Add chinese version of README
- Add introduction of graph visualization in LightRAG Server
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@@ -28,22 +28,10 @@
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</tr>
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</table>
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<div align="center">
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This repository hosts the code of LightRAG. The structure of this code is based on <a href="https://github.com/gusye1234/nano-graphrag">nano-graphrag</a>.
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<img src="https://i-blog.csdnimg.cn/direct/b2aaf634151b4706892693ffb43d9093.png" width="800" alt="LightRAG Diagram">
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</div>
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</div>
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</br>
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<img src="./README.assets/b2aaf634151b4706892693ffb43d9093.png" width="800" alt="LightRAG Diagram">
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<details>
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<summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
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🎉 News
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</summary>
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## 🎉 News
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- [X] [2025.03.18]🎯📢LightRAG now supports citation functionality.
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- [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
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@@ -63,8 +51,6 @@ This repository hosts the code of LightRAG. The structure of this code is based
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- [X] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
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- [X] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
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</details>
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<details>
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<summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
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Algorithm Flowchart
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@@ -779,7 +765,7 @@ For production level scenarios you will most likely want to leverage an enterpri
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create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
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CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
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ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
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-- drop if necessary
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drop INDEX entity_p_idx;
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drop INDEX vertex_p_idx;
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@@ -1081,33 +1067,33 @@ Valid modes are:
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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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| **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 | `4`（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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| **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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| **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 | `4`（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: `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers. `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. `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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</details>
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@@ -1132,166 +1118,9 @@ LightRag can be installed with API support to serve a Fast api interface to perf
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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 LightRAG Server offers a comprehensive knowledge graph visualization feature. It supports various gravity layouts, node queries, subgraph filtering, and more. **For more information about LightRAG Server, please refer to [LightRAG Server](./lightrag/api/README.md).**
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* The following code can be found in `examples/graph_visual_with_html.py`
