gardel/lightrag
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update sample env file and documentation

Browse Source 
- Change COSINE_THRESHOLD to 0.4
- Adjust TOP_K to 50
- Enhance API README details
This commit is contained in:
yangdx
2025-01-29 23:45:20 +08:00
parent e29682eef8
commit 46c9c7d95b
3 changed files with 24 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
.env.example
View File

@@ -14,8 +14,8 @@ MAX_EMBED_TOKENS=8192
#HISTORY_TURNS=3
#CHUNK_SIZE=1200
#CHUNK_OVERLAP_SIZE=100
#COSINE_THRESHOLD=0.2
#TOP_K=50
#COSINE_THRESHOLD=0.4 # 0.2 while not running API server
#TOP_K=50 # 60 while not running API server
# LLM Configuration (Use valid host. For local services, you can use host.docker.internal)
# Ollama example

9
README.md
View File

@@ -360,6 +360,8 @@ class QueryParam:
max_token_for_local_context: int = 4000
```
> default value of Top_k can be change by environment variables TOP_K.
### Batch Insert
```python
@@ -730,10 +732,10 @@ if __name__ == "__main__":
| **embedding\_func\_max\_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
| **llm\_model\_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` |
| **llm\_model\_name** | `str` | LLM model name for generation | `meta-llama/Llama-3.2-1B-Instruct` |
| **llm\_model\_max\_token\_size** | `int` | Maximum token size for LLM generation (affects entity relation summaries) | `32768` |
| **llm\_model\_max\_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `16` |
| **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) |
| **llm\_model\_max\_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `16`（default value changed by env var MAX_ASYNC) |
| **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation | |
| **vector\_db\_storage\_cls\_kwargs** | `dict` | Additional parameters for vector database (currently not used) | |
| **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) |
| **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 | `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` |
@@ -741,6 +743,7 @@ if __name__ == "__main__":
| **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}` |
### Error Handling
<details>
<summary>Click to view error handling details</summary>

26
lightrag/api/README.md
View File

@@ -98,6 +98,8 @@ After starting the lightrag-server, you can add an Ollama-type connection in the
LightRAG can be configured using either command-line arguments or environment variables. When both are provided, command-line arguments take precedence over environment variables.
For better performance, the API server's default values for TOP_K and COSINE_THRESHOLD are set to 50 and 0.4 respectively. If COSINE_THRESHOLD remains at its default value of 0.2 in LightRAG, many irrelevant entities and relations would be retrieved and sent to the LLM.
### Environment Variables
You can configure LightRAG using environment variables by creating a `.env` file in your project root directory. Here's a complete example of available environment variables:
@@ -111,6 +113,17 @@ PORT=9621
WORKING_DIR=/app/data/rag_storage
INPUT_DIR=/app/data/inputs
# RAG Configuration
MAX_ASYNC=4
MAX_TOKENS=32768
EMBEDDING_DIM=1024
MAX_EMBED_TOKENS=8192
#HISTORY_TURNS=3
#CHUNK_SIZE=1200
#CHUNK_OVERLAP_SIZE=100
#COSINE_THRESHOLD=0.4
#TOP_K=50
# LLM Configuration
LLM_BINDING=ollama
LLM_BINDING_HOST=http://localhost:11434
@@ -124,14 +137,8 @@ EMBEDDING_BINDING=ollama
EMBEDDING_BINDING_HOST=http://localhost:11434
EMBEDDING_MODEL=bge-m3:latest
# RAG Configuration
MAX_ASYNC=4
MAX_TOKENS=32768
EMBEDDING_DIM=1024
MAX_EMBED_TOKENS=8192
# Security
LIGHTRAG_API_KEY=
#LIGHTRAG_API_KEY=you-api-key-for-accessing-LightRAG
# Logging
LOG_LEVEL=INFO
@@ -186,10 +193,9 @@ PORT=7000 python lightrag.py
| --ssl | False | Enable HTTPS |
| --ssl-certfile | None | Path to SSL certificate file (required if --ssl is enabled) |
| --ssl-keyfile | None | Path to SSL private key file (required if --ssl is enabled) |
| --top-k | 50 | Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode. |
| --cosine-threshold | 0.4 | The cossine threshold for nodes and relations retrieval, works with top-k to control the retrieval of nodes and relations. |
For protecting the server using an authentication key, you can also use an environment variable named `LIGHTRAG_API_KEY`.
### Example Usage
#### Running a Lightrag server with ollama default local server as llm and embedding backends