New project with gemini thinking
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Madhu
parent
5f5bebfc9f
commit
38911478ce
1 changed files with 140 additions and 27 deletions
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@ -1,58 +1,171 @@
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import os
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import streamlit as st
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from agno.agent import Agent
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from agno.models.google import Gemini
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from agno.tools.duckduckgo import DuckDuckGoTools
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from agno.tools.yfinance import YFinanceTools
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import os
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from langchain_community.document_loaders import PyPDFLoader, WebBaseLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_qdrant import QdrantVectorStore
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from qdrant_client import QdrantClient
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from qdrant_client.models import Distance, VectorParams
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from agno.vectordb.pgvector import PgVector
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from agno.embedder.google import GeminiEmbedder
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import tempfile
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import bs4
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# Streamlit App Title
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st.title("AI Agent with Agno and Gemini Thinking")
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# Sidebar for API Key Input
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st.sidebar.header("Configuration")
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api_key = st.sidebar.text_input("Enter your Google API Key", type="password")
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if api_key:
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os.environ["GOOGLE_API_KEY"] = api_key
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google_api_key = st.sidebar.text_input("Enter your Google API Key", type="password")
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qdrant_api_key = st.sidebar.text_input("Enter your Qdrant API Key", type="password")
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qdrant_url = st.sidebar.text_input("Enter your Qdrant URL", placeholder="https://your-qdrant-url.com")
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if google_api_key:
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os.environ["GOOGLE_API_KEY"] = google_api_key
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# Initialize Qdrant Client
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def init_qdrant():
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if not qdrant_api_key or not qdrant_url:
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st.warning("Please provide Qdrant API Key and URL in the sidebar.")
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return None
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try:
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client = QdrantClient(url=qdrant_url, api_key=qdrant_api_key, timeout=60)
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client.get_collections() # Test connection
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return client
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except Exception as e:
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st.error(f"Failed to initialize Qdrant: {e}")
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return None
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qdrant_client = init_qdrant()
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# File/URL Upload Section
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st.sidebar.header("Upload Data")
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uploaded_file = st.sidebar.file_uploader("Upload a document", type=["txt", "pdf", "jpg", "png"])
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web_url = st.sidebar.text_input("Enter a web URL")
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# Document and Web URL Processing
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def process_document(file):
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try:
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with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
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tmp_file.write(file.getvalue())
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tmp_path = tmp_file.name
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loader = PyPDFLoader(tmp_path)
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documents = loader.load()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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texts = text_splitter.split_documents(documents)
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os.unlink(tmp_path)
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return texts
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except Exception as e:
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st.error(f"Error processing document: {e}")
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return []
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def process_web_url(url):
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try:
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loader = WebBaseLoader(
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web_paths=(url,),
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bs_kwargs=dict(
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parse_only=bs4.SoupStrainer(
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class_=("post-content", "post-title", "post-header")
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)
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),
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)
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documents = loader.load()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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texts = text_splitter.split_documents(documents)
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return texts
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except Exception as e:
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st.error(f"Error processing web URL: {e}")
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return []
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# Create and Populate Qdrant Vector Store
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COLLECTION_NAME = "agno_rag"
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def create_vector_store(texts):
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if not qdrant_client:
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return None
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try:
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# Create collection if it doesn't exist
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try:
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qdrant_client.create_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=VectorParams(size=1024, distance=Distance.COSINE)
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)
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st.success(f"Created new collection: {COLLECTION_NAME}")
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except Exception as e:
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if "already exists" not in str(e).lower():
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raise e
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# Initialize QdrantVectorStore
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vector_store = QdrantVectorStore(
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client=qdrant_client,
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collection_name=COLLECTION_NAME,
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embedding=GeminiEmbedder(dimensions=1024) # Add embedding model if needed
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)
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# Add documents to the vector store
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with st.spinner('Storing documents in Qdrant...'):
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vector_store.add_documents(texts)
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st.success("Documents successfully stored in Qdrant!")
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return vector_store
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except Exception as e:
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st.error(f"Error creating vector store: {e}")
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return None
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# Process Uploaded File or Web URL
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if uploaded_file:
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texts = process_document(uploaded_file)
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if texts:
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vector_store = create_vector_store(texts)
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elif web_url:
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texts = process_web_url(web_url)
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if texts:
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vector_store = create_vector_store(texts)
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# Initialize the Agent
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if api_key:
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if google_api_key and qdrant_client:
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thinking_agent = Agent(
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name="Thinking Agent",
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role="Think about the problem",
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model=Gemini(id="gemini-2.0-flash-exp", api_key=api_key),
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model=Gemini(id="gemini-2.0-flash-exp", api_key=google_api_key),
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instructions="Given the problem, think about it and provide a detailed explanation",
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show_tool_calls=True,
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markdown=True,
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)
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# Chat Interface
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st.header("Chat with the Agent")
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user_input = st.text_input("Ask a question or describe the problem:")
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# Display chat history if it exists
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if 'chat_history' not in st.session_state:
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st.session_state.chat_history = []
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for message in st.session_state.chat_history:
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with st.chat_message(message["role"]):
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st.write(message["content"])
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# Chat input using Streamlit's chat_input for better UX
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user_input = st.chat_input("Ask a question or describe a problem you'd like me to think about...")
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if user_input:
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# Process the user's input
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if uploaded_file:
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# Handle file upload
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file_content = uploaded_file.read()
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st.write("File content:", file_content)
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# Add logic to process the file content with the agent
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response = thinking_agent.run(f"Given this file content: {file_content}, answer: {user_input}")
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elif web_url:
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# Handle web URL
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st.write("Web URL:", web_url)
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# Add logic to process the web URL with the agent
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response = thinking_agent.run(f"Given this web URL: {web_url}, answer: {user_input}")
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else:
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# Handle normal chat
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response = thinking_agent.run(user_input)
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# Query the Qdrant vector store for relevant documents
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if 'vector_store' in locals():
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retriever = vector_store.as_retriever(
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search_type="similarity_score_threshold",
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search_kwargs={"k": 5, "score_threshold": 0.7}
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)
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relevant_docs = retriever.get_relevant_documents(user_input)
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# Display the response
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if relevant_docs:
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st.write("Relevant Documents:")
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for doc in relevant_docs:
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st.write(doc.page_content[:200] + "...")
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# Process the user's input with the agent
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response = thinking_agent.run(user_input)
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st.write("Agent's Response:")
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st.write(response.content)
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else:
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st.warning("Please enter your Google API Key in the sidebar to proceed.")
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st.warning("Please enter your Google API Key and Qdrant credentials in the sidebar to proceed.")
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