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```python
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import networkx as nx
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from pyvis.network import Network
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# Load the GraphML file
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G = nx.read_graphml('./dickens/graph_chunk_entity_relation.graphml')
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# Create a Pyvis network
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net = Network(notebook=True)
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# Convert NetworkX graph to Pyvis network
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net.from_nx(G)
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# Save and display the network
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net.show('knowledge_graph.html')
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```
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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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```python
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import os
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import json
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from lightrag.utils import xml_to_json
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from neo4j import GraphDatabase
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# Constants
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WORKING_DIR = "./dickens"
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BATCH_SIZE_NODES = 500
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BATCH_SIZE_EDGES = 100
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# Neo4j connection credentials
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NEO4J_URI = "bolt://localhost:7687"
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NEO4J_USERNAME = "neo4j"
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NEO4J_PASSWORD = "your_password"
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def convert_xml_to_json(xml_path, output_path):
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"""Converts XML file to JSON and saves the output."""
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if not os.path.exists(xml_path):
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print(f"Error: File not found - {xml_path}")
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return None
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json_data = xml_to_json(xml_path)
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if json_data:
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump(json_data, f, ensure_ascii=False, indent=2)
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print(f"JSON file created: {output_path}")
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return json_data
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else:
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print("Failed to create JSON data")
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return None
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def process_in_batches(tx, query, data, batch_size):
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"""Process data in batches and execute the given query."""
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for i in range(0, len(data), batch_size):
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batch = data[i:i + batch_size]
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tx.run(query, {"nodes": batch} if "nodes" in query else {"edges": batch})
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def main():
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# Paths
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xml_file = os.path.join(WORKING_DIR, 'graph_chunk_entity_relation.graphml')
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json_file = os.path.join(WORKING_DIR, 'graph_data.json')
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# Convert XML to JSON
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json_data = convert_xml_to_json(xml_file, json_file)
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if json_data is None:
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return
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# Load nodes and edges
