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--- | ||
layout: default | ||
title: Code contributions | ||
parent: Contributing | ||
nav_order: 1 | ||
permalink: /contributing/code | ||
--- | ||
# Contiributing code | ||
One way to contribute to ``llmware`` is by contributing to the code base. | ||
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We briefly describe some of the important modules of ``llmware`` next, so you can more easily navigate the code base. | ||
You may also take a look at our [fast start series from YouTube](https://www.youtube.com/playlist?list=PL1-dn33KwsmD7SB9iSO6vx4ZLRAWea1DB). | ||
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## Core modules | ||
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### Library | ||
<iframe width="560" height="315" src="https://www.youtube.com/embed/2xDefZ4oBOM?si=IAHkxpQkFwnWyYTL" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> | ||
The *library* module implements the classes **Library** and **LibraryCatalog**. | ||
The **Library** class implements the *library* concept. | ||
A *library* is a collection of documents, where a document can be PDF, an image, or an office document. | ||
It is responsible for parsing, text chunking, and indexing. | ||
In other words, it does the heavy lifting of adding content. | ||
In the following, we shortly describe the functions for adding documents to the library. | ||
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||
```python | ||
add_file( | ||
self, | ||
file_path): | ||
``` | ||
This method adds one document of any supported type to the library. | ||
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||
```python | ||
add_files( | ||
self, | ||
input_folder_path=None, | ||
encoding="utf-8", | ||
chunk_size=400, | ||
get_images=True,get_tables=True, | ||
smart_chunking=2, | ||
max_chunk_size=600, | ||
table_grid=True, | ||
get_header_text=True, | ||
table_strategy=1, | ||
strip_header=False, | ||
verbose_level=2, | ||
copy_files_to_library=True): | ||
``` | ||
This method adds the documents of one folder to the library. | ||
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||
```python | ||
add_website( | ||
self, | ||
url, | ||
get_links=True, | ||
max_links=5): | ||
``` | ||
This method adds a website, and links from the website, to the library. | ||
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```python | ||
add_wiki( | ||
self, | ||
topic_list, | ||
target_results=10): | ||
``` | ||
This method adds a wikipedia article to the library. | ||
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```python | ||
add_dialogs( | ||
self, | ||
input_folder=None): | ||
``` | ||
This method adds an AWS dialog transcript to the library. | ||
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||
```python | ||
add_image( | ||
self, | ||
input_folder=None): | ||
``` | ||
This method adds images to the libary. | ||
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||
```python | ||
add_pdf_by_ocr( | ||
self, | ||
input_folder=None): | ||
``` | ||
This method adds scanned PDFs to the library. | ||
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```python | ||
add_pdf( | ||
self, | ||
input_folder=None): | ||
``` | ||
This method adds PDFs to the library. | ||
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||
```python | ||
add_office( | ||
self, | ||
input_folder=None): | ||
``` | ||
This method adds office documents to the library. | ||
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### Embeddings | ||
<iframe width="560" height="315" src="https://www.youtube.com/embed/xQEk6ohvfV0?si=GAPle5gVdVPkYKWv" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> | ||
An *embedding* is a vector store and an embedding model. | ||
It is responsible for applying an embedding model to a library, storing the embeddings in a vector store, and providing access to the embeddings with natural language queries. | ||
We briefly describe the common methods offered by all vector stores below. | ||
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```python | ||
def create_new_embedding( | ||
self, | ||
doc_ids=None, | ||
batch_size=500): | ||
``` | ||
This method creates the embeddings and adds them to the vector store. | ||
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```python | ||
def search_index( | ||
self, | ||
query_embedding_vector, | ||
sample_count=10): | ||
``` | ||
This method searches the vector store given the query vector. | ||
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```python | ||
def delete_index(self): | ||
``` | ||
This method deletes the created vector store index. | ||
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### Prompts | ||
<iframe width="560" height="315" src="https://www.youtube.com/embed/swiu4oBVfbA?si=rKbgO3USADCqICqr" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> | ||
A *prompt* is an input to model. | ||
The prompt is used by the model to generate the response. | ||
One important use case is that users want to augment a prompt, or a series of prompts, with additional information. | ||
Next, we describe methods for augmenting a prompt with additional information. | ||
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```python | ||
def add_source_new_query( | ||
self, | ||
library, | ||
query=None, | ||
query_type="semantic", | ||
result_count=10): | ||
``` | ||
This method adds the results of the ``query`` to the prompt. | ||
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```python | ||
def add_source_query_results( | ||
self, | ||
query_results): | ||
``` | ||
This method adds previous results from a query as a source to the prompt. | ||
