---
title: DeployPythonicRAG
emoji: 📉
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: apache-2.0
---
# Deploying Pythonic Chat With Your Text File Application
In today's breakout rooms, we will be following the process that you saw during the challenge.
Today, we will repeat the same process - but powered by our Pythonic RAG implementation we created last week.
You'll notice a few differences in the `app.py` logic - as well as a few changes to the `aimakerspace` package to get things working smoothly with Chainlit.
> NOTE: If you want to run this locally - be sure to use `uv sync`, and then `uv run chainlit run app.py` to start the application outside of Docker.
## Reference Diagram (It's Busy, but it works)
### Anatomy of a Chainlit Application
[Chainlit](https://docs.chainlit.io/get-started/overview) is a Python package similar to Streamlit that lets users write a backend and a front end in a single (or multiple) Python file(s). It is mainly used for prototyping LLM-based Chat Style Applications - though it is used in production in some settings with 1,000,000s of MAUs (Monthly Active Users).
The primary method of customizing and interacting with the Chainlit UI is through a few critical [decorators](https://blog.hubspot.com/website/decorators-in-python).
> NOTE: Simply put, the decorators (in Chainlit) are just ways we can "plug-in" to the functionality in Chainlit.
We'll be concerning ourselves with three main scopes:
1. On application start - when we start the Chainlit application with a command like `chainlit run app.py`
2. On chat start - when a chat session starts (a user opens the web browser to the address hosting the application)
3. On message - when the users sends a message through the input text box in the Chainlit UI
Let's dig into each scope and see what we're doing!
### On Application Start:
The first thing you'll notice is that we have the traditional "wall of imports" this is to ensure we have everything we need to run our application.
```python
import os
from typing import List
from chainlit.types import AskFileResponse
from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader
from aimakerspace.openai_utils.prompts import (
UserRolePrompt,
SystemRolePrompt,
AssistantRolePrompt,
)
from aimakerspace.openai_utils.embedding import EmbeddingModel
from aimakerspace.vectordatabase import VectorDatabase
from aimakerspace.openai_utils.chatmodel import ChatOpenAI
import chainlit as cl
```
Next up, we have some prompt templates. As all sessions will use the same prompt templates without modification, and we don't need these templates to be specific per template - we can set them up here - at the application scope.
```python
system_template = """\
Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer."""
system_role_prompt = SystemRolePrompt(system_template)
user_prompt_template = """\
Context:
{context}
Question:
{question}
"""
user_role_prompt = UserRolePrompt(user_prompt_template)
```
> NOTE: You'll notice that these are the exact same prompt templates we used from the Pythonic RAG Notebook in Week 1 Day 2!
Following that - we can create the Python Class definition for our RAG pipeline - or *chain*, as we'll refer to it in the rest of this walkthrough.
Let's look at the definition first:
```python
class RetrievalAugmentedQAPipeline:
def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
self.llm = llm
self.vector_db_retriever = vector_db_retriever
async def arun_pipeline(self, user_query: str):
### RETRIEVAL
context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
context_prompt = ""
for context in context_list:
context_prompt += context[0] + "\n"
### AUGMENTED
formatted_system_prompt = system_role_prompt.create_message()
formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)
### GENERATION
async def generate_response():
async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
yield chunk
return {"response": generate_response(), "context": context_list}
```
Notice a few things:
1. We have modified this `RetrievalAugmentedQAPipeline` from the initial notebook to support streaming.
2. In essence, our pipeline is *chaining* a few events together:
1. We take our user query, and chain it into our Vector Database to collect related chunks
2. We take those contexts and our user's questions and chain them into the prompt templates
3. We take that prompt template and chain it into our LLM call
4. We chain the response of the LLM call to the user
3. We are using a lot of `async` again!
Now, we're going to create a helper function for processing uploaded text files.
First, we'll instantiate a shared `CharacterTextSplitter`.
```python
text_splitter = CharacterTextSplitter()
```
Now we can define our helper.
