4.7.1-alpha2 (#1153)

Co-authored-by: UUUUnotfound <31206589+UUUUnotfound@users.noreply.github.com>
Co-authored-by: Hexiao Zhang <731931282qq@gmail.com>
Co-authored-by: heheer <71265218+newfish-cmyk@users.noreply.github.com>

python/bge-rerank/README.md

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+# 接入 bge-rerank 重排模型
+
+## 不同模型推荐配置
+
+推荐配置如下：
+
+| 模型名           | 内存  | 显存  | 硬盘空间 | 启动命令      |
+| ---------------- | ----- | ----- | -------- | ------------- |
+| bge-rerank-base  | >=4GB | >=4GB | >=8GB    | python app.py |
+| bge-rerank-large | >=8GB | >=8GB | >=8GB    | python app.py |
+| bge-rerank-v2-m3 | >=8GB | >=8GB | >=8GB    | python app.py |
+
+## 源码部署
+
+### 1. 安装环境
+
+- Python 3.9, 3.10
+- CUDA 11.7
+- 科学上网环境
+
+### 2. 下载代码
+
+3 个模型代码分别为：
+
+1. [https://github.com/labring/FastGPT/tree/main/python/reranker/bge-reranker-base](https://github.com/labring/FastGPT/tree/main/python/reranker/bge-reranker-base)
+2. [https://github.com/labring/FastGPT/tree/main/python/reranker/bge-reranker-large](https://github.com/labring/FastGPT/tree/main/python/reranker/bge-reranker-large)
+3. [https://github.com/labring/FastGPT/tree/main/python/reranker/bge-rerank-v2-m3](https://github.com/labring/FastGPT/tree/main/python/reranker/bge-rerank-v2-m3)
+
+### 3. 安装依赖
+
+```sh
+pip install -r requirements.txt
+```
+
+### 4. 下载模型
+
+3个模型的 huggingface 仓库地址如下：
+
+1. [https://huggingface.co/BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)
+2. [https://huggingface.co/BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)
+3. [https://huggingface.co/BAAI/bge-rerank-v2-m3](https://huggingface.co/BAAI/bge-rerank-v2-m3)
+
+在对应代码目录下 clone 模型。目录结构：
+
+```
+bge-reranker-base/
+app.py
+Dockerfile
+requirements.txt
+```
+
+### 5. 运行代码
+
+```bash
+python app.py
+```
+
+启动成功后应该会显示如下地址：
+
+![](./rerank1.png)
+
+> 这里的 `http://0.0.0.0:6006` 就是请求地址。
+
+## docker 部署
+
+**镜像名分别为:**
+
+1. registry.cn-hangzhou.aliyuncs.com/fastgpt/bge-rerank-base:v0.1
+2. registry.cn-hangzhou.aliyuncs.com/fastgpt/bge-rerank-large:v0.1
+3. registry.cn-hangzhou.aliyuncs.com/fastgpt/bge-rerank-v2-m3:v0.1
+
+**端口**
+
+6006
+
+**环境变量**
+
+```
+ACCESS_TOKEN=访问安全凭证，请求时，Authorization: Bearer ${ACCESS_TOKEN}
+```
+
+**运行命令示例**
+
+```sh
+# auth token 为mytoken
+docker run -d --name reranker -p 6006:6006 -e ACCESS_TOKEN=mytoken --gpus all registry.cn-hangzhou.aliyuncs.com/fastgpt/bge-rerank-base:v0.1
+```
+
+**docker-compose.yml示例**
+
+```
+version: "3"
+services:
+  reranker:
+    image: registry.cn-hangzhou.aliyuncs.com/fastgpt/rerank:v0.2
+    container_name: reranker
+    # GPU运行环境，如果宿主机未安装，将deploy配置隐藏即可
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: all
+            capabilities: [gpu]
+    ports:
+      - 6006:6006
+    environment:
+      - ACCESS_TOKEN=mytoken
+
+```
+
+## 接入 FastGPT
+
+参考 [ReRank模型接入](https://doc/fastai.site/docs/development/configuration/#rerank-接入)

python/bge-rerank/bge-reranker-base/Dockerfile

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+FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
+
+# please download the model from https://huggingface.co/BAAI/bge-reranker-base and put it in the same directory as Dockerfile
+COPY ./bge-reranker-base ./bge-reranker-base
+
+COPY requirements.txt .
+
+RUN python3 -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+COPY app.py Dockerfile .
+
+ENTRYPOINT python3 app.py

