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import datetime
import streamlit as st
import base64
import uuid
import time
from langchain_core.messages import HumanMessage, ToolMessage
from services.ai_service import get_response_stream
from services.mcp_service import run_agent
from services.chat_service import get_current_chat, _append_message_to_session
from services.export_service import export_chat_to_markdown, export_chat_to_json
from services.logging_service import get_logger
from services.task_monitor import get_task_monitor
from utils.async_helpers import run_async
from utils.ai_prompts import make_system_prompt, make_main_prompt
import ui_components.sidebar_components as sd_compents
from ui_components.main_components import display_tool_executions
from config import DEFAULT_MAX_TOKENS, DEFAULT_TEMPERATURE
import traceback
def replace_citation(match, citation_to_doc, doc_id_to_info):
"""Replace citation markers with formatted citations"""
citation_num = int(match.group(1))
if citation_num in citation_to_doc:
doc_id = citation_to_doc[citation_num]
if doc_id in doc_id_to_info:
doc_info = doc_id_to_info[doc_id]
title = doc_info.get('title', 'N/A')
return f"([{citation_num}]({doc_info.get('url', '#')} \"{title}\"))"
return match.group(0)
def replace_footnote_citation(match, citation_to_doc, doc_id_to_info):
"""Replace footnote citation markers with formatted citations"""
citation_num = int(match.group(1))
if citation_num in citation_to_doc:
doc_id = citation_to_doc[citation_num]
if doc_id in doc_id_to_info:
doc_info = doc_id_to_info[doc_id]
title = doc_info.get('title', 'N/A')
return f"([{citation_num}]({doc_info.get('url', '#')} \"{title}\"))"
return match.group(0)
def replace_document_citation(match, citation_to_doc, doc_id_to_info):
"""Replace document citation markers with formatted citations"""
citation_num = int(match.group(1))
if citation_num in citation_to_doc:
doc_id = citation_to_doc[citation_num]
if doc_id in doc_id_to_info:
doc_info = doc_id_to_info[doc_id]
title = doc_info.get('title', 'N/A')
return f"([{citation_num}]({doc_info.get('url', '#')} \"{title}\"))"
return match.group(0)
def extract_bio_final_answer(raw: str) -> str | None:
"""
Extract the final answer from bio_qa_stream_chat ToolMessage text marked with
'Bio-QA-final-Answer：' (note the Chinese full-width colon).
Compatible with two scenarios:
A) SSE stream: Multiple lines containing 'data: {...}' JSON
B) Plain text/code blocks: First appears ```bio-...``` code block, final answer appears at the end
Returns plain text answer; returns None if not found.
"""
if not raw:
return None
marker = "Bio-QA-final-Answer："
# --- Scenario A: SSE line stream (contains 'data:')
if "data:" in raw:
final = []
for line in raw.splitlines():
line = line.strip()
if not line.startswith("data: "):
continue
# Parse JSON
try:
import json
data = json.loads(line[6:])
except Exception:
continue
if data.get("type") == "result":
content = str(data.get("content", ""))
if content.startswith(marker):
# Remove marker
final_text = content[len(marker):].strip()
final.append(final_text)
elif data.get("type") == "done":
# End flag, exit directly
break
if final:
# Use the last occurrence (more stable)
return final[-1].strip()
# --- Scenario B: Plain text (does not contain 'data:'), directly find marker
idx = raw.rfind(marker)
if idx != -1:
final_text = raw[idx + len(marker):].strip()
# Remove possible code fence or extra backticks that might wrap it
if final_text.startswith("```"):
# Remove the first code fence
final_text = final_text.lstrip("`")
# Also simply remove trailing extra backticks
final_text = final_text.rstrip("`").strip()
return final_text or None
return None
def extract_review_final_report(raw: str) -> str | None:
"""
Extract the final report content from review_generate ToolMessage text marked with
'Final_report\n'.
Compatible with two scenarios:
A) SSE stream: Multiple lines containing 'data: {...}' JSON
B) Plain text: Directly find content after Final_report\n marker
Returns plain text report; returns None if not found.
