🤖 RAG and Personal RAG: Context Development Agents - Feb 2025 🚀

February 12, 2025 · View on GitHub

Personal RAG Pipeline Creation with AI Interview Bot Workflow

This repository showcases a workflow for creating personal RAG pipelines using AI interview bots. It includes agent configurations for building and managing contextual information, leveraging vector stores to enhance RAG pipelines. The ultimate goal is to connect this knowledge store to agents for more relevant and targeted guidance.

Repo Map

Agent Roles and Workflow

In modern AI applications, specialized agents are increasingly used to handle specific tasks. This project is my exploration of using such agents to build a personal context data store, improving the performance and relevance of AI interactions. The core idea is that by engaging in "interviews" with AI agents, I can cultivate a robust contextual data knowledge store that reflects my unique experiences, skills, and goals.

During my experimentation, I created various agents, each tailored to a specific aspect of the context development process. These configurations reflect different approaches and strategies for building a comprehensive context profile and a powerful contextual data knowledge store.

For example, my initial "interviewer" agent asked questions randomly to gather information. A later version was connected to the contextual data vector store, allowing it to refine its questions based on existing data. This iterative approach aimed to optimize the interviewing process by targeting questions based on identified knowledge gaps and enriching the contextual data knowledge store.

The envisioned workflow (V1) can involve either a single agent handling all tasks or multiple specialized agents working together. I favor the latter approach for its modularity and maintainability, allowing each agent to focus on a specific part of the task. This isolation simplifies debugging, testing, and future enhancements to the agents and the overall contextual data knowledge store.

In addition to configurations that attempt to automate the entire context development process, there's also a standalone agent dedicated to parsing raw interview data and converting it into structured context snippets for inclusion in the knowledge store.

A planning agent is also included, which doesn't directly participate in interviewing or parsing but assists me in ideating and planning my context data development efforts, ensuring the knowledge store is comprehensive and well-organized.

Agent Configuration Summary

Agent Configuration	Description	Raw Config	JSON Config
General Interviewer	This agent conducts interviews with me to generate contextual data. It asks questions at random and structures the output in a specific format suitable for vector storage, contributing to the overall contextual data knowledge store.
Gap-Filler Interviewer	This agent identifies and fills in gaps in existing contextual data about me. It takes a proactive approach, probing areas that need development and enrichment to create a more complete context profile and a more comprehensive knowledge store.
Context Extractor	This agent acts as a text formatting tool, extracting contextual data from unstructured text and reformatting it in the third person. This is useful for converting existing documents into context snippets for inclusion in the contextual data knowledge store.
Context Planner	This agent assists me in planning my context data development efforts. It provides recommendations and suggestions for specific context snippets to develop, helping to guide the overall process of building a comprehensive and well-organized knowledge store.

Implementation Sketch

To set up an actual workflow for building my contextual data knowledge store, I would typically follow these steps:

Choose a Vector Database: Select a vector database to store my context data. Options include:
- Pinecone: A fully managed vector database ideal for production environments.
- Milvus: An open-source vector database that offers high performance and scalability.
- Weaviate: A graph-based vector database that supports complex data relationships.
- FAISS: A library for efficient similarity search and clustering of dense vectors, suitable for smaller-scale projects.
Set up the Agents: Configure the agents described above, ensuring they can access and interact with the vector database. This typically involves setting up API keys and authentication credentials, allowing them to contribute to and access the contextual data knowledge store.
Create a Data Pipeline: Implement a data pipeline to process the output from the agents and store it in the vector database. This might involve using tools like:
- Langchain: A framework for building applications powered by language models.
- ** নিজস্ব scripts:** Custom scripts written in Python or other languages to handle data transformation and loading into the knowledge store.
Develop a RAG Pipeline: Build a Retrieval-Augmented Generation (RAG) pipeline that retrieves context data from the vector database and uses it to enhance the responses of a large language model. This can be implemented using frameworks like Langchain or Haystack, enabling more relevant and targeted interactions with AI agents.

Use Cases

See use-cases/personal.md for personal use cases and use-cases/business.md for business use cases.

Workflow Examples

See workflows.md for detailed workflow examples.

Contributing

I welcome contributions to this repository! If you have any ideas for new agents, improvements to existing agents, or other enhancements, please feel free to submit a pull request.

I invite you to discuss these ideas further! You can find me at danielrosehill.com or reach out via email at public@danielrosehill.com.

Check out an example of a context-driven interview here.

Repository	Description
	Several workflows have been mapped out for using agents to proactively build a store of personal data. One approach involves connecting the interviewing agent to a growing personal contacts data store. Another approach involves using the interviewing agent and then manually feeding in the data.
	This Streamlit application models a basic AI agent interview process. The interviewing agent asks the user questions at random, and the user can periodically download the gathered contextual data.
	This repository contains general notes regarding the personal context or ideation idea and why it could be highly beneficial for AI users from a privacy protection standpoint.
	This is an individual configuration for a context data generation bot.
	This repository outlines the personal context repo idea.
	This is a demo public context repository.

12-Feb-25

Benefits of Proactive Contextual Data Generation

This approach offers a proactive method for generating personal contextual data, contrasting with reactive methods where data is extracted after user interaction with an AI tool. By deliberately creating and curating contextual data, users can:

Enhance Personalized AI: Provide AI agents with selective and purpose-built personal data, improving the relevance and accuracy of AI interactions.
Maintain Data Control: Empower users to control their own data store, avoiding vendor lock-in and ensuring portability across different AI tools.
Improve Efficiency: Streamline the RAG pipeline by focusing on high-quality, relevant data, reducing the workload on vector stores and RAG pipelines.

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