Added the app files with the fist version.

.dockerignore

@@ -0,0 +1,40 @@
+# Python
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.venv
+venv
+env
+pip-log.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+
+# Node
+node_modules
+npm-debug.log
+build
+
+# Development
+.git
+.gitignore
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# But allow the root .env file
+!.env
+
+# IDE
+.idea
+.vscode
+*.swp
+*.swo

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,7 @@
+__pycache__/
+.chainlit/
+.venv/
+.env
+# Node dependencies
+**/node_modules/
+frontend/node_modules/

Dockerfile

@@ -0,0 +1,64 @@
+# Stage 1: Build the React frontend
+FROM node:18 AS frontend-builder
+WORKDIR /app/frontend
+COPY frontend/package*.json ./
+RUN npm install
+COPY frontend/ ./
+# Explicitly set NODE_ENV to production to use .env.production
+ENV NODE_ENV=production
+RUN npm run build
+
+# Stage 2: Set up the Python backend and Nginx
+FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim
+
+# Install nginx
+RUN apt-get update && apt-get install -y nginx && rm -rf /var/lib/apt/lists/*
+
+# Create app directory and set permissions
+RUN mkdir /app && \
+    useradd -m -u 1000 user && \
+    chown -R user:user /app
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the backend app and set permissions
+COPY --chown=user . /app
+
+# Copy the built frontend from the previous stage
+COPY --from=frontend-builder --chown=user /app/frontend/build /app/static
+
+# Configure nginx as root
+COPY nginx.conf /etc/nginx/nginx.conf
+RUN mkdir -p /var/log/nginx && \
+    chown -R user:user /var/log/nginx && \
+    chown -R user:user /var/lib/nginx && \
+    touch /var/run/nginx.pid && \
+    chown -R user:user /var/run/nginx.pid
+
+# Switch to user for Python setup
+USER user
+
+# Set the environment variables
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH \
+    UVICORN_WS_PROTOCOL=websockets
+
+# Install the Python dependencies
+RUN uv sync
+
+# Switch back to root to create start script
+USER root
+
+# Create start script that handles both nginx and uvicorn
+RUN echo '#!/bin/bash\n\
+nginx -g "daemon off;" &\n\
+echo "Nginx started..."\n\
+su -c "bash -c \"source /app/.venv/bin/activate && uvicorn api:app --host 0.0.0.0 --port 8000\"" user\n\
+' > /app/start.sh && chmod +x /app/start.sh
+
+# Expose the Hugging Face port
+EXPOSE 7860
+
+# Run both nginx and the backend
+CMD ["./start.sh"]

README.md

@@ -0,0 +1,350 @@
+---
+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)
+
+![image](https://i.imgur.com/IaEVZG2.png)
+
+### 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: Since the response from LLMs can be slow, we tend to use streaming to start sending the tokens back as the LLM generates the tokens. It gives a user quick feedback and the app does not seem unresponsive or slow.**
+
+### 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: We cannot store everything in a global variable as some state will be specific to a user that is having a chat. We cannot expose the messages of one user chat session to another session. That would be incorrect. That is why each new chat has to be associated to a user session. In chainlit, the chat session related data is also saved in the user session itself. If the user is authnticated, the user specific details are saved in the user session.**
+
+### 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!
+
+<details>
+    <summary>Creating a Hugging Face Space</summary>
+
+1.  Navigate to the `Spaces` tab.
+
+![image](https://i.imgur.com/aSMlX2T.png)
+
+2. Click on `Create new Space`
+
+![image](https://i.imgur.com/YaSSy5p.png)
+
+3. Create the Space by providing values in the form. Make sure you've selected "Docker" as your Space SDK.
+
+![image](https://i.imgur.com/6h9CgH6.png)
+
+</details>
+
+<details>
+    <summary>Adding this Repository to the Newly Created Space</summary>
+
+1. Collect the SSH address from the newly created Space.
+
+![image](https://i.imgur.com/Oag0m8E.png)
+
+> NOTE: The address is the component that starts with `[email protected]: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!
+
+</details>
+
+<details>
+    <summary>Adding OpenAI Secrets to the Space</summary>
+
+1. Navigate to your Space settings.
+
+![image](https://i.imgur.com/zh0a2By.png)
+
+2. Navigate to `Variables and secrets` on the Settings page and click `New secret`:
+
+![image](https://i.imgur.com/g2KlZdz.png)
+
+3. In the `Name` field - input `OPENAI_API_KEY` in the `Value (private)` field, put your OpenAI API Key.
+
+![image](https://i.imgur.com/eFcZ8U3.png)
+
+4. The Space will begin rebuilding!
+
+</details>
+
+## 🎉
+
+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?
+
+**The application is not able to reason through the questions asked. In my initial try, the application was not able to generate a coherent answer to the second question. It seemed as if it got lost in the details and faced challenges in creating a coherent response. However, subsequently, I have found that it does answer the question and does a decent job but can tend to get lost sometimes. Also, it was unable to answer the last question which leads me to think that the model is not able to digest large pieces of information.**
+
+## 🚧 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!

