Pinecone N8n

PineconeOfficialPublished by Pinecone

Skill
Tool integration
Automation and integrations
MIT
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From the author

Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.

— the description by Pinecone

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides guidance on integrating Pinecone with n8n to create RAG pipelines and chat-with-docs workflows. It covers the configuration and best practices for two primary nodes: the Pinecone Assistant node, which offers a managed experience for document retrieval, and the Pinecone Vector Store node, which provides granular control over embedding models and indexing. The skill includes architectural patterns for ingestion and chat phases, metadata handling, and troubleshooting tips for common configuration tasks.

What it does

Guides users in selecting between Pinecone Assistant and Vector Store nodes based on project requirements.
Provides standard architectural patterns for two-phase RAG workflows (ingestion and chat).
Details configuration rules for assistant data, metadata filtering, and source tagging.
Offers best practices for chunking strategies and embedding model consistency.
Explains how to perform file operations like upload, update, and list within n8n.
Provides reference configurations for node connections and parameter settings.

When to use it

When building a RAG pipeline or document Q&A system in n8n.
When you need to configure or troubleshoot Pinecone nodes within an n8n workflow.
When deciding between managed assistant services or custom vector store implementations.
When you need to implement metadata filtering or multimodal file support in your Pinecone integration.

Advantages

Ensures adherence to recommended node usage and connection patterns.
Reduces setup time by providing clear configuration rules for credentials and parameters.
Helps maintain consistency in embedding models and chunking strategies.
Provides clear guidance on handling file metadata and citations.

Usage examples

“How do I set up a basic RAG workflow using the Pinecone Assistant node?”: The agent will outline the two-phase architecture (ingestion and chat), explain how to configure the Pinecone Assistant node with your host and assistant name, and provide the correct connection types for the AI Agent.
“I need more control over my embeddings. Should I use the Pinecone Vector Store node?”: The agent will confirm that the Vector Store node is the correct choice for custom embedding models and metadata filtering, and will guide you through setting up the required companion nodes like Embeddings OpenAI and Recursive Character Text Splitter.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/pinecone-io/pinecone-n8n/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip pinecone-n8n.zip -d .agents/skills

Documentation by Pinecone

Pinecone n8n Workflow Skill

<<invoke_this_skill>>

This skill helps you build n8n workflows with Pinecone nodes following best practices. It covers two Pinecone nodes:

Pinecone Assistant (@pinecone-database/n8n-nodes-pinecone-assistant) — recommended for most use cases
Pinecone Vector Store (@n8n/n8n-nodes-langchain.vectorStorePinecone) — for advanced control

Core rule: Always use the node's built-in resources and operations. Never suggest using the HTTP node to call the Pinecone REST API directly.

Step 1: Understand the user's scenario

Ask the user what they're trying to do:

Build a new workflow from scratch
Configure or understand a specific Pinecone node
Debug a workflow that isn't working
Review an existing workflow for best practices

Step 2: Node selection (for new workflows and configuration questions)

Always present the Pinecone Assistant node as the recommended choice first. Do NOT skip this step based on your own inference about which node fits better — even if the use case mentions specific triggers (Google Drive, webhooks, etc.) or file types (text, markdown, PDF), those details do not determine which node to use.

Only skip this step if:

The user explicitly names a specific node (e.g. "I want to use the Vector Store node", "help me set up pineconeAssistant")
The user is debugging or configuring an existing workflow that already has a specific Pinecone node in it

If the user has not named a node, always ask or recommend the Assistant node first. If the user said "use defaults" or you cannot ask, default to the Pinecone Assistant node and proceed with the Assistant path.

Ask the user which node they want to use, presenting these two options:

Pinecone Assistant (Recommended)

Fully managed RAG — Pinecone handles chunking, embedding, and indexing automatically
Built-in citations with file names and URLs
Simpler setup: no embedding model or text splitter needed in n8n
Great for: document Q&A, chat with files, knowledge base search

Pinecone Vector Store

Full control over embedding model, chunking strategy, and metadata
Works with any embedding model (OpenAI, Cohere, HuggingFace, etc.)
Required when: you need custom embeddings, have an existing Pinecone index, need metadata filtering, or need fine-grained control over chunking

