Create app.py

app.py

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+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 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 uuid
+import chainlit as cl
+
+### Global Section ###
+chat_model = ChatOpenAI(model="gpt-4o-mini")
+set_llm_cache(InMemoryCache())
+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)])
+core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+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))
+store = LocalFileStore("./cache/")
+cached_embedder = CacheBackedEmbeddings.from_bytes_store(core_embeddings, store, namespace=core_embeddings.model)
+vectorstore = QdrantVectorStore(client=client,collection_name=collection_name,embedding=cached_embedder)
+Loader = PyMuPDFLoader
+
+### On Chat Start (Session Start) Section ###
+@cl.on_chat_start
+async def on_chat_start():
+    files = await cl.AskFileMessage(
+        content="Please upload a PDF file to begin.",
+        accept=["application/pdf"],
+        max_size_mb=20,
+        timeout=180,
+    ).send()
+
+    if not files:
+        await cl.Message(content="No file was uploaded. Please try again.").send()
+        return
+
+    file = files[0]
+    msg = cl.Message(content=f"Processing `{file.name}`...")
+    await msg.send()
+
+    # Save the file locally
+    with open(file.name, "wb") as f:
+        f.write(file.content)
+
+    # Load and process the document
+    loader = Loader(file.name)
+    documents = loader.load()
+    docs = text_splitter.split_documents(documents)
+    for i, doc in enumerate(docs):
+        doc.metadata["source"] = f"source_{i}"
+
+    # Add documents to the vectorstore
+    vectorstore.add_documents(docs)
+
+    # Create retriever
+    retriever = vectorstore.as_retriever()
+
+    # Create RAG chain
+    global retrieval_augmented_qa_chain
+    retrieval_augmented_qa_chain = (
+        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+        | RunnablePassthrough.assign(context=itemgetter("context"))
+        | chat_prompt
+        | chat_model
+    )
+
+    await cl.Message(content=f"`{file.name}` processed. Ask me questions!.").send()
+
+    
+
+### Rename Chains ###
+@cl.author_rename
+def rename(orig_author: str):
+    return "AI PDF Assistant"
+
+### On Message Section ###
+@cl.on_message
+async def main(message: cl.Message):
+    response = retrieval_augmented_qa_chain.invoke({"question": message.content})
+    await cl.Message(content=response.content).send()