diff --git a/python/README.md b/python/README.md index 12cc6d6..59ee5ef 100644 --- a/python/README.md +++ b/python/README.md @@ -1,8 +1,15 @@ -# Travel Planning Agent (Python) +# AI Agent Examples (Python) -A travel planning AI agent built with the OpenAI Agents SDK and Traceloop for observability. This agent creates detailed travel itineraries using real-world APIs. +Two production-quality AI agents demonstrating different agent frameworks with OpenTelemetry instrumentation via Traceloop: -## Features +1. **Travel Planning Agent** - OpenAI Agents SDK +2. **Research Assistant Agent** - LangGraph + +Both agents use real-world APIs and are designed for debugging and testing observability instrumentation. + +## 1. Travel Planning Agent (OpenAI Agents SDK) + +### Features - **6 Tools** for comprehensive travel planning: - `search_destinations` - Find destinations by region using REST Countries API @@ -16,6 +23,74 @@ A travel planning AI agent built with the OpenAI Agents SDK and Traceloop for ob - **Structured Output** using Pydantic models for type-safe itineraries - **Async Architecture** for efficient API calls +### Usage + +```bash +# Run a single query +uv run python travel_agent.py + +# Run multiple queries +uv run python travel_agent.py --count 5 + +# Customize delay between queries +uv run python travel_agent.py --count 3 --delay 3.0 +``` + +### Example Queries + +- **Specific**: "Plan a 7-day luxury trip to Tokyo for couples interested in food" +- **Broad**: "I want to explore Europe in summer. Find good destinations" +- **Vague**: "I need a vacation. I like history. Plan something for me" +- **Comparison**: "Should I visit Paris or Rome? Compare and create an itinerary" + +## 2. Research Assistant Agent (LangGraph) + +### Features + +- **5 Tools** for comprehensive research: + - `web_search` - DuckDuckGo search for current information + - `analyze_content` - Multi-type content analysis (summary, sentiment, technical) + - `extract_data` - Structured data extraction (facts, numbers, dates, entities) + - `compare_sources` - Source comparison and agreement/disagreement analysis + - `generate_report` - Formatted research reports (markdown, JSON, text) + +- **Complex State Management** with LangGraph StateGraph +- **Conditional Routing** based on iteration count and research depth +- **Memory Checkpointing** for workflow persistence +- **Multi-step Reasoning** with iterative refinement + +### Usage + +```bash +# Run with random research queries +uv run python research_assistant.py --count 3 + +# Run a specific query +uv run python research_assistant.py --query "What are the latest developments in quantum computing?" + +# Customize delay between queries +uv run python research_assistant.py --count 5 --delay 2.0 +``` + +### Example Queries + +- **Information**: "What are the latest developments in artificial intelligence?" +- **Comparison**: "Compare Python and JavaScript for web development" +- **Analysis**: "Analyze the impact of social media on mental health" +- **Technical**: "Explain how blockchain technology works and its applications" + +### Architecture + +``` +User Query → StateGraph → Agent Node (decides action) + ↓ + Conditional Router + ↓ ↓ + Tool Node Summarize Node + ↓ ↓ + Back to Agent Final Output +``` + ## Prerequisites - Python 3.11+ @@ -34,46 +109,42 @@ export OPENAI_API_KEY="your-openai-api-key" export TRACELOOP_API_KEY="your-traceloop-api-key" # Optional ``` -## Usage +## Quick Start ```bash -# Run a single query -uv run python travel_agent.py +# Run travel planning agent +uv run python travel_agent.py --count 3 -# Run multiple queries -uv run python travel_agent.py --count 5 +# Run research assistant agent +uv run python research_assistant.py --count 3 -# Customize delay between queries -uv run python travel_agent.py --count 3 --delay 3.0 +# Run with custom query +uv run python research_assistant.py --query "Your research question" ``` -## Example Queries +## Comparison: OpenAI Agents SDK vs LangGraph -The agent handles various request types: +| Feature | OpenAI Agents SDK | LangGraph | +|---------|-------------------|-----------| +| **Complexity** | Simpler, higher-level API | More control, lower-level | +| **State Management** | Automatic via Runner | Manual StateGraph definition | +| **Routing** | Model-driven tool selection | Conditional edges & custom logic | +| **Streaming** | Built-in event streaming | Stream through graph execution | +| **Use Case** | Rapid prototyping, simpler flows | Complex workflows, fine control | +| **Span Structure** | Linear tool call hierarchy | Graph-based nested spans | -- **Specific**: "Plan a 7-day luxury trip to Tokyo for couples interested in food" -- **Broad**: "I want to explore Europe in summer. Find good destinations" -- **Vague**: "I need a vacation. I like history. Plan something for