--- title: Pinecone API Integration on Truto slug: pinecone category: Database canonical: "https://truto.one/integrations/detail/pinecone/" --- # Pinecone API Integration on Truto **Category:** Database **Status:** Generally available ## MCP-ready AI tools Truto exposes 47 tools for Pinecone that AI agents can call directly. - **get_single_pinecone_index_by_id** — Get a specific Pinecone index by id. Returns the full index object including name, host, metric, dimension, spec, deletion_protection, and status. Required: id. - **create_a_pinecone_index** — Create a new Pinecone index for dense vectors, sparse vectors, or full-text search documents. Returns the created index object including name, host, and status. Required: name and spec (with cloud and region). Dense vector indexes also require dimension and metric; sparse vector indexes require metric set to dotproduct. - **list_all_pinecone_indexes** — List all Pinecone indexes in the current project. Returns an array of index objects including name, dimension, metric, host, and status for each. - **update_a_pinecone_index_by_id** — Update configuration for a Pinecone index by id. Supports updating deletion_protection and tags. Required: id. - **delete_a_pinecone_index_by_id** — Delete a Pinecone index by id. Returns an empty response on success. Required: id. - **pinecone_indexes_create_for_model** — Create a Pinecone index with integrated embedding that automatically converts source text to dense or sparse vectors using a hosted model. Returns the created index object. Required: name, cloud, region, and embed (with model and field_map). - **pinecone_indexes_describe_index_stats** — Get statistics for a Pinecone index including dimension, index fullness, total vector count, and per-namespace vector counts. Required: index_host. - **update_a_pinecone_vector_by_id** — Update a Pinecone vector's dense values and/or metadata by id. Returns an empty response on success. - **delete_a_pinecone_vector_by_id** — Delete Pinecone vectors by id list, metadata filter, or by deleting all vectors in a namespace. Returns an empty response on success. Provide at least one of ids, filter, or deleteAll. - **pinecone_vectors_bulk_create** — Upsert (insert or update) vectors into a Pinecone index namespace. Existing records with the same id are fully overwritten. Returns upsertedCount. Required: vectors. - **get_single_pinecone_vector_by_id** — Fetch Pinecone vectors by their ids. Returns vector objects including id, values, and metadata. Required: ids. - **list_all_pinecone_vectors** — List vector ids in a Pinecone index namespace. Returns vector id objects. Optionally filter by id prefix. - **pinecone_vectors_search** — Query a Pinecone index for the most similar vectors to a given query vector or vector id. Returns matches including id, score, values, and metadata. Required: topK. - **create_a_pinecone_import** — Start an asynchronous bulk import of vectors from object storage (Amazon S3, Google Cloud Storage, or Azure Blob Storage) into a Pinecone serverless index. Returns the import `id` for tracking progress. Required: `uri`. - **delete_a_pinecone_import_by_id** — Cancel a Pinecone bulk import operation by id if it has not yet finished. Has no effect if the import is already complete. Returns an empty response on success. - **get_single_pinecone_import_by_id** — Get details of a specific Pinecone bulk import by id. Returns: id, uri, status, percent_complete, records_imported, created_at, finished_at, and an error message if the import failed. - **list_all_pinecone_imports** — List all recent and ongoing Pinecone bulk imports for an index. Returns an array of import objects each including id, uri, status, percent_complete, and records_imported. - **create_a_pinecone_collection** — Create a Pinecone collection (a static backup of a pod-based index). Returns the created collection object including its name. Required: name, source. Collections are a pod-only feature and are not supported for serverless indexes. - **list_all_pinecone_collections** — List all Pinecone collections in the current project. Returns: name, status, environment, size, vector_count, and dimension for each collection. Serverless indexes do not support collections. - **get_single_pinecone_collection_by_id** — Get details about a specific collection in Pinecone. Requires id. Returns name, status, environment, size, vector_count, and dimension of the collection. Serverless indexes do not support collections. - **delete_a_pinecone_collection_by_id** — Delete a collection in Pinecone. Requires id as collection_name. Returns a 202 status on successful deletion. Serverless indexes do not support collections. - **delete_a_pinecone_record_by_id** — Delete Pinecone records from a namespace by record IDs, a metadata filter expression, or delete all records at once. Returns an empty response on success. Required: namespace. Max 1000 IDs per request when deleting by ID. - **list_all_pinecone_records** — List record IDs stored in a Pinecone namespace. Returns an array of record id values. Required: namespace. - **pinecone_records_search** — Search a Pinecone namespace for semantically similar records using a query text or vector. Returns hits including _id, _score, and requested fields. Required: namespace, query (with top_k and inputs). - **create_a_pinecone_record** — Upsert