mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
synced 2026-05-02 18:45:05 -05:00
98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
import streamlit as st
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import nest_asyncio
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from io import BytesIO
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from phi.assistant import Assistant
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from phi.document.reader.pdf import PDFReader
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from phi.llm.openai import OpenAIChat
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from phi.knowledge import AssistantKnowledge
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from phi.tools.duckduckgo import DuckDuckGo
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from phi.embedder.openai import OpenAIEmbedder
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from phi.vectordb.pgvector import PgVector2
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from phi.storage.assistant.postgres import PgAssistantStorage
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# Apply nest_asyncio to allow nested event loops, required for running async functions in Streamlit
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nest_asyncio.apply()
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# Database connection string for PostgreSQL
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DB_URL = "postgresql+psycopg://ai:ai@localhost:5532/ai"
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# Function to set up the Assistant, utilizing caching for resource efficiency
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@st.cache_resource
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def setup_assistant(api_key: str) -> Assistant:
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llm = OpenAIChat(model="gpt-4o-mini", api_key=api_key)
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# Set up the Assistant with storage, knowledge base, and tools
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return Assistant(
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name="auto_rag_assistant", # Name of the Assistant
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llm=llm, # Language model to be used
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storage=PgAssistantStorage(table_name="auto_rag_storage", db_url=DB_URL),
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knowledge_base=AssistantKnowledge(
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vector_db=PgVector2(
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db_url=DB_URL,
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collection="auto_rag_docs",
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embedder=OpenAIEmbedder(model="text-embedding-ada-002", dimensions=1536, api_key=api_key),
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),
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num_documents=3,
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),
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tools=[DuckDuckGo()], # Additional tool for web search via DuckDuckGo
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instructions=[
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"Search your knowledge base first.",
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"If not found, search the internet.",
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"Provide clear and concise answers.",
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],
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show_tool_calls=True,
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search_knowledge=True,
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read_chat_history=True,
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markdown=True,
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debug_mode=True,
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)
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# Function to add a PDF document to the knowledge base
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def add_document(assistant: Assistant, file: BytesIO):
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reader = PDFReader()
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docs = reader.read(file)
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if docs:
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assistant.knowledge_base.load_documents(docs, upsert=True)
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st.success("Document added to the knowledge base.")
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else:
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st.error("Failed to read the document.")
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# Function to query the Assistant and return a response
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def query_assistant(assistant: Assistant, question: str) -> str:
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return "".join([delta for delta in assistant.run(question)])
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# Main function to handle Streamlit app layout and interactions
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def main():
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st.set_page_config(page_title="AutoRAG", layout="wide")
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st.title("🤖 Auto-RAG: Autonomous RAG with GPT-4o")
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api_key = st.sidebar.text_input("Enter your OpenAI API Key 🔑", type="password")
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if not api_key:
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st.sidebar.warning("Enter your OpenAI API Key to proceed.")
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st.stop()
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assistant = setup_assistant(api_key)
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uploaded_file = st.sidebar.file_uploader("📄 Upload PDF", type=["pdf"])
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if uploaded_file and st.sidebar.button("🛠️ Add to Knowledge Base"):
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add_document(assistant, BytesIO(uploaded_file.read()))
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question = st.text_input("💬 Ask Your Question:")
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# When the user submits a question, query the assistant for an answer
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if st.button("🔍 Get Answer"):
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# Ensure the question is not empty
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if question.strip():
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with st.spinner("🤔 Thinking..."):
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# Query the assistant and display the response
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answer = query_assistant(assistant, question)
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st.write("📝 **Response:**", answer)
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else:
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# Show an error if the question input is empty
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st.error("Please enter a question.")
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# Entry point of the application
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if __name__ == "__main__":
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main()
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