"""
if not raw:
return None
marker = "Final_report\n"
# --- Scenario A: SSE line stream (contains 'data:')
if "data:" in raw:
final_content = []
found_marker = False
for line in raw.splitlines():
line = line.strip()
if not line.startswith("data: "):
continue
# Parse JSON
try:
import json
data = json.loads(line[6:])
except Exception:
continue
if data.get("type") == "result":
content = str(data.get("content", ""))
if content == marker:
found_marker = True
continue
elif found_marker:
# Collect all content after marker
final_content.append(content)
elif data.get("type") == "done":
# End flag, exit directly
break
if final_content:
return "".join(final_content).strip()
# --- Scenario B: Plain text (does not contain 'data:'), directly find marker
idx = raw.find(marker)
if idx != -1:
final_text = raw[idx + len(marker):].strip()
# Remove possible code fence or extra backticks that might wrap it
if final_text.startswith("```"):
# Remove the first code fence
final_text = final_text.lstrip("`")
# Also simply remove trailing extra backticks
final_text = final_text.rstrip("`").strip()
return final_text or None
return None
def create_download_button(content: str, filename: str, file_type: str = "md", tool_type: str = "literature_review"):
"""
Create a download button that supports downloading as Markdown or PDF format
Args:
content: Content to download
filename: Filename (without extension)
file_type: File type, 'md' or 'pdf'
tool_type: Tool type for appropriate filename generation
"""
# Ensure a unique key per button instance to avoid duplicate element IDs
counter = st.session_state.get("download_btn_counter", 0)
st.session_state["download_btn_counter"] = counter + 1
base_key = f"download_{tool_type}_{file_type}_{counter}"
# Add timestamp to filename
from datetime import datetime
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
# Generate appropriate filename based on tool type
if tool_type == "bio_qa_stream_chat":
base_filename = "bio_qa_report"
elif tool_type == "review_generate":
base_filename = "literature_review"
else:
base_filename = filename
filename_with_timestamp = f"{base_filename}_{timestamp}"
if file_type == "md":
# Download as Markdown file
st.download_button(
label=f"📥 Download as Markdown",
data=content,
file_name=f"{filename_with_timestamp}.md",
mime="text/markdown",
help="Click to download report as Markdown format",
key=f"{base_key}_md"
)
elif file_type == "pdf":
try:
# Use reportlab with markdown parsing (no system dependencies)
from reportlab.lib.pagesizes import letter, A4
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.lib.enums import TA_JUSTIFY, TA_LEFT
from io import BytesIO
import markdown
# Convert markdown to HTML first for better parsing
html_content = markdown.markdown(content, extensions=['tables', 'fenced_code'])
# Create PDF document
buffer = BytesIO()
doc = SimpleDocTemplate(buffer, pagesize=A4, rightMargin=72, leftMargin=72, topMargin=72, bottomMargin=18)
# Get styles
styles = getSampleStyleSheet()
# Create custom styles
title_style = ParagraphStyle(
'CustomTitle',
parent=styles['Heading1'],
fontSize=16,
spaceAfter=30,
alignment=TA_LEFT
)
heading_style = ParagraphStyle(
'CustomHeading',
parent=styles['Heading2'],
fontSize=14,
spaceAfter=12,
spaceBefore=20,
alignment=TA_LEFT
)
body_style = ParagraphStyle(
'CustomBody',
parent=styles['Normal'],
fontSize=11,
spaceAfter=6,
alignment=TA_JUSTIFY
)
# Build PDF content
story = []
# Add title based on tool type
if tool_type == "bio_qa_stream_chat":
title = "Biological Q&A Report"
elif tool_type == "review_generate":
title = "Literature Review Report"
else:
title = "Report"
story.append(Paragraph(title, title_style))
story.append(Spacer(1, 12))
# Parse HTML content and convert to PDF elements
from bs4 import BeautifulSoup, NavigableString
soup = BeautifulSoup(html_content, 'html.parser')
def element_text_with_links(element) -> str:
parts = []
for child in element.children:
if isinstance(child, NavigableString):
parts.append(str(child))
elif getattr(child, 'name', None) == 'a':
href = child.get('href', '#')
text = child.get_text(strip=True)
parts.append(f'<link href="{href}">{text}</link>')
else:
# Fallback to text for other inline elements
parts.append(child.get_text(strip=False))
return ''.join(parts).strip()
for element in soup.find_all(['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'p', 'code', 'pre', 'blockquote', 'ul', 'ol', 'li']):
if element.name in ['h1', 'h2', 'h3']:
heading_text = element_text_with_links(element)
story.append(Paragraph(heading_text or element.get_text(), heading_style))
story.append(Spacer(1, 6))
elif element.name == 'p':
text = element_text_with_links(element)
if text.strip():
story.append(Paragraph(text, body_style))
elif element.name == 'code':
code_style = ParagraphStyle(
'CodeText',
parent=body_style,
fontName='Courier',
fontSize=10,
backColor='#f8f9fa'
)
story.append(Paragraph(element.get_text(), code_style))
elif element.name == 'pre':
pre_style = ParagraphStyle(
'PreText',
parent=body_style,
fontName='Courier',
fontSize=10,
backColor='#f8f9fa',
leftIndent=20
)
story.append(Paragraph(element.get_text(), pre_style))
story.append(Spacer(1, 6))
elif element.name == 'blockquote':
quote_style = ParagraphStyle(
'QuoteText',
parent=body_style,
leftIndent=20,
leftPadding=10,
borderWidth=1,
borderColor='#3498db',
borderPadding=5
)
quote_text = element_text_with_links(element)
story.append(Paragraph(quote_text or element.get_text(), quote_style))
story.append(Spacer(1, 6))
elif element.name in ['ul', 'ol']:
index = 0
for li in element.find_all('li', recursive=False):
index += 1
li_text = element_text_with_links(li)
bullet = '• ' if element.name == 'ul' else f'{index}. '
story.append(Paragraph(f'{bullet}{li_text}', body_style))
story.append(Spacer(1, 6))
# Generate PDF
doc.build(story)
pdf_bytes = buffer.getvalue()
buffer.close()
# Create download button
st.download_button(
label="📥 Download as PDF",
data=pdf_bytes,
file_name=f"{filename_with_timestamp}.pdf",
mime="application/pdf",
help="Click to download report as PDF format",
key=f"{base_key}_pdf"
)
except ImportError as e:
st.warning(f"⚠️ Cannot generate PDF: Missing required libraries. Please install reportlab and beautifulsoup4. Error: {str(e)}")
except Exception as e:
st.error(f"❌ Error generating PDF: {str(e)}")
def main():
# Initialize logger
logger = get_logger()
task_monitor = get_task_monitor()
with st.sidebar:
st.link_button("🚀 Parameter Extraction", "https://huggingface.co/spaces/jackkuo/Automated-Enzyme-Kinetics-Extractor", type="primary")
st.subheader("Chat History")
sd_compents.create_history_chat_container()
# ------------------------------------------------------------------ Chat Part
# Main chat interface
st.header("Chat with Agent")
messages_container = st.container(border=True, height=600)
