NDA Risk Detector with n8n & Vector Embeddings

This reference guide describes a production-style NDA Risk Detector built in n8n. The workflow automates NDA intake, splits and embeds contract text, stores it in a Redis vector index, and uses an AI agent with tools and memory to identify risky clauses. Results are logged to Google Sheets for auditability and follow-up.

1. Overview

The NDA Risk Detector workflow is designed for legal and product teams that need a repeatable, fast way to surface high-risk clauses in incoming NDAs. Instead of manually scanning each document, the workflow uses vector embeddings and retrieval-augmented generation to:

• Ingest NDA text or file URLs via a webhook endpoint
• Split long contracts into context-preserving chunks
• Generate vector embeddings for each chunk using a selected model
• Index those vectors in a Redis-based vector store
• Query the vector store during evaluation using a Tool layer and short-term Memory
• Run an AI Agent that produces structured risk assessments
• Append results to a Google Sheet for tracking and compliance

By combining embeddings with a vector store, the system can detect semantically similar risk patterns even when the NDA wording is different from your previous templates or examples.

2. Workflow Architecture

At a high level, the n8n workflow implements a data pipeline with two primary paths:

• Ingestion path – For reference NDAs, templates, or precedent clauses that you want to index as context.
• Evaluation path – For newly received NDAs that you want to analyze and score for risk.

Both paths share the core building blocks:

1. Webhook node – Entry point for NDA content.
2. Text Splitter node – Breaks NDA text into chunks with overlap.
3. Embeddings node – Generates vector representations for each chunk.
4. Redis Vector Store node – Stores or queries vector embeddings (index name nda_risk_detector in the template).
5. Tool node – Wraps the vector store for Agent usage.
6. Memory node – Maintains short-term conversational context.
7. Agent / Chat node – Orchestrates the language model, tools, and memory to produce a risk assessment.
8. Google Sheets node – Logs results to a spreadsheet for auditing and reporting.

The same architecture can support both batch ingestion of historical NDAs and real-time analysis of new agreements.

3. Node-by-Node Breakdown

3.1 Webhook Node (NDA Intake)

Role: Entry point for external systems to submit NDAs.

• Trigger type: Webhook node configured to accept POST requests.
• Payload: The workflow expects either:
  • Raw NDA text in the request body, or
  • A URL pointing to an NDA file that is processed upstream or by another part of your stack.
• Typical integrations: Form submissions, internal ingestion APIs, or a document upload UI that sends data to the webhook URL.

Configuration notes:

• Ensure the HTTP method is set to POST.
• Define a consistent JSON schema for incoming data (for example, { "nda_text": "...", "source_id": "..." }).
• Use authentication (e.g. header tokens) if the webhook is exposed externally.

3.2 Text Splitter Node (Document Chunking)

Role: Splits long NDA content into smaller chunks that are suitable for embedding and retrieval.

• Example settings from the template:
  • chunkSize: 400 characters
  • chunkOverlap: 40 characters
• Purpose:
  • Preserves local context within each chunk.
  • Prevents exceeding token limits of embedding models.
  • Improves retrieval granularity so the agent can reference specific clauses.

Edge cases:

• Very short NDAs may result in a single chunk. The workflow still operates correctly, but retrieval will be less granular.
• Highly formatted or scanned PDFs should be converted to clean text before reaching this node to avoid noisy chunks.

3.3 Embeddings Node (Vector Generation)

Role: Converts each text chunk into a numerical vector representation.

• Template example: Hugging Face Embeddings node.
• Alternatives: OpenAI embeddings or any other supported embeddings provider.
• Inputs: Chunked text from the Text Splitter node.
• Outputs: A vector (array of floats) associated with each text chunk.

Configuration:

• Set up credentials for your chosen provider (Hugging Face or OpenAI).
• Select an embedding model that:
  • Supports your primary language(s) of NDAs.
  • Provides sufficient semantic resolution for legal text.
  • Matches your latency and cost constraints.

Error handling:

• Handle provider-side rate limits by adding delays or batching (see Section 7).
• Log or route failures (e.g. invalid credentials, network issues) to an error-handling branch in your n8n workflow.

3.4 Redis Vector Store Node (Insert & Query)

Role: Serves as the vector database for storing and retrieving clause embeddings.

• Index name (example): nda_risk_detector.
• Modes of operation:
  • Insert mode: Used during ingestion to store embeddings and associated chunk metadata.
  • Query mode: Used during evaluation runs to retrieve the most relevant chunks.

Insert mode usage:

• Input: Embeddings + original text chunks.
• Recommended for:
  • Standard NDA templates.
  • Annotated examples of risky clauses.
  • Historical NDAs that represent your typical contract language.

Query mode usage:

• Input: Embedding of the clause or query text that the agent or workflow provides.
• Output: Top-N similar chunks with similarity scores and metadata.
• These results are routed to the Tool node so that the Agent can reference them as evidence.

Configuration notes:

• Provide Redis connection details and credentials in n8n.
• Ensure the vector index schema matches the dimensions of your embedding model.
• For sensitive data, use an encrypted Redis instance and restrict network access.

3.5 Tool Node (Vector Store Tooling Layer)

Role: Exposes the Redis vector store as a callable tool for the AI Agent.

• Allows the Agent to issue semantic search queries against the indexed NDA chunks.
• Returns retrieved clauses as structured data that the Agent can incorporate into its reasoning and final answer.

