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import chainlit as cl
from helper_functions import process_file, load_documents_from_url, store_uploaded_file
import models
import agents
import asyncio
@cl.on_chat_start
async def on_chat_start():
global qdrant_store
qdrant_store = models.semantic_tuned_Qdrant_vs
global retrieval_augmented_qa_chain
retrieval_augmented_qa_chain = agents.simple_rag_chain
res = await ask_action()
await handle_response(res)
@cl.author_rename
def rename(orig_author: str):
return "AI Assistant"
@cl.on_message
async def main(message: cl.Message):
await cl.Message(f"Processing `{message.content}`", disable_human_feedback=True)
if message.content.startswith("http://") or message.content.startswith("https://"):
message_type = "url"
else:
message_type = "question"
if message_type == "url":
try:
# Run the document loading and splitting in a thread
docs = await asyncio.to_thread(load_documents_from_url, message.content)
await cl.Message(content="loaded docs").send()
splits = await asyncio.to_thread(models.semanticChunker_tuned.split_documents, docs)
await cl.Message(content="split docs").send()
for i, doc in enumerate(splits):
doc.metadata["user_upload_source"] = f"source_{i}"
print(f"Processing {len(docs)} text chunks")
# Add to the qdrant_store asynchronously
await asyncio.to_thread(qdrant_store.add_documents, splits)
await cl.Message(f"Processing `{message.content}` done. You can now ask questions!").send()
except Exception as e:
await cl.Message(f"Error processing the document: {e}").send()
else:
# Handle the question as usual
response = await asyncio.to_thread(retrieval_augmented_qa_chain.invoke, {"question": message.content})
await cl.Message(content=response['content']).send()
res = await ask_action()
await handle_response(res)
## Chainlit helper functions
async def ask_action():
res = await cl.AskActionMessage(
content="Pick an action!",
actions=[
cl.Action(name="Question", value="question", label="Ask a question"),
cl.Action(name="File", value="file", label="Upload a file"),
cl.Action(name="Url", value="url", label="Upload a URL"),
],
).send()
return res
async def handle_response(res):
if res and res.get("value") == "file":
files = None
files = await cl.AskFileMessage(
content="Please upload a Text or PDF file to begin!",
accept=["text/plain", "application/pdf"],
max_size_mb=12,
).send()
file = files[0]
msg = cl.Message(
content=f"Processing `{file.name}`...", disable_human_feedback=True
)
await msg.send()
# load the file
file_path = store_uploaded_file(file)
docs = await asyncio.to_thread(process_file, file_path)
await cl.Message(content="loaded docs").send()
splits = await asyncio.to_thread(models.semanticChunker_tuned.split_documents, docs)
await cl.Message(content="split docs").send()
for i, doc in enumerate(splits):
doc.metadata["user_upload_source"] = f"source_{i}"
print(f"Processing {len(docs)} text chunks")
# Add to the qdrant_store
await asyncio.to_thread(qdrant_store.add_documents, splits)
await cl.Message(content="added to vs").send()
msg.content = f"Processing `{file.name}` done. You can now ask questions!"
await msg.update()
if res and res.get("value") == "url":
await cl.Message(content="Submit a url link in the message box below.").send()
if res and res.get("value") == "question":
await cl.Message(content="Ask away!").send()
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