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https://github.com/gusye1234/nano-graphrag
A simple, easy-to-hack GraphRAG implementation
https://github.com/gusye1234/nano-graphrag
gpt gpt-4o graphrag learning-by-doing llm rag
Last synced: 20 days ago
JSON representation
A simple, easy-to-hack GraphRAG implementation
- Host: GitHub
- URL: https://github.com/gusye1234/nano-graphrag
- Owner: gusye1234
- License: mit
- Created: 2024-07-25T07:53:58.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-10-25T10:27:23.000Z (about 2 months ago)
- Last Synced: 2024-10-30T09:55:17.531Z (about 2 months ago)
- Topics: gpt, gpt-4o, graphrag, learning-by-doing, llm, rag
- Language: Python
- Homepage:
- Size: 1.23 MB
- Stars: 1,194
- Watchers: 9
- Forks: 115
- Open Issues: 29
-
Metadata Files:
- Readme: readme.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
- Roadmap: docs/ROADMAP.md
Awesome Lists containing this project
- awesome-LLM-resourses - nano-GraphRAG - to-hack GraphRAG implementation. (知识库 RAG)
- StarryDivineSky - gusye1234/nano-graphrag - graphrag 大约有 1100 行代码。小巧便携（faiss、neo4j、ollama...）、异步且完全类型化。 (A01_文本生成_文本对话 / 大语言对话模型及数据)
- alan_awesome_llm - nano-GraphRAG - to-hack GraphRAG implementation. (RAG)
- alan_awesome_llm - nano-GraphRAG - to-hack GraphRAG implementation. (RAG)
README
A simple, easy-to-hack GraphRAG implementation
😭 [GraphRAG](https://arxiv.org/pdf/2404.16130) is good and powerful, but the official [implementation](https://github.com/microsoft/graphrag/tree/main) is difficult/painful to **read or hack**.
😊 This project provides a **smaller, faster, cleaner GraphRAG**, while remaining the core functionality(see [benchmark](#benchmark) and [issues](#Issues) ).
🎁 Excluding `tests` and prompts, `nano-graphrag` is about **1100 lines of code**.
👌 Small yet [**portable**](#Components)(faiss, neo4j, ollama...), [**asynchronous**](#Async) and fully typed.
## Install
**Install from source** (recommend)
```shell
# clone this repo first
cd nano-graphrag
pip install -e .
```
**Install from PyPi**
```shell
pip install nano-graphrag
```
## Quick Start
> [!TIP]
>
> **Please set OpenAI API key in environment: `export OPENAI_API_KEY="sk-..."`.**
> [!TIP]
> If you're using Azure OpenAI API, refer to the [.env.example](./.env.example.azure) to set your azure openai. Then pass `GraphRAG(...,using_azure_openai=True,...)` to enable.
> [!TIP]
> If you're using Amazon Bedrock API, please ensure your credentials are properly set through commands like `aws configure`. Then enable it by configuring like this: `GraphRAG(...,using_amazon_bedrock=True, best_model_id="us.anthropic.claude-3-sonnet-20240229-v1:0", cheap_model_id="us.anthropic.claude-3-haiku-20240307-v1:0",...)`. Refer to an [example script](./examples/using_amazon_bedrock.py).
> [!TIP]
>
> If you don't have any key, check out this [example](./examples/no_openai_key_at_all.py) that using `transformers` and `ollama` . If you like to use another LLM or Embedding Model, check [Advances](#Advances).
download a copy of A Christmas Carol by Charles Dickens:
```shell
curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt
```
Use the below python snippet:
```python
from nano_graphrag import GraphRAG, QueryParam
graph_func = GraphRAG(working_dir="./dickens")
with open("./book.txt") as f:
graph_func.insert(f.read())
# Perform global graphrag search
print(graph_func.query("What are the top themes in this story?"))
# Perform local graphrag search (I think is better and more scalable one)
print(graph_func.query("What are the top themes in this story?", param=QueryParam(mode="local")))
```
Next time you initialize a `GraphRAG` from the same `working_dir`, it will reload all the contexts automatically.
#### Batch Insert
```python
graph_func.insert(["TEXT1", "TEXT2",...])
```
Incremental Insert
`nano-graphrag` supports incremental insert, no duplicated computation or data will be added:
```python
with open("./book.txt") as f:
book = f.read()
half_len = len(book) // 2
graph_func.insert(book[:half_len])
graph_func.insert(book[half_len:])
```
> `nano-graphrag` use md5-hash of the content as the key, so there is no duplicated chunk.
