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74 lines
4.9 KiB
Markdown
74 lines
4.9 KiB
Markdown
---
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title: "Chat with a codebase using Qdrant and N8N"
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draft: false
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slug: qdrant-n8n
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short_description: Integration demo
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description: Building a RAG-based chatbot using Qdrant and N8N to chat with a codebase on GitHub
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preview_image: /blog/qdrant-n8n/preview/preview.jpg
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date: 2024-01-06T04:09:05+05:30
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author: Anush Shetty
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featured: false
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tags:
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- integration
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- n8n
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- blog
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---
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n8n (pronounced n-eight-n) helps you connect any app with an API. You can then manipulate its data with little or no code. With the Qdrant node on n8n, you can build AI-powered workflows visually.
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Let's go through the process of building a workflow. We'll build a chat with a codebase service.
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## Prerequisites
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- A running Qdrant instance. If you need one, use our [Quick start guide](https://qdrant.tech/documentation/quick-start/) to set it up.
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- An OpenAI API Key. Retrieve your key from the [OpenAI API page](https://platform.openai.com/account/api-keys) for your account.
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- A GitHub access token. If you need to generate one, start at the [GitHub Personal access tokens page](https://github.com/settings/tokens/).
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## Building the App
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Our workflow has two components. Refer to the [n8n quick start guide](https://docs.n8n.io/workflows/create/) to get acquainted with workflow semantics.
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- A workflow to ingest a GitHub repository into Qdrant
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- A workflow for a chat service with the ingested documents
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#### Workflow 1: GitHub Repository Ingestion into Qdrant
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For this workflow, we'll use the following nodes:
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- [Qdrant Vector Store - Insert](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant/#insert-documents): Configure with [Qdrant credentials](https://docs.n8n.io/integrations/builtin/credentials/qdrant/) and a collection name. If the collection doesn't exist, it's automatically created with the appropriate configurations.
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- [GitHub Document Loader](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.documentgithubloader/): Configure the GitHub access token, repository name, and branch. In this example, we'll use [qdrant/demo-food-discovery@main](https://github.com/qdrant/demo-food-discovery).
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- [Embeddings OpenAI](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.embeddingsopenai/): Configure with OpenAI credentials and the embedding model options. We use the [text-embedding-ada-002](https://platform.openai.com/docs/models/embeddings) model.
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- [Recursive Character Text Splitter](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter/): Configure the [text splitter options](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter/#node-parameters ). We use the defaults in this example.
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Connect the workflow to a manual trigger. Click "Test Workflow" to run it. You should be able to see the progress in real-time as the data is fetched from GitHub, transformed into vectors and loaded into Qdrant.
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#### Workflow 2: Chat Service with Ingested Documents
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The workflow use the following nodes:
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- [Qdrant Vector Store - Retrieve](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant/#retrieve-documents-for-agentchain): Configure with [Qdrant credentials](https://docs.n8n.io/integrations/builtin/credentials/qdrant/) and the name of the collection the data was loaded into in workflow 1.
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- [Retrieval Q&A Chain](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa/): Configure with default values.
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- [Embeddings OpenAI](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.embeddingsopenai/): Configure with OpenAI credentials and the embedding model options. We use the [text-embedding-ada-002](https://platform.openai.com/docs/models/embeddings) model.
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- [OpenAI Chat Model](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatopenai/): Configure with OpenAI credentials and the chat model name. We use [gpt-3.5-turbo](https://platform.openai.com/docs/models/gpt-3-5) for the demo.
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Once configured, hit the "Chat" button to initiate the chat interface and begin a conversation with your codebase.
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To embed the chat in your applications, consider using the [@n8n/chat](https://www.npmjs.com/package/@n8n/chat) package. Additionally, N8N supports scheduled workflows and can be triggered by events across various applications.
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## Further reading
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- [n8n Documentation](https://docs.n8n.io/)
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- [n8n Qdrant Node documentation](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant/#qdrant-vector-store)
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