fixes some issues
This commit is contained in:
Madhu
parent
fc02a7f3dd
commit
68f300d141
1 changed files with 158 additions and 114 deletions
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@ -7,8 +7,8 @@ from agno.models.openai import OpenAIChat
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from agno.embedder.openai import OpenAIEmbedder
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import tempfile
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import os
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from agno.document.chunking.document import DocumentChunking
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#initializing the session state variables
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def init_session_state():
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"""Initialize session state variables"""
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if 'openai_api_key' not in st.session_state:
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@ -23,55 +23,87 @@ def init_session_state():
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st.session_state.legal_team = None
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if 'knowledge_base' not in st.session_state:
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st.session_state.knowledge_base = None
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# Add a new state variable to track processed files
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if 'processed_files' not in st.session_state:
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st.session_state.processed_files = set()
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COLLECTION_NAME = "legal_documents" # Define your collection name
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def init_qdrant():
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"""Initialize Qdrant vector database"""
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if not st.session_state.qdrant_api_key:
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raise ValueError("Qdrant API key not provided")
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if not st.session_state.qdrant_url:
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raise ValueError("Qdrant URL not provided")
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return Qdrant(
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collection="legal_knowledge",
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url=st.session_state.qdrant_url,
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api_key=st.session_state.qdrant_api_key,
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https=True,
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timeout=None,
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distance="cosine"
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)
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"""Initialize Qdrant client with configured settings."""
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if not all([st.session_state.qdrant_api_key, st.session_state.qdrant_url]):
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return None
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try:
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# Create Agno's Qdrant instance which implements VectorDb
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vector_db = Qdrant(
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collection=COLLECTION_NAME,
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url=st.session_state.qdrant_url,
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api_key=st.session_state.qdrant_api_key,
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embedder=OpenAIEmbedder(
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id="text-embedding-3-small",
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api_key=st.session_state.openai_api_key
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)
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)
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return vector_db
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except Exception as e:
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st.error(f"🔴 Qdrant connection failed: {str(e)}")
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return None
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def process_document(uploaded_file, vector_db: Qdrant):
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"""Process document, create embeddings and store in Qdrant vector database"""
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"""
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Process document, create embeddings and store in Qdrant vector database
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Args:
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uploaded_file: Streamlit uploaded file object
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vector_db (Qdrant): Initialized Qdrant instance from Agno
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Returns:
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PDFKnowledgeBase: Initialized knowledge base with processed documents
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"""
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if not st.session_state.openai_api_key:
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raise ValueError("OpenAI API key not provided")
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os.environ['OPENAI_API_KEY'] = st.session_state.openai_api_key
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with tempfile.TemporaryDirectory() as temp_dir:
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temp_file_path = os.path.join(temp_dir, uploaded_file.name)
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with open(temp_file_path, "wb") as f:
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f.write(uploaded_file.getbuffer())
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try:
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# Save the uploaded file to a temporary location
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with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as temp_file:
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temp_file.write(uploaded_file.getvalue())
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temp_file_path = temp_file.name
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st.info("Loading and processing document...")
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# Create a PDFKnowledgeBase with the vector_db
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knowledge_base = PDFKnowledgeBase(
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path=temp_file_path, # Single string path, not a list
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vector_db=vector_db,
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reader=PDFReader(),
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chunking_strategy=DocumentChunking(
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chunk_size=1000,
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overlap=200
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)
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)
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# Load the documents into the knowledge base
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with st.spinner('📤 Loading documents into knowledge base...'):
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try:
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knowledge_base.load(recreate=True, upsert=True)
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st.success("✅ Documents stored successfully!")
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except Exception as e:
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st.error(f"Error loading documents: {str(e)}")
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raise
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# Clean up the temporary file
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try:
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embedder = OpenAIEmbedder(
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model="text-embedding-3-small",
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api_key=st.session_state.openai_api_key
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)
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os.unlink(temp_file_path)
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except Exception:
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pass
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# Creating knowledge base with explicit Qdrant configuration
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knowledge_base = PDFKnowledgeBase(
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path=temp_dir,
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vector_db=vector_db,
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reader=PDFReader(chunk=True),
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embedder=embedder,
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recreate_vector_db=True
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)
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knowledge_base.load()
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return knowledge_base
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except Exception as e:
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raise Exception(f"Error processing document: {str(e)}")
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return knowledge_base
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except Exception as e:
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st.error(f"Document processing error: {str(e)}")
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raise Exception(f"Error processing document: {str(e)}")
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def main():
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st.set_page_config(page_title="Legal Document Analyzer", layout="wide")
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@ -102,7 +134,7 @@ def main():
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qdrant_url = st.text_input(
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"Qdrant URL",
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value=st.session_state.qdrant_url if st.session_state.qdrant_url else "https://f499085c-b4bf-4bda-a9a5-227f62a9ca20.us-west-2-0.aws.cloud.qdrant.io:6333",
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value=st.session_state.qdrant_url if st.session_state.qdrant_url else "",
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help="Enter your Qdrant instance URL"
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)
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if qdrant_url:
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@ -111,8 +143,10 @@ def main():
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if all([st.session_state.qdrant_api_key, st.session_state.qdrant_url]):
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try:
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if not st.session_state.vector_db:
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# Make sure we're initializing a QdrantClient here
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st.session_state.vector_db = init_qdrant()
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st.success("Successfully connected to Qdrant!")
