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.gitattributes

@@ -33,3 +33,4 @@ 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
+data/How-to-Build-a-Career-in-AI.pdf filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,5 +1,11 @@
 import gradio as gr
 import os
+from aimakerspace.text_utils import PDFLoader, CharacterTextSplitter
+from aimakerspace.vectordatabase import VectorDatabase
+from aimakerspace.openai_utils.prompts import SystemRolePrompt, UserRolePrompt
+from aimakerspace.openai_utils.chatmodel import ChatOpenAI
+from aimakerspace.openai_utils.embedding import EmbeddingModel
+import asyncio
 
 def load_notebook():
     notebook_path = "Pythonic_RAG_Assignment.ipynb"
@@ -32,5 +38,102 @@ with gr.Blocks() as demo:
             3. Run: `jupyter notebook Pythonic_RAG_Assignment.ipynb`
             """)
 
+# Initialize the RAG pipeline
+def initialize_rag():
+    # Load the PDF
+    pdf_loader = PDFLoader("data/How-to-Build-a-Career-in-AI.pdf")
+    documents = pdf_loader.load_documents()
+    
+    # Split the documents
+    text_splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=300)
+    split_documents = text_splitter.split_texts(documents)
+    
+    # Create vector database
+    embedding_model = EmbeddingModel()
+    vector_db = VectorDatabase(embedding_model=embedding_model)
+    vector_db = asyncio.run(vector_db.abuild_from_list(split_documents))
+    
+    # Set up prompts
+    RAG_PROMPT_TEMPLATE = """ \
+    Use the provided context to answer the user's query.
+
+    You may not answer the user's query unless there is specific context in the following text.
+
+    If you do not know the answer, or cannot answer, please respond with "I don't know".
+    """
+    rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)
+    
+    USER_PROMPT_TEMPLATE = """ \
+    Context:
+    {context}
+
+    User Query:
+    {user_query}
+    """
+    user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)
+    
+    # Create ChatOpenAI instance
+    chat_openai = ChatOpenAI()
+    
+    # Create and return pipeline
+    return RetrievalAugmentedQAPipeline(vector_db_retriever=vector_db, llm=chat_openai)
+
+class RetrievalAugmentedQAPipeline:
+    def __init__(self, llm: ChatOpenAI, vector_db_retriever: VectorDatabase) -> None:
+        self.llm = llm
+        self.vector_db_retriever = vector_db_retriever
+
+    def run_pipeline(self, user_query: str) -> 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 = SystemRolePrompt(""" \
+        Use the provided context to answer the user's query.
+        You may not answer the user's query unless there is specific context in the following text.
+        If you do not know the answer, or cannot answer, please respond with "I don't know".
+        """).create_message()
+        
+        formatted_user_prompt = UserRolePrompt(""" \
+        Context:
+        {context}
+
+        User Query:
+        {user_query}
+        """).create_message(user_query=user_query, context=context_prompt)
+        
+        response = self.llm.run([formatted_system_prompt, formatted_user_prompt])
+        return response
+
+# Create Gradio interface
+def create_interface():
+    # Initialize RAG pipeline
+    rag_pipeline = initialize_rag()
+    
+    def query_rag(question):
+        return rag_pipeline.run_pipeline(question)
+    
+    with gr.Blocks(title="RAG Implementation") as demo:
+        gr.Markdown("# RAG Implementation Demo")
+        gr.Markdown("Ask questions about the 'How to Build a Career in AI' document")
+        
+        with gr.Row():
+            with gr.Column():
+                question = gr.Textbox(label="Your Question", placeholder="Type your question here...")
+                submit_btn = gr.Button("Submit")
+            
+            with gr.Column():
+                answer = gr.Textbox(label="Answer", lines=5)
+        
+        submit_btn.click(
+            fn=query_rag,
+            inputs=question,
+            outputs=answer
+        )
+    
+    return demo
+
 if __name__ == "__main__":
+    demo = create_interface()
     demo.launch() 

data/How-to-Build-a-Career-in-AI.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f510e0cf2d5a646f5d6fc253c153ef0a129a598bd3cab8379d395d535026332d
+size 3717673

requirements.txt

@@ -1,11 +1,14 @@
 jupyter
 notebook
 ipykernel
-numpy
+numpy>=1.24.0
 pandas
 matplotlib
 scikit-learn
 transformers
 datasets
 huggingface_hub
-openai 
+openai>=1.0.0
+python-dotenv>=1.0.0
+aimakerspace>=0.1.0
+asyncio>=3.4.3