Interact with local database

[GentleLemon]

A great project, but langchain has strong data analysis capabilities. We have a strong need for generating sql through natural language, querying the database, and even csv.

[dosubot[bot]]

Hey @GentleLemon, great to see you diving deeper into the Langchain-Chatchat ecosystem! How's everything going on your end?

I'm glad to hear that you're finding Langchain-Chatchat useful for your data analysis needs. To enhance its functionality to interact with a local database and generate SQL queries through natural language inputs, you can follow the steps I've outlined below:

1. SQL Query Generation: You can integrate a natural language processing (NLP) model that specializes in converting natural language to SQL queries. One popular choice is the use of pre-trained models like sqlparse or leveraging large language models that have been fine-tuned for SQL generation tasks.

2. Database Interaction: For interacting with a local database, you can use libraries such as sqlite3 for SQLite databases or sqlalchemy for a more database-agnostic approach. These libraries allow you to execute SQL queries, fetch results, and manipulate data in your database.

3. CSV File Interaction: For reading from and writing to CSV files, the pandas library is very efficient and user-friendly. It allows for easy data manipulation and can be used to read data from a CSV file into a DataFrame, which can then be queried or modified using natural language inputs that are converted to SQL or pandas queries.

Here's a simplified example of how you might start integrating these functionalities:

import pandas as pd
import sqlite3
from some_nlp_to_sql_model import nlp_to_sql  # This is a placeholder for the actual NLP to SQL model you choose

# Example function to convert natural language to SQL and execute it
def execute_natural_language_query(nl_query, db_connection):
    # Convert natural language to SQL
    sql_query = nlp_to_sql(nl_query)

    # Execute SQL query
    cursor = db_connection.cursor()
    cursor.execute(sql_query)
    results = cursor.fetchall()

    return results

# Example function to interact with a CSV file using a natural language query
def query_csv_with_natural_language(nl_query, csv_file_path):
    # Load CSV file into a DataFrame
    df = pd.read_csv(csv_file_path)

    # Convert natural language to a query (this could be SQL or a pandas query)
    # For simplicity, let's assume it's a pandas query here
    pandas_query = nlp_to_sql(nl_query)  # Assuming this function can also generate pandas queries

    # Query the DataFrame
    results = df.query(pandas_query)

    return results

# Example usage
db_connection = sqlite3.connect('my_local_database.db')
nl_query = "Show me all records where age is greater than 30"
db_results = execute_natural_language_query(nl_query, db_connection)

csv_file_path = 'my_data.csv'
csv_results = query_csv_with_natural_language(nl_query, csv_file_path)

This example assumes the existence of a function nlp_to_sql that can convert natural language queries into SQL or pandas queries. In reality, you would need to integrate or develop a model capable of this conversion. Large language models, when properly fine-tuned, can perform such tasks. However, integrating them would require additional setup, including defining the model architecture, training (or fine-tuning), and deploying the model for inference.

As for integrating these functionalities into the Langchain-Chatchat framework, you can consider creating a new Agent or extending the existing StructuredGLM3ChatAgent to include these capabilities. This would involve overriding the process method to handle the new types of inputs (i.e., natural language queries for SQL and CSV interactions), and possibly extending the BaseTool class to define new tools for these tasks.

Please note that while the Langchain-Chatchat architecture is designed to be extensible and customizable, integrating these new functionalities would require a good understanding of the existing codebase and careful design to ensure compatibility with the existing components and workflows.

I hope this helps! If you have any further questions or need more specific guidance, feel free to ask.

Sources

• webui_pages/dialogue/dialogue.py
• server/agent/custom_agent/ChatGLM3Agent.py

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

[github-actions[bot]]

这个问题已经被标记为 stale ，因为它已经超过 30 天没有任何活动。