Chat API

RAG-Powered Conversational AI

Python-based chat API integrating retrieval-augmented generation (RAG) with structured tool access for real-time data. Implemented conversation memory and semantic context retrieval pipelines to improve LLM accuracy in technical support and project assistance workflows.

Role: Backend Developer

Duration: 1 month

Team: Solo

Tech Stack

PythonFastAPILangChainRAG

Key Highlights

RAG integration for real-time data access

Conversation memory & semantic context retrieval

Technical support workflow optimization

Architecture

Chat API implements a modular architecture with clear separation of concerns: 1. **Conversation Manager**: Handles session state, message history, and context windowing for multi-turn conversations. 2. **RAG Pipeline**: Retrieves relevant documents using semantic search and formats them as context for the LLM. 3. **Tool Integration**: Structured tool access allows the LLM to query databases, APIs, and external services. 4. **Response Generator**: Combines conversation history, retrieved context, and tool results to generate accurate responses.

Challenges

01Managing context window limits while preserving conversation flow
02Implementing effective tool discovery and invocation
03Handling concurrent conversations without resource contention
04Balancing response quality with latency requirements

Key Learnings

Structured tool access significantly improves LLM capabilities
Conversation memory management is critical for multi-turn interactions
RAG pipelines need careful tuning for domain-specific content
FastAPI's async capabilities are essential for real-time chat

Future Work

→Streaming response support
→Multi-modal input (images, files)
→Advanced tool orchestration

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