diff --git a/examples/ClothingCLI/README.md b/examples/ClothingCLI/README.md
deleted file mode 100644
index 37e6d1f78..000000000
--- a/examples/ClothingCLI/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Marqo Simple CLI Demo
-
-### Prerequisites
-
-What things you need to install the software and how to install them.
-
-```
-Python 3.8
-```
-
-### Getting Started
-
-1. Download the Dataset from
- [Clothing Dataset](https://github.com/alexeygrigorev/clothing-dataset) into the directory where the `simple_marqo_demo.py` script is found.
-
-2. Run this command inside the script directory to setup an HTTP server:
- ```
- python3 -m http.server 8222
- ```
- This is for the marqo docker container to read files from local os.
- For more info on this please visit [this link](https://github.com/marqo-ai/marqo/issues/35).
-
-3. Make sure to run the Marqo docker container via the following command:
- ```
- docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
- ```
-
-4. Install Marqo
- ```
- pip install marqo
- ```
-
-5. Run the `simple_marqo_demo.py` script via the following command:
- ```
- python3 simple_marqo_demo.py
- ```
-
-For more information on Marqo's functions and features, please visit the [Marqo Documentation Page](https://marqo.pages.dev/).
-
-## Usage
-Feel free to checkout the code in order to have a better understanding on how Marqo functions are used :).
diff --git a/examples/ClothingCLI/simple_marqo_demo.py b/examples/ClothingCLI/simple_marqo_demo.py
deleted file mode 100644
index 7f4756360..000000000
--- a/examples/ClothingCLI/simple_marqo_demo.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import marqo
-import pprint
-import pandas as pd
-
-mq = marqo.Client(url='http://localhost:8882') # Connection to Marqo Docker Container
-
-dataset_path = "http://localhost:8222/" # Place your file path here (directory where http server is setup)
-
-def load_index(index_name: str, number_data: int) -> None:
- try:
- shirt_data = pd.read_csv('clothing-dataset/images.csv').head(number_data)[['image','label','kids']].to_dict('records')
-
- # dataset came from this link: https://github.com/alexeygrigorev/clothing-dataset-small
- # the .csv file has the following headers:
- # image, sender_id, label, kids
- # (image name, id of the sender who sent the pictures from sender_id, what kind of clothing it is, whether or not the clothing is for kids)
- # Dataset Example:.
- # 4285fab0-751a-4b74-8e9b-43af05deee22,124,Not sure,False
- # 70045b01-b350-4918-be74-2f627290ad7a,95,Skirt,False
-
-
- for data in shirt_data:
- path = "http://host.docker.internal:8222/clothing-dataset/images/" + data['image'] + ".jpg"
- data['image'] = path
-
- settings = {
- "treat_urls_and_pointers_as_images":True, # allows us to find an image file and index it
- "model":"ViT-B/16"
- }
-
- mq.create_index(index_name, **settings)
-
- mq.index(index_name).add_documents(shirt_data, tensor_fields=['image','label','kids'])
-
- print("Index successfully created.")
-
- except Exception as e:
- print("Index already exists.")
-
-def delete_index(index_name: str):
- try:
- mq.index(index_name).delete()
- print("Index successfully deleted.")
- except Exception as e:
- print("Index does not exist.")
-
-
-def delete_doc_from_index(index_name:str, doc_ids:list[str]):
- results = mq.index(index_name).delete_documents(ids=doc_ids)
- return results
-
-def search_index_text(index_name:str, query_text: str, search_method: str):
- results = mq.index(index_name).search(
- q=query_text,
- search_method=search_method,
- )
-
- # Marqo also has other features such as searhcing based on a specific attribute field and query fitlering
- # refer to the documentation on how these features work (https://marqo.pages.dev/)
- return results
-
-def search_index_image(index_name:str, image_name: str):
- # make sure the image is located inside the directory in which the python http server is running
-
- image_path = "http://host.docker.internal:8222/" + image_name
-
- results = mq.index(index_name).search(image_path)
-
- return results
-
-def get_index_stats(index_name: str) -> dict:
- results = mq.index(index_name).get_stats()
- return results
-
-
-
-def main():
- print("Welcome to Marqo Demo!")
- while True:
- action = int(input('''
-What would you like to do?
-1) Create an Index
-2) Delete an Index
-3) Search from an Index
-4) Show Index Stats
-5) Delete a document from an Index
-6) Quit
-
-Action: '''))
-
- if action == 1:
- index_name = input("Index name: ")
- no_of_items = int(input("No. of items in dataset: "))
-
- load_index(index_name, no_of_items)
- elif action == 2:
- index_name = input("Index name: ")
-
- delete_index(index_name)
- elif action == 3:
- index_name = input("Index name: ")
- search_type = input("Search Type (Text, Image): ")
-
- if search_type == 'Text':
- search_mode = str(input("Search Mode: (Lexical, Tensor)"))
- query_text = str(input("Query Text: "))
-
- results = search_index_text(index_name, query_text, search_mode.upper())
-
- pprint.pprint(results)
- elif search_type == 'Image':
- image_name = str(input("Image name (include MIME type .jpg or .png): "))
-
- results = search_index_image(index_name, image_name)
-
- pprint.pprint(results)
-
- elif action == 4:
- index_name = input("Index name: ")
- get_index_stats(index_name)
-
- elif action == 5:
- index_name = input("Index name: ")
- no_of_docs = int(input("No. of documents to delete: "))
- doc_ids = []
-
- for i in range(no_of_docs):
- doc_id = input("Document ID: ")
- doc_ids.append(doc_id)
-
- delete_doc_from_index(index_name, doc_ids)
-
- else:
- print("Goodbye")
- break
-
-main()
diff --git a/examples/ClothingStreamlit/README.md b/examples/ClothingStreamlit/README.md
deleted file mode 100644
index 983dff7c6..000000000
--- a/examples/ClothingStreamlit/README.md
+++ /dev/null
@@ -1,47 +0,0 @@
-# Marqo Streamlit Demo Application
-
-### Prerequisites
-
-What things you need to install the software and how to install them.
-
-```
-Python 3.8
-```
-
-### Getting Started
-
-1. Download the Dataset from
- [Clothing Dataset](https://github.com/alexeygrigorev/clothing-dataset) into the directory where the `streamlit_marqo_demo.py` script is found.
-
-2. Run this command inside the script directory to setup an HTTP server:
- ```
- python3 -m http.server 8222
- ```
- This is for the marqo docker container to read files from local os.
- For more info on this please visit [this link](https://github.com/marqo-ai/marqo/issues/35).
-
-3. Make sure to run the Marqo docker container via the following command:
- ```
- docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
- ```
-
-4. Install Streamlit. This can be done by via the following [link](https://docs.streamlit.io/library/get-started/installation).
-
-5. Install Marqo
- ```
- pip install marqo
- ```
- Note: if you are using Anaconda, make sure to install marqo in the anaconda environment.
-
-6. Once Streamlit is installed, we can start the Streamlit application by running the following command inside the directory where the `streamlit_marqo_demo.py` script is located:
- ```
- streamlit run streamlit_marqo_demo.py
- ```
-
-For more information on:
-- Marqo's functions and features, please visit the [Marqo Documentation Page](https://marqo.pages.dev/).
-
-- Streamlit's functions and features, please visit the [Streamlit Documentation Page](https://docs.streamlit.io/).
-
-## Usage
-Feel free to checkout the code in order to have a better understanding on how Marqo functions are used :).
diff --git a/examples/ClothingStreamlit/favicon.png b/examples/ClothingStreamlit/favicon.png
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diff --git a/examples/ClothingStreamlit/marqo-logo.jpg b/examples/ClothingStreamlit/marqo-logo.jpg
deleted file mode 100644
index ad1239e4e..000000000
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diff --git a/examples/ClothingStreamlit/streamlit_marqo_demo.py b/examples/ClothingStreamlit/streamlit_marqo_demo.py
deleted file mode 100644
index 3bd12a944..000000000
--- a/examples/ClothingStreamlit/streamlit_marqo_demo.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import os
-import requests
-import streamlit as st
-import pandas as pd
-from PIL import Image
-import pprint
-import marqo
-
-# Streamlit configuration settings
-st.set_page_config(
- page_title="Marqo Demo App",
- page_icon="favicon.png",
- layout="centered",
- initial_sidebar_state="collapsed",
- menu_items={}
-)
-
-mq = marqo.Client(url='http://localhost:8882') # Connection to Marqo Docker Container
-cwd = os.getcwd() # Get current working directory
-
-def load_index(number_data):
- try:
- shirt_data = pd.read_csv('clothing-dataset/images.csv').head(number_data)[['image','label','kids']].to_dict('records')
-
- for data in shirt_data:
- path = "http://host.docker.internal:8222/clothing-dataset/images/" + data['image'] + ".jpg"
- data['image'] = path
-
- settings = {
- "treat_urls_and_pointers_as_images":True, # allows us to find an image file and index it
- "model":"ViT-B/16"
- }
-
- mq.create_index("demo-search-index", **settings)
-
- with st.spinner("Creating Index..."):
- mq.index("demo-search-index").add_documents(shirt_data, tensor_fields=['image', 'label', 'kids'])
-
- st.success("Index successfully created.")
- except:
- st.error("Index already exists.")
-
-def delete_index():
- try:
- mq.index("demo-search-index").delete()
- st.success("Index successfully deleted.")
- except:
- st.error("Index does not exist.")
-
-def save_uploadedfile(uploadedfile):
- with open(os.path.join(cwd, uploadedfile.name), "wb") as f:
- f.write(uploadedfile.getbuffer())
- return uploadedfile.name
-
-def reset_state():
- st.session_state['results'] = {}
- st.session_state['page'] = -1
-
-def create_filter_str(filter_list):
- filter_string = ""
-
- if 'Kids' in filter_list:
- filter_string += 'kids:true'
- filter_list.remove('Kids')
- else:
- filter_string += 'kids:false'
-
- for field in filter_list:
- filter_string += f" AND label:({field})"
-
- print(filter_string)
- return filter_string
-
-def main():
- # Streamlit state variables (this is to save the state of the session for pagination of Marqo query results)
- if 'results' not in st.session_state:
- st.session_state['results'] = {}
-
- if 'page' not in st.session_state:
- st.session_state['page'] = -1
-
- # Index Settings Frontend
- with st.sidebar:
- st.write("Index Settings:")
- values = st.slider(
- label='Select a range of values',
- min_value=10.0,
- max_value=2000.0,
- value=1000.0,
- step=10.0)
-
- create_col, _, delete_col = st.columns([1,1,1])
-
- with create_col:
- create_btn = st.button('Create Index')
- if create_btn:
- load_index(int(values))
- with delete_col:
- delete_btn = st.button('Delete Index')
- if delete_btn:
- delete_index()
-
- # Main application frontend
- logo = Image.open("{}\marqo-logo.jpg".format(cwd))
- st.image(logo)
-
- search_text, search_image_url, search_image = None, None, None
- search_mode = st.radio("",("Text", "Image"), horizontal=True, on_change=reset_state)
- if search_mode == "Text":
- box_col, search_mode_col = st.columns([6,1])
- with box_col:
- search_text = st.text_input("Text Search")
-
- with search_mode_col:
- search_text_mode = st.radio("Search mode", ("Tensor", "Lexical"))
- else:
- image_input_col, image_type_col = st.columns([6,1])
-
- with image_type_col:
- image_type = st.radio("Image type", ("Web", "Local"))
-
- with image_input_col:
- if image_type=="Web":
- search_image_url = st.text_input("Provide an Image URL")
-
- else:
- search_image = st.file_uploader('Upload an Image', type=['jpg'])
-
- with st.expander("Search Settings"):
- attr_col, filter_col = st.columns(2)
- with attr_col:
- searchable_attr = st.multiselect('Searchable Attributes', ['Image', 'Label'], default=['Label'])
-
- with filter_col:
- filtering = st.multiselect('Pre-filtering Options', ['Dress', 'Hat', 'Longsleeve', 'Outwear', 'Pants', 'Shirt', 'Shoes', 'Shorts', 'Skirt', 'T-Shirt', 'Kids'], default=None)
-
-
- search_btn = st.button('Search')
-
- # Marqo Results logic
- if ((search_image is not None) or (search_image_url) or (search_text)) and search_btn:
- if search_text != "" and search_text != None:
- results = mq.index("demo-search-index").search(
- search_text,
- filter_string=create_filter_str(filtering),
- search_method=search_text_mode.upper(),
- searchable_attributes=[i.lower() for i in searchable_attr],
- limit=30
- )
-
- elif search_image_url != "" and search_image_url != None:
- results = mq.index("demo-search-index").search(
- search_image_url,
- filter_string=create_filter_str(filtering),
- searchable_attributes=[i.lower() for i in searchable_attr],
- limit=30
- )
-
- else:
- uploaded_img_name = save_uploadedfile(search_image)
-
- uploaded_img_path = f"http://host.docker.internal:8222/{uploaded_img_name}"
- print(uploaded_img_path)
-
- results = mq.index("demo-search-index").search(
- uploaded_img_path,
- filter_string=create_filter_str(filtering),
- searchable_attributes=[i.lower() for i in searchable_attr],
- limit=30
- )
-
- pprint.pprint(results)
-
- st.session_state['results'] = results
-
- if st.session_state['results']['hits']:
- st.session_state['page'] = 0
- else:
- st.session_state['page'] = -1
-
-
-
- # Results Pagination Logic
- if st.session_state['page'] > -1:
- prev_col, page_col, next_col = st.columns([1,9,1])
- with prev_col:
- prev_btn = st.button("Prev")
- if prev_btn and (st.session_state['page'] > 0):
- st.session_state['page']-=1
-
- with next_col:
- next_btn = st.button("Next")
- if next_btn and (st.session_state['page'] < 2):
- st.session_state['page'] += 1
-
- with page_col:
- st.markdown(' {}
'.format("Page " + str(st.session_state['page']+1)), unsafe_allow_html=True)
-
- if st.session_state['results'] != {}:
- if st.session_state['results']['hits']:
- st.write("Results (Top 30):")
- col = st.columns(5)
- for img in enumerate(st.session_state['results']['hits']):
- pic = img[1]['image']
-
- if pic is not None:
- if (img[0] >= st.session_state['page']*10) and (img[0] < (st.session_state['page']*10 + 10)):
- with col[(img[0]%10)//2]:
- if pic.startswith("http://host.docker.internal:8222/"):
- pic_url = 'http://localhost:8222/' + pic.split("http://host.docker.internal:8222/")[1]
- image = Image.open(requests.get(pic_url, stream=True).raw)
- st.image(image, caption=img[1]['label'])
- else:
- st.write("No results")
-
-
-main()
\ No newline at end of file
diff --git a/examples/GPT-examples/article/article.md b/examples/GPT-examples/article/article.md
deleted file mode 100644
index 3ea919ee8..000000000
--- a/examples/GPT-examples/article/article.md
+++ /dev/null
@@ -1,366 +0,0 @@
-# From "iron manual" to "ironman" - Augmenting GPT for fast editable memory to enable context aware question & answering
-
-TL:DR We show how [Marqo](https://github.com/marqo-ai/marqo) can be used as an update-able and domain specific memory to [GPT](https://en.wikipedia.org/wiki/GPT-3) to perform question and answering for products and chat agents. We also show how reference and hallucination checks can be easily implemented. A walk-through with images and animations is provided along with the [code](https://github.com/marqo-ai/marqo/tree/mainline/examples/GPT-examples) to reproduce.
-
-
- 
-
-
-## 1. Product Q & A - "Iron Manual"
-
-
- 
-
-
-## 2. NPC/chat agent with editable history - "Ironman"
-
-
- 
-
-
-## Introduction
-
-LLM’s can be used for many tasks with little (few-shot) to no (zero-shot) training data. A single LLM can be used for tasks like summarization, translation, question and answering, and classification. Despite LLM’s recent success there are still some limitations. For example, after the models are trained they are not easily updatable with new information. They also have a fixed input length. This places restrictions on the amount of context they can have inserted when being prompted. To overcome these limitations, we show how an external knowledge base can be used as part of the LLM to provide a fast and editable memory (i.e. document store) for it to draw from.
-
-
- 
-
-
-## Use case 1 - Product Q&A
-
-For the first use case, GPT is paired with Marqo to create a powerful search function for product documentation. This allows question and answering of its features. It is also able to provide a nice compact answer that is easy to read.
-
-### 1.1 The product documents
-
-To test the question answering capabilities, an “in the wild” use case was desired. A paper manual for a recently purchased clothes iron was selected. If already digitized text is available, this step can be skipped.
-
-
- 
-
-
-The manual is a particularly dry read. It consists of 7-pages of information related to the iron. Including information regarding its safe operation and maintenance.
-
-### 1.2 Preparing the documents
-
-Since the manual was on some paper, it needs to be digitized. AWS Textract was used to perform optical character recognition (OCR). The pages were two-columned which provided a challenge as the OCR output is left-to-right, causing the text to be intermingled. Bounding boxes are provided from the OCR output which would allow conceptual grouping of the text, however this was going to take too long. Instead, the OCR was performed again but with half the text blocked off by another piece of paper.
-
-
- 
-
-
-After scanning, there were seven documents, each representing a column of text from the manual. Below is an example of the text after OCR.
-
-```python
-"""
-Your iron has an Anti-Drip system, Anti-Scales system
-and Auto-Off function.
-Anti-Drip system: This is to prevent water from escaping
-from the soleplate when the iron is cold. During use, the
-anti-drip system may emit a loud 'clicking' sound,
-particularly when heating up or cooling down. This is
-normal and indicates that the system is functioning
-correctly.
-Anti-Scale system: The built-in anti-scale cartridge is
-designed to reduce the build-up of lime scale which
-occurs during steam ironing and will prolong the
-working life of your iron. The anti-calc cartridge is an
-integral part of the water tank and does not need to be
-replaced.
-Auto-Off function: This feature automatically switches
-off the steam iron if it has not been moved for a while.
-"""
-```
-
-## 1.3 Indexing the documents
-After creating a digital copy of the product manual, the next step is to index them into Marqo. Marqo embeds the documents using an encoder and allows for fast and efficient retrieval of relevant documents. Marqo provides both embedding based and lexical based retrieval. These retrieved documents are then going to be passed into a prompt for GPT. GPT is then asked to answer the query with respect to the retrieved documents (the “sources”).
-
-## 1.3.1 Installing Marqo
-We first install Marqo and the Marqo python client,
-
-```bash
-docker pull marqoai/marqo:0.0.12;
-docker rm -f marqo;
-docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:0.0.12
-pip install marqo
-```
-
-## 1.3.2 Indexing documents
-We then need to index the pages we have for the product. They need to be formatted as a python dictionary for ingestion.
-
-```python
-document1 = {"text":"Auto-Off function: This feature automatically switches
- off the steam iron if it has not been moved for a while.",
- "source":"page 1"}
-# other document content left out for clarity
-documents = [document1, document2, document3, document4, document5]
-```
-
-Once the documents are prepared, we can start indexing them using the python client. If no index settings are present, the default encoder is used.
-
-```python
-from marqo import Client
-mq = Client()
-index_name = "iron-docs"
-mq.create_index(index_name)
-```
-
-## 1.3.3 Searching documents
-At this point, Marqo can be used to search over the document embeddings using an approximate nearest neighbor algorithm (HNSW).
-```python
-results = mq.index(index_name).search("what is the loud clicking sound?")
-```
-or using lexical search which uses BM25.
-```python
-results = mq.index(index_name).search("what is the loud clicking sound?",
- search_method="LEXICAL")
-```
-## 1.4 Connecting Marqo to GPT
-The documents (product manual) can now be searched. Searching and retrieving relevant documents will provide the context for GPT to create a final answer. GPT requires an API key and can be obtained from the OpenAI website. The key then needs to be set as an environment variable.
-
-```bash
-export OPENAI_API_KEY="..."
-```
-
-## 1.4.1 Prompt creation
-The first thing that needs to be done is to create a prompt. There are a plethora of examples to draw from here but something like the following will get good results.
-
-```python
-template = """
-Given the following extracted parts of a long document ("SOURCES") and a question ("QUESTION"), create a final answer one paragraph long.
-Don't try to make up an answer and use the text in the SOURCES only for the answer. If you don't know the answer, just say that you don't know.
-QUESTION: {question}
-=========
-SOURCES:
-{summaries}
-=========
-ANSWER:
-"""
-```
-Here we instruct GPT to answer based on the context and not make anything up (this may not be perfect though). The question and answer is then inserted along with the context (“summaries”).
-
-To save time, we will use Langchain to help with the communication with GPT. Langchain can make it easy to setup interactions with LLM’s and removes a lot of the boiler plate code that would otherwise be required.
-```python
-pip install langchain
-from langchain.prompts import PromptTemplate
-prompt = PromptTemplate(template=template, input_variables=["summaries", "question"])
-```
-
-## 1.4.2 Preparing the context
-In order to connect GPT to Marqo, we need to format the results so they can be easily inserted into the prompt that was just created.
-
-```python
-from langchain.docstore.document import Document
-results = client.index(index_name).search(question)
-text = [res['_highlights']['text'] for res in results['hits']]
-docs = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts)]
-```
-To start with, we take just the highlights from Marqo. This is convenient because they are small pieces of text. They fit within the prompt and do not occupy too many tokens. Token limits matter because GPT (and LLM’s in general) will have a context length and often charge by token. This is the maximum number of tokens that can be used for the input. The more context, the larger the text (and background) that GPT can use. The drawback here is that the highlights might be too short to accurately answer the question.
-
-## 1.4.3 Token aware context truncating (optional)
-
- 
-
-
-To help with token limits while also having control over the input context length - a "dilation" procedure around the highlight can be performed to allow for more context. This means that some text before and after the highlight is included to allow for greater context for GPT. This is also very helpful as the pricing models for these models can be per token.
-
-```python
-highlights, texts = extract_text_from_highlights(results, token_limit=150)
-docs = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts)]
-```
-The next step is to provide the prompt with formatted context.
-
-## 1.4.4 GPT inference
-Now we have the prepared documents and prompt, we can call GPT using Langchain. We initiate an OpenAI class which communicates with the GPT API.
-
-```python
-from langchain.chains import LLMChain
-llm = OpenAI(temperature=0.9, model_name = "text-davinci-003")
-chain_qa = LLMChain(llm=llm, prompt=prompt)
-llm_results = chain_qa({"summaries": docs, "question": results['query']}, return_only_outputs=True)
-```
-
-The result is a dictionary with the text output from GPT. This essentially completes the required steps to augment GPT with an external knowledge base. However, we can add another feature to include and score sources of information that were used in the answer. This can be useful since LLM’s are known to hallucinate details. Providing the original sources can be used to check the output of the LLM’s results. In the next section we show how a re-ranker from two-stage retrieval can be repurposed to check which sources were used and provide a score.
-
-## 1.4.5 Rating sources
-After we have received a response in from the LLM, we can score the sources with respect to the LLM's response. This is in contrast to other methods that get the LLM themselves to cite the sources. From the experience here, that method was sometimes unreliable.
-
-
- 
-
-
-For the proposed method, we take a re-ranker (sentence-transformers cross-encoder) which would normally score each (query, document) pair to re-rank search results. Instead here, we score each (llm_result, document) pair. This provides a score for the "relevency" of the LLM's response with the provided sources. The idea being, the ones that were used will be most similar to the response.
-
-```python
-scores = predict_ce(llm_results['text'], texts)
-```
-and we can see scores for each piece of context with the LLM response.
-
-
- 
-
-
-The model is a classification model. A score of 1 is exact match and 0 is not a match. This method is not perfect though so some care should be taken. For example, GPT can be quite verbose when it does not know an answer. This response can include the original question and will cause false positives when scoring. Fine-tuning the re-ranker would probably reduce the false positives considerably.
-
-# Use case 2 - conversational agents with a story
-The second use case deals with a conversational agent that can draw on history (or background) as context to answer questions. This could be used for creating NPC's with a backstory or other chat agents that may need past context.
-
-
- 
-
-
-## 2.1 Indexing NPC data
-Now we have indexed the documents, we can search over them. In this case the documents are the backstories and the search is used context for the conversation. This search and retrieve step will provide the context for GPT to create a final answer.
-
-## 2.1.1 NPC data
-Here is an example of what the documents look like for the NPC's:
-```python
-document1 = {"name":"Sara Lee", "text":"my name is Sara Lee"}
-document2 = {"name":"Jack Smith", "text":"my name is Jack Smith"}
-document3 = {"name":"Sara Lee", "text":"Sara worked as a research assistant for a university before becoming a park ranger."}
-documents = [document1, document2, document3]
-```
-
-## 2.1.2 Indexing the data
-Now we can index the data. We need to get the Marqo client and create an index name.
-```python
-from marqo import Client
-mq = Client()
-index_name = "iron-docs"
-mq.create_index(index_name)
-```
-Now we index the documents
-```python
-results = mq.index(index_name).add_documents(documents, tensor_fields = ["name", "text"])
-```
-
-We can search and see what comes back.
-```python
-results = mq.index(index_name).search("sara lee")
-```
-
-Which gives the desired output
-```python
-In [32]: res['hits'][0]['_highlights']
-Out[32]: {'name': 'Sara Lee'}
-```
-
-Different characters can be easily selected (filtered). This means only their background can be searched.
-```python
-persona = "Jack Smith"
-results = mq.index(index_name).search('what is your hobby', filter_string=f'name:({persona})')
-```
-
-## 2.2 Connecting Marqo to GPT
-Now we have indexed the documents, we can search over them. In this case the documents are the backstories and the search is used context for the conversation. This search and retrieve step will provide the context for GPT to create a final answer.
-
-## 2.2.1 Prompt creation
-We use a prompt that contains some context and sets the stage for the LLM conversationalist.
-```python
-template = """
-The following is a conversation with a fictional superhero in a movie.
-BACKGROUND is provided which describes some of the history and powers of the superhero.
-The conversation should always be consistent with this BACKGROUND.
-Continue the conversation as the superhero in the movie.
-You are very funny and talkative and **always** talk about your superhero skills in relation to your BACKGROUND.
-BACKGROUND:
-=========
-{summaries}
-=========
-Conversation:
-{conversation}
-"""
-```
-Here we instruct GPT to answer for the character based on the background and to reference it where possible. Langchain is then used to create the prompt,
-
-```python
-pip install langchain
-from langchain.prompts import PromptTemplate
-prompt = PromptTemplate(template=template, input_variables=["summaries", "conversation"])
-```
-
-## 2.2.2 Preparing the context
-Here we truncate the context around the highlight from the previous search using Marqo. We use the token aware truncation which adds context from before and after the highlight.
-
-```python
-highlights, texts = extract_text_from_highlights(results, token_limit=150)
-docs = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts)]
-```
-The next step is to provide the prompt with formatted context.
-
-## 2.2.3 GPT inference
-Now we have the prepared documents and prompt, we can call GPT using Langchain. We initiate an OpenAI class which communicates with the GPT API,
-
-```python
-from langchain.chains import LLMChain
-lm = OpenAI(temperature=0.9, model_name = "text-davinci-003")
-chain_qa = LLMChain(llm=llm, prompt=prompt)
-llm_results = chain_qa({"summaries": docs, "conversation": "wow, what are some of your favorite things to do?", return_only_outputs=True)
-```
-The result is a dictionary with the text output from GPT. For example this is the response after the first text from the human,
-
-```python
- {'conversation': 'HUMAN:wow, what are some of your favorite things to do?',
- 'text': "SUPERHERO:I really enjoy working on cars, fishing, and playing video games. Those are some of the things that I like to do in my free time. I'm also really into maintaining and fixing stuff - I guess you could say it's one of my superhero powers! I have a lot of experience as an auto mechanic, so I'm really good at diagnosing and fixing problems with cars."}
- ```
-
-which aligns well with the background which was,
-
-```python
-['my hobbies is Working on cars, fishing, and playing video games',
- 'my favorite food is Steak',
- 'my favorite color is Blue']
-```
-
-## 2.3 Making it conversational
-The next step is to do some iterative prompting and inference to create a chat. We do this by iteratively updating the prompt with the question, searching across the background, formatting the context, calling the LLM and appending the result to the chat sequence (full code [here](https://github.com/marqo-ai/marqo/tree/mainline/examples/GPT-examples)).
-
-```python
-# how many background pieces of information to use
-n_background = 2
-# we keep track of the human and superhero responses
-history.append(f"\nHUMAN:{question}")
-# search for background related to the question
-results = mq.index(index_name).search(question, filter_string=f"name:({persona})", searchable_attributes=['text'], limit=20)
-# optionally crop the text to the highlighted region to fit within the context window
-highlights, texts = extract_text_from_highlights(results, token_limit=150)
-# add the truncated/cropped text to the data structure for langchain
-summaries = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts[:n_background])]
-# inference with the LLM
-chain_qa = LLMChain(llm=llm, prompt=prompt)
-llm_results = chain_qa({"summaries": summaries, "conversation": "\n".join(history)}, return_only_outputs=False)
-# add to the conversation history
-history.append(llm_results['text'])
-```
-We can now see the conversation and the agent drawing on its retrieved background.
-
-
- 
-
-
-This fits nicely with the background we gave the character,
-```python
-{'name': 'Evelyn Parker', 'text': 'my name is Evelyn Parker'},
-{'name': 'Evelyn Parker', 'text': 'my location is The city'},
-{'name': 'Evelyn Parker', 'text': 'my work_history is Evelyn worked as a line cook at several restaurants before attending culinary school and becoming a head chef.'},
-{'name': 'Evelyn Parker',
- 'text': 'my hobbies is Cooking, gardening, and reading'},
-{'name': 'Evelyn Parker', 'text': 'my favorite_food is Seafood'},
-{'name': 'Evelyn Parker', 'text': 'my dislikes is Cilantro'}
-```
-
-## 2.4 Editing the characters background
-We can patch, delete or add documents for the agents background with Marqo. Lets add something from the previous example,
-```python
-from iron_data import get_extra_data
-extra_docs = [{"text":text, "name":persona} for text in get_extra_data()]
-res = mq.index(index_name).add_documents(extra_docs, tensor_fields = ["name", "text"])
-```
-This adds some of the safety information from the iron manual. We will also take the bottom ranked results (i.e least relevant) to make it interesting. The following is the conversation - we can see it weaving its new background into the story nicely!
-
-
-
-
-
-
-## Conclusion
-We have shown how it is easy to make product question and answering and chat agents with an editable background using LLM’s like GPT and [Marqo](https://github.com/marqo-ai/marqo). We also showed how the limits of context length can be overcome by judicious truncation of the text. Reference scoring can be used to help verify the output. Although the results are really encouraging some care should still be made. GPT still has a habit of hallucinating results even with strong instructions and reference checks. If you are interested in combining GPT (or LLM’s in general) with Marqo — check out the [github](https://github.com/marqo-ai/marqo). Finally, if you are interested in running this in production, sign up for our [cloud](https://q78175g1wwa.typeform.com/to/d0PEuRPC).
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diff --git a/examples/GPT-examples/article/assets/readme.md b/examples/GPT-examples/article/assets/readme.md
deleted file mode 100644
index 674c7ca07..000000000
--- a/examples/GPT-examples/article/assets/readme.md
+++ /dev/null
@@ -1 +0,0 @@
-The assets for the article
diff --git a/examples/GPT-examples/data/p1.txt b/examples/GPT-examples/data/p1.txt
deleted file mode 100644
index 66bdc30ac..000000000
--- a/examples/GPT-examples/data/p1.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-
-IMPORTANT SAFETY INSTRUCTIONS
-Read this user manual carefully before first use and
-save it for future reference.
-This appliance is not intended for use by persons
-(including children) with reduced physical, sensory
-or mental capabilities or lack of experience and
-knowledge, unless they have been given supervision
-or instructions concerning use of the appliance by a
-person responsible for their safety.
-Children should be supervised to ensure that they do
-not play with the appliance.
-Cleaning and user maintenance shall not be made by
-children without supervision.
-This product has been designed for domestic use
-only. In case of any commercial use, inappropriate
-use or failure to comply with the instructions, the
-will not apply.
-manufacturer is not responsible, and the guarantee
-Before connecting your appliance, check if the mains
-voltage is the same as the voltage indicated on your
-appliance and that the power outlet has an earth
-connection. If unsure check with an electrician.
-Ensure before each use that the supply cord or any
-other parts are not damaged.
-Keep the iron and its cord out of reach of children
-when connected to mains power or cooling down.
-Never direct the steam towards persons or animals.
-Never direct the steam jet towards any other
-electrical or/and electronic appliances.
-DO NOT use the iron if it has been dropped, if there
-are visible signs of damage, malfunction or if it is
-leaking water.
\ No newline at end of file
diff --git a/examples/GPT-examples/data/p2.txt b/examples/GPT-examples/data/p2.txt
deleted file mode 100644
index f320b5df3..000000000
--- a/examples/GPT-examples/data/p2.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-
-unplugged and cooled any
-Always down before doing
-make sure the appliance is switched off,
-maintenance work.
-IMPORTANT! This iron must not be left unattended
-while it is connected to mains power and before it
-has cooled down. The soleplate of the iron can
-become extremely hot and may cause burns if
-touched.
-Do not unplug the appliance by pulling on the cord
-or on the appliance.
-Never immerse the iron, the stand, the power cord
-or the plug in water. Never hold them under a water
-tap.
-Do not allow the supply cord to come in contact with
-the soleplate when the iron is hot.
-If the supply cord is damaged, it must be replaced by
-qualified electrical person only or the product must
-be disposed.
-Disconnect from mains power when filling the
-reservoir in the iron with water.
-The iron must be used and rested on a flat, stable
-surface.
-Always place and operate the iron on a flat, solid,
-clean and dry surface. When placing the iron on its
-stand, ensure that the surface on which the stand is
-placed is stable.
-For additional protection, this appliance should be
-connected to a household residual current device
-(safety switch) with a rating of no more than 30mA.
-If unsure consult an electrician for advice.
\ No newline at end of file
diff --git a/examples/GPT-examples/data/p3.txt b/examples/GPT-examples/data/p3.txt
deleted file mode 100644
index f996f405c..000000000
--- a/examples/GPT-examples/data/p3.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-
-BEFORE FIRST USE
-Unpack the appliance and check if all parts are there
-and undamaged. Should this not be the case, return the
-product to Kmart for replacement.
-The packaging contains:
-One steam iron
-One filler cup
-Clean all parts before first use. See Cleaning and
-Maintenance
-Heat up the iron to its maximum temperature and iron
-over a piece of damp cloth for several minutes. This will
-burn-off any manufacturing residue from the iron.
-Smoke and odour may occur for a short period during
-this process, which is normal.
-FEATURES
-Your iron has an Anti-Drip system, Anti-Scales system
-and Auto-Off function.
-Anti-Drip system: This is to prevent water from escaping
-from the soleplate when the iron is cold. During use, the
-anti-drip system may emit a loud 'clicking' sound,
-particularly when heating up or cooling down. This is
-normal and indicates that the system is functioning
-correctly.
-Anti-Scale system: The built-in anti-scale cartridge is
-designed to reduce the build-up of lime scale which
-occurs during steam ironing and will prolong the
-working life of your iron. The anti-calc cartridge is an
-integral part of the water tank and does not need to be
-replaced.
-Auto-Off function: This feature automatically switches
-off the steam iron if it has not been moved for a while.
\ No newline at end of file
diff --git a/examples/GPT-examples/data/p4.txt b/examples/GPT-examples/data/p4.txt
deleted file mode 100644
index f360ada5e..000000000
--- a/examples/GPT-examples/data/p4.txt
+++ /dev/null
@@ -1,53 +0,0 @@
-
-PARTS
-D
-C
-B
-A
-A
-Soleplate
-E
-Spray button
-B
-Spray nozzle
-F
-Steam shot button
-C
-Water filling inlet
-G
-Cord bushing
-D
-Variable steam
-H
-Indicator lamp
-button
-I
-Temperature dial
-
-OPERATION
-Filling the water tank
-Ensure that the iron is cold and not connected to a
-power supply.
-Open the water filling cover (C). Hold the iron in a
-tilted position and fill the tank with plain water,
-using the filler cup.
-DO NOT exceed the max level on the tank.
-Close the water filling cover (C) by pressing down the
-cap until you hear a 'click'. To avoid water spillage,
-ensure that the water tank is properly closed.
-NOTES:
-To avoid calcium deposits, we recommend you use
-distilled water for your steam iron.
-To avoid damage or blockage of your steam iron, do not
-use any scented water or additives.
-Steam Ironing
-Your steam iron is equipped with the Eco Steam System.
-This function offers 3 steam levels for optimal ironing:
-ECO: Reduced amount of steam for energy saving,
-suitable for most fabric types
-Normal steam :suitable for most fabric types
-Power steam :increased steam output for remove
-stubborn creases
-Fill the water tank with plain water.
-Position the iron vertically on its stand on a suitable
-flat stable surface.
\ No newline at end of file
diff --git a/examples/GPT-examples/data/p5.txt b/examples/GPT-examples/data/p5.txt
deleted file mode 100644
index 038b9d93a..000000000
--- a/examples/GPT-examples/data/p5.txt
+++ /dev/null
@@ -1,47 +0,0 @@
-
-CLEANING AND MAINTENANCE
-Unplug the iron from mains power and allow it to cool
-completely before cleaning and storing.
-down
-Cleaning
-Clean the upper part of iron with a damp cloth. If
-necessary, use a mild detergent.
-Do not use alcohol, acetone, benzene, scourging
-cleaning agents, etc., to clean the iron. Do not use
-hard brushes or metallic objects.
-Remove any deposits on the soleplate with a damp
-cloth. To avoid scratching the finishing, never use
-metallic pad to clean the soleplate, and never place
-the iron on a rough surface.
-To remove synthetic residue from the soleplate, iron
-over an old cotton rag in high setting.
-Always store the emptied iron horizontally on a
-stable surface with a cloth protecting the soleplate.
-Self-clean function
-The self-cleaning function should be used on a regular
-basis, depending on usage and the hardness of water
-used.
-WARNING: Never pour white vinegar or any liquid
-cleaners into the water tank!
-Fill the water tank. Refer to OPERATION: Filling the
-water tank.
-Position the iron vertically on its stand on a suitable
-surface.
-Ensure that the steam control (D) is in OFF position.
-Connect the plug to a suitable mains power outlet.
-Set to maximum ironing temperature.
-Once the soleplate temperature is reached, unplug
-the iron from mains power.
-Hold the iron over a sink (away from your body),
-pressand hold the steam control (D) at "CALC CLEAN"
-position. Boiling water and steam will now emit from
-the holes in soleplate.
-Carefully shake the iron forwards and backwards to
-allow any scale and other deposits being expelled
-with the boiling water and the steam.
-Release the CALC CLEAN button when the water tank
-is empty.
-Plug the appliance into a mains power outlet socket,
-set a temperature and leave in operation for at least
-two minutes to dry the soleplate.
-Unplug the iron and let the iron cool down fully.
\ No newline at end of file
diff --git a/examples/GPT-examples/data/p6.txt b/examples/GPT-examples/data/p6.txt
deleted file mode 100644
index 88e819b20..000000000
--- a/examples/GPT-examples/data/p6.txt
+++ /dev/null
@@ -1,39 +0,0 @@
-
-IRONING TIPS
-IMPORTANT! always check whether a label with ironing instructions is attached to the garment. Always
-follow the ironing instructions attached to the garment.
