Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI)
Configured with Agent, Chat Trigger, Document Default Data Loader, Embeddings Open Ai, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- Web / API
- Data store
- Files / media
- Transform
- AI & models
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 clicking ‘Test workflow’ | Trigger | Manual Trigger | See node parameters |
| HTTP Request | Web / API | HTTP Request | See node parameters |
| Pinecone Vector Store | Data store | Vector Store Pinecone | 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 |
| AI Agent | AI & models | Agent | See node parameters |
| OpenAI Chat Model | AI & models | OpenAI Chat Model | See node parameters |
| Window Buffer Memory | Data store | Memory Buffer Window | See node parameters |
| Vector Store Tool | AI & models | Tool Vector Store | See node parameters |
| OpenAI Chat Model1 | AI & models | OpenAI Chat Model | See node parameters |
| Generate User Query Embedding | AI & models | Embeddings Open Ai | See node parameters |
| Pinecone Vector Store (Querying) | Data store | Vector Store Pinecone | See node parameters |
| Generate Embeddings | AI & models | Embeddings Open Ai | 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
openAiApipineconeApi
Workflow notes
Indexing content in the vector database
Indexing content in the vector database
This part of the workflow is responsible for extracting content, generating embeddings and sending them to the Pinecone vector store.
It requests the OpenAPI specifications from GitHub using a HTTP request. Then, it splits the file in chunks, generating embeddings for each chunk using OpenAI, and saving them in Pinecone vector DB.
Querying and response generation
Querying and response generation
This part of the workflow is responsible for the chat interface, querying the vector store and generating relevant responses.
It uses OpenAI GPT 4o-mini to generate responses.
RAG workflow in n8n
RAG workflow in n8n
This is an example of how to use RAG techniques to create a chatbot with n8n. It is an API documentation chatbot that can answer questions about the GitHub API. It uses OpenAI for generating embeddings, the gpt-4o-mini LLM for generating responses and Pinecone as a vector database.
Before using this template
- create OpenAI and Pinecone accounts
- obtain API keys OpenAI and Pinecone
- configure credentials in n8n for both
- ensure you have a Pinecone index named "n8n-demo" or adjust the workflow accordingly.
Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI)
Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI)
Purpose and structure
This folder contains the Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI) workflow. Its executable nodes use agent, chatTrigger, documentDefaultDataLoader, embeddingsOpenAi, httpRequest, lmChatOpenAi, manualTrigger, memoryBufferWindow, textSplitterRecursiveCharacterTextSplitter, toolVectorStore, 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 clicking ‘Test workflow’, 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
https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Manual/1373_Manual_Stickynote_Automation_Webhook.json MIT; preserve source notices.