Pinecone Assistant Node — Best Practices and Workflow Generation

Node package names

File operations (upload, list, delete): @pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant
Chat/retrieval as AI Agent tool: @pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistantTool

Prerequisites

Create a Pinecone Assistant in the Pinecone Console at https://app.pinecone.io/organizations/-/projects/-/assistant before running the workflow
Set up a Pinecone credential in n8n with your API key

Workflow architecture

The standard pattern is a two-phase workflow:

Phase 1 — Ingestion (run once or on a schedule):

Manual Trigger → Set file URLs → Split Out → HTTP Request (download) → Pinecone Assistant (uploadFile)

Phase 2 — Chat:

Chat Trigger → AI Agent ← Pinecone Assistant Tool (connected as ai_tool)
                        ← OpenAI Chat Model (connected as ai_languageModel)

Key configuration rules

assistantData parameter: Always include BOTH name and host fields:
```
{"name": "your-assistant-name", "host": "https://your-assistant-host.pinecone.io"}
```
Find your assistant's host in the Pinecone Console: open the assistant detail page and copy the host URL (format: https://<region>-data.<subdomain>.pinecone.io).

sourceTag: Always include in additionalFields:

{"sourceTag": "n8n:n8n_nodes_pinecone_assistant:pinecone_n8n_skill"}

Connection type: The Assistant Tool connects to the AI Agent via the ai_tool connection — NOT main
externalFileId: Set this to the file URL expression so Pinecone stores it as a reference for citations
Credential: Use pineconeApi credential type for both node variants
File metadata on upload: Add key-value metadata via additionalFields.metadata.metadataValues — an array of {"key": "...", "value": "..."} objects. The externalFileId is automatically added to metadata; do not include it manually. Example:
```
"additionalFields": {
  "metadata": {"metadataValues": [{"key": "department", "value": "legal"}]}
}
```
Metadata filtering on listFiles: Use additionalFields.metadataFilter.metadataValues (same {key, value} array) for simple equality filters, or additionalFields.advancedMetadataFilter (a JSON string) for operators like $or, $ne, $in. Cannot set both at once. Example simple filter:
```
"additionalFields": {
  "metadataFilter": {"metadataValues": [{"key": "department", "value": "legal"}]}
}
```
Multimodal PDF upload: Set additionalFields.multimodalFile: true on the uploadFile node when the PDF contains images or charts that should be indexed for visual retrieval. This is required for images to be retrievable later — it is not the default.

Generating workflow JSON for the Assistant path

Build the workflow to match what the user actually describes — their triggers, models, data sources, and structure. Ask about anything structurally significant they haven't mentioned. Only fall back to the defaults below when the user hasn't specified a value:

Assistant name: n8n-assistant (use n8n-assistant-1, n8n-assistant-2, etc. for multiples; must match an existing assistant in the Pinecone Console)
File URLs: sample Pinecone release notes PDFs
LLM model: gpt-5-mini
System message: generic prompt about retrieving from the assistant with citations

The JSON below is a reference configuration showing correct parameter values, required fields, and connection types for each node. Use it as a guide for how to configure the nodes — not as a template to copy verbatim. Placeholders to substitute:

[ASSISTANT_NAME] — assistant name
[ASSISTANT_HOST] — assistant host URL from the Pinecone Console (e.g. https://your-assistant-host.pinecone.io)
[USER_FILE_URLS_ARRAY] — JSON array of file URL strings, e.g. ["https://example.com/doc.pdf"]
[USER_MODEL] — LLM model name, e.g. gpt-5-mini
[USER_TOPIC] — short description of what the assistant knows, for the system message

