Managed Knowledge Layer for AI Applications

4/2/2026

This post introduces Pinecone Assistant as a managed knowledge layer for AI applications, moving beyond a simple retrieval stack. It details the end-to-end capabilities including document processing, embedding, retrieval, and answer generation. It highlights support for multimodal content, integration with multiple LLMs, and flexible deployment options (API, SDK, plugins, workflow tools). The post also announces a shift to a usage-based pricing model for Assistant, removing the per-assistant fixed fee to facilitate multi-tenant architectures. Upcoming features like upsert functionality and a Google Drive connector are mentioned.

2/11/2026

This post introduces the Pinecone Plugin for Claude Code, enabling developers to interact with Pinecone's vector database and managed RAG service directly within the Claude Code IDE. It provides natural language and explicit commands for managing indexes, querying Assistants, and building AI applications. Key features include index management (create, upsert, search with filters), and full Pinecone Assistant RAG capabilities (create, upload, sync, chat, context retrieval) directly through Claude Code commands. It also offers code generation for Pinecone quickstarts.

1/28/2026

Introduces the Pinecone Assistant n8n node, which integrates Pinecone Assistant's end-to-end RAG capabilities into n8n workflows. The node facilitates connecting any data source to AI-backed automation by enabling dynamic assistant selection, file management (upload, update, delete) with metadata, and context snippet retrieval with advanced metadata filtering. This provides a managed RAG service within n8n, simplifying AI workflow development.

9/17/2025

This post details a GTM engineering workflow using Pinecone's vector database and Assistant for semantic search powered sales outbound. It describes a FastAPI service that orchestrates web crawling for company news, reranking content using Pinecone's rerank service, retrieving relevant case studies via Pinecone Assistant's context API, and generating personalized emails with an LLM. The service is deployed to Google Cloud Run and integrated with Clay via an HTTP API action, enabling automated research and personalized outreach.

9/17/2025

This post demonstrates a practical application of the Pinecone Assistant for building a personalized knowledge base from unstructured personal notes (Google Docs). It highlights the ease of use for ingesting and querying documents, contrasting it with the limitations of general LLMs for large datasets. A code snippet for programmatic file upload via the Assistant API is provided, showcasing a method to overcome UI batch limits for faster ingestion.

6/18/2025

This post introduces support for new LLMs (OpenAI `gpt-4.1`, `o4-mini`, Anthropic `claude-3-7-sonnet`, Gemini `gemini-2.5-pro`) within Pinecone Assistant and exposes the `temperature` parameter for fine-grained control over LLM output. It also mentions infrastructure updates to facilitate faster model integration.

4/24/2025

This post details the integration of Pinecone with Rubrik Annapurna to enable secure, scalable agentic AI workflows. It highlights Rubrik Annapurna's role in controlled embedding generation from diverse enterprise data sources, including security and access control policies. Pinecone's vector database is utilized for low-latency, high-throughput semantic search of these embeddings, ensuring real-time data freshness and scalability. The integration emphasizes end-to-end data security, governance, and fine-grained access control for AI agents, addressing the challenges of production-grade AI infrastructure.

4/22/2025

This post introduces the capability for Pinecone Assistant to act as a remote Model Context Protocol (MCP) server. This allows AI agents to connect to Pinecone Assistant for contextual information retrieval without requiring the user to manage MCP server infrastructure. The post details the architecture, provides Python SDK examples for creating an assistant and uploading documents, and demonstrates integration with Langchain agents. It also outlines a workaround for using the remote MCP server with Claude Desktop via a local proxy.

4/22/2025

This post introduces three new MCP (Model Context Protocol) servers: Pinecone Assistant MCP (remote), Pinecone Assistant MCP (local), and Pinecone Developer MCP (local). The Pinecone Assistant MCPs enable seamless connection to Pinecone Assistant for AI agents and applications, abstracting server management. The Pinecone Developer MCP provides tools for database operations and searching Pinecone's documentation. The post also details the MCP protocol, its benefits for developer experience, simplified integrations, and automation, and provides a Node.js code example for connecting to the Pinecone Developer MCP.

