Retrieval Models
RetrievalRequest
Used to submit natural language queries:RetrievalResponse
Returned with the query result:Retrieval Methods
query()
Submit a natural language query:Complete Example
How Retrieval Works
- Document Embedding - Documents are embedded when created with
embed=Trueor viadocument.embed() - Query Embedding - Your natural language query is embedded using the same model
- Semantic Search - The system finds documents with similar embeddings
- Response Generation - A natural language response is generated from relevant documents
Requirements
For retrieval to work effectively:- Documents must be embedded before querying
- Use clear, specific queries
- Ensure documents contain relevant information
- The embedding model must be properly configured
Example Queries
Use Cases
- Question answering - Answer questions about stored documents
- Information retrieval - Find relevant information semantically
- Context search - Search through ESS and sequence contexts
- Knowledge base queries - Query organizational knowledge
Best Practices
- Embed documents promptly - Embed documents as soon as they’re created
- Use descriptive text - Write clear, detailed document text
- Specific queries - More specific queries yield better results
- Batch document creation - Create multiple related documents for comprehensive coverage
- Update embeddings - Re-embed documents after updates