-The iron heats up quicker than it cools down, therefore, you should start ironing the articles requiring the
-lowest temperature such as those made of synthetic fibre.
-If the fabric consists of various kinds of fibres, you must always select the lowest ironing temperature to
-iron the composition of those fibres.
-Silk and other fabrics that are likely to become shiny should always be ironed on the inner side. To prevent
-staining, do not spray water straight on silk or other delicate fabrics.
-To prevent staining, do not spray water straight on silk or other delicate fabrics.
-Velvet and other textures that rapidly become shiny should be ironed in one direction with light pressure
-applied. Always keep the iron moving at any moment.
-Pure wool fabrics (100% wool) may be ironed with the iron set to steam position. Preferably set steam
-button to the maximum position and use a dry cloth between the garment and the iron for protection.
-Do not touch plastic buttons with a hot iron because they may melt.
-Be careful around zippers and similar items to prevent the soleplate from scratching.
-Symbol
-Temperature setting
-Variable steam
-Steam shot
-Spray
-Fabric
-MIN
-x
-x
-Synthetic fiber
-x
-x
-Wool, Silk
-Cotton, Linen
-MAX
-possible
-x
-Attention! This symbol indicates: DO NOT IRON!
-not possible
-TECHNICAL DATA
-Rated voltage: 220-240V~ 50-60Hz
-Rate power: 2000-2400W
\ No newline at end of file
diff --git a/examples/GPT-examples/data/p7.txt b/examples/GPT-examples/data/p7.txt
deleted file mode 100644
index df412bf41..000000000
--- a/examples/GPT-examples/data/p7.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-
-12 Month Warranty
-Thank you for your purchase.
-Warrants your new product to be free from defects in materials and workmanship for the
-period stated above, from the date of purchase, provided that the product is used in accordance with
-accompanying recommendations or instructions where provided. This warranty is in addition to your rights
-under the Australian Consumer Law.
-Will provide you with your choice of a refund, repair or exchange (where possible) for this product if it
-becomes defective within the warranty period. Will bear the reasonable expense of claiming the
-warranty. This warranty will no longer apply where the defect is a result of alteration, accident, misuse, abuse
-or neglect.
-Please retain your receipt as proof of purchase and contact our Customer Service Centre on 1800 124 125
-(Australia) or 0800 945 995 (New Zealand) or alternatively, via Customer Help at com.au for any
-difficulties with your product. Warranty claims and claims for expense incurred in returning this product can
-be addressed to our Customer Service Centre at 690 Springvale Rd, Mulgrave Vic 3170.
-Our goods come with guarantees that cannot be excluded under the Australian Consumer Law. You are
-entitled to a replacement or refund for a major failure and compensation for any other reasonably foreseeable
-loss or damage. You are also entitled to have the goods repaired or replaced if the goods fail to be of
-acceptable quality and the failure does not amount to a major failure.
-For New Zealand customers, this warranty is in addition to statutory rights observed under New Zealand
-legislation.
\ No newline at end of file
diff --git a/examples/GPT-examples/ironman.py b/examples/GPT-examples/ironman.py
deleted file mode 100644
index 026f9a91d..000000000
--- a/examples/GPT-examples/ironman.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import pandas as pd
-from utilities import (
- marqo_prompt,
- extract_text_from_highlights,
- marqo_template,
- get_extra_data,
- reformat_npcs
-)
-from dotenv import load_dotenv
-from langchain.llms import OpenAI
-from langchain.docstore.document import Document
-from langchain.chains import LLMChain
-
-load_dotenv()
-
-if __name__ == "__main__":
-
- #############################################################
- # 0. Install Marqo
- #############################################################
-
- # run the following docker commands from the terminal to start marqo
- # docker rm -f marqo
- # docker pull marqoai/marqo:latest
- # docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
-
- #############################################################
- # 1. Create some data
- #############################################################
-
- NPCs = [{
- "name": "Sara Lee",
- "backstory": "Sara was born in a small village in the mountains. She was always fascinated with nature and as soon as she was old enough, she left her village to study environmental science at a university. She now works as a park ranger.",
- "location": "The mountains",
- "occupation": "Park ranger",
- "family_history": "Sara is an only child and her parents were both farmers. Growing up close to nature instilled in her a deep respect and appreciation for the environment.",
- "work_history": "Sara worked as a research assistant for a university before becoming a park ranger.",
- "favorite_color": "Yellow",
- "hobbies": "Hiking, bird watching, and photography",
- "favorite_food": "Fruits and vegetables",
- "dislikes": "Loud noises",
- },
- {
- "name": "Jack Smith",
- "backstory": "Jack was born and raised in the city. He has always had a love for cars and as soon as he could he began working on them. He now runs his own successful auto repair shop.",
- "location": "The city",
- "occupation": "Auto mechanic",
- "family_history": "Jack has a younger sister and his father was also a mechanic who ran his own shop.",
- "work_history": "Jack worked as a mechanic at several auto repair shops before opening his own business.",
- "favorite_color": "Blue",
- "hobbies": "Working on cars, fishing, and playing video games",
- "favorite_food": "Steak",
- "dislikes": "Celery",
- },
- {
- "name": "Evelyn Parker",
- "backstory": "Evelyn grew up in a small town in the countryside. She always had a passion for cooking and eventually moved to the city to attend culinary school. She now works as a chef at a popular restaurant.",
- "location": "The city",
- "occupation": "Chef",
- "family_history": "Evelyn is the youngest of three siblings. Her parents were farmers and instilled in her a love for cooking with fresh ingredients.",
- "work_history": "Evelyn worked as a line cook at several restaurants before attending culinary school and becoming a head chef.",
- "favorite_color": "Green",
- "hobbies": "Cooking, gardening, and reading",
- "favorite_food": "Seafood",
- "dislikes": "Cilantro",
- }]
-
- df = pd.DataFrame(reformat_npcs(NPCs))
- print(df.head())
-
- # make the data python dicts
- documents = df.to_dict(orient='records')
-
- #############################################################
- # 2. Setup Marqo
- #############################################################
-
- import marqo
- from marqo import Client
-
- marqo.set_log_level('WARN')
-
- mq = Client()
-
- index_name = "npc-chat"
-
- try:
- mq.index(index_name).delete()
- except:
- pass
-
- index_settings = {
- "index_defaults": {
- "normalize_embeddings": True,
- "text_preprocessing": {
- "split_length": 5,
- "split_overlap": 1,
- "split_method": "sentence"
- },
- }
- }
-
- # create the index - if no settings are present then sensible defaults are used
- mq.create_index(index_name, settings_dict=index_settings)
- res = mq.index(index_name).add_documents(documents, tensor_fields = ["name", "backstory", "location", "occupation",
- "family_history", "work_history", "favorite_color",
- "hobbies", "favorite_food", "dislikes"])
-
- #############################################################
- # 3. Regular NPC superhero
- #############################################################
-
- # select a character
- persona = "Evelyn Parker"
-
- # we pre-opulate them here to complete a conversation but it can easily be made interactive
- human_questions = [ "hi, what is your name?",
- "wow, what are some of your favorite things to do?",
- "are you scared of anything?",
- "where did you grow up?",
- "what do you dislike?"]
-
- history = []
- template = marqo_template()
- prompt = marqo_prompt(template)
-
- # how many pieces of context to use
- n_history = 2
-
- # setup the LLM API call
- llm = OpenAI(temperature=0.9)
-
- for question in human_questions:
-
- history.append(f"\nHUMAN:{question}")
- print(history[-1])
-
- # search for background related to the question
- results = mq.index(index_name).search(question, filter_string=f"name:({persona})", searchable_attributes=['text'], limit=20)
-
- # optionally crop the text to the highlighted region to fit within the context window
- highlights, texts = extract_text_from_highlights(results, token_limit=150)
-
- # add the truncated/cropped text to the data structure for langchain
- summaries = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts[:n_history])]
-
- # get the conversation history
- chain_qa = LLMChain(llm=llm, prompt=prompt)
-
- llm_results = chain_qa({"summaries": summaries, "conversation": "\n".join(history)}, return_only_outputs=False)
-
- history.append(llm_results['text'])
- print(history[-1])
-
- #############################################################
- # 3. IRONMAN
- #############################################################
-
- persona = "Evelyn Parker"
-
- # add some more info
- extra_docs = [{"text":text, "name":persona} for text in get_extra_data()]
- res = mq.index(index_name).add_documents(extra_docs, tensor_fields = ["name", "backstory", "location", "occupation",
- "family_history", "work_history", "favorite_color",
- "hobbies", "favorite_food", "dislikes"])
-
- # we pre-opulate them here to complete a conversation but it can easily be made interactive
- human_questions = [ "hi, what is your name?",
- "wow, what are some of your favorite things to do?",
- "are you scared of anything?",
- "where did you grow up?",
- "what do you dislike?"]
-
- history = []
- template = marqo_template()
- prompt = marqo_prompt(template)
-
- # how many pieces of context to use
- n_history = 2
-
- for question in human_questions:
-
- history.append(f"\nHUMAN:{question}")
- print(history[-1])
-
- # search for background related to the question
- results = mq.index(index_name).search(question, filter_string=f"name:({persona})", searchable_attributes=['text'], limit=20)
-
- # optionally crop the text to the highlighted region to fit within the context window
- highlights, texts = extract_text_from_highlights(results, token_limit=150)
-
- # add the truncated/cropped text to the data structure for langchain
- summaries = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts[-n_history:])]
-
- # get the conversation history
- chain_qa = LLMChain(llm=llm, prompt=prompt)
-
- llm_results = chain_qa({"summaries": summaries, "conversation": "\n".join(history)}, return_only_outputs=False)
-
- history.append(llm_results['text'])
- print(history[-1])
-
diff --git a/examples/GPT-examples/product_q_n_a.py b/examples/GPT-examples/product_q_n_a.py
deleted file mode 100644
index 72ae2b13c..000000000
--- a/examples/GPT-examples/product_q_n_a.py
+++ /dev/null
@@ -1,113 +0,0 @@
-from marqo import Client
-import pandas as pd
-import numpy as np
-
-from langchain.llms import OpenAI
-from langchain.docstore.document import Document
-from langchain.chains import LLMChain
-
-from dotenv import load_dotenv
-
-from utilities import (
- load_data,
- extract_text_from_highlights,
- qna_prompt,
- predict_ce,
- get_sorted_inds
-)
-
-load_dotenv()
-
-if __name__ == "__main__":
-
- #############################################################
- # 0. Install Marqo
- #############################################################
-
- # run the following docker commands from the terminal to start marqo
- # docker rm -f marqo
- # docker pull marqoai/marqo:latest
- # docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
-
-
- #############################################################
- # 1. Setup Marqo
- #############################################################
-
-
- mq = Client()
- index_name = "iron-docs"
-
- # (optinally) delete if it already exists
- try:
- mq.index(index_name).delete()
- except:
- pass
-
- # we can set some specific settings for the index. if they are not provided, sensible defaults are used
- index_settings = {
- "index_defaults": {
- "model": "flax-sentence-embeddings/all_datasets_v4_MiniLM-L6",
- "normalize_embeddings": True,
- "text_preprocessing": {
- "split_length": 3,
- "split_overlap": 1,
- "split_method": "sentence"
- },
- },
- }
-
- # create the index with custom settings
- mq.create_index(index_name, settings_dict=index_settings)
-
-
- #############################################################
- # 2. Load the data
- #############################################################
-
- df = load_data()
-
- # turn the data into a dict for indexing
- documents = df.to_dict(orient='records')
-
- #############################################################
- # 3. Index the data
- #############################################################
-
- # index the documents
- indexing = mq.index(index_name).add_documents(documents, tensor_fields=["cleaned_text"])
-
- #############################################################
- # 4. Search the data
- #############################################################
-
- # try a generic search
- q = "what is the rated voltage"
-
- results = mq.index(index_name).search(q)
- print(results['hits'][0])
-
- #############################################################
- # 5. Make it chatty
- #############################################################
-
- highlights, texts = extract_text_from_highlights(results, token_limit=150)
- docs = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts)]
- llm = OpenAI(temperature=0.9)
- chain_qa = LLMChain(llm=llm, prompt=qna_prompt())
- llm_results = chain_qa({"summaries": docs, "question": results['query']}, return_only_outputs=True)
- print(llm_results['text'])
-
- #############################################################
- # 6. Score the references
- #############################################################
-
- score_threshold = 0.20
- top_k = 3
- scores = predict_ce(llm_results['text'], texts)
- inds = get_sorted_inds(scores)
- scores = scores.cpu().numpy()
- scores = [np.round(s[0],2) for s in scores]
- references = [(str(np.round(scores[i],2)),texts[i]) for i in inds[:top_k] if scores[i] > score_threshold]
- df_ref = pd.DataFrame(references, columns=['score','sources'])
- print(df_ref)
diff --git a/examples/GPT-examples/readme.md b/examples/GPT-examples/readme.md
deleted file mode 100644
index 8ca29013f..000000000
--- a/examples/GPT-examples/readme.md
+++ /dev/null
@@ -1,30 +0,0 @@
-Original article [here](https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmenting-gpt-with-marqo-for-fast-editable-memory-to-enable-context-aware-question-answering).
-
-# Prerequisites
-1. You will need an OpenAI API key.
-```
-export OPENAI_API_KEY="..."
-```
-
-2. Install marqo
-```bash
-docker pull marqoai/marqo:0.0.12;
-docker rm -f marqo;
-docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:0.0.12
-pip install marqo
-```
-
-3. Install other dependencies
-```
-pip install -r requirements.txt
-```
-
-# 1. Product question and answering
-```python
-python product_q_n_a.py
-```
-
-# 2. Chat agent with history/NPC
-```python
-python ironman.py
-```
diff --git a/examples/GPT-examples/requirements.txt b/examples/GPT-examples/requirements.txt
deleted file mode 100644
index 8bf555a63..000000000
--- a/examples/GPT-examples/requirements.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-langchain==0.0.141
-marqo
-more_itertools==9.1.0
-nltk==3.8.1
-numpy==1.24.2
-pandas==2.0.0
-python-dotenv==1.0.0
-torch==2.0.0
-transformers==4.28.1
-openai==0.27.4
\ No newline at end of file
diff --git a/examples/GPT-examples/text.py b/examples/GPT-examples/text.py
deleted file mode 100644
index 6ada240ba..000000000
--- a/examples/GPT-examples/text.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from typing import Any, Dict, List, Optional, Union
-from types import FunctionType
-
-from functools import partial
-from more_itertools import windowed
-
-from nltk.tokenize import sent_tokenize, word_tokenize
-import nltk
-
-
-def _splitting_functions(split_by: str, language: str='english') -> FunctionType:
- """_summary_
- selects a text splitting function based on the method provided by 'split_by'
- Args:
- split_by (str): method to split the text by, 'character', 'word', 'sentence', 'passage'
- if not one of those allows for custom characters to split on
- language (str, optional): _description_. Defaults to 'english'.
- Raises:
- TypeError: _description_
- Returns:
- _type_: function for splitting text based on the method provided
- """
- if not isinstance(split_by, str):
- raise TypeError(f"expected str received {type(split_by)}")
-
- try:
- nltk.data.find("tokenizers/punkt")
- except LookupError:
- nltk.download("punkt")
-
- MAPPING = {
- 'character':list,
- 'word': partial(word_tokenize, language=language),
- 'sentence':partial(sent_tokenize, language=language),
- 'passage':lambda x:x.split("\n\n")
- }
-
- if split_by in MAPPING:
- return MAPPING[split_by]
-
- # can also have a custom split but leave that for now
- raise KeyError(f"unexpected split_by type of {split_by}")
-
-def _reconstruct_single_list(segmented_text: List[str], seperator: str = " ") -> str:
- """_summary_
- Args:
- segmented_text (List[str]): List of strings that were segmented
- seperator (str, optional): what to use as a seperator between tokens when re-joining into text. Defaults to " ".
- Returns:
- str: string of text that was re-joined
- """
- return seperator.join([t for t in segmented_text if t is not None])
-
-def _reconstruct_multi_list(segmented_text_list: List[List[str]], seperator: str = " ") -> List[str]:
- """_summary_
- Args:
- segmented_text_list (List[List[str]]): _description_
- seperator (str, optional): _description_. Defaults to " ".
- Returns:
- List[str]: _description_
- """
-
- results = []
- for seg in segmented_text_list:
- txt = _reconstruct_single_list(seg, seperator)
- if len(txt) > 0:
- results.append(txt)
-
- return results
-
-def check_make_string_valid(text: str, coerce: bool = True) -> str:
- """ does some simple validation and coercsion for empty strings
- Args:
- text (str): text of type str
- coerce (bool, optional): if an empty or None string is passed, return an empty string?. Defaults to True.
- Raises:
- TypeError: _description_
- Returns:
- _type_: something that is a string
- """
- empty_string = " "
-
- if text in [[], None, '', "", empty_string] and coerce:
- return empty_string
-
- if set(text) == set(" "):
- return empty_string
-
- if not isinstance(text, str):
- raise TypeError(f"text had type {type(text)} but expected str")
-
- return text
-
-def split_text(text: str, split_by: str = 'sentence', split_length: int = 2, split_overlap: int = 1,
- language: str = 'english', custom_seperator: str = None) -> List[str]:
- """ splits a single piece of text into smaller sub-texts based on splitting method (split_by).
- for example, the text can can be split at the character, word, sentence or passage level.
- optionally it can be split with a custom splitting string
- Args:
- text (str): _description_
- split_by (str, optional): _description_. Defaults to 'character'.
- split_length (int, optional): _description_. Defaults to 128.
- split_overlap (int, optional): _description_. Defaults to 16.
- language (str, optional): _description_. Defaults to 'english'.
- seperator (str, optional): _description_. Defaults to " ".
- Raises:
- TypeError: _description_
- Returns:
- List[str]: _description_
- """
-
- if split_length == 0:
- raise ValueError("split length must be > 0")
-
- # simple validation and correction
- text = check_make_string_valid(text, coerce=True)
-
- # don't split if it is not worth splitting
- if len(text) <= 1:
- return [text]
-
- # we need to treat character splitting differently
- if custom_seperator is None:
- seperator = '' if split_by == 'character' else ' '
- else:
- seperator = custom_seperator
-
- # determine how we want to split
- _func = _splitting_functions(split_by, language=language)
-
- # do the splitting
- split_text = _func(text)
-
- # concatenate individual elements based on split_length & split_stride
- segments = list(windowed(split_text, n=split_length, step=split_length - split_overlap))
-
- # reconstruct the segments. there is potential for a lossy process here as we
- # assume a uniform seperator when reconstructing the sentences
- text_splits = _reconstruct_multi_list(segments, seperator)
-
- return text_splits
\ No newline at end of file
diff --git a/examples/GPT-examples/utilities.py b/examples/GPT-examples/utilities.py
deleted file mode 100644
index 1a31d97b4..000000000
--- a/examples/GPT-examples/utilities.py
+++ /dev/null
@@ -1,710 +0,0 @@
-import glob
-import pandas as pd
-from langchain.prompts import PromptTemplate
-import re
-from text import split_text
-from typing import List, Literal
-import numpy as np
-import torch
-
-from transformers import (
- AutoTokenizer,
- AutoModelForSequenceClassification,
- GPT2TokenizerFast
-)
-
-def reformat_npcs(npcs):
- """
- helper function to make the data tidy
- """
- new_npcs = []
- for npc in npcs:
-
- # include a name field in each for filtering
- name = npc['name']
-
- for key,value in npc.items():
- doc = {"name":name, "text":f"my {key} is {value}"}
- new_npcs.append(doc)
- return new_npcs
-
-def marqo_template():
- """
- holds the prompt template
- """
- template = """The following is a conversation with a fictional superhero in a movie.
- BACKGROUND is provided which describes some the history and powers of the superhero.
- The conversation should always be consistent with this BACKGROUND.
- Continue the conversation as the superhero in the movie and **always** use something from the BACKGROUND.
- You are very funny and talkative and **always** talk about your superhero skills in relation to your BACKGROUND.
- BACKGROUND:
- =========
- {summaries}
- =========
- Conversation:
- {conversation}
- """
- return template
-
-def marqo_prompt(template = marqo_template()):
- """
- thin wrapper for prompt creation
- """
- PROMPT = PromptTemplate(template=template, input_variables=["summaries", "conversation"])
- return PROMPT
-
-def read_md_file(filename):
- """
- generic md/txt file reader
- """
- with open(filename, 'r') as f:
- return f.read()
-
-def clean_md_text(text):
- # Remove code blocks
- text = re.sub(r'```.*?```', '', text, flags=re.DOTALL)
-
- # Remove inline code
- text = re.sub(r'`.*?`', '', text)
-
- # Remove headings
- text = re.sub(r'#+.*?\n', '', text)
-
- # Remove horizontal lines
- text = re.sub(r'---*', '', text)
-
- # Remove links
- text = re.sub(r'\[.*?\]\(.*?\)', '', text)
-
- # Remove emphasis
- text = re.sub(r'\*\*.*?\*\*', '', text)
- text = re.sub(r'\*.*?\*', '', text)
-
- # Remove images
- text = re.sub(r'!\[.*?\]\(.*?\)', '', text)
-
- return text
-
-
-def load_all_files(files):
- """
- wrapper to load and clean text files
- """
-
- results = []
- for f in files:
- text = read_md_file(f)
- splitted_text = split_text(text, split_length=10, split_overlap=3)
- cleaned_text = [clean_md_text(_text) for _text in splitted_text]
- _files = [f]*(len(cleaned_text))
-
- results += list(zip(_files, splitted_text, cleaned_text))
-
- return pd.DataFrame(results, columns=['filename', 'text', 'cleaned_text'])
-
-def load_data():
- """
- wrapper to load all the data files
- """
- marqo_docs_directory = 'data/'
- files = glob.glob(marqo_docs_directory + 'p*.txt', recursive=True)
- files = [f for f in files if not f.startswith('_')]
- return load_all_files(files)
-
-def qna_prompt():
- """
- prompt template for q and a type answering
- """
-
- template = """Given the following extracted parts of a long document ("SOURCES") and a question ("QUESTION"), create a final answer one paragraph long.
- Don't try to make up an answer and use the text in the SOURCES only for the answer. If you don't know the answer, just say that you don't know.
- QUESTION: {question}
- =========
- SOURCES:
- {summaries}
- =========
- ANSWER:"""
- PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"])
- return PROMPT
-
-model_cache = dict()
-def load_ce(model_name='cross-encoder/ms-marco-MiniLM-L-6-v2'):
- """
- loads the sbert cross-encoder model
- """
-
- model = AutoModelForSequenceClassification.from_pretrained(model_name)
- tokenizer = AutoTokenizer.from_pretrained(model_name)
-
- return model, tokenizer
-
-def predict_ce(query, texts, model=None, key='ce'):
- """
- score all the queries with respect to the texts
- """
-
- if model is None:
- if key not in model_cache:
- model, tokenizer = load_ce()
- model_cache[key] = (model, tokenizer)
- else:
- (model, tokenizer) = model_cache[key]
-
- # create pairs
- softmax = torch.nn.Softmax(dim=0)
- N = len(texts)
- queries = [query]*N
- pairs = list(zip(queries, texts))
- features = tokenizer(pairs, padding=True, truncation=True, return_tensors="pt")
-
- model.eval()
- with torch.no_grad():
- scores = model(**features).logits
-
- return softmax(scores)
-
-def get_sorted_inds(scores):
- """
- return indexes based on sorted scores
- """
- return scores.argsort(0, descending=True)
-
-def extract_text_from_highlights(res, token_limit=256, truncate=True):
-
- highlights = []
- texts = []
- for ind,hit in enumerate(res[ResultsFields.hits]):
- highlight_dict = hit[ResultsFields.highlights]
- highlight_key = list(highlight_dict.keys())[0]
- highlight_text = list(highlight_dict.values())[0]
- text = hit[highlight_key]
-
- if truncate:
- text = " ".join(text.split())
- highlight_text = " ".join(highlight_text.split())
- text = truncate_text(text, token_limit, highlight_text)
-
- texts.append(text)
- highlights.append(highlight_text)
-
- return highlights, texts
-
-
-class ResultsFields:
- hits = 'hits'
- highlights = '_highlights'
-
-cached_tokenizers = dict()
-
-def _lies_between(offset_tuple, offset):
- """
- given a tuple of ints, determine if offset lies between them
- """
- return offset >= offset_tuple[0] and offset < offset_tuple[1]
-
-
-def _find_end_character_mapping(offset_mapping, offset):
- """assumes sorted offset_mapping. unless this was modified
- this will be the default from the tokenizer
- """
- # if the max length is bigger we just return the last index
- if offset >= max(offset_mapping[-1]):
- return [offset_mapping[-1]]
- return [ind for ind in offset_mapping if _lies_between(ind, offset)]
-
-def find_highlight_index_in_text(text, highlight):
- """
- return start and end character indices for the sub-string (highlight)
- """
- if highlight not in text:
- return (None, None)
-
- # returns left right
- left_ind = text.index(highlight)
- right_ind = left_ind + len(highlight)
-
- return (left_ind, right_ind)
-
-
-def get_token_indices(text: str, token_limit: int,
- method: Literal['start', 'end', 'center','offset'] = 'start',
- tokenizer = None,
- offset: int = None):
-
- # leave it here instead of a paramter
- default_tokenizer = 'gpt2'
-
- if tokenizer is None:
- if default_tokenizer not in cached_tokenizers:
- tokenizer = GPT2TokenizerFast.from_pretrained(default_tokenizer)
- cached_tokenizers[default_tokenizer] = tokenizer
- else:
- tokenizer = cached_tokenizers[default_tokenizer]
-
- tokenized_text = tokenizer(text, return_offsets_mapping=True)
- token_ids = tokenized_text['input_ids']
- character_offsets = tokenized_text['offset_mapping']
- text_token_len = len(token_ids)
-
- # need to get the offset from the start to hit the full size
- delta = text_token_len - token_limit
-
- # nothing to do if it fits already
- if delta <= 0:
- return [character_offsets[0], character_offsets[-1]]
-
- # convert offset into token space
- character_offset_tuple = _find_end_character_mapping(character_offsets, offset)
- token_offset = character_offsets.index(character_offset_tuple[0])
-
- is_odd_offset = 1
- if token_limit % 2 == 1: is_odd_offset = 0
-
- if method == 'start':
- ind_start = character_offsets[0]
- ind_end = character_offsets[token_limit-1]
-
- elif method == 'end':
- ind_start = character_offsets[delta]
- ind_end = character_offsets[-1]
-
- elif method == 'center':
- center_token = text_token_len//2
- left_ind = max(center_token - token_limit//2, 0)
- right_ind = min(center_token + token_limit//2, text_token_len)
- ind_start = character_offsets[left_ind]
- ind_end = character_offsets[right_ind-is_odd_offset]
-
- elif method == 'offset':
- center_token = token_offset
- left_ind = max(center_token - token_limit//2, 0)
- right_ind = min(center_token + token_limit//2, text_token_len)
- ind_start = character_offsets[left_ind]
- ind_end = character_offsets[right_ind-is_odd_offset]
-
- else:
- raise RuntimeError("incorrect method specified")
-
- return ind_start, ind_end
-
-def truncate_text(text, token_limit, highlight=None):
- """
- truncates text to a token limit centered on the highlight text
- """
-
- if highlight is None:
- method = 'start'
- center_ind = 0 # this will not be used for this start method
- else:
- # TODO the full context may ot get used if the highlight is not centered
- # we would need to add the excess to the end/start
-
- method = 'offset'
- # get indices of highlight
- inds = find_highlight_index_in_text(text, highlight)
- # get the center of the highlight in chars
- center_ind = (max(inds) - min(inds))//2 + min(inds)
- # now map this to tokens and get the left/right char indices to achieve token limit
-
- ind_left, ind_right = get_token_indices(text, token_limit, method=method, offset=center_ind)
- trunc_text = text[min(ind_left):max(ind_right)]
-
- return trunc_text
-
-def check_highlights_field(hit, highlight=ResultsFields.highlights):
- """
- check the validity of the highlights in the hit
- """
-
- if highlight in hit:
- if len(hit[highlight]) == 0:
- return False
- elif isinstance(hit[highlight], dict):
- if hit[highlight].values() == 0:
- return False
- else:
- return True
- else:
- raise RuntimeError("invalid hits and highlights")
- else:
- return False
-
-
-def test_truncate():
- import random
- import string
- tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
-
- text_en = ['hello this is a test sentence. this is another one? i think so. maybe throw in some more letteryz....xo']
- text_rm = [''.join(random.choices(string.ascii_uppercase + string.digits, k=10000)) for _ in range(10)]
- ks = [1, 32, 128, 1024, 2048]
- texts = text_en + text_rm
- methods = ['offset', 'start', 'end', 'center']
- for text in texts:
- k_gt = len(tokenizer(text)['input_ids'])
- for k in ks:
- for method in methods:
- ind_left, ind_right = get_token_indices(text, k, method=method, offset=2)
- trunc_text = text[min(ind_left):max(ind_right)]
- k_fn = len(tokenizer(trunc_text)['input_ids'])
-
- assert k_fn <= min(k,k_gt)
-
-
-def test_find_highlight_in_text():
-
- n_highlights = 5
-
- texts = [
- 'hello how are you',
- "I assume you only want to find the first occurrence of word in phrase. If that's the case, just use str.index to get the position of the first character. Then, add len(word) - 1 to it to get the position of the last character.",
- ]
-
- for text in texts:
- for _ in range(n_highlights):
- highlight_ind = sorted(np.random.choice(list(range(len(text))),2, replace=False))
- highlight = text[highlight_ind[0]:highlight_ind[1]]
- inds = find_highlight_index_in_text(text, highlight)
- assert text[inds[0]:inds[1]] == highlight
-
-
-def test_check_highlights_field():
- results_lexical = {
- 'hits': [
- {
- 'Title': 'Extravehicular Mobility Unit (EMU)',
- 'Description': 'The EMU is a spacesuit that provides environmental protection, mobility, life support, and'
- 'communications for astronauts',
- '_highlights': [],
- '_id': 'article_591',
- '_score': 0.61938936
- },
- {
- 'Title': 'The Travels of Marco Polo',
- 'Description': "A 13th-century travelogue describing Polo's travels",
- '_highlights': [],
- '_id': 'e00d1a8d-894c-41a1-8e3b-d8b2a8fce12a',
- '_score': 0.60237324
- }
- ],
- 'limit': 10,
- 'processingTimeMs': 49,
- 'query': 'What is the best outfit to wear on the moon?'
- }
-
- assert all(not check_highlights_field(hit, highlight=ResultsFields.highlights) for hit in results_lexical[ResultsFields.hits])
-
- results_tensor = {
- 'hits': [
- {
- 'Title': 'Extravehicular Mobility Unit (EMU)',
- 'Description': 'The EMU is a spacesuit that provides environmental protection, mobility, life support, and'
- 'communications for astronauts',
- '_highlights': {
- 'Description': 'The EMU is a spacesuit that provides environmental protection, '
- 'mobility, life support, and communications for astronauts'
- },
- '_id': 'article_591',
- '_score': 0.61938936
- },
- {
- 'Title': 'The Travels of Marco Polo',
- 'Description': "A 13th-century travelogue describing Polo's travels",
- '_highlights': {'Title': 'The Travels of Marco Polo'},
- '_id': 'e00d1a8d-894c-41a1-8e3b-d8b2a8fce12a',
- '_score': 0.60237324
- }
- ],
- 'limit': 10,
- 'processingTimeMs': 49,
- 'query': 'What is the best outfit to wear on the moon?'
- }
-
- assert all(check_highlights_field(hit, highlight=ResultsFields.highlights) for hit in results_tensor[ResultsFields.hits])
-
-
-def get_extra_data():
- """
- the extra data for the NPC's
- """
- text1 = """
-
- IMPORTANT SAFETY INSTRUCTIONS
- Read this user manual carefully before first use and
- save it for future reference.
- This appliance is not intended for use by persons
- (including children) with reduced physical, sensory
- or mental capabilities or lack of experience and
- knowledge, unless they have been given supervision
- or instructions concerning use of the appliance by a
- person responsible for their safety.
- Children should be supervised to ensure that they do
- not play with the appliance.
- Cleaning and user maintenance shall not be made by
- children without supervision.
- This product has been designed for domestic use
- only. In case of any commercial use, inappropriate
- use or failure to comply with the instructions, the
- will not apply.
- manufacturer is not responsible, and the guarantee
- Before connecting your appliance, check if the mains
- voltage is the same as the voltage indicated on your
- appliance and that the power outlet has an earth
- connection. If unsure check with an electrician.
- Ensure before each use that the supply cord or any
- other parts are not damaged.
- Keep the iron and its cord out of reach of children
- when connected to mains power or cooling down.
- Never direct the steam towards persons or animals.
- Never direct the steam jet towards any other
- electrical or/and electronic appliances.
- DO NOT use the iron if it has been dropped, if there
- are visible signs of damage, malfunction or if it is
- leaking water.
- """
-
- text2 = """
-
- unplugged and cooled any
- Always down before doing
- make sure the appliance is switched off,
- maintenance work.
- IMPORTANT! This iron must not be left unattended
- while it is connected to mains power and before it
- has cooled down. The soleplate of the iron can
- become extremely hot and may cause burns if
- touched.
- Do not unplug the appliance by pulling on the cord
- or on the appliance.
- Never immerse the iron, the stand, the power cord
- or the plug in water. Never hold them under a water
- tap.
- Do not allow the supply cord to come in contact with
- the soleplate when the iron is hot.
- If the supply cord is damaged, it must be replaced by
- qualified electrical person only or the product must
- be disposed.
- Disconnect from mains power when filling the
- reservoir in the iron with water.
- The iron must be used and rested on a flat, stable
- surface.
- Always place and operate the iron on a flat, solid,
- clean and dry surface. When placing the iron on its
- stand, ensure that the surface on which the stand is
- placed is stable.
- For additional protection, this appliance should be
- connected to a household residual current device
- (safety switch) with a rating of no more than 30mA.
- If unsure consult an electrician for advice.
-
- """
-
- text3 = """
- BEFORE FIRST USE
- Unpack the appliance and check if all parts are there
- and undamaged. Should this not be the case, return the
- product to Kmart for replacement.
- The packaging contains:
- One steam iron
- One filler cup
- Clean all parts before first use. See Cleaning and
- Maintenance
- Heat up the iron to its maximum temperature and iron
- over a piece of damp cloth for several minutes. This will
- burn-off any manufacturing residue from the iron.
- Smoke and odour may occur for a short period during
- this process, which is normal.
- FEATURES
- Your iron has an Anti-Drip system, Anti-Scales system
- and Auto-Off function.
- Anti-Drip system: This is to prevent water from escaping
- from the soleplate when the iron is cold. During use, the
- anti-drip system may emit a loud 'clicking' sound,
- particularly when heating up or cooling down. This is
- normal and indicates that the system is functioning
- correctly.
- Anti-Scale system: The built-in anti-scale cartridge is
- designed to reduce the build-up of lime scale which
- occurs during steam ironing and will prolong the
- working life of your iron. The anti-calc cartridge is an
- integral part of the water tank and does not need to be
- replaced.
- Auto-Off function: This feature automatically switches
- off the steam iron if it has not been moved for a while.
- """
-
- text4 = """
-
- PARTS
- D
- C
- B
- A
- A
- Soleplate
- E
- Spray button
- B
- Spray nozzle
- F
- Steam shot button
- C
- Water filling inlet
- G
- Cord bushing
- D
- Variable steam
- H
- Indicator lamp
- button
- I
- Temperature dial
-
- OPERATION
- Filling the water tank
- Ensure that the iron is cold and not connected to a
- power supply.
- Open the water filling cover (C). Hold the iron in a
- tilted position and fill the tank with plain water,
- using the filler cup.
- DO NOT exceed the max level on the tank.
- Close the water filling cover (C) by pressing down the
- cap until you hear a 'click'. To avoid water spillage,
- ensure that the water tank is properly closed.
- NOTES:
- To avoid calcium deposits, we recommend you use
- distilled water for your steam iron.
- To avoid damage or blockage of your steam iron, do not
- use any scented water or additives.
- Steam Ironing
- Your steam iron is equipped with the Eco Steam System.
- This function offers 3 steam levels for optimal ironing:
- ECO: Reduced amount of steam for energy saving,
- suitable for most fabric types
- Normal steam :suitable for most fabric types
- Power steam :increased steam output for remove
- stubborn creases
- Fill the water tank with plain water.
- Position the iron vertically on its stand on a suitable
- flat stable surface.
-
- """
-
- text5 = """
-
- CLEANING AND MAINTENANCE
- Unplug the iron from mains power and allow it to cool
- completely before cleaning and storing.
- down
- Cleaning
- Clean the upper part of iron with a damp cloth. If
- necessary, use a mild detergent.
- Do not use alcohol, acetone, benzene, scourging
- cleaning agents, etc., to clean the iron. Do not use
- hard brushes or metallic objects.
- Remove any deposits on the soleplate with a damp
- cloth. To avoid scratching the finishing, never use
- metallic pad to clean the soleplate, and never place
- the iron on a rough surface.
- To remove synthetic residue from the soleplate, iron
- over an old cotton rag in high setting.
- Always store the emptied iron horizontally on a
- stable surface with a cloth protecting the soleplate.
- Self-clean function
- The self-cleaning function should be used on a regular
- basis, depending on usage and the hardness of water
- used.
- WARNING: Never pour white vinegar or any liquid
- cleaners into the water tank!
- Fill the water tank. Refer to OPERATION: Filling the
- water tank.
- Position the iron vertically on its stand on a suitable
- surface.
- Ensure that the steam control (D) is in OFF position.
- Connect the plug to a suitable mains power outlet.
- Set to maximum ironing temperature.
- Once the soleplate temperature is reached, unplug
- the iron from mains power.
- Hold the iron over a sink (away from your body),
- pressand hold the steam control (D) at "CALC CLEAN"
- position. Boiling water and steam will now emit from
- the holes in soleplate.
- Carefully shake the iron forwards and backwards to
- allow any scale and other deposits being expelled
- with the boiling water and the steam.
- Release the CALC CLEAN button when the water tank
- is empty.
- Plug the appliance into a mains power outlet socket,
- set a temperature and leave in operation for at least
- two minutes to dry the soleplate.
- Unplug the iron and let the iron cool down fully.
- """
-
- text6 = """
-
-
- IRONING TIPS
- IMPORTANT! always check whether a label with ironing instructions is attached to the garment. Always
- follow the ironing instructions attached to the garment.
- The iron heats up quicker than it cools down, therefore, you should start ironing the articles requiring the
- lowest temperature such as those made of synthetic fibre.
- If the fabric consists of various kinds of fibres, you must always select the lowest ironing temperature to
- iron the composition of those fibres.
- Silk and other fabrics that are likely to become shiny should always be ironed on the inner side. To prevent
- staining, do not spray water straight on silk or other delicate fabrics.
- To prevent staining, do not spray water straight on silk or other delicate fabrics.
- Velvet and other textures that rapidly become shiny should be ironed in one direction with light pressure
- applied. Always keep the iron moving at any moment.
- Pure wool fabrics (100% wool) may be ironed with the iron set to steam position. Preferably set steam
- button to the maximum position and use a dry cloth between the garment and the iron for protection.
- Do not touch plastic buttons with a hot iron because they may melt.
- Be careful around zippers and similar items to prevent the soleplate from scratching.