Behavior:

• The Agent decides when to call the Tool based on its prompt and internal logic.
• The Tool returns the most relevant chunks for the Agent to cite as supporting evidence.

3.6 Memory Node (Short-term Context)

Role: Maintains a buffer of previous messages or steps so the Agent can consider earlier parts of the NDA or prior interactions.

• Typically configured as a buffer window memory.
• Stores a limited history to keep token usage manageable.

Use cases:

• Multi-step evaluations where the Agent reviews sections sequentially.
• Follow-up questions or clarifications during a review session.

Configuration considerations:

• If the memory window is too small, the Agent may lose important earlier context.
• If the window is too large, token usage and latency may increase unnecessarily.

3.7 Agent / Chat Node (AI Reasoning Layer)

Role: Central orchestration layer that combines the language model, Tool access, and Memory to produce the NDA risk assessment.

• Supported models: OpenAI or other LLM providers supported by n8n.
• Inputs:
  • Prompt instructions for risk analysis.
  • Relevant chunks from the Tool node.
  • Conversation context from Memory.
• Outputs: Structured risk assessment including:
  • Risk categories (for example, termination, indemnity, exclusivity).
  • Rationale and supporting references.
  • Risk severity scores or qualitative labels.

Configuration notes:

• Attach the Tool node so the Agent can query the vector store when needed.
• Attach the Memory node so the Agent can recall previous steps in the evaluation.
• Configure model credentials (e.g. OpenAI API key) in n8n credentials management.

3.8 Google Sheets Node (Logging & Audit Trail)

Role: Persists evaluation results to a Google Sheet for later review, reporting, and compliance checks.

• Operation: Append mode, adding a new row for each NDA evaluation.
• Typical columns:
  • NDA identifier or source link.
  • Timestamp of evaluation.
  • Summary of risk findings.
  • Risk categories and severity scores.
  • Optional: IDs or links to relevant chunks.

Configuration notes:

• Set up Google Sheets credentials in n8n.
• Map Agent output fields to specific spreadsheet columns.
• Use a dedicated sheet or tab for NDA risk logs to keep data organized.

4. Step-by-Step Setup Guide

1. Configure the Webhook node
   Set up a Webhook node in n8n to accept POST requests containing:
   • Raw NDA text, or
   • A URL reference to the NDA file.

   Integrate this endpoint with your existing form, upload UI, or ingestion API.

2. Add and tune the Text Splitter node
   Connect the Webhook output to a Text Splitter node and configure:
   • chunkSize (for example 400 characters in the template).
   • chunkOverlap (for example 40 characters).

   Larger chunks preserve more context but increase token usage and embedding cost. For legal text, 300-600 characters with 20-100 overlap is a common starting range.

3. Set up the Embeddings node
   Add a Hugging Face Embeddings node or an OpenAI Embeddings node:
   • Provide the appropriate API key via n8n credentials.
   • Select a model compatible with your language and quality requirements.

   Connect it to the Text Splitter so each chunk is converted into a vector.

4. Index embeddings into Redis
   Add a Redis Vector Store node configured with a dedicated index, for example:
   • indexName: nda_risk_detector

   In insert mode, send embeddings and their associated text chunks from the Embeddings node into Redis. Use this path to ingest:

   • Reference NDAs.
   • Templates.
   • Precedent clauses and annotated examples.
5. Wire the query path for evaluations
   For evaluation runs, configure the Redis Vector Store node in query mode:
   • Provide the query embedding (for example generated from a clause or from the NDA section under review).
   • Return the top relevant chunks to the Tool node.

   The Tool node will then expose this retrieval capability to the Agent.

6. Configure Agent and Memory
   Add an Agent (or Chat) node and:
   • Connect it to your language model provider (e.g. OpenAI credentials).
   • Attach the Memory node to maintain a short interaction history.
   • Attach the Tool node so the Agent can query the vector store during reasoning.

   Craft prompts that instruct the Agent to:

   • Identify risky NDA clauses.
   • Classify them into categories such as termination, indemnity, and exclusivity.
   • Assign risk severity and provide a clear rationale.
7. Log results in Google Sheets
   Finally, add a Google Sheets node configured to append a new row per evaluation:
   • Map Agent output fields (risk categories, severity, summary) to sheet columns.
   • Include timestamps and NDA identifiers for traceability.

5. Configuration Tips & Best Practices

5.1 Chunking Strategy

• Start with chunkSize between 300 and 600 characters for legal text.
• Use chunkOverlap between 20 and 100 characters to avoid splitting important clauses mid-sentence.
• Adjust based on:
  • Average NDA length.
  • Embedding cost constraints.
  • Observed retrieval quality.

5.2 Embedding Model Selection

• Higher-quality embeddings usually improve retrieval of nuanced legal language.
• If budget is a concern, run A/B tests across:
  • Different embedding models.
  • Different chunk sizes.

  and compare retrieval precision and recall on a small labeled test set.

5.3 Indexing Strategy in Redis

• Index both:
  • Standard, low-risk NDA templates.
  • Known risky clauses, ideally annotated with risk categories.
• This combination helps the vector store learn the semantic space of both acceptable and problematic language.

5.4 Prompt Design for the Agent

• Instruct the Agent to:
  • Cite specific retrieved chunks as evidence when flagging a risk.
  • Produce a concise risk score (for example on a 1-5 scale) per clause or per NDA.
  • Explicitly identify which