This repository contains the code and documentation for a sophisticated car search chatbot with advanced functionalities, leveraging vector databases and Large Language Models (LLMs). Below, you'll find a detailed overview of the project's functionalities and how to get started.

1. Vector Creation for Car Documents:

   • Create vectors for car documents to enable efficient and accurate document retrieval.

2. Index Creation in ChromaDB:

   • Organize and manage vectors by creating an index in ChromaDB and upserting (updating and inserting) these vectors locally.

3. LLM Integration:

   • Integrate a Large Language Model (LLM) to enhance the chatbot's ability to understand and generate human-like responses.

4. Document Retriever Creation:

   • Set up a retriever to fetch vectors from ChromaDB, ensuring relevant information is readily accessible.

5. Integration with LangChain:

   • Use the create_stuff_documents_chain function from LangChain to integrate our prompt and LLM, forming a cohesive unit.

6. Retrieval Chain Construction:

   • Create a retrieval chain to facilitate seamless communication between the LLM, document retriever, and the prompt.

7. Conversation History Management:

   • Use LangChain's history_langchain_format to store conversation history, allowing the chatbot to perform actions or generate messages based on previous human responses.

8. Secondary LLM for User Information Extraction:

   • After completing a conversation, pass all messages to a second LLM to extract the user's name and email, validate it, and provide the output in the desired format.

9. Local Storage of Information:

   • Store the extracted and validated user information in a local directory.

• docker build --build-arg OPENAI_API_KEY="api-key" -t chatbot .

• docker run chatbot

• Python 3.10
• Required Python libraries (detailed in requirements.txt)