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nodes = json_data.get('nodes', [])
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edges = json_data.get('edges', [])
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# Neo4j queries
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create_nodes_query = """
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UNWIND $nodes AS node
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MERGE (e:Entity {id: node.id})
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SET e.entity_type = node.entity_type,
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e.description = node.description,
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e.source_id = node.source_id,
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e.displayName = node.id
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REMOVE e:Entity
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WITH e, node
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CALL apoc.create.addLabels(e, [node.entity_type]) YIELD node AS labeledNode
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RETURN count(*)
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"""
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create_edges_query = """
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UNWIND $edges AS edge
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MATCH (source {id: edge.source})
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MATCH (target {id: edge.target})
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WITH source, target, edge,
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CASE
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WHEN edge.keywords CONTAINS 'lead' THEN 'lead'
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WHEN edge.keywords CONTAINS 'participate' THEN 'participate'
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WHEN edge.keywords CONTAINS 'uses' THEN 'uses'
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WHEN edge.keywords CONTAINS 'located' THEN 'located'
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WHEN edge.keywords CONTAINS 'occurs' THEN 'occurs'
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ELSE REPLACE(SPLIT(edge.keywords, ',')[0], '\"', '')
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END AS relType
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CALL apoc.create.relationship(source, relType, {
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weight: edge.weight,
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description: edge.description,
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keywords: edge.keywords,
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source_id: edge.source_id
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}, target) YIELD rel
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RETURN count(*)
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"""
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set_displayname_and_labels_query = """
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MATCH (n)
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SET n.displayName = n.id
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WITH n
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CALL apoc.create.setLabels(n, [n.entity_type]) YIELD node
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RETURN count(*)
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"""
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# Create a Neo4j driver
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driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
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try:
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# Execute queries in batches
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with driver.session() as session:
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# Insert nodes in batches
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session.execute_write(process_in_batches, create_nodes_query, nodes, BATCH_SIZE_NODES)
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# Insert edges in batches
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session.execute_write(process_in_batches, create_edges_query, edges, BATCH_SIZE_EDGES)
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# Set displayName and labels
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session.run(set_displayname_and_labels_query)