```python
def process_file(file: AskFileResponse):
import tempfile
import shutil
print(f"Processing file: {file.name}")
# Create a temporary file with the correct extension
suffix = f".{file.name.split('.')[-1]}"
with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_file:
# Copy the uploaded file content to the temporary file
shutil.copyfile(file.path, temp_file.name)
print(f"Created temporary file at: {temp_file.name}")
# Create appropriate loader
if file.name.lower().endswith('.pdf'):
loader = PDFLoader(temp_file.name)
else:
loader = TextFileLoader(temp_file.name)
try:
# Load and process the documents
documents = loader.load_documents()
texts = text_splitter.split_texts(documents)
return texts
finally:
# Clean up the temporary file
try:
os.unlink(temp_file.name)
except Exception as e:
print(f"Error cleaning up temporary file: {e}")
```
Simply put, this downloads the file as a temp file, we load it in with `TextFileLoader` and then split it with our `TextSplitter`, and returns that list of strings!
#### ❓ QUESTION #1:
Why do we want to support streaming? What about streaming is important, or useful?
#### Answer:
Streaming allows the application to provide responses immediately as they are available and hence improve user experience. It also enables to handle large data sets and improves performance.
### On Chat Start:
The next scope is where "the magic happens". On Chat Start is when a user begins a chat session. This will happen whenever a user opens a new chat window, or refreshes an existing chat window.
You'll see that our code is set-up to immediately show the user a chat box requesting them to upload a file.
```python
while files == None:
files = await cl.AskFileMessage(
content="Please upload a Text or PDF file to begin!",
accept=["text/plain", "application/pdf"],
max_size_mb=2,
timeout=180,
).send()
```
Once we've obtained the text file - we'll use our processing helper function to process our text!
After we have processed our text file - we'll need to create a `VectorDatabase` and populate it with our processed chunks and their related embeddings!
```python
vector_db = VectorDatabase()
vector_db = await vector_db.abuild_from_list(texts)
```
Once we have that piece completed - we can create the chain we'll be using to respond to user queries!
```python
retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
vector_db_retriever=vector_db,
llm=chat_openai
)
```
Now, we'll save that into our user session!
> NOTE: Chainlit has some great documentation about [User Session](https://docs.chainlit.io/concepts/user-session).
#### ❓ QUESTION #2:
Why are we using User Session here? What about Python makes us need to use this? Why not just store everything in a global variable?
#### Answer:
User session in chainlist persists data in the memory through the life-cycle of a chat session. Each user session is unique to a user and a given chat context. The concept of decorators in the python allow us to use chainlit's user session. We could use global variables to store data that is not user-specific, but this can lead to issues when multiple users interact with the application simultaneously and it could lead to conflicts too. Use of user sessions ensures that each user's data is kept isolated and secure.
### On Message
First, we load our chain from the user session:
```python
chain = cl.user_session.get("chain")
```
Then, we run the chain on the content of the message - and stream it to the front end - that's it!
```python
msg = cl.Message(content="")
result = await chain.arun_pipeline(message.content)
async for stream_resp in result["response"]:
await msg.stream_token(stream_resp)
```
### 🎉
With that - you've created a Chainlit application that moves our Pythonic RAG notebook to a Chainlit application!
## Deploying the Application to Hugging Face Space
Due to the way the repository is created - it should be straightforward to deploy this to a Hugging Face Space!
> NOTE: If you wish to go through the local deployments using `uv run chainlit run app.py` and Docker - please feel free to do so!
Creating a Hugging Face Space
1. Navigate to the `Spaces` tab.
2. Click on `Create new Space`
3. Create the Space by providing values in the form. Make sure you've selected "Docker" as your Space SDK.
Adding this Repository to the Newly Created Space
1. Collect the SSH address from the newly created Space.
> NOTE: The address is the component that starts with `git@hf.co:spaces/`.
2. Use the command:
```bash
git remote add hf HF_SPACE_SSH_ADDRESS_HERE
```
3. Use the command:
```bash
git pull hf main --no-rebase --allow-unrelated-histories -X ours
```
4. Use the command:
```bash
git add .
