From bbabc9e0ac31bbc2213fe2af5a083de73fdada51 Mon Sep 17 00:00:00 2001 From: Amrit Krishnan Date: Mon, 26 Jan 2026 19:21:26 -0500 Subject: [PATCH] Merge branch 'feature/knowledge-agent' of github.com:VectorInstitute/eval-agents into feature/knowledge-agent --- .env.example | 16 +- .pre-commit-config.yaml | 2 +- .../aieng/agent_evals/__init__.py | 33 + .../aieng/agent_evals/display.py | 339 +++ .../agent_evals/knowledge_agent/__init__.py | 49 + .../agent_evals/knowledge_agent/agent.py | 309 ++ .../agent_evals/knowledge_agent/config.py | 57 + .../agent_evals/knowledge_agent/evaluation.py | 442 +++ .../knowledge_agent/grounding_tool.py | 226 ++ .../agent_evals/knowledge_agent/session.py | 181 ++ aieng-eval-agents/pyproject.toml | 7 +- aieng-eval-agents/tests/aieng/__init__.py | 0 .../tests/aieng/agent_evals/__init__.py | 0 .../agent_evals/knowledge_agent/__init__.py | 0 .../knowledge_agent/test_evaluation.py | 177 ++ .../knowledge_agent/test_grounding_tool.py | 131 + .../knowledge_agent/test_session.py | 164 + .../knowledge_agent/01_grounding_basics.ipynb | 237 ++ .../knowledge_agent/02_agent_basics.ipynb | 392 +++ .../knowledge_agent/03_multi_turn.ipynb | 381 +++ implementations/knowledge_agent/README.md | 123 + implementations/knowledge_agent/gradio_app.py | 158 + pyproject.toml | 3 + uv.lock | 2663 +++++++++-------- 24 files changed, 4898 insertions(+), 1192 deletions(-) create mode 100644 aieng-eval-agents/aieng/agent_evals/display.py create mode 100644 aieng-eval-agents/aieng/agent_evals/knowledge_agent/__init__.py create mode 100644 aieng-eval-agents/aieng/agent_evals/knowledge_agent/agent.py create mode 100644 aieng-eval-agents/aieng/agent_evals/knowledge_agent/config.py create mode 100644 aieng-eval-agents/aieng/agent_evals/knowledge_agent/evaluation.py create mode 100644 aieng-eval-agents/aieng/agent_evals/knowledge_agent/grounding_tool.py create mode 100644 aieng-eval-agents/aieng/agent_evals/knowledge_agent/session.py create mode 100644 aieng-eval-agents/tests/aieng/__init__.py create mode 100644 aieng-eval-agents/tests/aieng/agent_evals/__init__.py create mode 100644 aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/__init__.py create mode 100644 aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_evaluation.py create mode 100644 aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_grounding_tool.py create mode 100644 aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_session.py create mode 100644 implementations/knowledge_agent/01_grounding_basics.ipynb create mode 100644 implementations/knowledge_agent/02_agent_basics.ipynb create mode 100644 implementations/knowledge_agent/03_multi_turn.ipynb create mode 100644 implementations/knowledge_agent/README.md create mode 100644 implementations/knowledge_agent/gradio_app.py diff --git a/.env.example b/.env.example index d00bf267..17d212a5 100644 --- a/.env.example +++ b/.env.example @@ -1,2 +1,14 @@ -ENV_VARIABLE_ONE= -ENV_VARIABLE_TWO= +# OpenAI-compatible LLM (using Gemini via OpenAI API) +OPENAI_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/" +OPENAI_API_KEY="..." # Or use GEMINI_API_KEY or GOOGLE_API_KEY + +# Model selection (see https://ai.google.dev/gemini-api/docs/models) +# Stable: gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite +# Preview: gemini-3-pro-preview, gemini-3-flash-preview +DEFAULT_PLANNER_MODEL="gemini-2.5-pro" +DEFAULT_WORKER_MODEL="gemini-2.5-flash" + +# LangFuse (optional, for tracing) +LANGFUSE_SECRET_KEY="sk-lf-..." +LANGFUSE_PUBLIC_KEY="pk-lf-..." +LANGFUSE_HOST="https://us.cloud.langfuse.com" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 52b23687..ceb8ab48 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -46,7 +46,7 @@ repos: rev: 1.9.1 hooks: - id: nbqa-ruff - args: [--fix, --exit-non-zero-on-fix, --ignore=D100] + args: [--fix, --exit-non-zero-on-fix, "--ignore=D100,D103,E402,F704,PLE1142"] ci: autofix_commit_msg: | diff --git a/aieng-eval-agents/aieng/agent_evals/__init__.py b/aieng-eval-agents/aieng/agent_evals/__init__.py index e69de29b..924e7d08 100644 --- a/aieng-eval-agents/aieng/agent_evals/__init__.py +++ b/aieng-eval-agents/aieng/agent_evals/__init__.py @@ -0,0 +1,33 @@ +"""Agent evaluation utilities and implementations. + +This package provides tools for building and evaluating AI agents, +including display utilities for Jupyter notebooks. +""" + +from .display import ( + create_console, + display_comparison, + display_evaluation_result, + display_example, + display_info, + display_metrics_table, + display_response, + display_source_table, + display_success, + display_warning, +) + + +__all__ = [ + # Display utilities + "create_console", + "display_response", + "display_source_table", + "display_comparison", + "display_example", + "display_evaluation_result", + "display_metrics_table", + "display_success", + "display_info", + "display_warning", +] diff --git a/aieng-eval-agents/aieng/agent_evals/display.py b/aieng-eval-agents/aieng/agent_evals/display.py new file mode 100644 index 00000000..10d3cae2 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/display.py @@ -0,0 +1,339 @@ +"""Rich display utilities for agent evaluation outputs. + +This module provides beautifully formatted console output for Jupyter notebooks +using the rich library. It can be used across all agent evaluation modules. +""" + +from typing import TYPE_CHECKING + +from rich.console import Console +from rich.markdown import Markdown +from rich.panel import Panel +from rich.table import Table +from rich.theme import Theme +from rich.tree import Tree + + +if TYPE_CHECKING: + from .knowledge_agent.grounding_tool import GroundedResponse + + +# Custom theme for consistent styling +KNOWLEDGE_AGENT_THEME = Theme( + { + "info": "cyan", + "success": "green", + "warning": "yellow", + "error": "red bold", + "highlight": "magenta", + "muted": "dim", + "agent": "bold blue", + "user": "bold green", + } +) + + +def create_console(force_jupyter: bool = True) -> Console: + """Create a configured rich Console for the knowledge agent. + + Parameters + ---------- + force_jupyter : bool, optional + Force Jupyter rendering mode, by default True. + + Returns + ------- + Console + Configured rich Console instance. + """ + return Console(theme=KNOWLEDGE_AGENT_THEME, force_jupyter=force_jupyter) + + +def display_response( + response: "GroundedResponse", + console: Console | None = None, + title: str = "Response", + show_sources: bool = True, + show_queries: bool = True, +) -> None: + """Display a grounded response with rich formatting. + + Parameters + ---------- + response : GroundedResponse + The grounded response to display. + console : Console, optional + Rich console to use. Creates one if not provided. + title : str, optional + Title for the response panel, by default "Response". + show_sources : bool, optional + Whether to show sources, by default True. + show_queries : bool, optional + Whether to show search queries, by default True. + """ + if console is None: + console = create_console() + + # Main response panel + console.print(Panel(Markdown(response.text), title=f"📖 {title}", border_style="blue")) + + # Stats bar + stats = f"[success]✓[/success] {len(response.search_queries)} queries | {len(response.sources)} sources" + console.print(f"\n{stats}\n") + + # Search queries + if show_queries and response.search_queries: + query_tree = Tree("[bold cyan]🔍 Search Queries[/bold cyan]") + for i, sq in enumerate(response.search_queries[:5], 1): + query_tree.add(f"[info]{i}.