Performing Semantic Search

In this tutorial, you’ll learn how to generate embeddings using OpenAI, store them in a Chroma vector database, and execute simple semantic searches. By the end, you’ll understand how to retrieve conceptually related documents—even when they share no exact keywords.

Table of Contents

1. Prerequisites
2. Step 1: Install and Import Dependencies
3. Step 2: Prepare Your Documents
4. Step 3: Create a Chroma Vector Store
5. Step 4: Execute Semantic Queries
6. How It Works
7. Further Reading

Prerequisites

• Python 3.7+
• An OpenAI API key
• Install the required libraries:

pip install langchain chromadb openai

export OPENAI_API_KEY="your_api_key_here"

Step 1: Install and Import Dependencies

Begin by importing LangChain’s embedding model and the Chroma vector store:

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

# Initialize the embeddings model
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")

Step 2: Prepare Your Documents

Here, we create a small collection of sports headlines. In real applications, you might load text from files, PDFs, or a database.

docs = [
    "Thrilling Finale Awaits: The Countdown to the Cricket World Cup Championship",
    "Global Giants Clash: Football World Cup Semi-Finals Set the Stage for Epic Showdowns",
    "Record Crowds and Unforgettable Moments: Highlights from the Cricket World Cup",
    "From Underdogs to Contenders: Football World Cup Surprises and Breakout Stars"
]

Step 3: Create a Chroma Vector Store

Chroma will automatically generate embeddings for each document and store them in a local vector database:

vectorstore = Chroma.from_texts(texts=docs, embedding=embeddings)

Step 4: Execute Semantic Queries

With your documents indexed, you can now query the vector store. Semantic search will return contextually related headlines, even without shared keywords.

Cricket Query

results_cricket = vectorstore.similarity_search("Rohit Sharma", k=2)
for doc in results_cricket:
    print(doc.page_content)

Output:

Record Crowds and Unforgettable Moments: Highlights from the Cricket World Cup
Thrilling Finale Awaits: The Countdown to the Cricket World Cup Championship

Football Query

results_football = vectorstore.similarity_search("Lionel Messi", k=2)
for doc in results_football:
    print(doc.page_content)

Output:

From Underdogs to Contenders: Football World Cup Surprises and Breakout Stars
Global Giants Clash: Football World Cup Semi-Finals Set the Stage for Epic Showdowns

How It Works

1. Embedding Generation
   Both documents and queries are transformed into vector embeddings by the same model.
2. Vector Similarity
   Chroma computes distances between the query vector and stored document vectors, retrieving the top-k closest matches.
3. Semantic Matching
   Unlike keyword-based search, semantic search finds conceptually related content—even if the exact terms differ.

Further Reading

• LangChain Documentation
• Chroma Vector Store
• OpenAI Embeddings API

Feel free to integrate this pattern into your QA systems, recommendation engines, or any application requiring intelligent text retrieval.