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PDF RAG Search Tool

Description​

PDF RAG Search is a versatile tool that enables semantic search capabilities within PDF documents. It supports both Node.js and browser environments, making it perfect for various PDF analysis scenarios.

PDF RAG Search Tool

Enhance your agents with:

  • PDF Processing: Efficient extraction and analysis of PDF content
  • Cross-Platform: Works in both Node.js and browser environments
  • Smart Chunking: Intelligent document segmentation for optimal results
  • Semantic Search: Find relevant information beyond keyword matching

Installation​

First, install the KaibanJS tools package and the required PDF processing library:

For Node.js:

npm install @kaibanjs/tools pdf-parse

For Browser:

npm install @kaibanjs/tools pdfjs-dist

API Key​

Before using the tool, ensure you have an OpenAI API key to enable the semantic search functionality.

Example​

Here's how to use the PDFSearch tool to enable your agent to search and analyze PDF content:

import { PDFSearch } from '@kaibanjs/tools';
import { Agent, Task, Team } from 'kaibanjs';

// Create the tool instance
const pdfSearchTool = new PDFSearch({
  OPENAI_API_KEY: 'your-openai-api-key',
  file: 'https://example.com/documents/sample.pdf'
});

// Create an agent with the tool
const documentAnalyst = new Agent({
    name: 'David', 
    role: 'Document Analyst', 
    goal: 'Extract and analyze information from PDF documents using semantic search', 
    background: 'PDF Content Specialist',
    tools: [pdfSearchTool]
});

// Create a task for the agent
const pdfAnalysisTask = new Task({
    description: 'Analyze the PDF document at {file} and answer: {query}',
    expectedOutput: 'Detailed answers based on the PDF content',
    agent: documentAnalyst
});

// Create a team
const pdfAnalysisTeam = new Team({
    name: 'PDF Analysis Team',
    agents: [documentAnalyst],
    tasks: [pdfAnalysisTask],
    inputs: {
        file: 'https://example.com/documents/sample.pdf',
        query: 'What would you like to know about this PDF?'
    },
    env: {
        OPENAI_API_KEY: 'your-openai-api-key'
    }
});

Advanced Example with Pinecone​

For more advanced use cases, you can configure PDFSearch with a custom vector store:

import { PineconeStore } from '@langchain/pinecone';
import { Pinecone } from '@pinecone-database/pinecone';
import { OpenAIEmbeddings } from '@langchain/openai';

const embeddings = new OpenAIEmbeddings({
  apiKey: process.env.OPENAI_API_KEY,
  model: 'text-embedding-3-small'
});

const pinecone = new Pinecone({
  apiKey: process.env.PINECONE_API_KEY
});

const pineconeIndex = pinecone.Index('your-index-name');
const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
  pineconeIndex
});

const pdfSearchTool = new PDFSearch({
  OPENAI_API_KEY: 'your-openai-api-key',
  file: 'https://example.com/documents/sample.pdf',
  embeddings: embeddings,
  vectorStore: vectorStore
});

Parameters​

  • OPENAI_API_KEY Required. Your OpenAI API key for embeddings and completions.
  • file Required. URL or local path to the PDF file to analyze.
  • embeddings Optional. Custom embeddings instance (defaults to OpenAIEmbeddings).
  • vectorStore Optional. Custom vector store instance (defaults to MemoryVectorStore).
  • chunkOptions Optional. Configuration for text chunking (size and overlap).
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