- Symbol
- Temperature setting
- Variable steam
- Steam shot
- Spray
- Fabric
- MIN
- x
- x
- Synthetic fiber
- x
- x
- Wool, Silk
- Cotton, Linen
- MAX
- possible
- x
- Attention! This symbol indicates: DO NOT IRON!
- not possible
- TECHNICAL DATA
- Rated voltage: 220-240V~ 50-60Hz
- Rate power: 2000-2400W
- """
-
- text7 = """
-
- 12 Month Warranty
- Thank you for your purchase from Kmart.
- Kmart Australia Ltd warrants your new product to be free from defects in materials and workmanship for the
- period stated above, from the date of purchase, provided that the product is used in accordance with
- accompanying recommendations or instructions where provided. This warranty is in addition to your rights
- under the Australian Consumer Law.
- Kmart will provide you with your choice of a refund, repair or exchange (where possible) for this product if it
- becomes defective within the warranty period. Kmart will bear the reasonable expense of claiming the
- warranty. This warranty will no longer apply where the defect is a result of alteration, accident, misuse, abuse
- or neglect.
- Please retain your receipt as proof of purchase and contact our Customer Service Centre on 1800 124 125
- (Australia) or 0800 945 995 (New Zealand) or alternatively, via Customer Help at Kmart.com.au for any
- difficulties with your product. Warranty claims and claims for expense incurred in returning this product can
- be addressed to our Customer Service Centre at 690 Springvale Rd, Mulgrave Vic 3170.
- Our goods come with guarantees that cannot be excluded under the Australian Consumer Law. You are
- entitled to a replacement or refund for a major failure and compensation for any other reasonably foreseeable
- loss or damage. You are also entitled to have the goods repaired or replaced if the goods fail to be of
- acceptable quality and the failure does not amount to a major failure.
- For New Zealand customers, this warranty is in addition to statutory rights observed under New Zealand
- legislation.
- """
-
- return [text1, text2, text3, text4, text5, text6, text7]
\ No newline at end of file
diff --git a/examples/GPT3NewsSummary/.gitignore b/examples/GPT3NewsSummary/.gitignore
deleted file mode 100644
index 96194bb2a..000000000
--- a/examples/GPT3NewsSummary/.gitignore
+++ /dev/null
@@ -1,132 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-pip-wheel-metadata/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-# Usually these files are written by a python script from a template
-# before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# pipenv
-# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-# However, in case of collaboration, if having platform-specific dependencies or dependencies
-# having no cross-platform support, pipenv may install dependencies that don't work, or not
-# install all needed dependencies.
-#Pipfile.lock
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-.idea
-data/*
\ No newline at end of file
diff --git a/examples/GPT3NewsSummary/README.md b/examples/GPT3NewsSummary/README.md
deleted file mode 100644
index 039e9b342..000000000
--- a/examples/GPT3NewsSummary/README.md
+++ /dev/null
@@ -1,123 +0,0 @@
-# Using Marqo to power topical news summarisation
-
-## Who am I?
-
-My name is Iain Mackie and I lead NLP investments at Creator Fund and previously worked in Quant trading. I am currently finishing my PhD in neural search systems at the University of Glasgow and recently won the $500k grand prize at the Alexa TaskBot Challenge.🤖
-
-Given my passion for startups and NLP...I could not be happier to announce that Creator Fund has led a £660,000 Pre-Seed investment into Marqo. Marqo allows search engines to think like humans through neural search. The Melbourne-based company is led by two senior engineers from Amazon Robotics and Australia.
-
-## What is neural search?
-
-Search, or information retrieval, is the study of retrieving relevant information given a user query. The most obvious example is Google, where a user inputs a query and Google will return a ranked list of web documents. Search is complex because:
-
- - Indexes can have millions or billions of documents.
- - Judgements mapping user requests can be incredibly sparse.
- - Documents can be long with many different modalities (text, structured information, images, and videos).
-
-
-Historically due to scale and computations, search systems were based on inverted indexes, where each word is stored in a database, making it easy to run simple algorithms over billions of documents. For example, for the query "good startup", search algorithms such as BM25 would return results with "good" and "startup" words. However, with the explosion of large language models (LLMs) with billions of parameters (SBERT, BERT, T5, GPT3, etc.), neural search systems have vastly improved search effectiveness. Specifically, LLMs can draw semantic connections between similar terms, i.e. between "good startup" and words like "scalable", "strong founders", and "high growth". In addition, there has also been breakthroughs in multimodal neural models (CLIP, DALLE, etc.) that allow for aligned multimodal indexong and search. So, for example, you could search for images or videos that align with our "good startup" query (maybe the picture of Marqo below!).
-
-The problem is that multimodal search can be extremely beneficial in many multimodal applications (search engines, image recommendation, product search, etc.). Still, effective and robust search systems require technical experts to implement and keep up-to-date with this fast-moving search environment. Now that's where Marqo comes in...
-
-## What is Marqo?
-
-Marqo is developing "tensor search for humans" that improves search relevance for multimodal search applications (text 💬, image 🖼️, video 📽️), while being simple to set up and scale. Whereas many current neural databases require specialised search engineers, Marqo is a simple Python API where developers can index and query within seconds. Specifically, Marqo effortlessly combines the robustness and expressiveness of OpenSearch, allowing for complex filters and lexical search, with your favourite multimodal neural search models such as S-Bert and CLIP. Furthermore, Marqo's cloud platform enables customers to pay-per-query and reduces costs due to pooled resources ☁️. They are already powering child-friendly search engines, NFT recommendation systems, and much much more!
-
-More than just the technology, we are backing two incredible founders. Jesse has a PhD in Physics from La Trogue, Postdocs at UCL and Stanford, and was a Lead Machine Learning Scientist at Alexa and Amazon Robotics. Tom Hamer has software degrees from Australian National University and Cambridge, and was a software engineer within AWS' ElasticSearch and Database teams. Together, they have built a team of search enthusiasts with the passion and skillset to enable developers worldwide effortless access to neural search.
-
-
- 
-
-Marqo's open-source Github Github has reached 1.1k+ 🌟 in 6 weeks (top 10 trending libraries!). They have also launched the cloud beta that allows customers to pay-per-query and reduces costs due to pooled resources (join waiting list). Lastly, they are building a community of search enthusiasts tackling different problems (Slack).
-
-## Topical news summarisation
-
-Now for the fun bit...
-
-I wanted to build a fun search application within minutes to show the ease and power of Marqo. I decided to build a news summarisation application, i.e. answer questions like "What is happening in business today?" that synthesises example news corpus (link).
-
-The plan is to use Marqo's search to provide useful context for a generation algorithm; we use OpenAI's GPT3 API (link). This is more formally called "retrieval-augmented generation" and helps with generation tasks that require specific knowledge that the model has not seen during training. For example, company-specific documents and news data that's "in the future". Overview of what we're planning:
-
-
- 
-
-Thus, we can see the problem when we solely ask GPT3, "What is happening in business today?" It does not know and thus generates a generic response:
-```
-Question: What is happening in business today?
-
-Answer:
-There is a lot happening in business today. The economy is slowly recovering from the recession, and businesses are starting to invest again...
-```
-In fact, anyone following the financial markets knows 'the "economy is slowly recovering" and "businesses are starting to invest again" is completely wrong!!
-
-To solve this, we need to start our Marqo docker container, which creates a Python API we'll interact with during this demo:
-```
-docker pull marqoai/marqo:latest;
-docker rm -f marqo;
-docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
-```
-
-Next, let's look at our example news documents corpus, which contains BBC and Reuters news content from 8th and 9th of November. We use "_id" as Marqo document identifier, the "date" the article was written, "website" indicating the web domain, "Title" for the headline, and "Description" for the article body:
-```
-[
-{
- '_id': '2',
- 'date': '2022-11-09',
- 'website': 'www.bbc.co.uk',
- 'Title': 'COP27: Time to pay the climate bill - vulnerable nations',
- 'Description': 'Leaders of countries flooded or parched due to climate change are pleading at the COP27 summit...'
-},...
-]
-```
-
-We then index our news documents that manage both the lexical and neural embeddings. By default, Marqo uses SBERT from neural text embeding and has complete OpenSearch lexical and metadata functionality natively.
-```
-from news import MARQO_DOCUMENTS
-
-DOC_INDEX_NAME = ''news-index'
-
-print('Establishing connection to marqo client.')
-mq = marqo.Client(url='http://localhost:8882')
-
-print('creating a Marqo index')
-mq.create_index(DOC_INDEX_NAME)
-
-print('Indexing documents')
-mq.index(DOC_INDEX_NAME).add_documents(MARQO_DOCUMENTS, tensor_fields= ["Title", "Description"])
-```
-
-Now we have indexed our news documents, we can simply use Marqo Python search API to return relevant context for our GPT3 generation. For query "q", we use the question and want to match news context based on the "Title" and "Description" text. We also want to filter our documents for "today", which was '2022-11-09'.
-```
-question = 'What is happening in business today?'
-date = '2022-11-09'
-results = mq.index(DOC_INDEX_NAME).search(
- q=question,
- searchable_attributes=['Title', 'Description'],
- filter_string=f"date:{date}"
- limit=5)
-```
-
-Next, we insert Marqo's search results into GPT3 prompt as context, and we try generating an answer again::
-```
-Background:
-Source 0) M&S warns of 'gathering storm' as shoppers squeezed || Marks and Spencer has warned of a "gathering storm" of higher costs for retailers and pressure on household budgets as it reported a fall in profits for the first half of the year. The High Street giant said trading would become "more challenging" after it revealed its profits dropped by 24%. It said "all parts" of retail would be affected...
-Source 1) Facebook-owner Meta to cut 11,000 staff || Meta, which owns Facebook, Instagram and WhatsApp, has announced that it will cut 13% of its workforce. The first mass lay-offs in the firm's history will result in 11,000 employees, from a worldwide headcount of 87,000, losing their jobs. Meta chief executive Mark Zuckerberg said the cuts were "the most difficult changes we've made in Meta's history". The news follows...
-Source 2) Allianz beats quarterly profit expectations, posts rosier 2022 outlook || German insurer Allianz on Wednesday posted a better-than-expected 17% rise in third-quarter net profit, helped by strength at its property and casualty division, and gave a more optimistic full-year outlook....
-Source 3) Georgia race goes to run-off as fight for US Senate neck-and-neck || Results are being declared in the US midterm elections, with control of Congress hanging in the balance. Republicans are likely to take control of the House of Representatives but the Senate fight is on a knife-edge. The race for the Senate seat in Georgia, which could determine the outcome, will not be decided until a runoff election on 6 December....
-Source 4) Tesla stock hits 2-year low after Musk sells $4 bln worth of shares || Tesla Inc (TSLA.O) shares slid to their lowest level in nearly two years on Wednesday after Chief Executive Elon Musk sold $3.95 billion worth of shares in the electric-vehicle maker. The shares were down 6.1% at $179.66 in afternoon trading. Musk's latest share sale fueled jitters about the fallout of his Twitter buy on the world's most valuable automaker, analysts...
-
-
-Question: What is happening in business today?
-
-Answer:
-There are a few major stories happening in business today. Firstly, Marks and Spencer has warned of a "gathering storm" of higher costs for retailers and pressure on household budgets. Secondly, Facebook-owner Meta is cutting 11,000 staff in its first mass lay-offs. And finally, Tesla stock has hit a 2-year low after CEO Elon Musk sold $4 billion worth of shares.
-```
-
-Sucess! You'll notice that using Marqo to add relevant and temporally correct context means we can build a news summarisation application with ease. So instead of wrong and vague answers, we get factually-grounded summaries based on retrieved facts such as:
-
- - Marks and Spencer has warned of a "gathering storm" of higher costs for retailers
- - Facebook-owner Meta is cutting 11,000 staff
- - Tesla stock has hit a 2-year low after CEO Elon Musk sold $4 billion worth of shares
-
-
-Full code: here (you'll need GPT3 API token)
diff --git a/examples/GPT3NewsSummary/assets/marqo_photo.jpeg b/examples/GPT3NewsSummary/assets/marqo_photo.jpeg
deleted file mode 100644
index 6e84889cc..000000000
Binary files a/examples/GPT3NewsSummary/assets/marqo_photo.jpeg and /dev/null differ
diff --git a/examples/GPT3NewsSummary/assets/overview.png b/examples/GPT3NewsSummary/assets/overview.png
deleted file mode 100644
index d93db3966..000000000
Binary files a/examples/GPT3NewsSummary/assets/overview.png and /dev/null differ
diff --git a/examples/GPT3NewsSummary/main.py b/examples/GPT3NewsSummary/main.py
deleted file mode 100644
index e1ae89c41..000000000
--- a/examples/GPT3NewsSummary/main.py
+++ /dev/null
@@ -1,115 +0,0 @@
-
-import marqo
-import json
-import openai
-
-from news import MARQO_DOCUMENTS
-
-# init GPT3 API
-openai.organization = None
-openai.api_key = None
-
-DOC_INDEX_NAME = 'news-index'
-output = './news_summaries.txt'
-queries = [
- # question # date filter
- ('How is the US Midterm Election going?', None),
- ('How is COP27 progressing?', None),
- ('What is happening in business today?', '2022-11-09'),
-
-]
-
-
-if __name__ == '__main__':
-
- print('Establishing connection to marqo client.')
- mq = marqo.Client(url='http://localhost:8882')
-
- #########################################################################
- ######################### MARQO INDEXING ################################
- #########################################################################
- # mq.index(DOC_INDEX_NAME).delete()
- try:
- print(f'document index build: {mq.index(DOC_INDEX_NAME).get_stats()}')
- except KeyboardInterrupt:
- raise
- except:
- print('Creating index')
- mq.create_index(DOC_INDEX_NAME)
-
- print('Indexing documents')
- mq.index(DOC_INDEX_NAME).add_documents(MARQO_DOCUMENTS, tensor_fields= ["Title", "Description"])
- print('Done')
-
-
- #########################################################################
- ######################### GPT3 GENERATION ###############################
- #########################################################################
-
- def get_no_context_prompt(question):
- """ GPT3 prompt without any context. """
- return f'Question: {question}\n\nAnswer:'
-
- def get_context_prompt(question, context):
- """ GPT3 prompt without text-based context from marqo search. """
- return f'Background: \n{context}\n\nQuestion: {question}\n\nAnswer:'
-
- def prompt_to_essay(prompt):
- """ Process GPT-3 prompt and clean string . """
- response = openai.Completion.create(
- engine="text-davinci-002",
- prompt=prompt,
- temperature=0.0,
- max_tokens=256,
- top_p=1.0,
- frequency_penalty=0.0,
- presence_penalty=0.0
- )
- return response['choices'][0]['text'].strip().replace('\n', ' ')
-
-
- #########################################################################
- ########################### EXPERIMENTS ################################
- #########################################################################
-
- # Write to news_summaries.txt for analysis.
- with open(output, 'w') as f_out:
- for question, date in queries:
- f_out.write('////////////////////////////////////////////////////////\n')
- f_out.write('////////////////////////////////////////////////////////\n')
-
- f_out.write(f'question: {question}, date filter: {date}\n')
-
- f_out.write('================= GPT3 NO CONTEXT ======================\n')
- # Build prompt without context.
- prompt = get_no_context_prompt(question)
- f_out.write(f'Prompt: \n{prompt}\n')
- summary = prompt_to_essay(prompt)
- f_out.write(f'{summary}\n\n')
-
- f_out.write('================= GPT3 + Marqo =======================\n')
- # Query Marqo and set filters based on user query
- if isinstance(date, str):
- results = mq.index(DOC_INDEX_NAME).search(q=question,
- searchable_attributes=['Title', 'Description'],
- filter_string=f"date:{date}",
- limit=5)
- else:
- results = mq.index(DOC_INDEX_NAME).search(q=question,
- searchable_attributes=['Title', 'Description'],
- limit=5)
-
- # Build context using Marqo's highlighting functionality.
- context = ''
- for i, hit in enumerate(results['hits']):
- title = hit['Title']
- text = hit['Description']
- # for section, text in hit['_highlights'].items():
- # context += text + '\n'
- context += f'Source {i}) {title} || {" ".join(text.split()[:60])}... \n'
- # Build prompt with Marqo context.
- prompt = get_context_prompt(question=question, context=context)
- f_out.write(f'Prompt: \n{prompt}\n')
- summary = prompt_to_essay(prompt)
- f_out.write(f'{summary}\n\n')
-
diff --git a/examples/GPT3NewsSummary/news.py b/examples/GPT3NewsSummary/news.py
deleted file mode 100644
index a4d088379..000000000
--- a/examples/GPT3NewsSummary/news.py
+++ /dev/null
@@ -1,145 +0,0 @@
-MARQO_DOCUMENTS = [
- {
- '_id': '1',
- 'date': '2022-11-09',
- 'website': 'www.bbc.co.uk',
- 'Title': 'M&S warns of \'gathering storm\' as shoppers squeezed',
- 'Description': 'Marks and Spencer has warned of a "gathering storm" of higher costs for retailers and pressure on household budgets as it reported a fall in profits for the first half of the year. The High Street giant said trading would become "more challenging" after it revealed its profits dropped by 24%. It said "all parts" of retail would be affected by the UK\'s economic climate, adding unviable firms would go bust. But M&S said its business could "prove more resilient" due to its clientele. Many UK businesses are being hit by rising energy bills, wage costs and raw materials prices. Consumers are also cutting back their spending, with the Bank of England warning the UK is facing its longest recession since records began. M&S said the "combined impacts of the cost-of-living squeeze" and the increased cost of doing business was "creating pressure on margins industry-wide".'
- },
- {
- '_id': '2',
- 'date': '2022-11-09',
- 'website': 'www.bbc.co.uk',
- 'Title': 'COP27: Time to pay the climate bill - vulnerable nations',
- 'Description': 'Leaders of countries flooded or parched due to climate change are pleading at the COP27 summit for an urgent financial lifeline from richer nations.\n\"We will not give up... the alternative consigns us to a watery grave,\" Bahamas Prime Minister Philip Davis said.Countries are meeting in Sharm el-Sheikh, Egypt to discuss next steps in curbing climate change. Front line nations want a special compensation fund for the climate impacts they cannot recover from. "I\'m not here to ask any of you to love the people of my country with the same passion as I do," Mr Davis told leaders. "I\'m asking what is it worth to you to have millions of climate refugees to turn into tens of millions, putting pressure on political and economic systems around the world,\" he said.'
- },
- {
- '_id': '3',
- 'date': '2022-11-09',
- 'website': 'www.bbc.co.uk',
- 'Title': 'World Cup 2022: Wales name injury doubt Joe Allen in 26-man squad',
- 'Description': 'Robert Page has named his 26-man squad for Wales\' first World Cup in 64 years, with midfielder Joe Allen included despite being a major injury doubt.Gareth Bale captains a squad that has no surprising inclusions or omissions. Rhys Norrington-Davies is out with a hamstring injury so Tom Lockyer is recalled more than a year since he last played for Wales, while a calf injury rules out forward Tyler Roberts. Wales face the United States in their opening World Cup match on 21 November. They are joined in Group B by Iran and England, who name their squad on Thursday.'
- },
- {
- '_id': '4',
- 'date': '2022-11-09',
- 'website': 'www.bbc.co.uk',
- 'Title': 'I regret Williamson job ended in resignation, says Rishi Sunak',
- 'Description': 'Rishi Sunak has said he regrets Sir Gavin Williamson\'s ministerial appointment ended in resignation amid bullying claims against him. The prime minister said Sir Gavin had been right to resign, adding his behaviour had been "unacceptable". Mr Sunak said he had not known of "specific concerns" about his conduct in two previous ministerial roles. But Labour\'s Sir Keir Starmer accused him of failing to "stand up to a run-of-the-mill bully\".'
- },
- {
- '_id': '5',
- 'date': '2022-11-09',
- 'website': 'www.bbc.co.uk',
- 'Title': 'Facebook-owner Meta to cut 11,000 staff',
- 'Description': 'Meta, which owns Facebook, Instagram and WhatsApp, has announced that it will cut 13% of its workforce. The first mass lay-offs in the firm\'s history will result in 11,000 employees, from a worldwide headcount of 87,000, losing their jobs. Meta chief executive Mark Zuckerberg said the cuts were "the most difficult changes we\'ve made in Meta\'s history". The news follows major lay-offs at Twitter, which cut about half its staff, and other tech firms. "I know this is tough for everyone, and I\'m especially sorry to those impacted," he wrote in a statement. Mr Zuckerberg blamed massive long-term expectations for growth based on the firm\'s rise in revenue during the pandemic.'
- },
- {
- '_id': '6',
- 'date': '2022-11-08',
- 'website': 'www.bbc.co.uk',
- 'Title': 'US election results: When will we know who won?',
- 'Description': 'As voters across the US wake up on Wednesday morning after the election, the results of the 2022 midterms are not yet completely clear - with officials across the country warning that elections may drag on for days or weeks. The expected delays are the result of a number of factors, including razor-thin margins between candidates, potentially contested elections, the possibility of recounts and even a likely run-off in Georgia - a key Senate battleground. Other delays will be due to the decentralised way in which elections are set up in the US, in which states have varied rules for how mail-in ballots are counted, and when. Live: Latest updates as contest goes to the wire. The issue of delays has been a politically contentious one since the 2020 presidential election, when then-President Donald Trump attempted to claim victory the following morning while votes were still being tallied, declaring continued voting counting a "fraud" and an "embarrassment".',
- },
- {
- '_id': '7',
- 'date': '2022-11-09',
- 'website': 'www.bbc.co.uk',
- 'Title': 'Georgia race goes to run-off as fight for US Senate neck-and-neck',
- 'Description': 'Results are being declared in the US midterm elections, with control of Congress hanging in the balance. Republicans are likely to take control of the House of Representatives but the Senate fight is on a knife-edge. The race for the Senate seat in Georgia, which could determine the outcome, will not be decided until a runoff election on 6 December. The contest there between Republican Herschel Walker and Democrat Raphael Warnock is too close to call. Democrat John Fetterman, who suffered a stroke in May, defeated his Republican opponent Mehmet Oz to flip a key Senate seat in Pennsylvania. But the results of two other races that will decide the contest have yet to be called - in Nevada and Arizona. If Democrats lose control of either chamber of Congress, Republicans will be able to block President Joe Biden\'s agenda. Meanwhile Florida\'s Republican governor Ron DeSantis, a possible 2024 presidential contender, has won a large victory to stay in office.',
- },
- {
- '_id': '8',
- 'date': '2022-11-08',
- 'website': 'www.bbc.co.uk',
- 'Title': 'Tesla recalls 40,000 cars over power-steering fault',
- 'Description': 'Tesla is recalling just over 40,000 cars in the US because of a potential power-steering problem. Elon Musk\'s electric-car manufacturer says the vehicles\' power-steering assist system may fail on rough roads or after hitting a pothole. The National Highway Traffic Safety Administration said it could require greater steering effort, especially at low speeds, increasing crash risks. Tesla has released a software update to recalibrate the system. It was unaware of any injuries or deaths in connection with the problem, it said. However, 314 vehicle alerts have been issued for US vehicles that may be related to the recall. The recall affects the 2017-21 Model S and Model X vehicles.',
- },
- {
- '_id': '9',
- 'date': '2022-11-08',
- 'website': 'www.bbc.co.uk',
- 'Title': 'COP27: Ukraine a reason to act fast on climate change - Rishi Sunak',
- 'Description': 'The war in Ukraine is a reason to act faster to tackle climate change, UK Prime Minister Rishi Sunak has told the UN climate summit COP27. "Climate and energy security go hand-in-hand," he said in his first international appearance since taking office. Leaders from 120 countries are meeting in Sharm el-Sheikh, Egypt to discuss next steps in curbing climate change. Key topics are compensation and support for the most-affected countries. "Putin\'s abhorrent war in Ukraine and rising energy prices across the world are not a reason to go slow on climate change. They are a reason to act faster," Mr Sunak said. "We can bequeath our children a greener planet and a more prosperous future [...] There really is room for hope," he added.',
- },
- {
- '_id': '10',
- 'date': '2022-11-08',
- 'website': 'www.bbc.co.uk',
- 'Title': 'Millions to receive latest cost-of-living payment',
- 'Description': 'Eight million people on low incomes who receive certain benefits will start getting the second instalment of a targeted cost-of-living payment. The £324 payment would be made directly into bank accounts between now and 23 November, the Department for Work and Pensions (DWP) said. Those who qualify via tax credits will receive it by the end of the month. A first grant of £326 was paid earlier in the year and, together, they are designed to assist with rising bills. When will I get the £400 energy discount? The support, totalling £650, is for those across the UK on certain benefits, including Universal Credit and pension credit. It is the largest part of direct financial assistance from the government to tackle costs such as energy bills. The funds are paid directly into the same accounts used to receive benefits payments, with the reference of the recipients\' national insurance number followed by "DWP COL".',
- },
- {
- '_id': '11',
- 'date': '2022-11-09',
- 'website': 'www.reuters.com',
- 'Title': 'Republican \'red wave\' turns into a ripple, Georgia Senate headed to runoff',
- 'Description': 'Republicans made modest gains in U.S. midterm elections but Democrats performed better than expected, as control of the Senate hinged on three races that remained too close to call on Wednesday afternoon. The Georgia U.S. Senate race between Democratic incumbent Raphael Warnock and Republican Herschel Walker will go to a Dec. 6 runoff, Edison Research projected. That means it could be weeks until control of that chamber is known, unless Democrats hold off challengers in Nevada and Arizona. Republicans picked up at least seven seats in the U.S. House of Representatives, Edison Research projected. That would be two more than needed to wrest control of the chamber from Democrats, though with 45 races still uncalled, the Republican margin could go up or down. But they appeared to be far short of the sweeping "red wave" victory they had sought, as Democrats were avoiding the kind of heavy midterm defeat that often plagues sitting presidents of either party. The results appeared to show voters punishing Biden for presiding over an economy hit by steep inflation, while also lashing out against Republican efforts to ban abortion and cast doubt on the nation\'s vote-counting process.'
- },
- {
- '_id': '12',
- 'date': '2022-11-09',
- 'website': 'www.reuters.com',
- 'Title': 'Tesla stock hits 2-year low after Musk sells $4 bln worth of shares',
- 'Description': 'Tesla Inc (TSLA.O) shares slid to their lowest level in nearly two years on Wednesday after Chief Executive Elon Musk sold $3.95 billion worth of shares in the electric-vehicle maker. The shares were down 6.1% at $179.66 in afternoon trading. Musk\'s latest share sale fueled jitters about the fallout of his Twitter buy on the world\'s most valuable automaker, analysts said. The billionaire\'s recent acquisition of Twitter sparked investor concerns including whether he would be distracted from running Tesla or divert resources to the social media company. Some investors also feared the possible impact on Tesla\'s sales and brand as well as potential pressure on Tesla from countries trying to control online speech. The latest sale brings the total amount of Tesla stock sold by Musk since last November to about $36 billion - despite his having said earlier that he would sell no more of his shares.'
- },
- {
- '_id': '13',
- 'date': '2022-11-08',
- 'website': 'www.reuters.com',
- 'Title': 'Meta will begin laying off employees on Wednesday morning - WSJ',
- 'Description': 'Meta Platforms Inc (META.O) will begin laying off employees on Wednesday morning, Chief Executive Mark Zuckerberg told hundreds of executives on Tuesday, the Wall Street Journal reported. Zuckerberg appeared downcast in Tuesday\'s meeting and said he was accountable for the company\'s missteps and his overoptimism about growth had led to overstaffing, the report added, citing people familiar with the matter. The specific employees losing their jobs will be informed over the course of the morning, the report said. Meta\'s head of human resources, Lori Goler, said employees who lose their jobs will be provided with at least four months of salary as severance, the WSJ reported, citing people familiar with the matter. Meta reported more than 87,000 employees at the end of September.'
- },
- {
- '_id': '14',
- 'date': '2022-11-09',
- 'website': 'www.reuters.com',
- 'Title': 'Disney\'s steep streaming costs push shares to 2-1/2-year low',
- 'Description': 'Shares in Walt Disney Co (DIS.N) tumbled 12% to the lowest since March 2020 on Wednesday, as ballooning costs at the entertainment giant\'s fast-growing streaming division cast a shadow on strong subscriber additions. Disney+ has attracted millions of subscribers and will launch an ad-supported tier next month, but executives\' promise of profitability next year and forecast for operating results in the next quarter failed to impress. The company missed analysts\' expectations for fourth-quarter earnings, after a $1.5 billion loss in its streaming division. "Disney\'s streaming results are indicative of the tightrope it is walking," said Fred Boxa, associate director at consulting firm Arthur D. Little. Finance chief Christine McCarthy, in a call with analysts on Tuesday, said the ad tier was not expected to meaningfully impact results until later in the financial year.'
- },
- {
- '_id': '15',
- 'date': '2022-11-08',
- 'website': 'www.reuters.com',
- 'Title': 'COP27: UAE and Egypt agree to build one of world\'s biggest wind farms',
- 'Description': 'The presidents of the United Arab Emirates (UAE) and Egypt witnessed the signing of an agreement on Tuesday to develop one of the world\'s largest onshore wind projects in Egypt, according to an official statement on the Gulf nation\'s state news agency. The memorandum of understanding was signed between the UAE\'s renewable energy firm Masdar alongside its joint venture with Egypt\'s main renewable energy developer Infinity and Hassan Allam Utilities, the statement on news agency WAM said. Masdar, which has a portfolio of renewable energy assets with a combined value of more than $20 billion and a total capacity of more than 15 GW, said the new project would be its biggest yet. The 10 GW farm would produce 47,790 GWh of clean energy annually and offset 23.8 million tonnes of carbon dioxide emissions, equivalent to around 9% of Egypt\'s current CO2 emissions."With this agreement to develop our largest ever project, Masdar is proud to bolster our contribution to Egypt\'s renewable energy goals," Masdar\'s CEO Mohamed Jameel al-Ramahi said.'
- },
- {
- '_id': '16',
- 'date': '2022-11-08',
- 'website': 'www.reuters.com',
- 'Title': 'Supreme Court urged to reject Pennsylvania\'s \'consent-by-registration\' law',
- 'Description': 'Reuters) - The U.S. Supreme Court on Tuesday heard arguments over whether states can require corporations to consent to the jurisdiction of their courts when they register to do business in the state, in a case that could dramatically expand state courts\' jurisdiction over corporations around the country. The dispute arises from a petition by Virginia resident Robert Mallory, who asked the nation\'s top court to revive his lawsuit in Pennsylvania state court against his former employer, Virginia-based Norfolk Southern Railway Co, claiming he developed colon cancer because of asbestos exposure on the job. Pennsylvania had a statute requiring companies to consent to its courts\' jurisdiction when they register to do business there. Norfolk Southern, one of the nation\'s largest railway operators, maintained that the law violates its due process rights under the U.S. Constitution\'s 14th Amendment. The Pennsylvania Supreme Court agreed, affirming the trial court\'s dismissal of Mallory\'s case. "Fidelity to the original public meaning of the 14th Amendment in this case means that the flesh-and-blood little guy wins and the Fortune 500 company loses," Ashley Keller of Keller Postman, a lawyer for Mallory, told the justices on Tuesday, urging them to reverse.'
- },
- {
- '_id': '17',
- 'date': '2022-11-09',
- 'website': 'www.reuters.com',
- 'Title': 'Show us the money: Developing world at COP27 seeks financing details',
- 'Description': 'Finance took centre stage at the COP27 climate talks on Wednesday, with U.N. experts publishing a list of projects worth $120 billion that investors could back to help poorer countries cut emissions and adapt to the impacts of global warming. A $3 billion water transfer project between Lesotho and Botswana and a $10 million plan to improve the public water system in Mauritius were among dozens of projects listed, including 19 in Africa.We can now show that a meaningful pipeline of investible opportunities does exist across the economies that need finance most," Mahmoud Mohieldin, one of the U.N. appointed experts, known as U.N. Climate Change High-Level Champions, said in a statement to accompany the report. n an effort to answer the argument by private sector financiers that it\'s too risky to invest more in emerging markets, the experts, who help the COP host-governments engage with business, pulled together a list of projects that could be funded more quickly.'
- },
- {
- '_id': '18',
- 'date': '2022-11-09',
- 'website': 'www.reuters.com',
- 'Title': 'Control of U.S. Congress unclear as Republican \'red wave\' fizzles',
- 'Description': 'Republicans made modest gains in U.S. midterm elections but Democrats performed better than expected, as control of the Senate hinged on three races that remained too close to call on Wednesday afternoon. The Georgia U.S. Senate race between Democratic incumbent Raphael Warnock and Republican Herschel Walker will go to a Dec. 6 runoff, Edison Research projected. That means it could be weeks until control of that chamber is known, unless Democrats hold off challengers in Nevada and Arizona. Republicans picked up at least eight seats in the U.S. House of Representatives, Edison Research projected. That would be three more than needed to wrest control of the chamber from Democrats, though with 44 races still uncalled the Republican margin could go up or down and might not be resolved for days. Even a slim House majority would let Republicans hem in Democratic President Joe Biden during his next two years in office, blocking legislation and launching potentially politically damaging investigations.',
- },
- {
- '_id': '19',
- 'date': '2022-11-09',
- 'website': 'www.reuters.com',
- 'Title': 'Allianz beats quarterly profit expectations, posts rosier 2022 outlook',
- 'Description': 'German insurer Allianz on Wednesday posted a better-than-expected 17% rise in third-quarter net profit, helped by strength at its property and casualty division, and gave a more optimistic full-year outlook.'
- },
- {
- '_id': '20',
- 'date': '2022-11-09',
- 'website': 'www.bbc.co.uk',
- 'Title': 'What is COP27 and why is it important?',
- 'Description': 'World leaders are discussing action to tackle climate change at the COP27 climate summit in Egypt. It follows a year of climate-related disasters and broken temperature records. UK Prime Minister Rishi Sunak is attending, having previously said he would not. United Nations (UN) climate summits are held every year, for governments to agree steps to limit global temperature rises. They are referred to as COPs, which stands for "Conference of the Parties". The parties are the attending countries that signed up to the original UN climate agreement in 1992. COP27 is the 27th annual UN meeting on climate. It is taking place in Sharm el-Sheikh until 18 November.'
- },
-
-]
-
-
diff --git a/examples/GPT3NewsSummary/news_summaries.txt b/examples/GPT3NewsSummary/news_summaries.txt
deleted file mode 100644
index 7974c2823..000000000
--- a/examples/GPT3NewsSummary/news_summaries.txt
+++ /dev/null
@@ -1,75 +0,0 @@
-////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////
-question: How is the US Midterm Election going?, date filter: None
-================= GPT3 NO CONTEXT ======================
-Prompt:
-Question: How is the US Midterm Election going?
-
-Answer:
-The US Midterm Election is going well.
-
-================= GPT3 + Marqo =======================
-Prompt:
-Background:
-Source 0) Georgia race goes to run-off as fight for US Senate neck-and-neck || Results are being declared in the US midterm elections, with control of Congress hanging in the balance. Republicans are likely to take control of the House of Representatives but the Senate fight is on a knife-edge. The race for the Senate seat in Georgia, which could determine the outcome, will not be decided until a runoff election on 6 December....
-Source 1) US election results: When will we know who won? || As voters across the US wake up on Wednesday morning after the election, the results of the 2022 midterms are not yet completely clear - with officials across the country warning that elections may drag on for days or weeks. The expected delays are the result of a number of factors, including razor-thin margins between candidates, potentially contested elections, the...
-Source 2) Republican 'red wave' turns into a ripple, Georgia Senate headed to runoff || Republicans made modest gains in U.S. midterm elections but Democrats performed better than expected, as control of the Senate hinged on three races that remained too close to call on Wednesday afternoon. The Georgia U.S. Senate race between Democratic incumbent Raphael Warnock and Republican Herschel Walker will go to a Dec. 6 runoff, Edison Research projected. That means it could...
-Source 3) Control of U.S. Congress unclear as Republican 'red wave' fizzles || Republicans made modest gains in U.S. midterm elections but Democrats performed better than expected, as control of the Senate hinged on three races that remained too close to call on Wednesday afternoon. The Georgia U.S. Senate race between Democratic incumbent Raphael Warnock and Republican Herschel Walker will go to a Dec. 6 runoff, Edison Research projected. That means it could...
-Source 4) Allianz beats quarterly profit expectations, posts rosier 2022 outlook || German insurer Allianz on Wednesday posted a better-than-expected 17% rise in third-quarter net profit, helped by strength at its property and casualty division, and gave a more optimistic full-year outlook....
-
-
-Question: How is the US Midterm Election going?
-
-Answer:
-The US Midterm Election is going neck-and-neck, with the Republicans likely to take control of the House of Representatives, but the Senate fight is on a knife-edge. The race for the Senate seat in Georgia, which could determine the outcome, will not be decided until a runoff election on 6 December.
-
-////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////
-question: How is COP27 progressing?, date filter: None
-================= GPT3 NO CONTEXT ======================
-Prompt:
-Question: How is COP27 progressing?
-
-Answer:
-The COP27 climate conference is progressing well. The conference is focused on finalizing the details of the Paris Agreement, which was reached in 2015.
-
-================= GPT3 + Marqo =======================
-Prompt:
-Background:
-Source 0) What is COP27 and why is it important? || World leaders are discussing action to tackle climate change at the COP27 climate summit in Egypt. It follows a year of climate-related disasters and broken temperature records. UK Prime Minister Rishi Sunak is attending, having previously said he would not. United Nations (UN) climate summits are held every year, for governments to agree steps to limit global temperature rises. They...
-Source 1) Show us the money: Developing world at COP27 seeks financing details || Finance took centre stage at the COP27 climate talks on Wednesday, with U.N. experts publishing a list of projects worth $120 billion that investors could back to help poorer countries cut emissions and adapt to the impacts of global warming. A $3 billion water transfer project between Lesotho and Botswana and a $10 million plan to improve the public water...
-Source 2) COP27: Time to pay the climate bill - vulnerable nations || Leaders of countries flooded or parched due to climate change are pleading at the COP27 summit for an urgent financial lifeline from richer nations. "We will not give up... the alternative consigns us to a watery grave," Bahamas Prime Minister Philip Davis said.Countries are meeting in Sharm el-Sheikh, Egypt to discuss next steps in curbing climate change. Front line nations...
-Source 3) COP27: Ukraine a reason to act fast on climate change - Rishi Sunak || The war in Ukraine is a reason to act faster to tackle climate change, UK Prime Minister Rishi Sunak has told the UN climate summit COP27. "Climate and energy security go hand-in-hand," he said in his first international appearance since taking office. Leaders from 120 countries are meeting in Sharm el-Sheikh, Egypt to discuss next steps in curbing climate change....
-Source 4) COP27: UAE and Egypt agree to build one of world's biggest wind farms || The presidents of the United Arab Emirates (UAE) and Egypt witnessed the signing of an agreement on Tuesday to develop one of the world's largest onshore wind projects in Egypt, according to an official statement on the Gulf nation's state news agency. The memorandum of understanding was signed between the UAE's renewable energy firm Masdar alongside its joint venture with...
-
-
-Question: How is COP27 progressing?
-
-Answer:
-COP27 is progressing well, with leaders from around the world discussing ways to tackle climate change. One key issue that has been raised is the need for more financing to help developing countries cut emissions and adapt to the impacts of global warming. Another issue that has been discussed is the importance of increasing renewable energy production, with the UAE and Egypt agreeing to develop one of the world's largest onshore wind projects.
