n8n provides native vector-store nodes for three database backends, each with a common five‑mode interface and the ability to connect directly to an AI Agent as a tool. Pinecone (cloud‑managed, serverless indexes supported from n8n v1.33.0) requires a matching embedding dimension and the correct “Retrieve Documents (As Tool for AI Agent)” operation mode for agentic RAG. Weaviate (self‑hosted via Docker or cloud via Weaviate Cloud Services) uses API‑key authentication against a collection name and supports all four core modes plus update. pgvector (self‑hosted, the Postgres extension that turns a standard PostgreSQL instance into a vector store) requires a manually provisioned table with an embedding vector(N) column. All three nodes share the identical operations model and can be wired as regular nodes for insert/retrieval, chained through a Vector Store Retriever for deterministic RAG, or connected directly to the AI Agent’s tool connector for agent‑driven retrieval.[reference:0][reference:1]

What are the five operation modes shared across all n8n vector store nodes?

Every n8n vector store node — Pinecone, Weaviate, and PGVector — exposes five operation modes selected from the “Operation Mode” dropdown. Get Many retrieves documents by ID with optional filters. Insert Documents stores new vectors and metadata into the vector database. Retrieve Documents (As Vector Store for Chain/Tool) provides documents to a retriever connected to a chain. Retrieve Documents (As Tool for AI Agent) connects directly to the AI Agent’s tool connector. Update Documents modifies existing documents by their ID.[reference:22][reference:23]

The mode selection determines both the available input/output connectors and which downstream nodes can attach. In regular node mode (Get Many, Insert, Update), the vector store sits in the standard node flow with no special connectors. In Retrieve Documents (As Tool for AI Agent) mode, the node exposes a tool connector that attaches directly to the AI Agent node’s tools input — the agent decides at runtime when to query the store.[reference:24] For deterministic retrieval where you always want to search the vector store before generating an answer, use the Question and Answer Chain → Vector Store Retriever → Vector Store pattern instead of the AI Agent tool mode, which guarantees a “Search → then Generate” order rather than the agent’s dynamic decision.[reference:25] For the complete AI Agent orchestration guide covering tool selection and multi‑tool patterns, see the n8n AI Nodes Reference.

How do you configure the Pinecone vector store node for serverless indexes and AI agent tools?

The Pinecone node authenticates via an API key from the Pinecone console. Create a Pinecone index before connecting the node — the index must have dimensions matching your embedding model (1,536 for text-embedding-3-small or text-embedding-ada-002; 3,072 for text-embedding-3-large) and use the cosine similarity metric.[reference:26] Serverless indexes, supported since n8n v1.33.0, require the “Retrieve Documents (As Tool for AI Agent)” operation — the older “chain/tool” mode does not work with serverless.[reference:27]

The single most common Pinecone configuration failures are: (1) using the wrong operation mode for serverless indexes — always use “As Tool for AI Agent” mode, not the legacy “chain/tool” mode for agent workflows; (2) leaving the Tool Description field empty — when this field is blank, n8n cannot build a valid JSON schema for OpenAI’s function calling and returns a null type error; (3) dimension mismatch between the embedding model and the index — the node will fail silently or return an error; and (4) using the deprecated “Answer questions with a vector store” sub‑node, which has known schema bugs in newer n8n versions and should be replaced by the main Pinecone node in tool mode.[reference:28][reference:29] Also set the Namespace option in the node to true only if your index actually uses one, and delete any old “Answer questions with a vector store” nodes — they have known bugs and are effectively deprecated.[reference:30] For the complete credential encryption and management reference, see the n8n Credential Nodes guide.

How do you set up the Weaviate vector store node with a cloud cluster or local Docker instance?

The Weaviate Vector Store node supports both self‑hosted and Weaviate Cloud deployments. For self‑hosted, start a Weaviate cluster via Docker Compose, then enter the instance URL and API key in the n8n credentials panel. For cloud, sign up for Weaviate Cloud Services, create a cluster, and use the provided Weaviate Cloud Endpoint and Weaviate API Key. The credential panel accepts the same fields regardless of deployment method.[reference:33][reference:34]

The node supports the same five modes as Pinecone and PGVector: Get Many, Insert Documents, Retrieve Documents (As Vector Store for Chain/Tool), Retrieve Documents (As Tool for AI Agent), and Update Documents. When used as a regular node (Insert or Get Many mode), the Weaviate Vector Store sits in the standard connection flow without an agent. When connected directly to an AI Agent as a tool, the connection is: AI Agent (tools connector) → Weaviate Vector Store node. The official Document Q&A template (n8n.io/workflows/7170) demonstrates the minimal RAG implementation: upload a PDF, generate embeddings with OpenAI, store in a Weaviate collection, and query via a Chat node with the Question and Answer Chain.[reference:35][reference:36] For selecting the optimal embedding model and chunk size for your RAG pipeline, see the section “Performance & Troubleshooting” below.

