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03cd7e06b3cc-0 | This document contains details about Pinecone releases. For information about using specific features, see our API reference.
June 21, 2023
The new gcp-starter region is now in public preview. This region has distinct limitations from other Starter Plan regions. gcp-starter is the default region for some new users.
Ap... | https://docs.pinecone.io/docs/release-notes |
03cd7e06b3cc-1 | You can now try out our new Node.js client for Pinecone.
February 14, 2023
New usage reports in the Pinecone console
You can now monitor your current and projected Pinecone usage with the Usage dashboard.
January 31, 2023
Pinecone is now available in AWS Marketplace
You can now sign up for Pinecone billing through Amaz... | https://docs.pinecone.io/docs/release-notes |
03cd7e06b3cc-2 | Fixes "Connection Reset by peer" error after long idle periods
Adds typing and explicit names for arguments in all client operations
Adds docstrings to all client operations
Adds Support for batch upserts by passing batch_size to the upsert method
Improves gRPC query results parsing performance
December 22, 2022
Pinec... | https://docs.pinecone.io/docs/release-notes |
03cd7e06b3cc-3 | October 31, 2022
Hybrid search (Early access)
Pinecone now supports keyword-aware semantic search with the new hybrid search indexes and endpoints. Hybrid search enables improved relevance for semantic search results by combining them with keyword search.
This is an early access feature and is available only by signing... | https://docs.pinecone.io/docs/release-notes |
03cd7e06b3cc-4 | Vertical scaling
You can now change the size of the pods for a live index to accommodate more vectors or queries without interrupting reads or writes. The p1 and s1 pod types are now available in 4 different sizes: 1x, 2x, 4x, and 8x. Capacity and compute per pod double with each size increment.
p2 pod type (Public Pre... | https://docs.pinecone.io/docs/release-notes |
03cd7e06b3cc-5 | Pinecone Console Guided Tour
You can now choose to follow a guided tour in the Pinecone Console. This interactive tutorial walks you through creating your first index, upserting vectors, and querying your data. The purpose of the tour is to show you all the steps you need to start your first project in Pinecone.
June 2... | https://docs.pinecone.io/docs/release-notes |
03cd7e06b3cc-6 | Query by ID
You can now query your Pinecone index using only the ID for another vector. This is useful when you want to search for the nearest neighbors of a vector that is already stored in Pinecone.
Improved index fullness accuracy
The index fullness metric in describe_index_stats() results is now more accurate.
Apr... | https://docs.pinecone.io/docs/release-notes |
03cd7e06b3cc-7 | pinecone_vector_count
pinecone_request_count_total
pinecone_request_error_count_total
pinecone_request_latency_seconds
pinecone_index_fullness (Public Preview)
Note: The accuracy of the pinecone_index_fullness metric is improved. This may result in changes from historic reported values. This metric is in public previe... | https://docs.pinecone.io/docs/release-notes |
155b13a20033-0 | Overview
This document describes the security protocols and practices in use by Pinecone.
API keys
Each Pinecone project has one or more API keys. In order to make calls to the Pinecone API, a user must provide a valid API key for the relevant Pinecone project.
Role-based access controls (RBAC)
Each Pinecone organizati... | https://docs.pinecone.io/docs/security |
155b13a20033-1 | Encryption in transit
Pinecone uses standard protocols to encrypt user data in transit. Clients open HTTPS or gRPC connections to the Pinecone API; the Pinecone API gateway uses gRPC connections to user deployments in the cloud. These HTTPS and gRPC connections use the TLS 1.2 protocol with 256-bit Advanced Encryption ... | https://docs.pinecone.io/docs/security |
8ef910d61d8e-0 | This page provides installation instructions, usage examples, and a reference for the Pinecone Node.JS client.
⚠️WarningThis is a public preview ("Beta") client. Test thoroughly before
using this client for production workloads. No SLAs or technical support
commitments are provided for this client. Expect potential b... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-1 | The following example creates an index that only indexes the "color" metadata field. Queries against this index cannot filter based on any other metadata field.
