commit bb65afab1f0e60f202f593744256da99922ace30
Author: hailin <hailin@gdzx.xyz>
Date:   Thu May 8 15:12:46 2025 +0800

    first commit!

diff --git a/Dockerfile b/Dockerfile
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+++ b/Dockerfile
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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY . /app
+
+# 安装系统依赖（必要时）
+RUN apt-get update && apt-get install -y build-essential && \
+    rm -rf /var/lib/apt/lists/*
+
+# 安装 Python 依赖
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 声明服务监听端口（供 Kubernetes 或 docker run -P 使用）
+EXPOSE 8000
+
+# 启动服务（可指定绑定 IP 和端口）
+CMD ["gunicorn", "app.main:app", "-k", "uvicorn.workers.UvicornWorker", "-c", "gunicorn_config.py"]
diff --git a/README.md b/README.md
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+++ b/README.md
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+# FAISS Enterprise RAG
+
+企业级部署方案，支持千万向量、本地高并发、热更新、容器部署。
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diff --git a/app/api/search.py b/app/api/search.py
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+++ b/app/api/search.py
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+from fastapi import APIRouter, HTTPException, Query
+from pydantic import BaseModel
+import numpy as np
+from app.core.index_manager import UserIndexManager
+from app.core.embedding import embedder
+from app.core.config import settings
+
+router = APIRouter()
+index_manager = UserIndexManager()
+
+class QueryRequest(BaseModel):
+    query: str
+
+@router.post("/search")
+def search_docs(request: QueryRequest, user_id: str = Query(..., description="用户ID")):
+    try:
+        query_vector = embedder.encode([request.query])
+        index = index_manager.get_index(user_id)
+        D, I = index.search(query_vector, settings.TOP_K)
+        return {
+            "user_id": user_id,
+            "query": request.query,
+            "results": [{"id": int(idx), "score": float(dist)} for dist, idx in zip(D, I)]
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
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diff --git a/app/api/upload.py b/app/api/upload.py
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+++ b/app/api/upload.py
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+from fastapi import APIRouter, UploadFile, File, Form
+import os
+from shutil import copyfileobj
+from scripts.rag_build_query import build_user_index
+
+router = APIRouter()
+
+@router.post("/upload")
+def upload_user_file(user_id: str = Form(...), file: UploadFile = File(...)):
+    user_doc_dir = os.path.join("docs", user_id)
+    os.makedirs(user_doc_dir, exist_ok=True)
+
+    file_path = os.path.join(user_doc_dir, file.filename)
+    with open(file_path, "wb") as f:
+        copyfileobj(file.file, f)
+
+    print(f"[UPLOAD] 文件已保存至 {file_path}")
+
+    # 自动重建用户索引
+    build_user_index(user_id)
+    print(f"[UPLOAD] 用户 {user_id} 的索引已重建")
+
+    return {"status": "ok", "filename": file.filename}
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diff --git a/app/core/config.py b/app/core/config.py
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+++ b/app/core/config.py
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+from pydantic import BaseSettings
+import os
+
+class Settings(BaseSettings):
+    INDEX_FILE: str = os.getenv("INDEX_FILE", "index_data/index.faiss")
+    EMBEDDING_DIM: int = 768
+    TOP_K: int = 5
+    DOC_PATH: str = "docs/"
+    DEVICE: str = "cpu"  # 可设置为 cuda:0
+    MODEL_NAME: str = "BAAI/bge-m3"
+
+settings = Settings()
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diff --git a/app/core/embedding.py b/app/core/embedding.py
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--- /dev/null
+++ b/app/core/embedding.py
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+from transformers import AutoTokenizer, AutoModel
+import torch
+import numpy as np
+from app.core.config import settings
+
+class BGEEmbedding:
+    def __init__(self):
+        self.tokenizer = AutoTokenizer.from_pretrained(settings.MODEL_NAME)
+        self.model = AutoModel.from_pretrained(settings.MODEL_NAME)
+        self.model.eval()
+
+    def encode(self, texts):
+        with torch.no_grad():
+            inputs = self.tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+            outputs = self.model(**inputs)
+            embeddings = self.mean_pooling(outputs, inputs['attention_mask'])
+            embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+            return embeddings.cpu().numpy()
