Building a RAG System with LangChain and FastAPI: From Development to Production

Retrieval-Augmented Generation (RAG) is one of the most exciting techniques in AI right now. It combines the precision of retrieving real information from massive datasets with the reasoning power of large language models. The result? Responses that are not just accurate, but deeply relevant. That’s why RAG is powering everything from chatbots and search engines to personalized content.

But here’s the catch: building a prototype is only half the battle. The real challenge lies in deployment: turning your idea into a reliable, scalable product.

In this article, I’ll show you how to build and deploy a RAG system using LangChain and FastAPI. You’ll learn how to go from a working prototype to a full-scale application ready for real users.

Let’s dive in!

What is RAG?

As we explore in a separate guide, Retrieval-Augmented Generation, or RAG, is a pretty advanced method in natural language processing that really levels up what language models can do. 

Instead of just relying on what the model already knows, RAG takes things further by pulling in new, relevant info from outside sources before generating a response.

Here’s how it works: when a user asks a question, the system doesn't just rely on the model’s pre-learned data. It first goes out, searches through a big set of documents or data sources, grabs the most relevant bits, and then feeds that into the language model. With both its built-in knowledge and this newly retrieved info, the model can create a response that’s way more accurate and up-to-date.

RAG blends retrieval and generation, so the responses aren’t just smart—they're also grounded in real, factual info. This makes it perfect for things like answering questions, chatbots, or even generating content where getting the facts right and understanding the context really matters.

Workflow of a RAG system. It begins with a user query, which is processed by the system to search external data sources for relevant information. The retrieved information is then fed into an LLM, which combines it with its pre-existing knowledge to generate an accurate and up-to-date response. Finally, the response is returned to the user. This process ensures that responses are grounded in factual, contextually relevant data.

Key components

When you’re building a RAG system, there are a few essential parts to get it up and running: document loaders, text splitting, indexing, retrieval models, and generative models. Let's break it down:

Document loaders, text splitting, and indexing

The first step is getting your data ready. That’s what document loaders, text splitting, and indexing do:

• Document Loaders: These tools pull in data from various sources (text files, PDFs, databases…) They convert that info into a format the system can actually use. Basically, they make sure all the important data is ready and in the right shape for the next steps.
• Text Splitting: Once the data is loaded, it gets chopped into smaller chunks. This is super important because smaller pieces are easier to search through, and language models work better with bite-sized bits of info due to their processing limits.
• Indexing: After splitting, you need to organize the data. Indexing turns those text chunks into vector representations. This setup makes it easy and fast for the system to search through all that data and find what’s most relevant to a user’s query.

Retrieval models

These are the heart of the RAG system. They’re responsible for digging through all that indexed data to find what you need.

• Vector Stores: These are databases designed to handle those vector representations of the text chunks. They make searching super efficient by using a method called vector similarity search, which compares the query to the stored vectors and pulls the best matches.
• Retrievers: These components do the actual searching. They take the user’s query, convert it into a vector, and then search the vector store to find the most relevant data. Once they grab that info, it’s passed along to the next step: generation.

Generative models

Now, this is where the magic happens. Once the relevant data is retrieved, the generative models take over and produce a final response.

• Language Models: These models create the actual response, making sure it’s coherent and fits the context. In a RAG system, they take both the retrieved data and their own internal knowledge to generate a response that’s up-to-date and accurate.
• Contextual Response Generation: The generative model blends the user’s question with the retrieved data to create a response that not only answers the question but also reflects the specific details from the relevant info it pulled.

Setting Up the Development Environment

Before building our RAG system, we need to make sure that our development environment is properly set up. Here’s what you’ll need:

• Python 3.10+: Make sure Python 3.10 or later is installed. You can check your Python version with the following command: python --version
• Virtual Environment: Next step is to use a virtual environment to keep your dependencies in one place. In order to do this, create a virtual environment in your project directory and activate it:

python3 -m venv ragenv
source ragenv/bin/activate   # For Linux/Mac
ragenv\Scripts\activate      # For Windows

Install Dependencies: Now, install the required packages using pip. 

pip install fastapi uvicorn langchain langchain-community openai langchain-openai faiss-cpu

Pro tip: Make sure to create a requirements.txt file to specify the necessary packages for your project. If you use: pip freeze > requirements.txt

This command will generate a requirements.txt file containing all installed packages and their versions, which you can use for deployment or sharing the environment with others.