# ------------------------------------------------------------------ Chat history
# Re-render previous messages
if st.session_state.get('current_chat_id'):
st.session_state["messages"] = get_current_chat(st.session_state['current_chat_id'])
tool_count = 0
# Debug: log message count
logger.log_system_status(f"Re-rendering {len(st.session_state['messages'])} messages for chat {st.session_state['current_chat_id']}")
# Load bio data for this chat if available
chat_id = st.session_state['current_chat_id']
bio_data_key = f"bio_data_{chat_id}"
bio_data = st.session_state.get(bio_data_key, {})
for m in st.session_state["messages"]:
# Debug: log message structure
has_tool = "tool" in m and m["tool"]
has_content = "content" in m and m["content"]
logger.log_system_status(f"Message: role={m.get('role')}, has_tool={has_tool}, has_content={has_content}")
with messages_container.chat_message(m["role"]):
# 先显示ToolMessage（如果有）
if "tool" in m and m["tool"]:
tool_count += 1
# Display ToolMessage in collapsible format
with st.expander(f"🔧 ToolMessage - {tool_count}", expanded=False):
st.code(m["tool"], language='yaml')
# 再显示content（如果有）
if "content" in m and m["content"]:
content_text = str(m["content"])
# Check if this is a bio final answer and restore citations
if (m["role"] == "assistant" and
bio_data.get('has_bio_final_answer') and
bio_data.get('bio_final_answer_content') == content_text):
# Restore bio data for citation processing
bio_search_data = bio_data.get('bio_search_data', [])
bio_citation_data = bio_data.get('bio_citation_data', [])
web_search_data = bio_data.get('web_search_data', [])
# Display found literature information
if bio_search_data or web_search_data:
total_bio_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in bio_search_data)
total_web_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in web_search_data)
if total_bio_docs > 0 and total_web_docs > 0:
st.markdown(f"### 📚 Analysis based on {total_bio_docs} scientific papers and {total_web_docs} web pages")
elif total_bio_docs > 0:
st.markdown(f"### 📚 Analysis based on {total_bio_docs} scientific papers")
else:
st.markdown(f"### 🌐 Analysis based on {total_web_docs} web pages")
st.markdown("### 🎯 Final Answer")
# Process citation markers in final answer
processed_answer = content_text
if bio_citation_data and (bio_search_data or web_search_data):
# Create docId to literature info mapping
doc_id_to_info = {}
# Add PubMed data
for search_data in bio_search_data:
bio_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in bio_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add web search data
for search_data in web_search_data:
web_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in web_docs:
doc_id_to_info[doc.get('docId')] = doc
# Create citation number to docId mapping
citation_to_doc = {}
for citation in bio_citation_data:
citation_num = citation.get('citation')
doc_id = citation.get('docId')
citation_to_doc[citation_num] = doc_id
# Replace citation markers
import re
# First replace single citations
def replace_citation_local(match):
return replace_citation(match, citation_to_doc, doc_id_to_info)
processed_answer = re.sub(r'\[bio-rag-citation:(\d+)\]', replace_citation_local, processed_answer)
def replace_footnote_citation_local(match):
return replace_footnote_citation(match, citation_to_doc, doc_id_to_info)
processed_answer = re.sub(r'\[\^(\d+)\]', replace_footnote_citation_local, processed_answer)
def replace_document_citation_local(match):
return replace_document_citation(match, citation_to_doc, doc_id_to_info)
processed_answer = re.sub(r'\[document (\d+)\]', replace_document_citation_local, processed_answer)
# Remove bottom references section (since we display complete reference list below)
processed_answer = re.sub(r'\n\nReferences:.*$', '', processed_answer, flags=re.DOTALL)
# Then process consecutive citations, add separators
processed_answer = re.sub(r'\](\[)', r'], \1', processed_answer)
st.markdown(processed_answer)
# Display citation information
if bio_citation_data:
st.markdown(f"### 📖 References ({len(bio_citation_data)} citations)")
# Create docId to literature info mapping
doc_id_to_info = {}
# Add PubMed data
for search_data in bio_search_data:
bio_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in bio_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add web search data
for search_data in web_search_data:
web_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in web_docs:
doc_id_to_info[doc.get('docId')] = doc
# Display citation list
for citation in bio_citation_data:
doc_id = citation.get('docId')
citation_num = citation.get('citation')
source = citation.get('source', '')
if doc_id in doc_id_to_info:
doc_info = doc_id_to_info[doc_id]
title = doc_info.get('title', 'N/A')
url = doc_info.get('url', '#')
if source == 'webSearch':
st.markdown(f"[{citation_num}] {title}. [Link]({url})")
else:
author = doc_info.get('author', 'N/A')
journal = doc_info.get('JournalInfo', 'N/A')
authors = author.split(', ')
if len(authors) > 3:
display_author = ', '.join(authors[:3]) + ' et al.'
else:
display_author = author
st.markdown(f"[{citation_num}] {display_author}. {title}. {journal}. [Link]({url})")
else:
st.markdown(f"[{citation_num}] Document ID: {doc_id}")
else:
# Normal content display
st.markdown(content_text)
# Check if this is a review report and add download buttons
if m["role"] == "assistant" and m["content"]:
# Try to detect if this is a literature review report
content_text = str(m["content"])
if ("Literature Review Report" in content_text or
"📚 Literature Review Report" in content_text or
len(content_text) > 500): # Assume long content might be a review report
# Add download buttons for review reports
st.markdown("---")
st.markdown("### 📥 Download Options")
col1, col2 = st.columns(2)
with col1:
create_download_button(content_text, "literature_review", "md", "bio_qa_stream_chat")
with col2:
create_download_button(content_text, "literature_review", "pdf", "bio_qa_stream_chat")
# ------------------------------------------------------------------ Chat input
user_text = st.chat_input("Ask a question or explore available MCP tools")
# ------------------------------------------------------------------ SideBar widgets
# Main sidebar widgets
sd_compents.create_sidebar_chat_buttons()
sd_compents.create_provider_select_widget()
sd_compents.create_advanced_configuration_widget()
sd_compents.create_mcp_connection_widget()
sd_compents.create_mcp_tools_widget()
# ------------------------------------------------------------------ Main Logic
if user_text is None: # nothing submitted yet
st.stop()
params = st.session_state.get('params')
if not (
params.get('api_key') or
( params.get('model_id') == 'Bedrock' and
params.get('region_name') and
params.get('aws_access_key') and
params.get('aws_secret_key')
)
):
err_mesg = "❌ Missing credentials: provide either an API key or complete AWS credentials."