records into a Pinecone namespace using NDJSON format, inserting new records or updating existing ones matched by id. Required: namespace. - **create_a_pinecone_backup** — Create a backup of a Pinecone index. Returns the new backup object including backup_id, status, source_index_name, cloud, region, and created_at. Required: index_name. - **delete_a_pinecone_backup_by_id** — Delete a Pinecone backup by id. Returns an empty response on success. Required: id. - **get_single_pinecone_backup_by_id** — Get the description of a Pinecone backup by id. Returns: backup_id, source_index_name, status, name, description, cloud, region, dimension, record_count, and created_at. Required: id. - **list_all_pinecone_backups** — List all Pinecone backups for a project. Returns an array of backup objects each including backup_id, source_index_name, status, name, cloud, region, and created_at. - **pinecone_backups_index** — Create an index from a specific backup in Pinecone. Requires backup_id. Returns restore_job_id (the created restore job’s identifier) and index_id (the newly created index identifier). - **get_single_pinecone_restore_job_by_id** — Get a description of a Pinecone restore job by id. Returns: restore_job_id, backup_id, target_index_name, status, percent_complete, created_at, completed_at. Required: id. - **list_all_pinecone_restore_jobs** — List all Pinecone restore jobs for a project. Returns an array of restore job objects including restore_job_id, backup_id, target_index_name, status, and percent_complete. - **list_all_pinecone_index_backups** — List all backups for a Pinecone index. Returns: backup_id, source_index_name, status, cloud, region, created_at. Required: index_name. - **create_a_pinecone_namespace** — Create a namespace in a Pinecone serverless index. Returns the created namespace including name, record_count, and schema. Required: name. Not supported for pod-based indexes. - **delete_a_pinecone_namespace_by_id** — Delete a namespace from a Pinecone serverless index by id. Permanently removes all data in the namespace — this action is irreversible. Returns an empty response on success. Required: id. Not supported for pod-based indexes. - **get_single_pinecone_namespace_by_id** — Get details of a Pinecone namespace by id. Returns: name, record_count. Required: id. Not supported for pod-based indexes. - **list_all_pinecone_namespaces** — List all namespaces in a Pinecone serverless index. Returns each namespace's name and record_count. Not supported for pod-based indexes. - **get_single_pinecone_model_by_id** — Get a description of a specific Pinecone hosted model by id. Returns: model name, type, vector_type, default_dimension, modality, provider_name, and supported_parameters. Required: id. - **list_all_pinecone_models** — List all embedding and reranking models hosted by Pinecone. Returns an array of models, each including model name, type, vector_type, default_dimension, modality, provider_name, and supported_parameters. - **create_a_pinecone_embedding** — Generate vector embeddings for input data using Pinecone's hosted embedding models. Returns: data (array of embedding value arrays), model name, and usage (total_tokens). Required: model and inputs. - **create_a_pinecone_rerank** — Rerank a list of documents by relevance to a query using Pinecone's inference API. Returns a ranked `data` array of results each containing `index`, `document`, and `score`, plus `usage.rerank_units`. Required: `model`, `query`, and `documents`. - **list_all_pinecone_vector_records** — List vector records from a Pinecone index by fetching vectors matched against metadata. Returns a collection of vectors from the specified index. Required: index_host. - **list_all_pinecone_backup_schedules** — List all backup_schedules for a specific index in Pinecone. Requires index_name. Returns schedule_id, name, index_id, project_id, schedule_type, frequency, retention_expire_after_days, enabled, next_scheduled_run, and created_at in the response. - **get_single_pinecone_backup_schedule_by_id** — Get details of a specific backup schedule in Pinecone. Requires id. Returns schedule_id, name, index_id, project_id, schedule_type, frequency, retention_expire_after_days, enabled, next_scheduled_run, and created_at. - **update_a_pinecone_backup_schedule_by_id** — Update a backup schedule in Pinecone. Requires id. Returns schedule_id, name, index_id, project_id, schedule_type, frequency, retention_expire_after_days, enabled, next_scheduled_run, and created_at in the response. - **delete_a_pinecone_backup_schedule_by_id** — Delete a backup schedule in Pinecone using id. This operation removes the schedule but does not delete backups created by it. Returns a 204 status code upon successful deletion. - **list_all_pinecone_backup_schedule_history** — List backup schedule history in Pinecone for a specific schedule_id. Returns details including backup_id, status, scheduled_execution_at, cloud, region, and created_at for each backup entry. ## How it works 1. **Link your customer's Pinecone account.** Use Truto's frontend SDK; we handle every OAuth and API key flow so you don't need to create the OAuth app. 2. **Authentication is automatic.** Truto refreshes tokens, stores credentials securely, and injects them into every API request. 3. **Call Truto's API to reach Pinecone.