Description

n8n-nodes-berget-mk

n8n community nodes for Berget AI, packaged as a single installable module. Maintained by Micke Kring.

Four nodes:

• Berget AI — multi-resource action node for one-shot calls. Resources: Chat (completions, classification, JSON Schema structured output, reasoning effort for GPT-OSS / GLM-4.7 / etc.), Image Analysis (vision-capable models), Rerank (document reranking), and Speech to Text (Swedish-tuned KB-Whisper, with optional diarization and word-level alignment). Can also be exposed as a tool to an AI Agent. (OCR is temporarily hidden — see CHANGELOG.md for 0.4.4 for details.)
• Berget AI Chat Model — sub-node that plugs into n8n’s built-in AI Agent, Basic LLM Chain, and other LangChain-based nodes. Exposes reasoning effort and the full standard LLM parameter set.
• Berget AI Embeddings Model — sub-node that plugs into n8n’s Vector Store nodes (Supabase, Qdrant, Pinecone, PGVector, etc.) and Question and Answer Chain.
• Berget AI Reranker — sub-node that plugs into Vector Store retrievers via the AiReranker connection, reordering candidates by relevance before they reach the agent or chain.

> ⚠️ Experimental — actively developed. This package is pre-1.0 and may break between minor releases. Pin a specific version in production workflows until 1.0.0. See CHANGELOG.md for breaking changes.

Install

In n8n: Settings → Community Nodes → Install and enter:

n8n-nodes-berget-mk

Then add a Berget AI API credential with your API key from berget.ai.

Typical workflows

One-shot chat / classification

1. Drop Berget AI onto the canvas, pick Resource = Chat, select a model, add a user message. Execute.

For classification or structured extraction tasks, set Options → Response Format = JSON Object or JSON Schema (with a schema you provide). The model is forced to return parseable JSON — no regex scraping of free-form text. The parsed JSON is exposed as a top-level output field on the node’s response, so a downstream IF, Set, or Switch node can reference its properties directly with expressions like {{ $json.output.category }} or {{ $json.output.contains }} — no extra parse step needed.

For reasoning-capable models (openai/gpt-oss-120b, zai-org/GLM-4.7-FP8, etc.), set Options → Reasoning Effort = High to crank up the model’s thinking budget. The parameter is silently ignored by non-reasoning models, so it’s safe to leave on. (Berget does not currently flag reasoning-capable models in /v1/models, so the model dropdown is not filtered — pick the reasoning model yourself.)

Agent with tools and memory

1. Add n8n’s built-in AI Agent.
2. Add Berget AI Chat Model and connect it to the Agent’s Chat Model socket.
3. Add Memory and Tool sub-nodes as needed — they work with Berget as the underlying LLM.

RAG with retrieval and reranking

1. Add a Vector Store node (Qdrant, Supabase, etc.) or a Question and Answer Chain.
2. Add Berget AI Embeddings Model and connect it to the Embedding socket.
3. Add Berget AI Reranker and connect it to the EmbeddingReranker socket — Vector Store will then retrieve a wider candidate set, the reranker reorders them by relevance, and only the best survive into the answer.
4. Index documents or query as usual.

Image analysis

1. Drop Berget AI onto the canvas, pick Resource = Image Analysis.
2. Pick a vision-capable model (the dropdown is filtered automatically).
3. Choose Input Type = Binary File (default — works with Form Trigger uploads, HTTP Request responses, etc.) or Image URL, and provide a Text Input prompt like "Describe what you see".
4. Execute.

Swedish speech transcription with speakers

1. Drop Berget AI onto the canvas, pick Resource = Speech to Text.
2. Provide the binary input data from a Form Trigger or HTTP Request.
3. Optional: enable Options → Diarize (Speaker Identification) — the response will include a speakertranscript field formatted as readable per-speaker paragraphs (SPEAKER00:n...nnSPEAKER_01:n...), alongside the raw segment-level timestamps and word-level data.
4. Optional: enable Word-Level Alignment for per-word timestamps useful in subtitle generation.
5. Optional: add Hotwords (comma-separated) for proper nouns and domain vocabulary.

Changelog

See CHANGELOG.md for what’s changed between releases.

License

MIT. Originally based on the open-source Berget AI n8n nodes.