What should I use for semantic search over internal content?

For semantic search over internal content, the usual best choice is a vector search / hybrid search stack rather than plain keyword search.

Good default options

1. Elasticsearch / OpenSearch with vector + keyword hybrid search

Best if you want:

• search across lots of internal docs
• keyword matching plus semantic matching
• filtering by metadata like team, date, access level
• a production-ready, familiar search platform

Why it’s strong:
You can combine:

• BM25 keyword search for exact terms, names, codes, acronyms
• embeddings + kNN/vector search for meaning-based retrieval

This is often the most practical enterprise choice.

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2. Postgres + pgvector

Best if:

• your corpus is smaller to medium-sized
• you already use Postgres
• you want something simple to operate

Why it’s strong:
Easy to get started, good for prototypes and modest production use.
Tradeoff: less powerful than dedicated search engines for large-scale hybrid retrieval.

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3. Dedicated vector databases

Examples:

• Pinecone
• Weaviate
• Milvus
• Qdrant

Best if:

• semantic retrieval is the core product
• you want fast vector similarity search
• you need flexible indexing and scaling

Tradeoff: many teams still pair these with a keyword engine for hybrid search.

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Important recommendation: use hybrid search

For internal content, pure semantic search often misses:

• exact product names
• ticket IDs
• policy codes
• acronyms
• filenames

So the best practical setup is usually:

1. chunk documents
2. generate embeddings
3. store vectors in a vector index
4. keep keyword/full-text search
5. combine results with hybrid ranking

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If you’re building an internal knowledge search tool

A strong default architecture is:

• Document store: S3 / SharePoint / Google Drive / Confluence / etc.
• Indexing: chunking + metadata extraction
• Embeddings: OpenAI, Cohere, or open-source models
• Search layer: Elasticsearch/OpenSearch or a vector DB
• Reranker: optional but highly recommended for better precision

A reranker can significantly improve result quality.

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Quick rule of thumb

• Need enterprise search with filters and keywords? → OpenSearch/Elasticsearch
• Already on Postgres and small scale? → pgvector
• Need vector-native infra? → Pinecone / Weaviate / Milvus / Qdrant
• Need best quality? → Hybrid search + reranker

If you want, I can recommend a specific stack based on:

• your content sources
• document volume
• latency requirements
• budget
• whether you want open-source or managed SaaS