Merge pull request #521 from magicyuan876/main

功能(lightrag): 添加文档状态跟踪和断点续传支持

README.md

@@ -278,10 +278,25 @@ class QueryParam:
 ### Batch Insert
 
 ```python
-# Batch Insert: Insert multiple texts at once
+# Basic Batch Insert: Insert multiple texts at once
 rag.insert(["TEXT1", "TEXT2",...])
+
+# Batch Insert with custom batch size configuration
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    addon_params={
+        "insert_batch_size": 20  # Process 20 documents per batch
+    }
+)
+rag.insert(["TEXT1", "TEXT2", "TEXT3", ...])  # Documents will be processed in batches of 20
 ```
 
+The `insert_batch_size` parameter in `addon_params` controls how many documents are processed in each batch during insertion. This is useful for:
+- Managing memory usage with large document collections
+- Optimizing processing speed
+- Providing better progress tracking
+- Default value is 10 if not specified
+
 ### Incremental Insert
 
 ```python
@@ -594,7 +609,7 @@ if __name__ == "__main__":
 | **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation |     |
 | **vector\_db\_storage\_cls\_kwargs** | `dict` | Additional parameters for vector database (currently not used) |     |
 | **enable\_llm\_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
-| **addon\_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"]}`: sets example limit and output language | `example_number: all examples, language: English` |
+| **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` |
 | **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
 | **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}` |
 

lightrag/base.py

@@ -1,5 +1,6 @@
 from dataclasses import dataclass, field
-from typing import TypedDict, Union, Literal, Generic, TypeVar
+from typing import TypedDict, Union, Literal, Generic, TypeVar, Optional, Dict, Any
+from enum import Enum
 
 import numpy as np
 
@@ -129,3 +130,42 @@ class BaseGraphStorage(StorageNameSpace):
 
     async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray, list[str]]:
         raise NotImplementedError("Node embedding is not used in lightrag.")
+
+
+class DocStatus(str, Enum):
+    """Document processing status enum"""
+
+    PENDING = "pending"
+    PROCESSING = "processing"
+    PROCESSED = "processed"
+    FAILED = "failed"
+
+
+@dataclass
+class DocProcessingStatus:
+    """Document processing status data structure"""
+
+    content_summary: str  # First 100 chars of document content
+    content_length: int  # Total length of document
+    status: DocStatus  # Current processing status
+    created_at: str  # ISO format timestamp
+    updated_at: str  # ISO format timestamp
+    chunks_count: Optional[int] = None  # Number of chunks after splitting
+    error: Optional[str] = None  # Error message if failed
+    metadata: Dict[str, Any] = field(default_factory=dict)  # Additional metadata
+
+
+class DocStatusStorage(BaseKVStorage):
+    """Base class for document status storage"""
+
+    async def get_status_counts(self) -> Dict[str, int]:
+        """Get counts of documents in each status"""
+        raise NotImplementedError
+
+    async def get_failed_docs(self) -> Dict[str, DocProcessingStatus]:
+        """Get all failed documents"""
+        raise NotImplementedError
+
+    async def get_pending_docs(self) -> Dict[str, DocProcessingStatus]:
+        """Get all pending documents"""
+        raise NotImplementedError

lightrag/kg/age_impl.py

@@ -1,7 +1,8 @@
 import asyncio
 import inspect
 import json
-import os, sys
+import os
+import sys
 from contextlib import asynccontextmanager
 from dataclasses import dataclass
 from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
@@ -22,8 +23,10 @@ from ..base import BaseGraphStorage
 
 if sys.platform.startswith("win"):
     import asyncio.windows_events
+
     asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
 
+
 class AGEQueryException(Exception):
     """Exception for the AGE queries."""
