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name: retrieval-patterns description: Search and retrieval strategies including semantic, hybrid, and reranking for RAG systems. Use when implementing retrieval mechanisms, optimizing search performance, comparing retrieval approaches, or when user mentions semantic search, hybrid search, reranking, BM25, or retrieval optimization. allowed-tools: Read, Write, Bash, Grep, Glob

Retrieval Patterns

Purpose: Provide comprehensive retrieval strategies, benchmarking tools, and implementation templates for building high-performance RAG retrieval systems using LlamaIndex and LangChain.

Activation Triggers:

• Implementing semantic search for RAG systems
• Optimizing retrieval performance and relevance
• Comparing different retrieval strategies
• Setting up hybrid search (vector + keyword)
• Implementing reranking mechanisms
• Benchmarking retrieval quality
• Multi-query retrieval patterns
• Conversational retrieval with context

Key Resources:

• scripts/benchmark-retrieval.py - Performance testing for different retrieval methods
• scripts/evaluate-retrieval-quality.py - Quality metrics (precision, recall, MRR, NDCG)
• templates/semantic-search.py - Pure vector similarity search
• templates/hybrid-search.py - Combined vector + BM25 search
• templates/reranking.py - Cross-encoder and LLM-based reranking
• templates/multi-query-retrieval.py - Query expansion and fusion
• examples/conversational-retrieval.py - Context-aware retrieval
• examples/metadata-filtering.py - Filtered retrieval with metadata

Retrieval Strategy Comparison

1. Semantic Search (Vector Only)

How it works: Embed query and documents, compute cosine similarity, return top-k matches

Strengths:

• Captures semantic meaning and context
• Works well with paraphrasing and synonyms
• Language-agnostic similarity

Weaknesses:

• May miss exact keyword matches
• Sensitive to embedding model quality
• Can struggle with rare terms or proper nouns

When to use:

• Natural language queries
• Conceptual similarity needed
• Multi-lingual scenarios

Performance: ~50-100ms per query (depends on index size)

Template: templates/semantic-search.py

2. Hybrid Search (Vector + BM25)

How it works: Combine semantic vector search with keyword-based BM25, merge results using Reciprocal Rank Fusion (RRF)

Strengths:

• Best of both worlds (semantic + lexical)
• Better recall than either method alone
• Robust to different query types

Weaknesses:

• More complex implementation
• Slightly higher latency
• Requires tuning fusion weights

When to use:

• Production RAG systems (recommended default)
• Mixed query types (conceptual + factual)
• When you need high recall

Performance: ~100-200ms per query

Template: templates/hybrid-search.py

Reciprocal Rank Fusion (RRF) formula:

RRF_score(d) = sum(1 / (k + rank_i(d)))
where k = 60 (constant), rank_i(d) = rank of document d in retriever i

3. Reranking

How it works: Initial retrieval (semantic or hybrid) returns top-k candidates (e.g., 20), then reranker scores all pairs (query, doc) and returns top-n (e.g., 5)

Strengths:

• Significantly improves relevance
• Captures query-document interactions
• Can use more powerful models

Weaknesses:

• Adds latency (100-500ms depending on reranker)
• Requires additional API calls or compute
• Only improves results within initial candidate set

When to use:

• When quality is critical
• Production systems with quality requirements
• After initial hybrid search

Performance: +100-500ms additional latency

Reranker options:

• Cross-encoder models (most accurate): Cohere rerank, sentence-transformers
• LLM-based (flexible): GPT-4, Claude with ranking prompts
• Custom models (optimized): Fine-tuned for your domain

Template: templates/reranking.py

4. Multi-Query Retrieval

How it works: Generate multiple query variations, retrieve for each, deduplicate and fuse results

Strengths:

• Increases recall by covering query ambiguity
• Handles different phrasings
• Can improve diversity of results

Weaknesses:

• 3-5x more retrieval calls
• Higher latency and cost
• May retrieve less focused results

When to use:

• Complex or ambiguous queries
• When single query formulation may miss relevant docs
• Exploration vs. precision use cases

Performance: 3-5x base retrieval time

Template: templates/multi-query-retrieval.py

Strategy Selection Decision Tree

Start
│
├─ Need highest quality? → Use Hybrid + Reranking
│
├─ Budget/latency constrained?
│  ├─ Yes → Semantic Search (simplest, fastest)
│  └─ No → Hybrid Search (best default)
│
├─ Queries are ambiguous/complex? → Multi-Query Retrieval
│
├─ Need exact keyword matches? → Hybrid Search
│
└─ Multilingual or conceptual similarity? → Semantic Search

Benchmarking Framework

Performance Metrics

Script: scripts/benchmark-retrieval.py

Measures:

• Latency (p50, p95, p99)
• Throughput (queries per second)
• Cost (API calls, compute)

Usage:

python scripts/benchmark-retrieval.py \
  --strategies semantic,hybrid,reranking \
  --queries queries.jsonl \
  --num-runs 100 \
  --output benchmark-results.json

Output:

{
  "semantic": {
    "latency_p50": 75.2,
    "latency_p95": 120.5,
    "latency_p99": 180.3,
    "throughput": 13.3,
    "cost_per_query": 0.0001
  },
  "hybrid": {
    "latency_p50": 145.8,
    "latency_p95": 220.1,
    "latency_p99": 300.5,
    "throughput": 6.9,
    "cost_per_query": 0.0002
  }
}

Quality Metrics

Script: scripts/evaluate-retrieval-quality.py

Measures:

• Precision@k: Percentage of retrieved docs that are relevant
• Recall@k: Percentage of relevant docs that were retrieved
• MRR (Mean Reciprocal Rank): 1/rank of first relevant result
• NDCG (Normalized Discounted Cumulative Gain): Graded relevance scoring
• Hit Rate: Percentage of queries with at least 1 relevant result

Usage:

python scripts/evaluate-retrieval-quality.py \
  --strategy hybrid \
  --test-set labeled-queries.jsonl \
  --k-values 1,3,5,10 \
  --output quality-metrics.json

Output:

{
  "precision@5": 0.78,
  "recall@5": 0.65,
  "mrr": 0.72,
  "ndcg@5": 0.81,
  "hit_rate@5": 0.92
}

Implementation Patterns

Pattern 1: Simple Semantic Search

Use case: Prototype, low latency requirements, conceptual queries

Template: templates/semantic-search.py

Implementation:

from llama_index.core import VectorStoreIndex
from llama_index.embeddings.openai import OpenAIEmbedding

# Initialize
embed_model = OpenAIEmbedding(model="text-embedding-3-small")
index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)

# Retrieve
retriever = index.as_retriever(similarity_top_k=5)
results = retriever.retrieve("query")

Pattern 2: Hybrid Search with Ensemble

Use case: Production systems, balanced performance/quality

Template: templates/hybrid-search.py

Implementation:

from langchain.retrievers import EnsembleRetriever, BM25Retriever
from langchain_community.vectorstores import FAISS

# Vector retriever
vector_retriever = FAISS.from_documents(docs, embeddings).as_retriever(
    search_kwargs={"k": 10}
)

# BM25 retriever
bm25_retriever = BM25Retriever.from_documents(docs)
bm25_retriever.k = 10

# Ensemble with RRF
ensemble = EnsembleRetriever(
    retrievers=[vector_retriever, bm25_retriever],
    weights=[0.5, 0.5]
)

results = ensemble.get_relevant_documents("query")

Pattern 3: Reranking Pipeline

Use case: Quality-critical applications

Template: templates/reranking.py

Implementation:

from llama_index.postprocessor.cohere_rerank import CohereRerank

# Initial retrieval (hybrid recommended)
initial_results = hybrid_retriever.retrieve("query", top_k=20)

# Rerank
reranker = CohereRerank(api_key=api_key, top_n=5)
reranked = reranker.postprocess_nodes(
    initial_results,
    query_str="query"
)

# Use top 5 reranked results
final_results = reranked[:5]

Pattern 4: Multi-Query Fusion

Use case: Complex queries, exploration scenarios

Template: templates/multi-query-retrieval.py

Implementation:

from langchain.retrievers import MultiQueryRetriever
from langchain.llms import OpenAI

# Generate query variations
retriever = MultiQueryRetriever.from_llm(
    retriever=base_retriever,
    llm=OpenAI(temperature=0.7)
)

# Automatically generates variations and fuses results
results = retriever.get_relevant_documents("query")

Advanced Patterns

Conversational Retrieval

Use case: Chatbots, multi-turn interactions

Example: examples/conversational-retrieval.py

Key features:

• Chat history compression
• Query rewriting with context
• Follow-up question handling

Implementation highlights:

# Rewrite query with conversation context
standalone_query = rewrite_with_history(current_query, chat_history)

# Retrieve with rewritten query
results = retriever.retrieve(standalone_query)

Metadata Filtering

Use case: Filtered search, access control, temporal queries

Example: examples/metadata-filtering.py

Key features:

