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Merge branch 'main' into feat-node-expand

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yangdx 2025-03-17 00:08:12 +08:00
commit 1c19290979
7 changed files with 13 additions and 10 deletions
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2
README.md
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@ -1061,7 +1061,7 @@ Valid modes are:
| **llm\_model\_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` | | **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\_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`（default value changed by env var MAX_TOKENS) | | **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\_max\_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `4`（default value changed by env var MAX_ASYNC) |
| **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation | | | **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation | |
| **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) | | **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** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |

3
env.example
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@ -50,7 +50,8 @@
# MAX_TOKEN_SUMMARY=500 # Max tokens for entity or relations summary # MAX_TOKEN_SUMMARY=500 # Max tokens for entity or relations summary
# SUMMARY_LANGUAGE=English # SUMMARY_LANGUAGE=English
# MAX_EMBED_TOKENS=8192 # MAX_EMBED_TOKENS=8192
# ENABLE_LLM_CACHE_FOR_EXTRACT=false # Enable LLM cache for entity extraction, defaults to false # ENABLE_LLM_CACHE_FOR_EXTRACT=true # Enable LLM cache for entity extraction
# MAX_PARALLEL_INSERT=2 # Maximum number of parallel processing documents in pipeline
### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal) ### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
LLM_BINDING=ollama LLM_BINDING=ollama

2
lightrag/api/README.md
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@ -224,7 +224,7 @@ LightRAG supports binding to various LLM/Embedding backends:
Use environment variables `LLM_BINDING` or CLI argument `--llm-binding` to select LLM backend type. Use environment variables `EMBEDDING_BINDING` or CLI argument `--embedding-binding` to select LLM backend type. Use environment variables `LLM_BINDING` or CLI argument `--llm-binding` to select LLM backend type. Use environment variables `EMBEDDING_BINDING` or CLI argument `--embedding-binding` to select LLM backend type.
### Entity Extraction Configuration ### Entity Extraction Configuration
* ENABLE_LLM_CACHE_FOR_EXTRACT: Enable LLM cache for entity extraction (default: false) * ENABLE_LLM_CACHE_FOR_EXTRACT: Enable LLM cache for entity extraction (default: true)
It's very common to set `ENABLE_LLM_CACHE_FOR_EXTRACT` to true for test environment to reduce the cost of LLM calls. It's very common to set `ENABLE_LLM_CACHE_FOR_EXTRACT` to true for test environment to reduce the cost of LLM calls.