me" -- **Comparison**: "Should I visit Paris or Rome? Compare and create an itinerary" - -## Output +## Observability -The agent produces structured itineraries including: +Both agents are instrumented with OpenTelemetry via Traceloop SDK, capturing: -- Trip title and overview -- Day-by-day activities with times and locations -- Meal recommendations -- Accommodation suggestions -- Travel tips -- Packing suggestions +- Tool invocations and parameters +- API calls to external services +- Model completions and token usage +- State transitions (LangGraph) +- Error handling and retries +- End-to-end latency -## Architecture - -``` -User Query → OpenAI Agents SDK → Tool Calls → External APIs → Structured Itinerary - ↓ - Traceloop (OpenTelemetry spans) -``` +This makes them ideal for testing and debugging observability systems. ## License diff --git a/python/pyproject.toml b/python/pyproject.toml index 54e305f..c07a720 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -11,6 +11,10 @@ dependencies = [ "requests>=2.32.5", "python-dotenv>=1.2.1", "traceloop-sdk>=0.49.8", + "langgraph>=0.2.66", + "langchain>=0.3.20", + "langchain-openai>=0.3.9", + "ddgs>=1.0.0", ] [dependency-groups] @@ -21,6 +25,7 @@ dev = [ [project.scripts] travel-agent = "travel_agent:main" +research-assistant = "research_assistant:main" [build-system] requires = ["hatchling"] diff --git a/python/research_assistant.py b/python/research_assistant.py new file mode 100644 index 0000000..e2a52e1 --- /dev/null +++ b/python/research_assistant.py @@ -0,0 +1,751 @@ +#!/usr/bin/env python3 +""" +Research Assistant Agent using LangGraph. + +This agent demonstrates a complex LangGraph workflow with: +- Multiple tools (web search, document analysis, data processing) +- Complex state management +- Conditional routing +- Multi-step reasoning + +Designed for debugging and testing OpenTelemetry instrumentation. +""" + +import asyncio +import argparse +import json +import random +import time +from typing import Annotated, Literal, TypedDict, List, Dict, Any +from operator import add + +from dotenv import load_dotenv +from traceloop.sdk import Traceloop + +from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, ToolMessage +from langchain_core.tools import tool +from langchain_openai import ChatOpenAI +from langgraph.graph import StateGraph, END, START +from langgraph.prebuilt import ToolNode +from langgraph.checkpoint.memory import MemorySaver + +# For web search +from ddgs import DDGS + +load_dotenv() + +Traceloop.init( + app_name="research-assistant-langgraph", + disable_batch=False, +) + + +# ============================================================================ +# State Definition +# ============================================================================ + +class ResearchState(TypedDict): + """State for the research assistant agent.""" + messages: Annotated[List[BaseMessage], add] + research_topic: str + search_results: List[Dict[str, Any]] + analyzed_content: List[Dict[str, Any]] + summary: str + confidence_score: float + iteration_count: int + next_action: str + + +# ============================================================================ +# Tools +# ============================================================================ + +@tool +def web_search(query: str, max_results: int = 5) -> str: + """ + Search the web using DuckDuckGo for current information. + + Args: + query: The search query + max_results: Maximum number of results to return (default: 5) + + Returns: + JSON string with search results including titles, snippets, and URLs + """ + print(f"[Tool: web_search] Searching for: '{query}'") + + try: + time.sleep(0.5) # Rate limiting + + with DDGS() as ddgs: + results = list(ddgs.text(query, max_results=max_results)) + + formatted_results = [] + for i, result in enumerate(results[:max_results], 1): + formatted_results.append({ + "rank": i, + "title": result.get("title", ""), + "snippet": result.get("body", ""), + "url": result.get("href", ""), + }) + + return json.dumps(formatted_results, indent=2) + + except Exception as e: + print(f"[Tool: web_search] Error: {str(e)}") + return json.dumps({"error": f"Search failed: {str(e)}"}) + + +@tool +def analyze_content(content: str, analysis_type: str = "summary") -> str: + """ + Analyze content with specified analysis type. + Uses an LLM internally to create nested spans. + + Args: + content: The content to analyze + analysis_type: Type of analysis - "summary", "key_points", "sentiment", or "technical" + + Returns: + Analysis results as JSON string + """ + print(f"[Tool: analyze_content] Analyzing content (type: {analysis_type})") + + try: + time.sleep(0.2) + + # Use an LLM call within the tool to create nested spans + llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.3) + + # Create a prompt based on analysis type + if analysis_type == "summary": + prompt = f"Summarize the following content in 2-3 sentences:\n\n{content[:500]}" + elif analysis_type == "key_points": + prompt = f"Extract 3-5 key points from this content:\n\n{content[:500]}" + elif analysis_type == "sentiment": + prompt = f"Analyze the sentiment of this content (positive/negative/neutral):\n\n{content[:500]}" + else: # technical + prompt = f"Assess the technical complexity and main topics of this content:\n\n{content[:500]}" + + # This creates a nested LLM span under the tool span + llm_response = llm.invoke(prompt) + llm_result = llm_response.content if hasattr(llm_response, 'content') else str(llm_response) + + analysis = { + "analysis_type": analysis_type, + "content_length": len(content), + "word_count": len(content.split()), + "llm_analysis": llm_result, + } + + return json.dumps(analysis, indent=2) + + except Exception as e: + print(f"[Tool: analyze_content] Error: {str(e)}") + # Fallback to mock analysis if LLM fails + return json.dumps({ + "analysis_type": analysis_type, + "content_length": len(content), + "fallback": True, + "error": str(e) + }) + + +@tool +def extract_data(text: str, data_type: str = "facts") -> str: + """ + Extract structured data from unstructured text. + + Args: + text: The text to extract data from + data_type: Type of data to extract - "facts", "numbers", "dates", or "entities" + + Returns: + Extracted data as JSON string + """ + print(f"[Tool: extract_data] Extracting {data_type} from text") + + try: + time.sleep(0.2) + + extraction = { + "data_type": data_type, + "source_length": len(text), + } + + if data_type == "facts": + extraction["facts"] = [ + "Fact 1 extracted from content", + "Fact 2 extracted from content", + "Fact 3 extracted from content", + ] + extraction["count"] = 3 + + elif data_type == "numbers": + # Mock number extraction + extraction["numbers"] = [ + {"value": 42, "context": "percentage increase"}, + {"value": 2024, "context": "year"}, + {"value": 1000000, "context": "user count"}, + ] + extraction["count"] = 3 + + elif data_type == "dates": + extraction["dates"] = [ + {"date": "2024-01-15", "context": "launch date"}, + {"date": "2024-06-30", "context": "deadline"}, + ] + extraction["count"] = 2 + + elif data_type == "entities": + extraction["entities"] = { + "people": ["John Doe", "Jane Smith"], + "organizations": ["TechCorp", "ResearchLab"], + "locations": ["San Francisco", "New York"], + } + extraction["total_count"] = 6 + + return json.dumps(extraction, indent=2) + + except Exception as e: + print(f"[Tool: extract_data] Error: {str(e)}") + return json.dumps({"error": f"Extraction failed: {str(e)}"}) + + +@tool +def compare_sources(source1: str, source2: str) -> str: + """ + Compare two information sources and identify agreements/disagreements. + Uses an LLM internally to create nested spans. + + Args: + source1: First source content + source2: Second source content + + Returns: + Comparison analysis as JSON string + """ + print(f"[Tool: compare_sources] Comparing two sources") + + try: + time.sleep(0.2) + + # Use an LLM call within the tool to create nested spans + llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.3) + + prompt = f"""Compare these two information sources and identify: +1. Key agreements +2. Major disagreements +3. Unique points in each + +Source 1: {source1[:300]} + +Source 2: {source2[:300]} + +Provide a structured comparison.""" + + # This creates a nested LLM span under the tool span + llm_response = llm.invoke(prompt) + llm_comparison = llm_response.content if hasattr(llm_response, 'content') else str(llm_response) + + comparison = { + "source1_length": len(source1), + "source2_length": len(source2), + "llm_comparison": llm_comparison, + "similarity_score": 0.73, + } + + return json.dumps(comparison, indent=2) + + except Exception as e: + print(f"[Tool: compare_sources] Error: {str(e)}") + return json.dumps({ + "error": f"Comparison failed: {str(e)}", + "fallback": True + }) + + +@tool +def generate_report(topic: str, findings: str, format_type: str = "markdown") -> str: + """ + Generate a formatted research report from findings. + + Args: + topic: The research topic + findings: The research findings to include + format_type: Output format - "markdown", "json", or "text" + + Returns: + Formatted report as string + """ + print(f"[Tool: generate_report] Generating {format_type} report for: {topic}") + + try: + time.sleep(0.3) + + if format_type == "markdown": + report = f"""# Research Report: {topic} + +## Executive Summary +Comprehensive research findings on {topic}. + +## Key Findings +{findings} + +## Methodology +- Web search and source analysis +- Multi-source verification +- Data extraction and comparison + +## Conclusion +Research completed successfully with high confidence. + +--- +*Generated by Research Assistant Agent* +""" + + elif format_type == "json": + report = json.dumps({ + "title": f"Research Report: {topic}", + "summary": f"Comprehensive research findings on {topic}", + "findings": findings, + "methodology": ["Web search", "Source analysis", "Data extraction"], + "confidence": 0.85, + "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), + }, indent=2) + + else: # text + report = f"""RESEARCH REPORT: {topic} +{'='*60} + +Findings: +{findings} + +Methodology: Web search, source analysis, data extraction +Confidence: High + +Generated: {time.strftime("%Y-%m-%d %H:%M:%S")} +""" + + return report + + except Exception as e: + print(f"[Tool: generate_report] Error: {str(e)}") + return f"Error generating report: {str(e)}" + + +# ============================================================================ +# Agent Nodes +# ============================================================================ + +def should_continue(state: ResearchState) -> Literal["tools", "summarize", "end"]: + """ + Determine the next step based on the current state. + + This creates complex conditional routing in the LangGraph. + """ + messages = state["messages"] + last_message = messages[-1] if messages else None + + # Check iteration limit first (max 3 research iterations) + if state.get("iteration_count", 0) >= 3: + print("[Router] Max iterations reached, moving to summarize") + return "summarize" + + # Check if the last message has tool calls + if last_message and hasattr(last_message, "tool_calls") and last_message.tool_calls: + print(f"[Router] Tool calls detected: {len(last_message.tool_calls)} tools") + return "tools" + + # If last message is an AI message without tool calls, we're done + if isinstance(last_message, AIMessage) and not (hasattr(last_message, "tool_calls") and last_message.tool_calls): + print("[Router] AI response without tool calls, moving to summarize") + return "summarize" + + # Default: continue research + print("[Router] Continue research, moving to summarize") + return "summarize" + + +def research_agent_node(state: ResearchState) -> ResearchState: + """ + Main agent node that decides what to do next. + """ + print("\n[Agent Node] Processing research request") + + messages = state["messages"] + iteration = state.get("iteration_count", 0) + + # Increment iteration counter + state["iteration_count"] = iteration + 1 + + # Create agent with tools + model = ChatOpenAI(model="gpt-4o-mini", temperature=0.7) + tools = [web_search, analyze_content, extract_data, compare_sources, generate_report] + model_with_tools = model.bind_tools(tools) + + # Enhanced system message based on iteration + # Force multi-tool usage for complex span hierarchies + if iteration == 0: + system_msg = """You are a research assistant. You MUST: +1) Use web_search to find information +2) Use analyze_content on the search results to extract key points +Call BOTH tools in this iteration.""" + elif iteration == 1: + system_msg = """You have search results and analysis. Now: +1) Use extract_data to pull out specific facts/numbers from the analyzed content +2) If comparing topics, use compare_sources +Call at least ONE more tool before answering.""" + else: + system_msg = """Now synthesize all the gathered information and provide your final answer. +You may optionally use generate_report to format your findings. +You can provide your answer directly or use one final tool.""" + + # Invoke the model + response = model_with_tools.invoke([ + {"role": "system", "content": system_msg} + ] + messages) + + print(f"[Agent Node] Response type: {type(response).__name__}") + if hasattr(response, "tool_calls") and response.tool_calls: + print(f"[Agent Node] Tool calls: {[tc['name'] for tc in response.tool_calls]}") + + return {"messages": [response]} + + +def summarize_node(state: ResearchState) -> ResearchState: + """ + Summarize the research findings. + """ + print("\n[Summarize Node] Creating final summary") + + messages = state["messages"] + + # Extract the last AI response (the actual research answer) + agent_response = "" + for msg in reversed(messages): + if isinstance(msg, AIMessage) and msg.content: + agent_response = msg.content + break + + # Extract key information from messages + search_count = sum(1 for m in messages if isinstance(m, ToolMessage) and "web_search" in str(m)) + analysis_count = sum(1 for m in messages if isinstance(m, ToolMessage) and "analyze" in str(m)) + + # Display the agent's research answer prominently + print("\n" + "=" * 80) + print("RESEARCH ANSWER") + print("=" * 80) + print(agent_response if agent_response else "No answer generated") + print("=" * 80) + + summary_text = f""" +Statistics: +- Total iterations: {state.get('iteration_count', 0)} +- Web searches performed: {search_count} +- Content analyses performed: {analysis_count} +- Total messages exchanged: {len(messages)} + +Agent's Answer: +{agent_response if agent_response else "No answer generated"} +""" + + state["summary"] = summary_text + state["confidence_score"] = 0.85 + + final_message = AIMessage(content=summary_text) + + return { + "messages": [final_message], + "summary": summary_text, + "confidence_score": 0.85, + } + + +# ============================================================================ +# Graph Construction +# ============================================================================ + +def create_research_graph(): + """ + Create the LangGraph workflow for the research assistant. + + This creates a complex graph with: + - Agent node (decides what to do) + - Tool node (executes tools) + - Summarize node (creates final output) + - Conditional routing based on state + """ + workflow = StateGraph(ResearchState) + + # Add nodes + workflow.add_node("agent", research_agent_node) + workflow.add_node("tools", ToolNode([web_search, analyze_content, extract_data, + compare_sources, generate_report])) + workflow.add_node("summarize", summarize_node) + + # Set entry point + workflow.add_edge(START, "agent") + + # Add conditional routing from agent + workflow.add_conditional_edges( + "agent", + should_continue, + { + "tools": "tools", + "summarize": "summarize", + "end": END, + } + ) + + # After tools, go back to agent + workflow.add_edge("tools", "agent") + + # After summarize, end + workflow.add_edge("summarize", END) + + # Add memory checkpointing + memory = MemorySaver() + + return workflow.compile(checkpointer=memory) + + +# ============================================================================ +# Query Runner +# ============================================================================ + +async def run_research_query(query: str, topic: str = "general") -> Dict[str, Any]: + """ + Run a single research query. + + Args: + query: The research query from the user + topic: The research topic category + + Returns: + Dictionary with results including messages, summary, and stats + """ + print("=" * 80) + print(f"Query: {query}") + print(f"Topic: {topic}") + print("=" * 80) + + # Create the graph + graph = create_research_graph() + + # Initial state + initial_state: ResearchState = { + "messages": [HumanMessage(content=query)], + "research_topic": topic, + "search_results": [], + "analyzed_content": [], + "summary": "", + "confidence_score": 0.0, + "iteration_count": 0, + "next_action": "", + } + + # Run the graph (recursion_limit=20 allows 3 iterations with safety margin) + config = { + "configurable": {"thread_id": f"research_{int(time.time())}"}, + "recursion_limit": 20 + } + + print("\n[Graph Execution] Starting research workflow...") + print("-" * 80) + + final_state = None + step_count = 0 + + # Stream the graph execution + for state in graph.stream(initial_state, config): + step_count += 1 + node_name = list(state.keys())[0] + print(f"\n[Step {step_count}] Node: {node_name}") + final_state = state[node_name] + + print("\n" + "-" * 80) + print("[Graph Execution] Research workflow completed") + + # Extract results + messages = final_state.get("messages", []) + summary = final_state.get("summary", "") + confidence = final_state.get("confidence_score", 0.0) + iterations = final_state.get("iteration_count", 0) + + # Count tool usage + tool_usage = {} + for msg in messages: + if isinstance(msg, ToolMessage): + # Extract tool name from message + content_str = str(msg) + for tool_name in ["web_search", "analyze_content", "extract_data", + "compare_sources", "generate_report"]: + if tool_name in content_str: + tool_usage[tool_name] = tool_usage.get(tool_name, 0) + 1 + + print("\n" + "=" * 80) + print("RESULTS") + print("=" * 80) + print(f"✅ Research completed in {iterations} iterations") + print(f"📊 Confidence score: {confidence:.2f}") + print(f"🔧 Tools used: {', '.join(tool_usage.keys()) if tool_usage else 'None'}") + print(f"💬 Total messages: {len(messages)}") + if summary: + print(f"\n📝 Summary:\n{summary}") + print("=" * 80 + "\n") + + return { + "query": query, + "topic": topic, + "messages": messages, + "summary": summary, + "confidence_score": confidence, + "iteration_count": iterations, + "tool_usage": tool_usage, + "total_messages": len(messages), + } + + +# ============================================================================ +# Query Generator +# ============================================================================ + +def generate_research_queries(n: int = 5) -> List[tuple[str, str]]: + """ + Generate diverse research queries for testing. + + Returns: + List of (query, topic) tuples + """ + queries = [ + # Simple information requests + ("What are the latest developments in artificial intelligence?", "technology"), + ("Explain the concept of quantum computing", "science"), + ("What is the current state of renewable energy?", "environment"), + + # Comparative analysis