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except Exception as e:
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print(f"Error occurred: {e}")
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finally:
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driver.close()
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if __name__ == "__main__":
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main()
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```
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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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@@ -1386,28 +1215,28 @@ Output your evaluation in the following JSON format:
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### Overall Performance Table
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| | **Agriculture** | | **CS** | | **Legal** | | **Mix** | |
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| --------------------------- | --------------------- | ------------------ | ------------ | ------------------ | --------------- | ------------------ | --------------- | ------------------ |
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| | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** |
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| **Comprehensiveness** | 32.4% | **67.6%** | 38.4% | **61.6%** | 16.4% | **83.6%** | 38.8% | **61.2%** |
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| **Diversity** | 23.6% | **76.4%** | 38.0% | **62.0%** | 13.6% | **86.4%** | 32.4% | **67.6%** |
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| **Empowerment** | 32.4% | **67.6%** | 38.8% | **61.2%** | 16.4% | **83.6%** | 42.8% | **57.2%** |
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| **Overall** | 32.4% | **67.6%** | 38.8% | **61.2%** | 15.2% | **84.8%** | 40.0% | **60.0%** |
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| | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** |
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| **Comprehensiveness** | 31.6% | **68.4%** | 38.8% | **61.2%** | 15.2% | **84.8%** | 39.2% | **60.8%** |
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| **Diversity** | 29.2% | **70.8%** | 39.2% | **60.8%** | 11.6% | **88.4%** | 30.8% | **69.2%** |
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| **Empowerment** | 31.6% | **68.4%** | 36.4% | **63.6%** | 15.2% | **84.8%** | 42.4% | **57.6%** |
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| **Overall** | 32.4% | **67.6%** | 38.0% | **62.0%** | 14.4% | **85.6%** | 40.0% | **60.0%** |
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| | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** |
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| **Comprehensiveness** | 26.0% | **74.0%** | 41.6% | **58.4%** | 26.8% | **73.2%** | 40.4% | **59.6%** |
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| **Diversity** | 24.0% | **76.0%** | 38.8% | **61.2%** | 20.0% | **80.0%** | 32.4% | **67.6%** |
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| **Empowerment** | 25.2% | **74.8%** | 40.8% | **59.2%** | 26.0% | **74.0%** | 46.0% | **54.0%** |
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| **Overall** | 24.8% | **75.2%** | 41.6% | **58.4%** | 26.4% | **73.6%** | 42.4% | **57.6%** |
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| | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** |
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| **Comprehensiveness** | 45.6% | **54.4%** | 48.4% | **51.6%** | 48.4% | **51.6%** | **50.4%** | 49.6% |
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| **Diversity** | 22.8% | **77.2%** | 40.8% | **59.2%** | 26.4% | **73.6%** | 36.0% | **64.0%** |
|
||||
| **Empowerment** | 41.2% | **58.8%** | 45.2% | **54.8%** | 43.6% | **56.4%** | **50.8%** | 49.2% |
|
||||
| **Overall** | 45.2% | **54.8%** | 48.0% | **52.0%** | 47.2% | **52.8%** | **50.4%** | 49.6% |
|
||||
| |**Agriculture**| |**CS**| |**Legal**| |**Mix**| |
|
||||
|----------------------|---------------|------------|------|------------|---------|------------|-------|------------|
|
||||
| |NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**|
|
||||
|**Comprehensiveness**|32.4%|**67.6%**|38.4%|**61.6%**|16.4%|**83.6%**|38.8%|**61.2%**|
|
||||
|**Diversity**|23.6%|**76.4%**|38.0%|**62.0%**|13.6%|**86.4%**|32.4%|**67.6%**|
|
||||
|**Empowerment**|32.4%|**67.6%**|38.8%|**61.2%**|16.4%|**83.6%**|42.8%|**57.2%**|
|
||||
|**Overall**|32.4%|**67.6%**|38.8%|**61.2%**|15.2%|**84.8%**|40.0%|**60.0%**|