How do you provision a pgvector table and connect the PGVector Vector Store node in n8n?

Run pgvector via Docker: docker pull pgvector/pgvector:pg16, then start a container with persistent storage. After the container is running, connect via psql and execute: CREATE DATABASE vectors;, then inside the new database run CREATE EXTENSION vector; to enable the pgvector extension. Create a table with CREATE TABLE embeddings (id bigserial PRIMARY KEY, embedding vector(N) NOT NULL, text text, metadata jsonb); — the vector dimension N must match your embedding model: 1,536 for text-embedding-3-small, 3,072 for text-embedding-3-large.[reference:37][reference:38]

In n8n, add the PGVector Vector Store node and create new credentials with the database name, username, and password from above. For the host, use localhost if n8n runs locally/natively; if n8n runs in a container alongside the pgvector container, use the Docker IP obtained via docker inspect. The node’s Options panel exposes Column Names — map the embedding, text, and metadata columns to match your table schema (default: embedding, text, metadata).[reference:39] For production, add an HNSW index to accelerate cosine similarity queries: CREATE INDEX ON embeddings USING hnsw (embedding vector_cosine_ops);. For the complete production deployment blueprint covering PostgreSQL pooling and high availability, see the n8n Docker Compose production stack guide.

How do you match embedding dimensions, choose chunk sizes, and validate the complete RAG pipeline?

The embedding model and vector store dimension must agree exactly. OpenAI’s text-embedding-3-small generates 1,536‑dimensional vectors (also the default for text-embedding-ada-002). The text-embedding-3-large model generates 3,072‑dimensional vectors — but supports downsizing via the dimensions parameter (e.g. 256, 512, 1,024). Match the index or table schema to the actual dimension your embedding node outputs.[reference:40][reference:41]

For chunking, the Recursive Character Text Splitter node defaults to a chunk size of 1,000 characters with an overlap of 200 — these values preserve context between adjacent chunks while keeping each chunk small enough for precise retrieval. Increase chunk size to 2,000–6,000 for long‑form documents or decrease to 500 for Q&A over short paragraphs.[reference:42][reference:43] The canonical six‑stage RAG pipeline in n8n chains: Data Loader → Text Splitter (chunk_size=1000, overlap=200) → Embeddings (OpenAI text-embedding-3-small, 1,536 dims) → Vector Store (Insert mode) → Vector Store Retriever → Question and Answer Chain. For a deterministic “Search → then Generate” order, use the Q&A Chain pattern rather than the AI Agent tool mode, which may generate an answer before searching.[reference:44] For complete end‑to‑end RAG pipeline templates, see the n8n OpenAI Prompt Chain Tutorial.

How do you attach multiple vector stores to a single AI agent and optimize retrieval performance?

A single AI Agent node can connect to multiple vector store nodes simultaneously — each store receives a distinct Tool Name and Tool Description, and the agent selects which store to query at runtime based on the user’s question. For example, attach one Pinecone store for HR policies (“Use this tool to search HR policies and employee handbooks”) and one pgvector store for product specs (“Use this tool to search product specifications and technical documentation”).[reference:46] The agent reads each description and chooses the appropriate store autonomously.

For production performance: Pinecone serverless indexes scale automatically and support hybrid search (semantic + lexical) with no operational overhead — optimal for teams without existing database infrastructure.[reference:47] pgvector reuses your existing PostgreSQL backup and tooling — no new infrastructure — and supports both HNSW (recommended for >10k vectors) and IVFFlat indexes. Use HNSW for production workloads: CREATE INDEX ON embeddings USING hnsw (embedding vector_cosine_ops);. Weaviate provides built‑in vectorization modules and multi‑tenant collections — optimal for organizations needing on‑premises deployment with full‑featured indexing. For comprehensive RAG pipeline architecture covering reranking, hybrid search, and production deployment patterns, see the n8n Vector Store Integration guide.