Merge branch 'HKUDS:main' into main

examples/graph_visual_with_html.py

@@ -11,6 +11,7 @@
 # Convert NetworkX graph to Pyvis network
 net.from_nx(G)
 
+
 # Add colors and title to nodes
 for node in net.nodes:
     node["color"] = "#{:06x}".format(random.randint(0, 0xFFFFFF))

examples/lightrag_api_ollama_demo.py

@@ -0,0 +1,164 @@
+from fastapi import FastAPI, HTTPException, File, UploadFile
+from pydantic import BaseModel
+import os
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import ollama_embedding, ollama_model_complete
+from lightrag.utils import EmbeddingFunc
+from typing import Optional
+import asyncio
+import nest_asyncio
+import aiofiles
+
+# Apply nest_asyncio to solve event loop issues
+nest_asyncio.apply()
+
+DEFAULT_RAG_DIR = "index_default"
+app = FastAPI(title="LightRAG API", description="API for RAG operations")
+
+DEFAULT_INPUT_FILE = "book.txt"
+INPUT_FILE = os.environ.get("INPUT_FILE", f"{DEFAULT_INPUT_FILE}")
+print(f"INPUT_FILE: {INPUT_FILE}")
+
+# Configure working directory
+WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}")
+print(f"WORKING_DIR: {WORKING_DIR}")
+
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=ollama_model_complete,
+    llm_model_name="gemma2:9b",
+    llm_model_max_async=4,
+    llm_model_max_token_size=8192,
+    llm_model_kwargs={"host": "http://localhost:11434", "options": {"num_ctx": 8192}},
+    embedding_func=EmbeddingFunc(
+        embedding_dim=768,
+        max_token_size=8192,
+        func=lambda texts: ollama_embedding(
+            texts, embed_model="nomic-embed-text", host="http://localhost:11434"
+        ),
+    ),
+)
+
+
+# Data models
+class QueryRequest(BaseModel):
+    query: str
+    mode: str = "hybrid"
+    only_need_context: bool = False
+
+
+class InsertRequest(BaseModel):
+    text: str
+
+
+class Response(BaseModel):
+    status: str
+    data: Optional[str] = None
+    message: Optional[str] = None
+
+
+# API routes
+@app.post("/query", response_model=Response)
+async def query_endpoint(request: QueryRequest):
+    try:
+        loop = asyncio.get_event_loop()
+        result = await loop.run_in_executor(
+            None,
+            lambda: rag.query(
+                request.query,
+                param=QueryParam(
+                    mode=request.mode, only_need_context=request.only_need_context
+                ),
+            ),
+        )
+        return Response(status="success", data=result)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# insert by text
+@app.post("/insert", response_model=Response)
+async def insert_endpoint(request: InsertRequest):
+    try:
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, lambda: rag.insert(request.text))
+        return Response(status="success", message="Text inserted successfully")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# insert by file in payload
+@app.post("/insert_file", response_model=Response)
+async def insert_file(file: UploadFile = File(...)):
+    try:
+        file_content = await file.read()
+        # Read file content
+        try:
+            content = file_content.decode("utf-8")
+        except UnicodeDecodeError:
+            # If UTF-8 decoding fails, try other encodings
+            content = file_content.decode("gbk")
+        # Insert file content
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, lambda: rag.insert(content))
+
+        return Response(
+            status="success",
+            message=f"File content from {file.filename} inserted successfully",
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# insert by local default file
+@app.post("/insert_default_file", response_model=Response)
+@app.get("/insert_default_file", response_model=Response)
+async def insert_default_file():
+    try:
+        # Read file content from book.txt
+        async with aiofiles.open(INPUT_FILE, "r", encoding="utf-8") as file:
+            content = await file.read()
+        print(f"read input file {INPUT_FILE} successfully")
+        # Insert file content
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, lambda: rag.insert(content))
+
+        return Response(
+            status="success",
+            message=f"File content from {INPUT_FILE} inserted successfully",
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8020)
+
+# Usage example
+# To run the server, use the following command in your terminal:
+# python lightrag_api_openai_compatible_demo.py
+
+# Example requests:
+# 1. Query:
+# curl -X POST "http://127.0.0.1:8020/query" -H "Content-Type: application/json" -d '{"query": "your query here", "mode": "hybrid"}'
+
+# 2. Insert text:
+# curl -X POST "http://127.0.0.1:8020/insert" -H "Content-Type: application/json" -d '{"text": "your text here"}'
+
+# 3. Insert file:
+# curl -X POST "http://127.0.0.1:8020/insert_file" -H "Content-Type: application/json" -d '{"file_path": "path/to/your/file.txt"}'
+
+# 4. Health check:
+# curl -X GET "http://127.0.0.1:8020/health"

examples/lightrag_openai_compatible_stream_demo.py

@@ -0,0 +1,55 @@
+import os
+import inspect
+from lightrag import LightRAG
+from lightrag.llm import openai_complete, openai_embedding
+from lightrag.utils import EmbeddingFunc
+from lightrag.lightrag import always_get_an_event_loop
+from lightrag import QueryParam
+
+# WorkingDir
+ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+WORKING_DIR = os.path.join(ROOT_DIR, "dickens")
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+print(f"WorkingDir: {WORKING_DIR}")
+
+api_key = "empty"
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=openai_complete,
+    llm_model_name="qwen2.5-14b-instruct@4bit",
+    llm_model_max_async=4,
+    llm_model_max_token_size=32768,
+    llm_model_kwargs={"base_url": "http://127.0.0.1:1234/v1", "api_key": api_key},
+    embedding_func=EmbeddingFunc(
+        embedding_dim=1024,
+        max_token_size=8192,
+        func=lambda texts: openai_embedding(
+            texts=texts,
+            model="text-embedding-bge-m3",
+            base_url="http://127.0.0.1:1234/v1",
+            api_key=api_key,
+        ),
+    ),
+)
+
+with open("./book.txt", "r", encoding="utf-8") as f:
+    rag.insert(f.read())
+
+resp = rag.query(
+    "What are the top themes in this story?",
+    param=QueryParam(mode="hybrid", stream=True),
+)
+
+
+async def print_stream(stream):
+    async for chunk in stream:
+        if chunk:
+            print(chunk, end="", flush=True)
+
+
+loop = always_get_an_event_loop()
+if inspect.isasyncgen(resp):
+    loop.run_until_complete(print_stream(resp))
+else:
+    print(resp)