Add Your OpenAI API Key: To integrate the OpenAI language model into your RAG system, you’ll need to provide your OpenAI API key:

1. Get your API key: If you don’t have one yet, you can generate your OpenAI API key by logging into your account on the OpenAI platform.
2. Create a .env file: In the root directory of your project, create a .env file to securely store your API key. The .env file allows you to load environment variables.
3. Add the API key to the .env file: Open the .env file and add the following line, replacing your-openai-api-key with your actual OpenAI key:OPENAI_API_KEY=your-openai-api-key
4. Load the API key in your code: Ensure your application loads this key when it runs. In your Python code, you can use the python-dotenv package to automatically load environment variables from the .env file:pip install python-dotenv
5. Then, in your Python script rag.py add:

from dotenv import load_dotenv
import os
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

Now, your OpenAI API key is securely loaded from the environment, and you're ready to start using it in your RAG system! 

PostgreSQL and PGVector (Optional): If you're planning on using PGVector for vector storage, make sure to install and set up PostgreSQL on your machine. You can use FAISS (which we will use in this article) or other vector databases that LangChain supports.

Docker (Optional): Docker can help you containerize your application to ensure consistent deployment across environments. If you plan to use Docker, make sure it’s installed on your machine as well.

Building the RAG Pipeline with LangChain

The first step in building a RAG system is preparing the data that the system will use to retrieve relevant information. This involves loading documents into the system, processing them, and then making sure that they are in a format that can be easily indexed and retrieved.

Document loaders

LangChain provides various document loaders to handle different data sources, such as text files, PDFs, or web pages. You can use these loaders to bring your documents into the system.

I have a fascination for polar bears, so I have decided to upload the following text file (my_document.txt) containing this information:“Polar Bears: The Arctic Giants

Polar bears (Ursus maritimus) are the largest land carnivores on Earth, and they’ve adapted perfectly to life in the extreme cold of the Arctic. Known for their thick white fur, which helps them blend into the snowy landscape, polar bears are powerful hunters, relying on sea ice to hunt seals, their primary source of food.

What’s fascinating about polar bears is their incredible adaptations to their environment. Beneath that thick fur is a layer of fat that can be up to 4.5 inches thick, providing insulation and energy reserves during the harsh winter months. Their large paws help them tread across both ice and open water, making them strong swimmers—able to cover great distances in search of food or new territory.

Unfortunately, polar bears are facing serious threats from climate change. As the Arctic warms, sea ice is melting earlier in the year and forming later, reducing the time polar bears have to hunt seals. Without enough food, many bears struggle to survive, and their population numbers are dwindling in some areas.

Polar bears play a crucial role in maintaining the health of the Arctic ecosystem, and their plight serves as a powerful reminder of the broader impacts of climate change on the world’s wildlife. Conservation efforts are underway to protect their habitat and ensure these majestic creatures continue to thrive in the wild.”

from langchain_community.document_loaders import TextLoader
loader = TextLoader('data/my_document.txt')
documents = loader.load()

Here is a simple text file loaded into the system, just as a pedagogical example, but you can add any type of document you want! For example, you can add internal documentation from your organization. The documents variable now holds the content of the file, ready to be processed.

Chunking the text

Large documents are often split into smaller chunks to make them easier to index and retrieve. This process is super important because smaller chunks are more manageable for the language model and allow for more precise retrieval. You can learn more in our guide on chunking strategies for AI and RAG. 

from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
document_chunks = splitter.split_documents(documents)

Here, the text is split into chunks of 500 characters with an overlap of 50 characters between chunks. This overlap helps maintain context across chunks during retrieval.

Once the data is prepared, the next step is to index it for efficient retrieval. Indexing involves converting the text chunks into vector embeddings and storing them in a vector store.

Embeddings

LangChain supports creating vector embeddings using various models, such as OpenAI or HuggingFace models. These embeddings represent the semantic meaning of the text chunks, which makes them suitable for similarity searches. 

In simple terms, embeddings are basically a way of taking text, like a paragraph from a document, and turning it into numbers that an AI model can understand. These numbers, or vectors, represent the meaning of the text in a way that makes it easier for AI systems to process.

from langchain_openai.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()

We’re using OpenAI’s embeddings to handle that transformation. First, we bring in OpenAI’s embedding tool, and then we initialize it so it’s ready to use. 

