Semantic search - quickstart

Cohere’s embedding models are available via the Embed endpoint. This endpoint enables you to embed text documents (multilingual) and images into the vector space.

Semantic search, powered by embeddings, enables applications to perform information retrieval based on the context or meaning of a document.

This quickstart guide shows you how to perform semantic search with the Embed endpoint.

1

Setup

First, install the Cohere Python SDK with the following command.

$pip install -U cohere

Next, import the library and create a client.

PYTHON
1import cohere
2
3co = cohere.ClientV2(
4    "COHERE_API_KEY"
5)  # Get your free API key here: https://dashboard.cohere.com/api-keys
2

Document Embeddings

First, embed the list of available documents using the Embed endpoint by specifying the input_type as search_document.

PYTHON
1# Define the documents
2documents = [
3    "Joining Slack Channels: Be sure to join relevant channels to stay informed and engaged.",
4    "Finding Coffee Spots: For your caffeine fix, cross the street to the café for artisan coffee.",
5    "Working Hours Flexibility: While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.",
6]
7
8# Embed the documents
9doc_emb = co.embed(
10    model="embed-english-v3.0",
11    input_type="search_document",
12    texts=documents,
13    embedding_types=["float"],
14).embeddings.float
3

Query Embedding

Next, embed the user query using the Embed endpoint by specifying the input_type as search_query.

PYTHON
1# Add the user query
2query = "Ways to connect with my teammates"
3
4# Embed the query
5query_emb = co.embed(
6    model="embed-english-v3.0",
7    input_type="search_query",
8    texts=[query],
9    embedding_types=["float"],
10).embeddings.float
4

Semantic Search

Then, perform semantic search by computing the similarity between the query embedding and the document embeddings, and then returning the most similar documents.

PYTHON
1import numpy as np
2
3
4# Compute dot product similarity and display results
5def return_results(query_emb, doc_emb, documents):
6    n = 2  # customize your top N results
7    scores = np.dot(query_emb, np.transpose(doc_emb))[0]
8    max_idx = np.argsort(-scores)[:n]
9
10    for rank, idx in enumerate(max_idx):
11        print(f"Rank: {rank+1}")
12        print(f"Score: {scores[idx]}")
13        print(f"Document: {documents[idx]}\n")
14
15
16return_results(query_emb, doc_emb, documents)
1Rank: 1
2Score: 0.23645164410153752
3Document: Joining Slack Channels: Be sure to join relevant channels to stay informed and engaged.
4
5Rank: 2
6Score: 0.1505097876657395
7Document: Finding Coffee Spots: For your caffeine fix, cross the street to the café for artisan coffee.

Further Resources

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