aimakerspace/openai_utils/chatmodel.py

@@ -0,0 +1,45 @@
+from openai import OpenAI, AsyncOpenAI
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+
+class ChatOpenAI:
+    def __init__(self, model_name: str = "gpt-4o-mini"):
+        self.model_name = model_name
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        if self.openai_api_key is None:
+            raise ValueError("OPENAI_API_KEY is not set")
+
+    def run(self, messages, text_only: bool = True, **kwargs):
+        if not isinstance(messages, list):
+            raise ValueError("messages must be a list")
+
+        client = OpenAI()
+        response = client.chat.completions.create(
+            model=self.model_name, messages=messages, **kwargs
+        )
+
+        if text_only:
+            return response.choices[0].message.content
+
+        return response
+    
+    async def astream(self, messages, **kwargs):
+        if not isinstance(messages, list):
+            raise ValueError("messages must be a list")
+        
+        client = AsyncOpenAI()
+
+        stream = await client.chat.completions.create(
+            model=self.model_name,
+            messages=messages,
+            stream=True,
+            **kwargs
+        )
+
+        async for chunk in stream:
+            content = chunk.choices[0].delta.content
+            if content is not None:
+                yield content

aimakerspace/openai_utils/embedding.py

@@ -0,0 +1,59 @@
+from dotenv import load_dotenv
+from openai import AsyncOpenAI, OpenAI
+import openai
+from typing import List
+import os
+import asyncio
+
+
+class EmbeddingModel:
+    def __init__(self, embeddings_model_name: str = "text-embedding-3-small"):
+        load_dotenv()
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        self.async_client = AsyncOpenAI()
+        self.client = OpenAI()
+
+        if self.openai_api_key is None:
+            raise ValueError(
+                "OPENAI_API_KEY environment variable is not set. Please set it to your OpenAI API key."
+            )
+        openai.api_key = self.openai_api_key
+        self.embeddings_model_name = embeddings_model_name
+
+    async def async_get_embeddings(self, list_of_text: List[str]) -> List[List[float]]:
+        embedding_response = await self.async_client.embeddings.create(
+            input=list_of_text, model=self.embeddings_model_name
+        )
+
+        return [embeddings.embedding for embeddings in embedding_response.data]
+
+    async def async_get_embedding(self, text: str) -> List[float]:
+        embedding = await self.async_client.embeddings.create(
+            input=text, model=self.embeddings_model_name
+        )
+
+        return embedding.data[0].embedding
+
+    def get_embeddings(self, list_of_text: List[str]) -> List[List[float]]:
+        embedding_response = self.client.embeddings.create(
+            input=list_of_text, model=self.embeddings_model_name
+        )
+
+        return [embeddings.embedding for embeddings in embedding_response.data]
+
+    def get_embedding(self, text: str) -> List[float]:
+        embedding = self.client.embeddings.create(
+            input=text, model=self.embeddings_model_name
+        )
+
+        return embedding.data[0].embedding
+
+
+if __name__ == "__main__":
+    embedding_model = EmbeddingModel()
+    print(asyncio.run(embedding_model.async_get_embedding("Hello, world!")))
+    print(
+        asyncio.run(
+            embedding_model.async_get_embeddings(["Hello, world!", "Goodbye, world!"])
+        )
+    )

aimakerspace/openai_utils/prompts.py

@@ -0,0 +1,78 @@
+import re
+
+
+class BasePrompt:
+    def __init__(self, prompt):
+        """
+        Initializes the BasePrompt object with a prompt template.
+
+        :param prompt: A string that can contain placeholders within curly braces
+        """
+        self.prompt = prompt
+        self._pattern = re.compile(r"\{([^}]+)\}")
+
+    def format_prompt(self, **kwargs):
+        """
+        Formats the prompt string using the keyword arguments provided.
+
+        :param kwargs: The values to substitute into the prompt string
+        :return: The formatted prompt string
+        """
+        matches = self._pattern.findall(self.prompt)
+        return self.prompt.format(**{match: kwargs.get(match, "") for match in matches})
+
+    def get_input_variables(self):
+        """
+        Gets the list of input variable names from the prompt string.
+
+        :return: List of input variable names
+        """
+        return self._pattern.findall(self.prompt)
+
+
+class RolePrompt(BasePrompt):
+    def __init__(self, prompt, role: str):
+        """
+        Initializes the RolePrompt object with a prompt template and a role.
+
+        :param prompt: A string that can contain placeholders within curly braces
+        :param role: The role for the message ('system', 'user', or 'assistant')
+        """
+        super().__init__(prompt)
+        self.role = role
+
+    def create_message(self, format=True, **kwargs):
+        """
+        Creates a message dictionary with a role and a formatted message.
+
+        :param kwargs: The values to substitute into the prompt string
+        :return: Dictionary containing the role and the formatted message
+        """
+        if format:
+            return {"role": self.role, "content": self.format_prompt(**kwargs)}
+        
+        return {"role": self.role, "content": self.prompt}
+
+
+class SystemRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "system")
+
+
+class UserRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "user")
+
+
+class AssistantRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "assistant")
+
+
+if __name__ == "__main__":
+    prompt = BasePrompt("Hello {name}, you are {age} years old")
+    print(prompt.format_prompt(name="John", age=30))
+
+    prompt = SystemRolePrompt("Hello {name}, you are {age} years old")
+    print(prompt.create_message(name="John", age=30))
+    print(prompt.get_input_variables())