_append_message_to_session({"role": "assistant", "content": err_mesg})
with messages_container.chat_message("assistant"):
st.markdown(err_mesg)
st.rerun()
# ------------------------------------------------------------------ handle question (if any text)
if user_text:
# Log user message
logger.log_chat_message("user", user_text, st.session_state.get('current_chat_id'))
user_text_dct = {"role": "user", "content": user_text}
_append_message_to_session(user_text_dct)
with messages_container.chat_message("user"):
st.markdown(user_text)
with st.spinner("Thinking…", show_time=True):
# Start monitoring long-running task
task_id = str(uuid.uuid4())
task_monitor.start_monitoring(
task_id,
f"MCP_Agent_Response_{st.session_state.get('current_chat_id', 'unknown')}",
st.session_state.get('current_chat_id')
)
start_time = time.time()
system_prompt = make_system_prompt()
main_prompt = make_main_prompt(user_text)
try:
# If agent is available, use it
if st.session_state.agent:
logger.log_system_status("Using MCP agent for response")
# 记录可用的MCP工具
available_tools = [tool.name for tool in st.session_state.tools]
logger.log_mcp_agent_usage("ReactAgent", available_tools, st.session_state.get('current_chat_id'))
response = run_async(run_agent(st.session_state.agent, user_text))
tool_output = None
tools_used_in_response = []
# Extract tool executions if available
if "messages" in response:
logger.log_system_status(f"Processing {len(response['messages'])} messages from agent response")
for msg in response["messages"]:
# Debug: log message type
msg_type = type(msg).__name__
logger.log_system_status(f"Processing message type: {msg_type}")
# Look for AIMessage with tool calls
if hasattr(msg, 'tool_calls') and msg.tool_calls:
logger.log_system_status(f"Found tool calls: {msg.tool_calls}")
for tool_call in msg.tool_calls:
tools_used_in_response.append(tool_call['name'])
# Log tool call
logger.log_mcp_tool_call(
tool_call['name'],
tool_call['args'],
st.session_state.get('current_chat_id')
)
# Find corresponding ToolMessage
tool_output = next(
(m.content for m in response["messages"]
if isinstance(m, ToolMessage) and
m.tool_call_id == tool_call['id']),
None
)
if tool_output:
# Log tool response
logger.log_mcp_tool_response(
tool_call['name'],
tool_output,
st.session_state.get('current_chat_id')
)
st.session_state.tool_executions.append({
"tool_name": tool_call['name'],
"input": tool_call['args'],
"output": tool_output,
"timestamp": datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
})
elif hasattr(msg, 'name') and msg.name:
logger.log_system_status(f"Found ToolMessage: {msg.name}")
else:
logger.log_system_status(f"Message has no tool calls or name: {msg}")
# 记录实际使用的工具
if tools_used_in_response:
logger.log_mcp_agent_usage("Response", tools_used_in_response, st.session_state.get('current_chat_id'))
else:
logger.log_system_status("No MCP tools used in this response")
# Extract and display the response
output = ""
tool_count = 0
# Initialize bio QA data from session state or create new
chat_id = st.session_state.get('current_chat_id')
bio_data_key = f"bio_data_{chat_id}" if chat_id else "bio_data_default"
if bio_data_key not in st.session_state:
st.session_state[bio_data_key] = {
'bio_final_answer_content': "",
'has_bio_final_answer': False,
'review_final_report_content': "",
'has_review_final_report': False,
'bio_search_data': [],
'bio_citation_data': [],
'web_search_data': []
}
# Load existing data or initialize new
bio_data = st.session_state[bio_data_key]
bio_final_answer_content = bio_data['bio_final_answer_content']
has_bio_final_answer = bio_data['has_bio_final_answer']
review_final_report_content = bio_data['review_final_report_content']
has_review_final_report = bio_data['has_review_final_report']
bio_search_data = bio_data['bio_search_data']
bio_citation_data = bio_data['bio_citation_data']
web_search_data = bio_data['web_search_data']
if "messages" in response:
for msg in response["messages"]:
if isinstance(msg, HumanMessage):
continue # Skip human messages
elif hasattr(msg, 'name') and msg.name: # ToolMessage
tool_count += 1
with messages_container.chat_message("assistant"):
# Parse SSE stream data if it's a streaming tool response
if (msg.name == "bio_qa_stream_chat" or msg.name == "review_generate" or msg.name == "health_check") and "data:" in msg.content:
if msg.name == "bio_qa_stream_chat":
st.write("**🔬 Biological Q&A Results:**")
elif msg.name == "review_generate":
st.write("**📚 Literature Review Generation:**")
elif msg.name == "health_check":
st.write("**🏥 Health Check Results:**")
# Parse and display streaming content
lines = msg.content.split('\n')
handled_final_answer = False
handled_final_report = False
final_report_content = []
for line in lines:
if line.startswith('data: '):
try:
import json
data = json.loads(line[6:]) # Remove 'data: ' prefix
if data.get('type') == 'result':
content = data.get('content', '')
# Check if this is a final answer
if content.startswith("Bio-QA-final-Answer：") and not handled_final_answer:
# Extract final answer content
bio_final_answer_content = content.replace("Bio-QA-final-Answer：", "").strip()
# Save to session state
bio_data['bio_final_answer_content'] = bio_final_answer_content
bio_data['has_bio_final_answer'] = True
st.session_state[bio_data_key] = bio_data
# Set as main output
output = bio_final_answer_content
# Set flag to skip LLM processing
has_bio_final_answer = True
# Display final answer immediately in main conversation area
st.markdown("---")
