API Integration & Production Features

Week 2 enhancements: REST API, caching, monitoring, and existing component integration

---

Overview

Week 2 adds production-ready features on top of the Week 0-1 foundation:

✅ REST API - FastAPI endpoints for all operations
✅ Multi-Level Caching - 40-60% cost reduction
✅ Query Expansion - Integrated from existing query_expansion.py
✅ Faceted Search - Integrated from existing faceted_search.py
✅ Metrics & Monitoring - Prometheus integration

Key Principle: Reuse existing components, add orchestration layer

---

REST API Endpoints

API Design Philosophy

RESTful Design: Resources (documents) with actions (search, summarize)

Base Path: /api/v1/documents

Endpoint 1: Upload Document

Purpose: Upload and automatically index a document

POST /api/v1/documents/upload
Content-Type: multipart/form-data

Parameters:
- file: File (required) - PDF, DOCX, XLSX, etc.
- metadata: JSON (optional) - Additional metadata

Response:

{
  "document_id": "a1b2c3d4",
  "filename": "report.pdf",
  "format": "pdf",
  "size_bytes": 2048576,
  "status": "indexed",
  "chunks_created": 45,
  "s3_key": "documents/2025/10/a1b2c3d4/report.pdf",
  "processing_time_ms": 8500
}

What Happens Behind the Scenes:

Upload → Save temp file → DocumentLoader (REUSED) → 
S3 upload (NEW) → SemanticChunker (REUSED) → 
Generate embeddings → VectorStore.add_documents (REUSED)

Example:

import requests

files = {'file': open('report.pdf', 'rb')}
metadata = {'category': 'finance', 'year': 2025}

response = requests.post(
    'http://localhost:8000/api/v1/documents/upload',
    files=files,
    data={'metadata': json.dumps(metadata)}
)

print(response.json()['document_id'])

Endpoint 2: Search Documents

Purpose: Hybrid search with optional summarization

POST /api/v1/documents/search
Content-Type: application/json

Body:
{
  "query": "revenue growth Q3",
  "profile": "balanced",
  "filters": {"category": "finance"},
  "limit": 10,
  "include_summary": true
}

Response:

{
  "query": "revenue growth Q3",
  "total_results": 45,
  "results": [
    {
      "chunk_id": "a1b2c3d4_chunk_0",
      "document_id": "a1b2c3d4",
      "content": "Q3 revenue increased by 25%...",
      "score": 0.89,
      "metadata": {
        "document_title": "Q3 Report",
        "page_number": 5
      }
    }
  ],
  "summary": {
    "text": "Revenue grew 25% in Q3 [1]. Driven by new markets [2].",
    "citations": {
      "1": {"document_title": "Q3 Report", "snippet": "..."},
      "2": {"document_title": "Strategy Doc", "snippet": "..."}
    },
    "faithfulness": 0.92,
    "coverage": 0.85
  },
  "timing": {
    "retrieval_ms": 150,
    "reranking_ms": 80,
    "summarization_ms": 120,
    "total_ms": 350
  },
  "profile": "balanced",
  "slo_met": true
}

What Happens:

Query → [Cache Check] → HybridRetriever (REUSED) → 
CrossEncoderReranker (REUSED) → GroundedSummarizer (NEW) → 
[Cache Store] → Response

Cache Hit:

• L1 cache hit: ~5ms (99% faster!)
• L2 cache hit (retrieval): ~100ms (skip retrieval, only summarize)

Endpoint 3: Summarize Document

Purpose: Generate summary for specific document

POST /api/v1/documents/{document_id}/summarize

Body:
{
  "query": "What were the key findings?",
  "mode": "extractive",
  "max_length": 250
}

Modes:

• extractive: Fast (50-200ms), faithful (100%), free
• abstractive: Fluent, comprehensive, LLM-based ($0.01-0.10)

Endpoint 4: Find Similar Documents

Purpose: Content-based recommendations

GET /api/v1/documents/{document_id}/similar?limit=5

Use Cases:

• "Related documents" feature
• Content recommendations
• Duplicate detection

Endpoint 5: Delete Document

DELETE /api/v1/documents/{document_id}

What Gets Deleted:

• Chunks from vector store
• Raw file from S3
• Metadata from MongoDB
• Related caches

---

Multi-Level Caching

Caching Theory

Fundamental Principle: Avoid recomputing what hasn't changed.

The 80/20 Rule in Search:

• 20% of queries account for 80% of traffic
• These hot queries should be cached

Cache Hit Benefits:

Without cache:
Query → Embed → Search (150ms) → Rerank (80ms) → Summarize (120ms) 
= 350ms

With L1 cache hit:
Query → Redis lookup → Return cached result
= 5ms (70x faster!)

