node-red-contrib-mistral 0.1.1

Node-RED nodes for the Mistral AI API: chat (text + vision + streaming), embeddings, and OCR.

npm install node-red-contrib-mistral

node-red-contrib-mistral

Node-RED nodes for the Mistral AI API — chat completions (with vision and streaming), embeddings, and OCR.

Table of contents

• Install
• Quick start
• Nodes
  • mistral-config
  • mistral-chat
  • mistral-embeddings
  • mistral-ocr
• Example flows
• Recipes
  • RAG with embeddings
  • Streaming to a dashboard
• License

Install

From the Node-RED editor:

1. Open the palette manager (hamburger menu → Manage palette).
2. Switch to the Install tab.
3. Search for node-red-contrib-mistral and click Install.

Or from the command line in your Node-RED user directory (typically ~/.node-red):

npm install node-red-contrib-mistral

Then restart Node-RED. Requires Node.js ≥ 18 and Node-RED ≥ 3.0.

Quick start

1. Drag a mistral chat node onto your flow.
2. Double-click it. In the Server field, click the pencil to create a new mistral-config entry.
3. Paste your Mistral API key (get one at console.mistral.ai) and save.
4. Wire an inject node (string payload) → mistral chat → debug.
5. Deploy, click inject, watch the response in the debug sidebar.

The config node is shared across all Mistral nodes — set it up once, reuse it everywhere.

Nodes

mistral-config

Holds the API key (stored encrypted in Node-RED's credentials store) and base URL. Not visible in the palette — it's only accessible from the editor panel of action nodes via the Server dropdown.

mistral-chat

Calls POST /chat/completions. Supports text, vision (image inputs), and streaming.

Inputs

Outputs (non-streaming)

Outputs (streaming) — one msg per chunk plus a final summary:

mistral-embeddings

Calls POST /embeddings to vectorize text. Default model: mistral-embed (1024 dims).

Inputs

Outputs

mistral-ocr

Calls POST /ocr to extract markdown from PDFs and images. Default model: mistral-ocr-latest.

Inputs

The node auto-detects document vs image from the URL extension by default. You can force a type in the editor.

Outputs

Example flows

Importable JSON flows are in examples/:

• test-flow.json — exercises chat, streaming, embeddings, and OCR.
• vision-flow.json — minimal Pixtral image-description flow.
• rag-flow.json — full RAG pattern (index → embed query → retrieve → answer).

Import in Node-RED: hamburger menu → Import → select a file to import (or paste the JSON content).

⚠️ The mistral-config ID in each example may collide with an existing config in your flow. Node-RED will prompt to Replace or Copy. Choose Replace to reuse your existing config (with your saved API key), or Copy and re-attach manually.

Recipes

RAG with embeddings

The rag-flow.json example shows the canonical pattern in 6 steps:

1. Index — inject array of documents → mistral-embeddings → function node stores {text, vec} pairs in flow.context.
2. Query — inject question → mistral-embeddings (single string → single vector).
3. Retrieve — function node computes cosine similarity vs the stored vectors and keeps the top-K.
4. Build prompt — same function node concatenates retrieved chunks into a context block.
5. Generate — mistral-chat answers from the context with a system prompt enforcing context-grounding.
6. Debug — final answer + the retrieved chunks for traceability.

For production, replace the in-memory flow.context store with a proper vector database (Qdrant, pgvector, Pinecone, Chroma) — the only nodes you need from this package are mistral-embeddings and mistral-chat.

Streaming to a dashboard

For a live-typing UI:

inject → mistral-chat (stream=true) → function (route by msg.complete) → ui-template (append chunk)
                                                                       → ui-template (mark complete)

Filter chunks (msg.complete === false) to your "append" UI handler, and the final message (msg.complete === true) to a "done" handler that displays usage stats.

Contributing

Issues and pull requests welcome on the project repository.

License

MIT © Damien Lachambre