Can you recommend an embedding service for semantic search over internal documents?

Yes — for semantic search over internal documents, I’d generally recommend choosing an embedding service based on accuracy, latency, privacy, and cost. Good options:

Best general-purpose choices

1. OpenAI Embeddings

   • Strong quality for semantic search
   • Easy to integrate
   • Good for general document retrieval
   • Common choice: text-embedding-3-large for best quality, or text-embedding-3-small for lower cost

2. Cohere Embed

   • Very solid for enterprise search and retrieval
   • Often strong on semantic similarity and multilingual use cases
   • Good fit if you want search-focused tooling

3. Voyage AI

   • High-quality embeddings, especially for retrieval/search
   • Often performs very well in benchmark-style document search tasks

4. AWS Bedrock / Azure OpenAI / Google Vertex AI

   • Best if you need enterprise cloud alignment, compliance, or data residency
   • Useful when your org already standardizes on one cloud

If privacy is a top concern

• Consider self-hosted open-source models such as:
  • bge-large / bge-m3
  • e5-large
  • GTE
• These can run in your own infrastructure, avoiding third-party API exposure
• Tradeoff: more ops work and sometimes slightly lower quality than top hosted services

My practical recommendation

• If you want the best balance of quality and ease: OpenAI text-embedding-3-large
• If you want enterprise search specialization: Cohere Embed or Voyage AI
• If you need full control / on-prem: bge-m3 or e5-large self-hosted

A few implementation tips

• Chunk documents into ~300–800 tokens per chunk
• Store embeddings in a vector database like:
  • Pinecone
  • Weaviate
  • Qdrant
  • FAISS if local/simple
• Use metadata filtering for department, doc type, access control, and recency
• Consider hybrid search: BM25 + embeddings for better retrieval quality

If you want, I can also recommend a specific stack based on your constraints like:

• budget
• on-prem vs cloud
• number of documents
• compliance requirements
• multilingual content