JavaScriptawait pinecone.createIndex({
createRequest: {
name: "example-index-2",
dimension: 1024,
metadataConfig: {
indexed: ["color"],
... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-2 | Upsert vectors
The following example upserts vectors to example-index.
JavaScriptconst index = pinecone.Index("example-index");
const upsertRequest = {
vectors: [
{
id: "vec1",
values: [0.1, 0.2, 0.3, 0.4],
metadata: {
genre: "drama",
},
},
{
id: "vec2",
values:... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-3 | Delete vectors
The following example deletes vectors by ID.
JavaScriptconst index = pinecone.Index("example-index");
await index.delete1({
ids: ["vec1", "vec2"],
namespace: "example-namespace",
});
Fetch vectors
The following example fetches vectors by ID.
JavaScriptconst index = pinecone.Index("example-index");
c... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-4 | Describe a collection
The following example returns a description of the collection example-collection.
JavaScriptconst collectionDescription = await pinecone.describeCollection({
collectionName: "example-collection",
});
Delete a collection
The following example deletes the collection example-collection.
JavaScript... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-5 | ParametersTypeDescriptionindexNamestringThe name of the index.patchRequestPatchRequest(Optional) Patch request parameters.
PatchRequest
ParametersTypeDescriptionreplicasnumber(Optional) The number of replicas to configure for this index.podTypestring(Optional) The new pod type for the index. One of ... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-6 | ParametersTypeDescriptionnamestringThe name of the collection to be created.sourcestringThe name of the source index to be used as the source for the collection.
Example:
JavaScriptawait pinecone.createCollection({
createCollectionRequest: {
name: "example-collection",
source: "example-index",
},
});
creat... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-7 | ParametersTypeDescriptionnamestrThe name of the index to be created. The maximum length is 45 characters.dimensionintegerThe dimensions of the vectors to be inserted in the index.metricstr(Optional) The distance metric to be used for similarity search: 'euclidean', 'cosine', or 'dotproduct'.podsint(Optional) The number... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-8 | // The following example creates an index that only indexes
// the 'color' metadata field. Queries against this index
// cannot filter based on any other metadata field.
await pinecone.createIndex({
createRequest: {
name: "example-index-2",
dimension: 1024,
metadata_config: {
indexed: ["color"],
... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-9 | ParametersTypeDescriptioncollectionNamestringThe name of the collection.
Example:
JavaScriptconst collectionDescription = await pinecone.describeCollection({
collectionName: "example-collection",
});
Return:
collectionMeta : object Configuration information and deployment status of the collection.
name : string Th... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-10 | database : object
name : string The name of the index.
dimension : integer The dimensions of the vectors to be inserted in the index.
metric : string The distance metric used for similarity search: 'euclidean', 'cosine', or 'dotproduct'.
pods : integer The number of pods the index uses, including replicas.
replicas : i... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-11 | listIndexes
pinecone.listIndexes()
Return a list of your Pinecone indexes.
Returns:
array of strings The names of the indexes in your project.
Example:
JavaScriptconst indexesList = await pinecone.listIndexes();
Index()
pinecone.Index(indexName: string)
Construct an Index object.
ParametersTypeDescrip... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-12 | ParametersTypeDescriptionrequestParametersDescribeIndexStatsOperationRequestDescribe index stats request wrapper
Types
DescribeIndexStatsOperationRequest
ParametersTypeDescriptiondescribeIndexStatsRequestDescribeIndexStatsRequestDescribe index stats request parameters
DescribeIndexStatsRequest
... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-13 | ParametersTypeDescriptionidsArrayThe vector IDs to fetch. Does not accept values containing spaces.namespacestring(Optional) The namespace containing the vectors.
Returns:
vectors : object Contains the vectors.
namespace : string The namespace of the vectors.