aimakerspace/text_utils.py

@@ -0,0 +1,136 @@
+import os
+from typing import List
+import PyPDF2
+
+
+class TextFileLoader:
+    def __init__(self, path: str, encoding: str = "utf-8"):
+        self.documents = []
+        self.path = path
+        self.encoding = encoding
+
+    def load(self):
+        if os.path.isdir(self.path):
+            self.load_directory()
+        elif os.path.isfile(self.path) and self.path.endswith(".txt"):
+            self.load_file()
+        else:
+            raise ValueError(
+                "Provided path is neither a valid directory nor a .txt file."
+            )
+
+    def load_file(self):
+        with open(self.path, "r", encoding=self.encoding) as f:
+            self.documents.append(f.read())
+
+    def load_directory(self):
+        for root, _, files in os.walk(self.path):
+            for file in files:
+                if file.endswith(".txt"):
+                    with open(
+                        os.path.join(root, file), "r", encoding=self.encoding
+                    ) as f:
+                        self.documents.append(f.read())
+
+    def load_documents(self):
+        self.load()
+        return self.documents
+
+
+class CharacterTextSplitter:
+    def __init__(
+        self,
+        chunk_size: int = 1000,
+        chunk_overlap: int = 200,
+    ):
+        assert (
+            chunk_size > chunk_overlap
+        ), "Chunk size must be greater than chunk overlap"
+
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+
+    def split(self, text: str) -> List[str]:
+        chunks = []
+        for i in range(0, len(text), self.chunk_size - self.chunk_overlap):
+            chunks.append(text[i : i + self.chunk_size])
+        return chunks
+
+    def split_texts(self, texts: List[str]) -> List[str]:
+        chunks = []
+        for text in texts:
+            chunks.extend(self.split(text))
+        return chunks
+
+
+class PDFLoader:
+    def __init__(self, path: str):
+        self.documents = []
+        self.path = path
+        print(f"PDFLoader initialized with path: {self.path}")
+
+    def load(self):
+        print(f"Loading PDF from path: {self.path}")
+        print(f"Path exists: {os.path.exists(self.path)}")
+        print(f"Is file: {os.path.isfile(self.path)}")
+        print(f"Is directory: {os.path.isdir(self.path)}")
+        print(f"File permissions: {oct(os.stat(self.path).st_mode)[-3:]}")
+        
+        try:
+            # Try to open the file first to verify access
+            with open(self.path, 'rb') as test_file:
+                pass
+            
+            # If we can open it, proceed with loading
+            self.load_file()
+            
+        except IOError as e:
+            raise ValueError(f"Cannot access file at '{self.path}': {str(e)}")
+        except Exception as e:
+            raise ValueError(f"Error processing file at '{self.path}': {str(e)}")
+
+    def load_file(self):
+        with open(self.path, 'rb') as file:
+            # Create PDF reader object
+            pdf_reader = PyPDF2.PdfReader(file)
+            
+            # Extract text from each page
+            text = ""
+            for page in pdf_reader.pages:
+                text += page.extract_text() + "\n"
+            
+            self.documents.append(text)
+
+    def load_directory(self):
+        for root, _, files in os.walk(self.path):
+            for file in files:
+                if file.lower().endswith('.pdf'):
+                    file_path = os.path.join(root, file)
+                    with open(file_path, 'rb') as f:
+                        pdf_reader = PyPDF2.PdfReader(f)
+                        
+                        # Extract text from each page
+                        text = ""
+                        for page in pdf_reader.pages:
+                            text += page.extract_text() + "\n"
+                        
+                        self.documents.append(text)
+
+    def load_documents(self):
+        self.load()
+        return self.documents
+
+
+if __name__ == "__main__":
+    loader = TextFileLoader("data/KingLear.txt")
+    loader.load()
+    splitter = CharacterTextSplitter()
+    chunks = splitter.split_texts(loader.documents)
+    print(len(chunks))
+    print(chunks[0])
+    print("--------")
+    print(chunks[1])
+    print("--------")
+    print(chunks[-2])
+    print("--------")
+    print(chunks[-1])