2
lightrag/api/docs/LightRagWithPostGRESQL.md
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@ -141,7 +141,7 @@ Start the LightRAG server using specified options:
lightrag-server --port 9621 --key sk-somepassword --kv-storage PGKVStorage --graph-storage PGGraphStorage --vector-storage PGVectorStorage --doc-status-storage PGDocStatusStorage lightrag-server --port 9621 --key sk-somepassword --kv-storage PGKVStorage --graph-storage PGGraphStorage --vector-storage PGVectorStorage --doc-status-storage PGDocStatusStorage
``` ```
Replace `the-port-number` with your desired port number (default is 9621) and `your-secret-key` with a secure key. Replace the `port` number with your desired port number (default is 9621) and `your-secret-key` with a secure key.
## Conclusion ## Conclusion

2
lightrag/api/utils_api.py
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@ -364,7 +364,7 @@ def parse_args(is_uvicorn_mode: bool = False) -> argparse.Namespace:
# Inject LLM cache configuration # Inject LLM cache configuration
args.enable_llm_cache_for_extract = get_env_value( args.enable_llm_cache_for_extract = get_env_value(
"ENABLE_LLM_CACHE_FOR_EXTRACT", False, bool "ENABLE_LLM_CACHE_FOR_EXTRACT", True, bool
) )
# Select Document loading tool (DOCLING, DEFAULT) # Select Document loading tool (DOCLING, DEFAULT)

6
lightrag/kg/postgres_impl.py
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@ -755,7 +755,7 @@ class PGDocStatusStorage(DocStatusStorage):
result = await self.db.query(sql, params, True) result = await self.db.query(sql, params, True)
docs_by_status = { docs_by_status = {
element["id"]: DocProcessingStatus( element["id"]: DocProcessingStatus(
content=result[0]["content"], content=element["content"],
content_summary=element["content_summary"], content_summary=element["content_summary"],
content_length=element["content_length"], content_length=element["content_length"],
status=element["status"], status=element["status"],
@ -1556,7 +1556,7 @@ TABLES = {
content_vector VECTOR, content_vector VECTOR,
create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP, create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
update_time TIMESTAMP, update_time TIMESTAMP,
chunk_id VARCHAR(255) NULL, chunk_id TEXT NULL,
CONSTRAINT LIGHTRAG_VDB_ENTITY_PK PRIMARY KEY (workspace, id) CONSTRAINT LIGHTRAG_VDB_ENTITY_PK PRIMARY KEY (workspace, id)
)""" )"""
}, },
@ -1570,7 +1570,7 @@ TABLES = {
content_vector VECTOR, content_vector VECTOR,
create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP, create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
update_time TIMESTAMP, update_time TIMESTAMP,
chunk_id VARCHAR(255) NULL, chunk_id TEXT NULL,
CONSTRAINT LIGHTRAG_VDB_RELATION_PK PRIMARY KEY (workspace, id) CONSTRAINT LIGHTRAG_VDB_RELATION_PK PRIMARY KEY (workspace, id)
)""" )"""
}, },

6
lightrag/lightrag.py
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@ -214,7 +214,7 @@ class LightRAG:
llm_model_max_token_size: int = field(default=int(os.getenv("MAX_TOKENS", 32768))) llm_model_max_token_size: int = field(default=int(os.getenv("MAX_TOKENS", 32768)))
"""Maximum number of tokens allowed per LLM response.""" """Maximum number of tokens allowed per LLM response."""
llm_model_max_async: int = field(default=int(os.getenv("MAX_ASYNC", 16))) llm_model_max_async: int = field(default=int(os.getenv("MAX_ASYNC", 4)))
"""Maximum number of concurrent LLM calls.""" """Maximum number of concurrent LLM calls."""
llm_model_kwargs: dict[str, Any] = field(default_factory=dict) llm_model_kwargs: dict[str, Any] = field(default_factory=dict)
@ -238,7 +238,7 @@ class LightRAG:
# Extensions # Extensions
# --- # ---
max_parallel_insert: int = field(default=int(os.getenv("MAX_PARALLEL_INSERT", 20))) max_parallel_insert: int = field(default=int(os.getenv("MAX_PARALLEL_INSERT", 2)))
"""Maximum number of parallel insert operations.""" """Maximum number of parallel insert operations."""
addon_params: dict[str, Any] = field( addon_params: dict[str, Any] = field(
@ -553,6 +553,7 @@ class LightRAG:
Args: Args:
input: Single document string or list of document strings input: Single document string or list of document strings
split_by_character: if split_by_character is not None, split the string by character, if chunk longer than split_by_character: if split_by_character is not None, split the string by character, if chunk longer than
chunk_token_size, it will be split again by token size.
split_by_character_only: if split_by_character_only is True, split the string by character only, when split_by_character_only: if split_by_character_only is True, split the string by character only, when
split_by_character is None, this parameter is ignored. split_by_character is None, this parameter is ignored.
ids: single string of the document ID or list of unique document IDs, if not provided, MD5 hash IDs will be generated ids: single string of the document ID or list of unique document IDs, if not provided, MD5 hash IDs will be generated
@ -574,6 +575,7 @@ class LightRAG:
Args: Args:
input: Single document string or list of document strings input: Single document string or list of document strings
split_by_character: if split_by_character is not None, split the string by character, if chunk longer than split_by_character: if split_by_character is not None, split the string by character, if chunk longer than
chunk_token_size, it will be split again by token size.
split_by_character_only: if split_by_character_only is True, split the string by character only, when split_by_character_only: if split_by_character_only is True, split the string by character only, when
split_by_character is None, this parameter is ignored. split_by_character is None, this parameter is ignored.
ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated