Implementation:Cohere ai Cohere python Vector Retrieval Pattern

Metadata	Value
Source	Cohere Semantic Search
Domains	Vector_Search, Information_Retrieval, Semantic_Search
Last Updated	2026-02-15 14:00 GMT
Implements	Principle:Cohere_ai_Cohere_python_Vector_Similarity_Search

Overview

Interface specification for integrating external vector databases with Cohere embeddings for similarity search.

Description

This is an External Tool Doc — it documents how to integrate external vector databases (not part of the Cohere SDK) with Cohere embeddings. The pattern involves: (1) embedding documents, (2) indexing in a vector DB, (3) embedding queries, (4) querying the vector DB, (5) optionally reranking results. The Cohere SDK provides embeddings; the vector DB provides storage and search.

Usage

Choose a vector database, embed and index documents, then query using embedding similarity. The SDK does not include a vector database — this is an external integration.

Code Reference

Source Location

N/A (external integration pattern)

Interface Specification

import numpy as np
from typing import List, Tuple

# Pattern: Vector similarity search with Cohere embeddings
def similarity_search(
    query_embedding: List[float],
    document_embeddings: List[List[float]],
    top_k: int = 10,
) -> List[Tuple[int, float]]:
    """
    Find top-K most similar documents using cosine similarity.

    Returns: List of (index, similarity_score) tuples, sorted by descending similarity.
    """
    query = np.array(query_embedding)
    docs = np.array(document_embeddings)

    # Cosine similarity
    similarities = np.dot(docs, query) / (
        np.linalg.norm(docs, axis=1) * np.linalg.norm(query)
    )

    top_indices = np.argsort(similarities)[::-1][:top_k]
    return [(int(idx), float(similarities[idx])) for idx in top_indices]

Import

N/A (external tools: numpy, faiss, pinecone, etc.)

I/O Contract

Direction	Description
Inputs	Query embedding (List[float]) from client.embed(), document embeddings indexed in vector DB
Outputs	Top-K document indices with similarity scores

Usage Examples

from cohere import Client
import numpy as np

client = Client()

# 1. Embed documents
documents = ["Doc about AI", "Doc about cooking", "Doc about physics"]
doc_response = client.embed(
    texts=documents,
    model="embed-english-v3.0",
    input_type="search_document",
)

# 2. Embed query
query_response = client.embed(
    texts=["Tell me about artificial intelligence"],
    model="embed-english-v3.0",
    input_type="search_query",
)

# 3. Compute cosine similarity (simple numpy example)
doc_embeds = np.array(doc_response.embeddings)
query_embed = np.array(query_response.embeddings[0])
similarities = np.dot(doc_embeds, query_embed) / (
    np.linalg.norm(doc_embeds, axis=1) * np.linalg.norm(query_embed)
)

# 4. Get top results
top_idx = np.argsort(similarities)[::-1]
for idx in top_idx:
    print(f"Score: {similarities[idx]:.4f} - {documents[idx]}")

# 5. Optionally rerank top candidates
top_docs = [documents[i] for i in top_idx[:10]]
reranked = client.rerank(
    model="rerank-v4.0-pro",
    query="Tell me about artificial intelligence",
    documents=top_docs,
    top_n=3,
)

Related Pages

• Principle:Cohere_ai_Cohere_python_Vector_Similarity_Search