aimakerspace/vectordatabase.py

@@ -0,0 +1,81 @@
+import numpy as np
+from collections import defaultdict
+from typing import List, Tuple, Callable
+from aimakerspace.openai_utils.embedding import EmbeddingModel
+import asyncio
+
+
+def cosine_similarity(vector_a: np.array, vector_b: np.array) -> float:
+    """Computes the cosine similarity between two vectors."""
+    dot_product = np.dot(vector_a, vector_b)
+    norm_a = np.linalg.norm(vector_a)
+    norm_b = np.linalg.norm(vector_b)
+    return dot_product / (norm_a * norm_b)
+
+
+class VectorDatabase:
+    def __init__(self, embedding_model: EmbeddingModel = None):
+        self.vectors = defaultdict(np.array)
+        self.embedding_model = embedding_model or EmbeddingModel()
+
+    def insert(self, key: str, vector: np.array) -> None:
+        self.vectors[key] = vector
+
+    def search(
+        self,
+        query_vector: np.array,
+        k: int,
+        distance_measure: Callable = cosine_similarity,
+    ) -> List[Tuple[str, float]]:
+        scores = [
+            (key, distance_measure(query_vector, vector))
+            for key, vector in self.vectors.items()
+        ]
+        return sorted(scores, key=lambda x: x[1], reverse=True)[:k]
+
+    def search_by_text(
+        self,
+        query_text: str,
+        k: int,
+        distance_measure: Callable = cosine_similarity,
+        return_as_text: bool = False,
+    ) -> List[Tuple[str, float]]:
+        query_vector = self.embedding_model.get_embedding(query_text)
+        results = self.search(query_vector, k, distance_measure)
+        return [result[0] for result in results] if return_as_text else results
+
+    def retrieve_from_key(self, key: str) -> np.array:
+        return self.vectors.get(key, None)
+
+    async def abuild_from_list(self, list_of_text: List[str]) -> "VectorDatabase":
+        embeddings = await self.embedding_model.async_get_embeddings(list_of_text)
+        for text, embedding in zip(list_of_text, embeddings):
+            self.insert(text, np.array(embedding))
+        return self
+
+
+if __name__ == "__main__":
+    list_of_text = [
+        "I like to eat broccoli and bananas.",
+        "I ate a banana and spinach smoothie for breakfast.",
+        "Chinchillas and kittens are cute.",
+        "My sister adopted a kitten yesterday.",
+        "Look at this cute hamster munching on a piece of broccoli.",
+    ]
+
+    vector_db = VectorDatabase()
+    vector_db = asyncio.run(vector_db.abuild_from_list(list_of_text))
+    k = 2
+
+    searched_vector = vector_db.search_by_text("I think fruit is awesome!", k=k)
+    print(f"Closest {k} vector(s):", searched_vector)
+
+    retrieved_vector = vector_db.retrieve_from_key(
+        "I like to eat broccoli and bananas."
+    )
+    print("Retrieved vector:", retrieved_vector)
+
+    relevant_texts = vector_db.search_by_text(
+        "I think fruit is awesome!", k=k, return_as_text=True
+    )
+    print(f"Closest {k} text(s):", relevant_texts)

api.py

@@ -0,0 +1,105 @@
+import os
+from typing import List
+from fastapi import FastAPI, HTTPException, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import asyncio
+import tempfile
+from aimakerspace.vectordatabase import VectorDatabase
+from aimakerspace.openai_utils.chatmodel import ChatOpenAI
+
+from app import (
+    RetrievalAugmentedQAPipeline,
+    process_file,
+    system_role_prompt,
+    user_role_prompt,
+)
+
+app = FastAPI()
+
+# Update CORS middleware configuration
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=[
+        "http://localhost:3001",    # Development React server
+        "http://localhost:7860",    # Production nginx server
+        "http://localhost",         # Just in case
+        "*",                        # Allow all origins in development
+    ],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+    expose_headers=["*"],
+)
+
+class ChatResponse(BaseModel):
+    response: str
+    context: List[tuple]
+
+class ChatRequest(BaseModel):
+    query: str
+
+@app.post("/api/upload", response_model=dict)
+async def upload_file(file: UploadFile = File(...)):
+    try:
+        # Create a temporary file to store the upload
+        with tempfile.NamedTemporaryFile(delete=False, suffix=f".{file.filename.split('.')[-1]}") as temp_file:
+            content = await file.read()
+            temp_file.write(content)
+            temp_file.flush()
+
+            # Process the file using existing function
+            texts = process_file(temp_file.name, file.filename)
+
+            # Create vector database
+            vector_db = VectorDatabase()
+            vector_db = await vector_db.abuild_from_list(texts)
+
+            # Create chat model
+            chat_openai = ChatOpenAI()
+
+            # Create pipeline
+            pipeline = RetrievalAugmentedQAPipeline(
+                vector_db_retriever=vector_db,
+                llm=chat_openai
+            )
+
+            # Store the pipeline in memory (Note: this is not production-ready)
+            if not hasattr(app, 'pipelines'):
+                app.pipelines = {}
+            pipeline_id = str(len(app.pipelines))
+            app.pipelines[pipeline_id] = pipeline
+
+            # Clean up temporary file
+            os.unlink(temp_file.name)
+
+            return {"pipeline_id": pipeline_id, "message": "File processed successfully"}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/api/chat/{pipeline_id}", response_model=ChatResponse)
+async def chat(pipeline_id: str, request: ChatRequest):
+    try:
+        if not hasattr(app, 'pipelines') or pipeline_id not in app.pipelines:
+            raise HTTPException(status_code=404, detail="Pipeline not found. Please upload a file first.")
+
+        pipeline = app.pipelines[pipeline_id]
+        result = await pipeline.arun_pipeline(request.query)
+
+        # Collect the streaming response
+        response_text = ""
+        async for chunk in result["response"]:
+            response_text += chunk
+
+        return ChatResponse(
+            response=response_text,
+            context=result["context"]
+        )
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

