Hybrid Retrieval in Retrieval-Augmented Generation (RAG)

Hybrid Retrieval is a technique that combines semantic retrieval (based on vector embeddings) and lexical retrieval (based on keyword matching) to improve the accuracy, flexibility, and relevance of information retrieval in a RAG architecture. This approach ensures both semantic understanding and precise keyword-based matching, addressing the limitations of each method when used independently.

How Hybrid Retrieval Works

1. Semantic Retrieval (Vector Search)

• How It Works:

  • Converts text (queries and documents) into high-dimensional embeddings using models like Sentence Transformers or OpenAI embeddings.
  • Uses similarity metrics (e.g., cosine similarity) to find semantically similar documents in a vector database.

• Strengths:

  • Captures the underlying meaning of text (e.g., synonyms, paraphrases).
  • Useful for queries with abstract or conceptual language.

• Weaknesses:

  • May miss exact matches for rare terms, proper nouns, or domain-specific keywords.

2. Lexical Retrieval (Keyword/Exact Match)

• How It Works:

  • Matches documents containing specific words or phrases in the query using traditional search engines (e.g., Elasticsearch or Solr).
  • Relies on term frequency, inverted indices, and query expansion techniques.

• Strengths:

  • Handles exact matches, rare terms, and highly specific queries well.
  • Works well for structured or domain-specific queries.

• Weaknesses:

  • Struggles with synonyms, misspellings, and nuanced meaning.

3. Combining the Two

Hybrid retrieval blends these two approaches to achieve the best of both worlds: - Semantic retrieval identifies documents with related meanings, even when exact terms differ. - Lexical retrieval ensures inclusion of documents with exact matches for key terms, boosting precision.

Hybrid Retrieval Workflow

1. Indexing:

   • Build a vector index for documents using embedding models.
   • Build a keyword index using a tool like Elasticsearch.

2. Querying:

   • A user query is processed for:
     • Semantic search: Embedding the query and retrieving similar vectors from the vector index.
     • Keyword search: Searching for exact matches in the keyword index.

3. Combining Results:

   • Results from both methods are merged, weighted, and ranked using strategies like:
     • Linear combination of scores.
     • Rules to prioritize one type over the other based on query characteristics.

4. RAG Context Building:

   • Top-ranked documents from the combined results are used as context for the LLM.

Example Implementations

1. Using Python for Hybrid Retrieval

Here’s a simple implementation using FAISS for semantic retrieval and Elasticsearch for keyword retrieval.

Setup:

• FAISS: A library for vector similarity search.
• Elasticsearch: A search engine for keyword-based search.

Code Implementation:

from elasticsearch import Elasticsearch
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Initialize Elasticsearch and FAISS
es = Elasticsearch("http://localhost:9200")
model = SentenceTransformer('all-MiniLM-L6-v2')

# Create a FAISS index
embedding_dim = 384  # Dimensionality of embeddings
faiss_index = faiss.IndexFlatL2(embedding_dim)

# Sample documents
documents = [
    {"id": 1, "text": "Artificial intelligence is transforming industries."},
    {"id": 2, "text": "Machine learning focuses on data-driven models."},
    {"id": 3, "text": "FAISS is great for vector similarity search."}
]

# Index documents in Elasticsearch and FAISS
for doc in documents:
    # Add to Elasticsearch
    es.index(index="documents", id=doc["id"], body={"text": doc["text"]})
    
    # Add to FAISS
    embedding = model.encode(doc["text"])
    faiss_index.add(np.array([embedding], dtype=np.float32))

# Hybrid search function
def hybrid_search(query, top_k=2):
    # Semantic search with FAISS
    query_embedding = model.encode(query).astype(np.float32)
    _, faiss_ids = faiss_index.search(np.array([query_embedding]), top_k)
    
    # Retrieve documents by FAISS IDs
    faiss_results = [documents[idx]["text"] for idx in faiss_ids[0]]
    
    # Keyword search with Elasticsearch
    es_results = es.search(index="documents", query={"match": {"text": query}}, size=top_k)
    es_results = [hit["_source"]["text"] for hit in es_results["hits"]["hits"]]
    
    # Combine results
    combined_results = list(set(faiss_results + es_results))
    return combined_results

# Example query
query = "How does machine learning work?"
results = hybrid_search(query)
print("Hybrid Results:", results)

2. Using Tools Like Weaviate or Pinecone

Modern vector databases like Weaviate, Pinecone, or Milvus support hybrid retrieval natively by integrating with tools like Elasticsearch or OpenSearch.

• Steps:
  1. Index your vector embeddings in the vector database.
  2. Connect your keyword-based search engine to the database.
  3. Use the hybrid search API to query both simultaneously and retrieve combined results.

Example: Hybrid Search in Pinecone

import pinecone
from sentence_transformers import SentenceTransformer

# Initialize Pinecone
pinecone.init(api_key="your-api-key", environment="us-west1-gcp")
index = pinecone.Index("hybrid-search")

# Query Pinecone with hybrid search
query = "What is artificial intelligence?"
query_embedding = model.encode(query).tolist()

# Hybrid query
results = index.query(
    vector=query_embedding,
    filter={"keyword": {"$contains": query}},
    top_k=5,
    include_metadata=True
)
print(results)

Real-World Use Cases for Hybrid Retrieval

1. Legal Document Search

• Legal queries often require exact phrase matching (e.g., specific legal terms) and semantic retrieval for related cases or laws.
• Example: A law firm searching for “intellectual property rights” finds both exact matches and semantically related cases.

2. E-Commerce Search

• Users often search for products using keywords (e.g., "wireless earbuds") while expecting semantically similar results (e.g., "Bluetooth headphones").
• Hybrid retrieval ensures both types of matches.

3. Medical Knowledge Systems

• Medical professionals searching for symptoms or treatments require exact matches for terminology and semantic understanding for similar conditions.
• Hybrid retrieval bridges this gap.

4. Chatbots and Virtual Assistants

• Chatbots use hybrid retrieval to provide accurate answers:
  • Lexical matches for domain-specific terms.
  • Semantic matches for generalized user queries.

5. Educational Content Recommendation

• Students querying for “calculus problem-solving” retrieve documents with exact matches and those explaining related topics like “integration techniques.”

Benefits of Hybrid Retrieval in RAG

Hybrid retrieval is indispensable in modern AI systems where both semantic meaning and exact matches are critical. By combining the strengths of semantic and lexical retrieval, it ensures high accuracy, relevance, and user satisfaction in applications ranging from chatbots to enterprise search engines. Would you like further clarification on specific aspects or additional examples?