 

lightrag/lightrag.py

@@ -4,7 +4,7 @@ from tqdm.asyncio import tqdm as tqdm_async
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
 from functools import partial
-from typing import Type, cast
+from typing import Type, cast, Dict
 
 from .llm import (
     gpt_4o_mini_complete,
@@ -32,12 +32,14 @@ from .base import (
     BaseVectorStorage,
     StorageNameSpace,
     QueryParam,
+    DocStatus,
 )
 
 from .storage import (
     JsonKVStorage,
     NanoVectorDBStorage,
     NetworkXStorage,
+    JsonDocStatusStorage,
 )
 
 # future KG integrations
@@ -172,6 +174,9 @@ class LightRAG:
     addon_params: dict = field(default_factory=dict)
     convert_response_to_json_func: callable = convert_response_to_json
 
+    # Add new field for document status storage type
+    doc_status_storage: str = field(default="JsonDocStatusStorage")
+
     def __post_init__(self):
         log_file = os.path.join("lightrag.log")
         set_logger(log_file)
@@ -263,7 +268,15 @@ class LightRAG:
             )
         )
 
-    def _get_storage_class(self) -> Type[BaseGraphStorage]:
+        # Initialize document status storage
+        self.doc_status_storage_cls = self._get_storage_class()[self.doc_status_storage]
+        self.doc_status = self.doc_status_storage_cls(
+            namespace="doc_status",
+            global_config=asdict(self),
+            embedding_func=None,
+        )
+
+    def _get_storage_class(self) -> dict:
         return {
             # kv storage
             "JsonKVStorage": JsonKVStorage,
@@ -284,6 +297,7 @@ class LightRAG:
             "TiDBGraphStorage": TiDBGraphStorage,
             "GremlinStorage": GremlinStorage,
             # "ArangoDBStorage": ArangoDBStorage
+            "JsonDocStatusStorage": JsonDocStatusStorage,
         }
 
     def insert(self, string_or_strings):
@@ -291,71 +305,139 @@ class LightRAG:
         return loop.run_until_complete(self.ainsert(string_or_strings))
 
     async def ainsert(self, string_or_strings):
-        update_storage = False
+        """Insert documents with checkpoint support
-        try:
-            if isinstance(string_or_strings, str):
-                string_or_strings = [string_or_strings]
 
-            new_docs = {
+        Args:
-                compute_mdhash_id(c.strip(), prefix="doc-"): {"content": c.strip()}
+            string_or_strings: Single document string or list of document strings
-                for c in string_or_strings
+        """
+        if isinstance(string_or_strings, str):
+            string_or_strings = [string_or_strings]
+
+        # 1. Remove duplicate contents from the list
+        unique_contents = list(set(doc.strip() for doc in string_or_strings))
+
+        # 2. Generate document IDs and initial status
+        new_docs = {
+            compute_mdhash_id(content, prefix="doc-"): {
+                "content": content,
+                "content_summary": self._get_content_summary(content),
+                "content_length": len(content),
+                "status": DocStatus.PENDING,
+                "created_at": datetime.now().isoformat(),
+                "updated_at": datetime.now().isoformat(),
             }
-            _add_doc_keys = await self.full_docs.filter_keys(list(new_docs.keys()))
+            for content in unique_contents
-            new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}
+        }
-            if not len(new_docs):
-                logger.warning("All docs are already in the storage")
-                return
-            update_storage = True
-            logger.info(f"[New Docs] inserting {len(new_docs)} docs")
 
-            inserting_chunks = {}
+        # 3. Filter out already processed documents
-            for doc_key, doc in tqdm_async(
+        _add_doc_keys = await self.doc_status.filter_keys(list(new_docs.keys()))
-                new_docs.items(), desc="Chunking documents", unit="doc"
+        new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}
+
+        if not new_docs:
+            logger.info("All documents have been processed or are duplicates")
+            return
+
+        logger.info(f"Processing {len(new_docs)} new unique documents")
+
+        # Process documents in batches
+        batch_size = self.addon_params.get("insert_batch_size", 10)
+        for i in range(0, len(new_docs), batch_size):
+            batch_docs = dict(list(new_docs.items())[i : i + batch_size])
+
+            for doc_id, doc in tqdm_async(
+                batch_docs.items(), desc=f"Processing batch {i//batch_size + 1}"
             ):
-                chunks = {
+                try:
