fix: 1. 配置切换 ollama -> vllm+TEI 2. 文本模型变更 ollama-cloud -> vllm 3. 增加 vl模型、rerank模型 4.增加qa知识上传方法 4.增加知识详情方法

.env.example

@@ -4,15 +4,28 @@ APP_VERSION="1.0"
 HOST="0.0.0.0"
 PORT=9600
 
-# LLM Configuration
+# LLM（Text） Configuration
-LLM_BINDING=ollama
+LLM_BINDING=vllm # ollama, vllm, openai
-LLM_BINDING_HOST=http://localhost:11434
+LLM_BINDING_HOST=http://192.168.6.115:8002/v1 # vLLM OpenAI API base
-LLM_MODEL=deepseek-v3.2:cloud
+LLM_MODEL=qwen2.5-7b-awq
+LLM_KEY=EMPTY # vLLM default key
+
+# LLM（Vision） Configuration
+VL_BINDING_HOST=http://192.168.6.115:8001/v1
+VL_MODEL=qwen2.5-vl-3b-awq
+VL_KEY=EMPTY
 
 # Embedding Configuration
-EMBEDDING_BINDING=ollama
+EMBEDDING_BINDING=tei # ollama, tei, openai
-EMBEDDING_BINDING_HOST=http://localhost:11434
+EMBEDDING_BINDING_HOST=http://192.168.6.115:8003 # TEI usually exposes /embed
-EMBEDDING_MODEL=bge-m3
+EMBEDDING_MODEL=BAAI/bge-m3 # model id in TEI
+EMBEDDING_KEY=EMPTY
+
+# Rerank - TEI
+RERANK_ENABLED=True
+RERANK_BINDING_HOST=http://192.168.6.115:8004
+RERANK_MODEL=BAAI/bge-reranker-v2-m3
+RERANK_KEY=EMPTY
 
 # Storage
 DATA_DIR=./index_data
@@ -20,3 +33,4 @@ DATA_DIR=./index_data
 # RAG Configuration
 EMBEDDING_DIM=1024
 MAX_TOKEN_SIZE=8192
+MAX_RAG_INSTANCES=5  # 最大活跃 RAG 实例数

app/api/routes.py

@@ -1,15 +1,14 @@
 import json
 import logging
 import os
-import io
 from fastapi import APIRouter, UploadFile, File, HTTPException, Body, Header, Depends
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
-from pypdf import PdfReader
 from lightrag import LightRAG, QueryParam
 from app.core.rag import get_rag_manager, llm_func
 from app.core.prompts import CUSTOM_RAG_RESPONSE_PROMPT
 from app.config import settings
+from app.core.ingest import process_pdf_with_images
 
 router = APIRouter()
 
@@ -41,6 +40,10 @@ class IngestResponse(BaseModel):
     status: str
     message: str
 
+class QAPair(BaseModel):
+    question: str
+    answer: str
+
 # ==========================================
 # 接口实现
 # ==========================================
@@ -67,7 +70,7 @@ async def query_knowledge_base(
             mode=request.mode, 
             top_k=request.top_k,
             stream=request.stream,
-            enable_rerank=False  # 显式关闭 Rerank 以消除 Warning
+            enable_rerank=settings.RERANK_ENABLED
         )
 
         # 处理流式输出 (SSE 协议)
@@ -85,7 +88,7 @@ async def query_knowledge_base(
                     mode=request.mode, 
                     top_k=request.top_k,
                     only_need_context=True,
-                    enable_rerank=False  # 显式关闭 Rerank 以消除 Warning
+                    enable_rerank=settings.RERANK_ENABLED
                 )
                 
