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feat: OSPP2025 - privateGPT 添加finRAG example

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ospp_privateGPT/Dockerfile.ubuntu
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@ -38,7 +38,7 @@ RUN poetry env use python3.11
RUN poetry config virtualenvs.create false
# 复制 poetry 配置文件
COPY pyproject.toml poetry.lock ./
# COPY pyproject.toml poetry.lock ./
# 安装 Python 依赖
RUN poetry lock && \

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3.10

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# finRAG 示例
这个示例展示了如何使用 PrivateGPT 处理 finRAG 金融数据集。finRAG 是一个包含金融问答对的数据集，可以用于训练和测试金融领域的 RAG (Retrieval-Augmented Generation) 系统。
## 功能特性
- 自动下载 finRAG 数据集从 Hugging Face
- 解析 Parquet 格式的金融数据
- 将数据上传到 PrivateGPT 进行向量化和存储
- 支持 OpenGauss 数据库作为向量存储后端
## 前置要求
- Docker 和 Docker Compose
- Python 3.10+ (如果本地运行)
- uv (Python 包管理器)
## 快速开始
### 1. 启动服务
首先启动 PrivateGPT 和相关服务：
```bash
# 在项目根目录下运行
docker compose up -d
```
这将启动以下服务：
- OpenGauss 数据库 (端口 5432)
- PrivateGPT 服务 (端口 8001)
- Ollama 服务 (端口 11434)
### 2. 等待服务就绪
等待所有服务启动完成，特别是 OpenGauss 数据库的健康检查通过。
### 3. 下载并上传数据
进入 finRAG 示例目录并运行数据下载和上传脚本：
```bash
cd examples/finRAG
uv run python download_dataset.py
```
这个脚本会：
1. 从 Hugging Face 下载 finRAG 数据集
2. 解析 `rag_text.parquet` 文件
3. 将前 10 条记录转换为文本格式
4. 通过 PrivateGPT API 上传到向量数据库
## 配置说明
### 环境变量
脚本使用以下默认配置：
- API 地址: `http://localhost:8001`
- 数据集: `parsee-ai/finRAG`
- 处理记录数: 10 条 (可在代码中修改)
### 修改处理数量
要处理更多数据，编辑 `download_dataset.py` 文件中的最后一行：
```python
if __name__ == "__main__":
ingest_finRAG_to_privategpt(n=100) # 修改这里的数字
```
## 数据格式
finRAG 数据集包含以下字段：
- `question`: 金融相关问题
- `context`: 相关上下文信息
- `answer`: 对应的答案
每条记录会被转换为以下格式的文本文件：
```
QUESTION:
[问题内容]
CONTEXT:
[上下文内容]
ANSWER:
[答案内容]
```
## API 端点
上传完成后，你可以通过以下 API 端点与 PrivateGPT 交互：
- 健康检查: `GET http://localhost:8001/health`
- 文档列表: `GET http://localhost:8001/v1/ingest/list`
- 搜索文档: `POST http://localhost:8001/v1/completions`
## 故障排除
### 常见问题
1. **容器启动失败**
```bash
# 检查容器状态
docker compose ps
# 查看日志
docker compose logs private-gpt-ubuntu
```
2. **API 连接失败**
- 确保 PrivateGPT 服务已完全启动
- 检查端口 8001 是否被占用
- 等待 OpenGauss 数据库健康检查通过
3. **数据下载失败**
- 检查网络连接
- 确保有足够的磁盘空间
- 验证 Hugging Face 访问权限
### 清理资源
停止并清理所有服务：
```bash
docker compose down -v
```
这将停止所有容器并删除相关卷。
## 项目结构
```
examples/finRAG/
├── README.md # 本文档
├── download_dataset.py # 数据下载和上传脚本
├── main.py # 主程序入口
├── pyproject.toml # Python 项目配置
└── uv.lock # 依赖锁定文件
```
## 依赖项
- `huggingface-hub`: 从 Hugging Face 下载数据集
- `pandas`: 数据处理
- `pyarrow`: Parquet 文件支持
- `requests`: HTTP 请求
- `tqdm`: 进度条显示

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ospp_privateGPT/examples/finRAG/download_dataset.py Normal file
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import os
import io
import logging
import pandas as pd
import requests
from huggingface_hub import snapshot_download
from tqdm import tqdm
API_BASE = "http://localhost:8001"
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def download_finRAG() -> str:
"""Download the finRAG dataset from Hugging Face to local cache.
Returns the local directory path."""
local_dir = snapshot_download(repo_id="parsee-ai/finRAG", repo_type="dataset")
logger.info("Downloaded finRAG to %s", local_dir)
return local_dir
def load_parquet(local_dir: str, fname: str = "rag_text.parquet") -> pd.DataFrame:
"""Load the specified Parquet file from the dataset folder."""
path = os.path.join(local_dir, fname)
if not os.path.exists(path):
raise FileNotFoundError(f"{path} not found")
df = pd.read_parquet(path)
logger.info("Loaded Parquet with %d rows, columns: %s", len(df), list(df.columns))
return df
def doc_row_to_text(row: pd.Series) -> str:
"""Format a DataFrame row into a single text blob for ingestion."""
row_dict = row.to_dict()
return "\n\n".join([f"{k.upper()}:\n{v}" for k, v in row_dict.items()])
def ingest_document_text(api_url: str, text: str, filename: str, timeout: int = 300) -> bool:
"""Upload a single document string via PrivateGPT Ingest File API."""
files = {
"file": (filename, io.BytesIO(text.encode("utf-8")), "text/plain"),
}
resp = requests.post(api_url, files=files, timeout=timeout)
if resp.status_code == 200:
return True
else:
logger.error("Failed ingest %s → %s: %s", filename, resp.status_code, resp.text)
return False
def ingest_finRAG_to_privategpt(n: int = 100):
"""Main pipeline: download, parse, and ingest first n docs from finRAG."""
local_dir = download_finRAG()
df = load_parquet(local_dir, "rag_text.parquet")
cols = [c for c in ["question", "context", "answer"] if c in df.columns]
ingest_api = f"{API_BASE}/v1/ingest/file"
success, fail = 0, 0
for idx, row in tqdm(df.head(n).iterrows()):
text = doc_row_to_text(row)
fname = f"finRAG_{idx:06d}.txt"
if ingest_document_text(ingest_api, text, fname):
success += 1
else:
fail += 1
logger.info("Ingestion finished: %d successes, %d failures", success, fail)
if __name__ == "__main__":
ingest_finRAG_to_privategpt(n=10)

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ospp_privateGPT/examples/finRAG/pyproject.toml Normal file
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[project]
name = "finrag"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"huggingface-hub>=0.35.2",
"pandas>=2.3.2",
"pyarrow>=21.0.0",
]

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ospp_privateGPT/examples/finRAG/uv.lock Normal file
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