Workflow:Cohere ai Cohere python Semantic Search With Rerank
| Knowledge Sources | |
|---|---|
| Domains | Embeddings, Reranking, Information_Retrieval, API_Client |
| Last Updated | 2026-02-15 14:00 GMT |
Overview
End-to-end process for implementing semantic search using Cohere embeddings for initial retrieval followed by the Rerank API for precision re-ordering of results.
Description
This workflow implements a two-stage retrieval pipeline: first, generate embeddings for a corpus and a query to perform approximate nearest neighbor (ANN) search, then apply Cohere's Rerank model to re-score and re-order the top candidates for higher relevance precision. The rerank step accepts raw text documents (no pre-embedding required) and returns ranked results with relevance scores.
Usage
Execute this workflow when building search systems, question-answering pipelines, or retrieval-augmented generation (RAG) applications where initial vector similarity retrieval needs to be refined for better precision. The rerank step is particularly effective at re-ordering results from any retrieval source (vector search, BM25, hybrid).
Execution Steps
Step 1: Generate Document Embeddings
Embed the document corpus using the embed() method with input_type set to search_document. Store the resulting vectors in a vector database or in-memory index for similarity search.
Key considerations:
- Use input_type="search_document" when embedding corpus documents
- The auto-batching feature handles large corpora automatically (96 items per batch)
- Choose the embedding model matching your language and dimensionality requirements
- Store embeddings alongside document text for the rerank step
Step 2: Embed the Query
Generate an embedding for the user's search query using the same model with input_type set to search_query. The asymmetric input types (search_document vs. search_query) are trained to optimize retrieval performance.
Key considerations:
- Use input_type="search_query" for query embeddings
- The query and document embeddings must use the same model
- Single queries bypass batching for minimal latency
Step 3: Perform Initial Retrieval
Compare the query embedding against the document embeddings using cosine similarity or another distance metric to retrieve the top-K candidate documents. This step uses an external vector database or in-memory search.
Key considerations:
- Retrieve a generous number of candidates (e.g., top 100) for the rerank step to refine
- The initial retrieval is approximate; the rerank step provides precision refinement
- This step is external to the Cohere SDK (uses a vector database)
Step 4: Rerank the Candidates
Pass the query and candidate documents to the rerank() method. The Rerank model cross-encodes each query-document pair to produce a relevance score, then returns documents sorted by descending relevance.
Key considerations:
- The rerank endpoint accepts documents as strings or as dictionaries with rank_fields
- The top_n parameter limits the number of returned results (default returns all)
- rank_fields specifies which dictionary keys to use for ranking (e.g., title, text)
- return_documents controls whether full document content is included in results
- max_chunks_per_doc splits long documents into chunks for more accurate scoring
- The V2 rerank endpoint (v2.rerank) provides the same functionality with V2 response types
Step 5: Process Ranked Results
Extract the reranked documents from the RerankResponse. Each result includes the document content, its original index, and a relevance_score between 0 and 1. Use the top results for display or as context for RAG.
Key considerations:
- Results are sorted by relevance_score in descending order
- The index field maps each result back to its position in the input documents list
- Relevance scores are not calibrated probabilities but are useful for relative ranking
- The meta field provides billing information for the rerank call