[/info] {sq}") + if len(response.search_queries) > 5: + query_tree.add(f"[muted]... and {len(response.search_queries) - 5} more[/muted]") + console.print(query_tree) + + # Sources + if show_sources and response.sources: + source_tree = Tree("[bold]📚 Sources[/bold]") + for i, source in enumerate(response.sources, 1): + source_tree.add(f"[muted][{i}][/muted] [link={source.uri}]{source.title}[/link]") + console.print(source_tree) + + +def display_source_table( + response: "GroundedResponse", + console: Console | None = None, +) -> None: + """Display sources as a formatted table. + + Parameters + ---------- + response : GroundedResponse + The grounded response containing sources. + console : Console, optional + Rich console to use. Creates one if not provided. + """ + if console is None: + console = create_console() + + if not response.sources: + console.print("[muted]No sources available[/muted]") + return + + source_table = Table(title="📚 Sources", show_header=True, header_style="bold green") + source_table.add_column("#", style="muted", width=3) + source_table.add_column("Title", style="white") + source_table.add_column("URL", style="blue", overflow="fold") + + for i, source in enumerate(response.sources, 1): + url_display = source.uri[:60] + "..." if len(source.uri) > 60 else source.uri + source_table.add_row(str(i), source.title, url_display) + + console.print(source_table) + + +def display_comparison( + response_a: str, + response_b: "GroundedResponse", + console: Console | None = None, + title_a: str = "Without Grounding", + title_b: str = "With Grounding", +) -> None: + """Display side-by-side comparison of grounded vs non-grounded responses. + + Parameters + ---------- + response_a : str + The non-grounded response text. + response_b : GroundedResponse + The grounded response. + console : Console, optional + Rich console to use. Creates one if not provided. + title_a : str, optional + Title for first response, by default "Without Grounding". + title_b : str, optional + Title for second response, by default "With Grounding". + """ + if console is None: + console = create_console() + + panel_a = Panel( + Markdown(response_a), + title=f"❌ {title_a}", + border_style="yellow", + subtitle="[muted]May be outdated[/muted]", + ) + + panel_b = Panel( + Markdown(response_b.text), + title=f"✅ {title_b}", + border_style="green", + subtitle=f"[muted]{len(response_b.sources)} sources[/muted]", + ) + + console.print(panel_a) + console.print(panel_b) + + +def display_example( + example_id: int, + problem: str, + category: str, + answer: str, + answer_type: str | None = None, + console: Console | None = None, +) -> None: + """Display a dataset example with rich formatting. + + Parameters + ---------- + example_id : int + The example ID. + problem : str + The problem/question text. + category : str + The problem category. + answer : str + The expected answer. + answer_type : str, optional + The answer type if available. + console : Console, optional + Rich console to use. Creates one if not provided. + """ + if console is None: + console = create_console() + + content = f"[bold]Problem:[/bold]\n{problem}\n\n[bold]Category:[/bold] {category}\n" + if answer_type: + content += f"[bold]Answer Type:[/bold] {answer_type}\n" + content += f"\n[success]Expected Answer:[/success]\n{answer}" + + console.print(Panel(content, title=f"📝 Example {example_id}", border_style="blue")) + + +def display_evaluation_result( + example_id: int, + problem: str, + ground_truth: str, + prediction: str, + sources_used: int, + search_queries: list[str], + contains_answer: bool, + console: Console | None = None, +) -> None: + """Display an evaluation result with rich formatting. + + Parameters + ---------- + example_id : int + The example ID. + problem : str + The problem text. + ground_truth : str + The expected answer. + prediction : str + The model's prediction. + sources_used : int + Number of sources used. + search_queries : list[str] + Search queries executed. + contains_answer : bool + Whether the prediction contains the expected answer. + console : Console, optional + Rich console to use. Creates one if not provided. + """ + if console is None: + console = create_console() + + status_icon = "[success]✓[/success]" if contains_answer else "[warning]✗[/warning]" + status_text = "MATCH" if contains_answer else "NO MATCH" + border_color = "green" if contains_answer else "yellow" + + # Truncate long texts + problem_display = problem[:200] + "..." if len(problem) > 200 else problem + pred_display = prediction[:300] + "..." if len(prediction) > 300 else prediction + + content = ( + f"{status_icon} [bold]{status_text}[/bold]\n\n" + f"[info]Problem:[/info]\n{problem_display}\n\n" + f"[info]Expected:[/info] {ground_truth}\n\n" + f"[info]Prediction:[/info]\n{pred_display}\n\n" + f"[muted]Sources: {sources_used} | Queries: {len(search_queries)}[/muted]" + ) + + console.print(Panel(content, title=f"Example {example_id}", border_style=border_color)) + + +def display_metrics_table( + metrics: dict[str, float | int | str], + title: str = "Evaluation Metrics", + console: Console | None = None, +) -> None: + """Display evaluation metrics as a formatted table. + + Parameters + ---------- + metrics : dict + Dictionary of metric names to values. + title : str, optional + Table title, by default "Evaluation Metrics". + console : Console, optional + Rich console to use. Creates one if not provided. + """ + if console is None: + console = create_console() + + table = Table(title=f"📊 {title}", show_header=True, header_style="bold cyan") + table.add_column("Metric", style="info") + table.add_column("Value", style="white", justify="right") + + for name, value in metrics.items(): + if isinstance(value, float): + table.add_row(name, f"{value:.1f}") + else: + table.add_row(name, str(value)) + + console.print(table) + + +def display_success(message: str, console: Console | None = None) -> None: + """Display a success message. + + Parameters + ---------- + message : str + The success message. + console : Console, optional + Rich console to use. + """ + if console is None: + console = create_console() + console.print(f"[success]✓[/success] {message}") + + +def display_info(message: str, console: Console | None = None) -> None: + """Display an info message. + + Parameters + ---------- + message : str + The info message. + console : Console, optional + Rich console to use. + """ + if console is None: + console = create_console() + console.print(f"[info]ℹ[/info] {message}") + + +def display_warning(message: str, console: Console | None = None) -> None: + """Display a warning message. + + Parameters + ---------- + message : str + The warning message. + console : Console, optional + Rich console to use. + """ + if console is None: + console = create_console() + console.print(f"[warning]⚠[/warning] {message}") diff --git a/aieng-eval-agents/aieng/agent_evals/knowledge_agent/__init__.py b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/__init__.py new file mode 100644 index 00000000..7561f2b2 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/__init__.py @@ -0,0 +1,49 @@ +"""Knowledge-grounded QA agent using Gemini with Google Search grounding. + +This package provides tools for building and evaluating knowledge-grounded +question answering agents using Gemini's built-in Google Search capability. + +Example +------- +>>> from aieng.agent_evals.knowledge_agent import ( +... KnowledgeGroundedAgent, +... DeepSearchQADataset, +... DeepSearchQAEvaluator, +... ) +>>> agent = KnowledgeGroundedAgent() +>>> response = agent.answer("What is the current population of Tokyo?") +>>> print(response.text) +""" + +from .agent import AsyncClientManager, KnowledgeGroundedAgent +from .config import KnowledgeAgentConfig +from .evaluation import ( + DeepSearchQADataset, + DeepSearchQAEvaluator, + DSQAExample, + EvaluationResult, +) +from .grounding_tool import GeminiGroundingTool, GroundedResponse, GroundingChunk +from .session import ConversationSession, Message, get_or_create_session + + +__all__ = [ + # Agent + "KnowledgeGroundedAgent", + "AsyncClientManager", + # Config + "KnowledgeAgentConfig", + # Grounding tool + "GeminiGroundingTool", + "GroundedResponse", + "GroundingChunk", + # Session management + "ConversationSession", + "Message", + "get_or_create_session", + # Evaluation + "DeepSearchQADataset", + "DeepSearchQAEvaluator", + "DSQAExample", + "EvaluationResult", +] diff --git a/aieng-eval-agents/aieng/agent_evals/knowledge_agent/agent.py b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/agent.py new file mode 100644 index 00000000..d81cf4ff --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/agent.py @@ -0,0 +1,309 @@ +"""Knowledge-grounded QA agent using Gemini with Google Search grounding. + +This module provides the main agent class for knowledge-grounded question +answering, using Gemini's built-in Google Search grounding capability. +""" + +import logging +from typing import TYPE_CHECKING + +from google import genai +from google.genai import types + +from .config import KnowledgeAgentConfig +from .grounding_tool import GeminiGroundingTool, GroundedResponse, GroundingChunk + + +if TYPE_CHECKING: + pass + + +logger = logging.getLogger(__name__) + + +# System instructions for the knowledge-grounded QA agent +KNOWLEDGE_AGENT_INSTRUCTIONS = """\ +You are a knowledge-grounded research assistant. Your role is to provide +accurate, comprehensive answers by searching the web for relevant information. + +## Guidelines + +1. **Search thoroughly**: Use Google Search to find relevant, up-to-date information. + Do not rely solely on your training data for facts that may have changed. + +2. **Cite your sources**: Always mention where you found the information and + include source URLs when available. + +3. **Be comprehensive**: For complex questions that require multiple pieces of + information, search multiple times to gather all relevant facts. + +4. **Be honest about uncertainty**: If you cannot find relevant information or if + search results are inconclusive, say so clearly rather than guessing. + +5. **Synthesize information**: When answering complex questions, synthesize findings + from multiple sources into a coherent response. + +## Response Format + +When answering questions: +- Provide a clear, direct answer first +- Include relevant context and details from your sources +- List the sources used at the end of your response + +Remember: Accuracy and completeness are more important than speed. +""" + + +class KnowledgeGroundedAgent: + """A knowledge-grounded QA agent using Gemini with Google Search. + + This agent uses Gemini's built-in Google Search grounding to answer questions + with real-time web information. + + Parameters + ---------- + config : KnowledgeAgentConfig, optional + Configuration settings. If not provided, creates default config. + model : str, optional + The model to use. If not provided, uses config.default_worker_model. + + Attributes + ---------- + config : KnowledgeAgentConfig + The configuration settings. + grounding_tool : GeminiGroundingTool + The Gemini grounding tool. + + Examples + -------- + >>> from aieng.agent_evals.knowledge_agent import KnowledgeGroundedAgent + >>> agent = KnowledgeGroundedAgent() + >>> response = agent.answer("Who won the 2024 Nobel Prize in Physics?") + >>> print(response.text) + """ + + def __init__( + self, + config: KnowledgeAgentConfig | None = None, + model: str | None = None, + ) -> None: + """Initialize the knowledge-grounded agent. + + Parameters + ---------- + config : KnowledgeAgentConfig, optional + Configuration settings. If not provided, creates default config. + model : str, optional + The model to use. If not provided, uses config.default_worker_model. + """ + if config is None: + config = KnowledgeAgentConfig() # type: ignore[call-arg] + + self.config = config + self.model = model or config.default_worker_model + + # Initialize Gemini client with grounding + self._client = genai.Client(api_key=config.openai_api_key) + + # Also create the grounding tool for direct access + self.grounding_tool = GeminiGroundingTool(config=config, model=self.model) + + def answer(self, question: str) -> GroundedResponse: + """Answer a question using Google Search grounding. + + Parameters + ---------- + question : str + The question to answer. + + Returns + ------- + GroundedResponse + The grounded response with text, search queries, and sources. + """ + logger.info(f"Answering question: {question[:100]}...") + + # Prepend system instructions to the question + prompt = f"{KNOWLEDGE_AGENT_INSTRUCTIONS}\n\n**Question:** {question}" + + response = self._client.models.generate_content( + model=self.model, + contents=prompt, + config=types.GenerateContentConfig( + tools=[types.Tool(google_search=types.GoogleSearch())], + ), + ) + + return self._parse_response(response) + + async def answer_async(self, question: str) -> GroundedResponse: + """Async version of answer for concurrent operations. + + Parameters + ---------- + question : str + The question to answer. + + Returns + ------- + GroundedResponse + The grounded response with text, search queries, and sources. + """ + logger.info(f"Answering question (async): {question[:100]}...") + + prompt = f"{KNOWLEDGE_AGENT_INSTRUCTIONS}\n\n**Question:** {question}" + + response = await self._client.aio.models.generate_content( + model=self.model, + contents=prompt, + config=types.GenerateContentConfig( + tools=[types.Tool(google_search=types.GoogleSearch())], + ), + ) + + return self._parse_response(response) + + def _parse_response(self, response: types.GenerateContentResponse) -> GroundedResponse: + """Parse a Gemini response into a GroundedResponse. + + Parameters + ---------- + response : types.GenerateContentResponse + The raw Gemini response. + + Returns + ------- + GroundedResponse + Parsed response with metadata. + """ + search_queries: list[str] = [] + sources: list[GroundingChunk] = [] + + if response.candidates and len(response.candidates) > 0: + candidate = response.candidates[0] + grounding_metadata = getattr(candidate, "grounding_metadata", None) + + if grounding_metadata: + if hasattr(grounding_metadata, "web_search_queries"): + search_queries = list(grounding_metadata.web_search_queries or []) + + if hasattr(grounding_metadata, "grounding_chunks"): + for chunk in grounding_metadata.grounding_chunks or []: + if hasattr(chunk, "web") and chunk.web: + sources.append( + GroundingChunk( + title=getattr(chunk.web, "title", "") or "", + uri=getattr(chunk.web, "uri", "") or "", + ) + ) + + return GroundedResponse( + text=response.text or "", + search_queries=search_queries, + sources=sources, + ) + + def format_answer(self, response: GroundedResponse) -> str: + """Format a grounded response for display. + + Parameters + ---------- + response : GroundedResponse + The grounded response to format. + + Returns + ------- + str + Formatted response with citations. + """ + return self.grounding_tool.format_response_with_citations(response) + + +class AsyncClientManager: + """Manages async client lifecycle with lazy initialization and cleanup. + + This class ensures clients are created only once and properly closed, + preventing resource warnings from unclosed event loops. + + Parameters + ---------- + config : KnowledgeAgentConfig, optional + Configuration object for client setup. If not provided, creates default. + + Examples + -------- + >>> manager = AsyncClientManager() + >>> agent = manager.agent + >>> response = await agent.answer_async("What is quantum computing?") + >>> print(response.text) + """ + + def __init__(self, config: KnowledgeAgentConfig | None = None) -> None: + """Initialize the client manager. + + Parameters + ---------- + config : KnowledgeAgentConfig, optional + Configuration object. If not provided, creates default config. + """ + self._config = config + self._agent: KnowledgeGroundedAgent | None = None + self._grounding_tool: GeminiGroundingTool | None = None + self._initialized = False + + @property + def config(self) -> KnowledgeAgentConfig: + """Get or create the config instance. + + Returns + ------- + KnowledgeAgentConfig + The configuration settings. + """ + if self._config is None: + self._config = KnowledgeAgentConfig() # type: ignore[call-arg] + return self._config + + @property + def grounding_tool(self) -> GeminiGroundingTool: + """Get or create the Gemini grounding tool. + + Returns + ------- + GeminiGroundingTool + The grounding tool instance. + """ + if self._grounding_tool is None: + self._grounding_tool = GeminiGroundingTool(config=self.config) + self._initialized = True + return self._grounding_tool + + @property + def agent(self) -> KnowledgeGroundedAgent: + """Get or create the knowledge-grounded agent. + + Returns + ------- + KnowledgeGroundedAgent + The knowledge-grounded QA agent. + """ + if self._agent is None: + self._agent = KnowledgeGroundedAgent(config=self.config) + self._initialized = True + return self._agent + + def close(self) -> None: + """Close all initialized clients and reset state.""" + self._agent = None + self._grounding_tool = None + self._initialized = False + + def is_initialized(self) -> bool: + """Check if any clients have been initialized. + + Returns + ------- + bool + True if any clients have been initialized. + """ + return self._initialized diff --git a/aieng-eval-agents/aieng/agent_evals/knowledge_agent/config.py b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/config.py new file mode 100644 index 00000000..1746400e --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/config.py @@ -0,0 +1,57 @@ +"""Configuration settings for the knowledge-grounded QA agent. + +This module provides centralized configuration management using Pydantic settings, +supporting environment variables and .env file loading. +""" + +from pydantic import AliasChoices, Field +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class KnowledgeAgentConfig(BaseSettings): + """Configuration settings for the knowledge-grounded QA agent. + + This class automatically loads configuration values from environment variables + and a .env file, providing type-safe access to all settings. + + Attributes + ---------- + openai_base_url : str + Base URL for OpenAI-compatible API (defaults to Gemini endpoint). + openai_api_key : str + API key for OpenAI-compatible API (accepts OPENAI_API_KEY, GEMINI_API_KEY, + or GOOGLE_API_KEY). + default_planner_model : str + Model name for planning/complex reasoning tasks. + default_worker_model : str + Model name for worker/simple tasks. + langfuse_public_key : str or None + Langfuse public key for tracing (optional). + langfuse_secret_key : str or None + Langfuse secret key for tracing (optional). + langfuse_host : str + Langfuse host URL. + + Examples + -------- + >>> from aieng.agent_evals.knowledge_agent.config import KnowledgeAgentConfig + >>> config = KnowledgeAgentConfig() + >>> print(config.default_worker_model) + 'gemini-2.5-flash' + """ + + model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", env_ignore_empty=True) + + # OpenAI-compatible API settings (works with Gemini via OpenAI endpoint) + openai_base_url: str = "https://generativelanguage.googleapis.com/v1beta/openai/" + openai_api_key: str = Field(validation_alias=AliasChoices("OPENAI_API_KEY", "GEMINI_API_KEY", "GOOGLE_API_KEY")) + + # Model selection (using stable Gemini 2.5 models) + # See https://ai.google.dev/gemini-api/docs/models for latest models + default_planner_model: str = "gemini-2.5-pro" + default_worker_model: str = "gemini-2.5-flash" + + # Langfuse configuration (optional) + langfuse_public_key: str | None = Field(default=None, pattern=r"^pk-lf-.*$") + langfuse_secret_key: str | None = Field(default=None, pattern=r"^sk-lf-.