                 # 获取上下文 (这步耗时较长，包含图遍历)
@@ -158,6 +161,55 @@ async def ingest_text(
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
+@router.post("/ingest/batch_qa")
+async def ingest_batch_qa(
+    qa_list: list[QAPair],
+    rag: LightRAG = Depends(get_current_rag)
+):
+    """
+    批量摄入 QA 对
+    - 自动将 QA 格式化为语义连贯的文本块
+    - 自动合并短 QA 以优化索引效率
+    """
+    if not qa_list:
+        return {"status": "skipped", "message": "Empty QA list"}
+
+    try:
+        # 1. 格式化并合并文本
+        batch_text = ""
+        current_batch_size = 0
+        MAX_BATCH_CHARS = 2000  # 约 1000 tokens，保守估计
+        
+        inserted_count = 0
+        
+        for qa in qa_list:
+            # 格式化单条 QA
+            entry = f"--- Q&A Entry ---\nQuestion: {qa.question}\nAnswer: {qa.answer}\n\n"
+            entry_len = len(entry)
+            
+            # 如果当前批次过大，先提交一次
+            if current_batch_size + entry_len > MAX_BATCH_CHARS:
+                await rag.ainsert(batch_text)
+                batch_text = ""
+                current_batch_size = 0
+                inserted_count += 1
+            
+            batch_text += entry
+            current_batch_size += entry_len
+            
+        # 提交剩余的文本
+        if batch_text:
+            await rag.ainsert(batch_text)
+            inserted_count += 1
+            
+        return {
+            "status": "success", 
+            "message": f"Successfully processed {len(qa_list)} QA pairs into {inserted_count} text chunks."
+        }
+    except Exception as e:
+        logging.error(f"QA批量导入失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
 @router.post("/ingest/file", response_model=IngestResponse)
 async def upload_file(
     file: UploadFile = File(...),
@@ -176,10 +228,8 @@ async def upload_file(
         
         # 根据文件类型解析
         if filename.endswith(".pdf"):
-            # PDF 解析逻辑
+            # PDF 解析逻辑 (支持图文)
-            pdf_reader = PdfReader(io.BytesIO(file_bytes))
+            content = await process_pdf_with_images(file_bytes)
-            for page in pdf_reader.pages:
-                content += page.extract_text() + "\n"
         elif filename.endswith(".txt") or filename.endswith(".md"):
             # 文本文件直接解码
             content = file_bytes.decode("utf-8")
@@ -247,6 +297,40 @@ async def list_documents(
         logging.error(f"获取文档列表失败: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
+@router.get("/documents/{doc_id}")
+async def get_document_detail(
+    doc_id: str,
+    rag: LightRAG = Depends(get_current_rag)
+):
+    """
+    获取文档详情
+    返回指定文档的完整内容
+    """
+    try:
+        # 读取 kv_store_full_docs.json
+        full_docs_path = os.path.join(rag.working_dir, "kv_store_full_docs.json")
+        if not os.path.exists(full_docs_path):
+            raise HTTPException(status_code=404, detail="Document store not found")
+            
+        with open(full_docs_path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+            
+        if doc_id not in data:
+            raise HTTPException(status_code=404, detail="Document not found")
+            
+        doc_info = data[doc_id]
+        return {
+            "id": doc_id,
+            "content": doc_info.get("content", ""),
+            "create_time": doc_info.get("create_time"),
+            "file_path": doc_info.get("file_path", "unknown")
+        }
+    except HTTPException:
+        raise
+    except Exception as e:
+        logging.error(f"获取文档详情失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
 @router.delete("/docs/{doc_id}")
 async def delete_document(
     doc_id: str,

app/config.py

@@ -11,15 +11,28 @@ class Settings(BaseSettings):
     # Data
     DATA_DIR: str = "./index_data"
 
-    # LLM
+    # LLM (Text) - vLLM
-    LLM_BINDING: str = "ollama"
+    LLM_BINDING: str = "vllm" # ollama, vllm, openai
-    LLM_BINDING_HOST: str = "http://localhost:11434"
+    LLM_BINDING_HOST: str = "http://192.168.6.115:8002/v1" # vLLM OpenAI API base
-    LLM_MODEL: str = "deepseek-v3.2:cloud"
+    LLM_MODEL: str = "qwen2.5-7b-awq"
+    LLM_KEY: str = "EMPTY" # vLLM default key
 
