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README.md
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@@ -566,7 +566,7 @@ rag.insert(text_content.decode('utf-8'))
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```
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</details>
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<details>
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<summary> <b>Using Neo4J for Storage</b> </summary>
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</details>
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### Delete
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```python
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rag = LightRAG(
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rag.delete_by_doc_id("doc_id")
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```
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<details>
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<summary>
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* The following code can be found in `examples/graph_visual_with_html.py`
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</details>
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<details>
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<summary> Graph visualization with
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* The following code can be found in `examples/graph_visual_with_neo4j.py`
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</details>
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### LightRAG init parameters
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<details>
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<summary>
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|----------------------------------------------| --- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
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| **working\_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` |
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| **kv\_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`, `OracleKVStorage` | `JsonKVStorage` |
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| **vector\_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`, `OracleVectorDBStorage` | `NanoVectorDBStorage` |
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| **graph\_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
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| **log\_level** | | Log level for application runtime | `logging.DEBUG` |
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| **chunk\_token\_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
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| **chunk\_overlap\_token\_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
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| **tiktoken\_model\_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
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| **entity\_extract\_max\_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` |
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| **entity\_summary\_to\_max\_tokens** | `int` | Maximum token size for each entity summary | `500` |
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| **node\_embedding\_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |
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| **node2vec\_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` |
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| **embedding\_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embed` |
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| **embedding\_batch\_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` |
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| **embedding\_func\_max\_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
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| **llm\_model\_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` |
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| **llm\_model\_name** | `str` | LLM model name for generation | `meta-llama/Llama-3.2-1B-Instruct` |
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| **llm\_model\_max\_token\_size** | `int` | Maximum token size for LLM generation (affects entity relation summaries) | `32768`（default value changed by env var MAX_TOKENS) |
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| **llm\_model\_max\_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `16`（default value changed by env var MAX_ASYNC) |
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| **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation | |
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| **vector\_db\_storage\_cls\_kwargs** | `dict` | Additional parameters for vector database, like setting the threshold for nodes and relations retrieval. | cosine_better_than_threshold: 0.2（default value changed by env var COSINE_THRESHOLD) |
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| **enable\_llm\_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
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| **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` |
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| **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` |
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| **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
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| **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}` |
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|**log\_dir** | `str` | Directory to store logs. | `./` |
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</details>
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### Error Handling
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<summary>Click to view error handling details</summary>
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The API includes comprehensive error handling:
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- File not found errors (404)
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- Processing errors (500)
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- Supports multiple file encodings (UTF-8 and GBK)
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</details>
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## Evaluation
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```
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</details>
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## API
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LightRag can be installed with API support to serve a Fast api interface to perform data upload and indexing/Rag operations/Rescan of the input folder etc..
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The documentation can be found [here](lightrag/api/README.md)
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## Graph viewer
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LightRag can be installed with Tools support to add extra tools like the graphml 3d visualizer.
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The documentation can be found [here](lightrag/tools/lightrag_visualizer/README.md)
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## Star History
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<a href="https://star-history.com/#HKUDS/LightRAG&Date">
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```
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</details>
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+
## Storage
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<details>
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| 572 |
<summary> <b>Using Neo4J for Storage</b> </summary>
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| 682 |
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| 683 |
</details>
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+
## Delete
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| 686 |
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| 687 |
```python
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rag = LightRAG(
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rag.delete_by_doc_id("doc_id")
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```
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+
## LightRAG init parameters
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<details>
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<summary> Parameters </summary>
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| **Parameter** | **Type** | **Explanation** | **Default** |
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|----------------------------------------------| --- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
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| **working\_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` |
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| **kv\_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`, `OracleKVStorage` | `JsonKVStorage` |
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| **vector\_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`, `OracleVectorDBStorage` | `NanoVectorDBStorage` |
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| **graph\_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
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| **log\_level** | | Log level for application runtime | `logging.DEBUG` |
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| **chunk\_token\_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
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| **chunk\_overlap\_token\_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
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| **tiktoken\_model\_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
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| **entity\_extract\_max\_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` |
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| **entity\_summary\_to\_max\_tokens** | `int` | Maximum token size for each entity summary | `500` |
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| **node\_embedding\_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |
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| **node2vec\_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` |
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| **embedding\_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embed` |
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| **embedding\_batch\_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` |
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| **embedding\_func\_max\_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
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| **llm\_model\_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` |
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| **llm\_model\_name** | `str` | LLM model name for generation | `meta-llama/Llama-3.2-1B-Instruct` |
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| **llm\_model\_max\_token\_size** | `int` | Maximum token size for LLM generation (affects entity relation summaries) | `32768`（default value changed by env var MAX_TOKENS) |
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| **llm\_model\_max\_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `16`（default value changed by env var MAX_ASYNC) |
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| **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation | |
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| **vector\_db\_storage\_cls\_kwargs** | `dict` | Additional parameters for vector database, like setting the threshold for nodes and relations retrieval. | cosine_better_than_threshold: 0.2（default value changed by env var COSINE_THRESHOLD) |
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| **enable\_llm\_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
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| **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` |
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| **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` |
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| **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
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| **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}` |
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|**log\_dir** | `str` | Directory to store logs. | `./` |
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</details>
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## Error Handling
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<details>
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<summary>Click to view error handling details</summary>
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+
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The API includes comprehensive error handling:
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- File not found errors (404)
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- Processing errors (500)
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- Supports multiple file encodings (UTF-8 and GBK)
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</details>
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## API
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LightRag can be installed with API support to serve a Fast api interface to perform data upload and indexing/Rag operations/Rescan of the input folder etc..
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+
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[LightRag API](lightrag/api/README.md)
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## Graph Visualization
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<details>
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<summary> <b>Graph visualization with html</b> </summary>
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* The following code can be found in `examples/graph_visual_with_html.py`
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</details>
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<details>
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<summary> <b>Graph visualization with Neo4</b> </summary>
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* The following code can be found in `examples/graph_visual_with_neo4j.py`
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</details>
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<details>
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<summary> <b>Graphml 3d visualizer</b> </summary>
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LightRag can be installed with Tools support to add extra tools like the graphml 3d visualizer.
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[LightRag Visualizer](lightrag/tools/lightrag_visualizer/README.md)
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</details>
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## Evaluation
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```
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</details>
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## Star History
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| 1165 |
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<a href="https://star-history.com/#HKUDS/LightRAG&Date">
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