lightrag/__init__.py

@@ -1,5 +1,5 @@
 from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
 
-__version__ = "1.0.4"
+__version__ = "1.0.5"
 __author__ = "Zirui Guo"
 __url__ = "https://github.com/HKUDS/LightRAG"

lightrag/lightrag.py

@@ -40,21 +40,37 @@
     NetworkXStorage,
 )
 
-from .kg.neo4j_impl import Neo4JStorage
-
-from .kg.oracle_impl import OracleKVStorage, OracleGraphStorage, OracleVectorDBStorage
-
-from .kg.milvus_impl import MilvusVectorDBStorge
-
-from .kg.mongo_impl import MongoKVStorage
-
 # future KG integrations
 
 # from .kg.ArangoDB_impl import (
 #     GraphStorage as ArangoDBStorage
 # )
 
 
+def lazy_external_import(module_name: str, class_name: str):
+    """Lazily import an external module and return a class from it."""
+
+    def import_class():
+        import importlib
+
+        # Import the module using importlib
+        module = importlib.import_module(module_name)
+
+        # Get the class from the module
+        return getattr(module, class_name)
+
+    # Return the import_class function itself, not its result
+    return import_class
+
+
+Neo4JStorage = lazy_external_import(".kg.neo4j_impl", "Neo4JStorage")
+OracleKVStorage = lazy_external_import(".kg.oracle_impl", "OracleKVStorage")
+OracleGraphStorage = lazy_external_import(".kg.oracle_impl", "OracleGraphStorage")
+OracleVectorDBStorage = lazy_external_import(".kg.oracle_impl", "OracleVectorDBStorage")
+MilvusVectorDBStorge = lazy_external_import(".kg.milvus_impl", "MilvusVectorDBStorge")
+MongoKVStorage = lazy_external_import(".kg.mongo_impl", "MongoKVStorage")
+
+
 def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
     """
     Ensure that there is always an event loop available.
@@ -68,7 +84,7 @@ def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
     try:
         # Try to get the current event loop
         current_loop = asyncio.get_event_loop()
-        if current_loop._closed:
+        if current_loop.is_closed():
             raise RuntimeError("Event loop is closed.")
         return current_loop
 

lightrag/llm.py

@@ -76,11 +76,24 @@ async def openai_complete_if_cache(
         response = await openai_async_client.chat.completions.create(
             model=model, messages=messages, **kwargs
         )
-    content = response.choices[0].message.content
-    if r"\u" in content:
-        content = content.encode("utf-8").decode("unicode_escape")
 
-    return content
+    if hasattr(response, "__aiter__"):
+
+        async def inner():
+            async for chunk in response:
+                content = chunk.choices[0].delta.content
+                if content is None:
+                    continue
+                if r"\u" in content:
+                    content = content.encode("utf-8").decode("unicode_escape")
+                yield content
+
+        return inner()
+    else:
+        content = response.choices[0].message.content
+        if r"\u" in content:
+            content = content.encode("utf-8").decode("unicode_escape")
+        return content
 
 
 @retry(
@@ -306,7 +319,7 @@ async def ollama_model_if_cache(
 
     response = await ollama_client.chat(model=model, messages=messages, **kwargs)
     if stream:
-        """ cannot cache stream response """
+        """cannot cache stream response"""
 
         async def inner():
             async for chunk in response:
@@ -447,6 +460,22 @@ class GPTKeywordExtractionFormat(BaseModel):
     low_level_keywords: List[str]
 
 
+async def openai_complete(
+    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+) -> Union[str, AsyncIterator[str]]:
+    keyword_extraction = kwargs.pop("keyword_extraction", None)
+    if keyword_extraction:
+        kwargs["response_format"] = "json"
+    model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
+    return await openai_complete_if_cache(
+        model_name,
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        **kwargs,
+    )
+
+
 async def gpt_4o_complete(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
 ) -> str:
@@ -890,6 +919,8 @@ async def llm_model_func(
         self, prompt, system_prompt=None, history_messages=[], **kwargs
     ) -> str:
         kwargs.pop("model", None)  # stop from overwriting the custom model name
+        kwargs.pop("keyword_extraction", None)
+        kwargs.pop("mode", None)
         next_model = self._next_model()
         args = dict(
             prompt=prompt,

lightrag/operate.py

@@ -222,7 +222,7 @@ async def _merge_edges_then_upsert(
                 },
             )
     description = await _handle_entity_relation_summary(
-        (src_id, tgt_id), description, global_config
+        f"({src_id}, {tgt_id})", description, global_config
     )
     await knowledge_graph_inst.upsert_edge(
         src_id,
@@ -572,7 +572,6 @@ async def kg_query(
             mode=query_param.mode,
         ),
     )
-
     return response
 
 

lightrag/utils.py

@@ -488,7 +488,7 @@ class CacheData:
 
 
 async def save_to_cache(hashing_kv, cache_data: CacheData):
-    if hashing_kv is None:
+    if hashing_kv is None or hasattr(cache_data.content, "__aiter__"):
         return
 
     mode_cache = await hashing_kv.get_by_id(cache_data.mode) or {}