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Langchain Database Agent with Keyword-Based Search and Visualization

Welcome to the Langchain Database Agent! This tool uses Langchain to create a powerful and intelligent agent that can interact with a database, conduct keyword-based searches, and visualize data based on user queries. The tool combines efficient keyword management, similarity search, and graphing capabilities, enabling users to derive meaningful insights from their data seamlessly.

Table of Contents

1. Features
2. How It Works
3. Installation
4. Usage
5. Configuration Options
6. Contributing
7. License

Features

• Langchain Agent: Uses Langchain to create an interactive, conversational agent for efficient data querying.
• Keyword-Based Search: Incorporates a unique keyword column in the database for easy and precise searches.
• Similarity Search with Embeddings: Utilizes keyword embeddings for similarity-based searches, making queries contextually relevant.
• Intermediary Table: Stores query results in an intermediary table for ease of processing and visualization.
• Data Visualization: Generates graphs and other visualizations based on user queries to help interpret data patterns quickly.

How It Works

This tool follows a unique approach to querying and visualizing data through the following steps:

1. Database Setup with Keywords:
   The database schema includes a special column, keywords, in each table. This column holds keywords relevant to the records, aiding in fast similarity searches. Additionally, a separate file saves these keywords along with their embeddings, which represent the semantic meaning of each keyword.

2. Embedding-Based Similarity Search:
   When a user interacts with the agent, they input keywords or phrases relevant to their query. The agent performs a similarity search by comparing these input keywords with stored embeddings. This step allows the agent to find records in the database that closely match the query's intent.

3. Query Execution and Storage in Intermediary Table:
   After identifying relevant keywords and their associated records, the agent performs a database query to retrieve the data. This data is then stored in an intermediary table, which serves as a temporary storage location for easy processing and visualization.

4. Data Visualization:
   Once the data is in the intermediary table, visualization tools process it to generate graphs and other visual representations. Users can quickly interpret the patterns and trends within their queried data, enhancing their decision-making process.

Installation

To install this tool, follow these steps:

1. Clone the Repository:

   git clone https://github.com/yourusername/langchain-database-agent.git
   cd langchain-database-agent

2. Install Dependencies: Ensure you have Python installed. Then, install the required packages:

   pip install -r requirements.txt

3. Database Setup:

   • Set up your database with a keywords column in each relevant table.
   • Ensure you have a way to generate and save embeddings for these keywords in the designated file.

4. Configure Settings: See Configuration Options below to adjust paths and database credentials.

Usage

1. Starting the Agent: Launch the Langchain agent to interact with the database:

   python agent.py

2. Interacting with the Agent:

   • Enter keywords or phrases relevant to your query.
   • The agent performs a similarity search using the keywords, identifies the best matches, and queries the database accordingly.

3. Viewing Results and Visualizations:

   • After querying, the results are stored in the intermediary table.
   • Access visualization tools to create graphs and charts based on the data in this table. For example:

     python visualize.py

     This will display graphs based on the data relevant to your query.

Configuration Options

In the configuration file (config.json), set the following:

• Database Credentials: Update the database URL, username, password, and other required connection details.
• Paths to Embedding Files: Specify the path to the keyword and embedding file to ensure the agent can access embeddings for similarity searches.
• Visualization Settings: Choose the type of graphs (e.g., line charts, bar graphs) and adjust display preferences as needed.

Contributing

Contributions are welcome! If you’d like to enhance this tool or fix an issue, please:

1. Fork the repository.
2. Create a feature branch.
3. Make your changes and push the branch.
4. Submit a pull request with a description of your updates.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Enjoy interacting with and visualizing your data through our Langchain Database Agent! If you have questions or suggestions, please feel free to open an issue or contribute.