app.py

@@ -0,0 +1,61 @@
+import os
+from typing import List
+from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader, PDFLoader
+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
+
+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)
+
+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):
+        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"
+
+        formatted_system_prompt = system_role_prompt.create_message()
+        formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)
+
+        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}
+
+text_splitter = CharacterTextSplitter()
+
+def process_file(file_path: str, file_name: str):
+    print(f"Processing file: {file_name}")
+
+    # Create appropriate loader based on file extension
+    if file_name.lower().endswith('.pdf'):
+        loader = PDFLoader(file_path)
+    else:
+        loader = TextFileLoader(file_path)
+
+    # Load and process the documents
+    documents = loader.load_documents()
+    texts = text_splitter.split_texts(documents)
+    return texts

frontend/.env.production

@@ -0,0 +1 @@
+REACT_APP_API_BASE_URL=

frontend/package.json

@@ -0,0 +1,48 @@
+{
+  "name": "chat-frontend",
+  "version": "0.1.0",
+  "private": true,
+  "dependencies": {
+    "@testing-library/jest-dom": "^5.17.0",
+    "@testing-library/react": "^13.4.0",
+    "@testing-library/user-event": "^13.5.0",
+    "@types/jest": "^27.5.2",
+    "@types/node": "^16.18.0",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "react-scripts": "5.0.1",
+    "typescript": "^4.9.5",
+    "web-vitals": "^2.1.4"
+  },
+  "scripts": {
+    "start": "PORT=3001 react-scripts start",
+    "build": "react-scripts build",
+    "test": "react-scripts test",
+    "eject": "react-scripts eject"
+  },
+  "eslintConfig": {
+    "extends": [
+      "react-app",
+      "react-app/jest"
+    ]
+  },
+  "browserslist": {
+    "production": [
+      ">0.2%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 1 chrome version",
+      "last 1 firefox version",
+      "last 1 safari version"
+    ]
+  },
+  "devDependencies": {
+    "@types/react": "^19.0.8",
+    "@types/react-dom": "^19.0.3",
+    "autoprefixer": "^10.4.16",
+    "postcss": "^8.4.31",
+    "tailwindcss": "^3.3.5"
+  }
+}

frontend/public/index.html

@@ -0,0 +1,21 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <meta name="theme-color" content="#000000" />
+    <meta name="description" content="Document Q&A Chat Application" />
+    <link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
+    <!--
+      manifest.json provides metadata used when your web app is installed on a
+      user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
+    -->
+    <link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
+    <title>Document Q&A Chat</title>
+  </head>
+  <body>
+    <noscript>You need to enable JavaScript to run this app.</noscript>
+    <div id="root"></div>
+  </body>
+</html>

frontend/public/manifest.json

@@ -0,0 +1,15 @@
+{
+  "short_name": "Document Q&A",
+  "name": "Document Q&A Chat Application",
+  "icons": [
+    {
+      "src": "favicon.ico",
+      "sizes": "64x64 32x32 24x24 16x16",
+      "type": "image/x-icon"
+    }
+  ],
+  "start_url": ".",
+  "display": "standalone",
+  "theme_color": "#000000",
+  "background_color": "#ffffff"
+}

frontend/src/App.tsx

@@ -0,0 +1,55 @@
+import React, { useState } from "react";
+import ChatInterface from "./components/ChatInterface";
+
+const API_BASE_URL = process.env.REACT_APP_API_BASE_URL || "";
+
+console.log("API_BASE_URL:", API_BASE_URL);
+
+const App: React.FC = () => {
+  const [pipelineId, setPipelineId] = useState<string | null>(null);
+
+  const handleFileUpload = async (file: File) => {
+    const formData = new FormData();
+    formData.append("file", file);
+
+    const url = `${API_BASE_URL}/api/upload`;
+    console.log("Uploading file to:", url);
+
+    try {
+      const response = await fetch(url, {
+        method: "POST",
+        body: formData,
+      });
+
+      if (!response.ok) {
+        const errorData = await response.json().catch(() => ({}));
+        throw new Error(errorData.detail || "Upload failed");
+      }
+
+      const data = await response.json();
+      console.log("Upload response:", data);
+      setPipelineId(data.pipeline_id);
+    } catch (error) {
+      console.error("Error uploading file:", error);
+      throw error;
+    }
+  };
+
+  return (
+    <div className="min-h-screen bg-gray-900">
+      <header className="bg-gray-800 border-b border-gray-700">
+        <div className="max-w-7xl mx-auto py-4 px-4">
+          <h1 className="text-2xl font-bold text-white">Document Q&A Chat</h1>
+        </div>
+      </header>
+      <main className="max-w-7xl mx-auto">
+        <ChatInterface
+          pipelineId={pipelineId}
+          onFileUpload={handleFileUpload}
+        />
+      </main>
+    </div>
+  );
+};
+
+export default App;