-
-////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////
-question: What is happening in business today?, date filter: 2022-11-09
-================= GPT3 NO CONTEXT ======================
-Prompt:
-Question: What is happening in business today?
-
-Answer:
-There is a lot happening in business today. The economy is slowly recovering from the recession, and businesses are starting to invest again. However, there is still a lot of uncertainty in the business world, and many companies are struggling to stay afloat.
-
-================= GPT3 + Marqo =======================
-Prompt:
-Background:
-Source 0) M&S warns of 'gathering storm' as shoppers squeezed || Marks and Spencer has warned of a "gathering storm" of higher costs for retailers and pressure on household budgets as it reported a fall in profits for the first half of the year. The High Street giant said trading would become "more challenging" after it revealed its profits dropped by 24%. It said "all parts" of retail would be affected...
-Source 1) Facebook-owner Meta to cut 11,000 staff || Meta, which owns Facebook, Instagram and WhatsApp, has announced that it will cut 13% of its workforce. The first mass lay-offs in the firm's history will result in 11,000 employees, from a worldwide headcount of 87,000, losing their jobs. Meta chief executive Mark Zuckerberg said the cuts were "the most difficult changes we've made in Meta's history". The news follows...
-Source 2) Allianz beats quarterly profit expectations, posts rosier 2022 outlook || German insurer Allianz on Wednesday posted a better-than-expected 17% rise in third-quarter net profit, helped by strength at its property and casualty division, and gave a more optimistic full-year outlook....
-Source 3) Georgia race goes to run-off as fight for US Senate neck-and-neck || Results are being declared in the US midterm elections, with control of Congress hanging in the balance. Republicans are likely to take control of the House of Representatives but the Senate fight is on a knife-edge. The race for the Senate seat in Georgia, which could determine the outcome, will not be decided until a runoff election on 6 December....
-Source 4) Tesla stock hits 2-year low after Musk sells $4 bln worth of shares || Tesla Inc (TSLA.O) shares slid to their lowest level in nearly two years on Wednesday after Chief Executive Elon Musk sold $3.95 billion worth of shares in the electric-vehicle maker. The shares were down 6.1% at $179.66 in afternoon trading. Musk's latest share sale fueled jitters about the fallout of his Twitter buy on the world's most valuable automaker, analysts...
-
-
-Question: What is happening in business today?
-
-Answer:
-There are a few major stories happening in business today. Firstly, Marks and Spencer has warned of a "gathering storm" of higher costs for retailers and pressure on household budgets. Secondly, Facebook-owner Meta is cutting 11,000 staff in its first mass lay-offs. And finally, Tesla stock has hit a 2-year low after CEO Elon Musk sold $4 billion worth of shares.
-
diff --git a/examples/GPT3NewsSummary/requirements.txt b/examples/GPT3NewsSummary/requirements.txt
deleted file mode 100644
index 7bb0a98e6..000000000
--- a/examples/GPT3NewsSummary/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-marqo
-openai
\ No newline at end of file
diff --git a/examples/GPT3NewsSummary/start_marqo.sh b/examples/GPT3NewsSummary/start_marqo.sh
deleted file mode 100644
index 32de58b19..000000000
--- a/examples/GPT3NewsSummary/start_marqo.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-
-docker pull marqoai/marqo:latest;
-docker rm -f marqo;
-docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
\ No newline at end of file
diff --git a/examples/ImageSearchGuide/ImageSearchGuide.md b/examples/ImageSearchGuide/ImageSearchGuide.md
deleted file mode 100644
index 494acb54b..000000000
--- a/examples/ImageSearchGuide/ImageSearchGuide.md
+++ /dev/null
@@ -1,219 +0,0 @@
-# How to implement text-to-image search on Marqo - in 5 lines of code
-
-*A step-to-step guide on how to search images with text (Text-to-Image Search) using [Marqo](https://www.marqo.ai/).*
-
-
-
-
-
-[Marqo](https://www.marqo.ai/) is an open-source tensor-based search engine that supports multi-modal search. In this article, we will
-introduce how to set up your own text-to-image search engine using marqo. The full code is available on Marqo's github [Github](imagesearchguide.ipynb).
-
-## Set up
-
-### Install marqo
-In this article, we select 5 images from the [coco dataset](https://cocodataset.org/#home) as examples.
-
- 
- 
- 
- 
- 
-
-
-First, we need to run marqo in Docker using the following command. This test is done on a x64 linux machine, for Mac users with M-series chips
-please check [here](https://github.com/marqo-ai/marqo#m-series-mac-users).
-
-```
-docker rm -f marqo
-docker pull marqoai/marqo:0.0.10
-docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:0.0.10
-```
-
-Now, we can create a new environment and install the Marqo client by:
-```
-conda create -n marqo-client python=3.8
-conda activate marqo-client
-
-pip install marqo matplotlib
-```
-Open Python and check the installation is successful by:
-```python
-import marqo
-mq = marqo.Client("http://localhost:8882")
-
-mq.get_marqo()
-```
-and you should have the output as:
-```python
-{'message': 'Welcome to Marqo', 'version': '0.0.10'}
-```
-At the time this article is written, we are using marqo with version 0.0.10.
-
-### Download images
-
-Now, you can download our examples images from [Github](./data). You should have the following directory diagram:
-
-
- 
-
-
-
-Done, you have finished all the set-up, let's do the real search!
-
-## Search with marqo
-
-### Create index
-
-First, we need to create a marqo index that provides you the access to all the necessary operations, e.g., indexing, searching. You can choose different models and parameters here.
-In this case, we just use a very basic setting by specifying the model ,enabling image search, and leaving others as default.
-
-```python
-index_name = 'image-search-guide'
-
-settings = {
- "model": "ViT-L/14",
- "treat_urls_and_pointers_as_images": True,
- }
-
-mq.create_index(index_name, **settings)
-```
-__Note__: To accomplish this multi-modal search task, we __MUST__ set `"treat_urls_and_pointers_as_imges": True` to enable the multi-modal search feature. As for the `model`, we need to
-select a model from [__CLIP families__](https://docs.marqo.ai/0.0.10/Models-Reference/dense_retrieval/) (`"ViT-L/14"` in this case).
-
-### Access local images
-Now, we need to add the images to the created index, which is a little tricky. Marqo is running in the docker, so it will not be able to access
-the local images.
-One solution is to upload all the images to Github and access them through urls. This is OK in this case as we only have 5 images. However, if we think big,
-are you really going to upload and download 1 million images with a larger dataset? I guess the answer is __NO__, so here is the solution.
-
-We can put the local images in a docker server for easier access from marqo in dock by
-```python
-import subprocess
-local_dir = "./data"
-pid = subprocess.Popen(['python3', '-m', 'http.server', '8222', '--directory', local_dir], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
-```
-
-With this step, marqo can access you image easily using http request, we just need to tell marqo where the image is:
-```python
-import glob
-import os
-
-# Find all the local images
-locators = glob.glob(local_dir + '*.jpg')
-
-# Generate docker path for local images
-docker_path = "http://host.docker.internal:8222/"
-image_docker = [docker_path + os.path.basename(f) for f in locators]
-
-print(image_docker)
-```
-```python
-output:
-['http://host.docker.internal:8222/image4.jpg',
- 'http://host.docker.internal:8222/image1.jpg',
- 'http://host.docker.internal:8222/image3.jpg',
- 'http://host.docker.internal:8222/image0.jpg',
- 'http://host.docker.internal:8222/image2.jpg']
-```
-
-All the local image are on a docker server for marqo to access now.
-
-### Add images to index
-
-Marqo requires the input (which we call `documents`) as a `list` of `dictionary`, we can convert the images into the required format
-
-```python
-documents = [{"image_docker" : image, "_id" : idx} for idx, image in enumerate(image_docker)]
-
-print(documents)
-```
-```python
-output:
-[{'image_docker': 'http://host.docker.internal:8222/image4.jpg', '_id': '0'},
- {'image_docker': 'http://host.docker.internal:8222/image1.jpg', '_id': '1'},
- {'image_docker': 'http://host.docker.internal:8222/image3.jpg', '_id': '2'},
- {'image_docker': 'http://host.docker.internal:8222/image0.jpg', '_id': '3'},
- {'image_docker': 'http://host.docker.internal:8222/image2.jpg', '_id': '4'}]
-```
-
-Add the documents into the previously created index using function `add_documents()`
-```python
-mq.index(index_name).add_documents(documents, tensor_fields=["image_docker"], device="cpu", client_batch_size= 1)
-```
-```python
-outputs:
-2022-12-30 04:27:45,846 logger:'marqo' INFO add_documents pre-processing: took 0.000s for 5 docs, for an average of 0.000s per doc.
-2022-12-30 04:27:45,848 logger:'marqo' INFO starting batch ingestion with batch size 1
-2022-12-30 04:27:47,130 logger:'marqo' INFO add_documents batch 0 roundtrip: took 1.281s to add 1 docs, for an average of 1.281s per doc.
-2022-12-30 04:27:47,131 logger:'marqo' INFO add_documents batch 0 Marqo processing: took 1.275s for Marqo to process & index 1 docs (server unbatched), for an average of 1.275s per doc.
-2022-12-30 04:27:48,182 logger:'marqo' INFO add_documents batch 1 roundtrip: took 1.050s to add 1 docs, for an average of 1.050s per doc.
-2022-12-30 04:27:48,183 logger:'marqo' INFO add_documents batch 1 Marqo processing: took 1.045s for Marqo to process & index 1 docs (server unbatched), for an average of 1.045s per doc.
-2022-12-30 04:27:49,255 logger:'marqo' INFO add_documents batch 2 roundtrip: took 1.070s to add 1 docs, for an average of 1.070s per doc.
-2022-12-30 04:27:49,256 logger:'marqo' INFO add_documents batch 2 Marqo processing: took 1.064s for Marqo to process & index 1 docs (server unbatched), for an average of 1.064s per doc.
-2022-12-30 04:27:50,386 logger:'marqo' INFO add_documents batch 3 roundtrip: took 1.129s to add 1 docs, for an average of 1.129s per doc.
-2022-12-30 04:27:50,388 logger:'marqo' INFO add_documents batch 3 Marqo processing: took 1.117s for Marqo to process & index 1 docs (server unbatched), for an average of 1.117s per doc.
-2022-12-30 04:27:51,462 logger:'marqo' INFO add_documents batch 4 roundtrip: took 1.073s to add 1 docs, for an average of 1.073s per doc.
-2022-12-30 04:27:51,463 logger:'marqo' INFO add_documents batch 4 Marqo processing: took 1.067s for Marqo to process & index 1 docs (server unbatched), for an average of 1.067s per doc.
-2022-12-30 04:27:51,573 logger:'marqo' INFO completed batch ingestion.
-2022-12-30 04:27:51,575 logger:'marqo' INFO add_documents completed. total time taken: 5.729s.
-```
-Yes, it is just this simple one line of code. And you can check the outputs for the indexing time. If you have CUDA GPU available, and want to speed up indexing, follow [this guide](https://docs.marqo.ai/0.0.10/using_marqo_with_a_gpu/) to enable CUDA on Marqo, and set `device="cuda"` in the `add_documents` call.
-
-Done, all the images are in Marqo and now we can search.
-
-
-### Search
-Finally, let us search and see the returned the results.
-
-Let's say we want to get the image "*A rider on a horse jumping over the barrier*". Here is the code.
-```python
-search_results = mq.index(index_name).search("A rider on a horse jumping over the barrier",
- searchable_attributes=['image_docker'], limit = 1,
- device='cpu')
-```
-```python
-output:
-2022-12-30 04:28:56,472 logger:'marqo' INFO search (tensor): took 0.364s to send query and received 1 results from Marqo (roundtrip). Marqo itself took 0.358s to execute the search.
-```
-Done, we get the result in just 0.36s without the help of GPU! So what do the results look like, then?
-```python
-print(search_results)
-```
-```python
-output:
-{'hits': [{'image_docker': 'http://host.docker.internal:8222/image1.jpg',
- '_id': '1',
- '_highlights': {'image_docker': 'http://host.docker.internal:8222/image1.jpg'},
- '_score': 0.6133688}],
- 'processingTimeMs': 358,
- 'query': 'A rider on a horse jumping over the barrier',
- 'limit': 1}
-```
-Well, very hard to understand? Don't worry, let's plot it and verify it with your eyes:
-```python
-import requests
-from PIL import Image
-
-fig_path = search_results["hits"][0]["image_docker"].replace(docker_path, local_dir)
-display(Image.open(fig_path))
-```
-output:
-
- 
-
-
-Isn't this the image you are look for, "*A rider on a horse jumping over the barrier*"? Searching image using text is just so simple.
-You must be thinking this is 5 images. What will happen in a larger dataset?
-Why not try it yourself, you can easily __change the parameters in the code__, __add more images into the directory__, and __test your searching results__.
-You can also check other advanced usages in our [Github](https://github.com/marqo-ai/marqo).
-
-## Take aways
-
-It is really easy to use marqo to achieve multi-modal searching, e.g., image-to-text, text-to-image, image-to-image, with the following steps:
-
-1. Environment setup: `conda`, `pip`
-2. Create index: `create_index()`
-3. Add documents into the index: `add_documents()`
-4. Search: `index().search()`
-
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diff --git a/examples/ImageSearchGuide/imagesearchguide.ipynb b/examples/ImageSearchGuide/imagesearchguide.ipynb
deleted file mode 100644
index ff21cd8fc..000000000
--- a/examples/ImageSearchGuide/imagesearchguide.ipynb
+++ /dev/null
@@ -1,361 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "907d6f05",
- "metadata": {},
- "source": [
- "# How to implement text-to-image search on Marqo - in 5 lines of code"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "033a1024",
- "metadata": {},
- "source": [
- "### Create index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "id": "5dd4df74",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'message': 'Welcome to Marqo', 'version': '0.0.10'}"
- ]
- },
- "execution_count": 79,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import marqo\n",
- "mq = marqo.Client(\"http://localhost:8882\")\n",
- "\n",
- "mq.get_marqo()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "id": "8e169d5b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'acknowledged': True,\n",
- " 'shards_acknowledged': True,\n",
- " 'index': 'image-search-guide'}"
- ]
- },
- "execution_count": 80,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "index_name = 'image-search-guide'\n",
- "\n",
- "settings = {\n",
- " \"model\": \"ViT-L/14\",\n",
- " \"treat_urls_and_pointers_as_images\": True,\n",
- " }\n",
- "\n",
- "mq.create_index(index_name, **settings)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7c8d9be2",
- "metadata": {},
- "source": [
- "### Add image into index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "id": "c434fd6a",
- "metadata": {},
- "outputs": [],
- "source": [
- "import subprocess\n",
- "local_dir = \"./data/\"\n",
- "pid = subprocess.Popen(['python3', '-m', 'http.server', '8222', '--directory', local_dir], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 108,
- "id": "271c9531",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['http://host.docker.internal:8222/image4.jpg', 'http://host.docker.internal:8222/image1.jpg', 'http://host.docker.internal:8222/image3.jpg', 'http://host.docker.internal:8222/image0.jpg', 'http://host.docker.internal:8222/image2.jpg']\n"
- ]
- }
- ],
- "source": [
- "import glob\n",
- "import os\n",
- "\n",
- "# Find all the local images\n",
- "locators = glob.glob(local_dir + '*.jpg')\n",
- "\n",
- "# Generate docker path for local images\n",
- "docker_path = \"http://host.docker.internal:8222/\"\n",
- "image_docker = [docker_path + os.path.basename(f) for f in locators]\n",
- "\n",
- "print(image_docker)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 109,
- "id": "cc01ad0f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['http://host.docker.internal:8222/image4.jpg',\n",
- " 'http://host.docker.internal:8222/image1.jpg',\n",
- " 'http://host.docker.internal:8222/image3.jpg',\n",
- " 'http://host.docker.internal:8222/image0.jpg',\n",
- " 'http://host.docker.internal:8222/image2.jpg']"
- ]
- },
- "execution_count": 109,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "image_docker"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "id": "6b718830",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[{'image_docker': 'http://host.docker.internal:8222/image4.jpg', '_id': '0'}, {'image_docker': 'http://host.docker.internal:8222/image1.jpg', '_id': '1'}, {'image_docker': 'http://host.docker.internal:8222/image3.jpg', '_id': '2'}, {'image_docker': 'http://host.docker.internal:8222/image0.jpg', '_id': '3'}, {'image_docker': 'http://host.docker.internal:8222/image2.jpg', '_id': '4'}]\n"
- ]
- }
- ],
- "source": [
- "documents = [{\"image_docker\" : image, \"_id\" : str(idx)} for idx, image in enumerate(image_docker)]\n",
- "\n",
- "print(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "id": "f8b4d6c4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'image_docker': 'http://host.docker.internal:8222/image4.jpg', '_id': '0'},\n",
- " {'image_docker': 'http://host.docker.internal:8222/image1.jpg', '_id': '1'},\n",
- " {'image_docker': 'http://host.docker.internal:8222/image3.jpg', '_id': '2'},\n",
- " {'image_docker': 'http://host.docker.internal:8222/image0.jpg', '_id': '3'},\n",
- " {'image_docker': 'http://host.docker.internal:8222/image2.jpg', '_id': '4'}]"
- ]
- },
- "execution_count": 97,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 98,
- "id": "223002e7",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-12-30 04:27:45,846 logger:'marqo' INFO add_documents pre-processing: took 0.000s for 5 docs, for an average of 0.000s per doc.\n",
- "2022-12-30 04:27:45,848 logger:'marqo' INFO starting batch ingestion with batch size 1\n",
- "2022-12-30 04:27:47,130 logger:'marqo' INFO add_documents batch 0 roundtrip: took 1.281s to add 1 docs, for an average of 1.281s per doc.\n",
- "2022-12-30 04:27:47,131 logger:'marqo' INFO add_documents batch 0 Marqo processing: took 1.275s for Marqo to process & index 1 docs (server unbatched), for an average of 1.275s per doc.\n",
- "2022-12-30 04:27:48,182 logger:'marqo' INFO add_documents batch 1 roundtrip: took 1.050s to add 1 docs, for an average of 1.050s per doc.\n",
- "2022-12-30 04:27:48,183 logger:'marqo' INFO add_documents batch 1 Marqo processing: took 1.045s for Marqo to process & index 1 docs (server unbatched), for an average of 1.045s per doc.\n",
- "2022-12-30 04:27:49,255 logger:'marqo' INFO add_documents batch 2 roundtrip: took 1.070s to add 1 docs, for an average of 1.070s per doc.\n",
- "2022-12-30 04:27:49,256 logger:'marqo' INFO add_documents batch 2 Marqo processing: took 1.064s for Marqo to process & index 1 docs (server unbatched), for an average of 1.064s per doc.\n",
- "2022-12-30 04:27:50,386 logger:'marqo' INFO add_documents batch 3 roundtrip: took 1.129s to add 1 docs, for an average of 1.129s per doc.\n",
- "2022-12-30 04:27:50,388 logger:'marqo' INFO add_documents batch 3 Marqo processing: took 1.117s for Marqo to process & index 1 docs (server unbatched), for an average of 1.117s per doc.\n",
- "2022-12-30 04:27:51,462 logger:'marqo' INFO add_documents batch 4 roundtrip: took 1.073s to add 1 docs, for an average of 1.073s per doc.\n",
- "2022-12-30 04:27:51,463 logger:'marqo' INFO add_documents batch 4 Marqo processing: took 1.067s for Marqo to process & index 1 docs (server unbatched), for an average of 1.067s per doc.\n",
- "2022-12-30 04:27:51,573 logger:'marqo' INFO completed batch ingestion.\n",
- "2022-12-30 04:27:51,575 logger:'marqo' INFO add_documents completed. total time taken: 5.729s.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[{'errors': False,\n",
- " 'processingTimeMs': 1275.352,\n",
- " 'index_name': 'image-search-guide',\n",
- " 'items': [{'_id': '0', 'result': 'created', 'status': 201}]},\n",
- " {'errors': False,\n",
- " 'processingTimeMs': 1045.022,\n",
- " 'index_name': 'image-search-guide',\n",
- " 'items': [{'_id': '1', 'result': 'created', 'status': 201}]},\n",
- " {'errors': False,\n",
- " 'processingTimeMs': 1064.125,\n",
- " 'index_name': 'image-search-guide',\n",
- " 'items': [{'_id': '2', 'result': 'created', 'status': 201}]},\n",
- " {'errors': False,\n",
- " 'processingTimeMs': 1116.58,\n",
- " 'index_name': 'image-search-guide',\n",
- " 'items': [{'_id': '3', 'result': 'created', 'status': 201}]},\n",
- " {'errors': False,\n",
- " 'processingTimeMs': 1067.055,\n",
- " 'index_name': 'image-search-guide',\n",
- " 'items': [{'_id': '4', 'result': 'created', 'status': 201}]}]"
- ]
- },
- "execution_count": 98,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mq.index(index_name).add_documents(documents, tensor_fields=[\"image_docker\"], device=\"cpu\", client_batch_size= 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "078e6114",
- "metadata": {},
- "source": [
- "### Search"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 99,
- "id": "f6b8d943",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-12-30 04:28:56,472 logger:'marqo' INFO search (tensor): took 0.364s to send query and received 1 results from Marqo (roundtrip). Marqo itself took 0.358s to execute the search.\n"
- ]
- }
- ],
- "source": [
- "search_results = mq.index(index_name).search(\"A rider on a horse jumping over the barrier\", \n",
- " searchable_attributes=['image_docker'], limit = 1,\n",
- " device='cpu')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 103,
- "id": "8c505794",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'hits': [{'image_docker': 'http://host.docker.internal:8222/image1.jpg',\n",
- " '_id': '1',\n",
- " '_highlights': {'image_docker': 'http://host.docker.internal:8222/image1.jpg'},\n",
- " '_score': 0.6133688}],\n",
- " 'processingTimeMs': 358,\n",
- " 'query': 'A rider on a horse jumping over the barrier',\n",
- " 'limit': 1}"
- ]
- },
- "execution_count": 103,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "search_results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 107,
- "id": "f95e588d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import requests\n",
- "from PIL import Image\n",
- "\n",
- "fig_path = search_results[\"hits\"][0][\"image_docker\"].replace(docker_path, local_dir)\n",
- "display(Image.open(fig_path))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "44a0a63a",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "conda_marqo-client",
- "language": "python",
- "name": "conda_marqo-client"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.15"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/ImageSearchLocalization/app.py b/examples/ImageSearchLocalization/app.py
deleted file mode 100644
index 646ce1d88..000000000
--- a/examples/ImageSearchLocalization/app.py
+++ /dev/null
@@ -1,191 +0,0 @@
-from PIL import Image, ImageOps, ImageDraw
-import streamlit as st
-from tempfile import NamedTemporaryFile
-import validators
-import requests
-import os
-import urllib3
-urllib3.disable_warnings()
-
-from marqo import Client
-
-client = Client()
-
-def load_image_from_path(image):
- """loads an image into PIL from a string path that is
- either local or a url
- Args:
- image (str): _description_
- Raises:
- ValueError: _description_
- Returns:
- ImageType: _description_
- """
-
- if os.path.isfile(image):
- img = Image.open(image)
- elif validators.url(image):
- img = Image.open(requests.get(image, stream=True).raw)
- else:
- raise ValueError(f"input str of {image} is not a local file or a valid url")
-
- return img
-
-def render_images(images, captions=None, boxes=None):
- """renders a list of image pointers
-
- Args:
- images (_type_): _description_
- captions (_type_, optional): _description_. Defaults to None.
- """
-
- if boxes is None:
- images = [ImageOps.expand(load_image_from_path(image), border=100, fill=(255,255,255) ) for image in images]
- else:
- images = [ImageOps.expand(draw_box(load_image_from_path(image), box), border=100, fill=(255,255,255) ) for box,image in zip(boxes,images)]
-
-
- if captions is None:
- st.image(images, use_column_width=False, width=230)
- else:
- st.image(images, caption=captions, use_column_width=False)
-
-
-def load_image(image_file):
- """loads an image using PIL
-
- Args:
- image_file (_type_): _description_
-
- Returns:
- _type_: _description_
- """
- img = Image.open(image_file)
- return img
-
-def draw_box(img, box):
- """draws a rectangle box on a PIL image
-
- Args:
- img (_type_): _description_
- box (_type_): _description_
-
- Returns:
- _type_: _description_
- """
- img1 = ImageDraw.Draw(img)
- img1.rectangle(box, outline = 'orange', width = 8)
- return img
-
-def main():
-
- #########################################
- # SETUP SOME PARAMTERS
- # this is for temporary storage of an image
- temp_image_dir = os.getcwd() + '/'
- # the field name for the image
- image_location = 'image_location'
- # some enums
- highlights = '_highlights'
- hits = 'hits'
-
- # specify the device
- device = 'cuda'
-
- docker_image_server_prefix = 'http://host.docker.internal:8222/'
- local_image_location = os.getcwd() + '/images/'
-
- #########################################
-
- st.title("image search with localisation")
-
- sentence = st.text_input("Please enter some text to start searching...")
-
- s1,s2,s3,s4,s5 = st.columns(5)
-
- options = ['None', 'yolox', 'dino/v2']
-
- option = s4.radio('Select the indexing method', options)
-
- if option == options[0]:
- index_name = 'visual-search'
- elif option == options[1]:
- index_name = 'visual-search-yolox'
- elif option == options[2]:
- index_name = 'visual-search-dino-v2'
- else:
- raise ValueError(f"unexpected option for {option}")
-
- option_reranker = s5.radio('Select the reranker',
- ['None', "google/owlvit-base-patch32"])
-
- if option_reranker == 'None':
- reranker = None
- else:
- reranker = option_reranker
-
- form1 = s1.form(key='my-form1')
- submit1 = form1.form_submit_button('Search with tensor...')
-
- form2 = s2.form(key='my-form2')
- submit2 = form2.form_submit_button('Search with tensor localisation...')
-
- image_file = s3.file_uploader("Upload Images", type=["png", "jpg", "jpeg"])
-
- if submit1:
- st.text("searching using '{}'...".format(sentence))
- res = client.index(index_name).search("{}".format(sentence), searchable_attributes=[image_location], reranker=reranker, device=device)
-
- if len(res[hits]) == 0:
- st.text(f"No results found for {sentence}")
- else:
- images = [i[image_location].replace(docker_image_server_prefix, local_image_location) for i in res['hits']]
- render_images(images)
-
- if submit2:
- st.text("searching using '{}'...".format(sentence))
- res = client.index(index_name).search("{}".format(sentence), searchable_attributes=[image_location], reranker=reranker, device=device)
- # get the image
- if len(res[hits]) == 0:
- st.text(f"No results found for {sentence}")
- else:
- # get the image
- images = [i[image_location].replace(docker_image_server_prefix, local_image_location) for i in res['hits']]
- boxes = [i[highlights][image_location] for i in res[hits]]
-
- # render text
- render_images(images, boxes=boxes)
-
-
- if image_file is not None:
-
- temp_file = NamedTemporaryFile(delete=False)
- if image_file:
- temp_file.write(image_file.getvalue())
-
- save_name = f'{temp_image_dir}temp.png'
- image = load_image(temp_file.name)
- image.save(save_name)
-
- query = save_name.replace(data_dir, docker_image_server_prefix)
- # To View Uploaded Image
- st.image(image, width=250)
-
- res = client.index(index_name).search("{}".format(query), searchable_attributes=[image_location],
- limit=9, device=device)
-
- # get the image
- if len(res['hits']) == 0:
- st.text(f"No results found for {sentence}")
- else:
- images = [i[image_location].replace(docker_image_server_prefix, local_image_location) for i in res['hits']]
-
- boxes = [i[highlights][image_location] for i in res[hits]]
-
- # render text
- render_images(images, boxes=boxes)
-
-
-
-
-main()
diff --git a/examples/ImageSearchLocalization/article.md b/examples/ImageSearchLocalization/article.md
deleted file mode 100644
index bc0d2942d..000000000
--- a/examples/ImageSearchLocalization/article.md
+++ /dev/null
@@ -1,216 +0,0 @@
-# Image search with localization and open-vocabulary reranking using Marqo, yolox, CLIP and OWL-ViT
-
-TL;DR: Here we show how image search can be evolved to add localization and re-ranking by leveraging Marqo, yolox, CLIP and OWL-ViT. Adding the extra dimension of localization can improve retrieval performance and enable new use cases for image search while also helping with explainability. Re-ranking with an open vocabulary detection model allows for even finer-grained localsiation. The first part of the article covers background information while the second part contains working code (also found [here](https://github.com/marqo-ai/marqo/blob/mainline/examples/ImageSearchLocalization/index_all_data.py)).
-
-
- 
-
-
-## Introduction
-
-Image search has come a long way. Originally if you wanted to search a collection of images you would use keyword based search across manually curated meta-data. Vector representations followed and provided an avenue for more direct ways to query the image content. This has developed even more with the advent of cross-modal models like CLIP that allow searching images with natural language. Here we show it can be evolved further using a modern search stack that adds localization and the ability to re-rank.
-
-## Image Search
-Popular modern image search (or image retrieval) is often based on embedding images into a latent space (e.g. by transforming images into vectors and tensors). A query is embedded into the same space while search results are found by finding the closest matching embedding and returning their corresponding images.
-
-
- 
-
-
-This single-stage retrieval based on embeddings is the same one that has been popular in many natural language processing applications like information retrieval, question and answering and chatbots. In many of these applications the matching documents are not just presented as part of the results but the part of the text that is the best match is also highlighted. This highlighting is what we can bring to image search via localization.
-
-
- 
-
-
-## Image Search + Localisation
-
-There are a number of ways to get localization in image search. There is a strong latency:relevancy trade off as more sophisticated methods take longer to process. However, there are two broad categories of localization - (1) heuristic - where a heuristic is used to obtain localization and, (2) model - where another ML model is used to provide the localization. The localization can also happen at the time of indexing ('index-time partitioning') or after an initial set of search results have been returned ("search-time localization"). The latter is akin to a second stage re-ranker from traditional two stage retrieval systems.
-
-### Index-time Partitioning
-
-
- 
-
-
-Here we will explain index-time partitioning for localization. In the indexing step, the image is partitioned into patches. Each of these patches and the original image are embedded and then stored in an index. This has the advantage that the time penalty is (mostly) paid off when indexing instead of searching.
-In the retrieval step the query is embedded and compared not just to the original image but to all the patches as well. This now allows the location of the sub-image to also be returned.
-
-
- 
-
-
-Variations of this approach can also be used to do 'augment-time-indexing'. Instead of the image being broken into sub-patches it is augmented any number of times using any number of operations. Each of these augmented images are then stored in the same way the sub-patches were.
-
-### Heuristic partitioning methods
-As explained above, one of the simplest ways to get localisation in image search is to partition the image into patches. This is done using a rule or heuristic to crop the image into other sub-images and store the embeddings for each of these patches. The simplest scheme for images is to split the image into an N x M equally sized patches and embed those. More sophisticated methods can be performed by other machine learning models like object detectors.
-
-
- 
-
-
-##S# Model based partitioning methods
-For the model based approaches, ideally we want "important" or relevant parts of the image to be detected by a model and proposed as the sub-images. Different use cases will have different requirements but surprisingly we can do some pretty good stuff with pretty generic models. To achieve this we can exploit some properties of object detectors and attention (i.e. transformers) based models.
-
-
- 
-
-
-For example, in two-stage detectors like Faster-RCNN the first stage consists of a region-proposal netwrok (RPN). The RPN is used to propose regions of the image that contains objects of interest and happens before any fine-grained classification occurs. The RPN is trainable and can be used to propose possible interesting parts of an image. Alternatives are to simply use a fast lighter-weight detector like yolo and make the output boxes the proposed regions (ignoring class) or use "objectness" scores to rank the proposed (now class agnostic) boxes. Finally, other alternatives exist like models that output "saliency" maps which can be obtained from supervised learning or through self-supervised methods like DINO. DINO has the added benefit that since it is self-supervised, it makes fine-tuning on custom datasets simple and a way to provide domain specific localisation.
-
-## Re-ranking
-An alternative approach to single-stage retrieval is two-stage retrieval. Two-stage retrieval consists of an initial retrieval of documents which are then reranked by a localization model or heuristic. The first stage is where the initial candidate documents are retrieved and the second stage can re-rank (i.e. re-order) the results based on another model or heuristic.
-
-
- 
-
-
-One of the reasons for this type of architecture is to strike a balance between speed and relevancy. For example, the first stage can trade off speed and relevancy to provide a fast initial selection while the re-ranker can then provide a better final ordering by using a different model. The re-ranker can be used to add additional diversity or context (e.g. personalisation) to the results ranking or to add other things like localization (for images or videos). The diagram above has an illustrated example of this - the first stage retrieval of images comes from dense embeddings (e.g. from CLIP) while the second-stage re-ranker re-orders them based on a second (different) model.
-
-### Search-time localization as re-ranking
-As we saw earlier, localization can be introduced by dividing the images into patches at index-time and then searching across the image and child patches. An alternative approach is to defer the localization to the second stage via a reranker.
-
-
- 
-
-
-There are multiple ways to do this, for example, you could do what was done at indexing time and divide each image in the retrieved results. However, doing that on its own ignores the crucial thing that we have now - the query. If we blindly divide the images and try and then match the query to the patches the additional information from the query is not used as effectively as it could. Instead, the proposal mechanism can be conditioned on the query. From this the results can then be re-ordered, for example by using the score that comes from the proposed regions.
-
-
- 
-
-
-Conditioning the proposals based on the query has its roots in tasks like visual question and answering. The way this differs from other object detection problems is that the output is no longer restricted to a fixed vocabulary of objects but can take free form queries ('open vocabulary'). One good candidate model for this is OWL-ViT (Vision Transformer for Open-World Localization). OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses CLIP as its backbone, while a vision transformer and a causal language model are used for the visual and text features respectively. Open-vocabulary classification is enabled by replacing the classification output with the class-name embeddings obtained from the text model.
-
-# Putting it all together
-
-In the previous section it was explained how image search works in general and how localization can be incorporated at both index and search time. In this section a full example with working code will be used to demonstrate each of these things in practice.
-
-## Image dataset
-For this example we are using about 10,000 images of various everyday objects. Here are some example images:
-
-
- 
-
-
-We are going to index this dataset using a couple of different methods and then search with and without the localization based reranker.
-
-## Starting Marqo
-
-We will be using Marqo to do the image search with localization that was explained previously (full code is also here). To start Marqo run the following from your terminal (assuming a cuda compatible GPU):
-
-```
-docker run --name marqo -it --privileged -p 8882:8882 --gpus all --add-host host.docker.internal:host-gateway -e MARQO_MODELS_TO_PRELOAD='[]' marqoai/marqo:0.0.10
-```
-
-If no GPU is available remove the --gpus all flag from the above command.
-
-## Preparing the documents
-
-We can either use the s3 urls directly or you can download the images and use them locally (see here for details). For now we will use the urls directly and create the documents for indexing.
-
-```python
-import pandas as pd
-import os
-
-df = pd.read_csv('files.csv')
-documents = [{"image_location":s3_uri, '_id':os.path.basename(s3_uri)} for s3_uri in df['s3_uri']]
-```
-
-## Indexing with localization
-Now we have the document we are going to index them using no index-time localization, using DINO and using yolox. We setup the client and the base settings.
-
-```python
-from marqo import Client
-client = Client()
-
-# setup the settings so we can comapre the different methods
-patch_methods = [None, "dino-v2", "yolox"]
-
-settings = {
- "index_defaults": {
- "treat_urls_and_pointers_as_images": True,
- "image_preprocessing": {
- "patch_method": None
- },
- "model":"ViT-B/32",
- "normalize_embeddings":True,
- },
-}
-```
-
-To use the different methods we change the method name. We will iterate through each method and index the images in a different index.
-
-```python
-for patch_method in patch_methods:
-
- index_name = f"visual_search-{str(patch_method).lower()}"
-
- settings['index_defaults']['image_preprocessing']['patch_method'] = patch_method
-
- response = client.create_index(index_name, settings_dict=settings)
-
- # index the documents on the GPU
- response = client.index(index_name).add_documents(documents, tensor_fields =["image_location"], device='cuda', client_batch_size=50)
-```
-
-If no GPU is available, set device='cpu'.
-
-## Searching with localization
-
-Now we will demonstrate how to use the two different methods to get localization in image search.
-
-### Search using index time localization
-We can now perform some searches against our indexed data and see the localization.
-
-```python
-response = client.index(index_name).search("brocolli", device="cuda")
-print(response['hits'][0])
-```
-
-We can see in the highlights field the coordinates of the bounding box that best matched the query.
-
-```python
-bbox = response['hits']['_highlights']['image_location']
-print(bbox)
-```
-
-The top six results are shown below with their corresponding top bounding box highlight.
-
-
- 
-
-
-The method here uses a pre-trained yolox model to propose the bounding boxes at indexing time and each of the sub-images are indexed alongside the original. Some filtering and non-max suppression (NMS) is applied and the maximum number of proposals per image is capped at ten. The class agnostic scores are used for the NMS. We can see the results from another method which is named dino-v2.
-
-
- 
-
-
-Dino-v2 uses base transformer models from DINO which is a self supervised representation learning method. Apart from being used as a pre-training step the attention maps from these models tend to focus on object within the images. These attention maps can be used to determine the salient or important parts of the images. The nice thing about this method is it is self-supervised and does not require labels or bounding boxes. It is also amenable to fine-tuning on domain specific data to provide better localization for specific tasks. The difference between dino-v1 and dino-v2 is that the proposals for v2 are generated per attention map, while v1 uses a summed attention map. This means the v1 generates fewer proposals than v2 (and means less storage is required).
-
-## Search using search time localization
-As described earlier, the alternative way to get localization is to have an object detector acting as a reranker and localizer. In Marqo we can specify the model here for re-ranking. The re-ranking model is OWL-ViT. OWL-ViT is an open vocabulary object detector that generates proposals after conditioning with a text prompt (or query). This conditional localisation is ideal to use as a reranker since we have the query to condition the model with for localisation.
-
-```python
-response = client.index(index_name).search("brocolli", device="cuda",
- searchable_attributes=['image_location'], reranker="owl/ViT-B/32")
-print(response['hits'][0])
-```
-
-The localisation provided by the reranker does not require any index time localisation. It can even be used with lexical search which does not use any embeddings for the first stage retrieval.
-We can see in the highlights field the coordinates of the bounding box that best matched the query after reranking,
-
-```python
-bbox = response['hits']['_highlights']['image_location']
-print(bbox)
-```
-
-and we can plot these results as well. The localisation is better here as the proposals are done in conjunction with the query.
-
-
- 
-
-
-# Conclusion
-We have shown how using a two-stage retrieval system can enable multiple avenues for adding localisation to image search. We showed how yolox and DINO could be leveraged to provide index time localisation. OWL-ViT was shown as a second stage reranker that also provides localisation. The methods discussed allow for a variety of trade-offs, including speed and relevency. To see how many more applications like this can be built, check out Marqo!
