Semantic search

Semantic search converts text into high-dimensional vectors using transformer embedding models, then finds documents with similar vector representations. This approach understands synonyms, paraphrases, and conceptual similarity—queries like “car” will match documents about “automobile” even without exact keyword overlap.

How it works

Semantic search uses dense vector embeddings to find documents with similar meaning to the query, enabling conceptual matching beyond keyword overlap.

Search process

Query embedding - Convert query text to dense vector using embedding model
Vector search - Find nearest neighbors in vector database index
Result retrieval - Return top-k most similar documents
Optional RAG - Generate answer using retrieved documents as context

When to use semantic search

Traditional keyword search fails when:

Query uses synonyms (“automobile” vs “car”)
Documents use different terminology for same concepts
User doesn’t know exact terms used in documents

Semantic search handles these by encoding meaning, not just keywords.

Key features

Supports any SentenceTransformers model
Optional semantic diversification removes near-duplicate results
Integrates with Groq or OpenAI for RAG answer generation
Metadata filters narrow results by category, date, source, or custom fields

Implementation

from vectordb.haystack.semantic_search import PineconeSemanticSearchPipeline

pipeline = PineconeSemanticSearchPipeline(
    "src/vectordb/haystack/semantic_search/configs/pinecone/arc.yaml"
)
result = pipeline.search("What is photosynthesis?", top_k=5)

for doc in result["documents"]:
    print(doc.content)

from vectordb.langchain.semantic_search import PineconeSemanticSearchPipeline

pipeline = PineconeSemanticSearchPipeline(
    "src/vectordb/langchain/semantic_search/configs/pinecone_arc.yaml"
)
results = pipeline.search(
    query="machine learning applications in healthcare",
    top_k=5,
    filters={"category": "medical"},
)

for doc in results["documents"]:
    print(f"Result: {doc.page_content[:100]}...")

if "answer" in results:
    print(f"Answer: {results['answer']}")

Configuration

Required settings

string

required

Pinecone API authentication key

string

required

Target index name for search

Optional settings

string

Document organization namespace for logical partitioning

string

default:"all-MiniLM-L6-v2"

Embedding model configuration (must match indexing model)

object

Optional LLM configuration for answer generation

Example configuration

pinecone:
  api_key: "${PINECONE_API_KEY}"
  index_name: "semantic-search"
  namespace: "production"
  metric: "cosine"

embeddings:
  model: "sentence-transformers/all-MiniLM-L6-v2"

rag:
  enabled: true
  generator_model: "gpt-4o-mini"

Search parameters

string

required

Search query text to embed and match against documents

integer

default:10

Number of results to return

dict

Metadata filters for pre-filtering (e.g., {"category": "tech"})

string

Isolated document collection within index

Embedding consistency

Critical: Query and documents must use the SAME embedding model. Mixing models (e.g., indexing with MiniLM, querying with OpenAI) will produce nonsensical results as vectors exist in different spaces.

Always ensure:

Query embedder matches document embedder
Same model version is used
Consistent preprocessing (normalization, truncation)

RAG integration

If an LLM is configured, the pipeline can generate answers using retrieved documents as context. This combines retrieval accuracy with generation fluency for question-answering applications.

results = pipeline.search(
    query="renewable energy technologies",
    top_k=5,
    filters={"category": "science"},
)

print(f"Query: {results['query']}")
for doc in results["documents"]:
    print(f"Score: {doc.score:.3f} - {doc.content[:80]}...")
if "answer" in results:
    print(f"RAG Answer: {results['answer']}")

Database support

Semantic search is available across all supported vector databases:

Pinecone - Managed vector database with namespaces
Weaviate - Open-source vector search with collections
Qdrant - High-performance search with payload filtering
Milvus - Scalable vector database with partition-key isolation
Chroma - Lightweight vector store for local development

Hybrid search

Combine dense and sparse retrieval with fusion

Reranking

Cross-encoder second-stage scoring

Diversity filtering

Post-retrieval redundancy reduction

MMR

Maximal marginal relevance for diversity

Getting Started

Core Concepts

Vector Databases

Retrieval Features

Advanced RAG

Data Management

How it works

Search process

When to use semantic search

Key features

Implementation

Configuration

Required settings

Optional settings

Example configuration

Search parameters

Embedding consistency

RAG integration

Database support

Hybrid search

Reranking

Diversity filtering

MMR

​How it works

​Search process

​When to use semantic search

​Key features

​Implementation

​Configuration

​Required settings

​Optional settings

​Example configuration

​Search parameters

​Embedding consistency

​RAG integration

​Database support

​Related features

Hybrid search

Reranking

Diversity filtering

MMR

How it works

Search process

When to use semantic search

Key features

Implementation

Configuration

Required settings

Optional settings

Example configuration

Search parameters

Embedding consistency

RAG integration

Database support

Related features