Autonomous Multi-Agent RAG & Tool Calling AI Platform
Candidate Portfolio Role: AI Systems & LLMOps Architect
Production Generative AI platform implementing LangGraph stateful multi-agent workflows, hybrid BM25 + dense vector retrieval, guardrail moderation, and real-time SSE token streaming.
Industry Problem Statement & Production Scope
Enterprise LLM deployments encounter hallucinations, stale context, prompt injection exploits, and unbound latency without semantic chunking, cross-encoder reranking, and sandboxed tool calling.
⚡ Live Distributed Service Topology
All Nodes Reporting NominalFastAPI SSE Streaming Gateway
HTTP / SSEHandles client connections with Server-Sent Events for word-by-word streaming.
NeMo Guardrail Filter
Internal HookScans prompts for prompt injection, jailbreaks, and PII before model dispatch.
Hybrid RAG Retriever (BM25 + Dense)
gRPC / HNSWCombines Pinecone dense vectors with Elasticsearch BM25 sparse indices.
Cohere Cross-Encoder Reranker
HTTPS APIReranks top-50 documents to select top-5 most relevant context chunks.
LangGraph Cyclical State Machine
Async Python GraphExecutes autonomous tool calling, fact-checking, and final synthesis.
📡 Simulated Gateway Ingress Endpoints
Live Response Payload Previews{
"query_id": "rag_0019283",
"input_query": "Explain PostgreSQL Row Level Security implementation in multi-tenant SaaS.",
"retrieved_documents_count": 5,
"reranker_top_score": 0.941,
"cited_sources": [
{
"title": "PostgreSQL 16 Security Guide",
"url": "https://docs.mockattempt.com/pg-rls"
},
{
"title": "Spring Data JPA Tenant Isolation Pattern",
"url": "https://docs.mockattempt.com/jpa-tenant"
}
],
"generated_response": "Row-Level Security (RLS) restricts row access based on the executing database user or runtime session variables (e.g. app.current_tenant_id)...",
"faithfulness_score": 0.98,
"total_tokens": 1420
}🗄️ Relational Data Models & Storage Architecture
| Entity / Table | Key Attributes & Schema | Architectural Purpose |
|---|---|---|
| knowledge_documents | id, title, content_hash, chunk_strategy, created_at | Source document master and ingestion metadata. |
| vector_embeddings | id, document_id, vector_index_id, chunk_index, token_length | Embedded chunks corresponding to Pinecone HNSW nodes. |
| agent_execution_traces | id, session_id, tool_name, input_arguments, tool_output, latency_ms | Granular trace history for LangSmith and observability. |
Download Complete Capstone Starter Kit
Contains complete Docker Compose orchestrations, database seed migrations, CI/CD pipeline declarations, Postman/Swagger API collections, and step-by-step developer deployment guide.