Overview
Custom AI agent leveraging Retrieval-Augmented Generation to provide context-aware coding assistance and documentation search, increasing developer productivity by 30%.
The Problem
Developers spend excessive time searching through documentation, Stack Overflow, and codebases. Generic AI assistants lack project-specific context and often provide outdated or incorrect information.
The Solution
Built a RAG-based chatbot that indexes project documentation, API references, and internal codebases into a vector database. Uses Gemini Pro for natural language understanding and code generation, with retrieval ensuring responses are grounded in actual documentation.
Project Gallery
Technical Architecture
Retrieval-Augmented Generation system with vector search and LLM integration
Document Ingestion Pipeline
Processes markdown, code files, and API docs into chunked embeddings
Vector Database (Chroma)
Stores and retrieves relevant documentation chunks based on semantic similarity
Gemini Pro Integration
Generates contextual responses using retrieved documentation as context
Code Execution Sandbox
Safely executes generated code snippets for validation
Methodology
- Indexed 50K+ documentation pages and 100K+ lines of code
- Chunking strategy: 512 tokens with 50-token overlap
- Embeddings: text-embedding-004 (768 dimensions)
- Retrieval: Top-5 chunks with MMR for diversity
- Prompt engineering: Few-shot examples for code generation
Results & Impact
Key Impact
- Reduced documentation search time from 15 min to 2 min
- Onboarding time for new developers cut by 40%
- Consistent code style through AI-suggested patterns
- Adopted by 25+ developers in organization
Challenges & Solutions
Hallucination Prevention
Strict retrieval filtering and confidence thresholds; cite sources in responses
Code Context Understanding
AST parsing and dependency graph analysis for better code comprehension
API Rate Limits
Caching layer for common queries and exponential backoff retry logic
Key Implementation
RAG Query Pipeline
class RAGCodeAssistant:
def __init__(self, vector_db, llm):
self.vector_db = vector_db
self.llm = llm
def query(self, user_question):
# Retrieve relevant documentation
docs = self.vector_db.similarity_search(
query=user_question,
k=5,
filter={"type": "documentation"}
)
# Build context from retrieved docs
context = "\n\n".join([
f"Source: {doc.metadata['source']}\n{doc.page_content}"
for doc in docs
])
# Generate response with context
prompt = f"""You are a coding assistant. Use the following documentation to answer the question.
Documentation:
{context}
Question: {user_question}
Answer with code examples where appropriate. Cite your sources."""
response = self.llm.generate(prompt)
return {
"answer": response,
"sources": [doc.metadata['source'] for doc in docs]
}