{
  "nodes": [
    {
      "parameters": {
        "options": {
          "systemMessage": "You are a helpful assistant. Use the Pinecone Assistant Tool to retrieve data about [USER_TOPIC]. Include the file name and file url in citations wherever referenced in output."
        }
      },
      "type": "@n8n/n8n-nodes-langchain.agent",
      "typeVersion": 2.2,
      "position": [2208, 784],
      "id": "e4c65881-120c-4a7c-854b-138611c8dfa3",
      "name": "AI Agent"
    },
    {
      "parameters": {
        "model": {"__rl": true, "mode": "list", "value": "[USER_MODEL]"},
        "options": {}
      },
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "typeVersion": 1.2,
      "position": [2144, 1008],
      "id": "b3ea858d-b62d-4022-8241-8872e403839a",
      "name": "OpenAI Chat Model"
    },
    {
      "parameters": {
        "content": "## 1. Upload files to Pinecone Assistant",
        "height": 384,
        "width": 1104,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [1616, 288],
      "typeVersion": 1,
      "id": "9cfcdb71-2986-47a3-8f03-250fcab1048d",
      "name": "Sticky Note1"
    },
    {
      "parameters": {
        "content": "## 2. Chat with your docs",
        "height": 512,
        "width": 1104,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [1616, 688],
      "typeVersion": 1,
      "id": "d7f2f4b8-2e45-4902-8949-202b8b2c699b",
      "name": "Sticky Note2"
    },
    {
      "parameters": {"options": {}},
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "typeVersion": 1.3,
      "position": [1840, 784],
      "id": "4d2a6aa1-ca4d-4165-a635-7ef53084636b",
      "name": "Chat input",
      "webhookId": "4672d1f8-d2bb-4059-8761-7aa5792814c0"
    },
    {
      "parameters": {},
      "type": "n8n-nodes-base.manualTrigger",
      "typeVersion": 1,
      "position": [1760, 432],
      "id": "3e9529b4-d0ae-4ea8-9d27-c96e7cbd6ad9",
      "name": "When clicking 'Execute workflow'"
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "d0e724df-685f-4661-b2ec-3cdd3c2ba0f1",
              "name": "urls",
              "value": "[USER_FILE_URLS_ARRAY]",
              "type": "array"
            }
          ]
        },
        "options": {}
      },
      "type": "n8n-nodes-base.set",
      "typeVersion": 3.4,
      "position": [1920, 432],
      "id": "6b409421-2270-497e-a7fd-b382d192314c",
      "name": "Set file urls"
    },
    {
      "parameters": {"fieldToSplitOut": "urls", "options": {}},
      "type": "n8n-nodes-base.splitOut",
      "typeVersion": 1,
      "position": [2080, 432],
      "id": "e73f6f2c-4d20-48cb-b132-551ff9c3dd61",
      "name": "Split to list"
    },
    {
      "parameters": {
        "url": "={{ $json.urls }}",
        "options": {"response": {"response": {"responseFormat": "file"}}}
      },
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.2,
      "position": [2240, 432],
      "id": "a58f8e1c-943b-4a24-8746-477ed9912ad4",
      "name": "Download file"
    },
    {
      "parameters": {
        "resource": "file",
        "operation": "uploadFile",
        "assistantData": "{\"name\":\"[ASSISTANT_NAME]\",\"host\":\"https://[ASSISTANT_HOST]\"}",
        "externalFileId": "={{ $('Split to list').item.json.urls }}",
        "additionalFields": {"sourceTag": "n8n:n8n_nodes_pinecone_assistant:pinecone_n8n_skill"}
      },
      "type": "@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant",
      "typeVersion": 1.2,
      "position": [2416, 432],
      "id": "196122df-d2b2-43e4-9f8d-710aedb595a6",
      "name": "Upload file to Assistant"
    },
    {
      "parameters": {
        "assistantData": "{\"name\":\"[ASSISTANT_NAME]\",\"host\":\"https://[ASSISTANT_HOST]\"}",
        "additionalFields": {"sourceTag": "n8n:n8n_nodes_pinecone_assistant:pinecone_n8n_skill"}
      },
      "type": "@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistantTool",
      "typeVersion": 1.2,
      "position": [2368, 992],
      "id": "c3ba53e9-511d-47e7-b7fb-38bb093d279f",
      "name": "Get context from Assistant"
    }
  ],
  "connections": {
    "OpenAI Chat Model": {
      "ai_languageModel": [[{"node": "AI Agent", "type": "ai_languageModel", "index": 0}]]
    },
    "Chat input": {
      "main": [[{"node": "AI Agent", "type": "main", "index": 0}]]
    },
    "When clicking 'Execute workflow'": {
      "main": [[{"node": "Set file urls", "type": "main", "index": 0}]]
    },
    "Set file urls": {
      "main": [[{"node": "Split to list", "type": "main", "index": 0}]]
    },
    "Split to list": {
      "main": [[{"node": "Download file", "type": "main", "index": 0}]]
    },
    "Download file": {
      "main": [[{"node": "Upload file to Assistant", "type": "main", "index": 0}]]
    },
    "Get context from Assistant": {
      "ai_tool": [[{"node": "AI Agent", "type": "ai_tool", "index": 0}]]
    }
  },
  "pinData": {},
  "meta": {"templateCredsSetupCompleted": false}
}