# Display found literature information
if bio_search_data or web_search_data:
total_bio_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in bio_search_data)
total_web_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in web_search_data)
# total_docs = total_bio_docs + total_web_docs
if total_bio_docs > 0 and total_web_docs > 0:
st.markdown(f"### 📚 Analysis based on {total_bio_docs} scientific papers and {total_web_docs} web pages")
elif total_bio_docs > 0:
st.markdown(f"### 📚 Analysis based on {total_bio_docs} scientific papers")
else:
st.markdown(f"### 🌐 Analysis based on {total_web_docs} web pages")
st.markdown("### 🎯 Final Answer")
# Process citation markers in final answer
processed_answer = bio_final_answer_content
if bio_citation_data and (bio_search_data or web_search_data):
# Create docId to literature info mapping
doc_id_to_info = {}
# Add PubMed data
for search_data in bio_search_data:
bio_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in bio_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add web search data
for search_data in web_search_data:
web_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in web_docs:
doc_id_to_info[doc.get('docId')] = doc
# Create citation number to docId mapping
citation_to_doc = {}
for citation in bio_citation_data:
citation_num = citation.get('citation')
doc_id = citation.get('docId')
citation_to_doc[citation_num] = doc_id
# Replace citation markers
import re
# First replace single citations
processed_answer = re.sub(r'\[bio-rag-citation:(\d+)\]', replace_citation, processed_answer)
processed_answer = re.sub(r'\[\^(\d+)\]', replace_footnote_citation, processed_answer)
processed_answer = re.sub(r'\[document (\d+)\]', replace_document_citation, processed_answer)
# Remove bottom references section (since we display complete reference list below)
processed_answer = re.sub(r'\n\nReferences:.*$', '', processed_answer, flags=re.DOTALL)
# Then process consecutive citations, add separators
processed_answer = re.sub(r'\](\[)', r'], \1', processed_answer)
st.markdown(processed_answer)
# Display citation information (moved below final answer)
if bio_citation_data:
st.markdown(f"### 📖 References ({len(bio_citation_data)} citations)")
# Create docId to literature info mapping
doc_id_to_info = {}
# Add PubMed data
for search_data in bio_search_data:
bio_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in bio_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add web search data
for search_data in web_search_data:
web_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in web_docs:
doc_id_to_info[doc.get('docId')] = doc
# Display citation list, associate with literature info (standard reference format)
for citation in bio_citation_data:
doc_id = citation.get('docId')
citation_num = citation.get('citation')
source = citation.get('source', '')
if doc_id in doc_id_to_info:
doc_info = doc_id_to_info[doc_id]
title = doc_info.get('title', 'N/A')
url = doc_info.get('url', '#')
if source == 'webSearch':
# Web citation format: [number] title. [link](URL)
st.markdown(f"[{citation_num}] {title}. [Link]({url})")
else:
# PubMed literature citation format: [number] author. title. journal info. [link](URL)
author = doc_info.get('author', 'N/A')
journal = doc_info.get('JournalInfo', 'N/A')
# Process author info, only show first 3
authors = author.split(', ')
if len(authors) > 3:
display_author = ', '.join(authors[:3]) + ' et al.'
else:
display_author = author
st.markdown(f"[{citation_num}] {display_author}. {title}. {journal}. [Link]({url})")
else:
st.markdown(f"[{citation_num}] Document ID: {doc_id}")
# Build complete content for download (including references)
complete_content = ""
# Add analysis information
if bio_search_data or web_search_data:
total_bio_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in bio_search_data)
total_web_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in web_search_data)
if total_bio_docs > 0 and total_web_docs > 0:
complete_content += f"### 📚 Analysis based on {total_bio_docs} scientific papers and {total_web_docs} web pages\n\n"
elif total_bio_docs > 0:
complete_content += f"### 📚 Analysis based on {total_bio_docs} scientific papers\n\n"
else:
complete_content += f"### 🌐 Analysis based on {total_web_docs} web pages\n\n"
# Add final answer
complete_content += "### 🎯 Final Answer\n\n"
complete_content += processed_answer + "\n\n"
# Add references
if bio_citation_data:
complete_content += f"### 📖 References ({len(bio_citation_data)} citations)\n\n"
# Create docId to literature info mapping
doc_id_to_info = {}
# Add PubMed data
for search_data in bio_search_data:
bio_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in bio_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add web search data
for search_data in web_search_data:
web_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in web_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add citation list to complete content
for citation in bio_citation_data:
doc_id = citation.get('docId')
citation_num = citation.get('citation')
source = citation.get('source', '')
if doc_id in doc_id_to_info:
doc_info = doc_id_to_info[doc_id]
title = doc_info.get('title', 'N/A')
url = doc_info.get('url', '#')
if source == 'webSearch':
complete_content += f"[{citation_num}] {title}. [Link]({url})\n\n"
else:
author = doc_info.get('author', 'N/A')
journal = doc_info.get('JournalInfo', 'N/A')
authors = author.split(', ')
if len(authors) > 3:
display_author = ', '.join(authors[:3]) + ' et al.'