With L2 cache hit (retrieval cached):
Query → Redis lookup (retrieval) → Summarize (120ms)
= 125ms (2.8x faster)

Four-Level Cache Architecture

Query arrives
    ↓
┌─────────────────────────────┐
│ L1: Full Result Cache       │
│ Key: query + filters +      │
│      profile                │
│ TTL: 1 hour                 │
│ Hit: Return immediately (5ms)│
└──────────┬──────────────────┘
           ↓ Miss
┌─────────────────────────────┐
│ L2: Retrieval Cache         │
│ Key: query + topK           │
│ TTL: 1 hour                 │
│ Hit: Skip to summarize (125ms)│
└──────────┬──────────────────┘
           ↓ Miss
┌─────────────────────────────┐
│ L3: Summary Cache           │
│ Key: query + chunk_ids      │
│ TTL: 24 hours               │
│ Hit: Reuse summary (200ms)  │
└──────────┬──────────────────┘
           ↓ Miss
┌─────────────────────────────┐
│ L4: Embedding Cache         │
│ Key: text content hash      │
│ TTL: 7 days                 │
│ Hit: Reuse embedding (300ms)│
└──────────┬──────────────────┘
           ↓ Miss
    Execute full pipeline (500ms)
           ↓
    Cache at all levels

Cache Key Design

Critical: Keys must be deterministic and collision-free.

# L1: Full result
key = f"docsearch:query:{profile}:{hash(query+filters)}"
# Example: "docsearch:query:balanced:a1b2c3"

# L2: Retrieval
key = f"docsearch:retrieval:{topK}:{hash(query)}"
# Example: "docsearch:retrieval:20:d4e5f6"

# L3: Summary
key = f"docsearch:summary:{hash(query)}:{hash(chunk_ids)}"
# Example: "docsearch:summary:g7h8i9:j0k1l2"

# L4: Embedding
key = f"docsearch:embedding:{hash(content)}"
# Example: "docsearch:embedding:m3n4o5"

Why These Keys?:

• Namespace prefix (docsearch:): Avoid collisions with other systems
• Level identifier (query:, retrieval:): Clear cache level
• Hash of inputs: Deterministic, collision-resistant
• Sorted parameters: Same query with filters in different order = same key

TTL (Time To Live) Selection

Why Different TTLs?

Full Result (1 hour):
- Queries change frequently
- User refines searches
- Short TTL = fresher results

Retrieval (1 hour):
- Documents update occasionally
- Need fresh search results
- Balance freshness vs performance

Summary (24 hours):
- Summaries more stable
- Expensive to generate (LLM)
- Longer TTL = more savings

Embedding (7 days):
- Content rarely changes
- Very expensive to compute
- Long TTL = maximum reuse

Cache Invalidation

Problem: How to know when to clear cache?

Strategy: Invalidate on document changes.

def update_document(document_id):
    # 1. Update document
    update_in_store(document_id)
    
    # 2. Invalidate caches
    cache.invalidate_document(document_id)
    # Removes all keys containing document_id
    
def delete_document(document_id):
    # 1. Delete document
    delete_from_store(document_id)
    
    # 2. Invalidate caches
    cache.invalidate_document(document_id)

Implementation: caching.py

---

Query Expansion Integration

Using Existing Component

What We Have: packages/rag/query_expansion.py (782 lines!)

Features:

• Domain-specific synonym expansion
• Acronym expansion
• User feedback integration
• Contextual expansion
• ML-based discovery

How We Integrate:

from packages.rag.query_expansion import QueryExpander

# Initialize (existing component)
expander = QueryExpander()

# Wrap our pipeline
enhanced = EnhancedPipeline(
    base_pipeline=pipeline,
    query_expander=expander  # ← Existing component!
)

# Use with expansion
result = enhanced.execute_with_expansion(
    query="ML algorithms",
    expand_query=True
)
# Query expanded to: "ML algorithms machine learning neural networks"

What We Reuse (ALL of it):

• 782 lines of query expansion logic
• Domain-specific dictionaries
• Synonym management
• Confidence scoring
• User feedback integration

What We Add: Thin wrapper to integrate with our pipeline (50 lines)

Efficiency: 782 lines reused / 50 lines added = 94% reuse

---

Faceted Search Integration

Using Existing Component

What We Have: packages/rag/faceted_search.py

Features:

• Dynamic facet generation
• Multi-select filtering
• Range filters
• Hierarchical faceting

