Skip to content

Prompt

A huge benefit of using LLMs is the ability to ask follow ups and get outputs that are based on the growing bank of context that forms within a chat. Is there any way to develop my own system for interfacing with LLMs that allows the user to have "chats" with an LLM via a frontend and then store those cohesively into a database (ie, the chat would be one entity and perhaps the outputs and prompts would be stored as associated/related entities in a database)

Output

Summary of Prompt: Daniel wants to develop a system that allows users to have "chats" with an LLM via a frontend, with each chat stored cohesively as one entity in a database, linking associated prompts and outputs.

To develop this kind of system, consider these approaches:

  1. Designing a Conversation Data Model:
  2. Structure your database so that each "chat" is a unique entity. This entity would have a primary key (like a chat_id) and store metadata (e.g., start time, user ID, topic, etc.).
  3. Each prompt and response pair would be a separate record linked to the chat_id, creating a relationship between the chat and its messages.
  4. You can use relational databases (like PostgreSQL) for this purpose, or NoSQL options (like MongoDB) if you prefer more flexibility in data structure.
  5. LangChain or LlamaIndex:
  6. Both frameworks support creating session-based interactions, where the context from previous interactions is carried forward. You can store a running session as a "chat" and save each message exchange (prompt-response pairs) under that session.
  7. They offer integrations that let you store these interactions directly in databases, with various pre-built modules to speed up the process of managing context.
  8. Creating a Custom Backend:
  9. Flask (Python) or Express (Node.js) can handle API requests to the OpenAI API, maintain chat sessions, and save conversations in your database.
  10. Each chat can be initiated as a new session, and the backend can store all interactions tied to that session.
  11. Use database transactions to ensure that prompts and responses are consistently linked to their parent chat.
  12. Frontend Development:
  13. For the user interface, JavaScript frameworks like React or Vue.js can be used to build a chat interface that resembles ChatGPT's. Each time a user sends a prompt, the frontend can send the data to your backend, which then communicates with OpenAI's API.
  14. Include session management features to start, continue, or end chats, with the backend handling which chat context to pull from and continue.
  15. Conversation Storage Example:
  16. Database Schema:
    • Chats table: {chat_id, user_id, start_time, end_time, topic}
    • Messages table: {message_id, chat_id, role (user/assistant), content, timestamp}
  17. This setup makes it easy to retrieve entire conversations, manage context, and even rehydrate old chats for follow-up interactions.
  18. Pre-Built Solutions:
  19. Rasa: While primarily a conversational AI framework, it could be configured to work with OpenAI’s API. It handles context and session management out of the box and allows for custom integrations, including database storage.
  20. Botpress: Another conversational platform that can be integrated with OpenAI. It also has modules for managing chat sessions and can be customized to save interactions as per your desired data structure.

By implementing a system like this, you can create a more structured, cohesive way to interface with LLMs, preserving context over chats and allowing for more detailed analysis or re-engagement later.