else:
display_author = author
complete_content += f"[{citation_num}] {display_author}. {title}. {journal}. [Link]({url})\n\n"
else:
complete_content += f"[{citation_num}] Document ID: {doc_id}\n\n"
# Add download buttons for Bio QA final answer (with complete content)
st.markdown("---")
st.markdown("### 📥 Download Options")
col1, col2 = st.columns(2)
with col1:
create_download_button(complete_content, "bio_qa_report", "md", "bio_qa_stream_chat")
with col2:
create_download_button(complete_content, "bio_qa_report", "pdf", "bio_qa_stream_chat")
# Save complete content to session history
_append_message_to_session({'role': 'assistant', 'content': complete_content})
# Force immediate rerender so Download Options appear without needing a new interaction
st.rerun()
handled_final_answer = True
# Check if this is a final report marker
elif content == "Final_report\n" and not handled_final_report:
handled_final_report = True
# Start collecting final report content
continue
elif handled_final_report:
# Collect final report content
final_report_content.append(content)
else:
# Try to parse JSON data and store
try:
import json
json_data = json.loads(content)
if json_data.get("type") == "search" and json_data.get("handler") == "QASearch":
handler_param = json_data.get('handlerParam', {})
source = handler_param.get('source', '')
if source == 'pubmed':
bio_search_data.append(json_data)
# Save to session state
bio_data['bio_search_data'] = bio_search_data
st.session_state[bio_data_key] = bio_data
st.write(f"🔍 Found {len(handler_param.get('bioDocs', []))} relevant papers")
elif source == 'webSearch':
web_search_data.append(json_data)
# Save to session state
bio_data['web_search_data'] = web_search_data
st.session_state[bio_data_key] = bio_data
st.write(f"🌐 Found {len(handler_param.get('bioDocs', []))} relevant web pages")
elif isinstance(json_data, list) and len(json_data) > 0 and "source" in json_data[0] and "citation" in json_data[0]:
# This is citation data
bio_citation_data.extend(json_data)
# Save to session state
bio_data['bio_citation_data'] = bio_citation_data
st.session_state[bio_data_key] = bio_data
st.write(f"📝 Generated citation information, {len(json_data)} citations total")
else:
st.write(content)
except json.JSONDecodeError:
# If not JSON, display content normally
st.write(content)
elif data.get('type') == 'done':
st.success("✅ Answer completed")
except json.JSONDecodeError:
continue
# Process collected final report content
if handled_final_report and final_report_content:
review_final_report_content = "".join(final_report_content).strip()
# Always display ToolMessage (collapsible)
with st.expander(f"🔧 ToolMessage - {tool_count} ({msg.name})", expanded=False):
st.code(msg.content, language='yaml')
# Display final report in main conversation area
with messages_container.chat_message("assistant"):
st.markdown("---")
st.markdown("### 📚 Literature Review Report")
st.markdown(review_final_report_content)
# Add download buttons to main conversation area (persistent)
st.markdown("---")
st.markdown("### 📥 Download Options")
col1, col2 = st.columns(2)
with col1:
create_download_button(review_final_report_content, "literature_review", "md", "review_generate")
with col2:
create_download_button(review_final_report_content, "literature_review", "pdf", "review_generate")
# Set flags and output
has_review_final_report = True
output = review_final_report_content
# Save final report to session history with download buttons info
_append_message_to_session({'role': 'assistant', 'content': review_final_report_content})
# Also save the original ToolMessage for reference
_append_message_to_session({'role': 'assistant', 'content': '', 'tool': msg.content})
# Force immediate rerender so Download Options appear without needing a new interaction
st.rerun()
else:
# Save tool message to session history
with st.expander(f"🔧 ToolMessage - {tool_count} ({msg.name})", expanded=False):
st.code(msg.content, language='yaml')
_append_message_to_session({'role': 'assistant', 'content': '', 'tool': msg.content})
else:
# For non-streaming or non-SSE returned tool messages, prioritize parsing bio_qa_stream_chat final answer
if msg.name == "bio_qa_stream_chat":
# Try to extract search data
try:
import json
import re
# Find JSON data blocks
json_matches = re.findall(r'```bio-chat-agent-task\n(.*?)\n```', msg.content, re.DOTALL)
for json_str in json_matches:
try:
json_data = json.loads(json_str)
if json_data.get("type") == "search" and json_data.get("handler") == "QASearch":
handler_param = json_data.get('handlerParam', {})
source = handler_param.get('source', '')
if source == 'pubmed':
bio_search_data.append(json_data)
# Save to session state
bio_data['bio_search_data'] = bio_search_data
st.session_state[bio_data_key] = bio_data
elif source == 'webSearch':
web_search_data.append(json_data)
# Save to session state
bio_data['web_search_data'] = web_search_data
st.session_state[bio_data_key] = bio_data
except json.JSONDecodeError:
continue
# Find citation data blocks
citation_matches = re.findall(r'```bio-resource-lookup\n(.*?)\n```', msg.content, re.DOTALL)
for citation_str in citation_matches:
try:
citation_data = json.loads(citation_str)
if isinstance(citation_data, list) and len(citation_data) > 0 and "source" in citation_data[0] and "citation" in citation_data[0]:
bio_citation_data.extend(citation_data)
# Save to session state
bio_data['bio_citation_data'] = bio_citation_data
st.session_state[bio_data_key] = bio_data
except json.JSONDecodeError:
continue
except Exception:
pass
extracted = extract_bio_final_answer(msg.content)
if extracted:
# Always display ToolMessage (collapsible)
with st.expander(f"🔧 ToolMessage - {tool_count} ({msg.name})", expanded=False):
st.code(msg.content, language='yaml')
# Then display final answer in main conversation area
with messages_container.chat_message("assistant"):