Vector stores

After generating the embeddings, the next step is to store them in a vector store like PGVector, FAISS, or any other supported by LangChain. This allows for fast and accurate retrieval of relevant documents when a query is made.

from langchain_community.vectorstores import FAISS
vector_store = FAISS.from_documents(document_chunks, embeddings)

In this case, we’re using FAISS, which is a great tool designed for searching through large sets of vectors. FAISS helps us find the most similar vectors really fast.

So, here’s what’s happening: We pull in FAISS from LangChain and use it to create what’s called a vector store. It’s like a special database that’s built to store and search through vectors efficiently.

The beauty of this setup is that, when we search later on, FAISS will be able to look through all those vectors, find the ones that are most similar to a given query, and return the corresponding document chunks.

With the data indexed, you can now implement the retrieval component, which is responsible for fetching relevant information based on user queries.

Retrieval 

Now we’re setting up a retriever. It's the component that goes through the indexed documents and finds the ones most relevant to a user’s query. 

Now, the cool thing is, you're not just searching randomly - you’re searching smartly with the power of embeddings, so the results you get are semantically similar to what the user is asking for. Let’s dig into the line of code:

retriever = vector_store.as_retriever(
	 search_type="similarity",
	 search_kwargs={"k": 5}
	)

First, we convert the vector store into a retriever. You know how you've already got this FAISS vector store packed with document embeddings? Well, now we’re telling it, "Hey, go ahead and use that to search for stuff when a user asks me a question."

Now, search_type is where the magic happens. The retriever can search in different ways, and you’ve got a few options here. Similarity search is the bread and butter of retrieval. It checks which documents are closest in meaning to the query. 

So when you say "search_type='similarity'," you're telling the retriever, "Find documents that are most similar to the query based on the embeddings we've generated." 

With search_kwargs={"k": 5} you fine-tune things. The k value tells the retriever how many documents to pull from the vector store. In this case, k=5 means, “Give me the top 5 most relevant documents. 

This is super powerful because it helps reduce the noise. Instead of getting a ton of results that are maybe sort of relevant, you’re only grabbing the most important pieces of information.

Querying

In this part of the code, we’re setting up the core engine of your RAG system using LangChain. You’ve already got your retriever, which can pull back the relevant documents based on a query. 

Now, we’re adding in the LLM and using it to actually generate the response based on the documents retrieved.

from langchain_openai import OpenAI  # Updated import
from langchain.chains import RetrievalQA

Here, we’re importing two key elements:

• OpenAI: This is your Large Language Model , which we’re pulling in from the langchain_openai package. This model will be responsible for generating text responses.
• RetrievalQA: This is the special LangChain feature that combines retrieval and QA (question answering). It connects your retriever (which finds the relevant documents) to your LLM (which generates the answer).

llm = OpenAI(openai_api_key=openai_api_key)

This line initializes the LLM using your OpenAI API key. Think of this as loading the brain of your system -  it’s the model that will take in text, understand it, and generate responses.

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

Here’s where things start to get really exciting. We’re setting up the QA Chain, which ties everything together. The RetrievalQA.from_chain_type() method is creating a question-answering chain, which is a way of saying, "Combine the retriever and the LLM to create a system that answers questions based on the retrieved documents."

Then, we are telling the chain to use the OpenAI LLM we just initialized to generate answers. After that we’re connecting the retriever you built earlier. The retriever is responsible for finding the relevant documents based on the user’s query.

Then we are setting chain_type="stuff": Okay, what’s "stuff" here? It’s actually a type of chain in LangChain. "Stuff" means we’re loading all the relevant retrieved documents into the LLM and having it generate a response based on everything. 

It is like dumping a bunch of notes on the LLM's desk and saying, "Here, use all this info to answer the question." 

There are other chain types too (like "map_reduce" or "refine"), but "stuff" is the simplest and most direct.

query = "Are polar bears in danger?"
response = qa_chain.invoke({"query": query})

This is where we actually ask the system a question and get a response back. The invoke() method triggers the whole pipeline. 

It takes your query, sends it to the retriever to fetch relevant documents, and then passes those documents to the LLM, which generates the final response.

print(response)

This last prints the response that was generated by the LLM. Based on the retrieved documents, the system generates a complete, well-informed answer to the query, which is printed out.

The final script looks like this:

from dotenv import load_dotenv
import os
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain_openai import OpenAI

# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

# Load the document
loader = TextLoader('data/my_document.txt')
documents = loader.load()

# Split the document into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
document_chunks = splitter.split_documents(documents)

# Initialize embeddings with OpenAI API key
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)

# FAISS expects document objects and the embedding model
vector_store = FAISS.from_documents(document_chunks, embeddings)

# Use the vector store's retriever
retriever = vector_store.as_retriever()

# Initialize the LLM (using OpenAI)
llm = OpenAI(openai_api_key=openai_api_key)

# Set up the retrieval-based QA chain
qa_chain = RetrievalQA.from_chain_type(
   llm=llm,
   chain_type="stuff",
   retriever=retriever
)

# Example query
query = "Are polar bears in danger?"
response = qa_chain.invoke({"query": query})

# Print the response
print(response)

Developing the API with FastAPI

Now it is time to build an API to interact with your RAG system. But why is this step necessary in deployment?

So, imagine you have been following this tutorial and you’ve got this amazing RAG system set up personalized to your documents and needs - it’s pulling in relevant information, processing queries, and generating smart responses. But how do you actually let users or other systems interact with it?

FastAPI is your middleman. It creates a simple, structured way for users or apps to ask your RAG system questions and get back answers.

FastAPI is asynchronous and high-performance. That means it can handle many requests at once without slowing down, which is super important when dealing with AI systems that might need to retrieve big chunks of data or run complex queries.

In this section, we will modify the previous script and create additional ones to ensure that your RAG system is accessible, scalable, and ready to handle real-world traffic.Let’s start by creating the routes that will handle incoming requests to the RAG system.

Writing your main.py file

In your working directory, create a main.py file. This is your entry point for the FastAPI app. This is going to be your FastAPI brain - this file will pull together all the API routes, dependencies, and the RAG system.

from fastapi import FastAPI
from endpoints import router
app = FastAPI()
app.include_router(router)

This is a pretty simple setup, but it’s clean. What we’re doing here is setting up a FastAPI instance, and then pulling in all the routes (or API paths) from another file, which we’ll create next.

Modify your rag.py script

So here’s where everything comes together. We are going to write a function that actually executes your RAG system when a user submits a query. 

async def get_rag_response(query: str):

This function is marked as async, meaning it’s asynchronous. This means that it can handle other things while it’s waiting for a response. 

Such a feature is especially useful when you’re dealing with retrieval-based systems where fetching documents or querying an LLM can take some time. In this way, FastAPI can process other requests while this one works in the background.

retriever = setup_rag_system()

Here we are calling the setup_rag_system() function, which, as we talked about earlier, initializes the entire retriever pipeline. This means:

• Your documents are loaded and chunked.
• Embeddings are generated.
• The FAISS vector store is set up for fast document retrieval.

This retriever will be responsible for fetching the relevant chunks of text based on the user’s query.

Now, when a user asks a question, the retriever goes through all the documents in the vector store and fetches the ones that are most relevant based on the query.

retrieved_docs = retriever.get_relevant_documents(query)

This invoke(query) method is fetching those relevant documents. Behind the scenes, it’s matching the query to the embedding vectors and pulling out the top matches based on similarity.

Now that we have the relevant documents, we need to format them for the LLM.

context = "\n".join([doc.page_content for doc in retrieved_docs])

Here, we are taking all the retrieved documents and combining their contents into a single string. This is important because the LLM expects a clean chunk of text to work with, not a bunch of separate pieces.

We’re using Python’s join() function to stitch these document chunks together into one coherent block of information. Each document’s content is stored in the doc.page_content field, and we’re joining them with new lines (\n).

Now we’re creating the prompt for the LLM. 

prompt = [f"Use the following information to answer the question:\n\n{context}\n\nQuestion: {query}"]

The prompt is structured in a way that tells the LLM to use the retrieved information to answer the user’s query. 

Now is the time to generate a response.

generated_response = llm.generate(prompt)  # Pass as a list of strings

Here, the OpenAI model is now tasked with taking the prompt and generating a context-aware response based on both the question and the relevant documents.

Finally, we return the generated response to whoever called the function (whether that’s a user, a frontend app, or another system).

return generated_response

This response is fully formed, contextual, and ready to be used in real-world applications.

