How I Built an AI-Powered Document Search Chatbot in 10 Minutes (And How You Can Too!)

Unlock the power of AI to search and analyze your documents instantly. Learn how to create your own document search analyst with VectorShift.

Hook: Ever wished you could ask a document a question and get an instant answer? Welcome to the future of document search! 📄✨

Building a Document Search Analyst: The Easy Way

Hello, fellow tech enthusiasts! Today, we’re diving into the creation of a Document Search Analyst—a chatbot that can sift through your documents and provide answers in a snap. Think of it as having a personal assistant who knows every page of your files. 📚🤖

Step 1: Set Up Your Pipeline

First, let's build our pipeline from scratch. We want our chatbot to handle a hefty document, like an Apple annual report, and answer questions efficiently.

1. Create a New Pipeline:

   • Click on "New" and then "Create pipeline."

2. Upload Your File:

   • Use a file node to input your document. You can upload manually or retrieve from the vector store.

   • For this example, I'll use a Microsoft annual report, but you can use any document.

Step 2: Process the File

To make our document searchable, we need to process it properly.

1. Add a File Loader:

   • Drag a file loader node and connect it to your file node.

   • This step converts your document into a format that the vector database can understand.

2. Integrate Vector Query:

   • Add a vector query node from the vector db tab.

   • Connect the output of the file loader to the document input of the vector query.

Step 3: Build the Chatbot Logic

Now, let's make our chatbot smart by integrating LLMs, chat memory, and prompts.

1. Add LLM (Large Language Model):

   • Use OpenAI LLM for natural language understanding.

2. Set Up Input Node:

   • This node will handle user questions. Connect it to the vector query node.

3. Incorporate Chat Memory:

   • This ensures the chatbot remembers the conversation context.

4. Define Variables:

   • Create variables for context (vector query), conversational history (chat memory), and question (input node).

Step 4: Configure the LLM

Tell the LLM how to behave using the system field.

1. Set the System Field:

   • Use this prompt: "You are an analyst chatbot specializing in answering questions given conversational history and context."

   • Ensure variable names match precisely with those defined earlier.

Step 5: Finalize and Test

Let's put it all together and test our chatbot.

1. Add an Output Node:

   • Connect it to the response of the LLM.

2. Save and Run:

   • Click on save and then run your pipeline.

3. Ask a Question:

   • Example: "What is the net profit for this year?" and watch as it retrieves the relevant info from your document!

Conclusion

And there you have it! You've built a powerful document search analyst that can help you quickly find information in any document. It's like having a superpower at your fingertips. 💪