LangChain Text File Chat

Overview

This repository showcases a Python-based chat application using LangChain, a library for building applications with language models. It emphasizes creating a conversational interface using OpenAI's language models, focusing on conversation history and dynamic prompting.

Key Features

• Language Model Interaction: Uses ChatOpenAI for engaging with OpenAI's language models.
• Conversation Tracking: Employs ConversationSummaryMemory to keep track of conversation history.
• Responsive Prompts: Utilizes ChatPromptTemplate and HumanMessagePromptTemplate for dynamic and context-aware prompt generation.
• Configuration Management: Manages settings via dotenv, facilitating environment variable handling.

How It Works

1. Setup: Initializes with necessary environment variables.
2. Model Configuration: Prepares the OpenAI chat model with detailed output options.
3. Memory Initialization: Sets up a system to store and recall conversation histories.
4. Prompt Handling: Configures prompts to dynamically incorporate previous dialogues and new inputs.
5. Chat Interface: Runs an interactive loop, processing user inputs and generating contextually relevant responses.

Repository Structure

• LICENSE: License agreement for the repository.
• README.md: Descriptive documentation of the project.
• facts: Directory containing the main components for the chat application.
  • emb: Subdirectory for Chroma's binary and SQLite3 data files.
  • facts.txt: Text file serving as the primary data source for document processing.
  • main.py: Script for initializing and querying the Chroma database.
  • prompt.py: Script for executing the question-answering chain.
  • redundant_filter_retriever.py: Script for filtering redundant information from retrieved results.
• main.py: Main script for running the chat application.

Usage Instructions

1. Install Dependencies: Set up Python and required libraries (dotenv, langchain).
2. Clone the Repository: Download the repository to your machine.
3. Configure Environment: Create and configure a .env file with necessary settings.
4. Run the Application: Execute main.py to start the chat application.
5. Engage with the Chatbot: Interact with the chatbot, which uses past interactions for context-aware responses.

For Chroma Embeddings

• Document Handling: Process and segment the facts.txt document.
• Database Creation: Build a Chroma database from segmented documents.
• Information Retrieval: Utilize the database for similarity searches related to queries.
• Query Handling: Use prompt.py to ask questions and receive answers based on facts.txt.

Applications

• Suitable for advanced chatbots and conversational agents needing memory and contextual understanding.
• Ideal for information retrieval from extensive text files, enhancing FAQ systems, knowledge bases, and educational tools.

This streamlined approach, combining LangChain and Chroma, offers efficient, context-aware, and sophisticated conversational experiences and data retrieval.