To sum it up, this function is doing the full retrieval and generation loop. Here’s a quick recap of the flow:

1. It sets up the retriever to find relevant documents.

2. Those documents are pulled based on the query.

3. The context from those documents is prepared for the LLM.

4. The LLM generates a final response using that context.

5. The response is returned to the user.

from dotenv import load_dotenv
import os

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAI

# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

# Initialize the LLM (using OpenAI)
llm = OpenAI(openai_api_key=openai_api_key)

# Function to set up the RAG system
def setup_rag_system():
   # Load the document
   loader = TextLoader('data/my_document.txt')
   documents = loader.load()

   # Split the document into chunks
   splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
   document_chunks = splitter.split_documents(documents)

   # Initialize embeddings with OpenAI API key
   embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)

   # Create FAISS vector store from document chunks and embeddings
   vector_store = FAISS.from_documents(document_chunks, embeddings)

   # Return the retriever for document retrieval with specified search_type
   retriever = vector_store.as_retriever(
       search_type="similarity",  # or "mmr" or "similarity_score_threshold"
       search_kwargs={"k": 5}  # Adjust the number of results if needed
   )
   return retriever

# Function to get the response from the RAG system
async def get_rag_response(query: str):
   retriever = setup_rag_system()

   # Retrieve the relevant documents using 'get_relevant_documents' method
   retrieved_docs = retriever.get_relevant_documents(query)

   # Prepare the input for the LLM: Combine the query and the retrieved documents into a single string
   context = "\n".join([doc.page_content for doc in retrieved_docs])

   # LLM expects a list of strings (prompts), so we create one by combining the query with the retrieved context
   prompt = [f"Use the following information to answer the question:\n\n{context}\n\nQuestion: {query}"]

   # Generate the final response using the language model (LLM)
   generated_response = llm.generate(prompt)
  
   return generated_response

Defining API Routes in endpoints.py

Next up, let’s create our endpoints.py file. This is where we’ll define the actual paths that users will call to interact with your RAG system.

from fastapi import APIRouter, HTTPException
from rag import get_rag_response
router = APIRouter()
@router.get("/query/")
async def query_rag_system(query: str):
    try:
        # Pass the query string to your RAG system and return the response
        response = await get_rag_response(query)
        return {"query": query, "response": response}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

We create an APIRouter to manage our API routes. The /query/ endpoint is defined to accept a **GET** request with a query string. It calls the get_rag_response function from your rag.py file, which handles the entire RAG pipeline (document retrieval + language generation).  If anything goes wrong, we raise an HTTP 500 error with a detailed message.

Running the Server with Uvicorn

With all that set up, you can now run your FastAPI app using Uvicorn. This is the web server that will allow users to access your API.

Go to the terminal and run:

uvicorn app.main:app --reload

app.main:app tells Uvicorn to look for the app instance in the main.py file and --reload  enables automatic reloading if you make any changes to your code.

Once the server is running, open your browser and go to http://127.0.0.1:8000/docs. FastAPI automatically generates Swagger UI documentation for your API, so you can test it right from your browser!

The FastAPI Swagger UI provides a clean and interactive interface for exploring and testing your API endpoints. Here, the /query/ endpoint allows users to input a query string and receive a response generated by the RAG pipeline.

Testing the API

With your FastAPI server running, head over to your browser or use Postman or Curl to make a GET request to your /query/ endpoint. 

In your browser, through Swagger:

Here we can see a successful response from the RAG system using a query: "Are polar bears in danger?". The system retrieves relevant information from its knowledge base and generates a coherent response using the GPT-3.5 Turbo-Instruct model. The detailed response includes not only the answer (Yes, polar bears are facing serious threats from climate change and their population numbers are dwindling) but also metadata such as token usage (number of tokens processed) and the model used for generation. The API is able to provide well-structured, context-aware answers by using both retrieval and generation mechanisms through FastAPI.

Let’s talk now about the benefits of Asynchronous Processing in FastAPI and why it makes such a big difference in handling those real-world API requests.

You’ve got your RAG system up and running. The API is taking in questions, retrieving relevant information from its knowledge base, and then generating responses with a language model. 

