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@@ -33,3 +33,5 @@ saved_model/**/* 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
+Blueprint-for-an-AI-Bill-of-Rights.pdf filter=lfs diff=lfs merge=lfs -text
+NIST.AI.600-1.pdf filter=lfs diff=lfs merge=lfs -text

Blueprint-for-an-AI-Bill-of-Rights.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ab34b52e55b3f6641d5db725333129a66c0bbbb33a060f62ad44d52d98935c0a
+size 11674910

Dockerfile

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+
+# # WORKS
+# # Use the official slim Python 3.9 image
+# FROM python:3.9-slim
+
+# # Install necessary build dependencies
+# RUN apt-get update && apt-get install -y build-essential python3-dev libffi-dev && apt-get clean
+
+# # Create a new user with a specific UID
+# RUN useradd -m -u 1000 user
+
+# # Set environment variables
+# ENV HOME=/home/user \
+#     PATH=/usr/local/bin:$PATH
+
+# # Set the working directory
+# WORKDIR $HOME/app
+
+# # Copy only the requirements file first to leverage Docker's layer caching
+# COPY --chown=user requirements.txt $HOME/app/requirements.txt
+
+# # Install dependencies
+# RUN pip install --no-cache-dir --upgrade pip && \
+#     pip install --no-cache-dir protobuf==3.20.0 && \
+#     pip install --no-cache-dir -r requirements.txt
+
+# # Install chainlit separately
+# RUN pip install --no-cache-dir chainlit
+
+# # Verify chainlit installation and check global binaries
+# RUN ls /usr/local/bin && pip show chainlit
+
+# # Copy the rest of the application files into the container
+# COPY --chown=user . $HOME/app
+
+# # Expose the port that Chainlit will run on
+# EXPOSE 7860
+
+# # Run Chainlit with the specified command
+# CMD ["chainlit", "run", "app.py", "--port", "7860"]
+
+
+# Use the official slim Python 3.9 image
+FROM python:3.9-slim
+
+# Install necessary build dependencies
+RUN apt-get update && apt-get install -y build-essential python3-dev libffi-dev && apt-get clean
+
+# Create a new user with a specific UID
+RUN useradd -m -u 1000 user
+
+# Set environment variables
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory
+WORKDIR $HOME/app
+
+# Copy only the requirements file first to leverage Docker's layer caching
+COPY --chown=user requirements.txt $HOME/app/requirements.txt
+
+# Print requirements.txt content for debugging
+RUN cat requirements.txt
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Install chainlit explicitly and verify its installation
+RUN pip install --no-cache-dir chainlit==0.7.700 && \
+    which chainlit && \
+    chainlit --version
+
+# Verify all installations
+RUN pip list
+
+# Copy the rest of the application files into the container
+COPY --chown=user . $HOME/app
+
+# Expose the port that Chainlit will run on
+EXPOSE 7860
+
+# Switch to the non-root user
+USER user
+
+# Run Chainlit with the specified command
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

LICENSE

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NIST.AI.600-1.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b98f5f456157e2de607322a9a2630175f93683754a455c469c0954e4e94a1b1c
+size 1204825

README.md

@@ -1,11 +1,2 @@
----
-title: AIE4 MIDTERM
-emoji: 🏆
-colorFrom: purple
-colorTo: green
-sdk: docker
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Midterm
+Midterm testing for bootcamp

app.py

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+import chainlit as cl
+import os
+import tiktoken
+from langchain.document_loaders import PyMuPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain_community.vectorstores import Qdrant
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from operator import itemgetter
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema import Document
+
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+
+# Tiktoken length function
+def tiktoken_len(text):
+    tokens = tiktoken.encoding_for_model("gpt-4o-mini").encode(text)
+    return len(tokens)
+
+# Function to load and split PDFs
+def load_and_split_pdfs_by_paragraphs(directory, chunk_size=500, chunk_overlap=50):
+    documents = []
+    
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        length_function=tiktoken_len,
+        separators=["\n\n", "\n"]
+    )
+    
+    for filename in os.listdir(directory):
+        if filename.endswith('.pdf'):
+            file_path = os.path.join(directory, filename)
+            
+            loader = PyMuPDFLoader(file_path)
+            pdf_documents = loader.load()
+            
+            for page_num, page in enumerate(pdf_documents):
+                splits = text_splitter.split_text(page.page_content)
+                for i, split in enumerate(splits):
+                    documents.append(Document(
+                        page_content=split,
+                        metadata={
+                            "filename": filename,
+                            "page_number": page_num + 1,
+                            "chunk_number": i + 1
+                        }
+                    ))
+    
+    return documents
+
+# RAG prompt template
+RAG_PROMPT = """
+CONTEXT:
+{context}
+
+QUERY:
+{question}
+
+You are a helpful assistant. Use the available context to answer the question. If you can't answer the question, say you don't know.
+"""
+
+@cl.on_chat_start
+async def start():
+    # Initialize the RAG system
+    cl.Message(content="Initializing the RAG system... This may take a moment.").send()
+    
+    # Set the directory containing your PDF files
+    current_directory = os.getcwd()  # Update this path
+    
+    # Load and split PDFs
+    docs = load_and_split_pdfs_by_paragraphs(current_directory)
+    
+    # Initialize embedding model
+    embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+    
+    # Create Qdrant vector store
+    qdrant_vectorstore = Qdrant.from_documents(
+        docs,
+        embedding_model,
+        location=":memory:",
+        collection_name="extending_context_window_llama_3",
+    )
+    
+    # Create retriever
+    qdrant_retriever = qdrant_vectorstore.as_retriever()
+    
+    # Create RAG prompt
+    rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
+    
+    # Initialize OpenAI chat model
+    openai_chat_model = ChatOpenAI(model="gpt-4o-mini")
+    
+    # Create the RAG chain
+    rag_chain = (
+        {"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
+        | rag_prompt
+        | openai_chat_model
+        | StrOutputParser()
+    )
+    
+    # Store the RAG chain in the user session
+    cl.user_session.set("rag_chain", rag_chain)
+    
+    cl.Message(content="RAG system initialized. You can now ask questions about the PDF documents.").send()
+
+@cl.on_message
+async def main(message: cl.Message):
+    # Retrieve the RAG chain from the user session
+    rag_chain = cl.user_session.get("rag_chain")
+    
+    # Use the RAG chain to process the user's question
+    response = rag_chain.invoke({"question": message.content})
+    
+    # Send the response back to the user
+    await cl.Message(content=response).send()
+
+# if __name__ == "__main__":
+#     cl.run()

chainlit.md

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+# Welcome to the RAG System Chatbot!
+
+This chatbot uses a Retrieval-Augmented Generation (RAG) system to answer your questions based on the content of uploaded PDF documents.
+
+## How to use
+
+Simply type your question in the chat box and press enter. The system will retrieve relevant information from the documents and generate an answer for you.
+
+Enjoy chatting!

requirements.txt

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+chainlit
+langchain
+PyMuPDF
+tiktoken
+openai
+qdrant-client
+
+langchain-community
+langchain_openai
+
+marshmallow==3.19.0
+dataclasses-json
+multidict==6.0.2
+idna==2.8
+
+python-dotenv==0.21.0
+dataclasses-json==0.5.7
+# Other dependencies with exact versions
+
+