Init Chroma and LLamaIndex Multi-Modal Demo (run-llama#8897)

* Init Chroma and LLamaIndex Multi-Modal Demo

* cr

* Create fleet_libraries_context.md (run-llama#8849)

* cr

* cr

* linting

---------

Co-authored-by: haotian zhang <haotianzhang@haotians-MacBook-Pro-3.local>
Co-authored-by: Andrew Zhou <44193474+adrwz@users.noreply.github.com>
Co-authored-by: Logan <logan.markewich@live.com>

docs/community/integrations/fleet_libraries_context.md

@@ -5,11 +5,13 @@ In this guide, we will be using Fleet Context to download the embeddings for Lla
 <br><br>
 
 ## Pre-requisites
-``` python
-!pip install llama-index 
+
+```
+!pip install llama-index
 !pip install --upgrade fleet-context
 ```
-``` python
+
+```
 import os
 import openai
 
@@ -20,6 +22,7 @@ openai.api_key = os.environ["OPENAI_API_KEY"]
 <br><br>
 
 ## Download Embeddings from Fleet Context
+
 We will be using Fleet Context to download the embeddings for the
 entirety of LlamaIndex\'s documentation (\~12k chunks, \~100mb of
 content). You can download for any of the top 1220 libraries by
@@ -34,33 +37,36 @@ type (class/function/attribute/etc), the parent section, and more. You
 can read more about this on their [Github
 page](https://github.com/fleet-ai/context/tree/main).
 
-``` python
+```python
 from context import download_embeddings
 
 df = download_embeddings("llamaindex")
 ```
 
 **Output**:
+
 ```shell
     100%|██████████| 83.7M/83.7M [00:03<00:00, 27.4MiB/s]
                                          id  \
-    0  e268e2a1-9193-4e7b-bb9b-7a4cb88fc735   
-    1  e495514b-1378-4696-aaf9-44af948de1a1   
-    2  e804f616-7db0-4455-9a06-49dd275f3139   
-    3  eb85c854-78f1-4116-ae08-53b2a2a9fa41   
-    4  edfc116e-cf58-4118-bad4-c4bc0ca1495e   
+    0  e268e2a1-9193-4e7b-bb9b-7a4cb88fc735
+    1  e495514b-1378-4696-aaf9-44af948de1a1
+    2  e804f616-7db0-4455-9a06-49dd275f3139
+    3  eb85c854-78f1-4116-ae08-53b2a2a9fa41
+    4  edfc116e-cf58-4118-bad4-c4bc0ca1495e
 ```
 
-``` python
-#Show some examples of the metadata
+```python
+# Show some examples of the metadata
 df["metadata"][0]
 display(Markdown(f"{df['metadata'][8000]['text']}"))
 ```
 
 **Output**:
+
 ```shell
 classmethod from_dict(data: Dict[str, Any], kwargs: Any) → Self classmethod from_json(data_str: str, kwargs: Any) → Self classmethod from_orm(obj: Any) → Model json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True*, dumps_kwargs: Any) → unicode Generate a JSON representation of the model, include and exclude arguments as per dict().
 ```
+
 <br><br>
 
 ## Create Pinecone Index for Hybrid Search in LlamaIndex
@@ -70,7 +76,7 @@ that we can do hybrid retrieval with both sparse vectors and dense
 vectors. Make sure you have a [Pinecone account](https://pinecone.io)
 before you proceed.
 
-``` python
+```python
 import logging
 import sys
 
@@ -79,27 +85,34 @@ logging.getLogger().handlers = []
 logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
 ```
 
-``` python
+```python
 import pinecone
 
-api_key = "..." # Add your Pinecone API key here
-pinecone.init(api_key=api_key, environment='us-east-1-aws') # Add your db region here
+api_key = "..."  # Add your Pinecone API key here
+pinecone.init(
+    api_key=api_key, environment="us-east-1-aws"
+)  # Add your db region here
 ```
 
-``` python
+```python
 # Fleet Context uses the text-embedding-ada-002 model from OpenAI with 1536 dimensions.
 
 # NOTE: Pinecone requires dotproduct similarity for hybrid search
 pinecone.create_index(
-    "quickstart-fleet-context", dimension=1536, metric="dotproduct", pod_type="p1"
+    "quickstart-fleet-context",
+    dimension=1536,
+    metric="dotproduct",
+    pod_type="p1",
 )
 
-pinecone.describe_index("quickstart-fleet-context") # Make sure you create an index in pinecone
+pinecone.describe_index(
+    "quickstart-fleet-context"
+)  # Make sure you create an index in pinecone
 ```
 
 <br>
 
-``` python
+```python
 from llama_index.vector_stores import PineconeVectorStore
 
 pinecone_index = pinecone.Index("quickstart-fleet-context")
@@ -109,12 +122,14 @@ vector_store = PineconeVectorStore(pinecone_index, add_sparse_vector=True)
 <br><br>
 
 ## Batch upsert vectors into Pinecone
+
 Pinecone recommends upserting 100 vectors at a time. We\'re going to do that after we modify the format of the data a bit.
 
-``` python
+```python
 import random
 import itertools
 
+
 def chunks(iterable, batch_size=100):
     """A helper function to break an iterable into chunks of size batch_size."""
     it = iter(iterable)
@@ -123,16 +138,22 @@ def chunks(iterable, batch_size=100):
         yield chunk
         chunk = tuple(itertools.islice(it, batch_size))
 
+
 # generator that generates many (id, vector, metadata, sparse_values) pairs
-data_generator = map(lambda row: {'id': row[1]['id'], 
-                                  'values': row[1]['values'], 
-                                  'metadata': row[1]['metadata'], 
-                                  'sparse_values': row[1]['sparse_values']}, df.iterrows())
+data_generator = map(
+    lambda row: {
+        "id": row[1]["id"],
+        "values": row[1]["values"],
+        "metadata": row[1]["metadata"],
+        "sparse_values": row[1]["sparse_values"],
+    },
+    df.iterrows(),
+)
 
 # Upsert data with 1000 vectors per upsert request
 for ids_vectors_chunk in chunks(data_generator, batch_size=100):
     print(f"Upserting {len(ids_vectors_chunk)} vectors...")
-    pinecone_index.upsert(vectors=ids_vectors_chunk) 
+    pinecone_index.upsert(vectors=ids_vectors_chunk)
 ```
 
 <br><br>
@@ -142,29 +163,32 @@ for ids_vectors_chunk in chunks(data_generator, batch_size=100):
 Finally, we\'re going to build the Pinecone vector store via LlamaIndex
 and query it to get results.
 
-``` python
+```python
 from llama_index import VectorStoreIndex
 from IPython.display import Markdown, display
 ```
 
-``` python
+```python
 index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
 ```
 
 <br><br>
 
 ## Query Your Index!
 
-``` python
-query_engine = index.as_query_engine(vector_store_query_mode="hybrid", similarity_top_k=8)
+```python
+query_engine = index.as_query_engine(
+    vector_store_query_mode="hybrid", similarity_top_k=8
+)
 response = query_engine.query("How do I use llama_index SimpleDirectoryReader")
 ```
 
-``` python
+```python
 display(Markdown(f"<b>{response}</b>"))
 ```
 
 **Output**:
+
 ```shell
 <b>To use the SimpleDirectoryReader in llama_index, you need to import it from the llama_index library. Once imported, you can create an instance of the SimpleDirectoryReader class by providing the directory path as an argument. Then, you can use the `load_data()` method on the SimpleDirectoryReader instance to load the documents from the specified directory.</b>
 ```