1/22/2025

This post announces the General Availability (GA) of Pinecone Assistant. Key technical contributions include the introduction of optimized Chat and Context APIs. The Chat API provides structured, grounded responses with citations in streaming or batch modes. The Context API, as the context engine, delivers structured context (expanded chunks with relevancy scores and references) without the generation step, making it suitable for agentic workflows. Custom instructions allow for fine-tuning assistant responses. Expanded input formats now include JSON, .md, and .docx, alongside PDF and .txt. Output can now be in JSON format. Region control for building assistants in EU or US regions is also introduced. The post also provides Python SDK examples for uploading files, using the context API with metadata filters and conversation history, and updating assistant instructions. An example demonstrates using the `json_response=True` parameter for JSON output.

11/21/2024

This post details the role of Pinecone Assistant in accelerating AI business by simplifying RAG-based system development. It highlights the Citation API for providing transparent and traceable answers with structured citations, custom formats, metadata filtering, and enhanced privacy. The Evaluation API is presented as a tool for demonstrating system performance with metrics. The post also emphasizes how Pinecone Assistant de-risks generative AI projects by making it easier to test ideas, iterate quickly, and scale effectively, integrating seamlessly into client workflows.

9/18/2024

This post announces the public preview of Pinecone Assistant, detailing new features such as expanded LLM support (Claude 3.5 Sonnet via Amazon Bedrock), an Evaluation API for benchmarking correctness and completeness, metadata filtering for file association and retrieval, and a new console UI. The Evaluation API provides metrics for correctness and completeness, contributing to an 'answer alignment score'. Metadata filtering allows for targeted retrieval based on attached key-value pairs.

7/1/2024

This post announces the beta launch of Pinecone Assistant, an API service for answering questions about proprietary data within applications. It highlights simplicity, high-quality results grounded in data with references, and full control over data. The post also mentions the doubling of the Enterprise quota for namespaces per serverless index to 100,000, enabling multitenant solutions. Additionally, it introduces a revamped Model Gallery for selecting and creating indexes with two clicks, and new sample apps for quickly setting up end-to-end GenAI applications.

6/25/2024

Introduced a new benchmarking scheme for AI assistants, including a novel 'Answer Alignment Score' metric based on correctness and completeness. This metric aims to better correlate with human judgment than existing unsupervised metrics by evaluating factual entailment, contradiction, and neutrality against ground truth. The post details the protocol for calculating this score and presents results comparing Pinecone Assistant against OpenAI assistants on three benchmark datasets (FinanceBench, Open Australian Legal, NQ-HARD), demonstrating Pinecone Assistant's superior performance.

6/25/2024

This post announces the beta release of Pinecone Assistant, an API service for answering complex questions about proprietary data. It simplifies AI assistant development by handling document ingestion, chunking, embedding, file storage, query planning, vector search, model orchestration, and reranking. The service leverages Pinecone's vector database and integrates with frontier models like GPT-4o from Azure OpenAI Service. Key features include a drag-and-drop upload and chat interface, a chat-completion-compatible API, and a focus on data privacy and security, with data used as context and reference without permanent model fine-tuning. The beta has initial limits on file storage and queries.

11/8/2023

Introduced Canopy, an open-source RAG framework that leverages Pinecone for vector storage and retrieval. Canopy simplifies RAG application development by handling data chunking, embedding, chat history management, query optimization, context retrieval, and augmented generation. It offers a free tier for up to 100K vectors and provides both a web service via Canopy Server and a library for custom applications. The framework includes components for Knowledge Base (data preparation), Context Engine (retrieval), and Canopy Chat Engine (full RAG workflow).

9/13/2023

This post details the integration of Pinecone as a Knowledge Base for Amazon Bedrock. It outlines the process for developers to use Pinecone within the Bedrock ecosystem for Retrieval Augmented Generation (RAG) applications. The technical steps include creating a Pinecone index, setting up AWS Secrets Manager for Pinecone API keys, configuring an S3 bucket as a data source, creating a Bedrock Knowledge Base that connects to Pinecone, and then setting up and testing a Bedrock Agent that utilizes this knowledge base. The post emphasizes the benefits of this integration, such as high performance, cost-effectiveness at scale, and enterprise readiness, by leveraging Pinecone's vector database capabilities for storing and retrieving relevant information to mitigate LLM hallucinations.