Template description
This n8n workflows builds another example of creating a knowledgebase assistant but demonstrates how a more deliberate and targeted approach to ingesting the data can produce much better results for your chatbot.
In this example, a government tax code policy document is used. Whilst we could split the document into chunks by content length, we often lose the context of chapters and sections which may be required by the user.
Our approach then is to first split the document into chapters and sections before importing into our vector store. Additionally, using metadata correctly is key to allow filtering and scoped queries.
Example
Human: "Tell me about what the tax code says about cargo for intentional commerce?"
AI: "Section 11.25 of the Texas Property Tax Code pertains to "MARINE CARGO CONTAINERS USED EXCLUSIVELY IN INTERNATIONAL COMMERCE." In this section, a person who is a citizen of a foreign country or an en..."
How it works
- The tax code policy document is downloaded as a zip file from the government website and its pages are extracted as separate chapters.
- Each chapter is then parsed and split into its sections using data manipulation expressions.
- Each section is then inserted into our Qdrant vector store tagged with its source, chapter and section numbers as metadata.
- When our AI Agent needs to retrieve data from our vector store, we use a custom workflow tool to perform the query to Qdrant.
- Because we're relying on Qdrant's advanced filtering capabilities, we perform the search using the Qdrant API rather than the Qdrant node.
- When the AI Agent, needs to pull full wording or extracts, we can use Qdrant's scroll API and metadata filtering to do so. This makes Qdrant behave like a key-value store for our document.
Requirements
- A Qdrant instance is required for the vector store and specifically for it's filtering functionality.
- Mistral.ai account for Embeddings and AI models.
Customising this workflow
Depending on your use-case, consider returning actual PDF pages (or links) to the user for the extra confirmation and to build trust.
Not using Mistral? You are able to replace but note to match the distance and dimension size of Qdrant collection to your chosen embedding model.
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Save time between meetings by allowing AI handle the chores of organising follow-up meetings and invites.
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OpenAI account for access to the LLM.
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Read the Blog: https://blog.n8n.io/how-to-extract-data-from-pdf-to-excel-spreadsheet-advance-parsing-with-n8n-io-and-llamaparse/
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This workflow will watch an email inbox for incoming invoices from suppliers
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LlamaParse is specifically designed to handle and convert complex PDF data structures such as tables to markdown.
Markdown is easily to process for LLM models and so the data extraction by our AI agent is more accurate and reliable.
The workflow exports the extracted data from the AI agent to Google Sheets once the job complete.
Requirements
The criteria of the email trigger must be configured to capture emails with attachments.
The gmail label "invoice synced" must be created before using this workflow.
A LlamaIndex.ai account to use the LlamaParse service.
An OpenAI account to use GPT for AI work.
Google Sheets to save the output of the data extraction process although this can be replaced for whatever your needs.
Customizing this workflow
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Not using Excel? Simple redirect the output of the AI agent to your accounting software of choice.
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To use this node you will need to set the Email Read node to use your mailboxes credentials and configure the Mindee and Google Sheets nodes to use your credentials.
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You can isolate the HTTP Request part into a separate workflow.
Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object.
Please reach out to Eduard is you need further assistance with you n8n workflows and automations!
Note that to use this template, you need to be on n8n version 1.19.4 or later.
Eduard