frontend/src/components/ChatInterface.tsx

@@ -0,0 +1,328 @@
+import React, { useState, useRef, useEffect } from "react";
+
+interface Message {
+  role: "user" | "assistant";
+  content: string;
+}
+
+interface ChatInterfaceProps {
+  pipelineId: string | null;
+  onFileUpload: (file: File) => Promise<void>;
+}
+
+const TypingIndicator = () => (
+  <div className="flex justify-start">
+    <div className="bg-gray-800 text-gray-100 p-4 rounded-lg flex items-center space-x-2">
+      <div
+        className="w-2 h-2 bg-gray-400 rounded-full animate-bounce"
+        style={{ animationDelay: "0ms" }}
+      ></div>
+      <div
+        className="w-2 h-2 bg-gray-400 rounded-full animate-bounce"
+        style={{ animationDelay: "150ms" }}
+      ></div>
+      <div
+        className="w-2 h-2 bg-gray-400 rounded-full animate-bounce"
+        style={{ animationDelay: "300ms" }}
+      ></div>
+    </div>
+  </div>
+);
+
+const LoadingState = () => (
+  <div className="flex flex-col items-center justify-center h-full">
+    <div className="bg-gray-800 p-8 rounded-lg shadow-lg max-w-md w-full">
+      <div className="flex items-center justify-center mb-6">
+        <div className="animate-spin rounded-full h-12 w-12 border-b-2 border-blue-500"></div>
+      </div>
+      <div className="space-y-4">
+        <div className="h-4 bg-gray-700 rounded animate-pulse"></div>
+        <div className="h-4 bg-gray-700 rounded animate-pulse w-3/4"></div>
+        <div className="h-4 bg-gray-700 rounded animate-pulse w-1/2"></div>
+      </div>
+      <div className="mt-6 text-gray-400 text-center">
+        Processing your document...
+      </div>
+    </div>
+  </div>
+);
+
+const ErrorState = ({ onRetry }: { onRetry: () => void }) => (
+  <div className="flex flex-col items-center justify-center h-full">
+    <div className="bg-gray-800 p-8 rounded-lg shadow-lg max-w-md w-full">
+      <div className="flex items-center justify-center mb-6 text-red-500">
+        <svg
+          className="w-12 h-12"
+          fill="none"
+          stroke="currentColor"
+          viewBox="0 0 24 24"
+        >
+          <path
+            strokeLinecap="round"
+            strokeLinejoin="round"
+            strokeWidth={2}
+            d="M12 9v2m0 4h.01m-6.938 4h13.856c1.54 0 2.502-1.667 1.732-3L13.732 4c-.77-1.333-2.694-1.333-3.464 0L3.34 16c-.77 1.333.192 3 1.732 3z"
+          />
+        </svg>
+      </div>
+      <div className="text-center">
+        <h3 className="text-lg font-medium text-white mb-2">
+          Document Upload Failed
+        </h3>
+        <p className="text-gray-400 mb-6">
+          There was an error processing your document. Please try again.
+        </p>
+        <button
+          onClick={onRetry}
+          className="bg-blue-600 text-white px-4 py-2 rounded-lg hover:bg-blue-700 transition-colors"
+        >
+          Try Again
+        </button>
+      </div>
+    </div>
+  </div>
+);
+
+const API_BASE_URL = process.env.REACT_APP_API_BASE_URL || "";
+
+const ChatInterface: React.FC<ChatInterfaceProps> = ({
+  pipelineId,
+  onFileUpload,
+}) => {
+  const [messages, setMessages] = useState<Message[]>([]);
+  const [input, setInput] = useState("");
+  const [isLoading, setIsLoading] = useState(false);
+  const [isUploading, setIsUploading] = useState(false);
+  const [uploadError, setUploadError] = useState(false);
+  const messagesEndRef = useRef<null | HTMLDivElement>(null);
+  const fileInputRef = useRef<HTMLInputElement>(null);
+
+  useEffect(() => {
+    if (pipelineId) {
+      setIsUploading(false);
+      setMessages([
+        {
+          role: "assistant",
+          content:
+            "👋 Your document has been processed! You can now ask questions about its content.",
+        },
+      ]);
+    }
+  }, [pipelineId]);
+
+  const scrollToBottom = () => {
+    messagesEndRef.current?.scrollIntoView({ behavior: "smooth" });
+  };
+
+  useEffect(() => {
+    scrollToBottom();
+  }, [messages]);
+
+  const handleFileUpload = async (
+    event: React.ChangeEvent<HTMLInputElement>
+  ) => {
+    const file = event.target.files?.[0];
+    if (file) {
+      try {
+        setIsUploading(true);
+        setUploadError(false);
+        await onFileUpload(file);
+      } catch (error) {
+        console.error("Error uploading file:", error);
+        setUploadError(true);
+      } finally {
+        setIsUploading(false);
+      }
+    }
+  };
+
+  const handleRetry = () => {
+    setUploadError(false);
+    // Reset file input
+    if (fileInputRef.current) {
+      fileInputRef.current.value = "";
+    }
+  };
+
+  const sendMessage = async (e: React.FormEvent) => {
+    e.preventDefault();
+    if (!input.trim() || !pipelineId) return;
+
+    const userMessage = input.trim();
+    setInput("");
+    setMessages((prev) => [...prev, { role: "user", content: userMessage }]);
+    setIsLoading(true);
+
+    try {
+      const response = await fetch(`${API_BASE_URL}/api/chat/${pipelineId}`, {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify({ query: userMessage }),
+      });
+
+      const data = await response.json();
+      setMessages((prev) => [
+        ...prev,
+        { role: "assistant", content: data.response },
+      ]);
+    } catch (error) {
+      console.error("Error sending message:", error);
+      setMessages((prev) => [
+        ...prev,
+        { role: "assistant", content: "Error: Failed to get response" },
+      ]);
+    } finally {
+      setIsLoading(false);
+    }
+  };
+
+  return (
+    <div className="flex flex-col h-screen bg-gray-900">
+      {isUploading ? (
+        <LoadingState />
+      ) : uploadError ? (
+        <ErrorState onRetry={handleRetry} />
+      ) : !pipelineId ? (
+        <div className="flex flex-col items-center justify-center h-full">
