FAISS vector store setup in LangChain - Mini Project: Build & Apply

FAISS Vector Store Setup with Langchain

📖 Scenario: You are building a simple document search tool. You want to store document embeddings in a vector store so you can quickly find similar documents later.

🎯 Goal: Set up a FAISS vector store using Langchain to hold document embeddings for fast similarity search.

📋 What You'll Learn

Create a list of documents with exact texts

Create an embedding function variable using OpenAIEmbeddings

Create a FAISS vector store from the documents and embeddings

Save the FAISS index to disk with a specific filename

💡 Why This Matters

🌍 Real World

Vector stores like FAISS help build fast search engines for documents, chatbots, and recommendation systems.

💼 Career

Knowledge of vector stores and embeddings is essential for AI developers, data scientists, and software engineers working with natural language processing.

1

Create the documents list

Create a list called documents with these exact strings: 'Hello world', 'Langchain is great', and 'FAISS vector store'.

LangChain

# Create the documents list
# Your code here

Need a hint?

Use a Python list with the exact strings given.

2

Create the embedding function

Create a variable called embedding_function and set it to OpenAIEmbeddings() from langchain.embeddings.openai.

LangChain

documents = ['Hello world', 'Langchain is great', 'FAISS vector store']
# Create the embedding_function variable
# Your code here

Need a hint?

Import OpenAIEmbeddings and create the variable exactly as shown.

3

Create the FAISS vector store

Create a variable called faiss_index by calling FAISS.from_texts(documents, embedding_function) from langchain.vectorstores.faiss.

LangChain

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS

documents = ['Hello world', 'Langchain is great', 'FAISS vector store']
embedding_function = OpenAIEmbeddings()
# Create the FAISS vector store called faiss_index
# Your code here

Need a hint?

Import FAISS and use the from_texts method with the exact variable names.

4

Save the FAISS index to disk

Call faiss_index.save_local('faiss_index') to save the FAISS vector store to a folder named faiss_index.

LangChain

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS

documents = ['Hello world', 'Langchain is great', 'FAISS vector store']
embedding_function = OpenAIEmbeddings()
faiss_index = FAISS.from_texts(documents, embedding_function)
# Save the FAISS index locally
# Your code here

Need a hint?

Use the save_local method on faiss_index with the exact folder name.