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ollama.py
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_community.llms import Ollama
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import streamlit as st
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import os
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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os.environ["LANGCHAIN_TRACING_V2"] = "true"
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os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
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# Prompt Template
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "You are a helpful assistant. Please respond to the user queries"),
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("user", "Question: {question}")
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]
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)
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# Streamlit app
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st.title('Langchain Demo With LLAMA2 API')
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input_text = st.text_input("Search the topic you want")
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# Ollama LLama2 LLM
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llm = Ollama(model="llama2")
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output_parser = StrOutputParser()
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chain = prompt | llm | output_parser
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# Display result when user inputs text
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if input_text:
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response = chain.invoke({"question": input_text})
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st.write(response)
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