rag基座已经搭建好

llm_server/rag_server.py

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+import torch
+import torch.nn.functional as F
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List
+from modelscope import AutoTokenizer, AutoModel, AutoModelForCausalLM
+from transformers import BitsAndBytesConfig
+
+# 在 load_models() 里添加量化配置
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+# ===================== 模型路径 =====================
+EMBED_MODEL_PATH = "/home/admin/minor_agent/modelscope/models/Qwen/Qwen3-Embedding-0___6B"
+RERANK_MODEL_PATH = "/home/admin/minor_agent/modelscope/models/Qwen/Qwen3-Reranker-4B"
+
+# ===================== 初始化 FastAPI =====================
+app = FastAPI(title="RAG Backend with 8-bit Qwen3 Models")
+
+# ===================== 全局变量（模型、分词器） =====================
+embed_tokenizer = None
+embed_model = None
+rerank_tokenizer = None
+rerank_model = None
+
+# ===================== 工具函数 =====================
+def last_token_pool(last_hidden_states, attention_mask):
+    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+    if left_padding:
+        return last_hidden_states[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        batch_size = last_hidden_states.shape[0]
+        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery:{query}'
+
+def format_rerank_instruction(instruction, query, doc):
+    if instruction is None:
+        instruction = 'Given a web search query, retrieve relevant passages that answer the query'
+    return f"<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {doc}"
+
+# ===================== 启动时加载模型（8‑bit） =====================
+@app.on_event("startup")
+def load_models():
+    global embed_tokenizer, embed_model, rerank_tokenizer, rerank_model
+
+    # ---------- 加载 Embedding 模型 ----------
+    print("Loading Embedding model with 16-bit quantization...")
+    embed_tokenizer = AutoTokenizer.from_pretrained(EMBED_MODEL_PATH, padding_side='left')
+    embed_model = AutoModel.from_pretrained(
+        EMBED_MODEL_PATH,
+        #load_in_8bit=True,                # 启用 8-bit 量化
+        device_map="auto",                # 自动分配到 GPU
+        dtype=torch.float16         # 计算精度保持 fp16
+    )
+    embed_model.eval()
+    print("Embedding model loaded.")
+
+    # ---------- 加载 Reranker 模型 ----------
+    print("Loading Reranker model with 8-bit quantization...")
+    rerank_tokenizer = AutoTokenizer.from_pretrained(RERANK_MODEL_PATH, padding_side='left')
+    rerank_model = AutoModelForCausalLM.from_pretrained(
+        RERANK_MODEL_PATH,
+        quantization_config=quantization_config,
+        device_map="auto",
+        dtype=torch.float16
+    )
+    rerank_model.eval()
+
+    # 预计算 Reranker 所需的固定 tokens
+    global token_true_id, token_false_id, prefix_tokens, suffix_tokens, max_rerank_length
+    token_false_id = rerank_tokenizer.convert_tokens_to_ids("no")
+    token_true_id  = rerank_tokenizer.convert_tokens_to_ids("yes")
+    max_rerank_length = 8192
+
+    prefix = "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n"
+    suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
+    prefix_tokens = rerank_tokenizer.encode(prefix, add_special_tokens=False)
+    suffix_tokens = rerank_tokenizer.encode(suffix, add_special_tokens=False)
+
+    print("Reranker model loaded.")
+
+# ===================== API 请求体定义 =====================
+class EmbedRequest(BaseModel):
+    task_description: str = "Given a web search query, retrieve relevant passages that answer the query"
+    queries: List[str]
+    documents: List[str]           # 可选，若只查询文本可只传 queries
+
+class EmbedResponse(BaseModel):
+    query_embeddings: List[List[float]]
+    doc_embeddings: List[List[float]]   # 当传入 documents 时返回
+
+class RerankRequest(BaseModel):
+    task_description: str = "Given a web search query, retrieve relevant passages that answer the query"
+    query: str
+    documents: List[str]
+
+class RerankResponse(BaseModel):
+    scores: List[float]
+
+# ===================== Embedding 端点 =====================
+@app.post("/embed", response_model=EmbedResponse)
+async def get_embeddings(req: EmbedRequest):
+    # 为 query 加上指令，document 不加指令
+    instructed_queries = [get_detailed_instruct(req.task_description, q) for q in req.queries]
+    # 所有文本拼成一个 batch：先是 queries，后是 documents（如果有）
+    input_texts = instructed_queries + req.documents
+
+    # 分词
+    batch_dict = embed_tokenizer(
+        input_texts,
+        padding=True,
+        truncation=True,
+        max_length=8192,
+        return_tensors="pt",
+    ).to(embed_model.device)
+
+    with torch.no_grad():
+        outputs = embed_model(**batch_dict)
+        embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+        embeddings = F.normalize(embeddings, p=2, dim=1)
+
+    # 拆分 query 和 document 的嵌入
+    nq = len(instructed_queries)
+    query_embs = embeddings[:nq].cpu().tolist()
+    doc_embs = embeddings[nq:].cpu().tolist() if req.documents else []
+
+    return EmbedResponse(query_embeddings=query_embs, doc_embeddings=doc_embs)
+
+# ===================== Reranker 端点 =====================
+@app.post("/rerank", response_model=RerankResponse)
+async def rerank_documents(req: RerankRequest):
+    # 构造带格式的输入对
+    pairs = [
+        format_rerank_instruction(req.task_description, req.query, doc)
+        for doc in req.documents
+    ]
+
+    # 处理输入，添加 prefix/suffix tokens 并 pad
+    inputs = rerank_tokenizer(
+        pairs,
+        padding=False,
+        truncation='longest_first',
+        return_attention_mask=False,
+        max_length=max_rerank_length - len(prefix_tokens) - len(suffix_tokens)
+    )
+    for i, ele in enumerate(inputs['input_ids']):
+        inputs['input_ids'][i] = prefix_tokens + ele + suffix_tokens
+    inputs = rerank_tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=max_rerank_length)
+    inputs = {k: v.to(rerank_model.device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        batch_scores = rerank_model(**inputs).logits[:, -1, :]
+        true_vector = batch_scores[:, token_true_id]
+        false_vector = batch_scores[:, token_false_id]
+        scores = torch.stack([false_vector, true_vector], dim=1)
+        scores = torch.nn.functional.log_softmax(scores, dim=1)
+        scores = scores[:, 1].exp().cpu().tolist()
+
+    return RerankResponse(scores=scores)
+
+# ===================== 健康检查 =====================
+@app.get("/health")
+def health():
+    return {"status": "ok"}