example_document.txt

ContextAgent: AI Assistant Backend

ContextAgent is a modular, production-ready AI assistant backend built with Python, LangChain, OpenAI API, and RAG (Retrieval-Augmented Generation) pipeline.

Key Features:
- RAG Pipeline: Embed documents and perform similarity search for context retrieval
- LangChain Agent: Chain of tools including Calculator and Google Search
- Conversational Memory: Maintains conversation history using LangChain's ConversationBufferMemory
- Document Ingestion: Support for PDFs, TXT, Markdown, and DOCX files
- Embeddings: OpenAI embeddings for document vectorization
- Vector Store: ChromaDB for fast document retrieval
- Environment-based Configuration: Secure API key management
- Swagger Documentation: Auto-generated API docs
- FastAPI Backend: High-performance async API

Architecture:
The system is built with a modular architecture that separates concerns:
- Routes: Handle HTTP requests and responses
- Chains: Manage LLM interactions and RAG pipelines
- Tools: Provide specific functionalities like calculations and web search
- Memory: Maintain conversation state
- Ingest: Handle document processing and vector storage
- Utils: Configuration and utility functions

Use Cases:
- Customer support chatbots
- Internal knowledge assistants
- PDF and document Q&A tools
- Custom agent workflows
- Research and analysis tools

The system can be deployed as a standalone service or integrated into existing applications. It provides a RESTful API that can be consumed by any frontend application.