Recommendica: AI Research Paper Recommendation Agent with Multi-Turn Query Expansion

The Challenge: Why Recommendica Was Built

Traditional semantic search systems for academic papers suffer from two critical flaws: they return top-K results regardless of actual relevance, and they’re limited by static local datasets. This leads to:

• Hallucinated citations when RAG systems reference irrelevant papers
• Missed discoveries from recent arXiv preprints not yet indexed
• Wasted API costs processing clearly off-topic queries

Recommendica solves these through an active Relevance Agent that dynamically refines searches and a live arXiv fallback that supplements local results when coverage is insufficient.

Core Architecture & Technical Stack

Service Orchestration

The system combines Django for business logic with React for the responsive frontend:

┌─────────────────┐    HTTP/SSE     ┌─────────────────┐
│ React Frontend  │ ◄─────────────► │ Django REST API │
└─────────────────┘                └────────┬────────┘
                                            │
                            ┌───────────────┼────────────────┐
                            ▼               ▼                ▼
                ┌─────────────────────┐ ┌─────────────┐ ┌─────────────┐
                │ ChromaDB Vector DB  │ │ Paddle Billing│ │ arXiv API   │
                └─────────────────────┘ └─────────────┘ └─────────────┘

Parallel Generation Engine

To optimize latency and cost:

• Documents partitioned into groups (default: 5 papers per chunk)
• Parallel workers (default: 3) process chunks concurrently
• Results collated and streamed in original query order

Key Features Breakdown

Multi-Turn Relevance Agent

The agent operates through an iterative loop:

1. Initial vector search retrieves candidate papers
2. LLM grades each on 0.0-1.0 relevance scale
3. If insufficient papers meet threshold (default: 0.5 score):
   • Analyzes rejection patterns
   • Dynamically rewrites query
   • Executes secondary search (max 2 iterations)

Live arXiv Fallback System

When local results are inadequate:

• Circuit breaker checks API status
• Rate limiter enforces 3s minimum request interval
• Results tagged with meta.source="arxiv_api"
• Failure tracking triggers 5-minute cooldown after 3 consecutive errors

Real-World Use Cases

• Literature Review Acceleration: PhD candidates identifying foundational papers with precise relevance filtering
• Citation Synthesis: Automated generation of survey papers with verified source adherence
• Research Discovery: Industry labs discovering cutting-edge preprints through the arXiv fallback

How It Works: Step-by-Step Workflow

1. Query Validation: Rejects empty/chitchat inputs while failing open on system errors
2. Initial Retrieval: Hybrid search combining dense vectors and BM25
3. Relevance Grading: LLM evaluates each candidate against original query intent
4. Dynamic Expansion: Rewrites queries when relevant papers < AGENT_MIN_RELEVANT_DOCS (default: 3)
5. Fallback Activation: Live arXiv query when local results remain insufficient
6. Verification: Computes faithfulness_score before final response

Comparison: Recommendica vs Traditional Approaches

Frequently Asked Questions (FAQ)

Conclusion & Next Steps

Recommendica represents a paradigm shift in academic search by combining agentic refinement with live data integration. The system is currently available at recommendica.nevatal.tech, with the pay-what-you-want model ensuring sustainable access for researchers worldwide.

For developers interested in the technical implementation, the architecture demonstrates several best practices including:

• Graceful degradation through circuit breakers
• Parallel processing of semantic chunks
• Deterministic verification metrics