Chat-With-Data is an open-source Streamlit application that integrates LangChain’s DataFrameAgent with OpenAI GPT models. Users can upload CSV, Excel, or connect to databases and ask questions in plain English. It returns insights, summaries, and visualizations on the fly. No coding required, making exploratory data analysis accessible to analysts and business users alike.
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Chat-With-Data
Chat-With-Data is an open-source Streamlit application that integrates LangChain’s DataFrameAgent with OpenAI GPT models. Users can upload CSV, Excel, or connect to databases and ask questions in plain English. It returns insights, summaries, and visualizations on the fly. No coding required, making exploratory data analysis accessible to analysts and business users alike.
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What is Chat-With-Data?
Chat-With-Data is a Python-based tool and web interface built on Streamlit, LangChain, and OpenAI’s GPT API. It automatically parses tabular datasets or database schemas and creates an AI agent that understands natural language queries about your data. Under the hood, it chunks large tables, builds an embedding index for semantic search, and formulates dynamic prompts to generate context-aware responses. Users ask questions like “What are the top 5 sales regions this quarter?” or “Show me a bar chart of revenue by category,” and receive answers or interactive plots without writing SQL or pandas code. The platform runs locally or on a server, ensuring data privacy while accelerating exploratory analysis for both technical and nontechnical users.
Who will use Chat-With-Data?
- Data analysts
- Business analysts
- Data scientists
- Financial analysts
- Researchers
- Non-technical business users
How to use the Chat-With-Data?
- Step1: Install the tool via pip: pip install chat-with-data
- Step2: Set your OpenAI API key as an environment variable (OPENAI_API_KEY)
- Step3: Launch the Streamlit app with: streamlit run main.py
- Step4: In the web UI, upload a CSV/Excel file or enter a database connection string
- Step5: Ask questions in natural language in the chat panel
- Step6: View generated insights, summaries, or visualizations directly in the interface
Platform
- Web
- Linux
- Mac
- Windows
Chat-With-Data's Core Features & Benefits
The Core Features
- Natural language querying of CSV and Excel files
- Support for database connections with SQLAlchemy
- Interactive Streamlit web interface
- On-the-fly data visualization with charts
- Integration with LangChain DataFrameAgent
- Semantic search over large tables via embeddings
The Benefits
- No coding required for data analysis
- Accelerates exploratory data workflows
- Accessible to non-technical users
- Automatic schema parsing and context-aware responses
- Customizable and extensible via Python
- Open-source and self-hosted for data privacy
Chat-With-Data's Main Use Cases & Applications
- Ad-hoc exploratory data analysis
- Business KPI reporting via conversational queries
- Rapid generation of charts and summaries
- Data-driven decision support in meetings
- Interactive data demos and training sessions
FAQs of Chat-With-Data
Chat-With-Data Company Information
Chat-With-Data Reviews
5/5
Chat-With-Data's Main Competitors and alternatives?
PandasAI
OpenAI Databrowse
ChatCSV
DataNitro
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