\ No newline at end of file
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@@ -1,9825 +0,0 @@
-,s3_uri
-0,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_long_distance_photo_with_a_airplane_in_it12e408b9-f10a-41d7-8bd7-dd66cc816201.jpg
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-9809,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itc97f8044-e798-4712-b31c-19816c3ba0b4.jpg
-9810,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itd54a663e-9ce5-449b-b7a1-99517289a504.jpg
-9811,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itd892b6a2-1fb0-42d6-bc10-ccf381fff857.jpg
-9812,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itd9c732a1-0c53-4041-839a-9f75e6418621.jpg
-9813,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_ite1143a77-794a-4534-bceb-223d4a62ba65.jpg
-9814,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_ite1334483-b86c-44cc-bfb5-78f5b429d502.jpg
-9815,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_ite2a40234-703d-49ea-a2d5-8df5fdfea233.jpg
-9816,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_ite47ee2e2-d5ba-4af4-af33-fa0f3e3d4760.jpg
-9817,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_ite58fce3d-d372-4aa8-97a5-464f2e32a5f4.jpg
-9818,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_iteb5feb36-df93-4a71-b1ed-6a7ba3dcff82.jpg
-9819,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itebbd604b-5aa1-41eb-8695-9cc4a9ac5a93.jpg
-9820,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itf075da13-5615-43c9-9e85-359e870aa05c.jpg
-9821,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itf2d32421-1f22-4666-a9ac-97a7b63d19f4.jpg
-9822,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itfb476617-ad62-4f7d-aad8-d794afa231d7.jpg
-9823,https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images/a_high_resolution_photo_of_a_street_with_a_zebra_in_itffa6cb1c-73e7-4467-a845-2e183cd06b18.jpg
diff --git a/examples/ImageSearchLocalization/index_all_data.py b/examples/ImageSearchLocalization/index_all_data.py
deleted file mode 100644
index dc1b83c1f..000000000
--- a/examples/ImageSearchLocalization/index_all_data.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#####################################################
-### STEP 0. Import and define any helper functions
-#####################################################
-
-from marqo import Client
-import os
-import pandas as pd
-from utils import download_data
-
-#####################################################
-### STEP 1. start Marqo
-#####################################################
-
-# Follow the instructions here https://github.com/marqo-ai/marqo
-
-#####################################################
-### STEP 2. Get the data for indexing
-#####################################################
-
-
-# this will pull directly from the s3 bucket if True, otherwise it will pull for local indexing
-use_remote = False
-in_docker = True
-
-data = pd.read_csv('files.csv', index_col=0)
-docker_path = 'http://host.docker.internal:8222/'
-local_dir = os.getcwd() + '/images/'
-
-locators = download_data(data=data, download_dir=local_dir, use_remote=use_remote, in_docker=in_docker, docker_path=docker_path)
-
-documents = [{"image_location":s3_uri, '_id':os.path.basename(s3_uri)} for s3_uri in locators]
-
-# if you have the images locally, see the instructions
-# here https://marqo.pages.dev/Advanced-Usage/images/ for the best ways to index
-
-
-#####################################################
-### STEP 3. Create the index(s)
-######################################################
-
-client = Client()
-
-# setup the settings so we can comapre the different methods
-index_name_prefix = "visual-search"
-patch_methods = ["dino/v1", None, "yolox"] #["dino/v1", "dino/v2", "frcnn", None, "yolox"]
-model_name = "ViT-B/32"
-batch_size = 24
-
-# set this to false if you do not want to delete the previous index of the same name
-delete_index = True
-
-settings = {
- "index_defaults": {
- "treat_urls_and_pointers_as_images": True,
- "image_preprocessing": {
- "patch_method": None
- },
- "model":None,
- "normalize_embeddings":True,
- },
-}
-
-for patch_method in patch_methods:
-
- suffix = '' if patch_method is None else f"-{patch_method.replace('/','-')}"
- index_name = index_name_prefix + suffix
-
- # update the settings we want to use
- settings['index_defaults']['model'] = model_name
- settings['index_defaults']['image_preprocessing']['patch_method'] = patch_method
-
- # optionally delete the index if it exists
- if delete_index:
- try:
- client.delete_index(index_name)
- except:
- print("index does not exist, cannot delete")
-
- # create the index with our settings
- response = client.create_index(index_name, settings_dict=settings)
-
-
- response = client.index(index_name).add_documents(documents, device='cuda', tensor_fields=["image_location"], client_batch_size=batch_size)
\ No newline at end of file
diff --git a/examples/ImageSearchLocalization/utils.py b/examples/ImageSearchLocalization/utils.py
deleted file mode 100644
index e63a7380f..000000000
--- a/examples/ImageSearchLocalization/utils.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import multiprocessing as mp
-from urllib.request import urlretrieve
-from pathlib import Path
-import os
-from functools import partial
-from typing import List
-import numpy as np
-import zipfile
-import glob
-import subprocess
-
-def download_data(data, download_dir, use_remote=True, in_docker=True, docker_path='http://host.docker.internal:8222/'):
-
- local_dir = download_dir
- if not use_remote:
- zip_file_url = 'https://marqo-public-datasets.s3.us-east-2.amazonaws.com/demos/ImageSearchLocalisation/images.zip'
- zip_file = download_file(zip_file_url, local_dir=local_dir)
- extract_zip(zip_file, local_dir=local_dir)
- # alternatively you can download the files individually
- #downloaded = download_parallel(urls=data['s3_uri'].tolist(), local_dir=local_dir)
- locators = glob.glob(local_dir + '*.jpg')
-
-
- # we start an image server for easier access from within docker
- pid = subprocess.Popen(['python3', '-m', 'http.server', '8222', '--directory', local_dir], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
- if in_docker:
- locators = [docker_path + os.path.basename(f) for f in locators]
- else:
- # now we create our documents for indexing - a list of python dicts with keys as field names
- locators = data['s3_uri']
-
- return locators
-
-def download_files(urls: str, local_dir: str) -> List[str]:
-
- results = []
- N = len(urls)
- for ii,url in enumerate(urls):
- result = download_file(url, local_dir)
- results.append(result)
- if ii % 10 == 0:
- print(f"{round(100*(ii+1)/N, 3)}%")
- return results
-
-def download_file(url: str, local_dir: str) -> str:
- """_summary_
-
- Args:
- url (str): _description_
- local_dir (str): local directory to download to
- """
-
- if not local_dir.endswith('/'): local_dir += '/'
-
- Path(local_dir).mkdir(exist_ok=True, parents=True)
-
- full_local_path = local_dir + os.path.basename(url)
-
- if not os.path.isfile(full_local_path):
- full_local_path, _ = urlretrieve(url, full_local_path)
-
- return full_local_path
-
-def download_parallel(urls: List[str], download_to_dir: str, n_processes=8) -> List[str]:
-
- N = len(urls)
- print(f"downloading {N} urls to {download_to_dir} using {n_processes} processes")
-
- func = partial(download_files, local_dir=download_to_dir)
-
- urls_split = np.array_split(urls, n_processes)
- urls_split = [split.tolist() for split in urls_split]
- with mp.Pool(n_processes) as pool:
- results = pool.map(func, urls_split)
-
- return results
-
-def extract_zip(zip_file, local_dir):
-
- with zipfile.ZipFile(zip_file, 'r') as zip_ref:
- zip_ref.extractall(local_dir)
diff --git a/examples/MultiLingual/README.md b/examples/MultiLingual/README.md
deleted file mode 100644
index 5ab3105cb..000000000
--- a/examples/MultiLingual/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-1. Clone the repository.
-2. Run Marqo, for instructions refer to the getting started guide.
-3. This demo is best run on a machine with a cuda-based GPU. If your machine doesn't have a cuda-based GPU, then go to the
-top of the `MultiLingual/eu_legal.py` file and set `DEVICE` to `cpu`.
-4. `pip install` all the imported packages found at the start of the `MultiLingual/eu_legal.py` file, if not yet installed
-5. `cd` into the `MultiLingual` directory.
-6. Run the code using `python3 eu_legal.py`
diff --git a/examples/MultiLingual/article.md b/examples/MultiLingual/article.md
deleted file mode 100644
index d781b61b4..000000000
--- a/examples/MultiLingual/article.md
+++ /dev/null
@@ -1,164 +0,0 @@
-# How I used Marqo to create a multilingual legal database in 5 key lines of code
-
-*A [machine learning transformer model](https://openai.com/dall-e-2/)
-from [OpenAI](https://openai.com/)
-generated this image. A transformer model will also power the following multilingual search solution.*
-
-
-The European Union has to deal with a peculiar problem - it has 24 official languages across 27 countries and these countries must abide by EU law. Experts in EU law have the complex task of navigating legal material in multiple languages.
-
-What if there was a system where a user (like a lawyer) could search through a database of documents in their preferred language, and get the closest matching document in another? What if this user wanted to give access to this database to a colleague that uses a different language?
-In this article, we present a solution that can search across multiple languages using a multilingual legal database built using Marqo, an open source tensor search engine, in just 5 key lines of code.
-## The dataset
-
-The MultiEURLEX dataset is a collection of 65 thousand laws in 23 EU languages. EU laws are published in all member languages. This means that we may come across the same law in multiple languages.
-
-## Scope for this proof of concept
-In the interest of time and for ease of replication, this proof-of-concept will be a database to store documents from two languages: Deutsch and English.
-We will also only use the dataset's validation splits with 5000 documents from each language.
-Note that the machine learning model that Marqo will be using, _stsb-xlm-r-multilingual_ (more about this model can be found
-[here](https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models) and
-[here](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual))
-can handle many more languages
-[than just these two](https://metatext.io/models/sentence-transformers-stsb-xlm-r-multilingual).
-
-The solution was run on an _ml.g4dn.2xlarge_ AWS machine. This comes with a Nvidia T4 GPU.
-The GPU speeds up the Marqo machine learning model which processes our documents as we insert them.
-These AWS machines are very easy to set up as
-[SageMaker Jupyter Notebook instances](https://aws.amazon.com/pm/sagemaker/).
-
-## The solution
-If we were to develop this on a traditional SQL database or search engine, we'd have to manually create a translation
-layer to process the queries, or link each document with handcrafted translations.
-
-An example of this would be to translate all the documents into English as they are stored. The search query would also
-be translated into English, and a keyword search would be performed using a technology like Elasticsearch. However this
-is problematic as a translated sentence is a lossy approximation of the source language and it introduces a significant component (real-time translation) into the system. This results in poorer search relevancy, worse latency, and additional system complexity.
-
-Tensor search, the technology that powers Marqo, outperforms traditional keyword search methods.
-
-First, we set up a Marqo instance on the machine, which has docker installed. Notice the `--gpus all` option.
-This allows Marqo to use GPUs it finds on the machine. If the machine you are using doesn't have GPUs, then remove this option from the command.
-```sh
-docker rm -f marqo;
-docker run --name marqo -it --privileged -p 8882:8882 --gpus all --add-host host.docker.internal:host-gateway marqoai/marqo:latest
-```
-We use pip to install the Marqo client (`pip install marqo`) and the datasets python package (`pip install datasets`).
-We will use the `datasets` package from [Hugging Face](https://huggingface.co/docs/datasets/index)
-to import the MultiEURLEX dataset.
-
-Then, we start work on our Python script. We start by loading the the validation splits for the English and Deutsch datasets:
-```python
-from datasets import load_dataset
-dataset_en = load_dataset('multi_eurlex', 'en', split="validation")
-dataset_de = load_dataset('multi_eurlex', 'de', split="validation")
-```
-We then import Marqo and set up the client. We tell the Marqo client to connect with the Marqo Docker container that we ran earlier.
-
-```python
-from marqo import Client
-mq = Client("http://localhost:8882")
-```
-Then, add a line telling Marqo to create the multilingual index:
-```python
-mq.create_index(index_name='my-multilingual-index', model='stsb-xlm-r-multilingual')
-```
-Notice that here is where we tell Marqo what model to use. After this, we'll iterate through each dataset, indexing each document as we go.
-One small adjustment we'll make is to split up text of very long documents (of over 100k chars) to make it easier to index and search.
-At the end of each loop, we call the `add_documents()` function to insert the document:
-```python
-mq.index(index_name='my-multilingual-index').add_documents(
- device='cuda', auto_refresh=False,
- documents=[{
- "_id": doc_id,
- "language": lang,
- 'text': sub_doc,
- 'celex_id': doc['celex_id'],
- 'labels': str(doc['labels'])
- }],
- tensor_fields=["language", "text", "labels"]
-)
-```
-Here we set the device argument as `"cuda"`. This tells Marqo to use the GPU it finds on the machine to index the document.
-If you don't have a GPU, remove this argument or set it to `"cpu"`. We encourage using a GPU as it will make the `add_documents`
-process significantly faster (our testing showed a 6–12x speed up).
-
-We also set the `auto_refresh` argument to `False`. When indexing large volumes of data we encourage you to set this to False, as it optimises the `add_documents` process.
-
-And that's the indexing process! Run the script to fill up the Marqo index with documents. It took us around 45 minutes
-with an AWS _ml.g4dn.2xlarge_ machine.
-
-### Searching the index
-
-
-We'll define the following search function that sets some parameters for the call to Marqo:
-```python
-# pprint is an inbuilt python formatter package that prints data in a readable way
-import pprint
-
-def search(query: str):
- result = mq.index(index_name='my-multilingual-index').search(q=query, searchable_attributes=["text"])
- for res in result["hits"]:
- pprint.pprint(res["_highlights"])
-```
-The first thing to notice is the call to the Marqo `search()` function. We set `searchable_attributes` to the `"text"` field.
-This is because this is the field that holds the content relevant for searching.
-
-We could print out the result straight away, but it contains the full original documents. These can be huge. Instead,
-we'll just print out the highlights from each document. These highlights also show us what part of the document Marqo
-found most relevant to the search query. We do this by printing the `_highlights` attribute from each hit.
-
-We search by passing a string query to the search function. For the search with query string:
-
-`"Laws about the fishing industry"`
-
-We get the following results as the top 2 highlights:
-```
-{'text': 'Consequently, catch limits and fishing effort limits for the cod stocks in the Baltic Sea should be established in accordance with the rules laid down in Council Regulation (EC) No 1098/2007 of 18 '...
-
-{'text': '(18)\n'
- 'Bei der Nutzung der Fangmöglichkeiten ist geltendes Unionsrecht uneingeschränkt zu befolgen -\n'
- 'HAT FOLGENDE VERORDNUNG ERLASSEN:\n'
- 'TITEL I\n'
- 'GELTUNGSBEREICH UND BEGRIFFSBESTIMMUNGEN\n'
- 'Artikel 1\n'...
-```
-The second result is from a German document. Using Google Translate, the German document's first line translates to
-
-`When using the fishing opportunities, applicable Union law to be strictly followed`
-
-Using Google Translate to translate the original fishing law query string into Deutsch gives us:
-
-`"Gesetze über die Fischereiindustrie"`
-
-Searching with this string gives us similar results to the English query. The first result is an English document, with the same highlight as the English query. Marqo identifies both queries strings as having similar meaning.
-
-Because we added the language code as a property of each document, we can filter for certain languages. We add a filter string to the search query:
-```python
-mq.index(index_name='my-multilingual-index').search(
- q=query,
- searchable_attributes=['text'],
- filter_string='language:en'
-)
-```
-Searching with this filter for `"Gesetze über saubere Energie"` (Google translation of `"Laws about clean energy"`) yields only English language results. The top 3 results are:
-```
-The electricity and water consumptions of products subject to this Regulation should be made more efficient by applying existing…
-
-Products subject to this Regulation should be made more energy efficient by applying existing non-proprietary cost-effective…
-
-The electricity consumption of products subject to this Regulation should be made more efficient by applying existing non-proprietary cost-effective technologies that can reduce the combined costs of purchasing and operating these products…
-```
-## Conclusion
-
-Marqo is a tensor search engine that can be deployed in just 3 lines of code and solve search problems using the latest
-ML models from HuggingFace and OpenAI. In this article I showed how I used Marqo to quickly set up a multilingual legal database.
-
-Marqo makes tensor search easy. Without needing to be a machine learning expert, you can use cutting edge machine
-learning models to create an unrivalled search experience with minimal code. Check out the full code for the demo
-[here](eu_legal.py). Check out (and contribute, if you can!) to our open source codebase [here](https://github.com/marqo-ai/marqo).
-
-
-
-
-
diff --git a/examples/MultiLingual/assets/fishing_search.gif b/examples/MultiLingual/assets/fishing_search.gif
deleted file mode 100644
index 9393ee107..000000000
Binary files a/examples/MultiLingual/assets/fishing_search.gif and /dev/null differ
diff --git a/examples/MultiLingual/assets/robot_lawyer.png b/examples/MultiLingual/assets/robot_lawyer.png
deleted file mode 100644
index df4d03863..000000000
Binary files a/examples/MultiLingual/assets/robot_lawyer.png and /dev/null differ
diff --git a/examples/MultiLingual/eu_legal.py b/examples/MultiLingual/eu_legal.py
deleted file mode 100644
index 8e86aaf49..000000000
--- a/examples/MultiLingual/eu_legal.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""
-This example uses the MultiEURLEX dataset.
-
-Log from running:
-Took 45 minutes on ml.g4dn.2xlarge
-"""
-# change this to 'cpu' if the machine you are running Marqo on doesn't have a
-# Nvidia GPU
-DEVICE = "cuda"
-
-# import marqo:
-from marqo import Client
-
-# import the huggingface datasets package:
-from datasets import load_dataset
-
-# import other python packages
-import datetime
-import json
-import pprint
-import logging
-
-# this will be the name of the index:
-INDEX_NAME = "my-multilingual-index"
-
-# this helps us see information about the HTTP requests
-logging.basicConfig(level=logging.DEBUG)
-
-# Create a new Marqo client:
-mq = Client("http://localhost:8882")
-
-
-def build_index():
- # Load the datasets. For this example we're just using the English and
- # Deutsch validation splits:
- dataset_en = load_dataset('multi_eurlex', 'en', split="validation")
- dataset_de = load_dataset('multi_eurlex', 'de', split="validation")
-
- # record the start time:
- t0 = datetime.datetime.now()
-
- # Create the index. The model we're using is multilingual:
- mq.create_index(index_name=INDEX_NAME, model='stsb-xlm-r-multilingual')
-
- # Let's break up large documents to make it easier to search:
- MAX_TEXT_LENGTH = 100000
-
- for ds, lang in [(dataset_en, "en"), (dataset_de, "de")]:
- num_docs_in_dataset = len(ds)
-
- for ii, doc in enumerate(ds):
- dumped = json.dumps(doc)
- # we'll set the doc ID to be the document's hash
- doc_id = str(hash(dumped))
-
- text_length = len(doc['text'])
- split_size = MAX_TEXT_LENGTH//2
- # break up the text of large documents:
- if text_length > MAX_TEXT_LENGTH:
- text_splits = [doc['text'][i: i + split_size] for i in range(0, text_length, split_size)]
- else:
- text_splits = [doc['text']]
-
- for i, sub_doc in enumerate(text_splits):
- # if a document is broken up, add the text's index to the end of the document:
- qualified_id = f"{doc_id}.{i}" if len(text_splits) > 1 else doc_id
- # create a dict to be posted
- to_post = dict(
- [(k, v) if k != "labels" else (k, str(v)) for k, v in doc. items() if k != 'text']
- + [("_id", qualified_id), ("language", lang), ('text', sub_doc)]
- )
- print(f"doc number {ii} out of {num_docs_in_dataset} docs in dataset {lang}. "
- f"_id: {qualified_id}, celex_id: {doc['celex_id']}, "
- f"json to send size: {len(json.dumps(to_post))}")
- # Index the document. The device is set to 'cuda' to take
- # advantage of the machine's GPU. If you don't have a GPU,
- # change this argument to 'cpu'.
- # We set auto_refresh to False which is optimal for indexing
- # a lot of documents.
- mq.index(index_name=INDEX_NAME).add_documents(
- documents=[to_post], device=DEVICE, auto_refresh=False,
- tensor_fields=["language", "text", "labels"]
- )
- t1 = datetime.datetime.now()
- print(f"finished indexing. Started at {t0}. Finished at {t1}. Took {t1 - t0}")
-
-
-def search(q):
- # Set searchable_attributes to 'text', which ensures that Marqo just
- # searches the 'text' field
- result = mq.index(INDEX_NAME).search(q=q, searchable_attributes=['text'])
- # Just print out the highlights, which makes the output easier to read
- for res in result["hits"]:
- pprint.pprint(res["_highlights"])
-
-
-# After you finishing indexing, comment out the following line to prevent going through
-# the whole indexing process again.
-build_index()
-
-# Replace 'my_search_query' with whatever text you want to search. In English or Deutsch!
-my_search_query = "Laws about the fishing industry"
-search(my_search_query)
diff --git a/examples/MultiModalSearch/article.md b/examples/MultiModalSearch/article.md
deleted file mode 100644
index 6c96624e0..000000000
--- a/examples/MultiModalSearch/article.md
+++ /dev/null
@@ -1,596 +0,0 @@
-# “Context Is All You Need” - Multimodal Vector Search with Personalization
-
-*TL:DR We show how both text and images can be used for multimodal search. This allows for multi-part queries, multimodal queries, searching via prompting, promoting/suppressing content by themes, per query curation and personalization. Additionally, we show how other query independent signals can be used to rank documents in addition to similarity. These features allow the creation and curation of high-quality search experiences, particularly for e-commerce and image heavy applications. The article [contains many examples](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#2-multimodal-search-in-practice) of multimodal search and a [walkthrough](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#3-detailed-example) of the [code](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/index_and_search.py) to reproduce these.*
-
-
- 
-
-
- An example of multimodal search that uses multimodal queries to curate search results based on text and images.
-
-
-## 1. Introduction to Multimodal Search
-
-Often the items we want to search over contain more than just text. For example, they may also contain images or videos. These modalities other than text will often contain a wealth of information that is not captured by the text. By incorporating these other modalities into search, the relevancy of results can be improved as well as unlocking new ways to search. Examples of multimodal search include domains like fashion and e-commerce which may have title, description as well as multiple images displaying the item. This data can also help disambiguate the subject of the image - for example if there are multiple items present like pants and a top, the text can provide the necessary context to identify the correct subject. The information contained in these data across modalities is complementary and rich.
-
-This articles has three main parts:
-1. [Introduction to Multimodal Search.](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#1-introduction)
-2. [Multimodal Search in Practice.](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#2-multimodal-search-in-practice)
-
- 2.1. [Multimodal Queries](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#21-multimodal-queries)
-
- 2.2. [Negation](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#22-negation)
-
- 2.3. [Excluding Low Quality Images](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#23-excluding-low-quality-images)
-
- 2.4. [Searching with Images](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#24-searching-with-images)
-
- 2.5. [Conditional Search with Popular or Liked Items](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#25-conditional-search-with-popular-or-liked-items)
-
- 2.6. [Searching as Prompting](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#26-searching-as-prompting)
-
- 2.7. [Ranking with Other Signals](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#27-ranking-with-other-signals)
-
- 2.8. [Multimodal Entities](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#28-multimodal-entities)
-
-3. [Detailed Example (with code)](https://github.com/marqo-ai/marqo/blob/mainline/examples/MultiModalSearch/article.md#3-detailed-example)
-
-
- 
-
-
- An example of multimodal "document" that contain both images and text.
-
-
-### 1.1 Multimodal Search
-
-Multimodal search is search that operates over multiple modalities. We can think of two ways of doing multimodal search, using multimodal queries and multimodal documents. In both cases, they may contain any combination of text and images. For clarity we will stick to two modalities for now, text and images but the concepts are not restricted to just those and can be extended to video or audio (for example).
-
-
- 
-
-
- An example of multimodal search using images and text to refine a search.
-
-
-### 1.2 Benefits
-
-There are numerous benefits to this multimodal approach. For example:
-
-- The mulit-modal representations of documents allows for utilizing not just text or images or a combination of these. This allows complementary information to be captured that is not present in either modality.
-- Using multimodal representations allows for updatable and editable meta data for documents without re-training a model or re-indexing large amounts of data.
-- Relevance feedback can be easily incorporated at a document level to improve or modify results.
-- Curating queries with additional context allows for personalization and curation of results on a per query basis without additional models or fine-tuning.
-- Curation can be performed in natural language.
-- Business logic can be incorporated into the search using natural language.
-
-## 2. Multimodal Search in Practice
-
-In this section we will walk through a number of ways multimodal search can be used to improve and curate results.
-
-### 2.1 Multimodal Queries
-
-Multimodal queries are queries that are made up of multiple components and/or multiple modalities. The benefit is that it effectively allows us to modify the scoring function for the approximate-knn to take into account additional similarities - for example, across multiple images or text and images. The similarity scoring will now be against a weighted collection of items rather than a single piece of text. This allows finer grained curation of search results than by using a single part query alone. We have seen previous examples of this earlier in the article already where both images and text are used to curate the search.
-
-Shown below is an example of this where the query has multiple components. The first query is for an item while the second query is used to further condition the results. This acts as a “soft” or “semantic” filter.
-
-```python
-query = {"green shirt":1.0, "short sleeves":1.0}
-```
-
-This multi-part query can be understood to be a form of manual [query expansion](https://en.wikipedia.org/wiki/Query_expansion). The animation below illustrates how the query can be used to modify search results.
-
-
- 
-
-
- An example of multimodal search two text queries to further refine the search.
-
-
-
-### 2.2 Negation
-
-In the previous examples we saw how multiple queries can be used to condition the search. In those examples, the terms were being added with a positive weighting. Another way to utilise these queries is to use negative weighting terms to move away from particular terms or concepts. Below is an example of a query with an additional negative term:
-
-```python
-query = {"green shirt":1.0, "short sleeves":1.0, "buttons":-1.0}
-```
-
-Now the search results are also moving away from the `buttons` while being drawn to the `green shirt` and `short sleeves`.
-
-
- 
-
-
- An example of multimodal search using negation to avoid certain concepts - `buttons` in this case.
-
-
-
-### 2.3 Excluding Low Quality Images
-
-Negation can help avoid particular things when returning results, like low-quality images or ones with artifacts. Avoiding things like low-quality images or [NSFW content](https://www.marqo.ai/blog/refining-image-quality-and-eliminating-nsfw-content-with-marqo) can be easily described using natural language as seen in the example query below:
-
-```python
-query = {"yellow handbag":1.0, "lowres, blurry, low quality":-1.1}
-```
-
-In the example below the initial results contain three low-quality images. These are denoted by a red mark for clarity and the poor image quality can be seen by the strong banding in the background of these images.
-
-
- 
-
-
- An example of multimodal search using negation to avoid low quality images. The low-quality images are denoted by a red dot next to them.
-
-
-An alternative is to use the same query to clean up existing data by using a positive weight to [actively identify low-quality images](https://www.marqo.ai/blog/refining-image-quality-and-eliminating-nsfw-content-with-marqo) for removal.
-
-### 2.4 Searching with Images
-
-In the earlier examples we have seen how searching can be performed using weighted combinations of images and text. Searching with images alone can also be performed to utilize image similarity to find similar looking items. An example query is below:
-
-```python
-query = {image_url:1.0}
-```
-
-It can also be easily extended in the same way as with text to include multiple multimodal terms.
-
-```python
-query = {image_url:1.0, "RED":1.0}
-```
-
-
- 
-
-
- An example of multimodal search using an image as the query before refining the search further with natural language.
-
-
-### 2.5 Conditional Search with Popular or Liked Items
-
-Another way to utilize the multimodal queries is to condition the query using a set of items. For example, this set could come from previously liked or purchased items. This will steer the search in the direction of these items and can be used to promote particular items or themes. This method can be seen as a form of [relevance feedback](https://en.wikipedia.org/wiki/Rocchio_algorithm) that uses items instead of variations on the query words themselves. To avoid any extra inference at search time, we can pre-compute the set of items vectors and fuse them into a context vector.
-
-```python
-query = {"backpack":1.0} query = {"backpack":1.0}
-context_vector1 = [.1, ...,.-.8] context_vector2 = [-.01, ...,.3]
-```
-
-Below is an example of two sets of 4 items that are going to be used to condition the search. The contribution for each item can also be adjusted to reflect the magnitude of its popularity.
-
-
- 
-
-
- Two sets of items based on different relevance feedback mechanisms that can be used to curate the search.
-
-
-
- 
-
-
- Two results sets for identical queries that were conditioned on two different sets of items. The search results are aligned with their conditioning.
-
-
-
-### 2.6 Searching as Prompting
-
-An alternative method to constructing multi-part queries is to append specific characteristics or styles to the end of a query. This is effectively the same as "prompting" in text to image generation models like [DALLE](https://openai.com/research/dall-e) and [Stable Diffusion](https://github.com/CompVis/stable-diffusion). For example, additional descriptors can be appended to a query to curate the results. An example query with additional prompting is below:
-
-```python
-query = {"handbag, bold colors, vibrant":1.0}
-```
-
-The impact of this prompting on the results can be seen in the animation.
-
-
- 
-
-
- Results that are curated with prompting.
-
-
-Another example query of searching as prompting:
-
-```python
-query = {"cozy sweater, xmas, festive, holidays":1.0}
-```
-
-
- 
-
-
- Results that are curated with prompting.
-
-
-
-### 2.7 Ranking with Other Signals
-
-In addition to curating the search with the methods outlined above, we can modify the similarity score to allow ranking with other signals. For example document specific values can be used to multiply or bias the vector similarity score. This allows for document specific concepts like overall popularity to impact the ranking. Below is the regular query and search results based on vector similarity alone. There are three low-quality images in the result set and can be identified by the strong banding in the background of the images (and are denoted by a red square).
-
-```python
-query = {"yellow handbag":1.0}
-```
-
-
- 
-
-
- Results that are based on similarity alone.
-
-
-To illustrate the ability to modify the score and use other signals for ranking we have calculated an [aesthetic score](https://github.com/LAION-AI/aesthetic-predictor) for each item. The aesthetic score is meant to identify "aesthetic" images and rate them between 1 and 10. We can now bias the score using this document (but query independent) field. An example is below:
-
-```python
-query = {"yellow handbag":1.0}
-score_modifiers = {
- "add_to_score":
- [
- {"field_name": "aesthetic_score", "weight": 0.02}]
- }
-```
-
-
- 
-
-
- Results that are based on similarity and aesthetic score.
-
-
-In the image above, the results have now been biased by the aesthetic score to remove the low-quality images (which have a low aesthetic score). This example uses aesthetic score but any other number of scalars can be used - for example ones based around sales and/or popularity.
-
-
-### 2.8 Multimodal Entities
-
-Multimodal entities or items are just that - representations that take into account multiple pieces of information. These can be images or text or some combination of both. Examples include using multiple display images for ecommerce. Using multiple images can aid retrieval and help disambiguate between the item for sale and other items in the images. If a multimodal model like [CLIP](https://openai.com/research/clip) is used, then the different modalities can be used together as they live in the same latent space.
-
-```python
-document = {"combined_text_image":
- {
- "image1":"https://some_image1.png",
- "image2":"https://some_image2.png",
- "image3":"https://some_image3.png",
- "title": "Fresh and Versatile: The Green Cotton T-Shirt for Effortless Style"
- "description": "Crafted from high-quality cotton fabric, this t-shirt offers a soft and breathable feel, ensuring all-day comfort."
- }
- }
-```
-
-
- 
-
-
-
-## 3. Detailed Example
-
-In the next section we will demonstrate how all of the above concepts can be implemented using [Marqo](https://github.com/marqo-ai/marqo).
-
-### 3.1 Dataset
-
-The dataset consists of ~220,000 e-commerce products with images, text and some meta-data. The items span many categories of items, from clothing and watches to bags, backpacks and wallets. Along with the images they also have an [aesthetic score](https://github.com/LAION-AI/aesthetic-predictor), caption, and price. We will use all these features in the following example. Some images from the dataset are below.
-
-
- 
-
-
- Some example images from the dataset.
-
-
-
-### 3.2 Installing Marqo
-
-The first thing to do is start [Marqo](https://github.com/marqo-ai/marqo). To start, we can run the following [docker command](https://marqo.pages.dev/0.0.21/) from a terminal (for M-series Mac users see [here](https://marqo.pages.dev/0.0.21/m1_mac_users/)).
-
-```bash
-docker pull marqoai/marqo:latest
-docker rm -f marqo
-docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
-```
-
-The next step is to install the python client (a REST API is also [available](https://docs.marqo.ai/0.0.21/)).
-
-```bash
-pip install marqo
-```
-
-### 3.3 Loading the Data
-
-The first step is to load the data. The images are hosted on s3 for easy access. We use a file that contains all the image pointers as well as the meta data for them (found [here](https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce_meta_data.csv)).
-
-```python
-import pandas as pd
-
-N = 100 # samples to use, the full dataset is ~220k
-filename = "https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce_meta_data.csv"
-data = pd.read_csv(filename, nrows=N)
-data['_id'] = data['s3_http']
-documents = data[['s3_http', '_id', 'price', 'blip_large_caption', 'aesthetic_score']].to_dict(orient='records')
-```
-
-### 3.4 Create the Index
-
-Now we have the data prepared, we can [set up the index](https://marqo.pages.dev/0.0.21/API-Reference/indexes/). We will use a [ViT-L-14 from open clip](https://github.com/mlfoundations/open_clip) as the model. This model is very good to start with. It is recommended to [use a GPU](https://marqo.pages.dev/0.0.21/using_marqo_with_a_gpu/) (at least 4GB VRAM) otherwise a [smaller model](https://marqo.pages.dev/0.0.21/Models-Reference/dense_retrieval/#open-clip) can be used (although results may be worse).
-
-```python
-from marqo import Client
-
-client = Client()
-
-index_name = 'multimodal'
-settings = {
- "index_defaults": {
- "treat_urls_and_pointers_as_images": True,
- "model": "open_clip/ViT-L-14/laion2b_s32b_b82k",
- "normalize_embeddings": True,
- },
- }
-
-response = client.create_index(index_name, settings_dict=settings)
-```
-
-### 3.5 Add Images to the Index
-
-Now we can [add images](https://marqo.pages.dev/0.0.21/API-Reference/documents/) to the index which can then be searched over. We can also select the device we want to use and also which fields in the data to embed. To use a GPU, change the device to `cuda` (see [here](https://marqo.pages.dev/0.0.21/using_marqo_with_a_gpu/) for how to use Marqo with a GPU).
-
-```python
-device = 'cpu' # use 'cuda' if a GPU is available
-
-res = client.index(index_name).add_documents(documents, client_batch_size=64, tensor_fields=["s3_http"], device=device)
-```
-
-### 3.6 Searching
-
-Now the images are indexed, we can start [searching](https://marqo.pages.dev/0.0.21/API-Reference/search/).
-
-```python
-query = "green shirt"
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-```
-
-### 3.7 Searching as Prompting
-
-Like in the examples above, it is easy to do more specific searches by adopting a similar style to prompting.
-
-```python
-query = "cozy sweater, xmas, festive, holidays"
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-```
-
-### 3.8 Searching with Semantic Filters
-
-Now we can extend the searching to use multi-part queries. These can act as "semantic filters" that can be based on any words to further refine the results.
-
-```python
-query = {"green shirt":1.0, "short sleeves":1.0}
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-```
-
-### 3.9 Searching with Negation
-
-In addition to additive terms, negation can be used. Here we remove buttons from long sleeve shirt examples.
-
-```python
-query = {"green shirt":1.0, "short sleeves":1.0, "buttons":-1.0}
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-```
-
-### 3.10 Searching with Images
-
-In addition to text, searching can be done with images alone.
-
-```python
-image_context_url = "https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/red_backpack.jpg"
-query = {image_context_url:1.0}
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-```
-
-### 3.11 Searching with Multimodal Queries
-
-The multi-part queries can span both text and images.
-
-```python
-# skateboard
-image_context_url = "https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/71iPk9lfhML._SL1500_.jpg"
-
-query = {"backpack":1.0, image_context_url:1.0}
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
-# trees/hiking
-image_context_url = "https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/trees.jpg"
-
-query = {"backpack":1.0, image_context_url:1.0}
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-```
-
-### 3.12 Searching with Ranking
-
-We can now extend the search to also include document specific values to boost the [ranking of documents](https://marqo.pages.dev/0.0.21/API-Reference/search/#score-modifiers) in addition to the vector similarity. In this example, each document has a field called `aesthetic_score` which can also be used to bias the score of each document.
-
-```python
-query = {"yellow handbag":1.0}
-
-# we define the extra document specific data to use for ranking
-# multiple fields can be used to multiply or add to the vector similarity score
-score_modifiers = {
- "add_to_score":
- [
- {"field_name": "aesthetic_score", "weight": 0.02}]
- }
-
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10, score_modifiers=score_modifiers)
-
-# now get the aggregate aesthetic score
-print(sum(r['aesthetic_score'] for r in res['hits']))
-
-# and compare to the non ranking version
-res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
-print(sum(r['aesthetic_score'] for r in res['hits']))
-```
-
-### 3.13 Searching with Popular or Liked Products
-
-Results at a per-query level can be personalized using sets of items. These items could be previously liked or popular items. To perform this we do it in two stages. The first is to calculate the "context vector" which is a condensed representation of the items. This is pre-computed and then stored to remove any additional overhead at query time. The context is generated by [creating documents](https://marqo.pages.dev/0.0.21/Advanced-Usage/document_fields/#multimodal-combination-object) of the item sets and retrieving the corresponding vectors.
-The first step is to create a new index to calculate the context vectors.
-```python
-# we create another index to create a context vector
-index_name_context = 'multimodal-context'
-settings = {
- "index_defaults": {
- "treat_urls_and_pointers_as_images": True,
- "model": "open_clip/ViT-L-14/laion2b_s32b_b82k",
- "normalize_embeddings": True,
- },
- }
-
-res = client.create_index(index_name_context, settings_dict=settings)
-```
-
-Then we [construct the objects](https://marqo.pages.dev/0.0.21/Advanced-Usage/document_fields/#multimodal-combination-object) from the sets of items we want to use for the context.
-
-```python
-# create the document that will be created from multiple images
-document1 = {"_id":"1",
- "multimodal":
- {
- "top_1":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/blue_backpack.jpg",
- "top_2":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/dark_backpack.jpeg",
- "top_3":'https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/green+_backpack.jpg',
- "top_4":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/red_backpack.jpg"
- }
- }
-
-# create the document that will be created from multiple images
-document2 = {"_id":"2",
- "multimodal":
- {
- "top_1":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/office_1.jpg",
- "top_2":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/office2.webp",
- "top_3":'https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/office_3.jpeg',
- "top_4":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/office_4.jpg"
- }
- }
-
-```
-
-We can now [define mappings](https://marqo.pages.dev/0.0.21/API-Reference/mappings/) objects to determine how we want to combine the different fields. We can then index the documents.
-
-```python
-# define how we want to combined
-mappings1 = {"multimodal":
- {"type": "multimodal_combination",
- "weights": {"top_1": 0.40,
- "top_2": 0.30,
- "top_3": 0.20,
- "top_4": 0.10,
- }}}
-
-# define how we want to combined
-mappings2 = {"multimodal":
- {"type": "multimodal_combination",
- "weights": {"top_1": 0.25,
- "top_2": 0.25,
- "top_3": 0.25,
- "top_4": 0.25,
- }}}
-
-# index the document
-res = client.index(index_name_context).add_documents([document1], tensor_fields=["multimodal"], device=device, mappings=mappings1)
-
-# index the other using a different mappings
-res = client.index(index_name_context).add_documents([document2], tensor_fields=["multimodal"], device=device, mappings=mappings2)
-```
-
-To get the vectors to use as context vectors at search time - we need to [retrieve the calculated vectors](https://marqo.pages.dev/0.0.21/API-Reference/documents/). We can then [create a context object](https://marqo.pages.dev/0.0.21/API-Reference/search/#context) that is used at search time.
