https://github.com/ksachdeva/langchain-graphrag
GraphRAG / From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/ksachdeva/langchain-graphrag
graphrag langchain llm rag
Last synced: 6 months ago
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GraphRAG / From Local to Global: A Graph RAG Approach to Query-Focused Summarization
- Host: GitHub
- URL: https://github.com/ksachdeva/langchain-graphrag
- Owner: ksachdeva
- License: apache-2.0
- Created: 2024-07-22T21:56:48.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-31T16:15:22.000Z (11 months ago)
- Last Synced: 2025-03-23T09:09:10.947Z (7 months ago)
- Topics: graphrag, langchain, llm, rag
- Language: Python
- Homepage: https://langchain-graphrag.readthedocs.io/en/latest/
- Size: 513 KB
- Stars: 101
- Watchers: 2
- Forks: 13
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GraphRAG - Powered by LangChain
[](https://langchain-graphrag.readthedocs.io/en/latest/)
[](https://github.com/pre-commit/pre-commit)
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/
## Guides
- [Text Unit Extraction](https://langchain-graphrag.readthedocs.io/en/latest/guides/text_units_extraction/)
- [Graph Extraction](https://langchain-graphrag.readthedocs.io/en/latest/guides/graph_extraction/)
## 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.
- Limited to OpenAI and AzureOpenAI models, with no support for other providers.
### Why relying 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
```bash
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.
#### 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.
```python
# 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,
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
```bash
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/
```bash
# 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.
#### Indexing
```bash
rye run simple-app-indexer --llm-type azure_openai --llm-model gpt-4o --embedding-type azure_openai --embedding-model text-embedding-3-small
```
```bash
# To see more options
$ rye run simple-app-indexer --help
Usage: main.py indexer index [OPTIONS]
╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --input-file FILE [default: None] [required] │
│ * --output-dir DIRECTORY [default: None] [required] │
│ * --cache-dir DIRECTORY [default: None] [required] │
│ * --llm-type [openai|azure_openai|ollama] [default: None] [required] │
│ * --llm-model TEXT [default: None] [required] │
│ * --embedding-type [openai|azure_openai|ollama] [default: None] [required] │
│ * --embedding-model TEXT [default: None] [required] │
│ --chunk-size INTEGER Chunk size for text splitting [default: 1200] │
│ --chunk-overlap INTEGER Chunk overlap for text splitting │
│ [default: 100] │
│ --ollama-num-context INTEGER Context window size for ollama model │
│ [default: None] │
│ --enable-langsmith --no-enable-langsmith Enable Langsmith │
│ [default: no-enable-langsmith] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
```
#### Global Search
```bash
rye run simple-app-global-search --llm-type azure_openai --llm-model gpt-4o --query "What are the top themes in this story?"
```
```bash
$ rye run simple-app-global-search --help
Usage: main.py query global-search [OPTIONS]
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --output-dir DIRECTORY [default: None] [required] │
│ * --cache-dir DIRECTORY [default: None] [required] │
│ * --llm-type [openai|azure_openai|ollama] [default: None] [required] │
│ * --llm-model TEXT [default: None] [required] │
│ * --query TEXT [default: None] [required] │
│ --level INTEGER Community level to search [default: 2] │
│ --ollama-num-context INTEGER Context window size for ollama model [default: None] │
│ --enable-langsmith --no-enable-langsmith Enable Langsmith [default: no-enable-langsmith] │
│ --help Show this message and exit. │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
```
#### Local Search
```bash
rye run simple-app-local-search --llm-type azure_openai --llm-model gpt-4o --query "Who is Scrooge, and what are his main relationships?" --embedding-type azure_openai --embedding-model text-embedding-3-small
```
```bash
$ rye run simple-app-local-search --help
Usage: main.py query local-search [OPTIONS]
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --output-dir DIRECTORY [default: None] [required] │
│ * --cache-dir DIRECTORY [default: None] [required] │
│ * --llm-type [openai|azure_openai|ollama] [default: None] [required] │
│ * --llm-model TEXT [default: None] [required] │
│ * --query TEXT [default: None] [required] │
│ --level INTEGER Community level to search [default: 2] │
│ * --embedding-type [openai|azure_openai|ollama] [default: None] [required] │
│ * --embedding-model TEXT [default: None] [required] │
│ --ollama-num-context INTEGER Context window size for ollama model [default: None] │
│ --enable-langsmith --no-enable-langsmith Enable Langsmith [default: no-enable-langsmith] │
│ --help Show this message and exit. │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
```
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