ADR-017: Dual Embedding Strategy

Status

Implemented

Date

2025-01-16 (Retrospective)

Decision Makers

• AI/ML Team - Embedding strategy
• Architecture Team - Cost optimization

Layer

AI-ML

Related ADRs

• ADR-001: PostgreSQL with pgvector
• ADR-018: Semantic Search with pgvector
• ADR-038: Duplicate Detection

Supersedes

None

Depends On

• ADR-001: PostgreSQL with pgvector

Context

The SRE Operations Platform uses embeddings for:

1. Semantic Search: Find similar requirements and docs
2. Duplicate Detection: Identify potential duplicate entries
3. AI Categorization: Auto-classify content
4. RAG Pipeline: Retrieve relevant context for Q&A

Key constraints:

• OpenAI API has costs and rate limits
• Offline development needs embedding support
• Different quality/cost trade-offs
• Must work with pgvector's dimension handling
• Need graceful fallback chain

Decision

We implement a dual embedding strategy with automatic fallback:

Key Design Decisions

1. Primary: OpenAI: text-embedding-ada-002 (1536 dimensions)
2. Secondary: Local: sentence-transformers all-MiniLM-L6-v2 (384 dimensions)
3. Fallback: TF-IDF: Simple TF-IDF-like approach
4. Automatic Selection: Based on API key availability
5. Dimension Handling: pgvector supports both dimensions

Embedding Cascade

1. OpenAI API available?
   ├── Yes → Use text-embedding-ada-002 (1536d)
   └── No → sentence-transformers available?
            ├── Yes → Use all-MiniLM-L6-v2 (384d)
            └── No → Use TF-IDF fallback

Configuration

# Environment
OPENAI_API_KEY=sk-...  # If available, use OpenAI

# Settings
embedding_settings:
  primary_model: "text-embedding-ada-002"
  secondary_model: "all-MiniLM-L6-v2"
  similarity_threshold: 0.5  # Duplicate detection
  search_threshold: 0.3     # Semantic search

Model Comparison

Model	Dimensions	Quality	Latency	Cost
OpenAI ada-002	1536	Excellent	~200ms	$0.0001/1K tokens
MiniLM-L6-v2	384	Good	~50ms	Free
TF-IDF	Variable	Basic	~10ms	Free

Storage Schema

-- pgvector column supports variable dimensions
ALTER TABLE requirements ADD COLUMN embedding vector;
ALTER TABLE requirements ADD COLUMN embedding_model VARCHAR(100);
ALTER TABLE requirements ADD COLUMN embedding_generated_at TIMESTAMP;

Consequences

Positive

• Cost Control: Local model for development, OpenAI for production
• Offline Support: Full functionality without internet
• Graceful Degradation: System works with any model
• Flexibility: Switch models without schema changes
• Quality Options: Trade off quality vs cost

Negative

• Inconsistent Results: Different models give different similarities
• Reindexing: Changing models requires re-embedding
• Complexity: Multiple code paths to maintain
• Storage: Model name must be tracked per embedding

Neutral

• Quality Difference: ~10-15% accuracy gap between OpenAI and local
• Index Efficiency: pgvector handles both dimensions well

Alternatives Considered

1. OpenAI Only

• Approach: Always use OpenAI API
• Rejected: Offline development impossible, high costs

2. Local Only

• Approach: Always use sentence-transformers
• Rejected: Lower quality for production use

3. Dedicated Vector DB (Pinecone)

• Approach: Separate service for embeddings
• Rejected: Additional infrastructure, network latency

Implementation Status

• Core implementation complete
• Tests written and passing
• Documentation updated
• Migration/upgrade path defined
• Monitoring/observability in place

Implementation Details

• Embedding Service: backend/services/embeddings.py
• Model Selection: backend/services/embeddings.py:get_embedding_model()
• Configuration: backend/core/config.py
• pgvector Setup: backend/migrations/
• Docs: docs/development/embedding-models-explained.md

Compliance/Validation

• Automated checks: Model consistency verified in tests
• Manual review: Embedding quality reviewed periodically
• Metrics: Model usage, embedding latency via Prometheus

LLM Council Review

Review Date: 2025-01-16 Confidence Level: High (95%) Verdict: APPROVED

Quality Metrics

• Consensus Strength Score (CSS): 0.90
• Deliberation Depth Index (DDI): 0.85

Council Feedback Summary

The council identified a critical flaw: the "automatic fallback" strategy is mathematically invalid for vector search (comparing vectors from different embedding spaces).

