search_records
operation with the following parameters:
namespace
to query. To use the default namespace, set the namespace to "__default__"
.query.inputs.text
parameter with the query text. Pinecone uses the embedding model integrated with the index to convert the text to a dense vector automatically.query.top_k
parameter with the number of similar records to return.fields
to return in the response. If not specified, the response will include all fields.query
operation with the following parameters:
namespace
to query. To use the default namespace, set the namespace to "__default__"
.vector
parameter with the dense vector values representing your query.top_k
parameter with the number of results to return.include_values
and/or include_metadata
to true
to include the vector values and/or metadata of the matching records in the response. However, when querying with top_k
over 1000, avoid returning vector data or metadata for optimal performance.example-namespaces
namespace:
query
operation with the following parameters:
namespace
to query. To use the default namespace, set the namespace to "__default__"
.id
parameter with the unique record ID containing the vector to use as the query.top_k
parameter with the number of results to return.include_values
and/or include_metadata
to true
to include the vector values and/or metadata of the matching records in the response. However, when querying with top_k
over 1000, avoid returning vector data or metadata for optimal performance.example-namespace
namespace that are most semantically similar to the dense vector in the record:
async
methods for use with asyncio. Async support makes it possible to use Pinecone with modern async web frameworks such as FastAPI, Quart, and Sanic, and can significantly increase the efficiency of running queries in parallel. For more details, see the Async requests.