*$") + langfuse_host: str = "https://us.cloud.langfuse.com" diff --git a/aieng-eval-agents/aieng/agent_evals/knowledge_agent/evaluation.py b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/evaluation.py new file mode 100644 index 00000000..18cbe0f7 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/evaluation.py @@ -0,0 +1,442 @@ +"""Evaluation utilities for DeepSearchQA benchmark. + +This module provides tools for loading, running, and evaluating the +DeepSearchQA benchmark dataset. +""" + +import asyncio +import logging +from pathlib import Path +from typing import TYPE_CHECKING + +import kagglehub +import pandas as pd +from pydantic import BaseModel, Field + + +if TYPE_CHECKING: + from .agent import KnowledgeGroundedAgent + + +logger = logging.getLogger(__name__) + + +class DSQAExample(BaseModel): + """A single example from the DeepSearchQA dataset. + + Attributes + ---------- + example_id : int + Unique identifier for the example. + problem : str + The research question/problem to solve. + problem_category : str + Category of the problem (e.g., "Politics & Government"). + answer : str + The ground truth answer. + answer_type : str + Type of answer (e.g., "Single Answer", "List"). + """ + + example_id: int + problem: str + problem_category: str + answer: str + answer_type: str + + +class EvaluationResult(BaseModel): + """Result of evaluating a single example. + + Attributes + ---------- + example_id : int + The example ID that was evaluated. + problem : str + The original problem/question. + ground_truth : str + The expected answer. + prediction : str + The model's generated answer. + search_queries : list[str] + Search queries executed by the model. + sources_used : int + Number of sources cited in the response. + is_correct : bool or None + Whether the answer is correct (None if not evaluated). + evaluation_notes : str + Additional notes about the evaluation. + """ + + example_id: int + problem: str + ground_truth: str + prediction: str + search_queries: list[str] = Field(default_factory=list) + sources_used: int = 0 + is_correct: bool | None = None + evaluation_notes: str = "" + + +class DeepSearchQADataset: + """Loader and manager for the DeepSearchQA dataset. + + This class handles downloading, loading, and accessing examples from + the DeepSearchQA benchmark dataset. + + Parameters + ---------- + cache_dir : str or Path, optional + Directory to cache the dataset. If not provided, uses kagglehub default. + + Examples + -------- + >>> dataset = DeepSearchQADataset() + >>> print(f"Total examples: {len(dataset)}") + >>> example = dataset[0] + >>> print(example.problem) + """ + + def __init__(self, cache_dir: str | Path | None = None) -> None: + """Initialize the dataset loader. + + Parameters + ---------- + cache_dir : str or Path, optional + Directory to cache the dataset. + """ + self._cache_dir = Path(cache_dir) if cache_dir else None + self._df: pd.DataFrame | None = None + self._examples: list[DSQAExample] | None = None + + def _download_dataset(self) -> Path: + """Download the dataset using kagglehub. + + Returns + ------- + Path + Path to the downloaded dataset directory. + """ + logger.info("Downloading DeepSearchQA dataset...") + path = kagglehub.dataset_download("deepmind/deepsearchqa") + return Path(path) + + def _load_data(self) -> None: + """Load the dataset into memory.""" + if self._df is not None: + return + + dataset_path = self._download_dataset() + csv_path = dataset_path / "DSQA-full.csv" + + if not csv_path.exists(): + raise FileNotFoundError(f"Dataset file not found: {csv_path}") + + self._df = pd.read_csv(csv_path) + + # Filter out rows with missing answers + original_count = len(self._df) + self._df = self._df.dropna(subset=["answer"]) + dropped_count = original_count - len(self._df) + if dropped_count > 0: + logger.info(f"Dropped {dropped_count} examples with missing answers") + + logger.info(f"Loaded {len(self._df)} examples from DeepSearchQA") + + # Convert to examples + self._examples = [ + DSQAExample( + example_id=row["example_id"], + problem=row["problem"], + problem_category=row["problem_category"], + answer=str(row["answer"]), # Ensure string type + answer_type=row["answer_type"], + ) + for _, row in self._df.iterrows() + ] + + @property + def dataframe(self) -> pd.DataFrame: + """Get the raw pandas DataFrame. + + Returns + ------- + pd.DataFrame + The full dataset as a DataFrame. + """ + self._load_data() + assert self._df is not None + return self._df + + @property + def examples(self) -> list[DSQAExample]: + """Get all examples as DSQAExample objects. + + Returns + ------- + list[DSQAExample] + All examples in the dataset. + """ + self._load_data() + assert self._examples is not None + return self._examples + + def __len__(self) -> int: + """Return the number of examples in the dataset.""" + self._load_data() + assert self._examples is not None + return len(self._examples) + + def __getitem__(self, index: int) -> DSQAExample: + """Get an example by index. + + Parameters + ---------- + index : int + The index of the example to retrieve. + + Returns + ------- + DSQAExample + The example at the given index. + """ + self._load_data() + assert self._examples is not None + return self._examples[index] + + def get_by_category(self, category: str) -> list[DSQAExample]: + """Get all examples in a specific category. + + Parameters + ---------- + category : str + The problem category to filter by. + + Returns + ------- + list[DSQAExample] + Examples matching the category. + """ + return [ex for ex in self.examples if ex.problem_category == category] + + def get_categories(self) -> list[str]: + """Get all unique problem categories. + + Returns + ------- + list[str] + List of unique category names. + """ + return list(self.dataframe["problem_category"].unique()) + + def sample(self, n: int = 10, random_state: int | None = None) -> list[DSQAExample]: + """Get a random sample of examples. + + Parameters + ---------- + n : int, optional + Number of examples to sample, by default 10. + random_state : int, optional + Random seed for reproducibility. + + Returns + ------- + list[DSQAExample] + Randomly sampled examples. + """ + sampled_df = self.dataframe.sample(n=min(n, len(self)), random_state=random_state) + return [ + DSQAExample( + example_id=row["example_id"], + problem=row["problem"], + problem_category=row["problem_category"], + answer=row["answer"], + answer_type=row["answer_type"], + ) + for _, row in sampled_df.iterrows() + ] + + +class DeepSearchQAEvaluator: + """Evaluator for running and scoring DeepSearchQA benchmark. + + This class manages the evaluation pipeline for testing agents on the + DeepSearchQA benchmark. + + Parameters + ---------- + agent : KnowledgeGroundedAgent + The agent to evaluate. + dataset : DeepSearchQADataset, optional + The dataset to use. If not provided, creates a new one. + + Examples + -------- + >>> from aieng.agent_evals.knowledge_agent import ( + ... KnowledgeGroundedAgent, + ... DeepSearchQAEvaluator, + ... ) + >>> agent = KnowledgeGroundedAgent() + >>> evaluator = DeepSearchQAEvaluator(agent) + >>> results = evaluator.evaluate_sample(n=5) + """ + + def __init__( + self, + agent: "KnowledgeGroundedAgent", + dataset: DeepSearchQADataset | None = None, + ) -> None: + """Initialize the evaluator. + + Parameters + ---------- + agent : KnowledgeGroundedAgent + The agent to evaluate. + dataset : DeepSearchQADataset, optional + The dataset to use. + """ + self.agent = agent + self.dataset = dataset or DeepSearchQADataset() + + def evaluate_example(self, example: DSQAExample) -> EvaluationResult: + """Evaluate a single example. + + Parameters + ---------- + example : DSQAExample + The example to evaluate. + + Returns + ------- + EvaluationResult + The evaluation result. + """ + logger.info(f"Evaluating example {example.example_id}...") + + try: + response = self.agent.answer(example.problem) + prediction = response.text + search_queries = response.search_queries + sources_used = len(response.sources) + except Exception as e: + logger.error(f"Error evaluating example {example.example_id}: {e}") + return EvaluationResult( + example_id=example.example_id, + problem=example.problem, + ground_truth=example.answer, + prediction=f"ERROR: {e}", + evaluation_notes=f"Evaluation failed: {e}", + ) + + return EvaluationResult( + example_id=example.example_id, + problem=example.problem, + ground_truth=example.answer, + prediction=prediction, + search_queries=search_queries, + sources_used=sources_used, + ) + + async def evaluate_example_async(self, example: DSQAExample) -> EvaluationResult: + """Async version of evaluate_example. + + Parameters + ---------- + example : DSQAExample + The example to evaluate. + + Returns + ------- + EvaluationResult + The evaluation result. + """ + logger.info(f"Evaluating example {example.example_id} (async)...") + + try: + response = await self.agent.answer_async(example.problem) + prediction = response.text + search_queries = response.search_queries + sources_used = len(response.sources) + except Exception as e: + logger.error(f"Error evaluating example {example.example_id}: {e}") + return EvaluationResult( + example_id=example.example_id, + problem=example.problem, + ground_truth=example.answer, + prediction=f"ERROR: {e}", + evaluation_notes=f"Evaluation failed: {e}", + ) + + return EvaluationResult( + example_id=example.example_id, + problem=example.problem, + ground_truth=example.answer, + prediction=prediction, + search_queries=search_queries, + sources_used=sources_used, + ) + + def evaluate_sample(self, n: int = 10, random_state: int | None = None) -> list[EvaluationResult]: + """Evaluate a random sample of examples. + + Parameters + ---------- + n : int, optional + Number of examples to evaluate, by default 10. + random_state : int, optional + Random seed for reproducibility. + + Returns + ------- + list[EvaluationResult] + Results for all evaluated examples. + """ + examples = self.dataset.sample(n=n, random_state=random_state) + return [self.evaluate_example(ex) for ex in examples] + + async def evaluate_sample_async( + self, + n: int = 10, + random_state: int | None = None, + max_concurrency: int = 3, + ) -> list[EvaluationResult]: + """Async evaluation of a random sample with concurrency control. + + Parameters + ---------- + n : int, optional + Number of examples to evaluate, by default 10. + random_state : int, optional + Random seed for reproducibility. + max_concurrency : int, optional + Maximum concurrent evaluations, by default 3. + + Returns + ------- + list[EvaluationResult] + Results for all evaluated examples. + """ + examples = self.dataset.sample(n=n, random_state=random_state) + semaphore = asyncio.Semaphore(max_concurrency) + + async def eval_with_semaphore(ex: DSQAExample) -> EvaluationResult: + async with semaphore: + return await self.evaluate_example_async(ex) + + tasks = [eval_with_semaphore(ex) for ex in examples] + return await asyncio.gather(*tasks) + + def results_to_dataframe(self, results: list[EvaluationResult]) -> pd.DataFrame: + """Convert evaluation results to a DataFrame. + + Parameters + ---------- + results : list[EvaluationResult] + The evaluation results. + + Returns + ------- + pd.DataFrame + Results as a DataFrame. + """ + return pd.DataFrame([r.model_dump() for r in results]) diff --git a/aieng-eval-agents/aieng/agent_evals/knowledge_agent/grounding_tool.py b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/grounding_tool.py new file mode 100644 index 00000000..7bf75861 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/grounding_tool.py @@ -0,0 +1,226 @@ +"""Gemini Google Search grounding tool for knowledge-grounded QA. + +This module provides tools for using Gemini's built-in Google Search grounding +capability to answer questions with real-time web information. +""" + +import logging +from typing import TYPE_CHECKING + +from google import genai +from google.genai import types +from pydantic import BaseModel, Field + + +if TYPE_CHECKING: + from .config import KnowledgeAgentConfig + + +logger = logging.getLogger(__name__) + + +class GroundingChunk(BaseModel): + """Represents a single grounding source from search results. + + Attributes + ---------- + title : str + Title of the source webpage. + uri : str + URL of the source webpage. + """ + + title: str = "" + uri: str = "" + + +class GroundedResponse(BaseModel): + """Response from Gemini with Google Search grounding. + + Attributes + ---------- + text : str + The generated response text. + search_queries : list[str] + The search queries that were executed. + sources : list[GroundingChunk] + The web sources used to ground the response. + """ + + text: str + search_queries: list[str] = Field(default_factory=list) + sources: list[GroundingChunk] = Field(default_factory=list) + + +class GeminiGroundingTool: + """Tool for generating responses grounded in Google Search results. + + This class uses the Gemini API with Google Search grounding to generate + responses that are backed by real-time web information. + + Parameters + ---------- + config : KnowledgeAgentConfig, optional + Configuration settings. If not provided, creates default config. + model : str, optional + The Gemini model to use. Defaults to config.default_worker_model. + + Examples + -------- + >>> from aieng.agent_evals.knowledge_agent import GeminiGroundingTool + >>> tool = GeminiGroundingTool() + >>> response = tool.search("What is the current population of Tokyo?") + >>> print(response.text) + """ + + def __init__( + self, + config: "KnowledgeAgentConfig | None" = None, + model: str | None = None, + ) -> None: + """Initialize the Gemini grounding tool. + + Parameters + ---------- + config : KnowledgeAgentConfig, optional + Configuration settings. If not provided, creates default config. + model : str, optional + The Gemini model to use. Defaults to config.default_worker_model. + """ + if config is None: + from .config import KnowledgeAgentConfig # noqa: PLC0415 + + config = KnowledgeAgentConfig() # type: ignore[call-arg] + + self.config = config + self.model = model or config.default_worker_model + + # Initialize Gemini client + self._client = genai.Client(api_key=config.openai_api_key) + + def search(self, query: str) -> GroundedResponse: + """Generate a response grounded in Google Search results. + + Parameters + ---------- + query : str + The question or query to answer. + + Returns + ------- + GroundedResponse + The response with text, search queries used, and sources. + """ + logger.info(f"Generating grounded response for: {query[:100]}...") + + response = self._client.models.generate_content( + model=self.model, + contents=query, + config=types.GenerateContentConfig( + tools=[types.Tool(google_search=types.GoogleSearch())], + ), + ) + + # Extract grounding metadata + search_queries: list[str] = [] + sources: list[GroundingChunk] = [] + + if response.candidates and len(response.candidates) > 0: + candidate = response.candidates[0] + grounding_metadata = getattr(candidate, "grounding_metadata", None) + + if grounding_metadata: + # Extract search queries + if hasattr(grounding_metadata, "web_search_queries"): + search_queries = list(grounding_metadata.web_search_queries or []) + + # Extract grounding chunks (sources) + if hasattr(grounding_metadata, "grounding_chunks"): + for chunk in grounding_metadata.grounding_chunks or []: + if hasattr(chunk, "web") and chunk.web: + sources.append( + GroundingChunk( + title=getattr(chunk.web, "title", "") or "", + uri=getattr(chunk.web, "uri", "") or "", + ) + ) + + return GroundedResponse( + text=response.text or "", + search_queries=search_queries, + sources=sources, + ) + + async def search_async(self, query: str) -> GroundedResponse: + """Async version of search for concurrent operations. + + Parameters + ---------- + query : str + The question or query to answer. + + Returns + ------- + GroundedResponse + The response with text, search queries used, and sources. + """ + logger.info(f"Generating grounded response (async) for: {query[:100]}...") + + response = await self._client.aio.models.generate_content( + model=self.model, + contents=query, + config=types.GenerateContentConfig( + tools=[types.Tool(google_search=types.GoogleSearch())], + ), + ) + + # Extract grounding metadata + search_queries: list[str] = [] + sources: list[GroundingChunk] = [] + + if response.candidates and len(response.candidates) > 0: + candidate = response.candidates[0] + grounding_metadata = getattr(candidate, "grounding_metadata", None) + + if grounding_metadata: + if hasattr(grounding_metadata, "web_search_queries"): + search_queries = list(grounding_metadata.web_search_queries or []) + + if hasattr(grounding_metadata, "grounding_chunks"): + for chunk in grounding_metadata.grounding_chunks or []: + if hasattr(chunk, "web") and chunk.web: + sources.append( + GroundingChunk( + title=getattr(chunk.web, "title", "") or "", + uri=getattr(chunk.web, "uri", "") or "", + ) + ) + + return GroundedResponse( + text=response.text or "", + search_queries=search_queries, + sources=sources, + ) + + def format_response_with_citations(self, response: GroundedResponse) -> str: + """Format a grounded response with inline citations. + + Parameters + ---------- + response : GroundedResponse + The grounded response to format. + + Returns + ------- + str + Formatted response text with citations appended. + """ + output_parts = [response.text] + + if response.sources: + output_parts.append("\n\n**Sources:**") + for i, source in enumerate(response.sources, 1): + if source.uri: + output_parts.append(f"[{i}] [{source.title}]({source.uri})") + + return "\n".join(output_parts) diff --git a/aieng-eval-agents/aieng/agent_evals/knowledge_agent/session.py b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/session.py new file mode 100644 index 00000000..5ebe8ca4 --- /dev/null +++ b/aieng-eval-agents/aieng/agent_evals/knowledge_agent/session.py @@ -0,0 +1,181 @@ +"""Session management for multi-turn conversations. + +This module provides utilities for managing conversation sessions, +maintaining history across multiple turns of a conversation. +""" + +import uuid +from typing import Any + +from pydantic import BaseModel + + +class Message(BaseModel): + """A single message in a conversation. + + Attributes + ---------- + role : str + The role of the message sender ("user" or "assistant"). + content : str + The content of the message. + """ + + role: str + content: str + + +class ConversationSession: + """Manages multi-turn conversation state. + + This class provides a simple interface for managing conversation + sessions, including history tracking and context management. + + Attributes + ---------- + session_id : str + Unique identifier for this session. + history : list[Message] + List of conversation messages. + + Examples + -------- + >>> session = ConversationSession() + >>> session.add_message("user", "What is the capital of France?") + >>> session.add_message("assistant", "The capital of France is Paris.") + >>> print(len(session)) + 2 + """ + + def __init__(self, session_id: str | None = None) -> None: + """Initialize a conversation session. + + Parameters + ---------- + session_id : str, optional + Unique identifier for the session. If not provided, generates a UUID. + """ + self.session_id = session_id or str(uuid.uuid4()) + self.history: list[Message] = [] + self.metadata: dict[str, Any] = {} + + def add_message(self, role: str, content: str) -> None: + """Add a message to the conversation history. + + Parameters + ---------- + role : str + The role of the message sender ("user" or "assistant"). + content : str + The content of the message. + """ + self.history.append(Message(role=role, content=content)) + + def add_user_message(self, content: str) -> None: + """Add a user message to the conversation. + + Parameters + ---------- + content : str + The user's message content. + """ + self.add_message("user", content) + + def add_assistant_message(self, content: str) -> None: + """Add an assistant message to the conversation. + + Parameters + ---------- + content : str + The assistant's message content. + """ + self.add_message("assistant", content) + + def get_history(self) -> list[dict[str, str]]: + """Get the full conversation history as dictionaries. + + Returns + ------- + list[dict[str, str]] + List of messages with role and content keys. + """ + return [{"role": m.role, "content": m.content} for m in self.history] + + def get_history_as_text(self) -> str: + """Get the conversation history formatted as text. + + Returns + ------- + str + Formatted conversation history. + """ + lines = [] + for msg in self.history: + prefix = "User" if msg.role == "user" else "Assistant" + lines.append(f"{prefix}: {msg.content}") + return "\n\n".join(lines) + + def get_last_n_messages(self, n: int) -> list[Message]: + """Get the last n messages from the conversation. + + Parameters + ---------- + n : int + Number of recent messages to retrieve. + + Returns + ------- + list[Message] + The last n messages from the conversation. + """ + return self.history[-n:] if len(self.history) >= n else self.history.copy() + + def clear(self) -> None: + """Clear the conversation history and reset the session.""" + self.history = [] + self.metadata = {} + self.session_id = str(uuid.uuid4()) + + def __len__(self) -> int: + """Return the number of messages in the conversation.""" + return len(self.history) + + def __repr__(self) -> str: + """Return a string representation of the session.""" + return f"ConversationSession(id={self.session_id}, messages={len(self)})" + + +def get_or_create_session( + session_state: dict[str, Any], + session_key: str = "session", +) -> ConversationSession: + """Get existing session or create a new one from Gradio state. + + This function manages conversation sessions for multi-turn interactions + in Gradio applications. + + Parameters + ---------- + session_state : dict[str, Any] + The Gradio session state dictionary. + session_key : str, optional + The key to use for storing the session, by