How can I batch process a large Pinecone dataset with an OpenAI agent in n8n?

Below is one approach to working around this limitation:

1. Batch the Result Set:
   Instead of having the Pinecone node return all the documents in one call using a very high limit, use a moderate limit (for example, 20 or 50) and then iterate over the entire dataset using a loop tool.

2. Use the SplitInBatches (Loop Over Items) Node:
   In n8n you can use the SplitInBatches node to process the results in smaller chunks. Each batch received from the Pinecone node can be passed into the Open AI agent node so that you’re never sending more data than the agent can handle at one time.

3. Combine/Accumulate Responses:
   Depending on your desired outcome, you may need to accumulate the responses from each batch before sending them into a final processing step. This ensures the agent eventually “sees” (or works on) all the data to generate comprehensive how-to steps.

4. Consider Adding a Wait Node:
   To avoid overwhelming the API or your website (if you’re also making additional requests), you might add a Wait node after each batch before processing the next one.

Below is an example of how the workflow could be structured in n8n:

JSONCopied
{
  "nodes": [
    {
      "id": "trigger",
      "name": "Manual Trigger",
      "type": "n8n-nodes-base.manualTrigger",
      "typeVersion": 1,
      "position": [250, 300]
    },
    {
      "id": "pinecone",
      "name": "Pinecone Vector Store",
      "type": "n8n-nodes-langchain.vectorstorepinecone",
      "typeVersion": 1,
      "position": [450, 300],
      "parameters": {
        "operation": "getMany",
        "limit": 50
      }
    },
    {
      "id": "splitBatches",
      "name": "Loop Over Items",
      "type": "n8n-nodes-base.splitInBatches",
      "typeVersion": 3,
      "position": [650, 300],
      "parameters": {
        "batchSize": 50
      }
    },
    {
      "id": "openAi",
      "name": "Open AI Agent",
      "type": "n8n-nodes-base.openAi",
      "typeVersion": 1,
      "position": [850, 300],
      "parameters": {
        "resource": "chat",
        "prompt": "Based on the following dataset chunk, provide technical how-to steps: {{$json[\"data\"]}}"
      }
    },
    {
      "id": "wait",
      "name": "Wait",
      "type": "n8n-nodes-base.wait",
      "typeVersion": 1,
      "position": [1050, 300],
      "parameters": {
        "unit": "seconds",
        "time": 2
      }
    }
  ],
  "connections": {
    "Manual Trigger": {
      "main": [
        [
          {
            "node": "Pinecone Vector Store",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Pinecone Vector Store": {
      "main": [
        [
          {
            "node": "Loop Over Items",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Loop Over Items": {
      "main": [
        [
          {
            "node": "Open AI Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Open AI Agent": {
      "main": [
        [
          {
            "node": "Wait",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Wait": {
      "main": [
        [
          {
            "node": "Loop Over Items",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

How This Addresses the Issue:

• The Pinecone node returns a fixed number of results per call, conforming to the limitations of what can be sent to Open AI.
• The SplitInBatches node (Loop Over Items) ensures that each batch is processed sequentially by the Open AI node.
• The Wait node helps prevent hitting any API rate limits if you have a large number of batches.

By iteratively processing each batch, the Open AI agent eventually sees all parts of your dataset without risking an overload in a single execution. This keeps each prompt within size limits while still covering the entire dataset.