-    # Embedding
+    # LLM (Vision) - vLLM
-    EMBEDDING_BINDING: str = "ollama"
+    VL_BINDING_HOST: str = "http://192.168.6.115:8001/v1"
-    EMBEDDING_BINDING_HOST: str = "http://localhost:11434"
+    VL_MODEL: str = "qwen2.5-vl-3b-awq"
-    EMBEDDING_MODEL: str = "bge-m3"
+    VL_KEY: str = "EMPTY"
+    
+    # Embedding - TEI
+    EMBEDDING_BINDING: str = "tei" # ollama, tei, openai
+    EMBEDDING_BINDING_HOST: str = "http://192.168.6.115:8003" # TEI usually exposes /embed
+    EMBEDDING_MODEL: str = "BAAI/bge-m3" # model id in TEI
+    EMBEDDING_KEY: str = "EMPTY"
+    
+    # Rerank - TEI
+    RERANK_ENABLED: bool = True
+    RERANK_BINDING_HOST: str = "http://192.168.6.115:8004"
+    RERANK_MODEL: str = "BAAI/bge-reranker-v2-m3"
+    RERANK_KEY: str = "EMPTY"
     
     # RAG Config
     EMBEDDING_DIM: int = 1024

app/core/ingest.py

@@ -0,0 +1,88 @@
+import base64
+import logging
+import httpx
+from io import BytesIO
+from app.config import settings
+
+async def vl_image_caption_func(image_data: bytes, prompt: str = "请详细描述这张图片") -> str:
+    """
+    使用 VL 模型 (vLLM OpenAI API) 生成图片描述
+    """
+    if not settings.VL_BINDING_HOST:
+        return "[Image Processing Skipped: No VL Model Configured]"
+
+    try:
+        # 1. 编码图片为 Base64
+        base64_image = base64.b64encode(image_data).decode('utf-8')
+        
+        # 2. 构造 OpenAI 格式请求
+        # vLLM 支持 OpenAI Vision API
+        url = f"{settings.VL_BINDING_HOST}/chat/completions"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {settings.VL_KEY}"
+        }
+        
+        payload = {
+            "model": settings.VL_MODEL,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{base64_image}"
+                            }
+                        }
+                    ]
+                }
+            ],
+            "max_tokens": 300
+        }
+        
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            response = await client.post(url, headers=headers, json=payload)
+            response.raise_for_status()
+            result = response.json()
+            description = result['choices'][0]['message']['content']
+            return f"[Image Description: {description}]"
+            
+    except Exception as e:
+        logging.error(f"VL Caption failed: {str(e)}")
+        return f"[Image Processing Failed: {str(e)}]"
+
+async def process_pdf_with_images(file_bytes: bytes) -> str:
+    """
+    解析 PDF，提取文本并对图片进行 Caption
+    """
+    import pypdf
+    from PIL import Image
+    
+    text_content = ""
+    pdf_file = BytesIO(file_bytes)
+    reader = pypdf.PdfReader(pdf_file)
+    
+    for page_num, page in enumerate(reader.pages):
+        # 1. 提取文本
+        page_text = page.extract_text()
+        text_content += f"--- Page {page_num + 1} Text ---\n{page_text}\n\n"
+        
+        # 2. 提取图片
+        if settings.VL_BINDING_HOST:
+            for count, image_file_object in enumerate(page.images):
+                try:
+                    # 获取图片数据
+                    image_data = image_file_object.data
+                    
+                    # 简单验证图片有效性
+                    # Image.open(BytesIO(image_data)).verify() 
+                    
+                    # 调用 VL 模型
+                    caption = await vl_image_caption_func(image_data)
+                    text_content += f"--- Page {page_num + 1} Image {count + 1} ---\n{caption}\n\n"
+                except Exception as e:
+                    logging.warning(f"Failed to process image {count} on page {page_num}: {e}")
+                    
+    return text_content

app/core/rag.py

@@ -1,11 +1,12 @@
 import logging
 import os
 import threading
-from collections import OrderedDict
+import httpx
-from typing import Optional
-
 import numpy as np
 import ollama
+from collections import OrderedDict
+from typing import Optional, List, Union
+
 from lightrag import LightRAG
 from lightrag.utils import EmbeddingFunc
 from lightrag.llm.ollama import ollama_embed
@@ -14,23 +15,22 @@ from app.config import settings
 # 全局 RAG 管理器
 rag_manager = None
 