+          <div className="bg-gray-800 p-8 rounded-lg shadow-lg max-w-md w-full">
+            <h2 className="text-2xl font-bold text-white mb-6 text-center">
+              Upload your document
+            </h2>
+            <label className="flex flex-col items-center justify-center w-full h-32 border-2 border-gray-600 border-dashed rounded-lg cursor-pointer hover:border-gray-500 hover:bg-gray-800/50 transition-all">
+              <div className="flex flex-col items-center justify-center pt-5 pb-6">
+                <svg
+                  className="w-8 h-8 mb-4 text-gray-400"
+                  aria-hidden="true"
+                  xmlns="http://www.w3.org/2000/svg"
+                  fill="none"
+                  viewBox="0 0 20 16"
+                >
+                  <path
+                    stroke="currentColor"
+                    strokeLinecap="round"
+                    strokeLinejoin="round"
+                    strokeWidth="2"
+                    d="M13 13h3a3 3 0 0 0 0-6h-.025A5.56 5.56 0 0 0 16 6.5 5.5 5.5 0 0 0 5.207 5.021C5.137 5.017 5.071 5 5 5a4 4 0 0 0 0 8h2.167M10 15V6m0 0L8 8m2-2 2 2"
+                  />
+                </svg>
+                <p className="mb-2 text-sm text-gray-400">
+                  <span className="font-semibold">Click to upload</span> or drag
+                  and drop
+                </p>
+                <p className="text-xs text-gray-400">PDF or TXT files</p>
+              </div>
+              <input
+                ref={fileInputRef}
+                type="file"
+                accept=".pdf,.txt"
+                onChange={handleFileUpload}
+                className="hidden"
+              />
+            </label>
+          </div>
+        </div>
+      ) : (
+        <div className="flex flex-col h-full max-w-5xl mx-auto w-full">
+          <div className="flex-1 overflow-y-auto p-4 space-y-4 mb-6">
+            {messages.map((message, index) => (
+              <div
+                key={index}
+                className={`flex ${
+                  message.role === "user" ? "justify-end" : "justify-start"
+                }`}
+              >
+                <div
+                  className={`max-w-[80%] p-4 rounded-lg shadow-lg ${
+                    message.role === "user"
+                      ? "bg-blue-600 text-white"
+                      : "bg-gray-800 text-gray-100"
+                  }`}
+                >
+                  {message.content}
+                </div>
+              </div>
+            ))}
+            {isLoading && <TypingIndicator />}
+            {messages.length === 0 && !isLoading && (
+              <div className="flex justify-center items-center h-full">
+                <div className="text-gray-400 text-center">
+                  <p>No messages yet</p>
+                  <p className="text-sm mt-2">
+                    Start by asking a question about your document
+                  </p>
+                </div>
+              </div>
+            )}
+            <div ref={messagesEndRef} />
+          </div>
+          <div className="px-4 pb-6">
+            <div className="bg-gray-800 rounded-lg p-2 shadow-lg border border-gray-700">
+              <form onSubmit={sendMessage} className="flex items-center gap-2">
+                <input
+                  type="text"
+                  value={input}
+                  onChange={(e) => setInput(e.target.value)}
+                  placeholder="Ask a question about your document..."
+                  className="flex-1 p-3 bg-transparent text-white placeholder-gray-400 focus:outline-none"
+                  disabled={isLoading}
+                />
+                <button
+                  type="submit"
+                  disabled={isLoading || !input.trim()}
+                  className="px-6 py-3 bg-blue-600 text-white rounded-lg hover:bg-blue-700 disabled:bg-gray-600 disabled:cursor-not-allowed transition-colors flex items-center gap-2 min-w-[100px] justify-center"
+                >
+                  {isLoading ? (
+                    <>
+                      <svg
+                        className="animate-spin h-4 w-4 text-white"
+                        xmlns="http://www.w3.org/2000/svg"
+                        fill="none"
+                        viewBox="0 0 24 24"
+                      >
+                        <circle
+                          className="opacity-25"
+                          cx="12"
+                          cy="12"
+                          r="10"
+                          stroke="currentColor"
+                          strokeWidth="4"
+                        ></circle>
+                        <path
+                          className="opacity-75"
+                          fill="currentColor"
+                          d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"
+                        ></path>
+                      </svg>
+                      <span>Sending...</span>
+                    </>
+                  ) : (
+                    <>
+                      <span>Send</span>
+                      <svg
+                        className="w-4 h-4"
+                        fill="none"
+                        stroke="currentColor"
+                        viewBox="0 0 24 24"
+                      >
+                        <path
+                          strokeLinecap="round"
+                          strokeLinejoin="round"
+                          strokeWidth={2}
+                          d="M14 5l7 7m0 0l-7 7m7-7H3"
+                        />
+                      </svg>
+                    </>
+                  )}
+                </button>
+              </form>
+            </div>
+          </div>
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default ChatInterface;