Other Pinecone Assistant operations

Upload File vs Update File — choosing the right operation

Scenario	Operation to use
File is guaranteed to be new (never ingested before)	`uploadFile`
File may already exist in the assistant (re-ingestion, scheduled refresh)	`updateFile`

updateFile is idempotent: it finds all files with the matching externalFileId, deletes them, then uploads the new version. If no file exists with that ID it behaves exactly like uploadFile. Use updateFile whenever a workflow may run more than once on the same source files.

Parameters for updateFile are identical to uploadFile: assistantData, externalFileId, inputDataFieldName, and all additionalFields including metadata, multimodalFile, and sourceTag.

{
  "parameters": {
    "resource": "file",
    "operation": "updateFile",
    "assistantData": "{\"name\":\"[ASSISTANT_NAME]\",\"host\":\"https://[ASSISTANT_HOST]\"}",
    "externalFileId": "={{ $('Split to list').item.json.urls }}",
    "additionalFields": {"sourceTag": "n8n:n8n_nodes_pinecone_assistant:pinecone_n8n_skill"}
  },
  "type": "@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant",
  "typeVersion": 1.2
}

List Files

Use resource: "file", operation: "listFiles" to retrieve files, optionally filtered by metadata.

{
  "parameters": {
    "resource": "file",
    "operation": "listFiles",
    "assistantData": "{\"name\":\"[ASSISTANT_NAME]\",\"host\":\"https://[ASSISTANT_HOST]\"}",
    "additionalFields": {
      "sourceTag": "n8n:n8n_nodes_pinecone_assistant:pinecone_n8n_skill",
      "metadataFilter": {
        "metadataValues": [{"key": "department", "value": "legal"}]
      }
    }
  },
  "type": "@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant",
  "typeVersion": 1.2
}

For complex filters, use advancedMetadataFilter (a JSON string) instead of metadataFilter — never both:

"additionalFields": {
  "advancedMetadataFilter": "{\"department\": {\"$in\": [\"legal\", \"finance\"]}}"
}

Get Context Snippets

Use resource: "contextSnippet", operation: "getContextSnippets" to retrieve relevant text or image chunks directly — useful when you need raw retrieved context as workflow data rather than a chat reply.

{
  "parameters": {
    "resource": "contextSnippet",
    "operation": "getContextSnippets",
    "assistantData": "{\"name\":\"[ASSISTANT_NAME]\",\"host\":\"https://[ASSISTANT_HOST]\"}",
    "query": "={{ $json.chatInput }}",
    "additionalFields": {
      "sourceTag": "n8n:n8n_nodes_pinecone_assistant:pinecone_n8n_skill",
      "includeMultimodalContext": true,
      "includeBinaryContent": true,
      "topK": 16,
      "snippetSize": 2048
    }
  },
  "type": "@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant",
  "typeVersion": 1.2
}

The same metadataFilter / advancedMetadataFilter options work on getContextSnippets to scope retrieval to files matching specific metadata. The includeBinaryContent flag only applies when includeMultimodalContext is true.

Pinecone Vector Store Node — Best Practices and Workflow Generation

Node package name

@n8n/n8n-nodes-langchain.vectorStorePinecone

Prerequisites

Create a Pinecone index in the Pinecone Console at https://app.pinecone.io/organizations/-/projects/-/indexes with the correct name and dimensions before running the workflow
Set up a Pinecone credential in n8n with your API key
Set up an OpenAI credential in n8n

Workflow architecture

Two-phase workflow with two separate vectorStorePinecone node instances:

Phase 1 — Ingestion (run once or on a schedule):

Manual Trigger → Set file URLs → Split Out → HTTP Request (download)
  → Pinecone Vector Store (insert mode)
       ↑ Default Data Loader ← Recursive Character Text Splitter
       ↑ Embeddings OpenAI

Phase 2 — Chat:

