Express.js API for CodeBox — user authentication, code project CRUD, and LangChain-powered AI code explanation.
npm install
npm run dev| Variable | Required | Description |
|---|---|---|
PORT | No | Server port (default 8000) |
MONGODB_URI | Yes | MongoDB connection string |
JWT_SECRET | Yes | JWT signing secret |
PINECONE_API_KEY | Yes | Pinecone API key |
PINECONE_INDEX | No | Index name (default codebox-code-chunks) |
PINECONE_CLOUD | No | Pinecone cloud provider (default aws) |
PINECONE_REGION | No | Pinecone region (default us-east-1) |
AI_API_KEY | Yes | OpenAI-compatible API key |
AI_MODEL | Yes | Chat model name |
AI_BASE_URL | Yes | OpenAI-compatible base URL |
EMBEDDING_MODEL | No | Embedding model (default text-embedding-3-small) |
EMBEDDING_DIMENSION | No | Vector dimension (default 1536, use 1024 for NVIDIA embed) |
CHUNK_SIZE | No | Max characters per chunk (default 800) |
CHUNK_OVERLAP | No | Overlap for line-based splits (default 120) |
RETRIEVAL_TOP_K | No | Candidates per retriever (default 6) |
RERANK_TOP_K | No | Final chunks after rerank (default 4) |
All routes require Authorization: Bearer <JWT>.
Send a message and receive an AI explanation.
Body
{
"message": "Explain this code",
"codeId": "optional-saved-code-id",
"sessionId": "optional-existing-session",
"codeSnippet": "optional live editor code",
"language": "javascript"
}Chunk and vector-index a saved code project into Pinecone.
Body:{ "codeId": "..." }
List chat sessions for the authenticated user.
Get all messages in a session.
View indexed chunks for a code project.
codebox-server/
├── controllers/chatController.js # Chat HTTP handlers
├── models/
│ ├── CodeChunk.model.js # Indexed code chunks
│ ├── ChatSession.model.js # Chat sessions
│ └── ChatMessage.model.js # Messages + retrieval metadata
├── services/
│ ├── ai/config.js # LLM + embedding clients
│ ├── chunking.js # Semantic code chunking
│ ├── indexing.js # Index pipeline
│ ├── pineconeStore.js # Pinecone vector store
│ ├── chat/chain.js # LangChain orchestration
│ └── retrieval/hybridRetriever.js # BM25 + vector + rerank
└── routers/route.js
- Chunk saved code into functions, classes, imports (line fallback with overlap)
- Embed chunks and store in Pinecone; metadata in MongoDB
- On chat query:
- BM25 search over in-memory chunk documents
- Vector similarity search in Pinecone (filtered by
codeId) - Reciprocal Rank Fusion to merge results
- LLM reranking to pick the best context
- LangChain prompt chain sends context + history to the LLM