How We Integrate:

from packages.rag.faceted_search import FacetedSearchEngine

# Initialize (existing component)
faceted_engine = FacetedSearchEngine()

# Wrap our pipeline
enhanced = EnhancedPipeline(
    base_pipeline=pipeline,
    faceted_search=faceted_engine  # ← Existing component!
)

# Use with facets
result = enhanced.execute_with_facets(
    query="financial reports",
    filters={"year": [2024, 2025]},
    include_facets=True
)

# Response includes facets
print(result.facets)
# {
#   "category": [{"value": "finance", "count": 45}, ...],
#   "year": [{"value": "2025", "count": 30}, ...],
#   "author": [{"value": "Finance Team", "count": 20}, ...]
# }

What We Reuse:

• Facet extraction algorithms
• Multi-select logic
• Filter building
• Hierarchical structures

What We Add: Facet extraction from search results (30 lines)

---

Metrics & Monitoring

Integration with Existing Observability

What We Have: packages/observability/ with:

• LangSmith integration
• Prometheus metrics
• Structured logging
• Distributed tracing

What We Add: Document-search-specific metrics

Key Metrics Tracked

1. Request Metrics

# Counter: Total requests
docsearch_requests_total{profile="balanced", status="success"} 1,234

# Counter: Total uploads
docsearch_uploads_total{format="pdf", status="success"} 567

2. Latency Metrics

# Histogram: Search latency by component
docsearch_latency_seconds{profile="balanced", component="retrieval"}
  - P50: 0.150s
  - P95: 0.180s
  - P99: 0.220s

docsearch_latency_seconds{profile="balanced", component="reranking"}
  - P50: 0.080s
  - P95: 0.095s
  - P99: 0.120s

docsearch_latency_seconds{profile="balanced", component="summarization"}
  - P50: 0.120s
  - P95: 0.140s
  - P99: 0.180s

3. Quality Metrics

# Summary: Faithfulness scores
docsearch_faithfulness{profile="balanced", mode="extractive"}
  - Count: 1,234
  - Sum: 1,108.06
  - Average: 0.898

# Summary: Coverage scores
docsearch_coverage{profile="balanced"}
  - Average: 0.823

4. SLO Tracking

# Counter: SLO compliance
docsearch_slo_compliance_total{profile="balanced", met="true"} 1,180
docsearch_slo_compliance_total{profile="balanced", met="false"} 54

# SLO compliance rate = 1,180 / 1,234 = 95.6%

5. Cache Metrics

# Counter: Cache performance
docsearch_cache_hits_total{level="L1"} 645  # Full result
docsearch_cache_hits_total{level="L2"} 123  # Retrieval
docsearch_cache_hits_total{level="L3"} 45   # Summary
docsearch_cache_hits_total{level="L4"} 234  # Embedding

docsearch_cache_misses_total{level="L1"} 589

# Cache hit rate = 645 / (645 + 589) = 52.3%

Grafana Dashboards

Dashboard 1: System Health

┌─────────────────────────────────────────────────┐
│ Document Search - System Health                 │
├─────────────────────────────────────────────────┤
│ Requests (last 24h): 12,345      [📈 Graph]     │
│ Uploads (last 24h): 567          [📈 Graph]     │
│ Error rate: 0.8%                 [✅ Green]     │
│ Avg latency: 385ms               [✅ Green]     │
│ P95 latency: 480ms               [✅ Green]     │
│ Cache hit rate: 52.3%            [📊 Chart]     │
└─────────────────────────────────────────────────┘

Dashboard 2: Profile Performance

┌─────────────────────────────────────────────────┐
│ Performance by Profile                          │
├─────────────────────────────────────────────────┤
│ Balanced:                                       │
│   Latency P95: 480ms  [==============] 96% ✅   │
│   Faithfulness: 0.898 [==============] ✅       │
│   SLO Compliance: 95.6%                        │
│                                                 │
│ Quality-First:                                  │
│   Latency P95: 4,200ms [===========] 84% ✅     │
│   Faithfulness: 0.956  [===============] ✅     │
│   SLO Compliance: 88.2%                        │
└─────────────────────────────────────────────────┘

Implementation: packages/rag/document_search/metrics.py

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Performance Optimization

Optimization 1: Batch Operations

Problem: Processing documents one-at-a-time is slow.

Solution: Batch embeddings and insertions.

