https://github.com/databytesun/csv-data-agent-with-deepseek-r1
Chat with your dataset.
https://github.com/databytesun/csv-data-agent-with-deepseek-r1
agent agent-based database dataquery deepseek deepseek-r1 huggingface-transformers langchain python
Last synced: 4 months ago
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Chat with your dataset.
- Host: GitHub
- URL: https://github.com/databytesun/csv-data-agent-with-deepseek-r1
- Owner: DataByteSun
- Created: 2025-02-03T14:00:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-04T14:44:14.000Z (over 1 year ago)
- Last Synced: 2025-07-06T15:04:32.745Z (11 months ago)
- Topics: agent, agent-based, database, dataquery, deepseek, deepseek-r1, huggingface-transformers, langchain, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.03 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# CSV Data Agent with DeepSeek-R1
## Overview
This project demonstrates how to interact with CSV data using a DeepSeek-R1-Distill-Qwen-32B. It integrates a streaming LLM from Hugging Face to process natural language queries on a pandas DataFrame. The project leverages `langchain`, `pandas`, and `huggingface_hub` to enable seamless data interaction.
## Features
- Uses a Hugging Face-hosted LLM `(deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)` for processing queries.
- Parses structured output from the LLM.
- Integrates `langchain` to create a pandas DataFrame agent.
- Allows querying CSV data using natural language prompts.
- Supports structured parsing for responses.
## Installation
1. Clone this repository:
```
git clone https://github.com/DataByteSun/CSV-Data-Agent-with-DeepSeek-R1.git
```
2. Create and activate a virtual environment (optional but recommended):
```
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
```
3. Install dependencies:
```
pip install -r requirements.txt
```
4. Set up your environment variables:
- Create a .env file in the project directory and add your Hugging Face API key(Get Free key from Huggung Face):
```
HUGGINGFACE_API_KEY=your_huggingface_api_key
```
- Load environment variables:
```
from dotenv import load_dotenv
load_dotenv()
```
## Usage
1. Load and preprocess your CSV data:
```
import pandas as pd
df = pd.read_csv("your_data.csv")
```
2. Instantiate the LLM and create an agent:
```
from huggingface_hub import InferenceClient
client = InferenceClient(api_key="your_huggingface_api_key")
agent = create_pandas_dataframe_agent(llm=client, df=df, verbose=True)
```
3. Query the agent using natural language:
```
result = agent.invoke("how many rows are there?")
```
## Expected Output (Screenshots)
- Running `agent.invoke("how many rows are there?")` may yield an output like:
- With a Prompt Engineering
Question: `How may patients were hospitalized during Mar 2021 in Alaska use column hospitalizedCumulative?`
> ⚠️ **Warning:**
> This code includes experimental components that may pose risks. Ensure thorough testing in a sandboxed environment to avoid potential vulnerabilities or data loss.
## Customization
Modify `model_name` in the Hugging Face API request to experiment with different LLMs.
Adjust parameters such as `temperature`, `max_tokens`, and `top_p` for fine-tuning responses.
## Contributions
Feel free to contribute by opening an issue or submitting a pull request.
## Contact
For questions or feedback, reach out to `surajpawar.in@gmail.com`.