Contributing code

One way to contribute to llmware is by contributing to the code base.

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.

Core modules

Library

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.

add_file(
    self,
    file_path):

This method adds one document of any supported type to the library.

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.

add_website(
    self,
    url,
    get_links=True,
    max_links=5):

This method adds a website, and links from the website, to the library.

add_wiki(
    self,
    topic_list,
    target_results=10):

This method adds a wikipedia article to the library.

add_dialogs(
    self,
    input_folder=None):

This method adds an AWS dialog transcript to the library.

add_image(
    self,
    input_folder=None):

This method adds images to the libary.

add_pdf_by_ocr(
    self,
    input_folder=None):

This method adds scanned PDFs to the library.

add_pdf(
    self,
    input_folder=None):

This method adds PDFs to the library.

add_office(
    self,
    input_folder=None):

This method adds office documents to the library.

Embeddings

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.

def create_new_embedding(
    self,
    doc_ids=None,
    batch_size=500):

This method creates the embeddings and adds them to the vector store.

def search_index(
    self,
    query_embedding_vector,
    sample_count=10):

This method searches the vector store given the query vector.

def delete_index(self):

This method deletes the created vector store index.

Prompts

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.

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.

def add_source_query_results(
    self,
    query_results):

This method adds previous results from a query as a source to the prompt.

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.

def add_source_wikipedia(
    self,
    topic,
    article_count=3,
    query=None):

This method adds wikipedia articles to the prompt based on the provided topic.

def add_source_yahoo_finance(
    self,
    ticker=None,
    key_list=None):

This method adds a Yahoo finance ticker to the prompt.

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.

def add_source_website(
    self,
    url,
    query=None):

This method adds the website pointed to by the url to the prompt.

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.

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.

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.

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.

def register_sentence_transformer_model(
    self,
    model_name,
    embedding_dims,
    context_window,
    display_name=None,
    link=""):

This method adds a new sentence transformer.

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.

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.

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.

Categories of code contributions

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. 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.

Bugs

If you encounter a bug, you can

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.

We encourage you to read How to create a Minimal, Reproducible Example from the Stackoverflow helpcenter, and the tag description of self-container, also from Stackoverflow.