|
||||
| |RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|
|
||||
|**Comprehensiveness**|31.6%|**68.4%**|38.8%|**61.2%**|15.2%|**84.8%**|39.2%|**60.8%**|
|
||||
|**Diversity**|29.2%|**70.8%**|39.2%|**60.8%**|11.6%|**88.4%**|30.8%|**69.2%**|
|
||||
|**Empowerment**|31.6%|**68.4%**|36.4%|**63.6%**|15.2%|**84.8%**|42.4%|**57.6%**|
|
||||
|**Overall**|32.4%|**67.6%**|38.0%|**62.0%**|14.4%|**85.6%**|40.0%|**60.0%**|
|
||||
| |HyDE|**LightRAG**|HyDE|**LightRAG**|HyDE|**LightRAG**|HyDE|**LightRAG**|
|
||||
|**Comprehensiveness**|26.0%|**74.0%**|41.6%|**58.4%**|26.8%|**73.2%**|40.4%|**59.6%**|
|
||||
|**Diversity**|24.0%|**76.0%**|38.8%|**61.2%**|20.0%|**80.0%**|32.4%|**67.6%**|
|
||||
|**Empowerment**|25.2%|**74.8%**|40.8%|**59.2%**|26.0%|**74.0%**|46.0%|**54.0%**|
|
||||
|**Overall**|24.8%|**75.2%**|41.6%|**58.4%**|26.4%|**73.6%**|42.4%|**57.6%**|
|
||||
| |GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|
|
||||
|**Comprehensiveness**|45.6%|**54.4%**|48.4%|**51.6%**|48.4%|**51.6%**|**50.4%**|49.6%|
|
||||
|**Diversity**|22.8%|**77.2%**|40.8%|**59.2%**|26.4%|**73.6%**|36.0%|**64.0%**|
|
||||
|**Empowerment**|41.2%|**58.8%**|45.2%|**54.8%**|43.6%|**56.4%**|**50.8%**|49.2%|
|
||||
|**Overall**|45.2%|**54.8%**|48.0%|**52.0%**|47.2%|**52.8%**|**50.4%**|49.6%|
|
||||
|
||||
## Reproduce
|
||||
|
||||
|
||||
559
lightrag/api/README-zh.md
Normal file
559
lightrag/api/README-zh.md
Normal file
@@ -0,0 +1,559 @@
|
||||
# LightRAG 服务器和 Web 界面
|
||||
|
||||
LightRAG 服务器旨在提供 Web 界面和 API 支持。Web 界面便于文档索引、知识图谱探索和简单的 RAG 查询界面。LightRAG 服务器还提供了与 Ollama 兼容的接口，旨在将 LightRAG 模拟为 Ollama 聊天模型。这使得 AI 聊天机器人（如 Open WebUI）可以轻松访问 LightRAG。
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## 入门指南
|
||||
|
||||
### 安装
|
||||
|
||||
* 从 PyPI 安装
|
||||
|
||||
```bash
|
||||
pip install "lightrag-hku[api]"
|
||||
```
|
||||
|
||||
* 从源代码安装
|
||||
|
||||
```bash
|
||||
# 克隆仓库
|
||||
git clone https://github.com/HKUDS/lightrag.git
|
||||
|
||||
# 切换到仓库目录
|
||||
cd lightrag
|
||||
|
||||
# 如有必要，创建 Python 虚拟环境
|
||||
# 以可编辑模式安装并支持 API
|
||||
pip install -e ".[api]"
|
||||
```
|
||||
|
||||
### 启动 LightRAG 服务器前的准备
|
||||
|
||||
LightRAG 需要同时集成 LLM（大型语言模型）和嵌入模型以有效执行文档索引和查询操作。在首次部署 LightRAG 服务器之前，必须配置 LLM 和嵌入模型的设置。LightRAG 支持绑定到各种 LLM/嵌入后端：
|
||||
|
||||
* ollama
|
||||
* lollms
|
||||
* openai 或 openai 兼容
|
||||
* azure_openai
|
||||
|
||||
建议使用环境变量来配置 LightRAG 服务器。项目根目录中有一个名为 `env.example` 的示例环境变量文件。请将此文件复制到启动目录并重命名为 `.env`。之后，您可以在 `.env` 文件中修改与 LLM 和嵌入模型相关的参数。需要注意的是，LightRAG 服务器每次启动时都会将 `.env` 中的环境变量加载到系统环境变量中。由于 LightRAG 服务器会优先使用系统环境变量中的设置，如果您在通过命令行启动 LightRAG 服务器后修改了 `.env` 文件，则需要执行 `source .env` 使新设置生效。
|
||||
|
||||
以下是 LLM 和嵌入模型的一些常见设置示例：
|
||||
|
||||
* OpenAI LLM + Ollama 嵌入
|
||||
|
||||
```
|
||||
LLM_BINDING=openai
|
||||
LLM_MODEL=gpt-4o
|
||||
LLM_BINDING_HOST=https://api.openai.com/v1
|
||||
LLM_BINDING_API_KEY=your_api_key
|
||||
MAX_TOKENS=32768 # 发送给 LLM 的最大 token 数（小于模型上下文大小）
|
||||
|
||||
EMBEDDING_BINDING=ollama
|
||||
EMBEDDING_BINDING_HOST=http://localhost:11434
|
||||
EMBEDDING_MODEL=bge-m3:latest
|
||||
EMBEDDING_DIM=1024
|
||||
# EMBEDDING_BINDING_API_KEY=your_api_key
|
||||
```
|
||||
|
||||
* Ollama LLM + Ollama 嵌入
|
||||
|
||||
```
|
||||
LLM_BINDING=ollama
|
||||
LLM_MODEL=mistral-nemo:latest
|
||||
LLM_BINDING_HOST=http://localhost:11434
|
||||
# LLM_BINDING_API_KEY=your_api_key
|
||||
MAX_TOKENS=8192 # 发送给 LLM 的最大 token 数（基于您的 Ollama 服务器容量）
|
||||
|
||||
EMBEDDING_BINDING=ollama
|
||||
EMBEDDING_BINDING_HOST=http://localhost:11434
|
||||
EMBEDDING_MODEL=bge-m3:latest
|
||||
EMBEDDING_DIM=1024
|
||||
# EMBEDDING_BINDING_API_KEY=your_api_key
|
||||
```
|
||||
|
||||
### 启动 LightRAG 服务器
|
||||
|
||||
LightRAG 服务器支持两种运行模式：
|
||||
* 简单高效的 Uvicorn 模式
|
||||
|
||||
```
|
||||
lightrag-server
|
||||
```
|
||||
* 多进程 Gunicorn + Uvicorn 模式（生产模式，不支持 Windows 环境）
|
||||
|
||||
```
|
||||
lightrag-gunicorn --workers 4
|
||||
```
|
||||
`.env` 文件必须放在启动目录中。启动时，LightRAG 服务器将创建一个文档目录（默认为 `./inputs`）和一个数据目录（默认为 `./rag_storage`）。这允许您从不同目录启动多个 LightRAG 服务器实例，每个实例配置为监听不同的网络端口。
|
||||
|
||||
以下是一些常用的启动参数：
|
||||
|
||||
- `--host`：服务器监听地址（默认：0.0.0.0）
|
||||
- `--port`：服务器监听端口（默认：9621）
|
||||
- `--timeout`：LLM 请求超时时间（默认：150 秒）
|
||||
- `--log-level`：日志级别（默认：INFO）
|
||||
- --input-dir：指定要扫描文档的目录（默认：./input）
|
||||
|
||||
### 启动时自动扫描
|
||||
|
||||
当使用 `--auto-scan-at-startup` 参数启动任何服务器时，系统将自动：
|
||||
|
||||
1. 扫描输入目录中的新文件
|
||||
2. 为尚未在数据库中的新文档建立索引
|
||||
3. 使所有内容立即可用于 RAG 查询
|
||||
|
||||
> `--input-dir` 参数指定要扫描的输入目录。您可以从 webui 触发输入目录扫描。
|
||||
|
||||
### Gunicorn + Uvicorn 的多工作进程
|
||||