Example:
JavaScriptconst fetchResponse = await index.fetch... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-14 | ParameterTypeDescriptionnamespacestring(Optional) The namespace to query.topKnumberThe number of results to return for each query.filterobject(Optional) The filter to apply. You can use vector metadata to limit your search. See https://www.pinecone.io/docs/metadata-filtering/.includeValuesboolean(Optional) Indicates wh... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-15 | Index.update()
index.update(requestParameters: UpdateOperationRequest)
Updates vectors in a namespace. If a value is included, it will overwrite the previous value.
If setMetadata is included in the updateRequest, the values of the fields specified in it will be added or overwrite the previous value.
Pa... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-16 | ParametersTypeDescriptionrequestParametersUpsertOperationRequestUpsert operation wrapper
Types
UpsertOperationRequest
ParametersTypeDescriptionupsertRequestUpsertRequestThe upsert request.
UpsertRequest
| Parameter | Type | Description |
| vectors | Array | An array containing the vectors to upsert. Rec... | https://docs.pinecone.io/docs/node-client |
8ef910d61d8e-17 | Example:
JavaScriptconst upsertResponse = await index.upsert({
upsertRequest: {
vectors: [
{
id: "vec1",
values: [0.1, 0.2, 0.3, 0.4],
metadata: {
genre: "drama",
},
},
{
id: "vec2",
values: [0.1, 0.2, 0.3, 0.4],
metadata: {
... | https://docs.pinecone.io/docs/node-client |
cc60f5f5e4a7-0 | This page provides installation instructions, usage examples, and a reference for the Pinecone Python client.
Getting Started
Installation
Use the following shell command to install the Python client for use with Python versions 3.6+:
Pythonpip3 install pinecone-client
Alternatively, you can install Pinecone in a Jupy... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-1 | pinecone.init(api_key="YOUR_API_KEY",
environment="YOUR_ENVIRONMENT")
pinecone.create_index("example-index", dimension=1024)
The following example creates an index that only indexes the "color" metadata field. Queries against this index cannot filter based on any other metadata field.
Pythonmetadata_con... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-2 | Describe index statistics
The following example returns statistics about the index example-index.
Pythonimport pinecone
pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENVIRONMENT")
index = pinecone.Index("example-index")
index_stats_response = index.describe_index_stats()
Upsert vectors
The following exampl... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-3 | query_response = index.query(
namespace="example-namespace",
top_k=10,
include_values=True,
include_metadata=True,
vector=[0.1, 0.2, 0.3, 0.4],
filter={
"genre": {"$in": ["comedy", "documentary", "drama"]}
}
)
Delete vectors
The following example deletes vectors by ID.
Pythonimport ... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-4 | Create collection
The following example creates the collection example-collection from example-index.
Pythonimport pinecone
pinecone.init(api_key="YOUR_API_KEY",
environment="YOUR_ENVIRONMENT")
pinecone.create_collection("example-collection", "example-index")
List collections
The following example retu... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-5 | configure_index()
pinecone.configure_index(index_name, **kwargs)
Configure an index to change pod type and number of replicas.
ParametersTypeDescriptionindex_namestrThe name of the indexreplicasint(Optional) The number of replicas to configure for this index.pod_typestr(Optional) The new pod ty... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-6 | ParametersTypeDescriptionnamestrThe name of the index to be created. The maximum length is 45 characters.dimensionintegerThe dimensions of the vectors to be inserted in the index.metricstr(Optional) The distance metric to be used for similarity search: 'euclidean', 'cosine', or 'dotproduct'.podsint(Optional) The number... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-7 | ## The following example creates an index that only indexes
## the 'color' metadata field. Queries against this index
## cannot filter based on any other metadata field.
metadata_config = {
'indexed': ['color']
}
pinecone.create_index('example-index-2', dimension=1024,
metadata_config=metada... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-8 | name : string The name of the collection.
size: integer The size of the collection in bytes.
status: string The status of the collection.
describe_index()
pinecone.describe_index(indexName)
Get a description of an index.
ParametersTypeDescriptionindex_namestrThe name of the index.