-
-```python
-
-# retrieve the embedding to use as a context for search
-indexed_documents = client.index(index_name_context).get_documents([document1['_id'], document2['_id']] , expose_facets=True)
-
-# get the embedding
-context_vector1 = indexed_documents['results'][0]['_tensor_facets'][0]['_embedding']
-context_vector2 = indexed_documents['results'][1]['_tensor_facets'][0]['_embedding']
-
-# create the context for the search
-context1 = {"tensor":
- [
- {'vector':context_vector1, 'weight':0.50}
- ]
- }
-
-# create the context for the search
-context2 = {"tensor":
- [
- {'vector':context_vector2, 'weight':0.50}
- ]
- }
-
-# now search
-query = {"backpack":1.0}
-res1 = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10, context=context1)
-
-res2 = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10, context=context2)
-```
-
-### 3.14 Indexing as Multimodal Objects
-
-For the final part of this example, we demonstrate how both text and images can be combined together as a single entity and allow multimodal representations.
-We will create a new index in the same way as before but with a new name.
-
-```python
-# we will create a new index for the multimodal objects
-index_name_mm_objects = 'multimodal-objects'
-settings = {
- "index_defaults": {
- "treat_urls_and_pointers_as_images": True,
- "model": "open_clip/ViT-L-14/laion2b_s32b_b82k",
- "normalize_embeddings": True,
- },
- }
-
-res = client.create_index(index_name_mm_objects, settings_dict=settings)
-```
-
- To index the documents as multimodal objects, we need to create a new field and add in what we want to use.
-
- ```python
- # now create the multimodal field in the documents
- for doc in documents:
- doc['multimodal'] = {
- 'blip_large_caption':doc['blip_large_caption'],
- 's3_http':doc['s3_http'],
- }
-```
-
-The next step is to index. The only change is an additional mappings object which details how we want to combine the different fields for each document.
-
-```python
-# the fields we do not want to embed
-non_tensor_fields = ['_id', 'price', 'blip_large_caption', 'aesthetic_score', 's3_http']
-
-# define how we want to combine the fields
-mappings = {"multimodal":
- {"type": "multimodal_combination",
- "weights":
- {"blip_large_caption": 0.20,
- "s3_http": 0.80,
- }
- }
- }
-
-# now index
-res = client.index(index_name_mm_objects).add_documents(documents, client_batch_size=64, tensor_fields=["multimodal"], device=device, mappings=mappings)
-```
-
-Finally we can search in the same way as before.
-
-```python
-query = "red shawl"
-res = client.index(index_name_mm_objects).search(query, searchable_attributes=['multimodal'], device=device, limit=10)
-```
-
-### 4. Conclusion
-
-To summarise, we have shown how vector search can be easily modified to enable a number of useful functions. These include the use of multimodal queries that comprise text and images, queries with negative terms, excluding low quality images, searching with images, per query search curation using popular items, verbose searching via prompting, ranking with external scalars and multimodal representations. If you are interested in learning more, then head to [Marqo](https://github.com/marqo-ai/marqo), see other [examples](https://github.com/marqo-ai/marqo/tree/mainline/examples) or read more in our [blog](https://www.marqo.ai/blog).
-
-
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-
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diff --git a/examples/MultiModalSearch/index_and_search.py b/examples/MultiModalSearch/index_and_search.py
deleted file mode 100644
index 74f733bdc..000000000
--- a/examples/MultiModalSearch/index_and_search.py
+++ /dev/null
@@ -1,282 +0,0 @@
-import pandas as pd
-import os
-
-from marqo import Client
-
-
-if __name__ == "__main__":
-
- #######################################################################
- ############ Install Marqo and the python client ############
- #######################################################################
-
- # run the following from the terminal
- # see https://marqo.pages.dev/0.0.21/
-
- """
- docker pull marqoai/marqo:latest
- docker rm -f marqo
- docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
-
- pip install marqo
- """
-
- #######################################################################
- ############ Read in the data ############
- #######################################################################
-
- N = 100 # the number of samples to use (full size is ~220k)
-
- filename = "https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce_meta_data.csv"
- data = pd.read_csv(filename, nrows=N)
- data['_id'] = data['s3_http']
- documents = data[['s3_http', '_id', 'price', 'blip_large_caption', 'aesthetic_score']].to_dict(orient='records')
-
- print("Finished reading data...")
-
- #######################################################################
- ############ Create the index ############
- #######################################################################
-
- # https://marqo.pages.dev/0.0.21/
- client = Client()
-
- # https://marqo.pages.dev/0.0.21/API-Reference/indexes/
- index_name = 'multimodal'
- settings = {
- "index_defaults": {
- "treat_urls_and_pointers_as_images": True,
- "model": "open_clip/ViT-L-14/laion2b_s32b_b82k",
- "normalize_embeddings": True,
- },
- }
-
- # if the index already exists it will cause an error
- res = client.create_index(index_name, settings_dict=settings)
- print("Finished creating index...")
-
- #######################################################################
- ############ Index the data (image only) ############
- #######################################################################
-
- # https://marqo.pages.dev/0.0.21/API-Reference/documents/
- device = 'cpu' # change to 'cuda' if GPU is available
- res = client.index(index_name).add_documents(documents, client_batch_size=64, tensor_fields=["s3_http"], device=device)
-
- print("Finished indexing data...")
-
- #######################################################################
- ############ Search ############
- #######################################################################
-
- # https://marqo.pages.dev/0.0.21/API-Reference/search/
- query = "green shirt"
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
-
- #######################################################################
- ############ Searching as prompting ############
- #######################################################################
-
- query = "cozy sweater, xmas, festive, holidays"
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
- #######################################################################
- ############ Searching with semantic filters ############
- #######################################################################
-
- # https://marqo.pages.dev/0.0.21/API-Reference/search/#query-q
- query = {"green shirt":1.0, "short sleeves":1.0}
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
-
- #######################################################################
- ############ Searching with negation ############
- #######################################################################
-
- query = {"green shirt":1.0, "short sleeves":1.0, "buttons":-1.0}
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
- #######################################################################
- ############ Searching with images ############
- #######################################################################
-
- image_context_url = "https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/red_backpack.jpg"
- query = {image_context_url:1.0}
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
- #######################################################################
- ############ Searching with multi-modal queries ############
- #######################################################################
-
- # skateboard
- image_context_url = "https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/71iPk9lfhML._SL1500_.jpg"
-
- query = {"backpack":1.0, image_context_url:1.0}
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
- # trees/hiking
- image_context_url = "https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/trees.jpg"
-
- query = {"backpack":1.0, image_context_url:1.0}
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
- #######################################################################
- ############ Searching with ranking ############
- #######################################################################
-
- query = {"yellow handbag":1.0}
-
- # https://marqo.pages.dev/0.0.21/API-Reference/search/#score-modifiers
- # we define the extra document specific data to use for ranking
- # multiple fields can be used to multiply or add to the vector similairty score
- score_modifiers = {
- "add_to_score":
- [
- {"field_name": "aesthetic_score", "weight": 0.02}]
- }
-
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10, score_modifiers=score_modifiers)
-
- # now get the aggregate aesthetic score
- print(sum(r['aesthetic_score'] for r in res['hits']))
-
- # and compare to the non ranking version
- res = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10)
-
- print(sum(r['aesthetic_score'] for r in res['hits']))
-
- print("Finished searching data...")
-
- #######################################################################
- ############ Guided search with popular products ############
- #######################################################################
-
- # we create another index to create a context vector
- # we create another index to create a context vector
- index_name_context = 'multimodal-context'
- settings = {
- "index_defaults": {
- "treat_urls_and_pointers_as_images": True,
- "model": "open_clip/ViT-L-14/laion2b_s32b_b82k",
- "normalize_embeddings": True,
- },
- }
-
- res = client.create_index(index_name_context, settings_dict=settings)
-
- # https://marqo.pages.dev/0.0.21/Advanced-Usage/document_fields/#multimodal-combination-object
- # create the document that will be created from multiple images
- document1 = {"_id":"1",
- "multimodal":
- {
- "top_1":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/blue_backpack.jpg",
- "top_2":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/dark_backpack.jpeg",
- "top_3":'https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/green+_backpack.jpg',
- "top_4":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/red_backpack.jpg"
- }
- }
-
- # create the document that will be created from multiple images
- document2 = {"_id":"2",
- "multimodal":
- {
- "top_1":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/office_1.jpg",
- "top_2":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/office2.webp",
- "top_3":'https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/office_3.jpeg',
- "top_4":"https://marqo-overall-demo-assets.s3.us-west-2.amazonaws.com/ecommerce/office_4.jpg"
- }
- }
-
- # https://marqo.pages.dev/0.0.21/API-Reference/mappings/
- # define how we want to comnbined
- mappings1 = {"multimodal": {"type": "multimodal_combination",
- "weights": {"top_1": 0.40,
- "top_2": 0.30,
- "top_3": 0.20,
- "top_4": 0.10,
- }}}
-
- # define how we want to comnbined
- mappings2 = {"multimodal": {"type": "multimodal_combination",
- "weights": {"top_1": 0.25,
- "top_2": 0.25,
- "top_3": 0.25,
- "top_4": 0.25,
- }}}
-
-
- # index the document
- res = client.index(index_name_context).add_documents([document1], tensor_fields=["multimodal"], device=device, mappings=mappings1)
-
- # index the other using a different mappings
- res = client.index(index_name_context).add_documents([document2], tensor_fields=["multimodal"], device=device, mappings=mappings2)
-
- # retrieve the embedding to use as a context for search
- indexed_documents = client.index(index_name_context).get_documents([document1['_id'], document2['_id']] , expose_facets=True)
-
- # get the embedding
- context_vector1 = indexed_documents['results'][0]['_tensor_facets'][0]['_embedding']
- context_vector2 = indexed_documents['results'][1]['_tensor_facets'][0]['_embedding']
-
- # create the context for the search
- context1 = {"tensor":
- [
- {'vector':context_vector1, 'weight':0.50}
- ]
- }
-
- # create the context for the search
- context2 = {"tensor":
- [
- {'vector':context_vector2, 'weight':0.50}
- ]
- }
-
- # now search
- query = {"backpack":1.0}
- res1 = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10, context=context1)
-
- res2 = client.index(index_name).search(query, searchable_attributes=['s3_http'], device=device, limit=10, context=context2)
-
-
- #######################################################################
- ############ Indexing as multi-modal objects ############
- #######################################################################
-
- # we will create a new index for the multimodal objects
- index_name_mm_objects = 'multimodal-objects'
- settings = {
- "index_defaults": {
- "treat_urls_and_pointers_as_images": True,
- "model": "open_clip/ViT-L-14/laion2b_s32b_b82k",
- "normalize_embeddings": True,
- },
- }
-
- res = client.create_index(index_name_mm_objects, settings_dict=settings)
- print(res)
-
- # now create the multi-modal field in the documents
- for doc in documents:
- doc['multimodal'] = {
- 'blip_large_caption':doc['blip_large_caption'],
- 's3_http':doc['s3_http'],
- }
-
- # define how we want to combine the fields
- mappings = {"multimodal":
- {"type": "multimodal_combination",
- "weights":
- {"blip_large_caption": 0.20,
- "s3_http": 0.80,
- }
- }
- }
-
- # now index
- res = client.index(index_name_mm_objects).add_documents(documents, client_batch_size=64, tensor_fields=["multimodal"], device=device, mappings=mappings)
-
- # now search
- query = "red shawl"
- res = client.index(index_name_mm_objects).search(query, searchable_attributes=['multimodal'], device=device, limit=10)
diff --git a/examples/MultiModalSearch/readme.md b/examples/MultiModalSearch/readme.md
deleted file mode 100644
index 64d44e385..000000000
--- a/examples/MultiModalSearch/readme.md
+++ /dev/null
@@ -1,7 +0,0 @@
-## Article
-
-The article is contained in `article.md` and the images are in the assets folder.
-
-## Code
-
-The file `index_and_search.py` contains the code to reproduce the article. First install the requirements found in `requirements.txt`
diff --git a/examples/MultiModalSearch/requirements.txt b/examples/MultiModalSearch/requirements.txt
deleted file mode 100644
index 078f1b4d1..000000000
--- a/examples/MultiModalSearch/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-pandas
-marqo==0.9.6
diff --git a/examples/SimpleWiki/README.md b/examples/SimpleWiki/README.md
deleted file mode 100644
index b6757fa53..000000000
--- a/examples/SimpleWiki/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# Simple Wikipedia Demo
-
-## Getting Started
-1. Download the Dataset from
- [Simple Wikipedia](https://drive.google.com/file/d/1OEqXeIdqaZb6BwzKIgw8G_sDi91fBawt/view?usp=sharing).
-
-2. Run Marqo;
-
- ```
- docker rm -f marqo;docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
- ```
- For mode detailed instructions, check the [getting started guide](index.md).
-
-3. Run the `simple_wiki_demo.py` script via the following command (Note it can take a bit of time to index depending on the computer):
- ```
- python3 simple_wiki_demo.py
- ```
-
-
diff --git a/examples/SimpleWiki/simple_wiki_demo.py b/examples/SimpleWiki/simple_wiki_demo.py
deleted file mode 100644
index 1a7b45fb5..000000000
--- a/examples/SimpleWiki/simple_wiki_demo.py
+++ /dev/null
@@ -1,145 +0,0 @@
-#####################################################
-### STEP 0. Import and define any helper functions
-#####################################################
-
-from marqo import Client
-from marqo.api.exceptions import MarqoApiError
-import numpy as np
-import json
-import pprint
-import copy
-import math
-
-def read_json(filename: str) -> dict:
- # reads a json file
- with open(filename, 'r', encoding='utf-8') as f:
- data = json.load(f)
- return data
-
-def replace_title(data: dict) -> dict:
- # removes the wikipedia from the title for better matching
- data['title'] = data['title'].replace('- Wikipedia', '')
- return data
-
-def split_big_docs(data, field='content', char_len=5e4):
- # there are some large documents which can cause issues for some users
- new_data = []
- for dat in data:
-
- content = dat[field]
- N = len(content)
-
- if N >= char_len:
- n_chunks = math.ceil(N / char_len)
- new_content = np.array_split(list(content), n_chunks)
-
- for _content in new_content:
- new_dat = copy.deepcopy(dat)
- new_dat[field] = ''.join(_content)
- new_data.append(new_dat)
- else:
- new_data.append(dat)
- return new_data
-
-#####################################################
-### STEP 1. load the data
-#####################################################
-
-# download the json formatted simplewiki from here -
-# https://www.kaggle.com/datasets/louisgeisler/simple-wiki?resource=download
-# or from
-# https://drive.google.com/file/d/1OEqXeIdqaZb6BwzKIgw8G_sDi91fBawt/view?usp=sharing
-dataset_file = "simplewiki.json"
-
-# get the data
-data = read_json(dataset_file)
-# clean up the title
-data = [replace_title(d) for d in data]
-# split big ones to make it easier for users on all hardware
-data = split_big_docs(data)
-print(f"loaded data with {len(data)} entries")
-
-#####################################################
-### STEP 2. start Marqo
-#####################################################
-
-# Follow the instructions here https://github.com/marqo-ai/marqo
-
-#####################################################
-### STEP 3. index some data with marqo
-#####################################################
-
-# we use an index name. the index name needs to be lower case.
-index_name = 'marqo-simplewiki-demo-all'
-
-# setup the client
-client = Client()
-
-# we create the index. Note if it already exists an error will occur
-# as you cannot overwrite an existing index
-# try:
-# client.delete_index(index_name)
-# except:
-# pass
-
-# we create the index and can set the model we want to use
-# the onnx models are typically faster on both CPU and GPU
-# to use non-onnx just use the name 'all_datasets_v4_MiniLM-L6'
-client.create_index(index_name, model='onnx/all_datasets_v4_MiniLM-L6')
-
-device = 'cpu'
-
-responses = client.index(index_name).add_documents(data, device=device, client_batch_size=20, tensor_fields=['title', 'content'])
-
-# optionally take a look at the responses
-#pprint.print(responses)
-
-#######################################
-### STEP 4. Searching with marqo ######
-#######################################
-
-
-# after indexing we can search using both keyword (lexical) and neural search
-# this will perform neural search across all indexed fields
-
-# lets create a query
-query = 'what is air made of?'
-
-results = client.index(index_name).search(query)
-
-# we can check the results - lets look at the top hit
-pprint.pprint(results['hits'][0])
-
-# we also get highlighting which tells us why this article was returned
-pprint.pprint(results['hits'][0]['_highlights'])
-
-# we can restrict the search to specific fields as well
-results = client.index(index_name).search(query, searchable_attributes=['content'])
-
-# we can check the results - lets look at the top hit
-pprint.pprint(results['hits'][0])
-
-# we can check the results - lets look at the top hit
-pprint.pprint(results['hits'][0])
-
-# we use lexical search instead of tensor search
-results = client.index(index_name).search(query, searchable_attributes=['content'], search_method='LEXICAL')
-
-# we can check the results - lets look at the top hit
-pprint.pprint(results['hits'][0])
-
-# we can check the results - lets look at the top hit
-pprint.pprint(results['hits'][0])
-
-
-# lets create another query
-query = 'what is a cube?'
-
-results = client.index(index_name).search(query, searchable_attributes=['content'])
-
-# we can check the results - lets look at the top hit
-pprint.pprint(results['hits'][0])
-
-# we also get highlighting which tells us why this article was returned
-pprint.pprint(results['hits'][0]['_highlights'])
-
diff --git a/examples/SpeechProcessing/.env_local b/examples/SpeechProcessing/.env_local
deleted file mode 100644
index 1b198ea61..000000000
--- a/examples/SpeechProcessing/.env_local
+++ /dev/null
@@ -1,2 +0,0 @@
-HF_TOKEN=""
-OPENAI_API_KEY=""
\ No newline at end of file
diff --git a/examples/SpeechProcessing/.gitignore b/examples/SpeechProcessing/.gitignore
deleted file mode 100644
index 1cb68e322..000000000
--- a/examples/SpeechProcessing/.gitignore
+++ /dev/null
@@ -1,168 +0,0 @@
-# project specific
-results/*
-audios/*
-SpeechSearch/downloads/*
-
-# vscode
-.vscode/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-# Usually these files are written by a python script from a template
-# before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-# For a library or package, you might want to ignore these files since the code is
-# intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-# However, in case of collaboration, if having platform-specific dependencies or dependencies
-# having no cross-platform support, pipenv may install dependencies that don't work, or not
-# install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-# This is especially recommended for binary packages to ensure reproducibility, and is more
-# commonly ignored for libraries.
-# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-# in version control.
-# https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-# and can be added to the global gitignore or merged into this file. For a more nuclear
-# option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
\ No newline at end of file
diff --git a/examples/SpeechProcessing/1. Download.py b/examples/SpeechProcessing/1. Download.py
deleted file mode 100644
index 07a6dafd0..000000000
--- a/examples/SpeechProcessing/1. Download.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import os
-from SpeechSearch.downloader import AudioWrangler
-
-
-def main():
- os.makedirs("audios", exist_ok=True)
- print("Downloading...")
- aw = AudioWrangler("audios")
- aw.download_from_file("coffee_links.txt")
- aw.download_from_file("podcast_links.txt")
- aw.download_from_file("meeting_links.txt")
- print("Done!")
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/SpeechProcessing/2. Process.py b/examples/SpeechProcessing/2. Process.py
deleted file mode 100644
index 2b648a625..000000000
--- a/examples/SpeechProcessing/2. Process.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import marqo
-import os
-import torch
-from dotenv import load_dotenv
-import time
-from SpeechSearch.transcriber import AudioTranscriber
-from SpeechSearch.indexer import index_transcriptions
-
-load_dotenv()
-
-
-def check_can_run() -> None:
- """Helper to check if there are any files to process"""
- if not os.path.exists("audios"):
- raise FileExistsError("There is no ./audios/ folder for input files!")
- os.makedirs("results", exist_ok=True)
-
-
-def main():
-
- marqo_device = "cpu"
-
- check_can_run()
-
- mq = marqo.Client(url="http://localhost:8882")
- index_name = "transcription-index"
-
- mq.delete_index(index_name)
- settings = {
- "index_defaults": {
- "text_preprocessing": {
- "split_length": 2,
- "split_overlap": 0,
- "split_method": "sentence",
- },
- },
- }
-
- response = mq.create_index(index_name, settings_dict=settings)
-
- if torch.cuda.is_available():
- local_device = "cuda"
- else:
- local_device = "cpu"
-
- print(f"Transcribing using {local_device} and indexing using {marqo_device}")
-
- print("NOTE: Transcribing and indexing may take some time...")
-
- if local_device == "cpu":
- print(
- "WARNING: You are using a CPU for speaker annotation and transcription. This can be very slow - it is recommended that you only attempt to use a small number of short audio files!"
- )
- print("Using the audio files downloaded in step 1 may take hours on CPU")
-
- at = AudioTranscriber(os.environ["HF_TOKEN"], local_device)
-
- for idx, f in enumerate(os.listdir("audios")):
- print(f"Processing file {idx+1} of {len(os.listdir('audios'))}...")
-
- at_start = time.perf_counter()
- if ".wav" not in f:
- continue
- file = os.path.join("audios", f)
-
- annotated_transcriptions = at.process_audio(file)
- at_end = time.perf_counter()
-
- print(
- f"Annotation and transcription of file {idx+1} took {at_end-at_start} seconds"
- )
-
- indx_start = time.perf_counter()
- index_transcriptions(
- annotated_transcriptions,
- index_name,
- mq,
- ["transcription"],
- marqo_device,
- )
- indx_end = time.perf_counter()
-
- print(f"Indexing of file {idx+1} took {indx_end-indx_start} seconds")
-
- print("Done!")
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/SpeechProcessing/3a. Search.py b/examples/SpeechProcessing/3a. Search.py
deleted file mode 100644
index 542be0917..000000000
--- a/examples/SpeechProcessing/3a. Search.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import marqo
-import soundfile as sf
-import os
-import librosa
-import numpy as np
-import json
-
-from SpeechSearch.searcher import search_transcripts
-
-
-def get_audio(audio_path: str, samplerate: int) -> np.ndarray:
- audio, _ = librosa.load(audio_path, sr=samplerate)
- return audio
-
-
-def main():
- mq = marqo.Client(url="http://localhost:8882")
-
- index_name = "transcription-index"
- while True:
- query = input("Enter a query: ")
- results, segments = search_transcripts(
- query=query, limit=2, index=index_name, mq=mq, audio_getter=get_audio
- )
-
- print(json.dumps(results, indent=4))
-
- fname = "-".join(
- ["".join([c for c in w if c.isalnum()]) for w in query.split()]
- )
-
- print(f"Results written to {fname} (txt and wav)!")
-
- for idx, s in enumerate(segments):
-
- with open(os.path.join("results", f"{fname}_{idx}.txt"), "w+") as f:
- f.write(s["transcription"])
-
- sf.write(
- os.path.join("results", f"{fname}_{idx}.wav"),
- s["audio"],
- s["samplerate"],
- )
-
- print("Done!")
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/SpeechProcessing/3b. Chat.py b/examples/SpeechProcessing/3b. Chat.py
deleted file mode 100644
index 130143b56..000000000
--- a/examples/SpeechProcessing/3b. Chat.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import marqo
-from SpeechSearch.chatter import answer_question
-
-
-def main():
- mq = marqo.Client(url="http://localhost:8882")
-
- index_name = "transcription-index"
- while True:
- query = input("Enter a query: ")
- answer = answer_question(
- query=query,
- limit=15,
- index=index_name,
- mq=mq,
- )
- print(answer)
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/SpeechProcessing/README.md b/examples/SpeechProcessing/README.md
deleted file mode 100644
index 5f676242b..000000000
--- a/examples/SpeechProcessing/README.md
+++ /dev/null
@@ -1,93 +0,0 @@
-# Search Speech Data
-
-Original article [here](https://www.marqo.ai/blog/speech-processing).
-
-## Overview
-
-This is a small project that indexes the transcriptions of audio files into Marqo to create a searchable database which can be queried with text and return corresponding segments of audio.
-
-### Examples
-After downloading and indexing the data, the system can answer domain specific questions about a range of topics (using `3b. Chat.py`):
-
-```
-Q: What pressure should the machine use to extract espresso?
-
-A: The pressure of the machine is an important factor when it comes to extracting espresso. Ideally, the machine should be set to 9 bars of pressure in order to get a strong and concentrated espresso with a thick and sweet flavour. A lower pressure may lead to a slower shot and a longer contact time, but it won't necessarily increase the extraction. Brewing too fast can lead to channeling, which reduces extraction and causes a harsh and sour taste.
-```
-
-```
-Q: What is the controversy around Samsungs Space Zoom feature?
-
-A: The controversy around Samsung's Space Zoom feature is that it implies that their phone cameras can take high definition pictures of the moon and other distant objects, whereas in reality the feature is only able to zoom in on far away objects with a low degree of clarity. This has raised concerns about the use of this technology for deep fake videos, as well as worries that Samsung may be purposely misrepresenting the capability of their phones.
-```
-
-```
-Q: What are some application of GANs that are beneficial to the research community?
-
-A: GANs have seen a lot of applications in recent years that are beneficial to the research community. GANs have been used to generate realistic images, improve security, generate music and more. GANs are especially helpful to students or practitioners in research fields as they can greatly help with the problems they see in their day-to-day. GANs are also being used for good in areas such as differential privacy and combating bias. GANs are being used in the medical field as well, where they can generate fake medical data for use in research without compromising any patient's privacy. GANs are also being used for fairness and for creating data for underrepresented populations. GANs have been used to learn from unlabeled real images and make a refiner that upgrades the synthetic image to make it more realistic.
-```
-
-
-## Getting Started
-
-1. Install requirements:
-
- ```
- python -m venv venv
- ```
-
- Linux/Mac
- ```
- source venv/bin/activate
- ```
- Windows
- ```
- venv\Scripts\activate
- ```
-
- ```
- pip install -r requirements.txt
- ```
-
-2. Install and run Marqo:
-
- [See the getting start guide for Marqo](https://github.com/marqo-ai/marqo#Getting-started)
-
-3. Install FFmpeg for your platform.
-4. [Log in to Hugging Face to get an API key for pyannot speaker-diarization](https://huggingface.co/pyannote/speaker-diarization)
-5. [Get an OpenAI API Key](https://platform.openai.com/account/api-keys). This repo uses the `text-davinci-003` model by default which will cost money to use if you are outside of your free trial period.
-6. Copy `.env_local` and rename it as `.env`, place the API keys in the `.env` file
-
-## Troubleshooting
-
-### Symbolic link creation related errors in `2. Process.py`
-
-PyAnnote may raise these errors if it is not running with the appropriate privileges, run the script as admin/super user to fix.
-
-## Usage
-
-Download the data:
-
-```
-python '1. Download.py'
-```
-
-Process and index the data:
-
-(for this step it is highly recommended that you have a GPU)
-
-```
-python '2. Process.py'
-```
-
-Search the data (this gives you an interactive prompt and returns direct excerpts):
-
-```
-python '3a. Search.py'
-```
-
-Question answer the data (this gives you an interactive prompt and returns natural language responses):
-
-```
-python '3b. chat.py'
-```
diff --git a/examples/SpeechProcessing/SpeechSearch/chatter.py b/examples/SpeechProcessing/SpeechSearch/chatter.py
deleted file mode 100644
index d3311d054..000000000
--- a/examples/SpeechProcessing/SpeechSearch/chatter.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import marqo
-from langchain.llms import OpenAI
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
-
-load_dotenv()
-
-TEMPLATE = """
-You are a question answerer, given the CONTEXT provided you will answer the QUESTION (also provided).
-If you are not sure of the answer then say 'I am sorry but I do not know the answer'
-Your answers should two to five sentences in length and only contain information relevant to the question. You should match the tone of the CONTEXT.
-The beginnings of the CONTEXT should be the most relevant so try and use that wherever possible, it is important that your answers a factual and don't make up information that is not in the CONTEXT.
-
-
-CONTEXT:
-=========
-{context}
-QUESTION:
-=========
-{question}
-"""
-
-
-def answer_question(
- query: str,
- limit: int,
- index: str,
- mq: marqo.Client,
-) -> str:
- print("Searching...")
- results = mq.index(index).search(
- q=query,
- limit=limit,
- )
- print("Done!")
-
- context = ". ".join([r["transcription"] for r in results["hits"]])
-
- prompt = PromptTemplate(template=TEMPLATE, input_variables=["context", "question"])
- llm = OpenAI(temperature=0.9, model_name="text-davinci-003")
- chain_qa = LLMChain(llm=llm, prompt=prompt)
- llm_results = chain_qa(
- {"context": context, "question": query}, return_only_outputs=True
- )
- return llm_results["text"]
diff --git a/examples/SpeechProcessing/SpeechSearch/downloader.py b/examples/SpeechProcessing/SpeechSearch/downloader.py
deleted file mode 100644
index 0af0df4d6..000000000
--- a/examples/SpeechProcessing/SpeechSearch/downloader.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import uuid
-import os
-from yt_dlp import YoutubeDL
-from pydub import AudioSegment
-import urllib.request
-import shutil
-from typing import List
-import hashlib
-from multiprocessing import Pool
-
-ABS_FILE_FOLDER = os.path.dirname(os.path.abspath(__file__))
-
-
-class AudioWrangler:
- def __init__(self, output_path: str, clean_up: bool = True):
-
- self.output_path = output_path
-
- self.tmp_dir = "downloads"
-
- os.makedirs(os.path.join(ABS_FILE_FOLDER, self.tmp_dir), exist_ok=True)
-
- if clean_up:
- self.clean_up()
-
- def download_from_file(self, file):
- urls = []
- with open(file, "r") as f:
- for url in f.readlines():
- urls.append(url.strip())
- self.multiprocess_read_url_sources(urls)
-
- def multiprocess_read_url_sources(self, sources: List[str]):
- pool = Pool(os.cpu_count())
- pool.map(self.read_url_source, sources)
-
- def read_url_source(self, source: str):
- if "www.youtube.com" in source:
- return self.download_from_youtube(source)
-
- return self.download_from_web(source)
-
- def clean_up(self):
- for file in os.listdir(os.path.join(ABS_FILE_FOLDER, self.tmp_dir)):
- os.remove(os.path.join(ABS_FILE_FOLDER, self.tmp_dir, file))
-
- def download_from_youtube(self, url: str):
- outf = os.path.join(
- ABS_FILE_FOLDER,
- self.tmp_dir,
- hashlib.sha256(url.encode("ascii")).hexdigest(),
- )
- ydl_opts = {
- "format": "bestaudio/best",
- "postprocessors": [
- {
- "key": "FFmpegExtractAudio",
- "preferredcodec": "mp3",
- "preferredquality": "192",
- }
- ],
- "fragment_retries": 10,
- "outtmpl": outf,
- }
- with YoutubeDL(ydl_opts) as ydl:
- ydl.download([url])
-
- outf = self.convert_to_wav(outf + ".mp3")
- outf = self._move_to_output(outf)
- return outf
-
- def download_from_web(self, url: str):
- outf = os.path.join(
- ABS_FILE_FOLDER,
- self.tmp_dir,
- hashlib.sha256(url.encode("ascii")).hexdigest() + f".wav",
- )
- req = urllib.request.Request(url=url, headers={"User-Agent": "Mozilla/5.0"})
- with urllib.request.urlopen(req) as response, open(outf, "wb") as out_file:
- shutil.copyfileobj(response, out_file)
- outf = self.convert_to_wav(outf)
- outf = self._move_to_output(outf)
- return outf
-
- def convert_to_wav(self, fpath: str):
- sound = AudioSegment.from_file(fpath)
- wav_path = "".join([p for p in fpath.split(".")[:-1]]) + ".wav"
- sound.export(wav_path, format="wav")
- return wav_path
-
- def _move_to_output(self, file):
- target = os.path.join(self.output_path, os.path.basename(file))
- shutil.move(file, target)
- return target
diff --git a/examples/SpeechProcessing/SpeechSearch/indexer.py b/examples/SpeechProcessing/SpeechSearch/indexer.py
deleted file mode 100644
index b775bc1e5..000000000
--- a/examples/SpeechProcessing/SpeechSearch/indexer.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import marqo
-
-from typing import List, Dict, Any
-
-
-def index_transcriptions(
- annotated_transcriptions: List[Dict[str, Any]],
- index: str,
- mq: marqo.Client,
- tensor_fields: List[str] = [],
- device: str = "cpu",
- batch_size: int = 32,
-) -> Dict[str, str]:
-
- # drop short transcriptions and transcriptions that consist of duplicated repeating
- # character artifacts
- annotated_transcriptions = [
- at
- for at in annotated_transcriptions
- if len(at["transcription"]) > 5 or len({*at["transcription"]}) > 4
- ]
-
- response = mq.index(index).add_documents(
- annotated_transcriptions,
- tensor_fields=tensor_fields,
- device=device,
- client_batch_size=batch_size
- )
-
- return response
diff --git a/examples/SpeechProcessing/SpeechSearch/searcher.py b/examples/SpeechProcessing/SpeechSearch/searcher.py
deleted file mode 100644
index d992c3870..000000000
--- a/examples/SpeechProcessing/SpeechSearch/searcher.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import marqo
-import numpy as np
-from typing import List, Dict, Union, Callable, Tuple, Any
-
-
-def search_transcripts(
- query: str,
- limit: int,
- index: str,
- mq: marqo.Client,
- audio_getter: Callable[[str, int], np.ndarray],
-) -> Tuple[Dict[str, List[Dict[str, Any]]], List[Dict[str, Union[str, np.ndarray]]]]:
- print("Searching...")
- results = mq.index(index).search(
- q=query,
- limit=limit,
- )
- print("Done!")
-
- segments = []
- print("Extracting audio for hits...")
- for result in results["hits"]:
- start = int(result["start"] * result["samplerate"])
- end = int(result["end"] * result["samplerate"])
-
- audio = audio_getter(result["file"], result["samplerate"])
-
- segment = {
- "transcription": result["transcription"],
- "audio": audio[start:end],
- "samplerate": result["samplerate"],
- }
- segments.append(segment)
- print("Done!")
- return results, segments
diff --git a/examples/SpeechProcessing/SpeechSearch/transcriber.py b/examples/SpeechProcessing/SpeechSearch/transcriber.py
deleted file mode 100644
index ddf2ef2bf..000000000
--- a/examples/SpeechProcessing/SpeechSearch/transcriber.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
-from pyannote.audio import Pipeline
-from tqdm import tqdm
-import librosa
-import numpy as np
-import os
-
-from typing import Set, List, Tuple, Dict, Any
-
-
-class AudioTranscriber:
- def __init__(
- self, hf_token: str, device: str = "cpu", transcription_batch_size: int = 4
- ):
-
- self.device = device
- self.sample_rate = 16000
-
- self.transcription_batch_size = transcription_batch_size
-
- self._model_size = "medium"
-
- self.transcription_model = Speech2TextForConditionalGeneration.from_pretrained(
- f"facebook/s2t-{self._model_size}-librispeech-asr"
- )
- self.transcription_model.to(self.device)
- self.transcription_processor = Speech2TextProcessor.from_pretrained(
- f"facebook/s2t-{self._model_size}-librispeech-asr"
- )
- self.annotation_pipeline = Pipeline.from_pretrained(
- "pyannote/speaker-diarization@2.1", use_auth_token=hf_token
- )
-
- def annotate(self, file: str) -> List[Tuple[float, float, Set[str]]]:
- diarization = self.annotation_pipeline(file)
- speaker_times = []
- for t in diarization.get_timeline():
- start, end = t.start, t.end
- # reduce to 30 second chunks in case of long segments
- while end - start > 0:
- speaker_times.append(
- (start, min(start + 30, end), diarization.get_labels(t))
- )
- start += 30
-
- return speaker_times
-
- def transcribe(self, datas: List[np.ndarray], samplerate: int = 16000) -> List[str]:
- batches = []
- batch = []
- i = 0
- for data in datas:
- # pad short audio
- if data.shape[0] < 400:
- data = np.pad(data, [(0, 400)], mode="constant")
-
- batch.append(data)
- i += 1
- if i > self.transcription_batch_size:
- batches.append(batch)
- i = 0
- batch = []
- if batch:
- batches.append(batch)
-
- transcriptions = []
- for batch in tqdm(
- batches, desc=f"Processing with batch size {self.transcription_batch_size}"
- ):
- inputs = self.transcription_processor(
- batch, sampling_rate=samplerate, return_tensors="pt", padding=True
- )
- generated_ids = self.transcription_model.generate(
- inputs["input_features"].to(self.device),
- attention_mask=inputs["attention_mask"].to(self.device),
- )
- transcription_batch = self.transcription_processor.batch_decode(
- generated_ids, skip_special_tokens=True
- )
-
- transcriptions += transcription_batch
-
- return transcriptions
-
- def _create_id(self, file: str, i: int):
- return os.path.basename(file) + f"_{i}"
-
- def process_audio(self, file: str) -> Dict[str, Any]:
- speaker_times = self.annotate(file)
- audio_data, samplerate = librosa.load(file, sr=self.sample_rate)
-
- datas = []
- for start, end, _ in speaker_times:
- datas.append(audio_data[int(start * samplerate) : int(end * samplerate)])
-
- transcriptions = self.transcribe(datas, samplerate)
-
- annotated_transcriptions = []
- for i in range(len(transcriptions)):
- annotated_transcriptions.append(
- {
- "_id": self._create_id(file, i),
- "speaker": [*speaker_times[i][2]],
- "start": speaker_times[i][0],
- "end": speaker_times[i][1],
- "transcription": transcriptions[i],
- "samplerate": samplerate,
- "file": file,
- }
- )
-
- return annotated_transcriptions
diff --git a/examples/SpeechProcessing/article/article.md b/examples/SpeechProcessing/article/article.md
deleted file mode 100644
index c819976de..000000000
--- a/examples/SpeechProcessing/article/article.md
+++ /dev/null
@@ -1,480 +0,0 @@
-# After All is Said and Indexed - Unlocking Information in Recorded Speech
-
-Speech is one of the most popular forms of communication and much of the world’s data is held in audio recordings of human speech, whether it be as videos, movies, TV, phone calls, meeting recordings and more. While abundant in nature, accessing the content of speech data is a difficult task, making it searchable is even harder.
-
-In this article we present a system which extracts audio from online sources, identifies speakers, transcribes their speech, indexes them into Marqo and then uses the resulting Marqo index as a source of truth to respond to questions about the content. We demonstrate the behaviour of this system by ingesting a diverse dataset from three distinct domains and then asking very domain specific questions to a chatbot provided context retrieved with Marqo.
-
-
-
-
-## The challenges of working with Audio
-
-The task of searching audio is a challenging problem. In the world of AI, audio is an especially challenging medium to work with due to its high dimensionality and its obfuscation of useful features when represented as a waveform in the time domain. The human ear can hear sounds up to around 20,000 Hz, this requires a sample rate of 40,000 Hz to represent; given that the average English speaker in the US speaks at a speed of 2.5 words per second some back of the napkin maths says that it requires an average of 16,000 floating point numbers to represent a single word at the fidelity required to match the human ear (in the time domain).
-
-In practice this large dimensionality problem is made more palatable by down-sampling the audio, we accept a loss in quality in exchange for more manageable data. Down-sampling to a 16,000 Hz sample rate using the numbers discussed before gives us a more manageable (but still large) average of 6,400 floating point numbers per word.
-
-Many AI systems do not operate directly on the waveform and instead transform windows of the wavefrom into the frequency domain to generate a spectogram, this involves converting chunks of the signal into the frequency domain with fast fourier transforms as depicted below.
