Finance Literacy Bot - RAG & ChatBot
Configured with Agent, Chat Trigger, Embeddings Hugging Face Inference, LM Chat Groq, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- AI & models
- Data store
- Integration
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 |
|---|---|---|---|
| When chat message received | Trigger | Chat Trigger | See node parameters |
| AI Agent | AI & models | Agent | See node parameters |
| Groq Chat Model | AI & models | LM Chat Groq | See node parameters |
| Pinecone Vector Store | Data store | Vector Store Pinecone | See node parameters |
| Embeddings HuggingFace Inference1 | AI & models | Embeddings Hugging Face Inference | See node parameters |
| SerpAPI | Integration | Tool Serp Api | 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
groqApihuggingFaceApipineconeApiserpApi
Workflow notes
Finance Literacy Bot - RAG & ChatBot
Finance Literacy Bot - RAG & ChatBot
Purpose and structure
This folder contains the Finance Literacy Bot - RAG & ChatBot workflow. Its executable nodes use agent, chatTrigger, embeddingsHuggingFaceInference, lmChatGroq, toolSerpApi, vectorStorePinecone. 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
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
Nik Bear Brown; rights confirmed 2026-10-19 MIT; preserve source notices.