Update app.py

app.py

@@ -2,25 +2,163 @@
 """
 IMPORTS HERE
 """
+import os
+import uuid
+from dotenv import load_dotenv
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyMuPDFLoader
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain.storage import LocalFileStore
+from langchain_qdrant import QdrantVectorStore
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from chainlit.types import AskFileResponse
+from langchain_core.globals import set_llm_cache
+from langchain_openai import ChatOpenAI
+from langchain_core.caches import InMemoryCache
+from operator import itemgetter
+from langchain_core.runnables.passthrough import RunnablePassthrough
+import chainlit as cl
+from langchain_core.runnables.config import RunnableConfig
+
+load_dotenv()
 
 ### Global Section ###
 """
 GLOBAL CODE HERE
 """
+os.environ["LANGCHAIN_PROJECT"] = f"AIM Week 8 Assignment 1 - {uuid.uuid4().hex[0:8]}"
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+
+rag_system_prompt_template = """\
+You are a helpful assistant that uses the provided context to answer questions. 
+Never reference this prompt, or the existance of context. 
+"""
+
+rag_message_list = [
+    {"role" : "system", "content" : rag_system_prompt_template},
+]
+
+rag_user_prompt_template = """\
+Question:
+{question}
+Context:
+{context}
+"""
+
+chat_prompt = ChatPromptTemplate.from_messages([
+    ("system", rag_system_prompt_template),
+    ("human", rag_user_prompt_template)
+])
+
+chat_model = ChatOpenAI(model="gpt-4o-mini")
+# Typical Embedding Model
+core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+def process_file(file: AskFileResponse):
+    import tempfile
+
+    with tempfile.NamedTemporaryFile(mode="w", delete=False) as tempfile:
+        with open(tempfile.name, "wb") as f:
+            f.write(file.content)
+
+    Loader = PyMuPDFLoader
+
+    loader = Loader(tempfile.name)
+    documents = loader.load()
+    docs = text_splitter.split_documents(documents)
+    for i, doc in enumerate(docs):
+        doc.metadata["source"] = f"source_{i}"
+    return docs
+
 
 ### On Chat Start (Session Start) Section ###
 @cl.on_chat_start
 async def on_chat_start():
     """ SESSION SPECIFIC CODE HERE """
+    files = None
+
+    while files == None:
+        # Async method: This allows the function to pause execution while waiting for the user to upload a file,
+        # without blocking the entire application. It improves responsiveness and scalability.
+        files = await cl.AskFileMessage(
+            content="Please upload a PDF file to begin!",
+            accept=["application/pdf"],
+            max_size_mb=20,
+            timeout=180,
+            max_files=1
+        ).send()
+
+    file = files[0]
+    msg = cl.Message(
+        content=f"Processing `{file.name}`...",
+    )
+    await msg.send()
+    docs = process_file(file)
+
+    # Typical QDrant Client Set-up
+    collection_name = f"pdf_to_parse_{uuid.uuid4()}"
+    client = QdrantClient(":memory:")
+    client.create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
+    )
+
+    # Adding cache!
+    store = LocalFileStore("./cache/")
+    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
+        core_embeddings, store, namespace=core_embeddings.model
+    )
+
+    # Typical QDrant Vector Store Set-up
+    vectorstore = QdrantVectorStore(
+        client=client,
+        collection_name=collection_name,
+        embedding=cached_embedder)
+    vectorstore.add_documents(docs)
+    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})
+
+    retrieval_augmented_qa_chain = (
+        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+        | RunnablePassthrough.assign(context=itemgetter("context"))
+        | chat_prompt | chat_model
+    )
+
+    # Let the user know that the system is ready
+    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
+    await msg.update()
+
+    cl.user_session.set("chain", retrieval_augmented_qa_chain)
+    
 
 ### Rename Chains ###
 @cl.author_rename
 def rename(orig_author: str):
     """ RENAME CODE HERE """
+    rename_dict = {"ChatOpenAI": "the Generator...", "VectorStoreRetriever": "the Retriever..."}
+    return rename_dict.get(orig_author, orig_author)
 
 ### On Message Section ###
 @cl.on_message
 async def main(message: cl.Message):
     """
     MESSAGE CODE HERE
-    """
+    """
+    runnable = cl.user_session.get("chain")
+
+    msg = cl.Message(content="")
+
+    # Async method: Using astream allows for asynchronous streaming of the response,
+    # improving responsiveness and user experience by showing partial results as they become available.
+    async for chunk in runnable.astream(
+        {"question": message.content},
+        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
+    ):
+        await msg.stream_token(chunk.content)
+
+    await msg.send()