Returns:
database :... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-9 | Example:
Pythonimport pinecone
pinecone.init(api_key='YOUR_API_KEY', environment='YOUR_ENVIRONMENT')
index_description = pinecone.describe_index('example-index')
list_collections()
pinecone.list_collections()
Return a list of the collections in your project.
Returns:
array of strings The names of the collections in... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-10 | ParametersTypeDescriptionidsarray(Optional) array of strings vectors to delete.delete_allboolean(Optional) Indicates that all vectors in the index namespace should be deleted.namespacestr(Optional) The namespace to delete vectors from, if applicable.filterobject(Optional) If specified, the metadata filter here will be ... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-11 | Example:
Pythonimport pinecone
pinecone.init(api_key='YOUR_API_KEY', environment='YOUR_ENVIRONMENT')
index = pinecone.Index('example-index')
index_stats_response = index.describe_index_stats()
Index.fetch()
Index.fetch(ids, **kwargs)
The Fetch operation looks up and returns vectors, by ID, from a single namespace. T... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-12 | ParametersTypeDescriptionnamespacestr(Optional) The namespace to query.top_kint64The number of results to return for each query.filterobject(Optional) The filter to apply. You can use vector metadata to limit your search. See https://www.pinecone.io/docs/metadata-filtering/.include_valuesboolean(Optional) Indicates whe... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-13 | query_response = index.query(
namespace='example-namespace',
top_k=10,
include_values=True,
include_metadata=True,
vector=[0.1, 0.2, 0.3, 0.4],
filter={
'genre': {'$in': ['comedy', 'documentary', 'drama']}
}
)
The following example queries the index example-index with a sparse-dense... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-14 | ParametersTypeDescriptionidstrThe vector's unique ID.values[float](Optional) Vector data.set_metadataobject(Optional) Metadata to set for the vector.namespacestr(Optional) The namespace containing the vector.
Example:
Pythonimport pinecone
pinecone.init(api_key='YOUR_API_KEY', environment='YOUR_ENVIRONMENT')
index = p... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-15 | Example:
Pythonimport pinecone
pinecone.init(api_key='YOUR_API_KEY', environment='YOUR_ENVIRONMENT')
index = pinecone.Index('example-index')
upsert_response = index.upsert(
vectors=[
{'id': "vec1", "values":[0.1, 0.2, 0.3, 0.4], "metadata": {'genre': 'drama'}},
{'id': "vec2", "values":[0.2, 0.3, 0.4,... | https://docs.pinecone.io/docs/python-client |
cc60f5f5e4a7-16 | upsert_response = index.upsert(
vectors=[
{'id': 'vec1',
'values': [0.1, 0.2, 0.3, 0.4],
'metadata': {'genre': 'drama'},
'sparse_values': {
'indices': [10, 45, 16],
'values': [0.5, 0.5, 0.2]
}},
{'id': 'vec2',
'values': [0.2, 0.3... | https://docs.pinecone.io/docs/python-client |
eb05f2902f12-0 | This document describes how to monitor the usage and costs for your Pinecone organization through the Pinecone console.
To view your Pinecone usage, you must be the organization owner for your organization. This feature is only available to organizations on the Standard or Enterprise plans.
To view your usage through t... | https://docs.pinecone.io/docs/monitoring-usage |
20c69b6bb780-0 | Overview
This category contains guides for tasks related to Pinecone billing.
Tasks
Setting up GCP Marketplace billing
Setting up AWS Marketplace billing
Changing your billing plan
Understanding subscription statuses
Updated 24 days ago Monitoring your usageUnderstanding subscription statusDid this page help you?YesNo | https://docs.pinecone.io/docs/manage-billing |
91092c370872-0 | Overview
This topic provides guidance on managing the cost of Pinecone. For the latest pricing details, see our pricing page. For help estimating total cost, see Understanding total cost. To see a calculation of your current usage and costs, see the usage dashboard in the Pinecone console.