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if st.session_state.vector_db:
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st.success("Successfully connected to Qdrant!")
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except Exception as e:
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st.error(f"Failed to connect to Qdrant: {str(e)}")
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@ -123,80 +157,90 @@ def main():
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uploaded_file = st.file_uploader("Upload Legal Document", type=['pdf'])
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if uploaded_file:
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with st.spinner("Processing document..."):
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try:
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knowledge_base = process_document(uploaded_file, st.session_state.vector_db)
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st.session_state.knowledge_base = knowledge_base
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# Initialize agents
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legal_researcher = Agent(
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name="Legal Researcher",
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role="Legal research specialist",
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model=OpenAIChat(model="gpt-4o"),
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tools=[DuckDuckGoTools()],
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knowledge=st.session_state.knowledge_base,
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search_knowledge=True,
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instructions=[
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"Find and cite relevant legal cases and precedents",
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"Provide detailed research summaries with sources",
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"Reference specific sections from the uploaded document",
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"Always search the knowledge base for relevant information"
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],
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show_tool_calls=True,
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markdown=True
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)
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contract_analyst = Agent(
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name="Contract Analyst",
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role="Contract analysis specialist",
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model=OpenAIChat(model="gpt-4o"),
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knowledge=knowledge_base,
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search_knowledge=True,
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instructions=[
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"Review contracts thoroughly",
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"Identify key terms and potential issues",
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"Reference specific clauses from the document"
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],
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markdown=True
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)
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legal_strategist = Agent(
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name="Legal Strategist",
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role="Legal strategy specialist",
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model=OpenAIChat(model="gpt-4o"),
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knowledge=knowledge_base,
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search_knowledge=True,
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instructions=[
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"Develop comprehensive legal strategies",
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"Provide actionable recommendations",
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"Consider both risks and opportunities"
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],
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markdown=True
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)
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# Legal Agent Team
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st.session_state.legal_team = Agent(
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name="Legal Team Lead",
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role="Legal team coordinator",
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model=OpenAIChat(model="gpt-4o"),
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team=[legal_researcher, contract_analyst, legal_strategist],
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knowledge=st.session_state.knowledge_base,
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search_knowledge=True,
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instructions=[
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"Coordinate analysis between team members",
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"Provide comprehensive responses",
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"Ensure all recommendations are properly sourced",
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"Reference specific parts of the uploaded document",
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"Always search the knowledge base before delegating tasks"
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],
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show_tool_calls=True,
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markdown=True
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)
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st.success("✅ Document processed and team initialized!")
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# Check if this file has already been processed
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if uploaded_file.name not in st.session_state.processed_files:
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with st.spinner("Processing document..."):
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try:
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# Process the document and get the knowledge base
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knowledge_base = process_document(uploaded_file, st.session_state.vector_db)
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except Exception as e:
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st.error(f"Error processing document: {str(e)}")
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if knowledge_base:
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st.session_state.knowledge_base = knowledge_base
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# Add the file to processed files
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st.session_state.processed_files.add(uploaded_file.name)
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# Initialize agents
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legal_researcher = Agent(
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name="Legal Researcher",
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role="Legal research specialist",
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model=OpenAIChat(id="gpt-4o"),
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tools=[DuckDuckGoTools()],
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knowledge=st.session_state.knowledge_base,
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search_knowledge=True,
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instructions=[
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"Find and cite relevant legal cases and precedents",
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"Provide detailed research summaries with sources",
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"Reference specific sections from the uploaded document",
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"Always search the knowledge base for relevant information"
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],
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show_tool_calls=True,
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markdown=True
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)
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contract_analyst = Agent(
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name="Contract Analyst",
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role="Contract analysis specialist",
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model=OpenAIChat(id="gpt-4o"),
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knowledge=st.session_state.knowledge_base,
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search_knowledge=True,
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instructions=[
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"Review contracts thoroughly",
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"Identify key terms and potential issues",
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"Reference specific clauses from the document"
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],
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markdown=True
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)
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legal_strategist = Agent(
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name="Legal Strategist",
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role="Legal strategy specialist",
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model=OpenAIChat(id="gpt-4o"),
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knowledge=st.session_state.knowledge_base,
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search_knowledge=True,
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instructions=[
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"Develop comprehensive legal strategies",
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"Provide actionable recommendations",
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"Consider both risks and opportunities"
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],
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markdown=True
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)
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# Legal Agent Team
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st.session_state.legal_team = Agent(
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name="Legal Team Lead",
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role="Legal team coordinator",
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model=OpenAIChat(id="gpt-4o"),
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team=[legal_researcher, contract_analyst, legal_strategist],
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knowledge=st.session_state.knowledge_base,
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search_knowledge=True,
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instructions=[
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"Coordinate analysis between team members",
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"Provide comprehensive responses",
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"Ensure all recommendations are properly sourced",
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"Reference specific parts of the uploaded document",
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"Always search the knowledge base before delegating tasks"
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],
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show_tool_calls=True,
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markdown=True
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)
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st.success("✅ Document processed and team initialized!")
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except Exception as e:
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st.error(f"Error processing document: {str(e)}")
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else:
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# File already processed, just show a message
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st.success("✅ Document already processed and team ready!")
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st.divider()
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st.header("🔍 Analysis Options")
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