-
-
-
-## Applications for Searching Speech Data
-
-For the avid users of Marqo you may have come across [our podcast demo](https://docs.marqo.ai/latest/End-to-End%20Examples/simple_podcast_demo/). This was an inspiration for this project.
-
-There are many applications for searchability of speech in commercial platforms, enterprise and industry.
-
-For platforms that run podcasts or host videos this approach could be used to enhance the retrieval of relevant search results - removing a reliance on metadata tagging, search results can include a snippet of the media as well as time stamps to reduce their need to navigate through and scrub over the media looking for a specific section.
-
-In an enterprise context this technology can be used to index recordings of meetings to provide organisations with a new capability, the retrieval, attribution and traceability of decision points. Speaker diarisation works much better when the number of speakers is known ahead of time; this could be extracted by integrating with meeting software to monitor concurrent speakers throughout the meeting, speaker names from the meeting can also be mapped to the transcribed sections and indexed.
-
-The traceability capability extends to industry applications. Indexing and archiving live communications data means that operational decisions can be later retrieved - this can be invaluable to posteriori analysis of decision making that led to a an outcome, whether that be an incident or a success.
-
-## Audio and Marqo
-
-Currently Marqo doesn’t support audio and instead only has support for images and text.
-
-As such this article discusses the process of building a speech processing front end that wrangles audio data into documents which can be indexed by Marqo. The process can be broken into the following steps which will be discussed in more detail in subsequent sections:
-
-- Ingestion
-- Speaker diarisation & speech to text
-- Indexing
-- Searching and information retrieval
-
-The full source code that is referred to in the article can be found [here](https://github.com/OwenPendrighElliott/AudioSearching)
-
-## Ingestion
-
-The downloader can ingest audio from a variety of sources, including YouTube videos, links to audio files and local files. The AudioWrangler class provides a number of methods for downloading and wrangling audio.
-
-In the constructor, the output directory and temporary directory are instantiated as instance variables, the temporary directory (for intermediate processing) is created if it does not exist, this is done relative to the location of the python script itself.
-
-```python
-ABS_FILE_FOLDER = os.path.dirname(os.path.abspath(__file__))
-
-class AudioWrangler():
- def __init__(self, output_path: str, clean_up: bool = True):
-
- self.output_path = output_path
-
- self.tmp_dir = 'downloads'
-
- os.makedirs(os.path.join(ABS_FILE_FOLDER, self.tmp_dir), exist_ok=True)
-
- if clean_up:
- self.clean_up()
-
- def convert_to_wav(self, fpath: str):
- sound = AudioSegment.from_file(fpath)
- wav_path = ''.join([p for p in fpath.split(".")[:-1]]) + ".wav"
- sound.export(wav_path, format="wav")
- return wav_path
-
- def _move_to_output(self, file):
- target = os.path.join(self.output_path, os.path.basename(file))
- shutil.move(file, target)
- return target
-```
-
-YouTube videos have their audio extracted as 192kpbs MP3 files using the yt_dlp library, Pydub is then used to convert this to WAV. Videos are given a unique name by taking the SHA256 hash of their URL.
-
-```python
-def download_from_youtube(self, url: str):
- outf = os.path.join(
- ABS_FILE_FOLDER,
- self.tmp_dir,
- hashlib.sha256(url.encode("ascii")).hexdigest(),
- )
- ydl_opts = {
- "format": "bestaudio/best",
- "postprocessors": [
- {
- "key": "FFmpegExtractAudio",
- "preferredcodec": "mp3",
- "preferredquality": "192",
- }
- ],
- "fragment_retries": 10,
- "outtmpl": outf,
- }
- with YoutubeDL(ydl_opts) as ydl:
- ydl.download([url])
-
- outf = self.convert_to_wav(outf + ".mp3")
- outf = self._move_to_output(outf)
- return outf
-```
-
-URLs that point to audio files can also be downloaded using the requests library.
-
-```python
-def download_from_web(self, url: str):
- outf = os.path.join(
- ABS_FILE_FOLDER,
- self.tmp_dir,
- hashlib.sha256(url.encode("ascii")).hexdigest() + f".wav",
- )
- req = urllib.request.Request(url=url, headers={"User-Agent": "Mozilla/5.0"})
- with urllib.request.urlopen(req) as response, open(outf, "wb") as out_file:
- shutil.copyfileobj(response, out_file)
- outf = self.convert_to_wav(outf)
- outf = self._move_to_output(outf)
- return outf
-```
-
-To make downloading in bulk easier, you can provide a file where each line is a link to either a YouTube video or an audio file, the program will download and convert all of them, there is also a multiprocessing version of this to download in parallel.
-
-```python
-def download_from_file(self, file):
- urls = []
- with open(file, "r") as f:
- for url in f.readlines():
- urls.append(url.strip())
- self.multiprocess_read_url_sources(urls)
-
-def multiprocess_read_url_sources(self, sources: List[str]):
- pool = Pool(os.cpu_count())
- pool.map(self.read_url_source, sources)
-
-def read_url_source(self, source: str):
- if "www.youtube.com" in source:
- return self.download_from_youtube(source)
-
- return self.download_from_web(source)
-```
-
-## The Data
-
-In the code for this article, three collections of audio sources are provided. These are stored as text files with a list of links (YouTube videos). The provided files include:
-
-- James Hoffman (videos about coffee)
-- The WAN Show (tech podcast with two speakers)
-- Expert Panels (long videos with multiple speakers)
-
-The expert panels file has two expert panels from OpenAI, one titled GANs for Good and one titled Optimizing BizOps with AI.
-
-## Speaker Diarisation and Speech to Text
-
-The speaker diarisation and speech to text functions are collated together in the AudioTranscriber class.
-
-The constructor takes in the Hugging Face token, device and batch size for transcription. These are stored into instance variables alongside the models and pipelines required for the audio transcription process
-
-```python
-class AudioTranscriber:
- def __init__(
- self, hf_token: str, device: str = "cpu", transcription_batch_size: int = 4
- ):
-
- self.device = device
- self.sample_rate = 16000
-
- self.transcription_batch_size = transcription_batch_size
-
- self._model_size = "medium"
-
- self.transcription_model = Speech2TextForConditionalGeneration.from_pretrained(
- f"facebook/s2t-{self._model_size}-librispeech-asr"
- )
- self.transcription_model.to(self.device)
- self.transcription_processor = Speech2TextProcessor.from_pretrained(
- f"facebook/s2t-{self._model_size}-librispeech-asr"
- )
- self.annotation_pipeline = Pipeline.from_pretrained(
- "pyannote/speaker-diarization@2.1", use_auth_token=hf_token
- )
-```
-
-Speaker diarisation is the process of identifying who is speaking at what time in an audio recording. The output of this is a list of segments with a start time, end time and speaker labels. As the model has no way of knowing who a speaker is by name these labels are of the form SPEAKER_00, SPEAKER_01, etc.
-
-We can use the start and end time extracted by the diarisation process to segment the audio by speaker. To do this we use the pyannote.audio library with the speaker-diarization V2.1 model. To use this model yourself you will need to accept the terms and conditions and obtain an API token from hugging face, details can be found [here](https://huggingface.co/pyannote/speaker-diarization).
-
-The annotation process is captured in the annotate method which takes in the path for an audio file and returns the speaker annotations, the heavy lifting is wrapped up behind the scenes in the annotation pipeline. Longer segments of speech are chunked into 30 second segments to avoid very long sections for single speaker audio.
-
-```python
-def annotate(self, file: str) -> List[Tuple[float, float, Set[str]]]:
- diarization = self.annotation_pipeline(file)
- speaker_times = []
- for t in diarization.get_timeline():
- start, end = t.start, t.end
- # reduce to 30 second chunks in case of long segments
- while end - start > 0:
- speaker_times.append(
- (start, min(start + 30, end), diarization.get_labels(t))
- )
- start += 30
-
- return speaker_times
-```
-
-The audio enters the annotation pipeline as a wave file in the temporal domain.
-
-
-
-The annotation process transforms this into a diarised log of which speaker spoke at what time and for how long. Overlapping speakers are also identified and segmented.
-
-
-
-The speaker times extracted by the annotation can then be used to divide the original audio into subsections for speech to text processing.
-
-For speech to text the fairseq S2T model family was used, as seen in the constructor for AudioTranscriber the pretrained medium model from hugging face is used by default. Segments of speech are batched before being passed to the preprocessor and the model.
-
-```python
-def transcribe(self, datas: List[np.ndarray], samplerate: int = 16000) -> List[str]:
- batches = []
- batch = []
- i = 0
- for data in datas:
- # pad short audio
- if data.shape[0] < 400:
- data = np.pad(data, [(0, 400)], mode="constant")
-
- batch.append(data)
- i += 1
- if i > self.transcription_batch_size:
- batches.append(batch)
- i = 0
- batch = []
- if batch:
- batches.append(batch)
-
- transcriptions = []
- for batch in tqdm(
- batches, desc=f"Processing with batch size {self.transcription_batch_size}"
- ):
- inputs = self.transcription_processor(
- batch, sampling_rate=samplerate, return_tensors="pt", padding=True
- )
- generated_ids = self.transcription_model.generate(
- inputs["input_features"].to(self.device),
- attention_mask=inputs["attention_mask"].to(self.device),
- )
- transcription_batch = self.transcription_processor.batch_decode(
- generated_ids, skip_special_tokens=True
- )
-
- transcriptions += transcription_batch
-
- return transcriptions
-```
-
-The diarisation and transcription are wrapped up together into one driver method which brings together the annotations and the diarisations into a document to be indexed in the next step.
-
-Each document has a speaker, start time, end time, transcription, samplerate and file.
-
-```python
-def process_audio(self, file: str) -> Dict[str, Any]:
- speaker_times = self.annotate(file)
- audio_data, samplerate = librosa.load(file, sr=self.sample_rate)
-
- datas = []
- for start, end, _ in speaker_times:
- datas.append(audio_data[int(start * samplerate) : int(end * samplerate)])
-
- transcriptions = self.transcribe(datas, samplerate)
-
- annotated_transcriptions = []
- for i in range(len(transcriptions)):
- annotated_transcriptions.append(
- {
- "_id": self._create_id(file, i),
- "speaker": [*speaker_times[i][2]],
- "start": speaker_times[i][0],
- "end": speaker_times[i][1],
- "transcription": transcriptions[i],
- "samplerate": samplerate,
- "file": file,
- }
- )
-
- return annotated_transcriptions
-```
-
-## Indexing
-
-The backbone of this entire application is the Marqo database, indexing the annotated transcriptions is made simple by Marqo. As described above each document has multiple fields however, we only want to calculate tensor embeddings for the ‘transcription’ field, to do this we simply pass the names of the other fields through to the non_tensor_fields argument.
-
-Some filtering is applied to remove short and erroneous transcribed segments.
-
-```python
-def index_transcriptions(
- annotated_transcriptions: List[Dict[str, Any]],
- index: str,
- mq: marqo.Client,
- tensor_fields: List[str] = [],
- device: str = "cpu",
- batch_size: int = 32,
-) -> Dict[str, str]:
-
- # drop short transcriptions and transcriptions that consist of duplicated repeating
- # character artifacts
- annotated_transcriptions = [
- at
- for at in annotated_transcriptions
- if len(at["transcription"]) > 5 or len({*at["transcription"]}) > 4
- ]
-
- response = mq.index(index).add_documents(
- annotated_transcriptions,
- tensor_fields=tensor_fields,
- device=device,
- client_batch_size=batch_size
- )
-
- return response
-```
-
-## Searching
-
-We are now able to search through our indexed transcriptions and retrieve the original audio snippets back from the system when done! This is a powerful capability in itself however what makes a system like this work well in practice is a human usable method of engagement, to enable this we can place a language model between the search results and the end user.
-
-## Generating Conversational Answers
-
-Returning verbatim text and audio snippets is useful for some application however many users simply want an easy to understand answer to their questions. To do this we can add a language model after the results from Marqo are returned to answer the question with natural language. This process can be viewed as follows:
-
-
-
-Our prompt is constructed as follows, the goal is to push to model to only answer questions that it knows the answer to and for it to generate accurate answers.
-
-```python
-TEMPLATE = """
-You are a question answerer, given the CONTEXT provided you will answer the QUESTION (also provided).
-If you are not sure of the answer then say 'I am sorry but I do not know the answer'
-Your answers should two to five sentences in length and only contain information relevant to the question. You should match the tone of the CONTEXT.
-The beginnings of the CONTEXT should be the most relevant so try and use that wherever possible, it is important that your answers a factual and don't make up information that is not in the CONTEXT.
-
-
-CONTEXT:
-=========
-{context}
-QUESTION:
-=========
-{question}
-"""
-```
-
-This context is used in the answer_question function which searches Marqo, uses the transcriptions to make a context and then sends it to the text-davinci-003 model as part of the prompt.
-
-```python
-def answer_question(
- query: str,
- limit: int,
- index: str,
- mq: marqo.Client,
-) -> str:
- print("Searching...")
- results = mq.index(index).search(
- q=query,
- limit=limit,
- )
- print("Done!")
-
- context = ". ".join([r["transcription"] for r in results["hits"]])
-
- prompt = PromptTemplate(template=TEMPLATE, input_variables=["context", "question"])
- llm = OpenAI(temperature=0.9, model_name="text-davinci-003")
- chain_qa = LLMChain(llm=llm, prompt=prompt)
- llm_results = chain_qa(
- {"context": context, "question": query}, return_only_outputs=True
- )
- return llm_results["text"]
-```
-
-This function is then called in the main for the chat component:
-
-```python
-import marqo
-from SpeechSearch.chatter import answer_question
-
-
-def main():
- mq = marqo.Client(url="http://localhost:8882")
-
- index_name = "transcription-index"
- while True:
- query = input("Enter a query: ")
- answer = answer_question(
- query=query,
- limit=15,
- index=index_name,
- mq=mq,
- )
- print(answer)
-
-
-if __name__ == "__main__":
- main()
-```
-
-The outputs are very impressive, below are some examples where all three data sets were indexed using Marqo. This is important to remember as the database now has information on Coffee, Generative Adversarial Networks, BizOps and technology news.
-
-Here are some coffee related questions:
-
-> Q: How does the Bialetti Moka Express work?
->
->
-> A: The Bialetti Moka Express is a coffee brewing machine that uses steam pressure to push hot water up and over coffee grounds. It is made up of a base, a filter basket filled with coffee, an upper chamber, and a filter plate. Hot water is filled into the base and is then pushed up through the coffee grounds and filter plate, creating pressure in the coffee and forcing the espresso up into the upper chamber. This process ensures a consistent extraction of the espresso, creating a sweeter and more complex cup of coffee.
->
-
-> Q: What is the history of the Bialetti logo?
->
->
-> A: The Bialetti logo is a drawing of a man with a moustache who was apparently based on Renato Bialetti, the company's founder who took over the family business in the 1950s. The logo was created by a cartoonist hired by the Bialetti family to represent the brand. The logo is an iconic representation of the Italian espresso and has become synonymous with the Bialetti Company.
->
-
-> Q: When making espresso with a light roasted coffee, what grind setting should I use?
->
->
-> A: A light roasted espresso should be brewed with a fine grind setting in order to properly extract all the flavours. For best results, the grind should be finer than that used for filter coffee. As a starting point, 18g of coffee should take around 30 seconds to extract. However, exact grind settings can vary from person to person, so it is best to experiment, taste and adjust until you achieve the desired result.
->
-
-> Q: What pressure should the machine use to extract espresso?
->
->
-> A: The pressure of the machine is an important factor when it comes to extracting espresso. Ideally, the machine should be set to 9 bars of pressure in order to get a strong and concentrated espresso with a thick and sweet flavour. A lower pressure may lead to a slower shot and a longer contact time, but it won't necessarily increase the extraction. Brewing too fast can lead to channeling, which reduces extraction and causes a harsh and sour taste.
->
-
-And some related to the WAN show podcast:
-
-> Q: What is the argument around right to repair?
->
->
-> A: The argument for right to repair is that people should have the freedom to repair their own devices as they please. It is based on the idea that you should have the right to make repairs yourself, or pay someone to do it for you, and that companies should not make it overly expensive or difficult to repair devices you own. The goal is to ensure that companies cannot restrict people’s ability to repair their own devices, and to make it easier for people to repair their own things if they choose. Right to repair advocates and crusaders have been pushing for people to have access to electronic replacement parts and the ability to fix their own phones.
->
-
-> Q: How does Samsung's space zoom work?
->
->
-> A: Samsung's space zoom works by using a combination of machine learning and post-processing features. It uses the camera to capture a low resolution image, and then uses machine learning to blend and add details from an algorithm that was trained on high resolution images of the moon. This allows the camera to add sharper details and give the impression of a zoom feature. In addition, post-processing features such as edge detection and motion blur reduction are used to further improve the quality of the image.
->
-
-> Q: What is the controversy around Samsungs Space Zoom feature?
->
->
-> A: The controversy around Samsung's Space Zoom feature is that it implies that their phone cameras can take high definition pictures of the moon and other distant objects, whereas in reality the feature is only able to zoom in on far away objects with a low degree of clarity. This has raised concerns about the use of this technology for deep fake videos, as well as worries that Samsung may be purposely misrepresenting the capability of their phones.
->
-
-And some results for the data in the expert panels:
-
-> Q: What does BizOps mean for AI?
->
->
-> A: BizOps stands for Business Operations, and it is an integral part of the AI industry. AI is used in many aspects of business operations, such as data collection and analysis, machine learning, supply chains, and marketing. AI is used to optimize efficiency and increase profitability, which means that BizOps can be improved using AI effectively. AI is also used for predictive analytics and creating automated workflows. BizOps also refers to the relationship between AI companies and their clients, as many AI firms are focused on providing solutions that will improve the efficiency and accuracy of their client's operations.
->
-
-> Q: What are some application of GANs that are beneficial to the research community?
->
->
-> A: GANs have seen a lot of applications in recent years that are beneficial to the research community. GANs have been used to generate realistic images, improve security, generate music and more. GANs are especially helpful to students or practitioners in research fields as they can greatly help with the problems they see in their day-to-day. GANs are also being used for good in areas such as differential privacy and combating bias. GANs are being used in the medical field as well, where they can generate fake medical data for use in research without compromising any patient's privacy. GANs are also being used for fairness and for creating data for underrepresented populations. GANs have been used to learn from unlabeled real images and make a refiner that upgrades the synthetic image to make it more realistic.
-
-## Shortcomings
-
-The weakest link in the systems is certainly the speech to text model, in particular its ability to deal with acronyms is lacking and these can often cause some strange artifacts. Additional experimentation with different models or fine-tuning models for domain specific applications would likely improve this.
-
-Other newer models, such as OpenAIs Whisper models do perform the speech to text processing better however they bring their own unique behaviours. For example, the Whisper models have a habit of putting quotations around lines for segments with overlapping speakers - which for the purposes of this application is not the desired behaviour.
-
-Things can also go wrong for shorter queries as the response can be very sensitive to the word choice, especially when similar language is used throughout the corpus. These queries can cause more irrelevant information to be returned which in turn makes the chat model exhibit hallucinatory behaviour.
-
-## Closing Thoughts
-
-Searching speech data is challenging, but when so much valuable data is stored as audio waveforms it is important the we have systems that can leverage this information. With the right tools and techniques, it's possible to make speech data searchable and indexable by platforms like Marqo. This article presents only a very simple implementation and already achieves high quality retrieval audio segments using text queries.
-
-The full code and setup instructions can be found on [Owen Elliott's GitHub](https://github.com/OwenPendrighElliott/SpeechSearch).
\ No newline at end of file
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diff --git a/examples/SpeechProcessing/article/assets/2023-04-12-after-all-is-said-and-indexed-temporal-sound.png b/examples/SpeechProcessing/article/assets/2023-04-12-after-all-is-said-and-indexed-temporal-sound.png
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diff --git a/examples/SpeechProcessing/coffee_links.txt b/examples/SpeechProcessing/coffee_links.txt
deleted file mode 100644
index 6503008cb..000000000
--- a/examples/SpeechProcessing/coffee_links.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-https://www.youtube.com/watch?v=pkbuFwHnJQY
-https://www.youtube.com/watch?v=OXbQx2hfA5k
-https://www.youtube.com/watch?v=mZrgzQvNhXg
-https://www.youtube.com/watch?v=J6yWOyNq0uw
-https://www.youtube.com/watch?v=8jAMOAmYCJI
-https://www.youtube.com/watch?v=BfDLoIvb0w4
-https://www.youtube.com/watch?v=upgQsA5kLAk
-https://www.youtube.com/watch?v=hXad5XBRTcI
-https://www.youtube.com/watch?v=_Py8JOi3REg
-https://www.youtube.com/watch?v=3oFV88PzEFE
-https://www.youtube.com/watch?v=WkYqHWThIpA
-https://www.youtube.com/watch?v=6IjFfl-8Gu8
-https://www.youtube.com/watch?v=tltBHjmIUJ0
-https://www.youtube.com/watch?v=7QYlExoEpvA
-https://www.youtube.com/watch?v=lFwJF-_SUr0
-https://www.youtube.com/watch?v=1eK0eidOA_U
-https://www.youtube.com/watch?v=aQOKa61YBYc
-https://www.youtube.com/watch?v=aTFsBqhpLes
-https://www.youtube.com/watch?v=F4wrUP4c5P4
-https://www.youtube.com/watch?v=hQaV3w_XNiw
-https://www.youtube.com/watch?v=er2voEn8ZDU
-https://www.youtube.com/watch?v=xb3IxAr4RCo
\ No newline at end of file
diff --git a/examples/SpeechProcessing/meeting_links.txt b/examples/SpeechProcessing/meeting_links.txt
deleted file mode 100644
index f65462bdd..000000000
--- a/examples/SpeechProcessing/meeting_links.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-https://www.youtube.com/watch?v=9d4jmPmTWmc
-https://www.youtube.com/watch?v=U2nXlwCwuO4
\ No newline at end of file
diff --git a/examples/SpeechProcessing/podcast_links.txt b/examples/SpeechProcessing/podcast_links.txt
deleted file mode 100644
index 8789de16e..000000000
--- a/examples/SpeechProcessing/podcast_links.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-https://www.youtube.com/watch?v=DK4N3LYLm5A
-https://www.youtube.com/watch?v=EAk48A0Nx4g
-https://www.youtube.com/watch?v=Sev3y1az04I
-https://www.youtube.com/watch?v=pX3NKFpjLwg
-https://www.youtube.com/watch?v=61VKVBdyRTw
-https://www.youtube.com/watch?v=4muVhuQNqeI
-https://www.youtube.com/watch?v=Orc9G5DbiCg
\ No newline at end of file
diff --git a/examples/SpeechProcessing/requirements.txt b/examples/SpeechProcessing/requirements.txt
deleted file mode 100644
index ed1803e65..000000000
--- a/examples/SpeechProcessing/requirements.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-langchain==0.0.136
-librosa==0.9.2
-marqo==0.9.1
-numpy==1.23.5
-pyannote.pipeline==2.3
-pydub==0.25.1
-python-dotenv==1.0.0
-SoundFile==0.10.3.post1
-torch==2.0.0+cu117
-tqdm==4.65.0
-transformers==4.27.4
-yt_dlp==2023.3.4
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diff --git a/examples/StableDiffusion/hot-dog-100k.md b/examples/StableDiffusion/hot-dog-100k.md
deleted file mode 100644
index 6e46e60fa..000000000
--- a/examples/StableDiffusion/hot-dog-100k.md
+++ /dev/null
@@ -1,214 +0,0 @@
-# Combining stable diffusion with semantic search to tag and query images at scale
-
-There has been an incredible advancement in image generation recently. One of the latest examples of this is [Stable Diffusion](https://github.com/CompVis/stable-diffusion#diffusers-integration). You provide a natural language prompt - *"A photo of a hot dog"* - and out comes a high fidelity image matching the caption . Given this great technological development, I decided to generate 100,000 images of hot dogs so you don't have to (the full code for this article is [here](https://github.com/marqo-ai/marqo/blob/mainline/examples/StableDiffusion/hot-dog-100k.py) and the dataset [here](https://drive.google.com/file/d/16_1MlX9GH-6v060jYA23eTJwH74fSU4L/view?usp=sharing)).
-
-
- 
-
-
-
-## The hot-dog 100k dataset
-I was pretty interested to see what the model could produce given the same prompt, particularly across a large number of images. I used Huggingface [diffusers library](https://github.com/huggingface/diffusers) to [set up the generation](https://github.com/CompVis/stable-diffusion#diffusers-integration) and just let it run. My original plan of 1 million hot-dogs in a day quickly unraveled as I soon realized the required 695 hot-dogs/minute would be unattainable so I had to settle for 13 hot-dogs/minute and ~93,000 (only 7,000 off) images.
-
-
- 
-
-
-
-Here is a sample of 100 images randomly selected. There is a pretty wide variety of dogs that are generated and some very interesting interpretations of what constitutes a hot dog.
-
-## Indexing the hot-dog 100k dataset
-
-To dig a bit deeper into hot dog-100k dataset, we can index the data using [Marqo](https://github.com/marqo-ai/marqo). This allows us to easily search the images and do some additional labeling and classifying of the images in the dataset. After downloading the dataset we start up Marqo:
-
-```
-pip install marqo
-docker pull marqoai/marqo:latest;
-docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
-```
-
-Once Marqo is up and running we can get the files ready for indexing:
-
-```python
-import glob
-import os
-from marqo import Client
-
-# this should be where the images are unzipped
-images_directory = 'hot-dog-100k/'
-
-# the images are accessed via docker from here - you will be able
-# to access them at something like http://[::]:8000/ or http://localhost:8000/
-docker_path = 'http://host.docker.internal:8222/'
-
-# we start an image server for easier access from within docker
-pid = subprocess.Popen(['python3', '-m', 'http.server', '8222', '--directory', images_directory], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
-
-# now find all the files
-files = glob.glob(images_directory + "/*.jpg")
-
-# we want to map the filename only with its docker path
-files_map = {os.path.basename(f):f for f in files}
-
-# update them to use the correct path
-files_docker = [f.replace(images_directory, docker_path) for f in files]
-
-# now we create our documents for indexing - a list of python dicts
-documents = [{"image_docker":file_docker, '_id':os.path.basename(file_docker)} for file_docker,file_local in zip(files_docker, files)]
-
-```
-
-Now we can start indexing:
-
-```python
-settings = {
- "model":'ViT-L/14',
- "treat_urls_and_pointers_as_images": True,
- }
-client.create_index("hot-dogs-100k", **settings)
-responses = client.index("hot-dogs-100k").add_documents(documents, device="cuda", client_batch_size=50, tensor_fields=["image_docker"])
-
-```
-Check we have our images in the index:
-
-```python
-print(client.index("hot-dogs-100k").get_stats())
-```
-
-## Cleaning the hot-dog 100k dataset
-
-One noticeable thing is the presence of some black images. These are caused by the built-in filtering that suppresses images which may be deemed NSFW. I couldn't be bothered to remove the filter so we have some of these images in the dataset. We can easily remove these though using Marqo. Since I am lazy I will just search for a blank image using a natural language description - *"a black image"*.
-
-```python
-results = client.index("hot-dogs-100k").search("a black image")
-
-```
-
- 
-
-
-
-Now we have a black image, we can search using that and find all the other duplicate black images and remove them from the dataset.
-
-```python
-query = 'a black image'
-
-results = client.index(index_name).search(query)
-
-# remove the blank images
-results = client.index(index_name).search(results['hits'][0]['image_docker'], limit=100)
-
-# we check the results - scores of very close to 1 are duplicated (this value can change depending on the task)
-documents_delete = [r['_id'] for r in results['hits'] if r['_score'] > 0.99999]
-
-client.index(index_name).delete_documents(documents_delete)
-```
-
-Now our dataset should be free from images that do not contain hot dogs.
-
-## Labeling the hot-dog 100k dataset
-
-Given the variety of hot dogs generated from a single prompt, I was keen to understand more. A quick search of the following query - *"two hot dogs"* yielded something of interest but more was to follow, *"a hamburger"* and *"a face"*.
-
-```python
-results = client.index("hot-dogs-100k").search("a face")
-```
-
- 
-
-
-
-Armed with this survey, I created a new index with the following four documents - *"one hot dog"*, *"two hot dogs"*, *"a hamburger"* and *"a face"*. We can use our dataset images as queries against these labels and get back scores for each. This is effectively doing [zero-shot learning](https://en.wikipedia.org/wiki/Zero-shot_learning) to provide a score for each category which could be thresholded to provide classification labels to each image.
-
-```python
-index_name = 'one_dog_two'
-
-# the documents here are actually serving as labels. the documents (i.e. label)
-# are returned in the order they most closely match and the score can be used for classification
-labels = [{"label":"one hot dog"}, {"label":"two hot dogs"},
- {"label":"a hamburger"}, {"label": "a face"}]
-
-# get a copy of the labels only
-label_strings = [list(a.values())[0] for a in labels]
-
-# we create a new index
-settings = {
- "model":'ViT-L/14',
- "treat_urls_and_pointers_as_images": True,
- }
-client.create_index(index_name, **settings)
-
-# add our labels to the index
-responses = client.index(index_name).add_documents(labels, tensor_fields=["label"])
-
-# loop through the documents and search against the labels to get scores
-for doc in documents:
-
- # the url for the image is what is used as the search - an image
- # note: you will want a gpu to index the whole dataset device="cuda"
- responses = client.index(index_name).search(doc['image_docker'], device='cpu')
-
- # now retrieve the score for each label and add it to our document
- for lab in label_strings:
- doc[lab.replace(' ','_')] = [r['_score'] for r in responses['hits'] if r['label'] == lab][0]
-
-```
-
-Now for each image we have the computed scores against each category which were just "documents" in our small index.
-
-## Updating the hot-dog 100k dataset
-
-We have now calculated scores for the different categories described previously. The next thing to do is update our indexed data to have the scores. If we re-index a document it will update it with any new information. We can remove our image field from the documents as it has already been indexed with a model previously.
-
-```python
-documents_image_docker = [doc.pop('image_docker') for doc in documents]
-responses = client.index("hot-dogs-100k").add_documents(documents, device='cpu', client_batch_size=50, tensor_fields=["image_docker"])
-```
-
-## Animating the hot-dog 100k dataset
-
-After all this work we can now animate the hot-dog-100k. We will do this by effectively "sorting" the vectors and creating a movie from these vectors accompanying images. Before we animate, we can make it more interesting as well by restricting the images we can search over by prefiltering on some of the scores we previously calculated. This effectively restricts the space we can use for the animation based on the filtering criteria.
-
-
- .gif)
-
-
-To animate the images based on their sorted vectors we can take an image as a start point (based on the query "a photo of a smiling face") and find the next closest one (as seen above). Repeat the process until no more images are left and you have walked across the [latent space](https://en.wikipedia.org/wiki/Latent_space). We are effectively solving a variant of the [traveling salesmen problem](https://en.wikipedia.org/wiki/Travelling_salesman_problem) - albeit with an approximate algorithm.
-
-```python
-# pick one to start
-results = client.index("hot-dogs-100k").search('a photo of a smiling face',
- searchable_attributes=['image_docker'], filter_string="a_face:[0.58 TO 0.99]")
-# find the document that matches closest with the query
-index = [ind for ind,doc in enumerate(documents) if doc['_id'] == results['hits'][0]['_id'] ][0]
-current_document = documents[index]
-# create a list to store the "sorted" documents
-ordered_documents = [current_document['_id']]
-
-for i in range(len(documents)):
-
- # remove current document
- client.index(index_name).delete_documents([current_document['_id']])
-
- # now search with it to get next best
- results = client.index(index_name).search(current_document['image_docker'], filter_string="a_face:[0.58 TO 0.99]",
- searchabel_attributes=['image_docker'], device='cuda')
-
- next_document = results['hits'][0]
-
- # now add it
- ordered_documents.append(next_document['_id'])
-
- current_document = next_document
-
-ordered_images = [files_map[f] for f in ordered_documents]
-```
-
-After we have done this walk we have the "sorted" list of images. These can then be animated in the order they appear in the list.
-
-
- 
-
-
-## Closing thoughts
-Its amazing to watch the progress in image generation and the multi-modality of it all. The fidelity of the images is incredible and the ability to maniuplate images via prompt engineering is very interesting. Of particular interest is how this will impact search given their shared models. If you are interested in this check out [Marqo](https://github.com/marqo-ai/marqo) for yourself and [sign up](https://q78175g1wwa.typeform.com/to/d0PEuRPC) for the cloud beta.
diff --git a/examples/StableDiffusion/hot-dog-100k.py b/examples/StableDiffusion/hot-dog-100k.py
deleted file mode 100644
index 225b5d19d..000000000
--- a/examples/StableDiffusion/hot-dog-100k.py
+++ /dev/null
@@ -1,182 +0,0 @@
-#####################################################
-### STEP 0. Import and define any helper functions
-#####################################################
-
-from marqo import Client
-from marqo.api.exceptions import MarqoApiError
-import torch
-import json
-import pprint
-import glob
-import os
-import pandas as pd
-import subprocess
-
-#####################################################
-### STEP 1. Setup some things and create the documents for indexing
-#####################################################
-
-# this should be where the images are unzipped
-# get the dataset from here https://drive.google.com/file/d/16_1MlX9GH-6v060jYA23eTJwH74fSU4L/view?usp=sharing
-images_directory = 'hot-dog-100k/'
-
-# the images are accessed via docker from here - you will be able
-# to access them at something like http://[::]:8000/ or http://localhost:8000/
-docker_path = 'http://host.docker.internal:8222/'
-
-# we start an image server for easier access from within docker
-pid = subprocess.Popen(['python3', '-m', 'http.server', '8222', '--directory', images_directory], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
-
-# now find all the files
-files = glob.glob(images_directory + "/*.jpg")
-
-# we want to map the filename only with its docker path
-files_map = {os.path.basename(f):f for f in files}
-
-# update them to use the correct path
-files_docker = [f.replace(images_directory, docker_path) for f in files]
-
-# now we create our documents for indexing - a list of python dicts with keys as field names
-documents = [{"image_docker":file_docker, '_id':os.path.basename(file_docker)} for file_docker,file_local in zip(files_docker, files)]
-
-#####################################################
-### STEP 2. Initial indexing
-#####################################################
-
-# we create the index and can set the model we want to use
-
-
-# get the marqo client
-client = Client()
-
-# index name - should be lowercase
-index_name = 'hot-dogs-100k'
-
-settings = {
- "model":'ViT-L/14',
- "treat_urls_and_pointers_as_images": True,
- }
-client.create_index(index_name, **settings)
-
-# Here we index. A gpu is recommended (device='cuda')
-responses = client.index(index_name).add_documents(documents, device='cpu', client_batch_size=50, tensor_fields=["image_docker"])
-
-
-#####################################################
-### Step 3. Add some labels via zero-shot learning
-#####################################################
-
-
-index_name = 'one_dog_two'
-
-# the documents here are actually serving as labels. the document (i.e. label)
-# that most closely matches is returned first and can be used as a label
-# each one gets scored and those scores can also be kept
-labels = [{"label":"one hot dog"}, {"label":"two hot dogs"},
- {"label":"a hamburger"}, {"label": "a face"}]
-
-# get a copy of the labels only
-label_strings = [list(a.values())[0] for a in labels]
-
-# we create a new index
-settings = {
- "model":'ViT-L/14',
- "treat_urls_and_pointers_as_images": True,
- }
-client.create_index(index_name, **settings)
-
-# add our labels to the index
-responses = client.index(index_name).add_documents(labels, tensor_fields=["label"])
-
-# loop through the documents and search against the labels to get scores
-for doc in documents:
-
- # the url for the image is what is used as the search - an image
- # note: you will want a gpu to index the whole dataset device="cuda"
- responses = client.index(index_name).search(doc['image_docker'], device='cpu')
-
- # now retrieve the score for each label and add it to our document
- for lab in label_strings:
- doc[lab.replace(' ','_')] = [r['_score'] for r in responses['hits'] if r['label'] == lab][0]
-
-documents_image_docker = [doc.pop('image_docker') for doc in documents]
-responses = client.index("hot-dogs-100k").add_documents(documents, device='cpu', client_batch_size=50, tensor_fields=["image_docker"])
-
-#####################################################
-### Step 4. Remove the black images
-#####################################################
-
-query = 'a black image'
-
-results = client.index(index_name).search(query)
-
-# remove the blank images
-results = client.index(index_name).search(results['hits'][0]['image_docker'], limit=100)
-
-# we check the results - scores of very close to 1 are duplicated (this value can change depending on the task)
-documents_delete = [r['_id'] for r in results['hits'] if r['_score'] > 0.99999]
-
-client.index(index_name).delete_documents(documents_delete)
-
-
-#####################################################
-### Step 5. order the images based on their similarity with each other
-#####################################################
-
-# pick one to start
-results = client.index("hot-dogs-100k").search('a photo of a smiling face',
- searchable_attributes=['image_docker'],
- filter_string="a_face:[0.58 TO 0.99] AND a_hamburger:[0.60 TO 0.99]", device='cuda')
-
-# find the document that matches closest with the query
-index = [ind for ind,doc in enumerate(documents) if doc['_id'] == results['hits'][0]['_id'] ][0]
-current_document = documents[index]
-# create a list to store the "sorted" documents
-ordered_documents = [current_document['_id']]
-
-for i in range(len(documents)):
-
- # remove current document
- client.index(index_name).delete_documents([current_document['_id']])
-
- # now search with it to get next best
- results = client.index(index_name).search(current_document['image_docker'],
- searchabel_attributes=['image_docker'],
- filter_string="a_face:[0.58 TO 0.99] AND a_hamburger:[0.60 TO 0.99]",
- device='cuda')
-
- next_document = results['hits'][0]
-
- # now add it
- ordered_documents.append(next_document['_id'])
-
- current_document = next_document
-
-ordered_images = [files_map[f] for f in ordered_documents]
-deleted_documents = [d for d in documents if d['_id'] in ordered_documents]
-
-#####################################################
-### Step 6. Animate them
-#####################################################
-import sys
-import subprocess
-from pathlib import Path
-
-def prepend_number(filename, number, new_dir):
- _dir, _name = os.path.split(filename)
-
- return _dir + f'/{new_dir}/' + number + '_' + _name
-
-def copyWithSubprocess(cmd):
- proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-
-save_base_dir = 'outputs/'
-Path(save_base_dir).mkdir(parents=True, exist_ok=True)
-
-new_images = [save_base_dir + f'{str(i).zfill(5)}' + os.path.basename(f) for i,f in enumerate(ordered_images)]
-
-for image, new_image in zip(ordered_images, new_images):
-
- cmd=['cp', image, new_image]
-
- copyWithSubprocess(cmd)
diff --git a/examples/podcast-search/README.md b/examples/podcast-search/README.md
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--- a/examples/podcast-search/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-- Clone the repository.
-- Run Marqo, for instructions refer to the getting started guide.
-- `cd` into the `podcast-search` directory.
-- Run the code using `python3 podcast_search_demo.py`
diff --git a/examples/podcast-search/data/podcast_data.csv b/examples/podcast-search/data/podcast_data.csv
deleted file mode 100644
index a990c6dcf..000000000
--- a/examples/podcast-search/data/podcast_data.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-name,description
-"The water crisis in Jackson, Mississippi","What’s happening in Jackson is hardly unique: Cities and states across the US are setting themselves up for failure by postponing expensive but critical work on aging water infrastructure. Climate change is making things worse, faster."