-async def llm_func(prompt, system_prompt=None, history_messages=[], **kwargs) -> str:
+# ==============================================================================
-    """定义 LLM 函数：使用 Ollama 生成回复"""
+# LLM Functions
-    # 移除可能存在的 model 参数，避免冲突
+# ==============================================================================
+
+async def ollama_llm_func(prompt, system_prompt=None, history_messages=[], **kwargs) -> str:
+    """Ollama LLM 实现"""
+    # 参数清理
     kwargs.pop('model', None)
     kwargs.pop('hashing_kv', None)
-    kwargs.pop('enable_cot', None)  # 移除不支持的参数
+    kwargs.pop('enable_cot', None)
     keyword_extraction = kwargs.pop("keyword_extraction", False)
     if keyword_extraction:
         kwargs["format"] = "json"
     
     stream = kwargs.pop("stream", False)
     think = kwargs.pop("think", False)
-    # Debug: 检查流式参数
-    if stream:
-        logging.info("LLM called with stream=True")
-    else:
-        logging.info("LLM called with stream=False")
     
     messages = []
     if system_prompt:
@@ -41,30 +41,169 @@ async def llm_func(prompt, system_prompt=None, history_messages=[], **kwargs) ->
     client = ollama.AsyncClient(host=settings.LLM_BINDING_HOST)
     if stream:
         async def inner():
-            # 使用 **kwargs 透传参数，确保 format 等顶级参数生效
             response = await client.chat(model=settings.LLM_MODEL, messages=messages, stream=True, think=think, **kwargs)
-
             async for chunk in response:
                 msg = chunk.get("message", {})
-
                 if "thinking" in msg and msg["thinking"]:
                     yield {"type": "thinking", "content": msg["thinking"]}
                 if "content" in msg and msg["content"]:
                     yield {"type": "content", "content": msg["content"]}
         return inner()
     else:
-        # 同步请求不要think，降低延迟
         response = await client.chat(model=settings.LLM_MODEL, messages=messages, stream=False, think=False, **kwargs)
         return response["message"]["content"]
 