python/bge-rerank/bge-reranker-base/app.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time: 2023/11/7 22:45
+@Author: zhidong
+@File: reranker.py
+@Desc:
+"""
+import os
+import numpy as np
+import logging
+import uvicorn
+import datetime
+from fastapi import FastAPI, Security, HTTPException
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from FlagEmbedding import FlagReranker
+from pydantic import Field, BaseModel, validator
+from typing import Optional, List
+
+app = FastAPI()
+security = HTTPBearer()
+env_bearer_token = 'ACCESS_TOKEN'
+
+class QADocs(BaseModel):
+    query: Optional[str]
+    documents: Optional[List[str]]
+
+
+class Singleton(type):
+    def __call__(cls, *args, **kwargs):
+        if not hasattr(cls, '_instance'):
+            cls._instance = super().__call__(*args, **kwargs)
+        return cls._instance
+
+
+RERANK_MODEL_PATH = os.path.join(os.path.dirname(__file__), "bge-reranker-base")
+
+class ReRanker(metaclass=Singleton):
+    def __init__(self, model_path):
+        self.reranker = FlagReranker(model_path, use_fp16=False)
+
+    def compute_score(self, pairs: List[List[str]]):
+        if len(pairs) > 0:
+            result = self.reranker.compute_score(pairs, normalize=True)
+            if isinstance(result, float):
+                result = [result]
+            return result
+        else:
+            return None
+
+class Chat(object):
+    def __init__(self, rerank_model_path: str = RERANK_MODEL_PATH):
+        self.reranker = ReRanker(rerank_model_path)
+
+    def fit_query_answer_rerank(self, query_docs: QADocs) -> List:
+        if query_docs is None or len(query_docs.documents) == 0:
+            return []
+
+        pair = [[query_docs.query, doc] for doc in query_docs.documents]
+        scores = self.reranker.compute_score(pair)
+
+        new_docs = []
+        for index, score in enumerate(scores):
+            new_docs.append({"index": index, "text": query_docs.documents[index], "score": score})
+        results = [{"index": documents["index"], "relevance_score": documents["score"]} for documents in list(sorted(new_docs, key=lambda x: x["score"], reverse=True))]
+        return results
+
+@app.post('/v1/rerank')
+async def handle_post_request(docs: QADocs, credentials: HTTPAuthorizationCredentials = Security(security)):
+    token = credentials.credentials
+    if env_bearer_token is not None and token != env_bearer_token:
+        raise HTTPException(status_code=401, detail="Invalid token")
+    chat = Chat()
+    try:
+        results = chat.fit_query_answer_rerank(docs)
+        return {"results": results}
+    except Exception as e:
+        print(f"报错：\n{e}")
+        return {"error": "重排出错"}
+
+if __name__ == "__main__":
+    token = os.getenv("ACCESS_TOKEN")
+    if token is not None:
+        env_bearer_token = token
+    try:
+        uvicorn.run(app, host='0.0.0.0', port=6006)
+    except Exception as e:
+        print(f"API启动失败！\n报错：\n{e}")

python/bge-rerank/bge-reranker-base/requirements.txt

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+fastapi==0.104.1
+transformers[sentencepiece]
+FlagEmbedding==1.2.8
+pydantic==1.10.13
+uvicorn==0.17.6
+itsdangerous
+protobuf

python/bge-rerank/bge-reranker-large/Dockerfile

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+FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
+
+# please download the model from https://huggingface.co/BAAI/bge-reranker-large and put it in the same directory as Dockerfile
+COPY ./bge-reranker-large ./bge-reranker-large
+
+COPY requirements.txt .
+
+RUN python3 -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+COPY app.py Dockerfile .
+
+ENTRYPOINT python3 app.py

python/bge-rerank/bge-reranker-large/app.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time: 2023/11/7 22:45
+@Author: zhidong
+@File: reranker.py
+@Desc:
+"""
+import os
+import numpy as np
+import logging
+import uvicorn
+import datetime
+from fastapi import FastAPI, Security, HTTPException
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from FlagEmbedding import FlagReranker
+from pydantic import Field, BaseModel, validator
+from typing import Optional, List
+
+app = FastAPI()
+security = HTTPBearer()
+env_bearer_token = 'ACCESS_TOKEN'
+
+class QADocs(BaseModel):
+    query: Optional[str]
+    documents: Optional[List[str]]
+
+
+class Singleton(type):
+    def __call__(cls, *args, **kwargs):
+        if not hasattr(cls, '_instance'):
+            cls._instance = super().__call__(*args, **kwargs)
+        return cls._instance
+
+
+RERANK_MODEL_PATH = os.path.join(os.path.dirname(__file__), "bge-reranker-large")
+
+class ReRanker(metaclass=Singleton):
+    def __init__(self, model_path):
+        self.reranker = FlagReranker(model_path, use_fp16=False)
+
+    def compute_score(self, pairs: List[List[str]]):
+        if len(pairs) > 0:
+            result = self.reranker.compute_score(pairs, normalize=True)
+            if isinstance(result, float):
+                result = [result]
+            return result
+        else:
+            return None
+
+class Chat(object):
+    def __init__(self, rerank_model_path: str = RERANK_MODEL_PATH):
+        self.reranker = ReRanker(rerank_model_path)
+
+    def fit_query_answer_rerank(self, query_docs: QADocs) -> List:
+        if query_docs is None or len(query_docs.documents) == 0:
+            return []
+
+        pair = [[query_docs.query, doc] for doc in query_docs.documents]
+        scores = self.reranker.compute_score(pair)
+
+        new_docs = []
+        for index, score in enumerate(scores):
+            new_docs.append({"index": index, "text": query_docs.documents[index], "score": score})
+        results = [{"index": documents["index"], "relevance_score": documents["score"]} for documents in list(sorted(new_docs, key=lambda x: x["score"], reverse=True))]
+        return results
+
+@app.post('/v1/rerank')
+async def handle_post_request(docs: QADocs, credentials: HTTPAuthorizationCredentials = Security(security)):
+    token = credentials.credentials
+    if env_bearer_token is not None and token != env_bearer_token:
+        raise HTTPException(status_code=401, detail="Invalid token")
+    chat = Chat()
+    try:
+        results = chat.fit_query_answer_rerank(docs)
+        return {"results": results}
+    except Exception as e:
+        print(f"报错：\n{e}")
+        return {"error": "重排出错"}
+
+if __name__ == "__main__":
+    token = os.getenv("ACCESS_TOKEN")
+    if token is not None:
+        env_bearer_token = token
+    try:
+        uvicorn.run(app, host='0.0.0.0', port=6006)
+    except Exception as e:
+        print(f"API启动失败！\n报错：\n{e}")