-"Your long Covid questions, answered","Millions of people have long Covid; countless more could get it. Dr. Monica Verduzco-Gutierrez answers question from Today, Explained listeners about the condition that has even doctors bewildered."
\ No newline at end of file
diff --git a/examples/podcast-search/data/transcripts/The water crisis in Jackson, Mississippi.txt b/examples/podcast-search/data/transcripts/The water crisis in Jackson, Mississippi.txt
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-9/8/22 / The Water Crisis in Jackson, Mississippi
-[HALF SECOND OF SILENCE]
-
-[BILLBOARD]
-
-NOEL KING (Host): In Jackson, Mississippi, here’s the past month: summed up.
-
- JACKSON RESIDENT It’s like playing Russian Roulette. You don’t if you’re gonna wake up with water. You don’t know if you got water. You don’t know what water the condition is in.
-
-Now, in response to the chaos, the city’s website has been updating residents.
-
-Yesterday, the most recent update: the water pressure is back. But the tap water is still not safe to drink unless it is boiled first.
-
- NEWSREEL: It’s a horrible situation in the entire city of Jackson, not just one individual. And I blame the city council, the governor, and the mayor because they’re all in confusion and no one seems to know how to solve the problem.
-
-Coming up on Today, Explained: how Jackson, Mississippi became ground zero - for today anyway - of our national infrastructure crisis.
-
-[THEME]
-
-NOEL: It’s Today, Explained. I’m Noel King. Kobee Vance, reporter with Mississippi Public Broadcasting. Let’s go back to the beginning. How did this all start?
-
-KOBEE VANCE (reporter, Mississippi Public Broadcasting): Back about two weeks ago, we saw a lot of rain come through the southeastern United States and here in Mississippi, a lot of that flooded into the Pearl River, upstream.
-
-SCORING
-
- NEWSREEL: The governor of Mississippi is now declaring a state of emergency after days of heavy rainfall in the central part of the state.
-
-KOBEE: The Jackson area saw a lot of flooding and with that flooding came a lot of extra chemicals in the water into Jackson's water plant. That meant they had to also raise the amount of treatment processes they were doing on those highly contaminated waters. The city of Jackson has a very limited staff. They have been understaffed at this water treatment plant for years and years now.
-
- NEWSREEL: Well, the EPA has made it clear that a staffing shortage is amplifying the city of Jackson’s continued water issues.
-
-KOBEE: And their equipment is exceptionally outdated. And so because of those factors, they just couldn't meet the demands of taking in water, processing it, and then putting it out into the system to maintain that pressure. So they had to decrease their water production in the short term, which couldn't keep up with the demand that Jackson residents needed. And when that happened, we saw water pressure across the city drop as the storage tanks that are used began to drain.
-
- NEWSREEL: At her Fondren home, water pressure is still too low to take a shower or wash clothes, but Hall said she tried to fill up her tub to have water to flush her toilet and wash dishes with.
-
-KOBEE: If a city's water pressure is not adequate, the water sits in the pipes at a lower rate and it can't flow as fast. You might have heard the phrase, moss can't grow on a rolling stone. Well, it kind of applies to whenever water can't flow quickly enough to get to these storage tanks, or it sits stagnant in some pipes, then bacteria has a chance to produce faster and faster. And when that happens, it can be dangerous for residents to drink the water. And the city of Jackson has been plagued by that for years.
-
- NEWSREEL: It came out of the faucet and it was thick and it was a different color. I couldn’t even give it to my dog.
-
-And that's something that Jacksonians have just come to live with. It's just that in this case, we've seen a bigger problem, which is they don't have water pressure at all.
-
- SCORING OUT
-
-NOEL: Why has this been going on for so long? This is the capital city!
-
-KOBEE: Jackson obviously has a history of racism. So that leads into something called white flight. When a lot of African-Americans moved into the city of Jackson, we began to see a lot of white people that held a majority of the capital – they left the city.
-
- NEWSREEL: Federally-mandated school desegregation had prompted waves of white residents to abandon the city. Between 1970 and 1990, Jackson went from being 60 percent white to being nearly 60 percent Black.
-
-KOBEE: They didn't move far away. These people are just on the very edge of the county lines. So these people can still benefit from having the things that are in Jackson, but they don't live there themselves. And their personal interest in the city has waned over time. Many look at the city as a stain on the state rather than something that they're proud of as a state capital.
-And so over a period of time, the city of Jackson lost their tax base. When that happens, they can't keep doing the same infrastructure updates that they had been doing in the past. So over a period of decades, the city has failed to be able to produce adequate maintenance on their systems. They haven't been able to replace pipes in their grounds across the city. And so these infrastructure failings are something that have compounded over years and years.
-
-NOEL: Who has been most directly affected by this?
-
-KOBEE: This is something that is heavily affecting those who live in South and West Jackson. Those residents live miles away from the water treatment plants. And so there is so much distance that has to be covered, they are almost always under a boil water notice. And oftentimes it's easier to count the weeks that they're not on a boil notice than they are.
-
- NEWSREEL: Somewhere, somebody has dropped the ball. And so the city council or the mayor or the governor or whatever needs to get together and get this done.
-
-KOBEE: The demographics of Jackson is 80% Black and a lot of Jackson residents are also low-income. And with that, it becomes a system that is increasingly putting a large tax burden on not just African-Americans, but people who have very low incomes and live far below the poverty rate.
-
-NOEL: The city knows it's in the middle of a crisis. The state knows it's in the middle of the crisis. Are those two entities – who presumably should have some of the same goals in mind – are they working together at this point? How are they responding?
-
-KOBEE: It has become a tricky situation to know how they're working together or not. During the first full week of the crisis, the governor and the mayor came together in a press conference, talking about how they wanted unity in this going forward to make sure that they were focusing on helping Jackson residents first.
-
- MISSISSIPPI GOV. TATE REEVES: There’ll be plenty of time in the future to play the blame game and y’all can do all of that you want to do, you can do that in real time if you want, but I ain’t got time for it.
-
-But then, we saw the governor come forward and immediately start blaming city administration for not maintaining the city's water plant adequately. And then on the city side, they're saying that they have been putting forward plans constantly for the past several years, which never get the funding they need.
-
- JACKSON MAYOR CHOKWE ANTAR LUMUMBA: I’ve been saying that it’s not a matter of if our system would fail, but when our system would fail.
-
-NOEL: Is the city providing people with water or with other types of aid?
-
-KOBEE: The city has been providing people with water for a while now. it's something they had been doing under an EPA order at fire stations across the area. And now the state has also stepped in to help with this issue from our Mississippi emergency management agency and our state national guard, as well as partners at for instance, our department of agriculture and commerce. And so with all of these entities coming together, they have been able to drastically step up water distribution efforts. I think the governor mentioned the other day, it was surpassing 5 million bottles of water passed out within a week. And so with all of that, people are getting the water they need right now, but this is something that has been an issue for so long that people have just gotten used to buying their own water in grocery stores. Some people spend maybe $25 a week if they live alone, but I've heard families spend maybe like a hundred dollars per week just on water for their children.
-
- NEWSREEL: People are fed up. I honestly don’t even want to bathe my baby in Jackson’s water.
-
- NEWSREEL: It’s sad. And I can’t even afford to move out of Jackson so I have to stay here and deal with this.
-
-NOEL: If I had been living like this for years, and I knew that it was in part because the state wouldn't help me, I'd be furious. Have you talked to anybody who's angry?
-
-SCORING
-
-KOBEE: People are pissed, I think it's fair to say. This is something that they have to a degree come to live with, come to learn how to live with, but they are not happy about it. Some people are questioning if they wanna pay their water bills. Why would you wanna pay your water bill if you can't drink the water and sometimes you don't have water coming out of your taps? It becomes a very regressive cycle. And so people are just really heavily frustrated. They are appreciative of all the work that's being done to try to get the water pressure back online, and then get that drinkable again. They're appreciative of the state stepping in and the federal government at this point. And they're appreciative of the national conversation that's ongoing now about Jackson, but it's just a matter of time until this happens again, unless something drastic happens and a lot of the systems are updated, more workers are added in Jackson, and residents are ensured that they're gonna have water in their homes for a longer period of time.
-
-NOEL: Where do things stand today with the water?
-
-KOBEE: Currently residents in Jackson do have water pressure. It is tentative because the, systems are still very fragile, but they do have water. They cannot drink it. Under the guidance of the department of health in Mississippi, they were lowering their standards of water output, so they could just meet the pressure demands of the city. Now they are currently working towards making sure that water is drinkable. And so currently residents are advised to not drink the water, not even brush their teeth with the water, although they can technically boil it, if they need water to drink in their homes.
-
- SCORING OUT
-[BREAK]
-
-[BUMPER]
-
- JACKSON MAYOR CHOKWE ANTAR LUMUMBA: I have described Jackson as the poster child of the infrastructure challenges that we see in this country.
-NOEL: It’s Today, Explained. Darren Olson is a water resources engineer based in Illinois. He is also a member of the American Society of Civil Engineers, where he vice chairs a committee that grades U.S. infrastructure, like a report card. Now, the last one was released in 2021. Out of 17 categories, the U-S got eleven Ds, four Cs, two Bs. All of that adds up to 17. So no As. Our national AVERAGE?
-DARREN OLSON (PE, AMERICAN SOCIETY OF CIVIL ENGINEERS): We got an overall grade of C minus. Not something that you would want to bring home to your parents or you'd want your child to bring home to you.
-NOEL: Can I ask what went through your head when you started reading about the water crisis in Jackson, Mississippi?
-DARREN: Well, while it made front line news these past few weeks, it's really not all that surprising that something like that can happen. You know, they're dealing with a horrible crisis down in Jackson, where nearly 150,000 people as of a few days ago were still without water. But throughout the country, there is a water crisis. And even here in Illinois, we've got a community, Dixmoor, that as of today is out of water as well. They have to get bottled water because they've had too many water main breaks and they aren't able to deliver water to their residents. So while it's making front line news right now, it's really not all that surprising.
-NOEL: Were the mechanics of what happened in Jackson immediately understandable to you? Was it something where you said, ‘oh, dear, this is very confusing’? Or something where you said, ‘oh, dear, this again’?
-DARREN: A little combination of both, and I think that's what we're going to start seeing, is that there is definitely a water infrastructure problem in the United States.
- SCORING
- NEWSREEL: Thousands of New Jersey residents are stocking up on bottled water after tests indicated water filters may not be protecting residents against elevated levels of lead.
- NEWSREEL: WSSE tells us there have been 365 water main breaks in our area this month alone.
- NEWSREEL: An alarming new report says more than 900,000 Californians do not have access to clean drinking water. More than 65% of the failing water systems are located in low-income communities, mostly in the central Valley.
-We have aging pipes and we have aging treatment plants that are beyond their useful life. You know, here in Chicago, we for a while were replacing water mains that were literally made of wood.
- CHICAGO OFFICIAL JOSH ELLIS: We are replacing water mains that we installed when President Roosevelt was in office - that’s Theodore Roosevelt.
-They were over 100 years old and way beyond their useful life.
- CHICAGO OFFICIAL JOSH ELLIS: Served us well, those trees did.
-That's one part of the problem and that's the part of the problem I think we understand a little bit more. But the part that's a little bit new is this climate change aspect to it. And so we've got this kind of a double whammy situation where we have aging water infrastructure that is now up against more severe storms, more drastic droughts. So that is the new part that I think is compounding the existing infrastructure crisis that we have.
-NOEL: Why does climate change – and the flooding that accompanies it – why does that matter? A pipe is meant to carry water. Why does it matter if it's carrying more water or less water?
-DARREN: I always relate water to something that's even more understandable to people, which is traffic. You can envision if you have roads that were built 50, 60, even 100 years ago, they were meant to carry a certain type of vehicle and a certain amount of vehicles. Those roads need to get increased both in lanes and in length. When we start talking about how many people are driving in the United States right now. Water is as simple as that is a very similar situation. With the roads, if you don't make them wider with more lanes, you obviously have horrific traffic issues. With water, it's the same thing. The pipes that were built 100 years ago and installed 100 years ago, they were meant for a certain type of climate that we were seeing then and were based on the standards of that time. Both of those have changed dramatically. The pipes that we put in the ground 50 years ago are simply not meant to handle what we're experiencing now. And not to mention the fact that they all have a lifespan, just like every piece of infrastructure has a certain lifespan to it. And we are now exceeding the lifespan of much of the water infrastructure that is underground.
- SCORING OUT
-NOEL: How much water infrastructure IS there in the U.S.?
-DARREN: So in the United States, they estimate that there's about 2.2 million miles of water main in the United States. It is generally, when you start talking about how much of this you can replace in a given year in 2020, it's estimated that about 12,000 miles of pipe were replaced. So that's less than 1% of the total amount of pipe that's in the ground that was able to be replaced in 2020. So you think about how many years it's going to take for us to upgrade all of this water infrastructure. It's really significant.
-NOEL: Why are we not handling it now? Is it that the money doesn't exist?
-DARREN: Yes. It is really – it’s expensive to replace this infrastructure. And water infrastructure is a little bit different than roads and bridges in that way back when, when this water infrastructure was put in place, much of it was federally funded. Back in the sixties, seventies, with the Clean Water Act, there was a huge federal boost of money that was put in to try to achieve the goals of the Clean Water Act. Since then, the funding responsibility has shifted dramatically. It has shifted from the federal side to really the local side. And, you know, when you talk about cities like Jackson, that water infrastructure that's in place, that is their responsibility to fix. And the funding generally has to come from the municipality. They can certainly get loans and there's ways to try to finance that. But to replace that infrastructure that was originally put in, it's on them. And it's under roads. And you can imagine how expensive it is to tear up the surface of the road, dig down five, six, sometimes ten feet down; pull out this large pipe out of the ground that's in deteriorating condition; put a new one back in; put all that material back; put those other utilities back; and then repave the road. When you explain it like that, just to do it for a block seems exhausting. And think about doing it for 2.2 million miles of pipe.
- JACKSON MAYOR CHOKWE ANTAR LUMUMBA: And so this is something that when the state joined me and shared that they would be bringing in resources, bringing in a team to support us, we welcomed that with open arms because we’ve been saying that we needed support. We’ve been saying that we need resources.
-NOEL: So Darren, all of this comes down to what?
-DARREN: What it comes down to is a lack of prioritization and a lack of funding to keep up this infrastructure.
- JACKSON MAYOR CHOKWE ANTAR LUMUMBA: This is due to decades - decades and decades - of possibly 30 years or more of deferred maintenance, a lack of capital improvements made to the system, a lack of human capital, a workforce plan, that accounted for the challenges that our water treatment facility suffers from.
-This infrastructure is very valuable. And for so many years, we have not prioritized maintaining those to the point now where we're looking at over the next ten years needing $2.6 trillion to get us from where we're at now, which is a C-minus as an overall grade for infrastructure, up to a B.
-NOEL: Are we as a nation taking money away from infrastructure, from roads and bridges and aviation and water and diverting it toward other stuff?
-DARREN: Well, certainly there's a fixed pool of resources and there are certainly many, many needs. I think what we're trying to do is – and one of the things that has occurred recently with the Infrastructure Investment and Jobs Act – is to try to let our elected officials know, and they heard that message and they passed that $1.2 trillion bipartisan infrastructure that actually brought in $550 billion of new spending.
- PRESIDENT JOE BIDEN: It’s a once-in-a-generation investment in America, unlike anything we’ve seen or done since we built the interstate highway system and the space race decades ago.
-It's not going to solve our infrastructure problem and it's a fraction of what we need. But it is really a move in the right direction.
-NOEL: But it's a fraction of what we need?
-DARREN: It is a fraction of what we need. That's how big the infrastructure gap is that it is a fraction of what we need.
-And we have an opportunity, whether it's the engineers, our elected officials. We can start to plan better for things like climate change and to make sure that whether it's our electric grid or our water mains or our levees or our ports, that they are built more resilient. And finally, we can use things like technology to track these 2.2 million miles of water main. We're able to start to understand where our weak points are, how those weak points intersect with communities that may be underserved. So we're able to prioritize from both an infrastructure perspective, but also for, you know, the need of the population where to invest our dollars first and where we're going to get the highest return on that investment.
-NOEL: What are the consequences for this country if we keep on with the mediocre slash disastrous grades? Do you have nightmares about flooding? Or do you envision flooding being like a minor consequence of where we stand right now?
-DARREN: Well, I'm a water resources engineer, so I have nightmares about flooding all the time. But you know what? That is one part of the consequence. You know, we will see more things like Jackson, Mississippi. We're going to see more headlines of, you know, out West with whole towns or states running out of water. But there's also the economic side of it as well.
- SCORING <12-tossing and turning (spare)>
-Every year this is costing the American household over $3,000 in in hidden costs for our poor infrastructure. Whether that is having to fix tires, because the roads are in such poor condition that you blew a tire, having to buy bottled water, maybe it's the electric grid went down and so your refrigerator shut down and you have to buy new food. These are real costs for the American people, and it's also costing businesses as well.
-And we don't want to have to wait for a bridge collapse or a whole city needing to boil its water for the focus of our elected officials to be on maintaining our infrastructure and investing in our infrastructure. And too many times, especially with the water system, whether it's a sanitary pipe or a drinking water pipe, it's out of sight and out of mind for 99% of our population. It then becomes difficult to try to convince people that they need to invest in that when they're not seeing it every day.
-SCORING BUMP
-NOEL: Today’s show was produced by Jillian Weinberger and Amanda Lewellyn with assists from Victoria Chamberlin and Tori Dominguez and edited by Matthew Collette. It was fact checked by Serena Soulis and it was engineered by Paul Robert Mounsey. It’s Today, Explained. I’m Noel King.
- SCORING OUT
\ No newline at end of file
diff --git a/examples/podcast-search/data/transcripts/Your long Covid questions, answered.txt b/examples/podcast-search/data/transcripts/Your long Covid questions, answered.txt
deleted file mode 100644
index 7c340d554..000000000
--- a/examples/podcast-search/data/transcripts/Your long Covid questions, answered.txt
+++ /dev/null
@@ -1,234 +0,0 @@
-Sept. 7, 2022 / Your long COVID questions, answered
-
-[HALF SECOND OF SILENCE]
-
-[BILLBOARD]
-
- SCORING IN
-
- Alida Aiello: This has all completely taken over my life.
-
-Colleen Powers: I have to plan out my days and weeks in terms of energy expenditure, like it's a limited and finite resource.
-
-Ben Sheehan: I really wish more people were talking about long COVID.
-
-Patricia Anderson: It's like my whole life has shrunk and gotten smaller.
-
-Cynthia Adinig: I didn't need any medications at all before COVID. I had no idea what life with long term chronic illness was like before.
-
-NOEL KING (host): You have questions about Long COVID. We have some answers. From a doctor. What we know, don’t know, and how we can help. That’s coming up on Today, Explained.
-
-Ben Sheehan: There are millions of people like me who are living with this, who have been looking for answers for over two years now.
-
-Patricia Anderson: So I haven't completely given up hope. It's just hard to hold on to it.
-
-SCORING OUT
-
-[THEME]
-
-NOEL: It’s Today Explained. I’m Noel King. Possibly the most important thing to understand about long COVID is that there is, at this point, not even an agreed-upon definition of what it is.
-
-MONICA VERDUZCO-GUTIERREZ (chair of rehabilitation medicine at the University of Texas Health Science Center at San Antonio): You know, we're still learning and we're trying to figure out what's going to be best for long COVID. We're still learning what's happening in the body.
-
-NOEL: Doctors have learned some things and so, to answer your questions, we called a doctor who has lots of experience with long COVID.
-
-MONICA: My name is Monica Verduzco-Gutierrez. I am the chair of rehabilitation medicine at the University of Texas Health Science Center at San Antonio. Here I have a practice where I take care of patients who are struggling with the effects of long COVID.
-
-NOEL: Dr. Gutierrez, we asked our listeners for their questions about COVID. Do you mind if I pose some of them to you? Help the listeners out?
-
-MONICA: Sure.
-
- Telephone beep
-
- Hi. My name is Aditya Vikram Mukherjee. I live in San Jose, California. What are long COVID symptoms and how are they different from quote unquote regular COVID?
-
-MONICA: So what I say is, if I've seen a patient with long COVID, I've seen one patient with long COVID because the symptoms are so different. Meaning, if you look at some of the studies looking at what are the symptoms of patients with long COVID, some of them have 200 symptoms that they're choosing from. But probably…
-
-NOEL: Oh my God.
-
-SCORING IN
-
-MONICA: The most common thing is fatigue. And we hear about this overwhelming, debilitating fatigue that is not just, oh, you know, I'm tired because I work full time and I try to work out and I don't sleep as much kind of thing. But no, this is truly a debilitating fatigue.
-
- Mark Harrington: General lack of energy.
-
- Ben Sheehan: I'll sometimes wake up as many as 15 to 18 times a night.
-
- Madelyn Bagby: It’s exhausting.
-
-MONICA: I had one patient that had had cancer before. And this person told me that their fatigue from COVID was 100 times worse than from when they had cancer.
-
-Other things that you might hear patients say is…
-
- Alida Aiello: Brain fog.
-
-MONICA: The brain fog can be different things. It could be concentration issues…
-
- Mark Harrington: Difficulty in focusing.
-
- Patricia Anderson: Confusion.
-
-MONICA: I hear a lot of difficulty finding words, like really simple words, just can't come to people's minds that they would be able to say before. Memory issues. Processing issues.
-
- Colleen Powers: I had trouble driving and I'd forget entire conversations that I had with my siblings or plots to movies that I had watched.
-
- Madelyn Bagby: Scrolling on my cell phone trying to text friends would induce motion sickness, nausea, headache.
-
-MONICA: Being able to handle as much work that they could do before.
-
- Alida Aiello: I began to get migraine headaches that I had never had before in my life.
-
-MONICA: Pain in joints, back pain, numbness, tingling…
-
- Colleen Powers: Body aches, chills, headaches.
-
-MONICA: Sore throat.
-
- Mark Harrington: Lack of appetite.
-
- Patricia Anderson: Dizziness.
-
-MONICA: The other thing that we see a lot is post-exertional malaise. And so what that is, is when someone does an activity that may have been, you know, pre COVID, they would easily do something, but it actually causes afterwards, a crash.
-
- Ben Sheehan: Anything that involves quick bursts of energy is almost completely off the table.
-
- Patricia Anderson: I know I have to stay under 3000 steps a day or I'll trigger a crash the next day.
-
-MONICA: And that's one of the cardinal symptoms of long COVID and also of Myalgic Encephalomyelitis or Chronic Fatigue Syndrome. So we're seeing more studies that show that some of these patients with long COVID are meeting some of the criteria for ME/CFS, as it's called.
-
-MONICA: The other thing is cardiac symptoms. So their heart rate might start racing.
-
- Colleen Powers: My heart rate would jump up to 160 if I got up to get myself a glass of water.
-
-MONICA: Some patients might develop clots. Then there's also the respiratory complaints. So shortness of breath, not able to do activities that they were doing before, exercising being very difficult. And you can say a system of the body and probably come up with a symptom that someone has that's related to long COVID. We're just seeing a lot of things across the board.
-
- SCORING OUT
-
- Telephone beep
-
- Susan Hootkins: My name is Susan Hootkins from Petaluma, California. What are the preexisting conditions that make it more likely for a person to end up with long COVID?
-
-MONICA: We're still learning. But things that we know is, people who are overweight is at risk, people who've had a history of diabetes are at risk, people who have a history of asthma. If you were hospitalized, if you're in the ICU, you're intubated, you're going to more likely have longer term symptoms than patients who had mild disease. But you could still have mild disease, be thin, be without asthma, and get long COVID. The other thing is if patients have Epstein-Barr virus reactivation, so that's a virus that causes mono, a lot of people have had it in the past. And COVID can trigger a reactivation of that.
-
-NOEL: If you do get COVID and here we're asking for the people who haven't had it yet. Is there anything you can do to keep it from becoming long COVID?
-
-MONICA: What I'm telling people and my friends is just like, take care of yourself when you have COVID. Don't overdo it. Try to get healthy. Try not to push yourself too much. Because there is literature that says, you know, people who push themselves too much then end up not doing as well. So I just tell people like, allow yourself to get better, allow yourself to rest, allow yourself that period of time to make sure that you get over COVID and don't try to push through it or do too much.
-
- Telephone beep
-
- My name is Alida Aiello, and I'm in Barre, Vermont. For those of us who are already experiencing the effects of long COVID, what additional complications or long term consequences could potentially be triggered by a second COVID infection?
-
-MONICA: So there is now studies that show that if you have reinfections, that it's not good. There's going to be more morbidity, there is more mortality. These patients have more symptoms. They have more visits to their physician's office in the next months, you know, new development of diseases. I mean, some of this study came out of the V.A. so we know that population is mostly a little bit older. But, you know, they have this data also from populations that are younger as well. So you do not want to continue to get COVID again.
-
- Telephone beep
-
- My name is Eric Honour and I'm in Fairfax, Virginia. If I'm fully vaccinated and I get COVID, then does my vaccination mean that I'm less likely to develop long COVID?
-
-MONICA: The answer is yes, but you can still be at risk of getting long COVID even if you're vaccinated. But I'm still encouraging vaccinations because they're doing their job as far as preventing severe disease and death.
-
- Telephone beep
-
- My name is Greg Rosen and I live in Baltimore, Maryland. I’ve had the unfortunate experience of dealing with long COVID since June 2020. I dealt with debilitating symptoms for nearly two years until doctors listened to me and believed me, and I got the treatments I needed. What should doctors and medical students understand about long COVID so that other patients don't feel like they're being gaslit by their doctors?
-
-MONICA: I know. I'm a big fan of ‘We have to believe patients.’ So I think for patients, continue to find someone that is going to believe you and want to help or at least, you know, realize what their limitations are and send you to someone who may have more experience. And then for clinicians, just, please believe your patients. And then the other thing is, probably our medical system needs to be able to acknowledge and better treat patients with chronic conditions, because in general, a fee for service model means, oh, I need to just see as many patients as quickly as possible to make money. It doesn't reward physicians who need to take an hour with a patient to work through, you know, ten plus symptoms and issues that they have.
-
- Telephone beep
-
- My name is Mark and I live in Patagonia, Argentina. My wife and I contracted COVID in May 2021. The most obvious ongoing symptom of long COVID that we have is our loss of smell. It's not so much a loss of smell as just a general confusion and rearrangement of the way we perceive smells and the associations that we make with them. To catch a bug that has the potential to drastically change one's perception of reality seems pretty weird and makes me wonder just how much we really do know about the long term effects of COVID. Is that something that's going to get better over time? Is it something that we're going to have to deal with for the rest of our lives?
-
-NOEL: Do we have any sense of when these symptoms go away?
-
-MONICA: In some patients, especially if they had fewer symptoms, their symptoms seemed to go away relatively sooner. And then there are other patients, especially more symptoms, it was going to last longer. So we need to see. We're building this plane while we're flying it. We're seeing what's happening in real time to people with long COVID and some of them, it continues to impact them from the beginning of the pandemic.
-
-NOEL: What's one thing that we don't currently know about long COVID that's really bugging you? What's one thing that if you could snap your fingers and know the answer, you would want to know the answer to?
-
-MONICA: Maybe one thing I do want right away is, let's get treatment trials started right away. If there is evidence of viral persistence, can we try the antivirals? I really wish we magically had a biomarker already that we could check and just know on people right away.
-
-NOEL: So that if I come in, there's some biomarker in me that tells you, my doctor, ‘oh, Noel's got long COVID.’
-
-MONICA: Exactly.
-
- SCORING IN
-
-NOEL: What is your best advice for people who have long COVID and who are finding it really, really hard to live with?
-
-MONICA: That we're working on it as fast as we can. And I know it's not fast enough for you, but I don't want them to give up hope that, you know, something can be done. I want them to be able to try to find some help. And sometimes the best help they get is from the community of people with long COVID, because it's an amazing community full of advocates, full of people who have so much knowledge about what's going on with long COVID. So, you know, find a community, find clinicians and practitioners who believe in you and who are willing to try to help you as much as possible. And let's not give up and all work on it together.
-
- SCORING BUMP
-
-NOEL: Coming up, people who have long COVID are taking action, they’re organizing, they’re trying to get disability laws changed! That’s all next.
-
- SCORING OUT
-[BREAK]
-
-[BUMPER]
-
-NOEL: We’re back. With long COVID.
-
-HANNAH DAVIS (co-founder of the Patient led Research Collaborative): I got it in March 2020, on March 25th. I was in the first wave, one of the original long haulers.
-
- SCORING IN
-
-HANNAH: My name is Hannah Davis, and I'm a co-founder of the Patient Led Research Collaborative, which is a group of long COVID patients that also researches long COVID.
-
-SCORING BUMP
-
-HANNAH: I, you know, I got sick and I wasn't actually too concerned because I was in my early thirties. I really was convinced it would be two weeks and then I would be recovered, you know, which was the narrative that was being pushed at the time. I had really bad brain fog from the beginning. I basically completely lost my memory. I lost all of my passwords and I had a low grade fever for 90 days. I had complete numbness in my arms and hands. I was given a POTS diagnosis, which is basically dysfunction of the autonomic nervous system, which is pretty common in long COVID patients. I'm still not recovered. I still have pretty bad cognitive impairment. I still have tinnitus, insomnia. I'm basically not able to walk more than 15 or 20 minutes a day. And I've, you know, of course, become extremely involved with this patient community and trying to do more activism because we see so many people basically going through the exact same thing that I've gone through. And we at this point know that many people with long COVID don't recover. And so the implications for our society are pretty massive.
-
- SCORING OUT
-
-NOEL: Long COVID ended Hannah’s previous career.
-
-HANNAH: I used to work in artificial intelligence. I did a lot with generative machine learning models. All of that is pretty foreign to me now. I don't have the capacity I used to. I am very lucky that I have a partner who is able to work full time and has supported me through most of this. I now work with the Patient Led Research Collaborative. We recently got some grants that allow part of our team to be paid, but I feel like I'm only able to work with this group because they are so accommodating. You know, it's a completely remote group. We make our own schedules. We take as much time off as we need. Right now we are doing a paper on mental health. We did a really large systematic paper in 2020 on the multiple symptoms of long COVID and also the timelines, like the trajectories, which symptoms resolve, which ones don't. And also, stigma and the way health care and the way providers and the community treats long haulers. We're working on a paper on reinfections. We're working on a paper on long COVID in lower and middle income countries, working on a project phenotyping long COVID, which means, you know, making different clusters of long COVID and seeing the patterns that people fall into. And more recently, we received several million dollars from a cryptocurrency founder that allows us to actually provide grants to external researchers. So these are people who have more resources than we do, who can work with biomedical samples and things like that. And so we're actually in a position now to be giving out funding for research, which is really helpful. But we also do a lot of patient engagement. This is like a long history of patient involvement in research. You know, there was a lot of it in the HIV/AIDS movement. There's been a lot of it in the ME/CFS movement which are both obviously post-viral conditions.
-
-NOEL: What do we still need to learn about long COVID? What are the questions you want answered soon?
-
-HANNAH: One thing, we see a lot of is there is a amazing community of post-viral researchers. And these people are not confused by long COVID. They don't describe long COVID as a mystery. They understand what happens after viruses. They knew this was coming. So I would say that there needs to be a general public education program on post-viral illness, what that looks like, how often it is, what to look for. But more specifically, I think, we don't know for sure a lot of solid treatments. So there's a couple major theories about what causes these kind of conditions. And based on those theories, there are a multitude of treatments to trial. So we really need a large scale clinical trial program to be able to recommend or not recommend each of those treatments while taking into consideration what point in the illness the treatments are implemented because that's a factor.
-
-NOEL: Yeah. So that's the research that needs to be done. I mean, from your perspective, what are the laws that need to change?
-
-HANNAH: We absolutely need legislation that provides provider education and public education. One of the things that has shocked me the most is that only 6% of medical schools fully teach post-viral illness. This is obviously something that needs to change after COVID and the lack of understanding of post-viral illness has created kind of bad long COVID clinics. And a lot of the long COVID clinics really need improvement grounded in this historical and scientific knowledge. So I think, you know, legislation that funds really high-quality clinics is really vital. And the other thing, of course, is treatment. If we start clinical trials now, we’re still not going to see the outcomes of those trials for several years. So that would be my second huge priority.
-
-NOEL: Are you aware of any legislation being proposed to address those changes you'd like to see?
-
-HANNAH: Yes, there's a couple. There's the Treat Long COVID Act…
-
- Angi Gonzalez, Spectrum News: Lawmakers, including Representative Ayana Pressley and Senator Ed Markey are also behind legislation to address long COVID, introducing versions of the TREAT Long COVID Act in both the House and Senate.
-
-HANNAH: Which would authorize HHS to award grants up to 2 million to health care providers, which would include community health centers. And there's also the Care for Long COVID Act…
-
- John Gonzalez, ABC News: Senator Tim Kaine, who has lingering nerve issues due to COVID, has introduced the Care for Long COVID Act.
-
-HANNAH: So that’s more focusing on education and dissemination of information on long COVID and also providing legal and social service assistance for people with long COVID.
-
- Angi Gonzalez, Spectrum News: It’s unclear whether the legislation will get the support it needs to pass.
-
-NOEL: In terms of disability accommodations that you'd like to see in workplaces, give me the sort of top four or five that you think could really be life changing for people with long COVID.
-
-HANNAH: I think the first is that, as of now, we have no treatments for long COVID. The only thing we know that is even close to a treatment is early rest and pacing. So we know that early rest in the first month or two is possibly preventative of a more severe and more long term long COVID. And we see so many employers rushing people to get back to work. We see so many people, you know, trying to exercise just a week or two after they got their COVID infection and trying to push through. You know, that's the big term that everyone tries to push through. And that's actually really harmful and really dangerous to getting this more severe form of long COVID. So allowing not only adequate time off but extensive time off, you know, allowing people to take 6 to 8 weeks off if they need to is really important. And then besides that, I would say working remotely and having a flexible schedule. A lot of people with long COVID, especially the form of myalgic encephalomyelitis long COVID have reversed sleep schedules. So you're up much later and you sleep much later. One of the major theories behind this illness is that there's a dysfunction in the way the brain actually drains at night. And this mechanism of brain drainage happens the most when you're sleeping. So sleep is so key. And a lot of people, people who take care of kids, people who are working, don't have the ability to adjust their sleep schedule, even though it's one of the most important things. From our standpoint, we are looking at a future where just an increasing number of people are getting long COVID every year and there is just no accommodation. There's nothing.
-
-SCORING IN
-
-HANNAH: I kick myself all of the time because I am certain I got COVID at the grocery store in mid-March when the CDC was saying we don't need masks. That never made sense. I don't know why I just listened to them. But I see that same thing happening right now. We should be masking everywhere. We should be implementing extremely high quality ventilation systems everywhere. I feel like people think that they are fine, because this narrative of a vaccine-only approach has really been pushed on us. But most studies that look at this are showing, people who are vaccinated are still getting long COVID. And there's a false safety to have if you had COVID once and got through it okay. There's no reason to think reinfections are safer or not going to lead to long COVID. We know they do. So I would say that reevaluate your risk level, re-implement your safety procedures, don't go anywhere there are crowds.
-
-SCORING BUMP
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-HANNAH: We are not protecting our society and we are going to learn the hard way. I think in a couple of years we are going to be a much, much sicker society and this is all going to be pretty axiomatic and maybe there will be protections put in place then. But right now, you can only protect yourself if you are putting these protections into place yourself. No one is protecting us.
-
-SCORING BUMP
-
-NOEL: Hannah Davis. She’s co-founder of the Patient Led Research Collaborative for long COVID.
-
-Earlier in the show we talked to Dr. Monica Verduzco-Gutierrez at the University of Texas Health Science Center at San Antonio.
-
-Thank you, sincerely, to all of the listeners who sent us their questions and stories about long COVID. We wish we could have answered all of them. We appreciate you reaching out.
-
-Today’s show was produced by Avishay Artsy. It was fact-checked by Serena Solin and Amanda Lewellyn. It was engineered by Efim Shapiro. Matthew Collette edited the show. I’m Noel King. It’s Today, Explained.
-
-SCORING OUT
-
-[10 SECONDS OF SILENCE]
-
-
-
-
diff --git a/examples/podcast-search/podcast_search_demo.py b/examples/podcast-search/podcast_search_demo.py
deleted file mode 100644
index d83ba80f4..000000000
--- a/examples/podcast-search/podcast_search_demo.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import marqo
-import pprint
-import pandas as pd
-
-
-####################################################
-### STEP 1: Load Data
-####################################################
-def load_data(file: str, number_data: int) -> dict:
- podcast_data = pd.read_csv(file).head(number_data)[['name', 'description']].to_dict('records')
-
- # dataset came from this link: https://www.vox.com/today-explained
- # the .csv file has the following headers:
- # name, description
- # (name of podcast, short description)
-
- # create a 'transcript' key and add the transcript text as values to each record
- id_counter = 1
- for data in podcast_data:
- path = "data/transcripts/" + data['name'] + ".txt"
- with open(path, 'r') as f:
- content = f.read()
- data['transcript'] = content
- data['_id'] = str(id_counter) # _id is a special key which is unique to every document
- id_counter += 1
-
- return podcast_data
-
-
-dataset_file = "data/podcast_data.csv"
-podcast_data = load_data(dataset_file, 2)
-'''
-format of podcast_data -
-[{'name': '....', 'description': '....', 'transcript': '....'},
-{'name': '....', 'description': '....', 'transcript': '....'}]
-'''
-
-#####################################################
-### STEP 2. Start Marqo
-#####################################################
-
-# Follow the instructions here https://github.com/marqo-ai/marqo
-
-####################################################
-### STEP 3: Index Data
-####################################################
-index_name = "marqo-podcast-search-demo"
-mq = marqo.Client(url='http://localhost:8882') # Connection to Marqo Docker Container
-mq.create_index(index_name)
-mq.index(index_name).add_documents(podcast_data, tensor_fields=['name', 'description', 'transcript'])
-stats = mq.index(index_name).get_stats() # get the stats for the index
-print(f"{stats['numberOfDocuments']} documents added to index: {index_name}")
-
-
-####################################################
-### STEP 4: Search using Marqo
-####################################################
-
-# let's create a query and perform tensor search
-query = 'what is long covid?'
-results = mq.index(index_name).search(query)
-
-# ['_highlights'] will return only the relevant portion rather than the whole transcript
-print("Result 1 -", end=" ")
-pprint.pprint(results['hits'][0]['_highlights']) # [0] returns the top hit
-print("Result 2 -", end=" ")
-pprint.pprint(results['hits'][1]['_highlights']) # [1] returns the second hit
-
-
-# let's create another query and perform tensor search on a particular field
-query = 'water issues in US'
-results = mq.index(index_name).search(query, searchable_attributes=['description'])
-
-print("Result 3 -", end=" ")
-pprint.pprint(results['hits'][0]['_highlights'])
-
-
-# let's create another query and perform lexical search on a particular field
-query = 'water crisis'
-results = mq.index(index_name).search(query, searchable_attributes=['name'], search_method='LEXICAL')
-
-print("Result 4 -", end=" ")
-pprint.pprint(results['hits'][0]['name'])
-print("Result 5 -", end=" ")
-pprint.pprint(results['hits'][0]['_highlights']) # [_highlights] will return an empty list if using lexical search