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# Conflicts:
#	README.md
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Samuel Chan
2025-01-11 10:40:09 +08:00
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</p>
<p>
<img src='https://img.shields.io/github/stars/hkuds/lightrag?color=green&style=social' />
<img src="https://img.shields.io/badge/python->=3.10-blue">
<img src="https://img.shields.io/badge/python-3.10-blue">
<a href="https://pypi.org/project/lightrag-hku/"><img src="https://img.shields.io/pypi/v/lightrag-hku.svg"></a>
<a href="https://pepy.tech/project/lightrag-hku"><img src="https://static.pepy.tech/badge/lightrag-hku/month"></a>
</p>
@@ -637,7 +637,7 @@ if __name__ == "__main__":
| **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation | |
| **vector\_db\_storage\_cls\_kwargs** | `dict` | Additional parameters for vector database (currently not used) | |
| **enable\_llm\_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
| **enable\_llm\_cache\_for\_entity\_extract** | `bool` | If `TRUE`, stores LLM results in cache for entity extraction; Good for beginners to debug your application | `FALSE` |
| **enable\_llm\_cache\_for\_entity\_extract** | `bool` | If `TRUE`, stores LLM results in cache for entity extraction; Good for beginners to debug your application | `TRUE` |
| **addon\_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"], "insert_batch_size": 10}`: sets example limit, output language, and batch size for document processing | `example_number: all examples, language: English, insert_batch_size: 10` |
| **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
| **embedding\_cache\_config** | `dict` | Configuration for question-answer caching. Contains three parameters:<br>- `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers.<br>- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.<br>- `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |
@@ -892,69 +892,6 @@ def extract_queries(file_path):
```
</details>
## Code Structure
```python
.
├── .github/
│ ├── workflows/
│ │ └── linting.yaml
├── examples/
│ ├── batch_eval.py
│ ├── generate_query.py
│ ├── graph_visual_with_html.py
│ ├── graph_visual_with_neo4j.py
│ ├── insert_custom_kg.py
│ ├── lightrag_api_openai_compatible_demo.py
│ ├── lightrag_api_oracle_demo..py
│ ├── lightrag_azure_openai_demo.py
│ ├── lightrag_bedrock_demo.py
│ ├── lightrag_hf_demo.py
│ ├── lightrag_lmdeploy_demo.py
│ ├── lightrag_nvidia_demo.py
│ ├── lightrag_ollama_demo.py
│ ├── lightrag_openai_compatible_demo.py
│ ├── lightrag_openai_demo.py
│ ├── lightrag_oracle_demo.py
│ ├── lightrag_siliconcloud_demo.py
│ └── vram_management_demo.py
├── lightrag/
│ ├── api/
│ │ ├── lollms_lightrag_server.py
│ │ ├── ollama_lightrag_server.py
│ │ ├── openai_lightrag_server.py
│ │ ├── azure_openai_lightrag_server.py
│ │ └── requirements.txt
│ ├── kg/
│ │ ├── __init__.py
│ │ ├── oracle_impl.py
│ │ └── neo4j_impl.py
│ ├── __init__.py
│ ├── base.py
│ ├── lightrag.py
│ ├── llm.py
│ ├── operate.py
│ ├── prompt.py
│ ├── storage.py
│ └── utils.py
├── reproduce/
│ ├── Step_0.py
│ ├── Step_1_openai_compatible.py
│ ├── Step_1.py
│ ├── Step_2.py
│ ├── Step_3_openai_compatible.py
│ └── Step_3.py
├── .gitignore
├── .pre-commit-config.yaml
├── get_all_edges_nx.py
├── LICENSE
├── README.md
├── requirements.txt
├── setup.py
├── test_neo4j.py
└── test.py
```
## Install with API Support
LightRAG provides optional API support through FastAPI servers that add RAG capabilities to existing LLM services. You can install LightRAG with API support in two ways: