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import os
from typing import List
from operator import itemgetter
from Chunking import ChunkingStrategy, TextLoaderAndSplitterWrapper
from langchain.schema.runnable import RunnablePassthrough
from langchain_openai import ChatOpenAI
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import Qdrant
import chainlit as cl
from chainlit.types import AskFileResponse
from chainlit.cli import run_chainlit
from uuid import uuid4
import tempfile
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
GPT_MODEL = "gpt-4o-mini"
# Used for Langsmith
unique_id = uuid4().hex[0:8]
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = f"LangSmith LCEL RAG - {unique_id}"
# Utility functions
def save_file(file: AskFileResponse,file_ext:str) -> str:
if file_ext == "application/pdf":
file_ext = ".pdf"
elif file_ext == "text/plain":
file_ext = ".txt"
else:
raise ValueError(f"Unknown file type: {file_ext}")
with tempfile.NamedTemporaryFile(
mode="wb", delete=False, suffix=file_ext
) as temp_file:
temp_file_path = temp_file.name
temp_file.write(file.content)
return temp_file_path
# Prepare the components that will form the chain
## Step 1: Create a prompt template
base_rag_prompt_template = """\
You are a helpful assistant that can answer questions related to the provided context. Repond I don't have that information if outside context.
Context:
{context}
Question:
{question}
"""
base_rag_prompt = ChatPromptTemplate.from_template(base_rag_prompt_template)
## Step 2: Create Embeddings model instance for creating embeddings
embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
## Step 2: Create the OpenAI chat model
base_llm = ChatOpenAI(model="gpt-4o-mini", tags=["base_llm"])
@cl.on_chat_start
async def on_chat_start():
msg = cl.Message(content="Welcome to the Chat with Files app powered by LCEL and OpenAI - RAG!")
await msg.send()
files = None
documents = None
# Wait for the user to upload a file
while files == None:
files = await cl.AskFileMessage(
content="Please upload a text or a pdf file to begin!",
accept=["text/plain", "application/pdf"],
max_size_mb=10,
max_files=1,
timeout=180,
).send()
## Load file and split into chunks
msg = cl.Message(content=f"Processing `{files[0].name}`...")
await msg.send()
current_file_path = save_file(files[0], files[0].type)
loader_splitter = TextLoaderAndSplitterWrapper(ChunkingStrategy.RECURSIVE_CHARACTER_CHAR_SPLITTER, current_file_path)
documents = loader_splitter.load_documents()
## Vectorising the documents
qdrant_vectorstore = Qdrant.from_documents(
documents=documents,
embedding=embedding_model,
location=":memory:"
)
qdrant_retriever = qdrant_vectorstore.as_retriever()
# create the chain on new chart session
retrieval_augmented_qa_chain = (
# INVOKE CHAIN WITH: {"question" : "<<SOME USER QUESTION>>"}
# "question" : populated by getting the value of the "question" key
# "context" : populated by getting the value of the "question" key and chaining it into the base_retriever
{"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
# "context" : is assigned to a RunnablePassthrough object (will not be called or considered in the next step)
# by getting the value of the "context" key from the previous step
| RunnablePassthrough.assign(context=itemgetter("context"))
# "response" : the "context" and "question" values are used to format our prompt object and then piped
# into the LLM and stored in a key called "response"
# "context" : populated by getting the value of the "context" key from the previous step
| {"response": base_rag_prompt | base_llm, "context": itemgetter("context")}
)
# Let the user know that the system is ready
msg = cl.Message(content=f"Processing `{files[0].name}` done. You can now ask questions!")
await msg.send()
cl.user_session.set("chain", retrieval_augmented_qa_chain)
@cl.on_message
async def main(message: cl.Message):
chain = cl.user_session.get("chain")
msg = cl.Message(content="")
response = chain.invoke({"question": message.content}, {"tags" : ["Demo Run"]})
msg.content= response["response"].content
await msg.send()
cl.user_session.set("chain", chain)
if __name__ == "__main__":
run_chainlit(__file__) |