Implement Intelligent Document Search Using AI

[monster0318]

Description

This project aims to build an AI-powered document search system that allows users to quickly and accurately find relevant information from a large collection of documents. Using natural language processing (NLP) and semantic search techniques, the system will understand user queries contextually and return precise search results beyond simple keyword matching.

Key goals include indexing documents intelligently, implementing semantic similarity search, and providing a user-friendly interface for query input and results display.

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Tasks

• Integrate a document ingestion pipeline to parse and index various document formats (PDF, DOCX, TXT)
• Implement embedding generation using pre-trained language models (e.g., OpenAI embeddings, Sentence Transformers)
• Build a vector search engine for semantic similarity search (e.g., using FAISS, Pinecone)
• Design and implement an API to handle search queries and return ranked results
• Develop a frontend interface to input queries and display results with snippet highlights
• Add filtering and sorting options by date, document type, or relevance
• Implement caching mechanisms for frequently searched queries to improve performance
• Write unit and integration tests for embedding, indexing, and search components
• Provide documentation for setup, usage, and API endpoints

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Code Snippet Examples

Generating Document Embeddings (using OpenAI API):

import { OpenAI } from "openai";

const openai = new OpenAI();

async function getEmbedding(text) {
  const response = await openai.embeddings.create({
    model: "text-embedding-3-small",
    input: text,
  });
  return response.data[0].embedding;
}

Vector Search Query Example (using FAISS):

import faiss
import numpy as np

# Load or build index
index = faiss.IndexFlatL2(768)  # 768-dim vector size for embeddings

# Query embedding
query_vector = np.array([/* embedding vector */], dtype='float32')

# Search top 5 similar documents
D, I = index.search(query_vector, 5)
print("Top documents:", I)

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Benefits

• Provides faster, more accurate document search leveraging AI and semantic understanding
• Improves user productivity by reducing time spent searching for relevant information
• Enables advanced search features such as natural language queries and contextual relevance
• Demonstrates practical integration of AI models with traditional search infrastructure
• Can be extended to various domains including legal, medical, and academic document management

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