|
||||
LightRAG 服务器可以在 `Gunicorn + Uvicorn` 预加载模式下运行。Gunicorn 的多工作进程（多进程）功能可以防止文档索引任务阻塞 RAG 查询。使用 CPU 密集型文档提取工具（如 docling）在纯 Uvicorn 模式下可能会导致整个系统被阻塞。
|
||||
|
||||
虽然 LightRAG 服务器使用一个工作进程来处理文档索引流程，但通过 Uvicorn 的异步任务支持，可以并行处理多个文件。文档索引速度的瓶颈主要在于 LLM。如果您的 LLM 支持高并发，您可以通过增加 LLM 的并发级别来加速文档索引。以下是几个与并发处理相关的环境变量及其默认值：
|
||||
|
||||
```
|
||||
WORKERS=2 # 工作进程数，不大于 (2 x 核心数) + 1
|
||||
MAX_PARALLEL_INSERT=2 # 一批中并行处理的文件数
|
||||
MAX_ASYNC=4 # LLM 的最大并发请求数
|
||||
```
|
||||
|
||||
### 将 Lightrag 安装为 Linux 服务
|
||||
|
||||
从示例文件 `lightrag.sevice.example` 创建您的服务文件 `lightrag.sevice`。修改服务文件中的 WorkingDirectory 和 ExecStart：
|
||||
|
||||
```text
|
||||
Description=LightRAG Ollama Service
|
||||
WorkingDirectory=<lightrag 安装目录>
|
||||
ExecStart=<lightrag 安装目录>/lightrag/api/lightrag-api
|
||||
```
|
||||
|
||||
修改您的服务启动脚本：`lightrag-api`。根据需要更改 python 虚拟环境激活命令：
|
||||
|
||||
```shell
|
||||
#!/bin/bash
|
||||
|
||||
# 您的 python 虚拟环境激活命令
|
||||
source /home/netman/lightrag-xyj/venv/bin/activate
|
||||
# 启动 lightrag api 服务器
|
||||
lightrag-server
|
||||
```
|
||||
|
||||
安装 LightRAG 服务。如果您的系统是 Ubuntu，以下命令将生效：
|
||||
|
||||
```shell
|
||||
sudo cp lightrag.service /etc/systemd/system/
|
||||
sudo systemctl daemon-reload
|
||||
sudo systemctl start lightrag.service
|
||||
sudo systemctl status lightrag.service
|
||||
sudo systemctl enable lightrag.service
|
||||
```
|
||||
|
||||
## Ollama 模拟
|
||||
|
||||
我们为 LightRAG 提供了 Ollama 兼容接口，旨在将 LightRAG 模拟为 Ollama 聊天模型。这使得支持 Ollama 的 AI 聊天前端（如 Open WebUI）可以轻松访问 LightRAG。
|
||||
|
||||
### 将 Open WebUI 连接到 LightRAG
|
||||
|
||||
启动 lightrag-server 后，您可以在 Open WebUI 管理面板中添加 Ollama 类型的连接。然后，一个名为 lightrag:latest 的模型将出现在 Open WebUI 的模型管理界面中。用户随后可以通过聊天界面向 LightRAG 发送查询。对于这种用例，最好将 LightRAG 安装为服务。
|
||||
|
||||
Open WebUI 使用 LLM 来执行会话标题和会话关键词生成任务。因此，Ollama 聊天补全 API 会检测并将 OpenWebUI 会话相关请求直接转发给底层 LLM。Open WebUI 的截图：
|
||||
|
||||
|
||||
|
||||
### 在聊天中选择查询模式
|
||||
|
||||
查询字符串中的查询前缀可以决定使用哪种 LightRAG 查询模式来生成响应。支持的前缀包括：
|
||||
|
||||
```
|
||||
/local
|
||||
/global
|
||||
/hybrid
|
||||
/naive
|
||||
/mix
|
||||
/bypass
|
||||
```
|
||||
|
||||
例如，聊天消息 "/mix 唐僧有几个徒弟" 将触发 LightRAG 的混合模式查询。没有查询前缀的聊天消息默认会触发混合模式查询。
|
||||
|
||||
"/bypass" 不是 LightRAG 查询模式，它会告诉 API 服务器将查询连同聊天历史直接传递给底层 LLM。因此用户可以使用 LLM 基于聊天历史回答问题。如果您使用 Open WebUI 作为前端，您可以直接切换到普通 LLM 模型，而不是使用 /bypass 前缀。
|
||||
|
||||
## API 密钥和认证
|
||||
|
||||
默认情况下，LightRAG 服务器可以在没有任何认证的情况下访问。我们可以使用 API 密钥或账户凭证配置服务器以确保其安全。
|
||||
|
||||
* API 密钥
|
||||
|
||||
```
|
||||
LIGHTRAG_API_KEY=your-secure-api-key-here
|
||||
WHITELIST_PATHS=/health,/api/*
|
||||
```
|
||||
|
||||
> 健康检查和 Ollama 模拟端点默认不进行 API 密钥检查。
|
||||
|
||||
* 账户凭证（Web 界面需要登录后才能访问）
|
||||
|
||||
LightRAG API 服务器使用基于 HS256 算法的 JWT 认证。要启用安全访问控制，需要以下环境变量：
|
||||
|
||||
```bash
|
||||
# JWT 认证
|
||||
AUTH_USERNAME=admin # 登录名
|
||||
AUTH_PASSWORD=admin123 # 密码
|
||||
TOKEN_SECRET=your-key # JWT 密钥
|
||||
TOKEN_EXPIRE_HOURS=4 # 过期时间
|
||||
```
|
||||
|
||||
> 目前仅支持配置一个管理员账户和密码。尚未开发和实现完整的账户系统。
|
||||
|
||||
如果未配置账户凭证，Web 界面将以访客身份访问系统。因此，即使仅配置了 API 密钥，所有 API 仍然可以通过访客账户访问，这仍然不安全。因此，要保护 API，需要同时配置这两种认证方法。
|
||||
|
||||
## Azure OpenAI 后端配置
|
||||
|
||||
可以使用以下 Azure CLI 命令创建 Azure OpenAI API（您需要先从 [https://docs.microsoft.com/en-us/cli/azure/install-azure-cli](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli) 安装 Azure CLI）：
|
||||
|
||||
```bash
|
||||
# 根据需要更改资源组名称、位置和 OpenAI 资源名称
|
||||
RESOURCE_GROUP_NAME=LightRAG
|
||||
LOCATION=swedencentral
|
||||
RESOURCE_NAME=LightRAG-OpenAI
|
||||
|
||||
az login
|
||||
az group create --name $RESOURCE_GROUP_NAME --location $LOCATION
|
||||
az cognitiveservices account create --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --kind OpenAI --sku S0 --location swedencentral
|
||||
az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME --model-format OpenAI --name $RESOURCE_NAME --deployment-name gpt-4o --model-name gpt-4o --model-version "2024-08-06" --sku-capacity 100 --sku-name "Standard"
|
||||
az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME --model-format OpenAI --name $RESOURCE_NAME --deployment-name text-embedding-3-large --model-name text-embedding-3-large --model-version "1" --sku-capacity 80 --sku-name "Standard"
|
||||
az cognitiveservices account show --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --query "properties.endpoint"
|
||||
az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_NAME
|
||||
```
|
||||
|
||||
最后一个命令的输出将提供 OpenAI API 的端点和密钥。您可以使用这些值在 `.env` 文件中设置环境变量。
|
||||
|
||||
```
|
||||
# .env 中的 Azure OpenAI 配置
|
||||
LLM_BINDING=azure_openai
|
||||