+
+    def mean_pooling(self, model_output, attention_mask):
+        token_embeddings = model_output[0]
+        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+embedder = BGEEmbedding()
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diff --git a/app/core/index.py b/app/core/index.py
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+++ b/app/core/index.py
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+
+import faiss
+import numpy as np
+import threading
+from app.core.config import settings
+
+class FaissIndexWrapper:
+    def __init__(self):
+        self.index_lock = threading.Lock()
+        self.index = None
+        self.load_index(settings.INDEX_FILE)
+
+    def load_index(self, path):
+        with self.index_lock:
+            self.index = faiss.read_index(path)
+            print(f"[FAISS] Index loaded from {path}")
+
+    def update_index(self, path):
+        """热更新替换当前索引"""
+        with self.index_lock:
+            new_index = faiss.read_index(path)
+            self.index = new_index
+            print(f"[FAISS] Index hot-swapped from {path}")
+
+    def search(self, vector: np.ndarray, top_k: int = 5):
+        with self.index_lock:
+            D, I = self.index.search(vector.astype(np.float32), top_k)
+            return D[0], I[0]
+
+# 单例，供 API 层引用
+faiss_index = FaissIndexWrapper()
diff --git a/app/core/index_manager.py b/app/core/index_manager.py
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+++ b/app/core/index_manager.py
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+import os
+import faiss
+
+class UserIndexManager:
+    def __init__(self, base_dir="index_data/"):
+        self.base_dir = base_dir
+        self.index_map = {}  # user_id -> faiss index
+        self._load_all_indexes()
+
+    def _load_all_indexes(self):
+        for fname in os.listdir(self.base_dir):
+            if fname.endswith(".index"):
+                user_id = fname.replace(".index", "")
+                path = os.path.join(self.base_dir, fname)
+                self.index_map[user_id] = faiss.read_index(path)
+                print(f"[INIT] Loaded index for user {user_id}")
+
+    def get_index(self, user_id):
+        if user_id not in self.index_map:
+            raise ValueError(f"Index for user {user_id} not loaded.")
+        return self.index_map[user_id]
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diff --git a/app/main.py b/app/main.py
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+++ b/app/main.py
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+from fastapi import FastAPI
+from app.api.search import router as search_router
+from app.api.upload import router as upload_router
+
+app = FastAPI(title="Enterprise FAISS RAG Server")
+
+app.include_router(search_router, prefix="/api", tags=["search"])
+app.include_router(upload_router, prefix="/api", tags=["upload"])
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diff --git a/gunicorn_config.py b/gunicorn_config.py
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+++ b/gunicorn_config.py
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+workers = 12  # 可按CPU核心调整
+timeout = 60
+preload_app = True
+bind = "0.0.0.0:8000"
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diff --git a/requirements.txt b/requirements.txt
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+++ b/requirements.txt
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+faiss-cpu
+fastapi
+uvicorn
+gunicorn
+pydantic
+numpy
+transformers
+torch
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diff --git a/scripts/build_index.py b/scripts/build_index.py
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+++ b/scripts/build_index.py
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+import os
+import faiss
+import numpy as np
+from app.core.embedding import embedder
+from app.core.config import settings
+
+def load_documents(doc_folder):
+    texts = []
+    for fname in os.listdir(doc_folder):
+        path = os.path.join(doc_folder, fname)
+        if os.path.isfile(path):
+            with open(path, "r", encoding="utf-8") as f:
+                texts.append(f.read())
+    return texts
+
+def build_faiss_index(docs, dim):
+    vectors = embedder.encode(docs)
+    index = faiss.IndexFlatIP(dim)
+    index.add(vectors)
+    return index
+
+if __name__ == "__main__":
+    print("[BUILD] Loading documents...")
+    docs = load_documents(settings.DOC_PATH)
+    print(f"[BUILD] Loaded {len(docs)} documents")
+
+    print("[BUILD] Building FAISS index...")
