Turn PDF documents into a searchable knowledge base for a chatbot

Splits a PDF into chunks, turns them into embeddings, and stores them so a chatbot can search and answer from it.

How the work actually flows

A straight line. Runs once per each text chunk.

Pattern: Sequence (1) ยท Multiple Instances with a priori Design-Time Knowledge (13)

flowchart TD trig(("user selects a pdf")):::human s0["download pdf from drive"]:::task s1["split text into chunks"]:::task s2[["generate embeddings per chunk"]]:::mi s3[("store embeddings in pinecone")]:::store trig --> s0 s0 --> s1 s1 -->|"one per each text chunk"| s2 s2 --> s3 out[/"searchable knowledge base created"/]:::out pay{{"instant answers from documents"}}:::pay s3 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
A stepRuns once per itemA personA record or sheetResultPayoff
Build size
Standard

A mid-size build with several tools working together.

Business functions
AI Chatbots & AssistantsKnowledge Base & RAGDocument Processing & OCRFile & Cloud Storage
Connects
Google DriveOpenAIPinecone

The problem it solves

You have important reference documents, like clinical guidelines, that people need answers from quickly, but nobody has time to search through a long PDF every time a question comes up. There is no easy way to ask a question and get an answer pulled directly from the source document.

Who it fits

A team building an AI chatbot that needs to answer questions from a specific set of reference documents.

How it works

  1. You select a PDF stored in Google Drive
  2. The system downloads it and splits the text into smaller chunks
  3. OpenAI converts each chunk into an embedding
  4. The embeddings are stored in a Pinecone database, organized by topic
  5. The chatbot can then search this database to answer questions
What you get

Documents your chatbot can search instantly

Turn any PDF into a searchable knowledge base your chatbot can pull answers from directly.

What you get

A searchable knowledge base in Pinecone that a chatbot can query for accurate, document-based answers.

What you need

A Google Drive account, an OpenAI API key, and a Pinecone account.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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