Cohere's embedding models are available via the Embed endpoint. This endpoint enables you to embed text documents (multilingual) and images into a 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. ::::::steps{titleSize="h2"} :::::step{title="Setup"} First, install the Cohere Python SDK with the following command. ```bash pip install -U cohere ``` Next, import the library and create a client. ::::tabs :::tab{title="Cohere Platform"} ```python PYTHON import cohere co = cohere.Client( "COHERE_API_KEY" ) # Get your free API key here: https://dashboard.cohere.com/api-keys ``` ::: :::tab{title="Private Deployment"} ```python PYTHON import cohere co = cohere.Client( api_key="", # Leave this blank base_url="", ) ``` ::: :::tab{title="Bedrock"} ```python PYTHON import cohere co = cohere.BedrockClient( aws_region="AWS_REGION", aws_access_key="AWS_ACCESS_KEY_ID", aws_secret_key="AWS_SECRET_ACCESS_KEY", aws_session_token="AWS_SESSION_TOKEN", ) # Get the model name: https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html ``` ::: :::tab{title="SageMaker"} ```python PYTHON import cohere co = cohere.SagemakerClient( aws_region="AWS_REGION", aws_access_key="AWS_ACCESS_KEY_ID", aws_secret_key="AWS_SECRET_ACCESS_KEY", aws_session_token="AWS_SESSION_TOKEN", ) ``` ::: :::tab{title="Azure AI"} ```python PYTHON import cohere co = cohere.Client( api_key="AZURE_API_KEY", base_url="AZURE_ENDPOINT", # example: "https://cohere-embed-v3-multilingual-xyz.eastus.models.ai.azure.com/" ) ``` ::: :::: ::::: :::::step{title="Document Embeddings"} First, embed the list of available documents using the Embed endpoint by specifying the `input_type` as `search_document`. ::::tabs :::tab{title="Cohere Platform"} ```python PYTHON # Define the documents documents = [ "Joining Slack Channels: Be sure to join relevant channels to stay informed and engaged.", "Finding Coffee Spots: For your caffeine fix, cross the street to the café for artisan coffee.", "Working Hours Flexibility: While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.", ] # Embed the documents doc_emb = co.embed( model="embed-v4.0", input_type="search_document", texts=documents, embedding_types=["float"], ).embeddings.float ``` ::: :::tab{title="Private Deployment"} ```python PYTHON # Define the documents documents = [ "Joining Slack Channels: Be sure to join relevant channels to stay informed and engaged.", "Finding Coffee Spots: For your caffeine fix, cross the street to the café for artisan coffee.", "Working Hours Flexibility: While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.", ] # Embed the documents doc_emb = co.embed( model="embed-v4.0", input_type="search_document", texts=documents, embedding_types=["float"], ).embeddings.float ``` ::: :::tab{title="Bedrock"} ```python PYTHON # Define the documents documents = [ "Joining Slack Channels: Be sure to join relevant channels to stay informed and engaged.", "Finding Coffee Spots: For your caffeine fix, cross the street to the café for artisan coffee.", "Working Hours Flexibility: While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.", ] # Embed the documents doc_emb = co.embed( model="YOUR_MODEL_NAME", input_type="search_document", texts=documents, embedding_types=["float"], ).embeddings.float ``` ::: :::tab{title="SageMaker"} ```python PYTHON # Define the documents documents = [ "Joining Slack Channels: Be sure to join relevant channels to stay informed and engaged.", "Finding Coffee Spots: For your caffeine fix, cross the street to the café for artisan coffee.", "Working Hours Flexibility: While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.", ] # Embed the documents doc_emb = co.embed( model="YOUR_ENDPOINT_NAME", input_type="search_document", texts=documents, embedding_types=["float"], ).embeddings.float ``` ::: :::tab{title="Azure AI"} ```python PYTHON # Define the documents documents = [ "Joining Slack Channels: Be sure to join relevant channels to stay informed and engaged.", "Finding Coffee Spots: For your caffeine fix, cross the street to the café for artisan coffee.", "Working Hours Flexibility: While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.", ] # Embed the documents doc_emb = co.embed( input_type="search_document", texts=documents, embedding_types=["float"], ).embeddings.float ``` ::: :::: ::::: :::::step{title="Query Embedding"} Next, embed the user query using the Embed endpoint by specifying the `input_type` as `search_query`. ::::tabs :::tab{title="Cohere Platform"} ```python PYTHON # Add the user query query = "Ways to connect with my teammates" # Embed the query query_emb = co.embed( model="embed-v4.0", input_type="search_query", texts=[query], embedding_types=["float"], ).embeddings.float ``` ::: :::tab{title="Private Deployment"} ```python PYTHON # Add the user query query = "Ways to connect with my teammates" # Embed the query query_emb = co.embed( model="embed-v4.0", input_type="search_query", texts=[query], embedding_types=["float"], ).embeddings.float ``` ::: :::tab{title="Bedrock"} ```python PYTHON # Add the user query query = "Ways to connect with my teammates" # Embed the query query_emb = co.embed( model="YOUR_MODEL_NAME", input_type="search_query", texts=[query], embedding_types=["float"], ).embeddings.float ``` ::: :::tab{title="SageMaker"} ```python PYTHON # Add the user query query = "Ways to connect with my teammates" # Embed the query query_emb = co.embed( model="embed-v4.0", input_type="search_query", texts=[query], embedding_types=["float"], ).embeddings.float ``` ::: :::tab{title="Azure AI"} ```python PYTHON # Add the user query query = "Ways to connect with my teammates" # Embed the query query_emb = co.embed( model="embed-v4.0", input_type="search_query", texts=[query], embedding_types=["float"], ).embeddings.float ``` ::: :::: ::::: :::step{title="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 PYTHON import numpy as np # Compute dot product similarity and display results def return_results(query_emb, doc_emb, documents): n = 2 # customize your top N results scores = np.dot(query_emb, np.transpose(doc_emb))[0] max_idx = np.argsort(-scores)[:n] for rank, idx in enumerate(max_idx): print(f"Rank: {rank+1}") print(f"Score: {scores[idx]}") print(f"Document: {documents[idx]}\n") return_results(query_emb, doc_emb, documents) ``` ```mdx wordWrap Rank: 1 Score: 0.262197161387274 Document: Joining Slack Channels: Be sure to join relevant channels to stay informed and engaged. Rank: 2 Score: 0.1266074257723145 Document: Working Hours Flexibility: While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed. ``` ::: :::::: ## Further Resources - [Embed endpoint API reference](/api) - [Documentation on embeddings](/guides/embeddings-vectors-search-retrieval-v2-text-embeddings-embeddings) - [LLM University module on semantic search](https://cohere.com/llmu#semantic-search) ## Related pages - [Changelog](../changelog.md) - [Cohere](../index.md) - [Cohere API](./cohere-api-index.md) - [Cohere Labs](./cohere-labs-index.md) - [Cohere Platform](./cohere-platform-index.md) - [Cookbooks](./cookbooks-index.md) - [Deployment Options](./deployment-options-index.md) - [Embeddings (Vectors, Search, Retrieval)](./embeddings-vectors-search-retrieval-index.md) - [Get Started](./get-started-index.md) - [Going to Production](./going-to-production-index.md) # Agent Instructions Cite this page’s canonical URL and keep its documentation version. 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