feat: 新增ini文件读取数据库配置方式，方便生产环境，修改Lightrag ainsert方法_add_doc_keys获取方式，原来只过滤存在的，但这会让失败的文档无法再次存储，新增--chunk_size和--chunk_overlap_size方便生产环境，新增llm_binding：openai-ollama 方便用openai的同时使用ollama embedding

config.ini

@@ -0,0 +1,13 @@
+[redis]
+uri = redis://localhost:6379
+
+[neo4j]
+uri = #
+username = neo4j
+password = 12345678
+
+[milvus]
+uri = #
+user = root
+password = Milvus
+db_name = lightrag

lightrag/api/lightrag_server.py

@@ -20,6 +20,7 @@ import shutil
 import aiofiles
 from ascii_colors import trace_exception, ASCIIColors
 import os
+import configparser
 
 from fastapi import Depends, Security
 from fastapi.security import APIKeyHeader
@@ -57,6 +58,52 @@ LIGHTRAG_SIZE = 7365960935  # it's a dummy value
 LIGHTRAG_CREATED_AT = "2024-01-15T00:00:00Z"
 LIGHTRAG_DIGEST = "sha256:lightrag"
 
+KV_STORAGE = "JsonKVStorage"
+DOC_STATUS_STORAGE = "JsonDocStatusStorage"
+GRAPH_STORAGE = "NetworkXStorage"
+VECTOR_STORAGE = "NanoVectorDBStorage"
+
+# 读取配置文件
+config = configparser.ConfigParser()
+config.read("config.ini")
+# Redis 配置
+redis_uri = config.get("redis", "uri", fallback=None)
+if redis_uri:
+    os.environ["REDIS_URI"] = redis_uri
+    KV_STORAGE = "RedisKVStorage"
+    DOC_STATUS_STORAGE = "RedisKVStorage"
+
+# Neo4j 配置
+neo4j_uri = config.get("neo4j", "uri", fallback=None)
+neo4j_username = config.get("neo4j", "username", fallback=None)
+neo4j_password = config.get("neo4j", "password", fallback=None)
+if neo4j_uri:
+    os.environ["NEO4J_URI"] = neo4j_uri
+    os.environ["NEO4J_USERNAME"] = neo4j_username
+    os.environ["NEO4J_PASSWORD"] = neo4j_password
+    GRAPH_STORAGE = "Neo4JStorage"
+
+# Milvus 配置
+milvus_uri = config.get("milvus", "uri", fallback=None)
+milvus_user = config.get("milvus", "user", fallback=None)
+milvus_password = config.get("milvus", "password", fallback=None)
+milvus_db_name = config.get("milvus", "db_name", fallback=None)
+if milvus_uri:
+    os.environ["MILVUS_URI"] = milvus_uri
+    os.environ["MILVUS_USER"] = milvus_user
+    os.environ["MILVUS_PASSWORD"] = milvus_password
+    os.environ["MILVUS_DB_NAME"] = milvus_db_name
+    VECTOR_STORAGE = "MilvusVectorDBStorge"
+
+# MongoDB 配置
+mongo_uri = config.get("mongodb", "uri", fallback=None)
+mongo_database = config.get("mongodb", "LightRAG", fallback=None)
+if mongo_uri:
+    os.environ["MONGO_URI"] = mongo_uri
+    os.environ["MONGO_DATABASE"] = mongo_database
+    KV_STORAGE = "MongoKVStorage"
+    DOC_STATUS_STORAGE = "MongoKVStorage"
+
 
 def get_default_host(binding_type: str) -> str:
     default_hosts = {
@@ -337,6 +384,18 @@ def parse_args() -> argparse.Namespace:
         help="Embedding model name (default: from env or bge-m3:latest)",
     )
 
+    parser.add_argument(
+        "--chunk_size",
+        default=1200,
+        help="chunk token size default 1200",
+    )
+
+    parser.add_argument(
+        "--chunk_overlap_size",
+        default=100,
+        help="chunk token size default 1200",
+    )
+
     def timeout_type(value):
         if value is None or value == "None":
             return None
@@ -551,7 +610,7 @@ def get_api_key_dependency(api_key: Optional[str]):
 
 def create_app(args):
     # Verify that bindings arer correctly setup
-    if args.llm_binding not in ["lollms", "ollama", "openai"]:
+    if args.llm_binding not in ["lollms", "ollama", "openai", "openai-ollama"]:
         raise Exception("llm binding not supported")
 
     if args.embedding_binding not in ["lollms", "ollama", "openai"]:
@@ -692,22 +751,32 @@ def create_app(args):
     )
 