** The Proxy API is a 1-to-1 mapping of the Pinecone API. 4. **Get a unified response format.** Every response uses a single shape, with cursor-based pagination and data in the `result` field. ## Use cases - **Embed RAG into your SaaS product** — Let your customers connect their own Pinecone account so your AI features (chatbots, copilots, smart search) retrieve context from their proprietary data without you hosting their vectors. - **Offer a Pinecone destination for data sync** — If you run an ETL, CMS, helpdesk, or CRM, let users automatically push their content into Pinecone as vectors so it becomes searchable by their AI stack. - **Multi-tenant vector isolation for your users** — Provision a dedicated Pinecone namespace or index per end-customer directly from your app, giving each tenant isolated vector storage without manual setup. - **Compliance-ready data lifecycle management** — Automatically delete vectors, records, or chunks from a customer's Pinecone index when they remove the source document in your app, keeping GDPR and audit workflows clean. - **No-code AI workflow building blocks** — Expose Pinecone as a step in your visual workflow or automation builder so non-technical users can embed text, upsert vectors, and run semantic searches without writing code. ## What you can build - **Index picker for end-user onboarding** — Use list_all_pinecone_indexes and get_single_pinecone_index_by_id to let users pick or auto-provision the index your app should read from and write to. - **Continuous knowledge sync to Pinecone** — Push incremental updates from your app into the user's index using pinecone_vectors_bulk_create or pinecone_documents_bulk_create whenever source content changes. - **In-app semantic search and RAG retrieval** — Power AI responses by calling pinecone_vectors_search, pinecone_documents_search, or pinecone_records_search with metadata filters scoped to the active user or workspace. - **Native embedding and reranking pipeline** — Use create_a_pinecone_embedding to vectorize text on the fly and create_a_pinecone_rerank to reorder retrieved results before passing them to your LLM. - **Namespace-per-tenant provisioning** — Automatically call create_a_pinecone_namespace when a new workspace, project, or customer is created, and clean up with delete_a_pinecone_namespace_by_id on offboarding. - **Bulk historical import from object storage** — Trigger create_a_pinecone_import for large initial syncs from S3/GCS and monitor progress via list_all_pinecone_imports and get_single_pinecone_import_by_id. ## FAQs ### How do end users authenticate their Pinecone account? Pinecone uses API key authentication. Through Truto, your users provide their Pinecone API key during the connect flow, and Truto securely stores and injects it into every request — you never have to handle the credential yourself. ### Can we provision isolated storage per end-customer? Yes. You can either create a dedicated index per tenant with create_a_pinecone_index, or use a shared index and isolate tenants via create_a_pinecone_namespace. Truto exposes both lifecycle operations including delete and list endpoints. ### Does Truto support Pinecone's serverless inference for embeddings and reranking? Yes. create_a_pinecone_embedding generates vectors from raw text using Pinecone's hosted models, and create_a_pinecone_rerank reorders search results by relevance — both are accessible through the same unified connection. ### How do we keep vectors in sync when source data changes or is deleted? Use pinecone_vectors_bulk_create or pinecone_documents_bulk_create for upserts, and delete_a_pinecone_vector_by_id, delete_a_pinecone_record_by_id, or delete_a_pinecone_chunk_by_id to remove stale data — typically triggered from your app's own create/update/delete webhooks. ### Can we run hybrid search combining keyword and vector queries? Yes. pinecone_documents_search supports Pinecone's document schema which combines BM25 keyword scoring with vector similarity, and pinecone_vectors_search supports dense, sparse, and metadata-filtered queries. ### How do we handle very large initial data loads for a new customer? Use create_a_pinecone_import to ingest large datasets directly from object storage (S3/GCS) asynchronously, then poll get_single_pinecone_import_by_id or list_all_pinecone_imports to track completion before switching to incremental bulk upserts. ## Related reading - [Connect Pinecone to Claude: Automate embeddings and record management](https://truto.one/blog/connect-pinecone-to-claude-automate-embeddings-and-record-management/) — Learn how to connect Pinecone to Claude using a managed MCP server. Automate vector search, index management, and RAG ingestion workflows with AI. - [Connect Pinecone to ChatGPT: Search Vectors and Manage Index Resources](https://truto.one/blog/connect-pinecone-to-chatgpt-search-vectors-and-manage-index-resources/) — Learn how to connect Pinecone to ChatGPT using a managed MCP server. Execute vector searches, manage serverless indexes, and orchestrate RAG pipelines natively. - [Connect Pinecone to AI Agents: Manage vector indexes and search data](https://truto.one/blog/connect-pinecone-to-ai-agents-manage-vector-indexes-and-search-data/) — Learn how to connect Pinecone to AI agents using Truto's /tools endpoint. Discover architectural patterns for vector search, index management, and tool calling.