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import time |
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import streamlit as st |
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from langchain.memory import ConversationBufferWindowMemory, ConversationBufferMemory, StreamlitChatMessageHistory |
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from langchain.prompts import MessagesPlaceholder |
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from langchain.chat_models import ChatOpenAI |
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from langchain.embeddings import VoyageEmbeddings |
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from langchain.vectorstores import DeepLake |
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from langchain.retrievers.document_compressors import CohereRerank |
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from langchain.retrievers import ContextualCompressionRetriever |
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from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate |
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from dotenv import load_dotenv |
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from operator import itemgetter |
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough, RunnableLambda |
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from langchain.output_parsers import StructuredOutputParser, ResponseSchema |
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load_dotenv() |
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response_schemas = [ |
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ResponseSchema(name="Name", description="The candidate name."), |
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ResponseSchema(name="Experience", description="The work experiences of the candidate."), |
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ResponseSchema(name="Skills", description="The skills of the candidate."), |
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ResponseSchema(name="Projects", description="The projects of the candidate."), |
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ResponseSchema(name="Summary", description="Final conclusion about the chosen candidate as the best choice") |
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] |
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output_parser = StructuredOutputParser.from_response_schemas(response_schemas) |
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format_instructions = output_parser.get_format_instructions() |
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system_message = ( |
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"You are a experienced Hiring manager. " |
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"You are looking for a relevant candidates to fill a position in your company. Always look for Job roles, " |
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"You have a list of requirements asked by the user. Look for major skills and projects in the document." |
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" Always look for relevant experiences, projects and skills. " |
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"and finally the link to the resume. IF the link is a PATH TO A FILE, then output only the the filename not the path." |
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) |
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human_message = ("You are provided with a list of resumes. " |
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"Find the most relevant candidates for the job from " |
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"{context} " |
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"that suits the job decription " |
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"{query}" |
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"Answer the query in a format that is easy to read." |
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"------------" |
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"{format_instructions}" |
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) |
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@st.cache_resource |
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def init_retriever(dataset_path="hub://p1utoze/default", |
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embeddings_model="voyage-lite-01", |
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): |
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embeddings = VoyageEmbeddings(model=embeddings_model, show_progress_bar=True) |
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db = DeepLake(token=st.secrets["deep_lake_token"], dataset_path=dataset_path, read_only=True, embedding=embeddings) |
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retriever = db.as_retriever() |
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retriever.search_kwargs['top_k'] = "5" |
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retriever.search_kwargs['distance_metric'] = 'cos' |
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compressor = CohereRerank() |
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compression_retriever = ContextualCompressionRetriever( |
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base_compressor=compressor, base_retriever=retriever |
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) |
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return compression_retriever |
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@st.cache_resource |
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def load_model(): |
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chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.4) |
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return chat |
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@st.cache_resource |
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def load_memory(): |
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msgs = StreamlitChatMessageHistory(key="langchain_messages") |
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memory = ConversationBufferMemory(return_messages=True) |
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return memory |
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def typewriter(text: str, title: str, speed: int): |
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tokens = text.split() |
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container = st.empty() |
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for index in range(len(tokens) + 1): |
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curr_full_text = " ".join(tokens[:index]) |
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if index == len(tokens): |
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container.markdown(curr_full_text) |
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else: |
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container.markdown(curr_full_text + "▌") |
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time.sleep(1 / speed) |
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def model_pipeline(memory): |
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compression_retriever = init_retriever( |
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"hub://p1utoze/resumes", |
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embeddings_model="voyage-lite-01", |
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) |
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chat = load_model() |
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system_message_prompt = SystemMessagePromptTemplate.from_template(system_message) |
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human_message_prompt = HumanMessagePromptTemplate.from_template( |
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human_message, |
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partial_variables={"format_instructions": format_instructions}, |
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input_variables=["query", "context"] |
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) |
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prompt = ChatPromptTemplate.from_messages( |
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[system_message_prompt, |
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MessagesPlaceholder(variable_name="history"), |
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human_message_prompt] |
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) |
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print(memory.load_memory_variables({})) |
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setup_and_retrieval = RunnableParallel( |
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{"context": compression_retriever, |
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"query": RunnablePassthrough(), |
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"history": RunnableLambda(memory.load_memory_variables) | itemgetter("history") |
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}, |
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) |
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chain = ( |
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setup_and_retrieval |
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| prompt |
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| chat |
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| output_parser |
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) |
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return chain |
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