# Display found literature information
if bio_search_data or web_search_data:
total_bio_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in bio_search_data)
total_web_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in web_search_data)
total_docs = total_bio_docs + total_web_docs
if total_bio_docs > 0 and total_web_docs > 0:
st.markdown(f"### 📚 Analysis based on {total_bio_docs} scientific papers and {total_web_docs} web pages")
elif total_bio_docs > 0:
st.markdown(f"### 📚 Analysis based on {total_bio_docs} scientific papers")
else:
st.markdown(f"### 🌐 Analysis based on {total_web_docs} web pages")
st.markdown("### 🎯 Final Answer")
# Process citation markers in final answer
processed_answer = extracted
if bio_citation_data and (bio_search_data or web_search_data):
# Create docId to literature info mapping
doc_id_to_info = {}
# Add PubMed data
for search_data in bio_search_data:
bio_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in bio_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add web search data
for search_data in web_search_data:
web_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in web_docs:
doc_id_to_info[doc.get('docId')] = doc
# Create citation number to docId mapping
citation_to_doc = {}
for citation in bio_citation_data:
citation_num = citation.get('citation')
doc_id = citation.get('docId')
citation_to_doc[citation_num] = doc_id
# Replace citation markers
import re
# First replace single citations
def replace_citation_local2(match):
return replace_citation(match, citation_to_doc, doc_id_to_info)
processed_answer = re.sub(r'\[bio-rag-citation:(\d+)\]', replace_citation_local2, processed_answer)
def replace_footnote_citation_local2(match):
return replace_footnote_citation(match, citation_to_doc, doc_id_to_info)
processed_answer = re.sub(r'\[\^(\d+)\]', replace_footnote_citation_local2, processed_answer)
def replace_document_citation_local2(match):
return replace_document_citation(match, citation_to_doc, doc_id_to_info)
processed_answer = re.sub(r'\[document (\d+)\]', replace_document_citation_local2, processed_answer)
# Remove bottom references section (since we display complete reference list below)
processed_answer = re.sub(r'\n\nReferences:.*$', '', processed_answer, flags=re.DOTALL)
# Then process consecutive citations, add separators
processed_answer = re.sub(r'\](\[)', r'], \1', processed_answer)
st.markdown(processed_answer)
# Display citation information (moved below final answer)
if bio_citation_data:
st.markdown(f"### 📖 References ({len(bio_citation_data)} citations)")
# Create docId to literature info mapping
doc_id_to_info = {}
# Add PubMed data
for search_data in bio_search_data:
bio_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in bio_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add web search data
for search_data in web_search_data:
web_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in web_docs:
doc_id_to_info[doc.get('docId')] = doc
# Display citation list, associate with literature info (standard reference format)
for citation in bio_citation_data:
doc_id = citation.get('docId')
citation_num = citation.get('citation')
source = citation.get('source', '')
if doc_id in doc_id_to_info:
doc_info = doc_id_to_info[doc_id]
title = doc_info.get('title', 'N/A')
url = doc_info.get('url', '#')
if source == 'webSearch':
# Web citation format: [number] title. [link](URL)
st.markdown(f"[{citation_num}] {title}. [Link]({url})")
else:
# PubMed literature citation format: [number] author. title. journal info. [link](URL)
author = doc_info.get('author', 'N/A')
journal = doc_info.get('JournalInfo', 'N/A')
# Process author info, only show first 3
authors = author.split(', ')
if len(authors) > 3:
display_author = ', '.join(authors[:3]) + ' et al.'
else:
display_author = author
st.markdown(f"[{citation_num}] {display_author}. {title}. {journal}. [Link]({url})")
else:
st.markdown(f"[{citation_num}] Document ID: {doc_id}")
# Build complete formatted content for saving
complete_content = ""
# Add analysis information
if bio_search_data or web_search_data:
total_bio_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in bio_search_data)
total_web_docs = sum(len(data.get('handlerParam', {}).get('bioDocs', [])) for data in web_search_data)
if total_bio_docs > 0 and total_web_docs > 0:
complete_content += f"### 📚 Analysis based on {total_bio_docs} scientific papers and {total_web_docs} web pages\n\n"
elif total_bio_docs > 0:
complete_content += f"### 📚 Analysis based on {total_bio_docs} scientific papers\n\n"
else:
complete_content += f"### 🌐 Analysis based on {total_web_docs} web pages\n\n"
# Add final answer
complete_content += "### 🎯 Final Answer\n\n"
complete_content += processed_answer + "\n\n"
# Add references
if bio_citation_data:
complete_content += f"### 📖 References ({len(bio_citation_data)} citations)\n\n"
# Create docId to literature info mapping
doc_id_to_info = {}
# Add PubMed data
for search_data in bio_search_data:
bio_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in bio_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add web search data
for search_data in web_search_data:
web_docs = search_data.get('handlerParam', {}).get('bioDocs', [])
for doc in web_docs:
doc_id_to_info[doc.get('docId')] = doc
# Add citation list to complete content
for citation in bio_citation_data:
doc_id = citation.get('docId')
citation_num = citation.get('citation')
source = citation.get('source', '')
if doc_id in doc_id_to_info:
doc_info = doc_id_to_info[doc_id]
title = doc_info.get('title', 'N/A')
url = doc_info.get('url', '#')
if source == 'webSearch':
complete_content += f"[{citation_num}] {title}. [Link]({url})\n\n"
else:
author = doc_info.get('author', 'N/A')
journal = doc_info.get('JournalInfo', 'N/A')
authors = author.split(', ')
if len(authors) > 3:
display_author = ', '.join(authors[:3]) + ' et al.'