LLM_BINDING_HOST=your-azure-endpoint
|
||||
LLM_MODEL=your-model-deployment-name
|
||||
LLM_BINDING_API_KEY=your-azure-api-key
|
||||
AZURE_OPENAI_API_VERSION=2024-08-01-preview # 可选，默认为最新版本
|
||||
EMBEDDING_BINDING=azure_openai # 如果使用 Azure OpenAI 进行嵌入
|
||||
EMBEDDING_MODEL=your-embedding-deployment-name
|
||||
```
|
||||
|
||||
## LightRAG 服务器详细配置
|
||||
|
||||
API 服务器可以通过三种方式配置（优先级从高到低）：
|
||||
|
||||
* 命令行参数
|
||||
* 环境变量或 .env 文件
|
||||
* Config.ini（仅用于存储配置）
|
||||
|
||||
大多数配置都有默认设置，详细信息请查看示例文件：`.env.example`。数据存储配置也可以通过 config.ini 设置。为方便起见，提供了示例文件 `config.ini.example`。
|
||||
|
||||
### 支持的 LLM 和嵌入后端
|
||||
|
||||
LightRAG 支持绑定到各种 LLM/嵌入后端：
|
||||
|
||||
* ollama
|
||||
* lollms
|
||||
* openai 和 openai 兼容
|
||||
* azure_openai
|
||||
|
||||
使用环境变量 `LLM_BINDING` 或 CLI 参数 `--llm-binding` 选择 LLM 后端类型。使用环境变量 `EMBEDDING_BINDING` 或 CLI 参数 `--embedding-binding` 选择嵌入后端类型。
|
||||
|
||||
### 实体提取配置
|
||||
* ENABLE_LLM_CACHE_FOR_EXTRACT：为实体提取启用 LLM 缓存（默认：true）
|
||||
|
||||
在测试环境中将 `ENABLE_LLM_CACHE_FOR_EXTRACT` 设置为 true 以减少 LLM 调用成本是很常见的做法。
|
||||
|
||||
### 支持的存储类型
|
||||
|
||||
LightRAG 使用 4 种类型的存储用于不同目的：
|
||||
|
||||
* KV_STORAGE：llm 响应缓存、文本块、文档信息
|
||||
* VECTOR_STORAGE：实体向量、关系向量、块向量
|
||||
* GRAPH_STORAGE：实体关系图
|
||||
* DOC_STATUS_STORAGE：文档索引状态
|
||||
|
||||
每种存储类型都有几种实现：
|
||||
|
||||
* KV_STORAGE 支持的实现名称
|
||||
|
||||
```
|
||||
JsonKVStorage JsonFile(默认)
|
||||
MongoKVStorage MogonDB
|
||||
RedisKVStorage Redis
|
||||
TiDBKVStorage TiDB
|
||||
PGKVStorage Postgres
|
||||
OracleKVStorage Oracle
|
||||
```
|
||||
|
||||
* GRAPH_STORAGE 支持的实现名称
|
||||
|
||||
```
|
||||
NetworkXStorage NetworkX(默认)
|
||||
Neo4JStorage Neo4J
|
||||
MongoGraphStorage MongoDB
|
||||
TiDBGraphStorage TiDB
|
||||
AGEStorage AGE
|
||||
GremlinStorage Gremlin
|
||||
PGGraphStorage Postgres
|
||||
OracleGraphStorage Postgres
|
||||
```
|
||||
|
||||
* VECTOR_STORAGE 支持的实现名称
|
||||
|
||||
```
|
||||
NanoVectorDBStorage NanoVector(默认)
|
||||
MilvusVectorDBStorge Milvus
|
||||
ChromaVectorDBStorage Chroma
|
||||
TiDBVectorDBStorage TiDB
|
||||
PGVectorStorage Postgres
|
||||
FaissVectorDBStorage Faiss
|
||||
QdrantVectorDBStorage Qdrant
|
||||
OracleVectorDBStorage Oracle
|
||||
MongoVectorDBStorage MongoDB
|
||||
```
|
||||
|
||||
* DOC_STATUS_STORAGE 支持的实现名称
|
||||
|
||||
```
|
||||
JsonDocStatusStorage JsonFile(默认)
|
||||
PGDocStatusStorage Postgres
|
||||
MongoDocStatusStorage MongoDB
|
||||
```
|
||||
|
||||
### 如何选择存储实现
|
||||
|
||||
您可以通过环境变量选择存储实现。在首次启动 API 服务器之前，您可以将以下环境变量设置为特定的存储实现名称：
|
||||
|
||||
```
|
||||
LIGHTRAG_KV_STORAGE=PGKVStorage
|
||||
LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
|
||||
LIGHTRAG_GRAPH_STORAGE=PGGraphStorage
|
||||
LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
|
||||
```
|
||||
|
||||
在向 LightRAG 添加文档后，您不能更改存储实现选择。目前尚不支持从一个存储实现迁移到另一个存储实现。更多信息请阅读示例 env 文件或 config.ini 文件。
|
||||
|
||||
### LightRag API 服务器命令行选项
|
||||
|
||||
| 参数 | 默认值 | 描述 |
|
||||
|-----------|---------|-------------|
|
||||
| --host | 0.0.0.0 | 服务器主机 |
|
||||
| --port | 9621 | 服务器端口 |
|
||||
| --working-dir | ./rag_storage | RAG 存储的工作目录 |
|
||||
| --input-dir | ./inputs | 包含输入文档的目录 |
|
||||
| --max-async | 4 | 最大异步操作数 |
|
||||
| --max-tokens | 32768 | 最大 token 大小 |
|
||||
| --timeout | 150 | 超时时间（秒）。None 表示无限超时（不推荐） |
|
||||
| --log-level | INFO | 日志级别（DEBUG、INFO、WARNING、ERROR、CRITICAL） |
|
||||
| --verbose | - | 详细调试输出（True、False） |
|
||||
| --key | None | 用于认证的 API 密钥。保护 lightrag 服务器免受未授权访问 |
|
||||
| --ssl | False | 启用 HTTPS |
|
||||
| --ssl-certfile | None | SSL 证书文件路径（如果启用 --ssl 则必需） |
|
||||
| --ssl-keyfile | None | SSL 私钥文件路径（如果启用 --ssl 则必需） |
|
||||
| --top-k | 50 | 要检索的 top-k 项目数；在"local"模式下对应实体，在"global"模式下对应关系。 |
|
||||
| --cosine-threshold | 0.4 | 节点和关系检索的余弦阈值，与 top-k 一起控制节点和关系的检索。 |
|
||||
| --llm-binding | ollama | LLM 绑定类型（lollms、ollama、openai、openai-ollama、azure_openai） |
|
||||
| --embedding-binding | ollama | 嵌入绑定类型（lollms、ollama、openai、azure_openai） |
|
||||
| auto-scan-at-startup | - | 扫描输入目录中的新文件并开始索引 |
|
||||
|
||||
### 使用示例
|
||||
|
||||
#### 使用 ollama 默认本地服务器作为 llm 和嵌入后端运行 Lightrag 服务器
|
||||
|
||||
Ollama 是 llm 和嵌入的默认后端，因此默认情况下您可以不带参数运行 lightrag-server，将使用默认值。确保已安装 ollama 并且正在运行，且默认模型已安装在 ollama 上。
|
||||
|
||||
```bash
|
||||
# 使用 ollama 运行 lightrag，llm 使用 mistral-nemo:latest，嵌入使用 bge-m3:latest
|
||||
lightrag-server
|
||||
|
||||
# 使用认证密钥
|
||||
lightrag-server --key my-key
|
||||
```
|
||||
|
||||
#### 使用 lollms 默认本地服务器作为 llm 和嵌入后端运行 Lightrag 服务器
|
||||
|
||||
```bash
|
||||
# 使用 lollms 运行 lightrag，llm 使用 mistral-nemo:latest，嵌入使用 bge-m3:latest
|