+    index = build_faiss_index(docs, settings.EMBEDDING_DIM)
+
+    print(f"[BUILD] Saving index to {settings.INDEX_FILE}")
+    faiss.write_index(index, settings.INDEX_FILE)
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diff --git a/scripts/index_permissions.json b/scripts/index_permissions.json
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+++ b/scripts/index_permissions.json
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+{
+  "user_001": ["common.index", "engineering.index"],
+  "user_002": ["common.index", "hr.index"],
+  "user_003": []
+}
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diff --git a/scripts/permissions.py b/scripts/permissions.py
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+++ b/scripts/permissions.py
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+import json
+
+PERMISSION_PATH = "scripts/index_permissions.json"
+
+def get_user_allowed_indexes(user_id: str):
+    try:
+        with open(PERMISSION_PATH, "r", encoding="utf-8") as f:
+            permission_map = json.load(f)
+        return permission_map.get(user_id, [])
+    except Exception as e:
+        print(f"[Permission Error] {e}")
+        return []
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diff --git a/scripts/rag_build_query.py b/scripts/rag_build_query.py
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+++ b/scripts/rag_build_query.py
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+import os
+import faiss
+from typing import List
+from llama_index import (
+    SimpleDirectoryReader,
+    VectorStoreIndex,
+    ServiceContext,
+    PromptTemplate,
+)
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.vector_stores.faiss import FaissVectorStore
+from llama_index.llms.base import ChatMessage
+
+# 假设你要用的本地嵌入模型
+EMBED_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+USER_INDEX_PATH = "index_data"
+USER_DOC_PATH = "docs"
+
+def build_user_index(user_id: str):
+    doc_dir = os.path.join(USER_DOC_PATH, user_id)
+    if not os.path.exists(doc_dir):
+        raise FileNotFoundError(f"文档目录不存在: {doc_dir}")
+    
+    documents = SimpleDirectoryReader(doc_dir).load_data()
+    embed_model = HuggingFaceEmbedding(model_name=EMBED_MODEL_NAME)
+    service_context = ServiceContext.from_defaults(embed_model=embed_model)
+    
+    # 构建向量索引
+    index = VectorStoreIndex.from_documents(
+        documents,
+        service_context=service_context,
+        vector_store=FaissVectorStore()
+    )
+    
+    # 保存为用户专属 .index 文件
+    index_path = os.path.join(USER_INDEX_PATH, f"{user_id}.index")
+    faiss.write_index(index.vector_store.index, index_path)
+    print(f"[BUILD] 为用户 {user_id} 构建并保存了索引 → {index_path}")
+
+from scripts.permissions import get_user_allowed_indexes
+
+def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
+    index_path = os.path.join(USER_INDEX_PATH, f"{user_id}.index")
+    if not os.path.exists(index_path):
+        raise FileNotFoundError(f"[ERROR] 用户 {user_id} 的索引不存在")
+    
+    embed_model = HuggingFaceEmbedding(model_name=EMBED_MODEL_NAME)
+    service_context = ServiceContext.from_defaults(embed_model=embed_model)
+
+    # 加载索引
+    vector_store = FaissVectorStore.from_persist_path(index_path)
+    index = VectorStoreIndex.from_vector_store(vector_store, service_context=service_context)
+    
+    retriever = index.as_retriever(similarity_top_k=top_k)
+    nodes = retriever.retrieve(question)
+
+    # 构造 Prompt
+    context_str = "\n\n".join([n.get_content() for n in nodes])
+    prompt_template = PromptTemplate(
+        "请根据以下内容回答用户问题：\n\n{context}\n\n问题：{query}"
+    )
+    final_prompt = prompt_template.format(
+        context=context_str,
+        query=question,
+    )
+
+    print("[PROMPT构建完成]")
+    return final_prompt
+
+# 示例：
+if __name__ == "__main__":
+    uid = "user_001"
+    build_user_index(uid)
+    prompt = query_user_rag(uid, "这份资料中提到了哪些关键点？")
+    print("\n------ 最终构建的 Prompt 给 LLM 使用 ------\n")
+    print(prompt)
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diff --git a/scripts/update_index.py b/scripts/update_index.py
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+++ b/scripts/update_index.py
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+import time
+import os
+from app.core.index import faiss_index
+from app.core.config import settings
+
+def watch_and_reload(interval=600):
+    print("[HOT-RELOAD] Watching index file for updates...")
+    last_mtime = None
+    while True:
+        try:
+            mtime = os.path.getmtime(settings.INDEX_FILE)
+            if last_mtime is None:
+                last_mtime = mtime
+            elif mtime != last_mtime:
+                print("[HOT-RELOAD] Detected new index file. Reloading...")
+                faiss_index.update_index(settings.INDEX_FILE)
+                last_mtime = mtime
+        except Exception as e:
+            print(f"[HOT-RELOAD] Error: {e}")
+        time.sleep(interval)
+
+if __name__ == "__main__":
+    watch_and_reload()
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