Chat Trigger → AI Agent ← Pinecone Vector Store (retrieve-as-tool mode)
                               ↑ Embeddings OpenAI (same model as insert)
                        ← OpenAI Chat Model

Key configuration rules

Two node instances: Use one vectorStorePinecone in insert mode for ingestion and a separate one in retrieve-as-tool mode for chat
Embedding model consistency: The SAME embedding model and dimensions MUST be used in both insert and retrieve-as-tool nodes — they share a single Embeddings OpenAI node via the ai_embedding connection
Required companion nodes for the insert node:
- @n8n/n8n-nodes-langchain.embeddingsOpenAi — set dimensions to match your index (1536 for text-embedding-3-small)
- @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter — connects to the data loader
- @n8n/n8n-nodes-langchain.documentDefaultDataLoader — set dataType: binary and textSplittingMode: custom
toolDescription: Write a clear, specific description on the retrieve-as-tool node — the AI Agent uses this to decide when to query it
topK: Use 20 as the default; increase for broader recall, decrease to reduce token usage
Metadata for citations: In the Default Data Loader, add external_file_url as a metadata field set to ={{ $json.urls }} so the AI Agent can cite sources
Credential: Use pineconeApi credential type

Chunking guidance

3000 chars / 500 overlap: Good default for long documents (articles, reports, documentation)
500–1000 chars / 100–200 overlap: Better for Q&A-style retrieval or short, dense content
Reference: https://www.pinecone.io/learn/chunking-strategies/

Index setup instructions

When generating a workflow, always tell the user to:

Go to https://app.pinecone.io/organizations/-/projects/-/indexes and create an index
Set the name to match the pineconeIndex value in the workflow
Select the embedding model that matches the n8n Embeddings node (text-embedding-3-small → 1536 dimensions)

Generating workflow JSON for the Vector Store path

Build the workflow to match what the user actually describes — their triggers, embedding model, chunking strategy, data sources, and structure. Ask about anything structurally significant they haven't mentioned. Only fall back to the defaults below when the user hasn't specified a value:

Index name: n8n-index (use n8n-index-1, n8n-index-2, etc. for multiples; must match an existing Pinecone index)
File URLs: sample Pinecone release notes PDFs
LLM model: gpt-5-mini
Embedding model: text-embedding-3-small (1536 dimensions)
Chunk size: 3000 chars / 500 overlap
System message / tool description: generic prompt about retrieving from the index with citations

[INDEX_NAME] — index name
[USER_FILE_URLS_ARRAY] — JSON array of file URL strings, e.g. ["https://example.com/doc.pdf"]
[USER_MODEL] — LLM model name, e.g. gpt-5-mini
[USER_TOPIC] — short description of the data, for the AI Agent system message
[USER_TOOL_DESCRIPTION] — description of what data the vector store contains, for the tool node

