Pinecone Setup Guide for n8n

Pinecone offers serverless vector storage with sub-50ms query latency at scale. For law firms and professional services managing 10,000+ documents, it outperforms self-hosted solutions like Qdrant or Chroma when you need zero infrastructure overhead.

This guide walks you through connecting Pinecone to n8n for production knowledge base systems. You'll configure authentication, structure your index for legal/financial documents, and build a working Q&A retrieval workflow.

Pinecone Account (Free Tier Works) Sign up at pinecone.io. Free tier includes 1 index with 100K vectors and 1GB storage. Sufficient for testing with 5,000-10,000 document chunks.

n8n Instance (Cloud or Self-Hosted) Cloud version at n8n.io works immediately. Self-hosted requires Docker or npm installation. Version 0.220.0+ required for native Pinecone nodes.

OpenAI API Key You'll need this to generate embeddings. GPT-3.5-turbo embeddings cost $0.0001 per 1K tokens. Budget $5-10 for initial testing with 1,000 documents.

1. Log into Pinecone and click "Create Index"

2. Configure index settings:

• Index Name: firm-knowledge-base (use lowercase, hyphens only)
• Dimensions: 1536 (matches OpenAI text-embedding-ada-002 output)
• Metric: cosine (standard for semantic search)
• Pod Type: s1.x1 for starter (handles 100K vectors)
• Replicas: 1 (increase to 2-3 for production high availability)

3. Click "Create Index" and wait 60-90 seconds for provisioning

4. Copy your credentials from the dashboard:

• API

  API

  Click to read the full definition in our AI & Automation Glossary.

  Key (starts with pcsk_)
• Environment (format: us-east1-gcp or similar)
• Index Host URL (format: firm-knowledge-base-abc123.svc.us-east1-gcp.pinecone.io)

Store these in your password manager. You'll need them for n8n authentication.

1. Open n8n and create a new workflow

2. Add a Pinecone node to the canvas Search for "Pinecone Vector Store" in the node panel. If missing, update n8n to version 0.220.0+.

3. Click "Create New Credential" in the node settings

4. Enter your Pinecone credentials:

• API

  API

  Click to read the full definition in our AI & Automation Glossary.

  Key: Paste your pcsk_ key
• Environment: Enter your region (example: us-east1-gcp)

5. Test the connection Click "Test Credential". You should see "Connection successful". If it fails, verify your API

key hasn't expired and your IP isn't blocked by Pinecone's firewall.

6. Save the credential as "Pinecone Production"

This workflow converts PDFs/Word docs into searchable vectors. Use this pattern for client files, case law, or internal knowledge bases.

1. Add an "HTTP Request" node (or "Google Drive" node for cloud files)

Configure to fetch your source documents. Example for local files:

• Method: GET
• URL: https://yourdomain.com/documents/client-agreement.pdf
• Response Format: Binary

2. Add a "Extract from File" node

Connect it after HTTP Request:

• Operation: Extract Text
• Binary Property: data
• Output Format: Plain Text

3. Add a "Code" node to chunk the text

Large documents must be split into 500-1000 token chunks. Paste this function:

const text = $input.item.json.text;
const chunkSize = 800; // tokens, roughly 600 words
const overlap = 100; // prevents context loss at boundaries

function chunkText(text, size, overlap) {
  const words = text.split(/\s+/);
  const chunks = [];
  
  for (let i = 0; i < words.length; i += size - overlap) {
    const chunk = words.slice(i, i + size).join(' ');
    chunks.push({
      text: chunk,
      chunkIndex: Math.floor(i / (size - overlap)),
      sourceFile: $input.item.json.fileName
    });
  }
  
  return chunks;
}

return chunkText(text, chunkSize, overlap).map(chunk => ({ json: chunk }));

4. Add an "OpenAI" node for embeddings

• Resource: Embeddings
• Model: text-embedding-ada-002
• Input: {{ $json.text }}

This converts each text chunk into a 1536-dimension vector.

5. Add the "Pinecone Vector Store" node

• Operation: Insert
• Index Name: firm-knowledge-base
• Vector: {{ $json.embedding }} (from OpenAI node)
• ID: {{ $json.sourceFile }}-chunk-{{ $json.chunkIndex }}
• Metadata: Add these fields:

  • text: {{ $json.text }}
  • source: {{ $json.sourceFile }}
  • chunkIndex: {{ $json.chunkIndex }}
  • uploadDate: {{ $now.toISO() }}

6. Execute the workflow

Start with 5-10 test documents. Monitor the execution panel for errors. Each document should produce 10-50 chunks depending on length.