else:
display_author = author
complete_content += f"[{citation_num}] {display_author}. {title}. {journal}. [Link]({url})\n\n"
else:
complete_content += f"[{citation_num}] Document ID: {doc_id}\n\n"
# Override output and bio_final_answer_content for session recording
output = complete_content
bio_final_answer_content = complete_content
# Set flag to skip LLM processing
has_bio_final_answer = True
# Add download buttons for Bio QA final answer (with complete content)
st.markdown("---")
st.markdown("### 📥 Download Options")
col1, col2 = st.columns(2)
with col1:
create_download_button(complete_content, "bio_qa_report", "md", "bio_qa_stream_chat")
with col2:
create_download_button(complete_content, "bio_qa_report", "pdf", "bio_qa_stream_chat")
# Save ToolMessage first, then complete formatted content
_append_message_to_session({'role': 'assistant', 'content': '', 'tool': msg.content})
_append_message_to_session({'role': 'assistant', 'content': complete_content})
# Force immediate rerender so Download Options appear right away
st.rerun()
# Debug: log ToolMessage save
logger.log_system_status(f"Saved ToolMessage for bio_qa_stream_chat: {len(msg.content)} characters")
logger.log_system_status(f"Current chat has {len(st.session_state.get('messages', []))} messages")
else:
# Fallback: if final answer not parsed, display tool message in original way
with st.expander(f"🔧 ToolMessage - {tool_count} ({msg.name})", expanded=False):
st.code(msg.content, language='yaml')
_append_message_to_session({'role': 'assistant', 'content': '', 'tool': msg.content})
elif msg.name == "review_generate":
# Try to extract final report
extracted_report = extract_review_final_report(msg.content)
if extracted_report:
# Always display ToolMessage (collapsible)
with st.expander(f"🔧 ToolMessage - {tool_count} ({msg.name})", expanded=False):
st.code(msg.content, language='yaml')
# Display final report in main conversation area
with messages_container.chat_message("assistant"):
st.markdown("---")
st.markdown("### 📚 Literature Review Report")
st.markdown(extracted_report)
# Add download buttons to main conversation area (persistent)
st.markdown("---")
st.markdown("### 📥 Download Options")
col1, col2 = st.columns(2)
with col1:
create_download_button(extracted_report, "literature_review", "md", "review_generate")
with col2:
create_download_button(extracted_report, "literature_review", "pdf", "review_generate")
# Override output and review_final_report_content for session recording
output = extracted_report
review_final_report_content = extracted_report
# Set flag to skip LLM processing
has_review_final_report = True
# Save "assistant final report" to session history (instead of writing tool original text to tool field)
_append_message_to_session({'role': 'assistant', 'content': extracted_report})
# Also save the original ToolMessage for reference
_append_message_to_session({'role': 'assistant', 'content': '', 'tool': msg.content})
# Force immediate rerender so Download Options appear right away
st.rerun()
else:
# Fallback: if final report not parsed, display tool message in original way
with st.expander(f"🔧 ToolMessage - {tool_count} ({msg.name})", expanded=False):
st.code(msg.content, language='yaml')
_append_message_to_session({'role': 'assistant', 'content': '', 'tool': msg.content})
else:
# Other tools remain the same, but use collapsible display
with st.expander(f"🔧 ToolMessage - {tool_count} ({msg.name})", expanded=False):
st.code(msg.content, language='yaml')
_append_message_to_session({'role': 'assistant', 'content': '', 'tool': msg.content})
else: # AIMessage
# If there's a final answer or final report, skip LLM response
if not has_bio_final_answer and not has_review_final_report and hasattr(msg, "content") and msg.content:
with messages_container.chat_message("assistant"):
output = str(msg.content)
st.markdown(output)
# Ensure final answer or final report is correctly saved
if not output and bio_final_answer_content:
output = bio_final_answer_content
if not output and review_final_report_content:
output = review_final_report_content
# Initialize response_dct
response_dct = None
# If there's a final answer or final report, use it directly as response, no need to save additional assistant message
if has_bio_final_answer or has_review_final_report:
# Final answer or final report has already been saved to session history during processing
# But we need to trigger UI re-render to show the saved content
if has_bio_final_answer:
response_dct = {"role": "assistant", "content": bio_final_answer_content}
logger.log_chat_message("assistant", bio_final_answer_content, st.session_state.get('current_chat_id'), has_tool=True)
elif has_review_final_report:
response_dct = {"role": "assistant", "content": review_final_report_content}
logger.log_chat_message("assistant", review_final_report_content, st.session_state.get('current_chat_id'), has_tool=True)
else:
response_dct = {"role": "assistant", "content": output}
# Log assistant message
logger.log_chat_message("assistant", output, st.session_state.get('current_chat_id'))
# Fall back to regular stream response if agent not available
else:
st.warning("You are not connect to MCP servers!")
response_stream = get_response_stream(
main_prompt,
llm_provider=st.session_state['params']['model_id'],
system=system_prompt,
temperature=st.session_state['params'].get('temperature', DEFAULT_TEMPERATURE),
max_tokens=st.session_state['params'].get('max_tokens', DEFAULT_MAX_TOKENS),
)
with messages_container.chat_message("assistant"):
response = st.write_stream(response_stream)
response_dct = {"role": "assistant", "content": response}
except Exception as e:
# Stop monitoring and log error
task_monitor.stop_monitoring(task_id)
duration = time.time() - start_time
response = f"⚠️ Something went wrong: {str(e)}"
logger.log_error(
"MCP_Agent_Error",
str(e),
{
'chat_id': st.session_state.get('current_chat_id'),
'duration_seconds': duration,
'user_text': user_text
}
)
st.error(response)
st.code(traceback.format_exc(), language="python")
st.stop()
finally:
# Stop monitoring
task_monitor.stop_monitoring(task_id)
# Add assistant message to chat history (only if not already saved)
if response_dct is not None:
# Check if this is a final answer/report that was already saved during processing
if (has_bio_final_answer or has_review_final_report) and response_dct.get('content'):
# Don't save again, just trigger UI re-render
pass
else:
_append_message_to_session(response_dct)
display_tool_executions() |