{
  "nodes": [
    {
      "parameters": {
        "mode": "insert",
        "pineconeIndex": {
          "__rl": true,
          "value": "[INDEX_NAME]",
          "mode": "list",
          "cachedResultName": "[INDEX_NAME]"
        },
        "options": {}
      },
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
      "typeVersion": 1.3,
      "position": [224, 0],
      "id": "163c0a37-427c-4da7-a194-203ac216c88e",
      "name": "Pinecone Vector Store"
    },
    {
      "parameters": {
        "options": {"dimensions": 1536}
      },
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "typeVersion": 1.2,
      "position": [336, 608],
      "id": "a034bcb9-d813-4fd0-a68a-426c1f2b2aba",
      "name": "Embeddings OpenAI"
    },
    {
      "parameters": {"options": {}},
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "typeVersion": 1.3,
      "position": [-512, 896],
      "id": "56182cf7-2bda-4513-a2f9-18fb70c718ec",
      "name": "When chat message received",
      "webhookId": "30328376-a9e3-4f11-b1eb-2bbaadbea688"
    },
    {
      "parameters": {
        "options": {
          "systemMessage": "You are a helpful assistant. Only use the Pinecone Vector Store Tool to retrieve data about [USER_TOPIC]. Include the file name and file url in citations wherever referenced in output."
        }
      },
      "type": "@n8n/n8n-nodes-langchain.agent",
      "typeVersion": 2.2,
      "position": [-336, 896],
      "id": "36bb8361-edfd-47ff-aaf1-48f3c31fb1f7",
      "name": "AI Agent"
    },
    {
      "parameters": {
        "model": {
          "__rl": true,
          "value": "[USER_MODEL]",
          "mode": "list",
          "cachedResultName": "[USER_MODEL]"
        },
        "options": {}
      },
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "typeVersion": 1.2,
      "position": [-336, 1200],
      "id": "22d00f25-44eb-40c1-8dae-b757e4b66690",
      "name": "OpenAI Chat Model"
    },
    {
      "parameters": {
        "mode": "retrieve-as-tool",
        "toolDescription": "[USER_TOOL_DESCRIPTION]",
        "pineconeIndex": {
          "__rl": true,
          "value": "[INDEX_NAME]",
          "mode": "list",
          "cachedResultName": "[INDEX_NAME]"
        },
        "topK": 20,
        "options": {}
      },
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
      "typeVersion": 1.3,
      "position": [-16, 1104],
      "id": "15c599b7-93bb-44b7-b95c-190200de2a20",
      "name": "Pinecone Vector Store Tool"
    },
    {
      "parameters": {
        "content": "## 1. Process new files, embed, and upsert to Pinecone index",
        "height": 576,
        "width": 1744,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [-608, -80],
      "typeVersion": 1,
      "id": "18f22d77-fb10-4fa7-b942-2fafd7ca30c3",
      "name": "Sticky Note"
    },
    {
      "parameters": {
        "content": "## 2. Chat with your docs",
        "height": 640,
        "width": 1744,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [-608, 784],
      "typeVersion": 1,
      "id": "7b7be380-821c-4012-8f1a-1606527d2544",
      "name": "Sticky Note1"
    },
    {
      "parameters": {},
      "type": "n8n-nodes-base.manualTrigger",
      "typeVersion": 1,
      "position": [-512, 0],
      "id": "a0a3b917-765b-445e-9866-d3fc651ef6a8",
      "name": "When clicking 'Execute workflow'"
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "d0e724df-685f-4661-b2ec-3cdd3c2ba0f1",
              "name": "urls",
              "value": "[USER_FILE_URLS_ARRAY]",
              "type": "array"
            }
          ]
        },
        "options": {}
      },
      "type": "n8n-nodes-base.set",
      "typeVersion": 3.4,
      "position": [-352, 0],
      "id": "651439de-5525-4d0a-b055-1d722e81c0aa",
      "name": "Set file urls"
    },
    {
      "parameters": {"fieldToSplitOut": "urls", "options": {}},
      "type": "n8n-nodes-base.splitOut",
      "typeVersion": 1,
      "position": [-192, 0],
      "id": "8878265e-9e6f-4d9a-b533-53100205eb38",
      "name": "Split to list"
    },
    {
      "parameters": {
        "chunkSize": 3000,
        "chunkOverlap": 500,
        "options": {}
      },
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "typeVersion": 1,
      "position": [416, 352],
      "id": "347ba65b-9fad-4b92-a90f-6690df8e2a7e",
      "name": "Recursive Character Text Splitter"
    },
    {
      "parameters": {
        "url": "={{ $json.urls }}",
        "options": {"response": {"response": {"responseFormat": "file"}}}
      },
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.2,
      "position": [-32, 0],
      "id": "edf963af-18ee-4605-9b1e-49118c56b52d",
      "name": "Download file"
    },
    {
      "parameters": {
        "dataType": "binary",
        "textSplittingMode": "custom",
        "options": {
          "metadata": {
            "metadataValues": [
              {"name": "external_file_url", "value": "={{ $json.urls }}"}
            ]
          }
        }
      },
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "typeVersion": 1.1,
      "position": [336, 192],
      "id": "e9b99301-3af1-4d19-ab78-3fd0e8946fd1",
      "name": "Default data loader"
    }
  ],
  "connections": {
    "Embeddings OpenAI": {
      "ai_embedding": [[
        {"node": "Pinecone Vector Store", "type": "ai_embedding", "index": 0},
        {"node": "Pinecone Vector Store Tool", "type": "ai_embedding", "index": 0}
      ]]
    },
    "When chat message received": {
      "main": [[{"node": "AI Agent", "type": "main", "index": 0}]]
    },
    "OpenAI Chat Model": {
      "ai_languageModel": [[{"node": "AI Agent", "type": "ai_languageModel", "index": 0}]]
    },
    "Pinecone Vector Store Tool": {
      "ai_tool": [[{"node": "AI Agent", "type": "ai_tool", "index": 0}]]
    },
    "When clicking 'Execute workflow'": {
      "main": [[{"node": "Set file urls", "type": "main", "index": 0}]]
    },
    "Set file urls": {
      "main": [[{"node": "Split to list", "type": "main", "index": 0}]]
    },
    "Split to list": {
      "main": [[{"node": "Download file", "type": "main", "index": 0}]]
    },
    "Recursive Character Text Splitter": {
      "ai_textSplitter": [[{"node": "Default data loader", "type": "ai_textSplitter", "index": 0}]]
    },
    "Download file": {
      "main": [[{"node": "Pinecone Vector Store", "type": "main", "index": 0}]]
    },
    "Default data loader": {
      "ai_document": [[{"node": "Pinecone Vector Store", "type": "ai_document", "index": 0}]]
    }
  },
  "pinData": {},
  "meta": {"templateCredsSetupCompleted": false}
}