>
> However, each time you insert, the communities of graph will be re-computed and the community reports will be re-generated
Naive RAG
`nano-graphrag` supports naive RAG insert and query as well:
```python
graph_func = GraphRAG(working_dir="./dickens", enable_naive_rag=True)
...
# Query
print(rag.query(
"What are the top themes in this story?",
param=QueryParam(mode="naive")
)
```
### Async
For each method `NAME(...)` , there is a corresponding async method `aNAME(...)`
```python
await graph_func.ainsert(...)
await graph_func.aquery(...)
...
```
### Available Parameters
`GraphRAG` and `QueryParam` are `dataclass` in Python. Use `help(GraphRAG)` and `help(QueryParam)` to see all available parameters! Or check out the [Advances](#Advances) section to see some options.
## Components
Below are the components you can use:
| Type | What | Where |
| :-------------- | :----------------------------------------------------------: | :-----------------------------------------------: |
| LLM | OpenAI | Built-in |
| | Amazon Bedrock | Built-in |
| | DeepSeek | [examples](./examples) |
| | `ollama` | [examples](./examples) |
| Embedding | OpenAI | Built-in |
| | Amazon Bedrock | Built-in |
| | Sentence-transformers | [examples](./examples) |
| Vector DataBase | [`nano-vectordb`](https://github.com/gusye1234/nano-vectordb) | Built-in |
| | [`hnswlib`](https://github.com/nmslib/hnswlib) | Built-in, [examples](./examples) |
| | [`milvus-lite`](https://github.com/milvus-io/milvus-lite) | [examples](./examples) |
| | [faiss](https://github.com/facebookresearch/faiss?tab=readme-ov-file) | [examples](./examples) |
| Graph Storage | [`networkx`](https://networkx.org/documentation/stable/index.html) | Built-in |
| | [`neo4j`](https://neo4j.com/) | Built-in([doc](./docs/use_neo4j_for_graphrag.md)) |
| Visualization | graphml | [examples](./examples) |
| Chunking | by token size | Built-in |
| | by text splitter | Built-in |
- `Built-in` means we have that implementation inside `nano-graphrag`. `examples` means we have that implementation inside an tutorial under [examples](./examples) folder.
- Check [examples/benchmarks](./examples/benchmarks) to see few comparisons between components.
- **Always welcome to contribute more components.**
## Advances
Some setup options
- `GraphRAG(...,always_create_working_dir=False,...)` will skip the dir-creating step. Use it if you switch all your components to non-file storages.
Only query the related context
`graph_func.query` return the final answer without streaming.
If you like to interagte `nano-graphrag` in your project, you can use `param=QueryParam(..., only_need_context=True,...)`, which will only return the retrieved context from graph, something like:
````
# Local mode
-----Reports-----
```csv
id, content
0, # FOX News and Key Figures in Media and Politics...
1, ...
```
...
# Global mode
----Analyst 3----
Importance Score: 100
Donald J. Trump: Frequently discussed in relation to his political activities...
...
````
You can integrate that context into your customized prompt.
Prompt
`nano-graphrag` use prompts from `nano_graphrag.prompt.PROMPTS` dict object. You can play with it and replace any prompt inside.
Some important prompts:
- `PROMPTS["entity_extraction"]` is used to extract the entities and relations from a text chunk.
- `PROMPTS["community_report"]` is used to organize and summary the graph cluster's description.
- `PROMPTS["local_rag_response"]` is the system prompt template of the local search generation.
- `PROMPTS["global_reduce_rag_response"]` is the system prompt template of the global search generation.
- `PROMPTS["fail_response"]` is the fallback response when nothing is related to the user query.
Customize Chunking
`nano-graphrag` allow you to customize your own chunking method, check out the [example](./examples/using_custom_chunking_method.py).
Switch to the built-in text splitter chunking method:
```python
from nano_graphrag._op import chunking_by_seperators
GraphRAG(...,chunk_func=chunking_by_seperators,...)
```
LLM Function
In `nano-graphrag`, we requires two types of LLM, a great one and a cheap one. The former is used to plan and respond, the latter is used to summary. By default, the great one is `gpt-4o` and the cheap one is `gpt-4o-mini`
You can implement your own LLM function (refer to `_llm.gpt_4o_complete`):
```python
async def my_llm_complete(
prompt, system_prompt=None, history_messages=[], **kwargs
) -> str:
# pop cache KV database if any
hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
# the rest kwargs are for calling LLM, for example, `max_tokens=xxx`
...
# YOUR LLM calling
response = await call_your_LLM(messages, **kwargs)
return response
```
Replace the default one with:
```python
# Adjust the max token size or the max async requests if needed
GraphRAG(best_model_func=my_llm_complete, best_model_max_token_size=..., best_model_max_async=...)
