Local chatbot with retrieval augmented generation (RAG) Gemini
Configured with Agent, Chat Trigger, Document Default Data Loader, Embeddings Ollama, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- AI & models
- Files / media
- Transform
- Trigger
- Data store
- Web / API
Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.
Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.
How it works
The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.
| Node | Category | Type | Operation |
|---|---|---|---|
| Embeddings Ollama | AI & models | Embeddings Ollama | See node parameters |
| Default Data Loader | Files / media | Document Default Data Loader | See node parameters |
| Recursive Character Text Splitter | Transform | Text Splitter Recursive Character Text Splitter | See node parameters |
| When chat message received | Trigger | Chat Trigger | See node parameters |
| On form submission1 | Trigger | Form Trigger | See node parameters |
| Qdrant Vector Store | Data store | Vector Store Qdrant | See node parameters |
| AI Agent1 | AI & models | Agent | See node parameters |
| Simple Memory1 | Data store | Memory Buffer Window | See node parameters |
| Embeddings Ollama2 | AI & models | Embeddings Ollama | See node parameters |
| Qdrant Vector Store2 | Data store | Vector Store Qdrant | See node parameters |
| Google Gemini Chat Model | AI & models | LM Chat Google Gemini | See node parameters |
| HTTP Request | Web / API | HTTP Request | POST |
| Merge | Transform | Merge | See node parameters |
Setup
- Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
- Connect your own credentials through n8n’s credential store. Replace
REPLACE_WITH_*values with your resource identifiers. - Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
- Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.
Requirements
An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.
Credential types declared in the source
googlePalmApiollamaApiqdrantApi
Workflow notes
Data Ingestion
Data Ingestion
**Add data to the semantic database
RAG Chatbot
RAG Chatbot
*Chat with your data
Local chatbot with retrieval augmented generation (RAG) Gemini
Local chatbot with retrieval augmented generation (RAG) Gemini
Purpose and structure
This folder contains the Local chatbot with retrieval augmented generation (RAG) Gemini workflow. Its executable nodes use agent, chatTrigger, documentDefaultDataLoader, embeddingsOllama, formTrigger, httpRequest, lmChatGoogleGemini, memoryBufferWindow, merge, textSplitterRecursiveCharacterTextSplitter, vectorStoreQdrant. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.
Setup
Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.
Entry points
When chat message received, On form submission1
Verification
Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.
Attribution
https://github.com/algonacci/rag-evaluation/blob/5a0256acc2f538259f7ec60a71f6991a0365fb41/sumber.json MIT; preserve source notices.