Debugging guidance

When a user reports a broken workflow, ask which node is failing and what error they see, then refer to these common issues:

Error / Symptom	Cause	Fix
"Assistant not found"	`assistantData.name` doesn't match an existing assistant	Create the assistant in the Pinecone Console first, then match the name exactly
"Invalid credentials"	Pinecone API key not set or wrong credential on the node	Re-select the `pineconeApi` credential on each Pinecone node
"Dimension mismatch"	Embedding model dimensions don't match the index	Index and embeddings node must use the same dimensions (e.g., 1536 for text-embedding-3-small)
Tool never called by Agent	`toolDescription` is vague	Make the description specific: what data is in the store, what questions it can answer
No results returned	Ingestion didn't run, or topK too low	Run Phase 1 first; try increasing topK to 20+
HTTP node calling Pinecone API	Wrong pattern	Replace with `@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant` or `@n8n/n8n-nodes-langchain.vectorStorePinecone`

Best practices review checklist

When reviewing an existing workflow:

No HTTP nodes calling Pinecone endpoints — replace any with the proper Pinecone node
Vector Store: same Embeddings node connected to both insert and retrieve-as-tool instances
Assistant: assistantData includes both name and host; sourceTag is present in additionalFields
toolDescription is specific and descriptive (not generic like "a vector store")
Vector Store metadata: external_file_url stored in the Default Data Loader metadata for source citations
Assistant file metadata: files uploaded with meaningful key-value metadata when filtering will be needed later
Metadata filter: listFiles and getContextSnippets use a metadata filter (not both filter fields at once) when scoping to a subset of files
Multimodal upload: multimodalFile: true is set on uploadFile when PDFs contain images or charts — this is required to index visual content and is NOT the default
Multimodal retrieval: includeMultimodalContext: true and includeBinaryContent: true on getContextSnippets when the workflow needs image chunks alongside text
Chunk size is appropriate for the content type (see chunking guidance above)
Index exists with correct dimensions before the workflow runs

Files

ATTRIBUTION.mdAdded by AutoGineer853 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md28.0 KiB

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

Pinecone

Publisher

Pinecone (Official)

Source

pinecone-io/skills · skills/pinecone-n8n · commit 885dc53

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Pinecone.

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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download pinecone-n8n.zip

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What it does

When to use it

Advantages

Usage examples

Install

Documentation by Pinecone

Pinecone n8n Workflow Skill

Step 1: Understand the user's scenario

Step 2: Node selection (for new workflows and configuration questions)

Pinecone Assistant Node — Best Practices and Workflow Generation

Node package names

Prerequisites

Workflow architecture

Key configuration rules

Generating workflow JSON for the Assistant path

Other Pinecone Assistant operations

Upload File vs Update File — choosing the right operation

List Files

Get Context Snippets

Pinecone Vector Store Node — Best Practices and Workflow Generation

Node package name

Prerequisites

Workflow architecture

Key configuration rules

Chunking guidance

Index setup instructions

Generating workflow JSON for the Vector Store path

Debugging guidance

Best practices review checklist

Files

Requirements and compatibility

Security

Attribution and licence

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