-async def embedding_func(texts: list[str]) -> np.ndarray:
+async def openai_llm_func(prompt, system_prompt=None, history_messages=[], **kwargs) -> str:
-    """定义 Embedding 函数：使用 Ollama 计算向量"""
+    """OpenAI 兼容 LLM 实现 (适用于 vLLM)"""
+    # 参数清理
+    kwargs.pop('model', None)
+    kwargs.pop('hashing_kv', None)
+    kwargs.pop('enable_cot', None)
+    keyword_extraction = kwargs.pop("keyword_extraction", False)
+    
+    # vLLM/OpenAI 不直接支持 format="json"，通常需要在 prompt 中指示或使用 response_format
+    # 这里简单处理：如果需要 json，在 prompt 中暗示（LightRAG 的 prompt 通常已经包含 json 指令）
+    if keyword_extraction:
+        kwargs["response_format"] = {"type": "json_object"}
+
+    stream = kwargs.pop("stream", False)
+    # think 参数是 DeepSeek 特有的，OpenAI 标准接口不支持，暂时忽略
+    kwargs.pop("think", None)
+
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+    messages.extend(history_messages)
+    messages.append({"role": "user", "content": prompt})
+
+    url = f"{settings.LLM_BINDING_HOST}/chat/completions"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {settings.LLM_KEY}"
+    }
+    
+    payload = {
+        "model": settings.LLM_MODEL,
+        "messages": messages,
+        "stream": stream,
+        **kwargs
+    }
+
+    if stream:
+        async def inner():
+            async with httpx.AsyncClient(timeout=120.0) as client:
+                async with client.stream("POST", url, headers=headers, json=payload) as response:
+                    response.raise_for_status()
+                    async for line in response.aiter_lines():
+                        if line.startswith("data: "):
+                            data_str = line[6:]
+                            if data_str.strip() == "[DONE]":
+                                break
+                            try:
+                                import json
+                                chunk = json.loads(data_str)
+                                delta = chunk['choices'][0]['delta']
+                                if 'content' in delta:
+                                    yield {"type": "content", "content": delta['content']}
+                                # vLLM 可能不返回 thinking 字段，除非是 DeepSeek 模型且配置了
+                            except:
+                                pass
+        return inner()
+    else:
+        async with httpx.AsyncClient(timeout=120.0) as client:
+            response = await client.post(url, headers=headers, json=payload)
+            response.raise_for_status()
+            result = response.json()
+            return result['choices'][0]['message']['content']
+
+async def llm_func(prompt, system_prompt=None, history_messages=[], **kwargs) -> str:
+    """LLM 调度函数"""
+    if settings.LLM_BINDING == "ollama":
+        return await ollama_llm_func(prompt, system_prompt, history_messages, **kwargs)
+    elif settings.LLM_BINDING in ["vllm", "openai", "custom"]:
+        return await openai_llm_func(prompt, system_prompt, history_messages, **kwargs)
+    else:
+        raise ValueError(f"Unsupported LLM_BINDING: {settings.LLM_BINDING}")
+
+# ==============================================================================
+# Embedding Functions
+# ==============================================================================
+
+async def ollama_embedding_func(texts: list[str]) -> np.ndarray:
     return await ollama_embed(
         texts, 
         embed_model=settings.EMBEDDING_MODEL, 
         host=settings.EMBEDDING_BINDING_HOST
     )
 
+async def tei_embedding_func(texts: list[str]) -> np.ndarray:
+    """TEI (Text Embeddings Inference) Embedding 实现"""
+    url = f"{settings.EMBEDDING_BINDING_HOST}/embed" # TEI 标准接口
+    headers = {"Content-Type": "application/json"}
+    if settings.EMBEDDING_KEY and settings.EMBEDDING_KEY != "EMPTY":
+        headers["Authorization"] = f"Bearer {settings.EMBEDDING_KEY}"
+    
+    payload = {
+        "inputs": texts,
+        "truncate": True # TEI 参数，防止超长报错
+    }
+    
+    async with httpx.AsyncClient(timeout=60.0) as client:
+        response = await client.post(url, headers=headers, json=payload)
+        response.raise_for_status()
+        # TEI 返回: [[0.1, ...], [0.2, ...]]
+        embeddings = response.json()
+        return np.array(embeddings)
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    """Embedding 调度函数"""
+    if settings.EMBEDDING_BINDING == "ollama":
+        return await ollama_embedding_func(texts)
+    elif settings.EMBEDDING_BINDING == "tei":
+        return await tei_embedding_func(texts)
+    else:
+        # 默认回退到 ollama 或者报错
+        raise ValueError(f"Unsupported EMBEDDING_BINDING: {settings.EMBEDDING_BINDING}")
+
+# ==============================================================================
+# Rerank Functions
+# ==============================================================================
+
+async def tei_rerank_func(query: str, documents: list[str]) -> np.ndarray:
+    """TEI Rerank 实现"""
+    if not documents:
+        return np.array([])
+        
+    url = f"{settings.RERANK_BINDING_HOST}/rerank"
+    headers = {"Content-Type": "application/json"}
+    if settings.RERANK_KEY and settings.RERANK_KEY != "EMPTY":
+        headers["Authorization"] = f"Bearer {settings.RERANK_KEY}"
+        
+    payload = {
+        "query": query,
+        "texts": documents,
+        "return_text": False
+    }
+    
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        response = await client.post(url, headers=headers, json=payload)
+        response.raise_for_status()
+        # TEI 返回: [{"index": 0, "score": 0.99}, {"index": 1, "score": 0.5}]
+        results = response.json()
+        
+        # LightRAG 期望返回一个分数数组，对应输入的 documents 顺序
+        # TEI 返回的结果是排序过的，我们需要根据 index 还原顺序
+        scores = np.zeros(len(documents))
+        for res in results:
+            idx = res['index']
+            scores[idx] = res['score']
+            
+        return scores
+
+# ==============================================================================
+# RAG Manager
+# ==============================================================================
+
 class RAGManager:
     def __init__(self, capacity: int = 3):
         self.capacity = capacity
@@ -73,44 +212,44 @@ class RAGManager:
 