The total cost of Pinecone us... | https://docs.pinecone.io/docs/managing-cost |
91092c370872-1 | Back up inactive indexes
When a specific index is not in use, back it up using collections and delete the inactive index. When you're ready to use these vectors again, you can create a new index from the collection. This new index can also use a different index type or size. Because it's relatively cheap to store colle... | https://docs.pinecone.io/docs/managing-cost |
91092c370872-2 | Learn about choosing index type and size
Learn about monitoring usage
Updated about 1 month ago Understanding organizationsUnderstanding costDid this page help you?YesNo | https://docs.pinecone.io/docs/managing-cost |
97a99c842e81-0 | Overview
This topic describes the calculation of total cost for Pinecone, including an example. All prices are examples; for the latest pricing details, please see our pricing page. While our pricing page lists rates on an hourly basis for ease of comparison, this topic lists prices per minute, as this is how Pinecone ... | https://docs.pinecone.io/docs/understanding-cost |
97a99c842e81-1 | The following equation calculates the total costs accrued over time:
(Number of pods) * (pod size) * (number of replicas) * (minutes pod exists) * (pod price per minute)
(collection storage in GB) * (collection storage time in minutes) * (collection storage price per GB per minute)
To see a calculation of your curre... | https://docs.pinecone.io/docs/understanding-cost |
97a99c842e81-2 | Billing componentValueNumber of pods1Number of replicas3Pod sizex2Total pod count6Minutes in January44,640Pod-minutes (pods * minutes)267,840Pod price per minute$0.0012Collection storage1 GBCollection storage minutes44,640Price per storage minute$0.00000056
The invoice for this example is given in Table 2 below:
Table ... | https://docs.pinecone.io/docs/understanding-cost |
97a99c842e81-3 | Next steps
Learn about choosing index type and size
Learn about monitoring usage
Updated about 1 month ago Managing costMonitoring your usageDid this page help you?YesNo | https://docs.pinecone.io/docs/understanding-cost |
13b908d24904-0 | Overview
A Pinecone organization is a set of projects that use the same billing. Organizations allow one or more users to control billing and project permissions for all of the projects belonging to the organization. Each project belongs to an organization.
For a guide to adding users to an organization, see Add users... | https://docs.pinecone.io/docs/organizations |
13b908d24904-1 | Organization rolePermissions in organizationOrganization ownerProject owner for all projectsCreate projectsManage billingManags organization membersOrganization memberCreate projectsJoin projects when invitedRead access to billing
Organization single sign-on (SSO)
SSO allows organizations to manage their teams' access ... | https://docs.pinecone.io/docs/organizations |
b05ea8e9b206-0 | You can limit your vector search based on metadata. Pinecone lets you attach metadata key-value pairs to vectors in an index, and specify filter expressions when you query the index.
Searches with metadata filters retrieve exactly the number of nearest-neighbor results that match the filters. For most cases, the search... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-1 | null value, we recommend you remove that key from the metadata payload.
For example, the following would be valid metadata payloads:
JSON{
"genre": "action",
"year": 2020,
"length_hrs": 1.5
}
{
"color": "blue",
"fit": "straight",
"price": 29.99,
"is_jeans": true
}
Supported metadata size
P... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-2 | ...means the "genre" takes on both values.
