Spaces:
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Added Required Files
Browse files- README.md +9 -1
- app.py +54 -0
- requirements.txt +32 -0
README.md
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---
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license: apache-2.0
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title: Search Engine
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sdk: streamlit
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emoji: 🏃
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colorFrom: red
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colorTo: yellow
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short_description: Search Engine With LLM
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---
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app.py
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import streamlit as st
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from langchain_groq import ChatGroq
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from langchain_community.utilities import ArxivAPIWrapper,WikipediaAPIWrapper
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from langchain_community.tools import ArxivQueryRun,WikipediaQueryRun,DuckDuckGoSearchRun
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from langchain.agents import initialize_agent,AgentType
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from langchain.callbacks import StreamlitCallbackHandler
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import os
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from dotenv import load_dotenv
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## Code
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####
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## Arxiv and wikipedia Tools
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arxiv_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
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arxiv=ArxivQueryRun(api_wrapper=arxiv_wrapper)
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api_wrapper=WikipediaAPIWrapper(top_k_results=1,doc_content_chars_max=200)
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wiki=WikipediaQueryRun(api_wrapper=api_wrapper)
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search=DuckDuckGoSearchRun(name="Search")
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st.title("🔎 LangChain - Chat with search")
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"""
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In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
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Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
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"""
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## Sidebar for settings
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st.sidebar.title("Settings")
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api_key=st.sidebar.text_input("Enter your Groq API Key:",type="password")
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if "messages" not in st.session_state:
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st.session_state["messages"]=[
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{"role":"assisstant","content":"Hi,I'm a chatbot who can search the web. How can I help you?"}
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]
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for msg in st.session_state.messages:
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st.chat_message(msg["role"]).write(msg['content'])
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if prompt:=st.chat_input(placeholder="What is machine learning?"):
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st.session_state.messages.append({"role":"user","content":prompt})
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st.chat_message("user").write(prompt)
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llm=ChatGroq(groq_api_key=api_key,model_name="Llama3-8b-8192",streaming=True)
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tools=[search,arxiv,wiki]
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search_agent=initialize_agent(tools,llm,agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,handling_parsing_errors=True)
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with st.chat_message("assistant"):
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st_cb=StreamlitCallbackHandler(st.container(),expand_new_thoughts=False)
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response=search_agent.run(st.session_state.messages,callbacks=[st_cb])
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st.session_state.messages.append({'role':'assistant',"content":response})
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st.write(response)
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requirements.txt
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langchain
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python-dotenv
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ipykernel
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langchain-community
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pypdf
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bs4
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arxiv
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pymupdf
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wikipedia
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langchain-text-splitters
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langchain-openai
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chromadb
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sentence_transformers
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langchain_huggingface
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faiss-cpu
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langchain_chroma
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duckdb
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pandas
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openai
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langchain-groq
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duckduckgo_search==5.3.1b1
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pymupdf
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arxiv
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wikipedia
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mysql-connector-python
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SQLAlchemy
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validators==0.28.1
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youtube_transcript_api
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unstructured
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pytube
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numexpr
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huggingface_hub
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