Now, if that whole process were synchronous, the system would sit there, waiting for each task to complete before moving on to the next. For example, while it's retrieving documents, it wouldn’t be able to process any new requests. 

• Non-blocking I/O: Async functions let the server handle multiple requests at once, rather than waiting for one to finish before starting another.
• Improved Performance: For tasks like retrieving documents from a vector store or generating text, async processing makes sure that the API can manage high loads efficiently.
• Better User Experience: Clients get faster responses, and your API stays responsive even under heavy use.

RAG Deployment Strategies

Let’s talk about deployment strategies  - a very important step in turning your RAG system from a prototype into a fully operational product. The goal here is to make sure your system is packaged, deployed, and ready to scale so it can handle real-world users.

Containerization with Docker

First up, let’s talk about Docker. Docker is like a magic box that packages up everything your RAG system needs -its code, dependencies, configurations - and wraps it all into a neat little container. 

This makes sure that wherever you deploy your app, it behaves exactly the same. You can run your app in different environments, but since it’s in a container, you don’t have to worry about the "it works on my machine" problem.

You create a Dockerfile, which is a set of instructions that tells Docker how to set up your app’s environment, install the necessary packages, and start running. Once that’s in place, you can build a Docker image from it and run your application inside a container. It’s efficient, repeatable, and super portable.

Cloud deployment

Now, once your system is packed up and ready to go, you’re likely going to want to deploy it to the cloud. This is where it gets interesting because deploying your RAG system to the cloud means it can be accessed from anywhere in the world. Plus, it gives you scalability, reliability, and access to other cloud services that can boost your system.

Let’s look at a couple more popular cloud platforms, including Azure and Google Cloud:

AWS (Amazon Web Services

AWS offers tools like Elastic Beanstalk, which makes deployment really easy. You basically hand off your Docker container, and AWS takes care of scaling, load balancing, and monitoring. If you need more control, you can use Amazon ECS, which lets you run your Docker containers on a cluster of servers and scale them up or down depending on your needs.

Heroku

Heroku is another option that simplifies deployment. You just push your code, and Heroku handles the infrastructure for you. It’s a great choice if you don’t want to get too deep into the nuts and bolts of managing cloud resources.

Microsoft Azure

Azure offers Azure App Service, which allows you to deploy and manage your RAG system with ease, providing built-in support for auto-scaling, load balancing, and continuous deployment. 

For more flexibility, you can use Azure Kubernetes Service (AKS) to manage your Docker containers at scale, ensuring your system can handle high traffic with the ability to dynamically adjust resources as needed.

Google Cloud Platform (GCP)

GCP has Google Cloud Run, a fully managed platform that allows you to deploy your containers and scale them automatically based on traffic. 

If you want more control over your infrastructure, you can go with Google Kubernetes Engine (GKE), which gives you the power to manage and scale your Docker containers across multiple nodes, with the added benefit of deep integration with Google’s cloud services like AI and machine learning APIs.

Each platform has its strengths, whether you want simplicity and automation or more granular control over your deployment. 

Final Thoughts

We’ve covered a lot in this article, walking through the process of building a RAG system using LangChain and FastAPI. RAG systems are a huge step forward in natural language processing because they bring in external information, giving AI the power to generate more accurate, relevant, and contextually aware responses.

With LangChain, we’ve got a solid framework that handles everything from loading documents, splitting text, and creating embeddings, to retrieving information based on user queries. 

Then, FastAPI steps in to give us a fast, async-ready web framework, helping us to deploy the RAG system as a scalable API. 

Together, these tools make it easier to build AI applications that can handle complex queries, deliver precise answers, and ultimately provide a better user experience.

Now, it’s time for you to take what you’ve learned and apply it to your own projects.

Think about the possibilities: querying your own company’s internal knowledge base, automating document review processes, or creating intelligent chatbots for client support.

Don’t forget - there are so many ways to extend this setup! You could experiment with POST requests to send more complex data structures, or even explore WebSocket connections for real-time interactions. 

I encourage you to dig deeper, experiment, and see where this can take you. This is just the beginning of what you can achieve with LangChain, FastAPI, and modern AI tools! Here are some resources I recommend: 

• The GitHub repository for this tutorial
• Build RAG Systems with LangChain Course
• Agentic RAG: Step-by-Step Tutorial With Demo Project
• Developing LLM Applications with LangChain Course
• Introduction to FastAPI Course