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A simple, easy-to-hack GraphRAG implementation

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sometimes, while using nano-graphrag, the progress stuck while doing Entity Extraction process. while stuck, the output of terminal looks like below: ``` "Processed 26 chunks, 378 entities found(duplicated), 263 relations(duplicated)"...

### Issue Hello, I have a similar error with #57 using no_openai_key_at_all.py. I already used qwen2:ctx32k to replace qwen2. How could I fix this problem? Thanks. ### Error ``` ⠹...

- Implement RedisKVStorage for key-value storage. - Implement MilvusVectorStorage for vector storage. - Implement NebulaGraphStorage for graph storage. Refer to #9 - Add tests for NebulaGraphStorage to ensure functionality. TODO...

Hello ! Great project, so much easy to understand/hack than the microsoft one Is there any plan to support the prompt tuning feature ? https://microsoft.github.io/graphrag/posts/prompt_tuning/overview/ It would be really nice...

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my error: `BadRequestError Traceback (most recent call last) Cell In[10], [line 5](vscode-notebook-cell:?execution_count=10&line=5) [3](vscode-notebook-cell:?execution_count=10&line=3) nest_asyncio.apply() [4](vscode-notebook-cell:?execution_count=10&line=4) import asyncio ----> [5](vscode-notebook-cell:?execution_count=10&line=5) rag.insert(FAKE_TEXT) File ~/test/nano_graphrag/graphrag.py:217, in GraphRAG.insert(self, string_or_strings) [215](https://vscode-remote+ssh-002dremote-002b103-002e183-002e119-002e49.vscode-resource.vscode-cdn.net/root/test/~/test/nano_graphrag/graphrag.py:215) def insert(self, string_or_strings): [216](https://vscode-remote+ssh-002dremote-002b103-002e183-002e119-002e49.vscode-resource.vscode-cdn.net/root/test/~/test/nano_graphrag/graphrag.py:216)...

For example the Goal and Target response length and format repeated. Is this intentional repetition or some kind of error? PROMPTS[ "local_rag_response" ] = """---Role--- You are a helpful assistant...

good first issue

See `TypeError: unsupported operand type(s) for +=: 'NoneType' and 'str'` when writing graph Not sure exactly how to reproduce, flaky behavior when using large corpus with azure openai.

Must have for real world application: 1) To actually use this in any application we must be able to generate multiple independent Knowledge Graph for RAG for diffirent users this...