Remove api demo (reference to LightRAG Server instead)

examples/lightrag_api_ollama_demo.py

@@ -1,188 +0,0 @@
-from fastapi import FastAPI, HTTPException, File, UploadFile
-from contextlib import asynccontextmanager
-from pydantic import BaseModel
-import os
-from lightrag import LightRAG, QueryParam
-from lightrag.llm.ollama import ollama_embed, ollama_model_complete
-from lightrag.utils import EmbeddingFunc
-from typing import Optional
-import asyncio
-import nest_asyncio
-import aiofiles
-from lightrag.kg.shared_storage import initialize_pipeline_status
-
-# Apply nest_asyncio to solve event loop issues
-nest_asyncio.apply()
-
-DEFAULT_RAG_DIR = "index_default"
-
-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)
-
-
-async def init():
-    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_embed(
-                texts, embed_model="nomic-embed-text", host="http://localhost:11434"
-            ),
-        ),
-    )
-
-    # Add initialization code
-    await rag.initialize_storages()
-    await initialize_pipeline_status()
-
-    return rag
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    global rag
-    rag = await init()
-    print("done!")
-    yield
-
-
-app = FastAPI(
-    title="LightRAG API", description="API for RAG operations", lifespan=lifespan
-)
-
-
-# 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: multipart/form-data" -F "file=@path/to/your/file.txt"
-
-# 4. Health check:
-# curl -X GET "http://127.0.0.1:8020/health"

examples/lightrag_api_openai_compatible_demo.py

@@ -1,204 +0,0 @@
-from fastapi import FastAPI, HTTPException, File, UploadFile
-from contextlib import asynccontextmanager
-from pydantic import BaseModel
-import os
-from lightrag import LightRAG, QueryParam
-from lightrag.llm.openai import openai_complete_if_cache, openai_embed
-from lightrag.utils import EmbeddingFunc
-import numpy as np
-from typing import Optional
-import asyncio
-import nest_asyncio
-from lightrag.kg.shared_storage import initialize_pipeline_status
-
-# 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")
-
-# Configure working directory
-WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}")
-print(f"WORKING_DIR: {WORKING_DIR}")
-LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4o-mini")
-print(f"LLM_MODEL: {LLM_MODEL}")
-EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
-print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
-EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
-print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
-BASE_URL = os.environ.get("BASE_URL", "https://api.openai.com/v1")
-print(f"BASE_URL: {BASE_URL}")
-API_KEY = os.environ.get("API_KEY", "xxxxxxxx")
-print(f"API_KEY: {API_KEY}")
-
-if not os.path.exists(WORKING_DIR):
-    os.mkdir(WORKING_DIR)
-
-
-# LLM model function
-
-
-async def llm_model_func(
-    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
-) -> str:
-    return await openai_complete_if_cache(
-        model=LLM_MODEL,
-        prompt=prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        base_url=BASE_URL,
-        api_key=API_KEY,
-        **kwargs,
-    )
-
-
-# Embedding function
-
-
-async def embedding_func(texts: list[str]) -> np.ndarray:
-    return await openai_embed(
-        texts=texts,
-        model=EMBEDDING_MODEL,
-        base_url=BASE_URL,
-        api_key=API_KEY,
-    )
-
-
-async def get_embedding_dim():
-    test_text = ["This is a test sentence."]
-    embedding = await embedding_func(test_text)
-    embedding_dim = embedding.shape[1]
-    print(f"{embedding_dim=}")
-    return embedding_dim
-
-
-# Initialize RAG instance
-async def init():
-    embedding_dimension = await get_embedding_dim()
-
-    rag = LightRAG(
-        working_dir=WORKING_DIR,
-        llm_model_func=llm_model_func,
-        embedding_func=EmbeddingFunc(
-            embedding_dim=embedding_dimension,
-            max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
-            func=embedding_func,
-        ),
-    )
-
-    await rag.initialize_storages()
-    await initialize_pipeline_status()
-
-    return rag
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    global rag
-    rag = await init()
-    print("done!")
-    yield
-
-
-app = FastAPI(
-    title="LightRAG API", description="API for RAG operations", lifespan=lifespan
-)
-
-# 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))
-
-
-@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))
-
-
-@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))
-
-
-@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: multipart/form-data" -F "file=@path/to/your/file.txt"
-
-# 4. Health check:
-# curl -X GET "http://127.0.0.1:8020/health"