For example, queries with the following filters will match the vector:
JSON{"genre":"comedy"}
{"genre": {"$in":["documentary","action"]}}
{"$and": [{"genre": "comedy"}, {"genre":"documentary"}]}
Queries with the following filter will not match the vector:
JSON{ "$and": [{ "... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-3 | index.upsert([
("A", [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], {"genre": "comedy", "year": 2020}),
("B", [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2], {"genre": "documentary", "year": 2019}),
("C", [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3], {"genre": "comedy", "year": 2019}),
("D", [0.4, 0.4, 0.4, 0.4, 0.4, ... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-4 | ])
await index.upsert({
vectors: [
{
id: "A",
values: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
metadata: { genre: "comedy", year: 2020 },
},
{
id: "B",
values: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
metadata: { genre: "documentary", year: 2019 },
},
{
... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-5 | metadata: { genre: "drama" },
},
{
id: "E",
values: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
metadata: { genre: "drama" },
},
],
});
curl -i -X POST https://YOUR_INDEX-YOUR_PROJECT.svc.YOUR_ENVIRONMENT.pinecone.io/vectors/upsert \
-H 'Api-Key: YOUR_API_KEY' \
-H 'Content-Type: appli... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-6 | "metadata": {"genre": "documentary", "year": 2019}
},
{
"id": "C",
"values": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
"metadata": {"genre": "comedy", "year": 2019}
},
{
"id": "D",
"values": [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4],
"metadata": {"g... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-7 | Projects on the gcp-starter environment do not support metadata strings containing the character Δ.
Querying an index with metadata filters
Metadata filter expressions can be included with queries to limit the search to only vectors matching the filter expression.
For example, we can search the previous movies index fo... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-8 | # Returns:
# {'matches': [{'id': 'B',
# 'metadata': {'genre': 'documentary', 'year': 2019.0},
# 'score': 0.0800000429,
# 'values': []}],
# 'namespace': ''}
const queryResponse = await index.query({
vector: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
filter: { genre: { $in: [... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-9 | curlcurl -i -X POST https://YOUR_INDEX-YOUR_PROJECT.svc.YOUR_ENVIRONMENT.pinecone.io/query \
-H 'Api-Key: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"vector": [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
"filter": {"genre": {"$in": ["comedy", "documentary", "drama"]}},
"topK": 1,
"in... | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-10 | A drama from 2020 (equivalent to the previous example):
JSON{
"$and": [{ "genre": { "$eq": "drama" } }, { "year": { "$gte": 2020 } }]
}
A drama or a movie from 2020:
JSON{
"$or": [{ "genre": { "$eq": "drama" } }, { "year": { "$gte": 2020 } }]
} | https://docs.pinecone.io/docs/metadata-filtering |
b05ea8e9b206-11 | Deleting vectors by metadata filter
To specify vectors to be deleted by metadata values, pass a metadata filter expression to the delete operation. This deletes all vectors matching the metadata filter expression.
Projects in the gcp-starter region do not support deleting by metadata.
Example
This example deletes all v... | https://docs.pinecone.io/docs/metadata-filtering |
ff471843e1bb-0 | After your data is indexed, you can start sending queries to Pinecone.
The Query operation searches the index using a query vector. It retrieves the IDs of the most similar vectors in the index, along with their similarity scores. t can optionally include the result vectors' values and metadata too. You specify the num... | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-1 | # Returns:
# {'matches': [{'id': 'C',
# 'score': -1.76717265e-07,
# 'values': [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]},
# {'id': 'B',
# 'score': 0.080000028,
# 'values': [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]},
# {'... | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-2 | topK: 3,
includeValues: true,
},
namespace: "example-namespace",
});
// Returns:
// {'matches': [{'id': 'C',
// 'score': -1.76717265e-07,
// 'values': [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]},
// {'id': 'B',
// 'score': 0.080000028,
// ... | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-3 | -H 'Content-Type: application/json' \
-d '{
"vector":[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
"topK": 3,
"includeValues": true
}' | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-4 | # Output:
# {
# "matches":[
# {
# "id": "C",
# "score": -1.76717265e-07,
# "values": [0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3]
# },
# {
# "id": "B",
# "score": 0.080000028,
# "values": [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]
# },
# {
# "id": "D",
# "sco... | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-5 | Depending on your data and your query, you may not get top_k results. This happens when top_k is larger than the number of possible matching vectors for your query.