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```python | ||
def add_source_library( | ||
self, | ||
library_name): | ||
``` | ||
This method adds an entire library to the prompt. | ||
We recommend that you only use this when the library is sufficiently small. | ||
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```python | ||
def add_source_wikipedia( | ||
self, | ||
topic, | ||
article_count=3, | ||
query=None): | ||
``` | ||
This method adds wikipedia articles to the prompt based on the provided ``topic``. | ||
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```python | ||
def add_source_yahoo_finance( | ||
self, | ||
ticker=None, | ||
key_list=None): | ||
``` | ||
This method adds a Yahoo finance ticker to the prompt. | ||
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```python | ||
def add_source_knowledge_graph( | ||
self, | ||
library, | ||
query): | ||
``` | ||
This method adds the summary output elements from a knowledge graph based on the provided ``query``. | ||
Please note that this method is experimental, i.e. unstable, and is subject to change dramatically in each new version. | ||
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```python | ||
def add_source_website( | ||
self, | ||
url, | ||
query=None): | ||
``` | ||
This method adds the website pointed to by the ``url`` to the prompt. | ||
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```python | ||
def add_source_document( | ||
self, | ||
input_fp, | ||
input_fn, | ||
query=None): | ||
``` | ||
This method adds a document, or documents, of any supported type to the prompt. | ||
If documents are added, then the ``query`` parameter can be used to filter the documents. | ||
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```python | ||
def add_source_last_interaction_step( | ||
self): | ||
``` | ||
This method adds the last interaction to the prompt. | ||
The use case for this is to enable interactive dialog, i.e. chatting. | ||
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### Model Catalog | ||
A *model catalog* is a collection of models. | ||
In the following, we briefly describe the methods for adding new models to the catalog. | ||
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```python | ||
def register_new_hf_generative_model( | ||
self, | ||
hf_model_name=None, | ||
context_window=2048, | ||
prompt_wrapper="<INST>", | ||
display_name=None, | ||
temperature=0.3, | ||
trailing_space="", | ||
link=""): | ||
``` | ||
This method adds a new generative model from hugging face. | ||
Users can therefore add models from hugging face that are unsupported currently. | ||
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```python | ||
def register_sentence_transformer_model( | ||
self, | ||
model_name, | ||
embedding_dims, | ||
context_window, | ||
display_name=None, | ||
link=""): | ||
``` | ||
This method adds a new sentence transformer. | ||
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```python | ||
def register_gguf_model( | ||
self, | ||
model_name, | ||
gguf_model_repo, | ||
gguf_model_file_name, | ||
prompt_wrapper=None, | ||
eos_token_id=0, | ||
display_name=None, | ||
trailing_space="", | ||
temperature=0.3, | ||
context_window=2048, | ||
instruction_following=True): | ||
``` | ||
This method adds a new GGUF model. | ||
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```python | ||
def register_open_chat_model( | ||
cls, | ||
model_name, | ||
api_base=None, | ||
model_type="chat", | ||
display_name=None, | ||
context_window=4096, | ||
instruction_following=True, | ||
prompt_wrapper="", | ||
temperature=0.5): | ||
``` | ||
This method adds any chat model that is available through a web API, e.g. a chat model that is available locally | ||
via localhost. | ||
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```python | ||
def register_ollama_model( | ||
cls, | ||
model_name, | ||
host="localhost", | ||
port=11434, | ||
model_type="chat", | ||
raw=False, | ||
stream=False, | ||
display_name=None, | ||
context_window=4096, | ||
instruction_following=True, | ||
prompt_wrapper="", | ||
temperature=0.5): | ||
``` | ||
This method adds an OLLama model that is available through a web API. | ||
The method is similar to the ``register_open_chat_model`` method above. | ||
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### Categories of code contributions | ||
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#### New or Enhancement to existing Features | ||
You want to submit a code contribution that adds a new feature or enhances an existing one? | ||
Then the best way to start is by opening a discussion in our [GitHub discussions](https://github.com/llmware-ai/llmware/discussions). | ||
Please do this before you work on it, so you do not put effort into it just to realise after submission that | ||
it will not be merged. | ||
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#### Bugs | ||
If you encounter a bug, you can | ||
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- File an issue about the bug. | ||
- Provide a self-contained minimal example that reproduces the bug, which is extremely important. | ||
- Provide possible solutions for the bug. | ||
- Submit a pull a request to fix the bug. | ||
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We encourage you to read [How to create a Minimal, Reproducible Example](https://stackoverflow.com/help/minimal-reproducible-example) from the Stackoverflow helpcenter, and the tag description of [self-container](https://stackoverflow.com/tags/self-contained/info), also from Stackoverflow. |
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