Building a Smart Retriever in LangChain using Gemini Embeddings and FAISS

In modern AI applications, retrievers play a key role in helping language models find and use the right information. Whether you’re building a chatbot, a search assistant, or a document analysis tool, retrievers allow your system to efficiently access relevant data before generating a response.

In this Blog, we’ll walk through how to build a smart retriever using LangChain, Google Gemini embeddings, and FAISS (Facebook AI Similarity Search).
By the end, you’ll understand how retrieval works, how to connect Gemini embeddings, and how to perform semantic searches across your documents.

What You’ll Learn

By following this guide, you’ll learn:

• What retrievers are and how they work in LangChain

• How to generate text embeddings using Gemini

• How to use FAISS for vector storage and search

• How to retrieve the most relevant documents based on similarity

• How to extend the retriever with a large language model (LLM)

This guide is beginner-friendly and assumes only basic familiarity with Python.

What is LangChain?

LangChain is a framework that helps developers build AI applications with large language models (LLMs).
It provides components for:

• Prompt handling and chaining

• Document loading and retrieval

• Integration with different model providers and APIs

• Memory and contextual reasoning

LangChain simplifies the process of connecting your own data with intelligent models such as Gemini, Claude, or GPT.

What are Embeddings and FAISS?

Embeddings

Embeddings are numerical representations of text.
They transform sentences into high-dimensional vectors that capture meaning.

For example:

"Apple the fruit"   → [0.12, 0.55, -0.33, ...]
"Apple the company" → [0.89, 0.11, 0.42, ...]

Texts with similar meanings will have vectors that are close together.
This allows us to search for semantically related information rather than exact keywords.

FAISS

FAISS (Facebook AI Similarity Search) is an open-source library developed by Meta AI.
It provides highly efficient indexing and similarity search for dense vector data.
FAISS is widely used for building scalable retrievers in AI systems.

Setting Up the Environment

Before writing any code, let’s set up the development environment.

Step 1: Create a project folder

mkdir gemini_faiss_retriever
cd gemini_faiss_retriever

Step 2: Create and activate a virtual environment

python -m venv venv
source venv/bin/activate   # Mac/Linux
venv\Scripts\activate      # Windows

Step 3: Install the required packages

pip install langchain langchain-google-genai faiss-cpu python-dotenv

Step 4: Create a .env file

Create a file named .env in your project directory and add your Gemini API key:

GOOGLE_API_KEY=your_google_api_key_here

Note: Never share your .env file publicly or upload it to GitHub.
If you are using GitHub, make sure your .gitignore file includes .env.

Step 1: Import Dependencies

Create a Python file named retriever_faiss.py and start with these imports:

from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.schema import Document
from dotenv import load_dotenv
import os

load_dotenv()

Step 2: Initialize Gemini Embeddings

Use Google’s Gemini embedding model to convert text into meaningful vectors.

embedding_model = GoogleGenerativeAIEmbeddings(
    model="models/gemini-embedding-001",
    google_api_key=os.getenv("GOOGLE_API_KEY")
)

This model transforms input text into high-dimensional vectors that capture semantic similarity.

Step 3: Create Example Documents

For testing purposes, let’s define a few short text documents.

documents = [
    Document(page_content="LangChain simplifies the process of working with LLMs."),
    Document(page_content="FAISS helps search for similar text efficiently."),
    Document(page_content="Gemini provides advanced embedding and reasoning capabilities."),
    Document(page_content="Retrievers fetch relevant context for better AI answers.")
]

Step 4: Build and Store the FAISS Vectorstore

Now we’ll create a FAISS index from the embedded documents.

vectorstore = FAISS.from_documents(
    documents=documents,
    embedding=embedding_model
)

This step converts all documents into embeddings and stores them in a searchable FAISS index.

Step 5: Create a Retriever

Next, turn the FAISS vectorstore into a retriever interface.

retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

Here, k=2 means the retriever will return the two most similar documents to a query.

Step 6: Query the Retriever

Now, test your retriever with a question.

query = "How can I find relevant context for LLMs?"
results = retriever.get_relevant_documents(query)

for i, doc in enumerate(results, start=1):
    print(f"\nResult {i}:")
    print(doc.page_content)

FAISS will compare the query vector with all stored document vectors and return the closest matches.

Example Output

Result 1:
Retrievers fetch relevant context for better AI answers.

Result 2:
LangChain simplifies the process of working with LLMs.

You’ve just built a working retriever using Gemini embeddings and FAISS.

Step 7: Extending with an LLM

Now that your retriever is working, you can connect it with an LLM to create a question-answering pipeline.

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains import RetrievalQA

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    api_key=os.getenv("GOOGLE_API_KEY")
)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    chain_type="stuff"
)

query = "What does FAISS do?"
response = qa_chain.invoke({"query": query})

print("\nQuestion:", query)
print("Answer:", response["result"])

# Try Another Question
query2 = "What is LangChain used for?"
response2 = qa_chain.invoke({"query": query2})

print("\nQuestion:", query2)
print("Answer:", response2["result"])

Outputs:

Question: What does FAISS do?
Answer: FAISS helps search for similar text efficiently.

Question: What is LangChain used for?
Answer: LangChain is used to simplify the process of working with LLMs (Large Language Models).

This setup combines both retrieval and generation.
The retriever fetches relevant context, and the LLM generates a polished answer.

Conclusion

You’ve now built a smart retriever in LangChain using Gemini embeddings and FAISS.
Here’s what you learned:

• How embeddings represent text meaning

• How FAISS enables efficient similarity search

• How to use LangChain to build a retriever

• How to combine retrieval with an LLM for intelligent responses

This foundation forms the basis of Retrieval-Augmented Generation (RAG) systems a key technique for grounding AI models with external knowledge.