```
5. Use the command:
```bash
git commit -m "Deploying Pythonic RAG"
```
6. Use the command:
```bash
git push hf main
```
7. The Space should automatically build as soon as the push is completed!
> NOTE: The build will fail before you complete the following steps!
Adding OpenAI Secrets to the Space
1. Navigate to your Space settings.
2. Navigate to `Variables and secrets` on the Settings page and click `New secret`:
3. In the `Name` field - input `OPENAI_API_KEY` in the `Value (private)` field, put your OpenAI API Key.
4. The Space will begin rebuilding!
## 🎉
You just deployed Pythonic RAG!
Try uploading a text file and asking some questions!
#### ❓ Discussion Question #1:
Upload a PDF file of the recent DeepSeek-R1 paper and ask the following questions:
1. What is RL and how does it help reasoning?
2. What is the difference between DeepSeek-R1 and DeepSeek-R1-Zero?
3. What is this paper about?
Does this application pass your vibe check? Are there any immediate pitfalls you're noticing?
#### Answer:
1. What is RL and how does it help reasoning?
LLM Response: Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by taking actions in an environment in order to maximize some notion of cumulative reward. In the context of reasoning, RL helps improve the reasoning capabilities of language models by allowing them to evolve through a process of trial and error, receiving feedback based on their performance on reasoning tasks. This method enables the models, such as DeepSeek-R1-Zero and DeepSeek-R1 discussed in the context, to develop powerful reasoning behaviors without relying on supervised data, which can be time-consuming to collect. The result is that models can display enhanced reasoning performance through large-scale RL processes, such as those employed in the study.
2. What is the difference between DeepSeek-R1 and DeepSeek-R1-Zero?
LLM Response: DeepSeek-R1 and DeepSeek-R1-Zero have several differences. DeepSeek-R1-Zero initially exhibits strong reasoning capabilities and autonomously develops powerful reasoning behaviors but struggles with issues like poor readability and language mixing. In contrast, DeepSeek-R1 aims to improve these aspects by incorporating a small amount of high-quality data as a cold start, enhancing reasoning performance and convergence.
Additionally, while DeepSeek-R1 demonstrates better performance on benchmarks like IF-Eval, AlpacaEval2.0, and ArenaHard, DeepSeek-R1-Zero achieves high accuracy without safety reinforcement learning (RL). However, DeepSeek-R1 tends to refuse certain queries after safety RL, resulting in lower performance on the Chinese SimpleQA benchmark compared to DeepSeek-V3. Overall, DeepSeek-R1 focuses on user-friendly improvements and instruction-following capabilities, whereas DeepSeek-R1-Zero leans more towards initial reasoning strength without these enhancements.
3. What is this paper about?
LLM Response: I don't know the answer.
##### Aspect Tested:
1. Ability to correctly respond to the asked question?
2. How informative is the answer?
3. Can the LLM summarize the document?
3. Would application give similar answers when asked same questions again?
4. Responsiveness/latency of the application
##### Vide Check:
1. Ability to correctly respond to the asked questions?
The LLM correctly responds to the questions about RL and the differences between DeepSeek-R1 and DeepSeek-R1-Zero. However, it fails to answer the question about the paper's content.
2. How informative is the answer?
The answers provided are detailed and informative, especially for the first two questions. They explain the concepts clearly and provide relevant comparisons and details.
3. Can the LLM summarize the document?
The LLM does not demonstrate summarization capabilities in the provided responses. It explicitly states "I don't know the answer" for the question about the paper's content, indicating a lack of summarization.
4. Would the application give similar answers when asked the same questions again?
Application did give similar responses when tried to ask same questions again, including I don't know for last question.
5. Responsiveness/latency of the application?
Due to use of streaming and user_session, there wasn't much lag and responses seem pretty fast and responsiveness.
## 🚧 CHALLENGE MODE 🚧
For the challenge mode, please instead create a simple FastAPI backend with a simple React (or any other JS framework) frontend.
You can use the same prompt templates and RAG pipeline as we did here - but you'll need to modify the code to work with FastAPI and React.
Deploy this application to Hugging Face Spaces!