landing_page/qdrant-landing/content/documentation/embeddings/cohere.md

title, weight, aliases

title	weight	aliases
Cohere	700	../integrations/cohere/

Cohere

Qdrant is compatible with Cohere co.embed API and its official Python SDK that might be installed as any other package:

pip install cohere

The embeddings returned by co.embed API might be used directly in the Qdrant client's calls:

import cohere
import qdrant_client

from qdrant_client.http.models import Batch

cohere_client = cohere.Client("<< your_api_key >>")
qdrant_client = qdrant_client.QdrantClient()
qdrant_client.upsert(
    collection_name="MyCollection",
    points=Batch(
        ids=[1],
        vectors=cohere_client.embed(
            model="large",
            texts=["The best vector database"],
        ).embeddings,
    ),
)

If you are interested in seeing an end-to-end project created with co.embed API and Qdrant, please check out the "Question Answering as a Service with Cohere and Qdrant" article.

Embed v3

Embed v3 is a new family of Cohere models, released in November 2023. The new models require passing an additional parameter to the API call: input_type. It determines the type of task you want to use the embeddings for.

• input_type="search_document" - for documents to store in Qdrant
• input_type="search_query" - for search queries to find the most relevant documents
• input_type="classification" - for classification tasks
• input_type="clustering" - for text clustering

While implementing semantic search applications, such as RAG, you should use input_type="search_document" for the indexed documents and input_type="search_query" for the search queries. The following example shows how to index documents with the Embed v3 model:

import cohere
import qdrant_client

from qdrant_client.http.models import Batch

cohere_client = cohere.Client("<< your_api_key >>")
qdrant_client = qdrant_client.QdrantClient()
qdrant_client.upsert(
    collection_name="MyCollection",
    points=Batch(
        ids=[1],
        vectors=cohere_client.embed(
            model="embed-english-v3.0",  # New Embed v3 model
            input_type="search_document",  # Input type for documents
            texts=["Qdrant is the a vector database written in Rust"],
        ).embeddings,
    ),
)

Once the documents are indexed, you can search for the most relevant documents using the Embed v3 model:

qdrant_client.search(
    collection_name="MyCollection",
    query=cohere_client.embed(
        model="embed-english-v3.0",  # New Embed v3 model
        input_type="search_query",  # Input type for search queries
        texts=["The best vector database"],
    ).embeddings[0],
)

According to Cohere's documentation, all v3 models can use dot product, cosine similarity, and Euclidean distance as the similarity metric, as all metrics return identical rankings.