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Browse files- app.py +32 -30
- requirements.txt +1 -1
app.py
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@@ -1,6 +1,5 @@
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import chainlit as cl
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from dotenv import load_dotenv
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from langchain_openai import OpenAIEmbeddings
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from langchain_core.prompts import ChatPromptTemplate
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@@ -11,25 +10,27 @@ from langchain.schema.runnable import RunnablePassthrough
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from langchain_openai import ChatOpenAI
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from langchain.schema.runnable.config import RunnableConfig
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from langchain_core.output_parsers import StrOutputParser
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from langchain_community.document_loaders import UnstructuredPDFLoader
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load_dotenv()
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RAG_PROMPT = """
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CONTEXT:
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{context}
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QUERY:
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{question}
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You house builder and can only provide your answers from the context.
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You can only provide a response in danish
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Don't tell in your response that you are getting it from the context.
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"""
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text_splitter = RecursiveCharacterTextSplitter(
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@@ -76,37 +77,38 @@ text_splitter = RecursiveCharacterTextSplitter(
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# )
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loader = UnstructuredPDFLoader("
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embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
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# vector_store = Pinecone.from_documents(data, embedding_model, index_name=
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retriever = vector_store.as_retriever()
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rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
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model = ChatOpenAI(model="gpt-3.5-turbo")
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@cl.on_chat_start
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async def main():
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mecanic_qa_chain = ""
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mecanic_qa_chain = (
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)
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cl.user_session.set("runnable", mecanic_qa_chain)
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@cl.on_message
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async def on_message(message: cl.Message):
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runnable = cl.user_session.get("runnable")
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msg = cl.Message(content="")
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async for chunk in runnable.astream(
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):
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import chainlit as cl
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import os
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from dotenv import load_dotenv
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from langchain_openai import OpenAIEmbeddings
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_openai import ChatOpenAI
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from langchain.schema.runnable.config import RunnableConfig
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from langchain_core.output_parsers import StrOutputParser
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_community.document_loaders import UnstructuredPDFLoader
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load_dotenv()
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# RAG_PROMPT = """
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# CONTEXT:
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# {context}
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# QUERY:
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# {question}
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# You house builder and can only provide your answers from the context.
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# You can only provide a response in danish
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# Don't tell in your response that you are getting it from the context.
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# """
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text_splitter = RecursiveCharacterTextSplitter(
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# )
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loader = UnstructuredPDFLoader("./br_femogfirs.pdf")
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# loader = UnstructuredPDFLoader("./br_syvoghalvfjerds.pdf")br_femogfirs.pdf
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# data = loader.load_and_split(text_splitter)
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data = loader.load()
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# embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
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# vector_store = Pinecone.from_documents(data, embedding_model, index_name="bygnings-regl-rag-1")
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# retriever = vector_store.as_retriever()
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# rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
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# model = ChatOpenAI(model="gpt-3.5-turbo")
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@cl.on_chat_start
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async def main():
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mecanic_qa_chain = ""
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# mecanic_qa_chain = (
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# {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
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# | RunnablePassthrough.assign(context=itemgetter("context"))
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# | rag_prompt | model | StrOutputParser()
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# )
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cl.user_session.set("runnable", mecanic_qa_chain)
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@cl.on_message
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async def on_message(message: cl.Message):
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runnable = cl.user_session.get("runnable")
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# msg = cl.Message(content="")
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# async for chunk in runnable.astream(
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# {"question":message.content},
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# config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
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# ):
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# await msg.stream_token(chunk)
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requirements.txt
CHANGED
@@ -13,4 +13,4 @@ pdf2image
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bitsandbytes
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pillow_heif
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opencv-python-headless
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bitsandbytes
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pillow_heif
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opencv-python-headless
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pikepdf
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