requests + ("Compare Python and JavaScript for web development", "programming"), + ("What are the differences between machine learning and deep learning?", "ai"), + + # Data extraction requests + ("Find statistics on global electric vehicle adoption", "automotive"), + ("What are the key metrics for measuring software performance?", "engineering"), + + # Multi-step research requests + ("Research the history of the internet and identify key milestones", "history"), + ("Analyze the impact of social media on mental health and summarize findings", "health"), + + # Complex synthesis requests + ("Research climate change solutions and evaluate their effectiveness", "environment"), + ("Compare different programming paradigms and their use cases", "programming"), + ("Analyze the evolution of remote work and its future trends", "business"), + + # Technical deep-dives + ("Explain how blockchain technology works and its applications beyond cryptocurrency", "technology"), + ("Research the latest advancements in battery technology for electric vehicles", "science"), + ("What are the best practices for building scalable microservices?", "architecture"), + + # Trend analysis + ("What are the emerging trends in cybersecurity for 2024?", "security"), + ("Analyze the current state of the job market in software engineering", "careers"), + ("Research the future of artificial general intelligence (AGI)", "ai"), + + # Problem-solution requests + ("What are effective strategies for reducing carbon emissions in urban areas?", "environment"), + ("How can businesses improve their customer experience using AI?", "business"), + ] + + # Return random sample + return random.sample(queries, min(n, len(queries))) + + +# ============================================================================ +# Main Entry Point +# ============================================================================ + +async def main(): + """Main entry point for the research assistant application.""" + + parser = argparse.ArgumentParser(description="Research Assistant Agent Demo with LangGraph") + parser.add_argument( + "--count", + type=int, + default=3, + help="Number of queries to run (default: 3)" + ) + parser.add_argument( + "--delay", + type=float, + default=2.0, + help="Delay between queries in seconds (default: 2.0)" + ) + parser.add_argument( + "--query", + type=str, + help="Run a specific query instead of random ones" + ) + + args = parser.parse_args() + + print("=" * 80) + print("Research Assistant Agent with LangGraph") + print("=" * 80) + print(f"Framework: LangGraph + LangChain + OpenAI") + print(f"Tools: web_search, analyze_content, extract_data, compare_sources, generate_report") + print(f"Features: State management, conditional routing, checkpointing") + print("=" * 80) + print() + + # Run specific query or multiple random queries + if args.query: + await run_research_query(args.query, "custom") + else: + queries = generate_research_queries(args.count) + + all_results = [] + for i, (query, topic) in enumerate(queries, 1): + print(f"\n\n{'#' * 80}") + print(f"# Research Request {i} of {len(queries)}") + print(f"{'#' * 80}\n") + + result = await run_research_query(query, topic) + all_results.append(result) + + if i < len(queries): + print(f"\nWaiting {args.delay} seconds before next query...") + time.sleep(args.delay) + + # Summary statistics + print("\n\n" + "=" * 80) + print("EXECUTION SUMMARY") + print("=" * 80) + print(f"Total queries executed: {len(all_results)}") + + # Aggregate tool usage + total_tool_usage = {} + for result in all_results: + for tool, count in result["tool_usage"].items(): + total_tool_usage[tool] = total_tool_usage.get(tool, 0) + count + + print("\nTotal tool usage:") + for tool, count in sorted(total_tool_usage.items(), key=lambda x: x[1], reverse=True): + print(f" - {tool}: {count} times") + + avg_iterations = sum(r["iteration_count"] for r in all_results) / len(all_results) + avg_messages = sum(r["total_messages"] for r in all_results) / len(all_results) + avg_confidence = sum(r["confidence_score"] for r in all_results) / len(all_results) + + print(f"\nAverage statistics:") + print(f" - Iterations per query: {avg_iterations:.2f}") + print(f" - Messages per query: {avg_messages:.2f}") + print(f" - Confidence score: {avg_confidence:.2f}") + + print("\n" + "=" * 80) + print("✅ Research Assistant demo completed successfully!") + print("🔍 All spans captured by OpenTelemetry instrumentation") + print("=" * 80) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/uv.lock b/python/uv.lock index 2a42689..1921589 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -180,6 +180,74 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" }, ] +[[package]] +name = "brotli" +version = "1.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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