frontend/src/index.css

@@ -0,0 +1,3 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;

frontend/src/index.tsx

@@ -0,0 +1,16 @@
+import React from "react";
+import ReactDOM from "react-dom/client";
+import "./index.css";
+import App from "./App";
+
+const root = ReactDOM.createRoot(
+  document.getElementById("root") as HTMLElement
+);
+root.render(
+  <React.StrictMode>
+    <App />
+  </React.StrictMode>
+);
+
+// Add an empty export to make this file a module
+export {};

frontend/tailwind.config.js

@@ -0,0 +1,7 @@
+module.exports = {
+  content: ["./src/**/*.{js,jsx,ts,tsx}"],
+  theme: {
+    extend: {},
+  },
+  plugins: [],
+};

frontend/tsconfig.json

@@ -0,0 +1,20 @@
+{
+  "compilerOptions": {
+    "target": "es5",
+    "lib": ["dom", "dom.iterable", "esnext"],
+    "allowJs": true,
+    "skipLibCheck": true,
+    "esModuleInterop": true,
+    "allowSyntheticDefaultImports": true,
+    "strict": true,
+    "forceConsistentCasingInFileNames": true,
+    "noFallthroughCasesInSwitch": true,
+    "module": "esnext",
+    "moduleResolution": "node",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "noEmit": true,
+    "jsx": "react-jsx"
+  },
+  "include": ["src"]
+}

nginx.conf

@@ -0,0 +1,48 @@
+worker_processes 1;
+error_log /var/log/nginx/error.log warn;
+pid /var/run/nginx.pid;
+
+events {
+    worker_connections 1024;
+}
+
+http {
+    include /etc/nginx/mime.types;
+    default_type application/octet-stream;
+    access_log /var/log/nginx/access.log combined;
+    sendfile on;
+    keepalive_timeout 65;
+
+    # Add these settings for handling large file uploads
+    client_max_body_size 10M;
+    proxy_read_timeout 300;
+    proxy_connect_timeout 300;
+    proxy_send_timeout 300;
+
+    server {
+        listen 7860;
+        root /app/static;
+        index index.html;
+
+        location / {
+            try_files $uri $uri/ /index.html;
+            add_header Cache-Control "no-cache";
+        }
+
+        location /api {
+            proxy_pass http://localhost:8000;
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection 'upgrade';
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+            proxy_cache_bypass $http_upgrade;
+
+            # Add these for better file upload handling
+            proxy_request_buffering off;
+            proxy_buffering off;
+        }
+    }
+}

pyproject.toml

@@ -0,0 +1,17 @@
+[project]
+name = "aie5-deploypythonicrag"
+version = "0.1.0"
+description = "Simple Pythonic RAG App"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "chainlit>=2.0.4",
+    "numpy>=2.2.2",
+    "openai>=1.59.9",
+    "pydantic==2.10.1",
+    "pypdf2>=3.0.1",
+    "websockets>=14.2",
+    "fastapi>=0.115.6",
+    "uvicorn>=0.34.0",
+    "python-multipart>=0.0.18"
+]

uv.lock

@@ -0,0 +1,933 @@
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+requires-python = ">=3.13"
+
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+    { name = "chainlit" },
+    { name = "fastapi" },
+    { name = "numpy" },
+    { name = "openai" },
+    { name = "pydantic" },
+    { name = "pypdf2" },
+    { name = "python-multipart" },
+    { name = "uvicorn" },
+    { name = "websockets" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "chainlit", specifier = ">=2.0.4" },
+    { name = "fastapi", specifier = ">=0.115.6" },
+    { name = "numpy", specifier = ">=2.2.2" },
+    { name = "openai", specifier = ">=1.59.9" },
+    { name = "pydantic", specifier = "==2.10.1" },
+    { name = "pypdf2", specifier = ">=3.0.1" },
+    { name = "python-multipart", specifier = ">=0.0.18" },
+    { name = "uvicorn", specifier = ">=0.34.0" },
+    { name = "websockets", specifier = ">=14.2" },
+]
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