langchain-graphrag 0.0.2b10

Implementation of GraphRAG (https://arxiv.org/pdf/2404.16130)

GraphRAG

This is an implementation of GraphRAG as described in

https://arxiv.org/pdf/2404.16130

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Official implementation by the authors of the paper is available at:

https://github.com/microsoft/graphrag/

Why re-implementation 🤔?

Personal Preference

While I generally prefer utilizing and refining existing implementations, as re-implementation often isn't optimal, I decided to take a different approach after encountering several challenges with the official version.

Issues with the Official Implementation

• Lacks integration with popular frameworks like LangChain, LlamaIndex, etc.
• Complexity due to dependence on the datashaper package, making it harder to understand.
• Limited to OpenAI and AzureOpenAI models, with no support for other providers.

Why reling on established frameworks like LangChain?

Using an established foundation like LangChain offers numerous benefits. It abstracts various providers, whether related to LLMs, embeddings, vector stores, etc., allowing for easy component swapping without altering core logic or adding complex support. More importantly, a solid foundation like this lets you focus on the problem's core logic rather than reinventing the wheel.

LangChain also supports advanced features like batching and streaming, provided your components align with the framework’s guidelines. For instance, using chains (LCEL) allows you to take full advantage of these capabilities.

Modularity & Extensibility focused design

The APIs are designed to be modular and extensible. You can replace any component with your own implementation as long as it implements the required interface.

Given the nature of the domain, this is important for conducting experiments by swapping out various components.

Install

pip install langchain-graphrag

Projects

There are 2 projects in the repo:

langchain_graphrag

This is the core library that implements the GraphRAG paper. It is built on top of the langchain library.

The concepts described in GraphRAG paper are implemented in a modular fashion with easy extensibility and replacement in mind.

An example code for local search using the API

Below is a snippet taken from the example-app to show the style of API and extensibility offered by the library.

Almost all the components (classes/functions) can be replaced by your own implementations. The library is designed to be modular and extensible.

# Reload the vector Store that stores
# the entity name & description embeddings
entities_vector_store = ChromaVectorStore(
    collection_name="entity_name_description",
    persist_directory=str(vector_store_dir),
    embedding_function=make_embedding_instance(
        embedding_type=embedding_type,
        embedding_model=embedding_model,
        cache_dir=cache_dir,
    ),
)

# Build the Context Selector using the default
# components; You can supply the various components
# and achieve as much extensibility as you want
# Below builds the one using default components.
context_selector = ContextSelector.build_default(
    entities_vector_store=entities_vector_store,
    entities_top_k=10,
    community_level=cast(CommunityLevel, level),
)

# Context Builder is responsible for taking the
# result of Context Selector & building the
# actual context to be inserted into the prompt
# Keeping these two separate further increases
# extensibility & maintainability
context_builder = ContextBuilder.build_default(
    token_counter=TiktokenCounter(),
)

# load the artifacts
artifacts = load_artifacts(artifacts_dir)

# Make a langchain retriever that relies on
# context selection & builder
retriever = LocalSearchRetriever(
    context_selector=context_selector,
    context_builder=context_builder,
    artifacts=artifacts,
)

# Build the LocalSearch object
local_search = LocalSearch(
    prompt_builder=LocalSearchPromptBuilder(),
    llm=make_llm_instance(llm_type, llm_model, cache_dir),
    retriever=retriever,
)

# it's a callable that returns the chain
search_chain = local_search()

# you could invoke
# print(search_chain.invoke(query))

# or, you could stream
for chunk in search_chain.stream(query):
    print(chunk, end="", flush=True)

Clone the repo

git clone https://github.com/ksachdeva/langchain-graphrag.git

Open in VSCode devcontainer (Recommended)

Devcontainer will install all the dependencies

If not using devcontainer

Make sure you have rye installed. See https://rye.astral.sh/

# sync all the dependencies
rye sync

examples/simple-app

This is a simple typer based CLI app.

In terms of configuration it is limited by the number of command line options exposed.

That said, the way core library is written you can easily replace any component by your own implementation i.e. your choice of LLM, embedding models etc. Even some of the classes as long as they implement the required interface.

Note:

Make sure to rename .env.example with .env if you are using OpenAI or AzureOpenAI and fill in the necessary environment variables.

# To generate the index
# default set azure_openai/gpt4-o/text-embedding-3-small
# you can change the model and other parameters from command line
rye run simple-app-indexer

# To see more options
rye run simple-app-indexer --help

# To do global search/query
# defaults are azure_openai/gpt4-o/text-embedding-3-small
# you can change the model and other parameters from command line
rye run simple-app-global-search --query "What are the top themes in this story?"

# To do local search/query
# defaults are azure_openai/gpt4-o/text-embedding-3-small
# you can change the model and other parameters from command line
rye run simple-app-local-search --query "Who is Scrooge, and what are his main relationships?"

See examples/simple-app/README.md for more details.

Roadmap / Things to do

The state of the library is far from complete.

Here are some of the things that need to be done to make it more useful:

• Add more guides
• Document the APIs
• Add more tests