python/bge-rerank/bge-reranker-large/requirements.txt

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+fastapi==0.104.1
+transformers[sentencepiece]
+FlagEmbedding==1.2.8
+pydantic==1.10.13
+uvicorn==0.17.6
+itsdangerous
+protobuf

python/bge-rerank/bge-reranker-v2-m3/Dockerfile

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+FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
+
+# please download the model from https://huggingface.co/BAAI/bge-reranker-v2-m3 and put it in the same directory as Dockerfile
+COPY ./bge-reranker-v2-m3 ./bge-reranker-v2-m3
+
+COPY requirements.txt .
+
+RUN python3 -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+COPY app.py Dockerfile .
+
+ENTRYPOINT python3 app.py

python/bge-rerank/bge-reranker-v2-m3/app.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time: 2023/11/7 22:45
+@Author: zhidong
+@File: reranker.py
+@Desc:
+"""
+import os
+import numpy as np
+import logging
+import uvicorn
+import datetime
+from fastapi import FastAPI, Security, HTTPException
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from FlagEmbedding import FlagReranker
+from pydantic import Field, BaseModel, validator
+from typing import Optional, List
+
+app = FastAPI()
+security = HTTPBearer()
+env_bearer_token = 'ACCESS_TOKEN'
+
+class QADocs(BaseModel):
+    query: Optional[str]
+    documents: Optional[List[str]]
+
+
+class Singleton(type):
+    def __call__(cls, *args, **kwargs):
+        if not hasattr(cls, '_instance'):
+            cls._instance = super().__call__(*args, **kwargs)
+        return cls._instance
+
+
+RERANK_MODEL_PATH = os.path.join(os.path.dirname(__file__), "bge-reranker-v2-m3")
+
+class ReRanker(metaclass=Singleton):
+    def __init__(self, model_path):
+        self.reranker = FlagReranker(model_path, use_fp16=False)
+
+    def compute_score(self, pairs: List[List[str]]):
+        if len(pairs) > 0:
+            result = self.reranker.compute_score(pairs, normalize=True)
+            if isinstance(result, float):
+                result = [result]
+            return result
+        else:
+            return None
+
+class Chat(object):
+    def __init__(self, rerank_model_path: str = RERANK_MODEL_PATH):
+        self.reranker = ReRanker(rerank_model_path)
+
+    def fit_query_answer_rerank(self, query_docs: QADocs) -> List:
+        if query_docs is None or len(query_docs.documents) == 0:
+            return []
+
+        pair = [[query_docs.query, doc] for doc in query_docs.documents]
+        scores = self.reranker.compute_score(pair)
+
+        new_docs = []
+        for index, score in enumerate(scores):
+            new_docs.append({"index": index, "text": query_docs.documents[index], "score": score})
+        results = [{"index": documents["index"], "relevance_score": documents["score"]} for documents in list(sorted(new_docs, key=lambda x: x["score"], reverse=True))]
+        return results
+
+@app.post('/v1/rerank')
+async def handle_post_request(docs: QADocs, credentials: HTTPAuthorizationCredentials = Security(security)):
+    token = credentials.credentials
+    if env_bearer_token is not None and token != env_bearer_token:
+        raise HTTPException(status_code=401, detail="Invalid token")
+    chat = Chat()
+    try:
+        results = chat.fit_query_answer_rerank(docs)
+        return {"results": results}
+    except Exception as e:
+        print(f"报错：\n{e}")
+        return {"error": "重排出错"}
+
+if __name__ == "__main__":
+    token = os.getenv("ACCESS_TOKEN")
+    if token is not None:
+        env_bearer_token = token
+    try:
+        uvicorn.run(app, host='0.0.0.0', port=6006)
+    except Exception as e:
+        print(f"API启动失败！\n报错：\n{e}")

python/bge-rerank/bge-reranker-v2-m3/requirements.txt

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+fastapi==0.104.1
+transformers[sentencepiece]
+FlagEmbedding==1.2.8
+pydantic==1.10.13
+uvicorn==0.17.6
+itsdangerous
+protobuf