This workflow takes a user question and returns the 3 most relevant document excerpts.

1. Create a new workflow with a "Webhook" trigger

• Method: POST
• Path: knowledge-base-query
• Response Mode: Last Node

2. Add an "OpenAI" node to embed the question

• Resource: Embeddings
• Model: text-embedding-ada-002
• Input: {{ $json.body.question }}

3. Add a "Pinecone Vector Store" node for search

• Operation: Query
• Index Name: firm-knowledge-base
• Query Vector: {{ $json.embedding }}
• Top K: 3 (returns 3 best matches)
• Include Metadata: true

4. Add a "Code" node to format results

const matches = $input.item.json.matches;

const formattedResults = matches.map((match, index) => ({
  rank: index + 1,
  relevanceScore: match.score.toFixed(3),
  excerpt: match.metadata.text,
  source: match.metadata.source,
  chunkIndex: match.metadata.chunkIndex
}));

return [{ json: { results: formattedResults } }];

5. Add a "Respond to Webhook

" node

• Response Body: {{ $json }}

6. Test with a sample question

Send a POST request to your webhook

URL:

{
  "question": "What are the termination clauses in client agreements?"
}

You should receive 3 ranked excerpts with relevance scores above 0.75 for good matches.

Raw excerpts are useful, but a synthesized answer improves user experience.

1. Insert an "OpenAI" node after the Pinecone query

• Resource: Chat
• Model: gpt-4o-mini (faster and cheaper than GPT-4)
• Messages: System + User message

System Message:

You are a legal knowledge assistant for [Firm Name]. Answer questions using only the provided document excerpts. If the excerpts don't contain the answer, say "I don't have enough information in the knowledge base to answer that."

Cite sources using this format: [Source: filename.pdf, Section X]

User Message:

Question: `{{ $('Webhook').item.json.body.question }}`

Relevant excerpts:
`{{ $json.results.map(r => `[${r.rank}] ${r.excerpt} (Source: ${r.source})`).join('\n\n') }}`

Provide a clear, concise answer with source citations.

2. Update the "Respond to Webhook

" node

Return both the GPT answer and the raw excerpts:

{
  "answer": "`{{ $json.choices[0].message.content }}`",
  "sources": "`{{ $('Code').item.json.results }}`"
}

Batch Upserts for Large Document Sets

Instead of inserting vectors one at a time, batch them in groups of 100:

const vectors = $input.all().map(item => ({
  id: `${item.json.sourceFile}-${item.json.chunkIndex}`,
  values: item.json.embedding,
  metadata: {
    text: item.json.text,
    source: item.json.sourceFile,
    chunkIndex: item.json.chunkIndex
  }
}));

// Split into batches of 100
const batches = [];
for (let i = 0; i < vectors.length; i += 100) {
  batches.push(vectors.slice(i, i + 100));
}

return batches.map(batch => ({ json: { vectors: batch } }));

Set the Pinecone node to "Upsert" operation and pass {{ $json.vectors }}.

Namespace Strategy for Multi-Client Firms

Use namespaces to isolate client data:

• Namespace: client-{{ $json.clientId }}

This prevents cross-client data leakage and enables per-client access controls.

Metadata Filtering for Precise Searches

Add filters to the Pinecone query node:

{
  "filter": {
    "source": { "$eq": "employment-agreements" },
    "uploadDate": { "$gte": "2024-01-01" }
  }
}

This restricts searches to specific document types or date ranges.

"Index not found" error Verify the index name matches exactly (case-sensitive). Check the Pinecone dashboard to confirm the index exists and is active.

Low relevance scores (below 0.6) Your embeddings may not match your query style. Try rephrasing questions to match document language, or fine-tune your chunking strategy to preserve more context.

Rate limit errors during bulk uploads Free tier limits to 100 requests/minute. Add a "Wait" node with 1-second delay between batches, or upgrade to a paid plan.

Missing metadata in query results Ensure "Include Metadata" is set to true in the Query operation. Metadata isn't returned by default.

10,000 documents (average 5 pages each):

• Embedding cost: ~$15 (one-time)
• Pinecone storage: Free tier sufficient
• Query cost: $0.0001 per query (negligible)

Monthly operating cost: $0-5 for most small-to-midsize firms.

Upgrade to paid Pinecone ($70/month) when you exceed 100K vectors or need multiple indexes.