-                    compute_mdhash_id(dp["content"], prefix="chunk-"): {
+                    # Update status to processing
-                        **dp,
+                    doc_status = {
-                        "full_doc_id": doc_key,
+                        "content_summary": doc["content_summary"],
+                        "content_length": doc["content_length"],
+                        "status": DocStatus.PROCESSING,
+                        "created_at": doc["created_at"],
+                        "updated_at": datetime.now().isoformat(),
                     }
-                    for dp in chunking_by_token_size(
+                    await self.doc_status.upsert({doc_id: doc_status})
-                        doc["content"],
+
-                        overlap_token_size=self.chunk_overlap_token_size,
+                    # Generate chunks from document
-                        max_token_size=self.chunk_token_size,
+                    chunks = {
-                        tiktoken_model=self.tiktoken_model_name,
+                        compute_mdhash_id(dp["content"], prefix="chunk-"): {
+                            **dp,
+                            "full_doc_id": doc_id,
+                        }
+                        for dp in chunking_by_token_size(
+                            doc["content"],
+                            overlap_token_size=self.chunk_overlap_token_size,
+                            max_token_size=self.chunk_token_size,
+                            tiktoken_model=self.tiktoken_model_name,
+                        )
+                    }
+
+                    # Update status with chunks information
+                    doc_status.update(
+                        {
+                            "chunks_count": len(chunks),
+                            "updated_at": datetime.now().isoformat(),
+                        }
                     )
-                }
+                    await self.doc_status.upsert({doc_id: doc_status})
-                inserting_chunks.update(chunks)
-            _add_chunk_keys = await self.text_chunks.filter_keys(
-                list(inserting_chunks.keys())
-            )
-            inserting_chunks = {
-                k: v for k, v in inserting_chunks.items() if k in _add_chunk_keys
-            }
-            if not len(inserting_chunks):
-                logger.warning("All chunks are already in the storage")
-                return
-            logger.info(f"[New Chunks] inserting {len(inserting_chunks)} chunks")
 
-            await self.chunks_vdb.upsert(inserting_chunks)
+                    try:
+                        # Store chunks in vector database
+                        await self.chunks_vdb.upsert(chunks)
 
-            logger.info("[Entity Extraction]...")
+                        # Extract and store entities and relationships
-            maybe_new_kg = await extract_entities(
+                        maybe_new_kg = await extract_entities(
-                inserting_chunks,
+                            chunks,
-                knowledge_graph_inst=self.chunk_entity_relation_graph,
+                            knowledge_graph_inst=self.chunk_entity_relation_graph,
-                entity_vdb=self.entities_vdb,
+                            entity_vdb=self.entities_vdb,
-                relationships_vdb=self.relationships_vdb,
+                            relationships_vdb=self.relationships_vdb,
-                global_config=asdict(self),
+                            global_config=asdict(self),
-            )
+                        )
-            if maybe_new_kg is None:
-                logger.warning("No new entities and relationships found")
-                return
-            self.chunk_entity_relation_graph = maybe_new_kg
 
-            await self.full_docs.upsert(new_docs)
+                        if maybe_new_kg is None:
-            await self.text_chunks.upsert(inserting_chunks)
+                            raise Exception(
-        finally:
+                                "Failed to extract entities and relationships"
-            if update_storage:
+                            )
-                await self._insert_done()
+
+                        self.chunk_entity_relation_graph = maybe_new_kg
+
+                        # Store original document and chunks
+                        await self.full_docs.upsert(
+                            {doc_id: {"content": doc["content"]}}
+                        )
+                        await self.text_chunks.upsert(chunks)
+
+                        # Update status to processed