     async def get_rag(self, user_id: str) -> LightRAG:
         """获取指定用户的 LightRAG 实例 (LRU 缓存)"""
-        # 1. 尝试从缓存获取
         with self.lock:
             if user_id in self.cache:
                 self.cache.move_to_end(user_id)
                 logging.debug(f"Cache hit for user: {user_id}")
                 return self.cache[user_id]
         
-        # 2. 缓存未命中，需要初始化
         logging.info(f"Initializing RAG instance for user: {user_id}")
         user_data_dir = os.path.join(settings.DATA_DIR, user_id)
         
-        # 严格隔离：确保工作目录绝对干净，不应包含上级目录或其他用户数据
         if not os.path.exists(user_data_dir):
             os.makedirs(user_data_dir)
             
-        # 实例化 LightRAG
+        # 准备参数
-        # 关键修复：确保 working_dir 严格指向用户子目录
+        rag_params = {
-        rag = LightRAG(
+            "working_dir": user_data_dir,
-            working_dir=user_data_dir,
+            "llm_model_func": llm_func,
-            llm_model_func=llm_func,
+            "llm_model_name": settings.LLM_MODEL,
-            llm_model_name=settings.LLM_MODEL,
+            "llm_model_max_async": 4, # vLLM 并发能力强，可以调高
-            llm_model_max_async=1,
+            "max_parallel_insert": 1,
-            max_parallel_insert=1,
+            "embedding_func": EmbeddingFunc(
-            embedding_func=EmbeddingFunc(
                 embedding_dim=settings.EMBEDDING_DIM,
                 max_token_size=settings.MAX_TOKEN_SIZE,
                 func=embedding_func
             ),
-            embedding_func_max_async=4,
+            "embedding_func_max_async": 8, # TEI 并发强
-            enable_llm_cache=True
+            "enable_llm_cache": True
-        )
+        }
+        
+        # 如果启用了 Rerank，注入 rerank_model_func
+        if settings.RERANK_ENABLED:
+            logging.info("Rerank enabled for RAG instance")
+            rag_params["rerank_model_func"] = tei_rerank_func
+            
+        rag = LightRAG(**rag_params)
         
-        # 异步初始化存储
         await rag.initialize_storages()
         
-        # 3. 放入缓存并处理驱逐
         with self.lock:
-            # 双重检查，防止在初始化期间其他线程已经创建了
             if user_id in self.cache:
                 self.cache.move_to_end(user_id)
                 return self.cache[user_id]
@@ -118,23 +257,19 @@ class RAGManager:
             self.cache[user_id] = rag
             self.cache.move_to_end(user_id)
             
-            # 检查容量
             while len(self.cache) > self.capacity:
                 oldest_user, _ = self.cache.popitem(last=False)
-                logging.info(f"Evicting RAG instance for user: {oldest_user} due to capacity limit")
+                logging.info(f"Evicting RAG instance for user: {oldest_user}")
-                # 这里依赖 Python GC 回收资源
                 
         return rag
 
 def initialize_rag_manager():
-    """初始化全局 RAG 管理器"""
     global rag_manager
     rag_manager = RAGManager(capacity=settings.MAX_RAG_INSTANCES)
     logging.info(f"RAG Manager initialized with capacity: {settings.MAX_RAG_INSTANCES}")
     return rag_manager
 
 def get_rag_manager() -> RAGManager:
-    """获取全局 RAG 管理器"""
     if rag_manager is None:
         return initialize_rag_manager()
     return rag_manager