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Document Search & Summarization - Documentation Index

Complete guide to all documentation resources

📚 Main Documentation​

Complete Guide ⭐​

The comprehensive, educational guide covering everything from theory to practice.

Sections:

  1. Theoretical Foundations (Information Retrieval, BM25, Vector Search)
  2. Architecture & Design (Profile-based approach)
  3. Information Retrieval Deep Dive (Hybrid search, RRF, query expansion)
  4. Summarization Techniques (Extractive vs abstractive, grounding)
  5. Profile-Based Configuration (Balanced, Latency-First, Quality-First)
  6. Implementation Guide (Quick start, advanced usage)
  7. Evaluation & Quality Assurance (RAGAS metrics)
  8. Production Best Practices (Caching, monitoring, error handling)
  9. Advanced Topics (Multi-doc, streaming, hierarchical)

Length: ~800 lines
Audience: Developers, ML engineers, architects
Level: Beginner to Advanced

Quick Reference 📖​

One-page cheat sheet for quick lookup.

Contents:

  • Profile comparison table
  • 3-step quick start
  • Key concepts summary
  • Configuration patterns
  • Common tuning parameters
  • Caching strategy
  • Error handling
  • Monitoring metrics
  • API reference
  • Troubleshooting guide
  • Decision tree

Length: ~300 lines
Audience: Practitioners needing quick answers
Level: All levels

💻 Code & Examples​

Example Code Repository​

Practical, runnable examples demonstrating real-world usage.

Examples:

  • Basic search and summarization
  • Profile comparison
  • Advanced retrieval techniques
  • Grounded summarization patterns
  • Production deployment

Location: /Users/sparshagarwal/Desktop/work/recohut/recoagent/examples/document_search_demo.py

Implementation Files​

Core Module (packages/rag/document_search/):

  • store.py - DocumentStore interface + OpenSearch
  • retriever.py - HybridDocumentRetriever + QueryExpander
  • summarizer.py - GroundedSummarizer (extractive + abstractive)
  • pipeline.py - DocumentSearchPipeline + profiles
  • test_fixtures.py - Test dataset (10 queries, 3 user stories)
  • README.md - Module documentation

📊 Planning & Design​

Refined Implementation Plan​

Production-ready plan incorporating RAG best practices.

Highlights:

  • Profile-based architecture
  • RAGAS evaluation framework
  • User story driven approach
  • 8-week implementation roadmap
  • Cost analysis
  • SLO definitions

Length: ~1,000 lines
Audience: Project managers, technical leads
Level: Strategic

Week 0 Completion Summary​

Detailed summary of foundation phase implementation.

Contents:

  • Components built (2,190 lines of code)
  • Architecture overview
  • Test dataset
  • SLO targets
  • Integration points
  • Next steps

Length: ~450 lines
Audience: Team members, stakeholders
Level: Status update

🎓 Learning Path​

For Beginners​

  1. Start: Quick Reference - Get oriented (15 min)
  2. Learn: Main Guide - Quick Start (20 min)
  3. Practice: Basic Example (30 min)
  4. Explore: Run examples/document_search_demo.py (15 min)

Total Time: ~80 minutes to first working system

For Practitioners​

  1. Review: Quick Reference (10 min)
  2. Deep Dive: Main Guide - Architecture (30 min)
  3. Configure: Profile Selection (20 min)
  4. Deploy: Production Best Practices (40 min)

Total Time: ~100 minutes to production deployment

For ML Engineers​

  1. Theory: Theoretical Foundations (45 min)
  2. Algorithms: Information Retrieval Deep Dive (60 min)
  3. Evaluation: RAGAS Metrics (30 min)
  4. Advanced: Advanced Topics (45 min)

Total Time: ~3 hours for deep understanding

🎯 By Use Case​

Customer Support / Knowledge Base​

Compliance / Legal Research​

Interactive Chat / Auto-Complete​

🔧 By Component​

Query Expansion​

Reranking​

Summarization​

Grounding & Citations​

📖 API Documentation​

Classes​

DocumentStore (store.py)

  • Interface for unified storage
  • OpenSearch implementation with k-NN + BM25
  • Reciprocal Rank Fusion
  • Faceted navigation

HybridDocumentRetriever (retriever.py)

  • BM25 + Vector retrieval
  • Query expansion (PRF, HyDE)
  • Intent detection
  • Deduplication

GroundedSummarizer (summarizer.py)

  • Extractive (TextRank)
  • Abstractive (LLM-based)
  • Citation tracking
  • Faithfulness verification

DocumentSearchPipeline (pipeline.py)

  • Profile-based factory
  • Component composition
  • SLO enforcement
  • Batch execution

🧪 Testing & Evaluation​

Test Fixtures​

Location: packages/rag/document_search/test_fixtures.py

Contents:

  • 10 test queries across 3 user stories
  • Ground truth answers
  • Expected latencies
  • SLO requirements

Usage:

from packages.rag.document_search.test_fixtures import get_all_fixtures

fixtures = get_all_fixtures()
for test_case in fixtures:
    result = pipeline.execute(test_case.query, test_case.filters)
    assert result.slo_met

RAGAS Evaluation​

Guide: Evaluation & Quality Assurance

Metrics:

  • Context Precision
  • Context Recall
  • Faithfulness
  • Answer Relevancy

🔍 Search This Documentation​

By Keyword​

By Question​

📞 Getting Help​

Community​

  • GitHub Issues: Report bugs, request features
  • Discussions: Ask questions, share patterns
  • Discord: Real-time help (link in repo)

Support​

Contributing​

  • Code: See Contributing Guide
  • Documentation: PRs welcome for improvements
  • Examples: Share your patterns and use cases

Last Updated: October 9, 2025
Documentation Version: 1.0
Module Version: 0.1.0