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dbcfa6e
1
Parent(s):
7a2f93b
Adds third selection
Browse files
app.py
CHANGED
@@ -8,11 +8,11 @@ from langchain_core.caches import InMemoryCache
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from operator import itemgetter
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from langchain_core.runnables.passthrough import RunnablePassthrough
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from langchain_qdrant import QdrantVectorStore, Qdrant
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from langchain_community.document_loaders import
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import uuid
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import chainlit as cl
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import os
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from helper_functions import process_file, add_to_qdrant
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chat_model = ChatOpenAI(model="gpt-4o-mini")
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te3_small = OpenAIEmbeddings(model="text-embedding-3-small")
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@@ -38,24 +38,81 @@ chat_prompt = ChatPromptTemplate.from_messages([("system", rag_system_prompt_tem
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@cl.on_chat_start
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async def on_chat_start():
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qdrant_client = QdrantClient(url=os.environ["QDRANT_ENDPOINT"], api_key=os.environ["QDRANT_API_KEY"])
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qdrant_store = Qdrant(
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client=qdrant_client,
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collection_name="kai_test_docs",
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embeddings=te3_small
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)
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res = await cl.AskActionMessage(
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content="Pick an action!",
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actions=[
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cl.Action(name="Question", value="question", label="Ask a question"),
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cl.Action(name="File", value="file", label="Upload a file
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],
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).send()
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if res and res.get("value") == "file":
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files = None
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files = await cl.AskFileMessage(
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content="Please upload a
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accept=["text/plain", "application/pdf"],
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max_size_mb=12,
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).send()
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@@ -82,33 +139,8 @@ async def on_chat_start():
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msg.content = f"Processing `{file.name}` done. You can now ask questions!"
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await msg.update()
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if res and res.get("value") == "question":
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await cl.Message(content="Ask away!").send()
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# Load the style guide from the local file system
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style_guide_path = "./public/CoExperiences Writing Style Guide V1 (2024).pdf"
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loader = PyMuPDFLoader(style_guide_path)
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style_guide_docs = loader.load()
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style_guide_text = "\n".join([doc.page_content for doc in style_guide_docs])
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retriever = qdrant_store.as_retriever()
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global retrieval_augmented_qa_chain
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retrieval_augmented_qa_chain = (
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{
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"context": itemgetter("question") | retriever,
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"question": itemgetter("question"),
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"writing_style_guide": lambda _: style_guide_text
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}
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| RunnablePassthrough.assign(context=itemgetter("context"))
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| chat_prompt
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| chat_model
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)
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@cl.author_rename
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def rename(orig_author: str):
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return "AI Assistant"
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@cl.on_message
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async def main(message: cl.Message):
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response = retrieval_augmented_qa_chain.invoke({"question": message.content})
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await cl.Message(content=response.content).send()
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from operator import itemgetter
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from langchain_core.runnables.passthrough import RunnablePassthrough
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from langchain_qdrant import QdrantVectorStore, Qdrant
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from langchain_community.document_loaders import PyPDFLoader
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import uuid
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import chainlit as cl
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import os
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from helper_functions import process_file, load_documents_from_url, add_to_qdrant
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chat_model = ChatOpenAI(model="gpt-4o-mini")
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te3_small = OpenAIEmbeddings(model="text-embedding-3-small")
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@cl.on_chat_start
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async def on_chat_start():
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qdrant_client = QdrantClient(url=os.environ["QDRANT_ENDPOINT"], api_key=os.environ["QDRANT_API_KEY"])
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global qdrant_store
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qdrant_store = Qdrant(
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client=qdrant_client,
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collection_name="kai_test_docs",
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embeddings=te3_small
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)
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res = await ask_action()
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await handle_response(res)
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# Load the style guide from the local file system
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style_guide_path = "./public/CoExperiences Writing Style Guide V1 (2024).pdf"
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loader = PyPDFLoader(style_guide_path)
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style_guide_docs = loader.load()
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style_guide_text = "\n".join([doc.page_content for doc in style_guide_docs])
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retriever = qdrant_store.as_retriever()
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global retrieval_augmented_qa_chain
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retrieval_augmented_qa_chain = (
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{
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"context": itemgetter("question") | retriever,
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"question": itemgetter("question"),
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"writing_style_guide": lambda _: style_guide_text
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}
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| RunnablePassthrough.assign(context=itemgetter("context"))
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| chat_prompt
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| chat_model
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)
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@cl.author_rename
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def rename(orig_author: str):
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return "Marketing Assistant"
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@cl.on_message
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async def main(message: cl.Message, message_type: str):
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if message_type == "url":
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# load the file
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docs = load_documents_from_url(message.content)
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splits = text_splitter.split_documents(docs)
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for i, doc in enumerate(splits):
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doc.metadata["user_upload_source"] = f"source_{i}"
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print(f"Processing {len(docs)} text chunks")
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# Add to the qdrant_store
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qdrant_store.add_documents(
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documents=splits
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)
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await cl.Message(f"Processing `{response.url}` done. You can now ask questions!").send()
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else:
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response = retrieval_augmented_qa_chain.invoke({"question": message.content})
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await cl.Message(content=response.content).send()
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res = await ask_action()
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await handle_response(res)
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## Chainlit helper functions
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async def ask_action():
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res = await cl.AskActionMessage(
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content="Pick an action!",
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actions=[
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cl.Action(name="Question", value="question", label="Ask a question"),
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cl.Action(name="File", value="file", label="Upload a file"),
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cl.Action(name="Url", value="url", label="Upload a URL"),
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],
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).send()
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return res
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async def handle_response(res):
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if res and res.get("value") == "file":
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files = None
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files = await cl.AskFileMessage(
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content="Please upload a Text or PDF file to begin!",
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accept=["text/plain", "application/pdf"],
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max_size_mb=12,
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).send()
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msg.content = f"Processing `{file.name}` done. You can now ask questions!"
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await msg.update()
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if res and res.get("value") == "url":
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await cl.Message(content="Submit a url link in the message box below.").send()
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if res and res.get("value") == "question":
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await cl.Message(content="Ask away!").send()
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