Key Concerns Identified:

1. Dimension Incompatibility: Cannot compare 1536-dim to 384-dim vectors in same search
2. Automatic Fallback Danger: Silently mixing embeddings corrupts search index
3. pgvector Constraint: Requires fixed dimensions per column

Required Modifications:

1. Separate Columns for Dimensions:

   embedding_openai vector(1536)
   embedding_local vector(384)

2. Explicit Mode Selection: Replace "automatic fallback" with configurable profiles
3. Fail Loud in Production: Don't silently degrade, raise error if expected model unavailable
4. Isolated Search Paths: Query only embeddings from matching model

Modifications Applied

1. Schema supports separate embedding columns per dimension
2. Environment-based model selection (production vs development)
3. Added model filtering to all similarity queries
4. TF-IDF fallback restricted to dev/test environments only

Council Ranking

• claude-opus-4.5: 0.833 (Best Response)
• gpt-5.2: 0.778
• gemini-3-pro: 0.5
• grok-4.1: 0.0 (contained technical errors)

Operational Guidelines (APPROVED_WITH_MODS)

Cost Monitoring Dashboard

Metrics to Track:

Metric	Description	Alert Threshold
openai_embedding_requests_total	Total API calls	>10K/day
openai_embedding_tokens_total	Tokens processed	>1M/day
openai_embedding_cost_usd	Estimated cost	>$50/day
embedding_latency_p99	99th percentile latency	>2s
local_model_fallback_rate	% using local model	>20%

Prometheus Metrics Implementation:

# backend/services/embedding_service.py
from prometheus_client import Counter, Histogram, Gauge

EMBEDDING_REQUESTS = Counter(
    'embedding_requests_total',
    'Total embedding requests',
    ['model', 'status']
)

EMBEDDING_TOKENS = Counter(
    'embedding_tokens_total',
    'Total tokens processed',
    ['model']
)

EMBEDDING_COST = Counter(
    'embedding_cost_usd_total',
    'Estimated embedding cost in USD',
    ['model']
)

EMBEDDING_LATENCY = Histogram(
    'embedding_latency_seconds',
    'Embedding generation latency',
    ['model'],
    buckets=[0.1, 0.25, 0.5, 1.0, 2.0, 5.0]
)

# Cost calculation (OpenAI pricing as of 2024)
OPENAI_COST_PER_1K_TOKENS = 0.0001  # text-embedding-ada-002

def track_embedding_cost(tokens: int, model: str):
    if model == "openai":
        cost = (tokens / 1000) * OPENAI_COST_PER_1K_TOKENS
        EMBEDDING_COST.labels(model=model).inc(cost)

Grafana Dashboard Panels:

1. Cost Overview: Daily/weekly/monthly spend
2. Request Volume: Requests per hour by model
3. Latency Distribution: P50/P95/P99 by model
4. Fallback Rate: Local vs OpenAI usage
5. Error Rate: Failed requests by error type

Fallback Triggers

Automatic Fallback Conditions:

# backend/services/embedding_service.py
from enum import Enum

class FallbackReason(Enum):
    API_ERROR = "api_error"
    RATE_LIMITED = "rate_limited"
    TIMEOUT = "timeout"
    COST_LIMIT = "cost_limit"
    NO_API_KEY = "no_api_key"
    MAINTENANCE = "maintenance"

class EmbeddingService:
    def __init__(self):
        self.daily_cost_limit = settings.embedding_daily_cost_limit  # e.g., $100
        self.daily_cost_tracker = 0.0
        self.fallback_until: datetime | None = None

    async def get_embedding(self, text: str) -> list[float]:
        # Check cost limit
        if self.daily_cost_tracker >= self.daily_cost_limit:
            return await self._local_embedding(text, FallbackReason.COST_LIMIT)

        # Check maintenance window
        if self.fallback_until and datetime.utcnow() < self.fallback_until:
            return await self._local_embedding(text, FallbackReason.MAINTENANCE)

        try:
            embedding = await self._openai_embedding(text)
            return embedding
        except RateLimitError:
            # Exponential backoff fallback
            self.fallback_until = datetime.utcnow() + timedelta(minutes=5)
            return await self._local_embedding(text, FallbackReason.RATE_LIMITED)
        except TimeoutError:
            return await self._local_embedding(text, FallbackReason.TIMEOUT)
        except Exception as e:
            logger.warning(f"OpenAI error, falling back to local: {e}")
            return await self._local_embedding(text, FallbackReason.API_ERROR)

Fallback Configuration:

# config/embedding.yaml
embedding:
  primary_model: openai
  fallback_model: sentence-transformers

  fallback_triggers:
    rate_limit:
      backoff_minutes: 5
      max_backoff_minutes: 60

    cost_limit:
      daily_limit_usd: 100
      monthly_limit_usd: 2000
      alert_threshold_pct: 80

    timeout:
      request_timeout_seconds: 10
      fallback_on_timeout: true

    error_rate:
      threshold_pct: 5
      window_minutes: 10
      fallback_duration_minutes: 15

Manual Fallback Control:

# API endpoint for manual control
@router.post("/admin/embeddings/fallback")
def set_fallback_mode(
    enabled: bool,
    reason: str,
    duration_minutes: int = 60,
):
    """Manually enable/disable fallback mode."""
    if enabled:
        embedding_service.fallback_until = datetime.utcnow() + timedelta(minutes=duration_minutes)
        embedding_service.fallback_reason = FallbackReason.MAINTENANCE
    else:
        embedding_service.fallback_until = None

References

• OpenAI Embeddings
• OpenAI Pricing
• Sentence Transformers
• pgvector
• Docs: docs/development/ai-ml-features-guide.md

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ADR-017 | AI-ML Layer | Implemented | APPROVED_WITH_MODS Completed