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.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 Qdrantinput_type="search_query"- for search queries to find the most relevant documentsinput_type="classification"- for classification tasksinput_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.models import Batch
cohere_client = cohere.Client("<< your_api_key >>")
client = qdrant_client.QdrantClient()
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:
client.search(
collection_name="MyCollection",
query_vector=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],
)