Querying by namespace
You can organize the vectors added to an index into partitions, or "namespaces," to limit queries and other vector operations to only... | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-6 | topK: 1,
includeMetadata: true
filters: {
"genre": {"$eq": "documentary"}
},
}
}) | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-7 | curl -i -X POST https://YOUR_INDEX-YOUR_PROJECT.svc.YOUR_ENVIRONMENT.pinecone.io/query \
-H 'Api-Key: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"vector": [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
"filter": {"genre": {"$in": ["comedy", "documentary", "drama"]}},
"topK": 1,
"includ... | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-8 | will be returned.
Examples
The following example queries the index example-index with a sparse-dense vector.
Pythoncurlquery_response = index.query(
namespace="example-namespace",
top_k=10,
vector=[0.1, 0.2, 0.3, 0.4],
sparse_vector={
'indices': [10, 45, 16],
'values': [0.5, 0.5, 0.2]
... | https://docs.pinecone.io/docs/query-data |
ff471843e1bb-9 | Limitations
Avoid returning vector data and metadata when top_k is greater than 1000. This means queries with top_k over 1000 should not contain include_metadata=True or include_data=True. For more limitations, see: Limits.Updated 5 days ago Sparse-dense embeddingsFiltering with metadataDid this page help you?YesNo | https://docs.pinecone.io/docs/query-data |
089cf6017e8f-0 | In addition to inserting and querying data, there are other ways you can interact with vector data in a Pinecone index. This section walks through the various vector operations available.
Connect to an index
If you're using a Pinecone client library to access an index, you'll need to open a session with the index:
Pyt... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-1 | Describe index statistics
Get statistics about an index, such as vector count per namespace:
PythonJavaScriptcurlindex.describe_index_stats()
const index = pinecone.Index("pinecone-index");
const indexStats = await index.describeIndexStats();
console.log(indexStats.data);
curl -i -X GET https://YOUR_INDEX-PROJECT_NAME.... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-2 | # Returns:
# {'namespace': '',
# 'vectors': {'id-1': {'id': 'id-1',
# 'values': [0.568879, 0.632687092, 0.856837332, ...]},
# 'id-2': {'id': 'id-2',
# 'values': [0.00891787093, 0.581895, 0.315718859, ...]}}}
const fetchedVectors = await index.fetch(["id-1", "id-... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-3 | # "vectors": {
# "id-1": {
# "id": "id-1",
# "values": [0.568879, 0.632687092, 0.856837332, ...]
# },
# "id-2": {
# "id": "id-2",
# "values": [0.00891787093, 0.581895, 0.315718859, ...]
# }
# },
# "namespace": ""
# } | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-4 | Updating vectors
There are two methods for updating vectors and metadata, using full or partial updates.
Full update
Full updates modify the entire item, that is vectors and metadata. Updating an item by id is done the same way as inserting items. (Write operations in Pinecone are idempotent.)
The Upsert operation writ... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-5 | PythonJavaScriptcurlindex.fetch(["id-3"])
const fetchedVectors = await index.fetch(["id-3"]);
curl -i -X GET https://YOUR_INDEX-YOUR_PROJECT.svc.YOUR_ENVIRONMENT.pinecone.io/vectors/fetch?ids=id-3 \
-H 'Api-Key: YOUR_API_KEY'
Partial update
The Update operation performs partial updates that allow changes to part of ... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-6 | will be returned.
To update the value of item ("id-3", [3., 3.], {"type": "doc", "genre": "drama"}):
PythonJavaScriptcurlindex.update(id="id-3", values=[4., 2.])
await index.update({
id: "id-3",
values: [4, 2],
});
curl -i -X POST https://YOUR_INDEX-YOUR_PROJECT.svc.YOUR_ENVIRONMENT.pinecone.io/vectors/update \
-... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-7 | argument. Any other fields will remain unchanged.
To update the metadata of item ("id-3", [4., 2.], {"type": "doc", "genre": "drama"}):
PythonJavaScriptcurlindex.update(id="id-3", set_metadata={"type": "web", "new": "true"})
await index.update({
id: "id-3",
setMetadata: {
type: "web",
new: "true",
},
});
... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-8 | The updated item would now be ("id-3", [4., 2.], {"type": "web", "genre": "drama", "new": "true"}).