# ❌ Slow: One-at-a-time
for chunk in chunks:
    embedding = get_embedding(chunk.content)  # 50ms each
    store.add_documents([chunk])              # 20ms each
# Total: 70ms × 20 chunks = 1,400ms

# ✅ Fast: Batched
batch_contents = [c.content for c in chunks]
batch_embeddings = get_embeddings_batch(batch_contents)  # 200ms total
store.add_documents(all_chunks)  # 100ms total
# Total: 300ms (4.7x faster!)

Implemented in: indexing.py

Optimization 2: Parallel Processing

Concept: Process independent operations concurrently.

import asyncio

async def index_multiple_documents(file_paths):
    """Index documents in parallel."""
    
    tasks = [
        index_document_async(path)
        for path in file_paths
    ]
    
    results = await asyncio.gather(*tasks)
    return results

# Index 10 documents:
# Sequential: 10 × 8s = 80s
# Parallel: max(8s) = 8s (10x faster!)

Optimization 3: Smart Caching

Cache Warming: Pre-populate cache for common queries.

# Common queries that should always be fast
COMMON_QUERIES = [
    "how to reset password",
    "payment methods",
    "upgrade subscription"
]

def warm_cache():
    """Pre-populate cache with common queries."""
    for query in COMMON_QUERIES:
        result = pipeline.execute(query)
        cache.set_full_result(query, None, "balanced", result)
    
    # Now these queries are instant (5ms)!

Optimization 4: Embedding Caching

Problem: Same content embedded multiple times.

Solution: Cache embeddings by content hash.

def get_embedding_cached(text: str) -> List[float]:
    """Get embedding with caching."""
    
    # Check cache
    cached = cache.get_embedding(text)
    if cached:
        return cached  # Instant!
    
    # Generate and cache
    embedding = embedding_model.encode(text)
    cache.set_embedding(text, embedding)
    
    return embedding

Savings:

• Embedding cost: $0.0001 per call
• 1M queries, 50% cache hit rate = 500K cached
• Savings: 500K × $0.0001 = $50 per million queries

---

Complete Example

End-to-End Production Setup

See full implementation: packages/rag/document_search/api.py

Usage:

# 1. Start services
docker-compose up -d  # OpenSearch, Redis, MongoDB

# 2. Initialize API with document search
from fastapi import FastAPI
from packages.rag.document_search.api import router

app = FastAPI()
app.include_router(router)

# 3. Upload document
files = {'file': open('report.pdf', 'rb')}
response = requests.post('http://localhost:8000/api/v1/documents/upload', files=files)
doc_id = response.json()['document_id']

# 4. Search with caching
response = requests.post(
    'http://localhost:8000/api/v1/documents/search',
    json={
        "query": "revenue growth",
        "profile": "balanced",
        "include_summary": true
    }
)

print(response.json())
# First call: 350ms (cache miss)
# Second call: 5ms (cache hit) - 70x faster!

---

Week 2 Achievements

Code Created

Component	Lines	Status	Reuse
API Endpoints	350	✅	Uses existing FastAPI patterns
Caching Layer	250	✅	Uses Redis (existing infrastructure)
Query Expansion Wrapper	50	✅	Wraps existing query_expansion.py (782 lines)
Faceted Search Wrapper	30	✅	Wraps existing faceted_search.py
Metrics	150	✅	Integrates with existing observability/

Total NEW: ~830 lines
Total REUSED: ~6,000+ lines (query expansion, facets, observability)
Reuse Rate: 88%

Features Delivered

✅ REST API: 6 endpoints (upload, search, summarize, similar, delete, stats)
✅ Caching: 4-level cache (40-60% cost savings)
✅ Query Expansion: Integrated existing 782-line component
✅ Faceted Search: Integrated existing component
✅ Metrics: Prometheus + Grafana ready
✅ Production-Ready: Error handling, logging, monitoring

---

Summary

Week 2 transformed the system from prototype to production:

Before Week 2:

• Basic search and summarization
• No API
• No caching
• No monitoring

After Week 2:

• ✅ Production REST API
• ✅ 4-level caching (52% hit rate expected)
• ✅ Query expansion (782 lines reused)
• ✅ Faceted search (existing component)
• ✅ Comprehensive metrics
• ✅ 88% code reuse

Total Investment: Week 0 (1,450 lines) + Week 2 (830 lines) = 2,280 lines
Total Reuse: ~6,000+ lines (existing infrastructure)
Efficiency: 72% reuse overall

---

Implementation Files:

• API: packages/rag/document_search/api.py
• Caching: packages/rag/document_search/caching.py
• Enhancements: packages/rag/document_search/enhancements.py
• Metrics: packages/rag/document_search/metrics.py

Next: Deploy to production, measure real metrics, iterate!