• Pre-filtering before vector search
• Hybrid metadata + semantic filtering
• Dynamic filter construction

Implementation highlights:

# Filter by metadata before retrieval
retriever = index.as_retriever(
    similarity_top_k=5,
    filters=MetadataFilters(
        filters=[
            ExactMatchFilter(key="source", value="documentation"),
            DateFilter(key="timestamp", after="2024-01-01")
        ]
    )
)

Optimization Guidelines

1. Embedding Model Selection

Fast & Cheap:

• OpenAI text-embedding-3-small (1536 dim, $0.02/1M tokens)
• Suitable for most use cases

High Quality:

• OpenAI text-embedding-3-large (3072 dim, $0.13/1M tokens)
• Cohere embed-english-v3.0 (1024 dim, $0.10/1M tokens)

Domain-Specific:

• Fine-tune open-source models (e.g., sentence-transformers)

2. Top-K Tuning

Initial Retrieval:

• Semantic only: k=5-10
• Hybrid: k=10-20 (for reranking)
• Multi-query: k=3-5 per query

Final Results:

• RAG generation: 3-5 chunks typical
• Search results: 10-20 results

3. Latency Optimization

Techniques:

• Cache embeddings for common queries
• Use approximate nearest neighbor (ANN) indexes
• Async/parallel retrieval for multi-query
• Batch queries when possible

Target latencies:

• Semantic: <100ms
• Hybrid: <200ms
• Hybrid + Reranking: <500ms

4. Cost Optimization

Strategies:

• Use smaller embedding models for development
• Implement query caching
• Batch embedding generation
• Use open-source rerankers when possible

Testing & Validation

Benchmarking Workflow

# 1. Collect test queries
# Create queries.jsonl with representative queries

# 2. Run performance benchmark
python scripts/benchmark-retrieval.py \
  --strategies semantic,hybrid \
  --queries queries.jsonl \
  --num-runs 100

# 3. Run quality evaluation (requires labeled data)
python scripts/evaluate-retrieval-quality.py \
  --strategy hybrid \
  --test-set labeled-queries.jsonl \
  --k-values 3,5,10

# 4. Compare results
# Analyze benchmark-results.json and quality-metrics.json

A/B Testing

Pattern:

import random

def retrieve_with_strategy(query, user_id):
    # A/B test: 50% hybrid, 50% semantic
    strategy = "hybrid" if hash(user_id) % 2 == 0 else "semantic"

    if strategy == "hybrid":
        return hybrid_retriever.retrieve(query)
    else:
        return semantic_retriever.retrieve(query)

Common Pitfalls

1. Using Only Semantic Search

Problem: Misses exact keyword matches Solution: Default to hybrid search for production

2. Not Reranking

Problem: Initial retrieval may have poor ordering Solution: Add reranking for quality-critical applications

3. Wrong Top-K Values

Problem: Too few = miss relevant docs, too many = noise Solution: Benchmark with your data (typically 5-10 for final results)

4. Ignoring Latency

Problem: Multi-query without caching can be slow Solution: Profile each component, optimize critical paths

5. No Quality Metrics

Problem: Can't measure improvement Solution: Implement evaluation with labeled test set

Resources

Scripts:

• benchmark-retrieval.py - Measure latency, throughput, cost
• evaluate-retrieval-quality.py - Precision, recall, MRR, NDCG

Templates:

• semantic-search.py - Vector-only retrieval
• hybrid-search.py - Vector + BM25 with RRF
• reranking.py - Cross-encoder and LLM reranking
• multi-query-retrieval.py - Query expansion and fusion

Examples:

• conversational-retrieval.py - Chat context handling
• metadata-filtering.py - Filtered retrieval

Documentation:

• LlamaIndex Retrievers: https://developers.llamaindex.ai/python/framework/understanding/
• LangChain Retrievers: https://python.langchain.com/docs/modules/data_connection/retrievers/
• Hybrid Search Guide: https://python.langchain.com/docs/how_to/hybrid/
• Ensemble Retrievers: https://python.langchain.com/docs/how_to/ensemble_retriever/

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Supported Frameworks: LlamaIndex, LangChain Vector Stores: FAISS, Chroma, Pinecone, Weaviate, Qdrant Rerankers: Cohere, cross-encoders, LLM-based

Best Practice: Start with hybrid search, add reranking if quality is critical, benchmark with your data