Both vector and metadata can be updated at once by including both values and set_metadata arguments. To update the "id-3" item we write:
PythonJavaScriptcurlindex.update(id="id-3", values=[1., 2.], set_metadata={"type": ... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-9 | The updated item would now be ("id-3", [1., 2.], {"type": "webdoc", "genre": "drama", "new": "true"}).
Deleting vectors
The Delete operation deletes vectors, by ID, from an index.
Alternatively, it can also delete all vectors from an index or namespace.
When deleting large numbers of vectors, limit the scope of delete ... | https://docs.pinecone.io/docs/manage-data |
089cf6017e8f-10 | namespace.
Projects on the gcp-starter environment do not support the deleteAll parameter.
Example:
PythonJavaScriptcurlindex.delete(deleteAll='true', namespace='example-namespace')
await index.delete1([], true, "example-namespace");
curl -i -X DELETE "https://YOUR_INDEX-YOUR_PROJECT.svc.YOUR_ENVIRONMENT.pinecone.io/ve... | https://docs.pinecone.io/docs/manage-data |
b55bd8ef143b-0 | After creating a Pinecone index, you can start inserting vector embeddings and metadata into the index.
Inserting the vectors
Connect to the index:
Pythoncurlindex = pinecone.Index("pinecone-index")
# Not applicable
Insert the data as a list of (id, vector) tuples. Use the Upsert operation to write vectors into a ... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-1 | PythonJavaScriptcurl# Insert sample data (5 8-dimensional vectors)
index.upsert([
("A", [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]),
("B", [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]),
("C", [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]),
("D", [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]),
("E", [0.5, 0.5, 0.5, ... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-2 | {
id: "A",
values: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
},
{
id: "B",
values: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
},
{
id: "C",
values: [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
},
{
id: "D",
values: [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-3 | },
],
});
curl -i -X POST https://YOUR_INDEX-YOUR_PROJECT.svc.YOUR_ENVIRONMENT.pinecone.io/vectors/upsert \
-H 'Api-Key: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"vectors": [
{
"id": "A",
"values": [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
},
{
"id... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-4 | "id": "D",
"values": [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]
},
{
"id": "E",
"values": [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
}
]
}' | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-5 | Immediately after the upsert response is received, vectors may not be visible to queries yet. In most situations, you can check if the vectors have been received by checking for the vector counts returned by describe_index_stats() to be updated. This technique may not work if the index has multiple replicas. The databa... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-6 | Sending upserts in parallel
By default, all vector operations block until the response has been received. But using our client they can be made asynchronous. For the Batching Upserts example this can be done as follows:
PythonShell# Upsert data with 100 vectors per upsert request asynchronously
# - Create pinecone.Inde... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-7 | Pinecone is thread-safe, so you can launch multiple read requests and multiple write requests in parallel. Launching multiple requests can help with improving your throughput. However, reads and writes can’t be performed in parallel, therefore writing in large batches might affect query latency and vice versa.
If you e... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-8 | ("B", [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2], {"genre": "documentary", "year": 2019}),
("C", [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3], {"genre": "comedy", "year": 2019}),
("D", [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4], {"genre": "drama"}),
("E", [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], {"genre": "drama"}... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-9 | metadata: { genre: "comedy", year: 2020 },
},
{
id: "B",
values: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
metadata: { genre: "documentary", year: 2019 },
},
{
id: "C",
values: [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
metadata: { genre: "comedy", year: 2019 },
},... | https://docs.pinecone.io/docs/insert-data |
b55bd8ef143b-10 | metadata: { genre: "drama" },
},
],
});
curl -i -X POST https://YOUR_INDEX-YOUR_PROJECT.svc.YOUR_ENVIRONMENT.pinecone.io/vectors/upsert \
-H 'Api-Key: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"vectors": [
{
"id": "A",
"values": [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,... | https://docs.pinecone.io/docs/insert-data |
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