     # Initialize RAG
-    if args.llm_binding in ["lollms", "ollama"]:
+    if args.llm_binding in ["lollms", "ollama", "openai-ollama"]:
         rag = LightRAG(
             working_dir=args.working_dir,
             llm_model_func=lollms_model_complete
             if args.llm_binding == "lollms"
-            else ollama_model_complete,
+            else ollama_model_complete
+            if args.llm_binding == "ollama"
+            else openai_alike_model_complete,
             llm_model_name=args.llm_model,
             llm_model_max_async=args.max_async,
             llm_model_max_token_size=args.max_tokens,
+            chunk_token_size=int(args.chunk_size),
+            chunk_overlap_token_size=int(args.chunk_overlap_size),
             llm_model_kwargs={
                 "host": args.llm_binding_host,
                 "timeout": args.timeout,
                 "options": {"num_ctx": args.max_tokens},
                 "api_key": args.llm_binding_api_key,
-            },
+            }
+            if args.llm_binding == "lollms" or args.llm_binding == "ollama"
+            else {},
             embedding_func=embedding_func,
+            kv_storage=KV_STORAGE,
+            graph_storage=GRAPH_STORAGE,
+            vector_storage=VECTOR_STORAGE,
+            doc_status_storage=DOC_STATUS_STORAGE,
         )
     else:
         rag = LightRAG(
@@ -715,7 +784,13 @@ def create_app(args):
             llm_model_func=azure_openai_model_complete
             if args.llm_binding == "azure_openai"
             else openai_alike_model_complete,
+            chunk_token_size=int(args.chunk_size),
+            chunk_overlap_token_size=int(args.chunk_overlap_size),
             embedding_func=embedding_func,
+            kv_storage=KV_STORAGE,
+            graph_storage=GRAPH_STORAGE,
+            vector_storage=VECTOR_STORAGE,
+            doc_status_storage=DOC_STATUS_STORAGE,
         )
 
     async def index_file(file_path: Union[str, Path]) -> None:

lightrag/lightrag.py

@@ -361,7 +361,13 @@ class LightRAG:
         }
 
         # 3. Filter out already processed documents
-        _add_doc_keys = await self.doc_status.filter_keys(list(new_docs.keys()))
+        # _add_doc_keys = await self.doc_status.filter_keys(list(new_docs.keys()))
+        _add_doc_keys = {
+            doc_id
+            for doc_id in new_docs.keys()
+            if (current_doc := await self.doc_status.get_by_id(doc_id)) is None
+            or current_doc["status"] == DocStatus.FAILED
+        }
         new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}
 
         if not new_docs:
@@ -573,7 +579,7 @@ class LightRAG:
         _not_stored_doc_keys = await self.full_docs.filter_keys(list(new_docs.keys()))
         if len(_not_stored_doc_keys) < len(new_docs):
             logger.info(
-                f"Skipping {len(new_docs)-len(_not_stored_doc_keys)} already existing documents"
+                f"Skipping {len(new_docs) - len(_not_stored_doc_keys)} already existing documents"
             )
         new_docs = {k: v for k, v in new_docs.items() if k in _not_stored_doc_keys}
 
@@ -618,7 +624,7 @@ class LightRAG:
             batch_docs = dict(list(new_docs.items())[i : i + batch_size])
             for doc_id, doc in tqdm_async(
                 batch_docs.items(),
-                desc=f"Level 1 - Spliting doc in batch {i//batch_size + 1}",
+                desc=f"Level 1 - Spliting doc in batch {i // batch_size + 1}",
             ):
                 try:
                     # Generate chunks from document

lightrag/storage.py

@@ -445,6 +445,9 @@ class JsonDocStatusStorage(DocStatusStorage):
         await self.index_done_callback()
         return data
 
+    async def get_by_id(self, id: str):
+        return self._data.get(id)
+
     async def get(self, doc_id: str) -> Union[DocProcessingStatus, None]:
         """Get document status by ID"""
         return self._data.get(doc_id)

requirements.txt

@@ -4,6 +4,7 @@ aiofiles
 aiohttp
 aioredis
 asyncpg
+configparser
 
 # database packages
 graspologic
@@ -17,13 +18,19 @@ numpy
 ollama
 openai
 oracledb
+pipmaster
 psycopg-pool
 psycopg[binary,pool]
 pydantic
 pymilvus
 pymongo
 pymysql
+
+
+PyPDF2
+python-docx
 python-dotenv
+python-pptx
 pyvis
 setuptools
 # lmdeploy[all]