GraphRAG(cheap_model_func=my_llm_complete, cheap_model_max_token_size=..., cheap_model_max_async=...)
```
You can refer to this [example](./examples/using_deepseek_as_llm.py) that use [`deepseek-chat`](https://platform.deepseek.com/api-docs/) as the LLM model
You can refer to this [example](./examples/using_ollama_as_llm.py) that use [`ollama`](https://github.com/ollama/ollama) as the LLM model
#### Json Output
`nano-graphrag` will use `best_model_func` to output JSON with params `"response_format": {"type": "json_object"}`. However there are some open-source model maybe produce unstable JSON.
`nano-graphrag` introduces a post-process interface for you to convert the response to JSON. This func's signature is below:
```python
def YOUR_STRING_TO_JSON_FUNC(response: str) -> dict:
"Convert the string response to JSON"
...
```
And pass your own func by `GraphRAG(...convert_response_to_json_func=YOUR_STRING_TO_JSON_FUNC,...)`.
For example, you can refer to [json_repair](https://github.com/mangiucugna/json_repair) to repair the JSON string returned by LLM.
Embedding Function
You can replace the default embedding functions with any `_utils.EmbedddingFunc` instance.
For example, the default one is using OpenAI embedding API:
```python
@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
async def openai_embedding(texts: list[str]) -> np.ndarray:
openai_async_client = AsyncOpenAI()
response = await openai_async_client.embeddings.create(
model="text-embedding-3-small", input=texts, encoding_format="float"
)
return np.array([dp.embedding for dp in response.data])
```
Replace default embedding function with:
```python
GraphRAG(embedding_func=your_embed_func, embedding_batch_num=..., embedding_func_max_async=...)
```
You can refer to an [example](./examples/using_local_embedding_model.py) that use `sentence-transformer` to locally compute embeddings.
Storage Component
You can replace all storage-related components to your own implementation, `nano-graphrag` mainly uses three kinds of storage:
**`base.BaseKVStorage` for storing key-json pairs of data**
- By default we use disk file storage as the backend.
- `GraphRAG(.., key_string_value_json_storage_cls=YOURS,...)`
**`base.BaseVectorStorage` for indexing embeddings**
- By default we use [`nano-vectordb`](https://github.com/gusye1234/nano-vectordb) as the backend.
- We have a built-in [`hnswlib`](https://github.com/nmslib/hnswlib) storage also, check out this [example](./examples/using_hnsw_as_vectorDB.py).
- Check out this [example](./examples/using_milvus_as_vectorDB.py) that implements [`milvus-lite`](https://github.com/milvus-io/milvus-lite) as the backend (not available in Windows).
- `GraphRAG(.., vector_db_storage_cls=YOURS,...)`
**`base.BaseGraphStorage` for storing knowledge graph**
- By default we use [`networkx`](https://github.com/networkx/networkx) as the backend.
- We have a built-in `Neo4jStorage` for graph, check out this [tutorial](./docs/use_neo4j_for_graphrag.md).
- `GraphRAG(.., graph_storage_cls=YOURS,...)`
You can refer to `nano_graphrag.base` to see detailed interfaces for each components.
## FQA
Check [FQA](./docs/FAQ.md).
## Roadmap
See [ROADMAP.md](./docs/ROADMAP.md)
## Contribute
`nano-graphrag` is open to any kind of contribution. Read [this](./docs/CONTRIBUTING.md) before you contribute.
## Benchmark
- [benchmark for English](./docs/benchmark-en.md)
- [benchmark for Chinese](./docs/benchmark-zh.md)
- [An evaluation](./examples/benchmarks/eval_naive_graphrag_on_multi_hop.ipynb) notebook on a [multi-hop RAG task](https://github.com/yixuantt/MultiHop-RAG)
## Projects that used `nano-graphrag`
- [Medical Graph RAG](https://github.com/MedicineToken/Medical-Graph-RAG): Graph RAG for the Medical Data
- [LightRAG](https://github.com/HKUDS/LightRAG): Simple and Fast Retrieval-Augmented Generation
- [fast-graphrag](https://github.com/circlemind-ai/fast-graphrag): RAG that intelligently adapts to your use case, data, and queries
> Welcome to pull requests if your project uses `nano-graphrag`, it will help others to trust this repo❤️
## Issues
- `nano-graphrag` didn't implement the `covariates` feature of `GraphRAG`
- `nano-graphrag` implements the global search different from the original. The original use a map-reduce-like style to fill all the communities into context, while `nano-graphrag` only use the top-K important and central communites (use `QueryParam.global_max_consider_community` to control, default to 512 communities).