+                        doc_status.update(
+                            {
+                                "status": DocStatus.PROCESSED,
+                                "updated_at": datetime.now().isoformat(),
+                            }
+                        )
+                        await self.doc_status.upsert({doc_id: doc_status})
+
+                    except Exception as e:
+                        # Mark as failed if any step fails
+                        doc_status.update(
+                            {
+                                "status": DocStatus.FAILED,
+                                "error": str(e),
+                                "updated_at": datetime.now().isoformat(),
+                            }
+                        )
+                        await self.doc_status.upsert({doc_id: doc_status})
+                        raise e
+
+                except Exception as e:
+                    import traceback
+
+                    error_msg = f"Failed to process document {doc_id}: {str(e)}\n{traceback.format_exc()}"
+                    logger.error(error_msg)
+                    continue
+
+                finally:
+                    # Ensure all indexes are updated after each document
+                    await self._insert_done()
 
     async def _insert_done(self):
         tasks = []
@@ -591,3 +673,26 @@ class LightRAG:
                 continue
             tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback())
         await asyncio.gather(*tasks)
+
+    def _get_content_summary(self, content: str, max_length: int = 100) -> str:
+        """Get summary of document content
+
+        Args:
+            content: Original document content
+            max_length: Maximum length of summary
+
+        Returns:
+            Truncated content with ellipsis if needed
+        """
+        content = content.strip()
+        if len(content) <= max_length:
+            return content
+        return content[:max_length] + "..."
+
+    async def get_processing_status(self) -> Dict[str, int]:
+        """Get current document processing status counts
+
+        Returns:
+            Dict with counts for each status
+        """
+        return await self.doc_status.get_status_counts()

lightrag/storage.py

@@ -3,7 +3,7 @@ import html
 import os
 from tqdm.asyncio import tqdm as tqdm_async
 from dataclasses import dataclass
-from typing import Any, Union, cast
+from typing import Any, Union, cast, Dict
 import networkx as nx
 import numpy as np
 from nano_vectordb import NanoVectorDB
@@ -19,6 +19,9 @@ from .base import (
     BaseGraphStorage,
     BaseKVStorage,
     BaseVectorStorage,
+    DocStatus,
+    DocProcessingStatus,
+    DocStatusStorage,
 )
 
 
@@ -315,3 +318,47 @@ class NetworkXStorage(BaseGraphStorage):
 
         nodes_ids = [self._graph.nodes[node_id]["id"] for node_id in nodes]
         return embeddings, nodes_ids
+
+
+@dataclass
+class JsonDocStatusStorage(DocStatusStorage):
+    """JSON implementation of document status storage"""
+
+    def __post_init__(self):
+        working_dir = self.global_config["working_dir"]
+        self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
+        self._data = load_json(self._file_name) or {}
+        logger.info(f"Loaded document status storage with {len(self._data)} records")
+
+    async def filter_keys(self, data: list[str]) -> set[str]:
+        """Return keys that don't exist in storage"""
+        return set([k for k in data if k not in self._data])
+
+    async def get_status_counts(self) -> Dict[str, int]:
+        """Get counts of documents in each status"""
+        counts = {status: 0 for status in DocStatus}
+        for doc in self._data.values():
+            counts[doc["status"]] += 1
+        return counts
+
+    async def get_failed_docs(self) -> Dict[str, DocProcessingStatus]:
+        """Get all failed documents"""
+        return {k: v for k, v in self._data.items() if v["status"] == DocStatus.FAILED}
+
+    async def get_pending_docs(self) -> Dict[str, DocProcessingStatus]:
+        """Get all pending documents"""
+        return {k: v for k, v in self._data.items() if v["status"] == DocStatus.PENDING}
+
+    async def index_done_callback(self):
+        """Save data to file after indexing"""
+        write_json(self._data, self._file_name)
+
+    async def upsert(self, data: dict[str, dict]):
+        """Update or insert document status
+
+        Args:
+            data: Dictionary of document IDs and their status data
+        """
+        self._data.update(data)
+        await self.index_done_callback()
+        return data