||||
# 在 .env 或 config.ini 中配置 LLM_BINDING=lollms 和 EMBEDDING_BINDING=lollms
|
||||
lightrag-server
|
||||
|
||||
# 使用认证密钥
|
||||
lightrag-server --key my-key
|
||||
```
|
||||
|
||||
#### 使用 openai 服务器作为 llm 和嵌入后端运行 Lightrag 服务器
|
||||
|
||||
```bash
|
||||
# 使用 openai 运行 lightrag，llm 使用 GPT-4o-mini，嵌入使用 text-embedding-3-small
|
||||
# 在 .env 或 config.ini 中配置：
|
||||
# LLM_BINDING=openai
|
||||
# LLM_MODEL=GPT-4o-mini
|
||||
# EMBEDDING_BINDING=openai
|
||||
# EMBEDDING_MODEL=text-embedding-3-small
|
||||
lightrag-server
|
||||
|
||||
# 使用认证密钥
|
||||
lightrag-server --key my-key
|
||||
```
|
||||
|
||||
#### 使用 azure openai 服务器作为 llm 和嵌入后端运行 Lightrag 服务器
|
||||
|
||||
```bash
|
||||
# 使用 azure_openai 运行 lightrag
|
||||
# 在 .env 或 config.ini 中配置：
|
||||
# LLM_BINDING=azure_openai
|
||||
# LLM_MODEL=your-model
|
||||
# EMBEDDING_BINDING=azure_openai
|
||||
# EMBEDDING_MODEL=your-embedding-model
|
||||
lightrag-server
|
||||
|
||||
# 使用认证密钥
|
||||
lightrag-server --key my-key
|
||||
```
|
||||
|
||||
**重要说明：**
|
||||
- 对于 LoLLMs：确保指定的模型已安装在您的 LoLLMs 实例中
|
||||
- 对于 Ollama：确保指定的模型已安装在您的 Ollama 实例中
|
||||
- 对于 OpenAI：确保您已设置 OPENAI_API_KEY 环境变量
|
||||
- 对于 Azure OpenAI：按照先决条件部分所述构建和配置您的服务器
|
||||
|
||||
要获取任何服务器的帮助，使用 --help 标志：
|
||||
```bash
|
||||
lightrag-server --help
|
||||
```
|
||||
|
||||
注意：如果您不需要 API 功能，可以使用以下命令安装不带 API 支持的基本包：
|
||||
```bash
|
||||
pip install lightrag-hku
|
||||
```
|
||||
|
||||
## API 端点
|
||||
|
||||
所有服务器（LoLLMs、Ollama、OpenAI 和 Azure OpenAI）都为 RAG 功能提供相同的 REST API 端点。当 API 服务器运行时，访问：
|
||||
|
||||
- Swagger UI：http://localhost:9621/docs
|
||||
- ReDoc：http://localhost:9621/redoc
|
||||
|
||||
您可以使用提供的 curl 命令或通过 Swagger UI 界面测试 API 端点。确保：
|
||||
|
||||
1. 启动适当的后端服务（LoLLMs、Ollama 或 OpenAI）
|
||||
2. 启动 RAG 服务器
|
||||
3. 使用文档管理端点上传一些文档
|
||||
4. 使用查询端点查询系统
|
||||
5. 如果在输入目录中放入新文件，触发文档扫描
|
||||
|
||||
### 查询端点
|
||||
|
||||
#### POST /query
|
||||
使用不同搜索模式查询 RAG 系统。
|
||||
|
||||
```bash
|
||||
curl -X POST "http://localhost:9621/query" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"query": "您的问题", "mode": "hybrid", ""}'
|
||||
```
|
||||
|
||||
#### POST /query/stream
|
||||
从 RAG 系统流式获取响应。
|
||||
|
||||
```bash
|
||||
curl -X POST "http://localhost:9621/query/stream" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"query": "您的问题", "mode": "hybrid"}'
|
||||
```
|
||||
|
||||
### 文档管理端点
|
||||
|
||||
#### POST /documents/text
|
||||
直接将文本插入 RAG 系统。
|
||||
|
||||
```bash
|
||||
curl -X POST "http://localhost:9621/documents/text" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"text": "您的文本内容", "description": "可选描述"}'
|
||||
```
|
||||
|
||||
#### POST /documents/file
|
||||
向 RAG 系统上传单个文件。
|
||||
|
||||
```bash
|
||||
curl -X POST "http://localhost:9621/documents/file" \
|
||||
-F "file=@/path/to/your/document.txt" \
|
||||
-F "description=可选描述"
|
||||
```
|
||||
|
||||
#### POST /documents/batch
|
||||
一次上传多个文件。
|
||||
|
||||
```bash
|
||||
curl -X POST "http://localhost:9621/documents/batch" \
|
||||
-F "files=@/path/to/doc1.txt" \
|
||||
-F "files=@/path/to/doc2.txt"
|
||||
```
|
||||
|
||||
#### POST /documents/scan
|
||||
|
||||
触发输入目录中新文件的文档扫描。
|
||||
|
||||
```bash
|
||||
curl -X POST "http://localhost:9621/documents/scan" --max-time 1800
|
||||
```
|
||||
|
||||
> 根据所有新文件的预计索引时间调整 max-time。
|
||||
|
||||
#### DELETE /documents
|
||||
|
||||
从 RAG 系统中清除所有文档。
|
||||
|
||||
```bash
|
||||
curl -X DELETE "http://localhost:9621/documents"
|
||||
```
|
||||
|
||||
### Ollama 模拟端点
|
||||
|
||||
#### GET /api/version
|
||||
|
||||
获取 Ollama 版本信息。
|
||||
|
||||
```bash
|
||||
curl http://localhost:9621/api/version
|
||||
```
|
||||
|
||||
#### GET /api/tags
|
||||
|
||||
获取 Ollama 可用模型。
|
||||
|
||||
```bash
|
||||
curl http://localhost:9621/api/tags
|
||||
```
|
||||
|
||||
#### POST /api/chat
|
||||
|
||||
处理聊天补全请求。通过根据查询前缀选择查询模式将用户查询路由到 LightRAG。检测并将 OpenWebUI 会话相关请求（用于元数据生成任务）直接转发给底层 LLM。
|
||||
|
||||
```shell
|
||||
curl -N -X POST http://localhost:9621/api/chat -H "Content-Type: application/json" -d \
|
||||
'{"model":"lightrag:latest","messages":[{"role":"user","content":"猪八戒是谁"}],"stream":true}'
|
||||
```
|
||||
|
||||
> 有关 Ollama API 的更多信息，请访问：[Ollama API 文档](https://github.com/ollama/ollama/blob/main/docs/api.md)
|
||||
|
||||
#### POST /api/generate
|
||||
|
||||
处理生成补全请求。为了兼容性目的，该请求不由 LightRAG 处理，而是由底层 LLM 模型处理。
|
||||
|
||||
### 实用工具端点
|
||||
|
||||
#### GET /health
|
||||
检查服务器健康状况和配置。
|
||||
|
||||
```bash
|
||||
curl "http://localhost:9621/health"
|
||||
|
||||
```
|
||||
Reference in New Issue
Block a user