Merge branch 'main' into feat-node-expand

README.md

@@ -1061,7 +1061,7 @@ Valid modes are:
 | **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`（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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |                                                                                                               |
 | **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`                                                                                                      |

env.example

@@ -50,7 +50,8 @@
 # MAX_TOKEN_SUMMARY=500        # Max tokens for entity or relations summary
 # SUMMARY_LANGUAGE=English
 # 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_BINDING=ollama

lightrag/api/README.md

@@ -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.
 
 ### 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.
 

lightrag/api/docs/LightRagWithPostGRESQL.md

@@ -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
 ```
 
-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
 

lightrag/api/utils_api.py

@@ -364,7 +364,7 @@ def parse_args(is_uvicorn_mode: bool = False) -> argparse.Namespace:
 
     # Inject LLM cache configuration
     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)

lightrag/kg/postgres_impl.py

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

lightrag/lightrag.py

@@ -214,7 +214,7 @@ class LightRAG:
     llm_model_max_token_size: int = field(default=int(os.getenv("MAX_TOKENS", 32768)))
     """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."""
 
     llm_model_kwargs: dict[str, Any] = field(default_factory=dict)
@@ -238,7 +238,7 @@ class LightRAG:
     # 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."""
 
     addon_params: dict[str, Any] = field(
@@ -553,6 +553,7 @@ class LightRAG:
         Args:
             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
+            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 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
@@ -574,6 +575,7 @@ class LightRAG:
         Args:
             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
+            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 is None, this parameter is ignored.
             ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated