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Overview

PropertyValue
Workflows3 (2 parent + 1 sub-workflow)
TriggersHTTP (2 parent workflows) + sub-workflow
IntegrationsAnthropic (Claude), Pinecone (vector DB)
Credential typesFLEXCRED (Anthropic), INSTCRED (Pinecone)
FeaturesRAG retrieval, file upload/download, sub-workflows, data ingestion, knowledge base
Cost15 + 5 + 0 credits
ComplexityComplex
This use case generates business proposals by searching a knowledge base of historical proposals (via RAG), then using Claude to draft a new proposal based on the requirements document and similar past work.

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Folder structure

proposal-generation/
  config.ts
  version.json
  project.json
  workflows/
    proposal-generation-package.json    # Main: generates proposal
    document-retrieval.json             # RAG: finds similar documents
    markdown-to-pdf-subworkflow.json    # Sub-workflow: converts to PDF

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Workflow architecture

User uploads requirements document
  → proposal-generation-package (HTTP trigger)
    → Extract text from document
    → Search knowledge base (Pinecone) for similar proposals
    → Generate proposal with Claude (using similar docs as context)
    → Call pdf-converter sub-workflow
      → markdown-to-pdf-subworkflow
        → Convert markdown to PDF
        → Upload PDF via Codika Upload File (with execution metadata override)
        → Return documentId to parent
    → Codika Submit Result (with PDF documentId)

User can also:
  → document-retrieval (HTTP trigger)
    → Search knowledge base without generating a proposal
    → Return matching documents

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config.ts highlights

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Three workflow definitions

export const WORKFLOW_FILES = [
  'workflows/proposal-generation-package.json',
  'workflows/document-retrieval.json',
  'workflows/markdown-to-pdf-subworkflow.json',
];

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Main workflow (HTTP trigger with file upload input)

{
  workflowTemplateId: 'proposal-generation-package',
  workflowId: 'proposal-generation-package',
  workflowName: 'Generate Proposal',
  integrationUids: ['anthropic'],
  triggers: [{
    triggerId: crypto.randomUUID(),
    type: 'http' as const,
    url: webhookUrl,
    method: 'POST' as const,
    title: 'Generate Proposal',
    inputSchema: getProposalInputSchema(),
  }],
  outputSchema: getProposalOutputSchema(),
  cost: 15,
}

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File input schema

function getProposalInputSchema(): FormInputSchema {
  return [
    {
      type: 'section',
      title: 'Proposal Configuration',
      collapsible: false,
      inputSchema: [
        {
          key: 'requirements_file',
          type: 'file',
          label: 'Requirements Document',
          description: 'Upload the RFP or requirements document',
          required: true,
          maxSize: 50 * 1024 * 1024,
          allowedMimeTypes: ['application/pdf', '.docx', '.doc'],
        },
        {
          key: 'proposal_language',
          type: 'select',
          label: 'Proposal Language',
          required: true,
          defaultValue: 'french',
          options: [
            { value: 'french', label: 'French' },
            { value: 'english', label: 'English' },
            { value: 'dutch', label: 'Dutch' },
          ],
        },
      ],
    },
    {
      type: 'section',
      title: 'Additional Context',
      collapsible: true,
      inputSchema: [
        {
          key: 'notes',
          type: 'array',
          label: 'Additional Notes',
          description: 'Extra context to include in the proposal',
          itemField: { type: 'text', maxLength: 10000, rows: 6 },
          minItems: 0,
          maxItems: 10,
        },
      ],
    },
  ];
}

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File output schema

function getProposalOutputSchema(): FormOutputSchema {
  return [
    {
      key: 'proposal_pdf',
      type: 'file',
      label: 'Generated Proposal',
      description: 'PDF proposal document',
    },
    {
      key: 'executive_summary',
      type: 'text',
      label: 'Executive Summary',
    },
  ];
}

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Sub-workflow definition

{
  workflowTemplateId: 'markdown-to-pdf-subworkflow',
  workflowId: 'markdown-to-pdf-subworkflow',
  workflowName: 'Markdown to PDF Converter',
  integrationUids: [],
  triggers: [{
    triggerId: crypto.randomUUID(),
    type: 'subworkflow' as const,
    title: 'Convert Markdown to PDF',
    inputSchema: [
      { name: 'markdownContent', type: 'string' },
      { name: 'fieldKey', type: 'string' },
      { name: 'fileName', type: 'string' },
      { name: 'executionId', type: 'string' },
      { name: 'executionSecret', type: 'string' },
      { name: 'docTitle', type: 'string' },
      { name: 'docSubtitle', type: 'string' },
    ],
    calledBy: ['proposal-generation-package'],
  }],
  outputSchema: [],
  cost: 0,
}

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Knowledge base access

knowledgeBaseAccess: {
  processDocTags: ['proposal', 'rfp'],
  processInstanceDocTags: ['proposal'],
}
This enables the workflow to access tagged documents from both the process-level and instance-level knowledge bases.

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Data ingestion configuration

For embedding documents into the vector store: 
export function getDataIngestionConfig(): ProcessDataIngestionConfigInput {
  return {
    workflowTemplateId: 'proposal-embedding-ingestion',
    workflowName: 'Embedding Ingestion',
    n8nWorkflowJsonBase64: loadAndEncodeWorkflow(__dirname, 'workflows/embedding-workflow.json'),
    webhooks: {
      embed: '{{PROCDATA_PROCESS_ID_ATADCORP}}/embed',
      delete: '{{PROCDATA_PROCESS_ID_ATADCORP}}/embed-delete',
    },
    purpose: 'Embed KB documents into Pinecone for RAG retrieval',
    cost: 2,
  };
}

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Key patterns demonstrated

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1. Multi-workflow architecture

Three workflows with clear separation of concerns — main generation, retrieval, and PDF conversion.

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2. Sub-workflow with file upload

The PDF sub-workflow receives execution metadata from the parent and uses executionIdOverride / executionSecretOverride on the Codika Upload File node.

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3. SUBWKFL placeholder

Parent calls sub-workflow via: 
"value": "{{SUBWKFL_markdown-to-pdf-subworkflow_LFKWBUS}}"

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4. File input and output

Users upload a document (requirements file), the workflow processes it, generates a PDF, uploads it via Codika Upload File, and returns the documentId as a file type output field.

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5. RAG data ingestion

Separate data ingestion workflow embeds documents into Pinecone. Deployed independently via: 
codika-helper deploy process-data-ingestion ./proposal-generation

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6. Knowledge base tags

Documents are filtered by tags, allowing fine-grained access control over which documents the workflow can read.

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7. Cost differentiation

Main workflow costs 15 credits, retrieval costs 5, sub-workflow costs 0 (attributed to parent).

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Deploy sequence

# 1. Validate
codika-helper verify use-case ./proposal-generation

# 2. Deploy use case (all 3 workflows)
codika-helper deploy use-case ./proposal-generation

# 3. Deploy data ingestion (separate)
codika-helper deploy process-data-ingestion ./proposal-generation

# 4. Test retrieval
codika-helper trigger document-retrieval \
  --payload '{"query": "software development proposal"}' \
  --poll

# 5. Test full generation (with file upload via platform UI)
# File uploads require the platform UI — CLI trigger only supports JSON payloads