default "session". + + Returns + ------- + ConversationSession + The conversation session. + + Examples + -------- + >>> # In a Gradio app + >>> def chat_handler(query, history, session_state): + ... session = get_or_create_session(session_state) + ... session.add_user_message(query) + ... # Process and get response... + ... session.add_assistant_message(response) + ... return response + """ + if session_key not in session_state: + session_state[session_key] = ConversationSession() + return session_state[session_key] diff --git a/aieng-eval-agents/pyproject.toml b/aieng-eval-agents/pyproject.toml index 1565996e..303b660e 100644 --- a/aieng-eval-agents/pyproject.toml +++ b/aieng-eval-agents/pyproject.toml @@ -7,7 +7,12 @@ requires-python = ">=3.12,<4.0" readme = "README.md" license = "MIT" dependencies = [ - "kagglehub>=0.3.13", + "kagglehub>=0.4.1", + "pydantic-settings>=2.12.0", + "pydantic>=2.12.0", + "google-genai>=1.52.0", + "pandas>=2.3.0", + "rich>=13.9.0", ] [dependency-groups] diff --git a/aieng-eval-agents/tests/aieng/__init__.py b/aieng-eval-agents/tests/aieng/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/aieng-eval-agents/tests/aieng/agent_evals/__init__.py b/aieng-eval-agents/tests/aieng/agent_evals/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/__init__.py b/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_evaluation.py b/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_evaluation.py new file mode 100644 index 00000000..49f95c2b --- /dev/null +++ b/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_evaluation.py @@ -0,0 +1,177 @@ +"""Tests for DeepSearchQA evaluation utilities.""" + +from unittest.mock import patch + +import pandas as pd +import pytest +from aieng.agent_evals.knowledge_agent.evaluation import ( + DeepSearchQADataset, + DSQAExample, + EvaluationResult, +) + + +class TestDSQAExample: + """Tests for the DSQAExample model.""" + + def test_example_creation(self): + """Test creating an example.""" + example = DSQAExample( + example_id=0, + problem="What is the capital of France?", + problem_category="Geography", + answer="Paris", + answer_type="Single Answer", + ) + assert example.example_id == 0 + assert example.problem == "What is the capital of France?" + assert example.problem_category == "Geography" + assert example.answer == "Paris" + assert example.answer_type == "Single Answer" + + +class TestEvaluationResult: + """Tests for the EvaluationResult model.""" + + def test_result_creation(self): + """Test creating an evaluation result.""" + result = EvaluationResult( + example_id=1, + problem="Test question", + ground_truth="Expected answer", + prediction="Model answer", + search_queries=["query1"], + sources_used=2, + ) + assert result.example_id == 1 + assert result.ground_truth == "Expected answer" + assert result.prediction == "Model answer" + assert len(result.search_queries) == 1 + assert result.sources_used == 2 + assert result.is_correct is None + + def test_result_defaults(self): + """Test default values for evaluation result.""" + result = EvaluationResult( + example_id=0, + problem="Q", + ground_truth="A", + prediction="B", + ) + assert result.search_queries == [] + assert result.sources_used == 0 + assert result.is_correct is None + assert result.evaluation_notes == "" + + +class TestDeepSearchQADataset: + """Tests for the DeepSearchQADataset class.""" + + @pytest.fixture + def mock_csv_data(self): + """Create mock CSV data.""" + return { + "example_id": [0, 1, 2], + "problem": ["Q1", "Q2", "Q3"], + "problem_category": ["Cat A", "Cat B", "Cat A"], + "answer": ["A1", "A2", "A3"], + "answer_type": ["Single Answer", "List", "Single Answer"], + } + + @patch("aieng.agent_evals.knowledge_agent.evaluation.kagglehub.dataset_download") + @patch("aieng.agent_evals.knowledge_agent.evaluation.pd.read_csv") + def test_dataset_loading(self, mock_read_csv, mock_download, mock_csv_data): + """Test loading the dataset.""" + mock_download.return_value = "/fake/path" + mock_read_csv.return_value = pd.DataFrame(mock_csv_data) + + with patch("pathlib.Path.exists", return_value=True): + dataset = DeepSearchQADataset() + examples = dataset.examples + + assert len(examples) == 3 + assert examples[0].problem == "Q1" + + @patch("aieng.agent_evals.knowledge_agent.evaluation.kagglehub.dataset_download") + @patch("aieng.agent_evals.knowledge_agent.evaluation.pd.read_csv") + def test_dataset_length(self, mock_read_csv, mock_download, mock_csv_data): + """Test getting dataset length.""" + mock_download.return_value = "/fake/path" + mock_read_csv.return_value = pd.DataFrame(mock_csv_data) + + with patch("pathlib.Path.exists", return_value=True): + dataset = DeepSearchQADataset() + assert len(dataset) == 3 + + @patch("aieng.agent_evals.knowledge_agent.evaluation.kagglehub.dataset_download") + @patch("aieng.agent_evals.knowledge_agent.evaluation.pd.read_csv") + def test_dataset_indexing(self, mock_read_csv, mock_download, mock_csv_data): + """Test indexing into the dataset.""" + mock_download.return_value = "/fake/path" + mock_read_csv.return_value = pd.DataFrame(mock_csv_data) + + with patch("pathlib.Path.exists", return_value=True): + dataset = DeepSearchQADataset() + example = dataset[1] + + assert example.example_id == 1 + assert example.problem == "Q2" + + @patch("aieng.agent_evals.knowledge_agent.evaluation.kagglehub.dataset_download") + @patch("aieng.agent_evals.knowledge_agent.evaluation.pd.read_csv") + def test_get_by_category(self, mock_read_csv, mock_download, mock_csv_data): + """Test filtering by category.""" + mock_download.return_value = "/fake/path" + mock_read_csv.return_value = pd.DataFrame(mock_csv_data) + + with patch("pathlib.Path.exists", return_value=True): + dataset = DeepSearchQADataset() + cat_a_examples = dataset.get_by_category("Cat A") + + assert len(cat_a_examples) == 2 + assert all(ex.problem_category == "Cat A" for ex in cat_a_examples) + + @patch("aieng.agent_evals.knowledge_agent.evaluation.kagglehub.dataset_download") + @patch("aieng.agent_evals.knowledge_agent.evaluation.pd.read_csv") + def test_get_categories(self, mock_read_csv, mock_download, mock_csv_data): + """Test getting unique categories.""" + mock_download.return_value = "/fake/path" + mock_read_csv.return_value = pd.DataFrame(mock_csv_data) + + with patch("pathlib.Path.exists", return_value=True): + dataset = DeepSearchQADataset() + categories = dataset.get_categories() + + assert "Cat A" in categories + assert "Cat B" in categories + + @patch("aieng.agent_evals.knowledge_agent.evaluation.kagglehub.dataset_download") + @patch("aieng.agent_evals.knowledge_agent.evaluation.pd.read_csv") + def test_sample(self, mock_read_csv, mock_download, mock_csv_data): + """Test random sampling.""" + mock_download.return_value = "/fake/path" + mock_read_csv.return_value = pd.DataFrame(mock_csv_data) + + with patch("pathlib.Path.exists", return_value=True): + dataset = DeepSearchQADataset() + sample = dataset.sample(n=2, random_state=42) + + assert len(sample) == 2 + assert all(isinstance(ex, DSQAExample) for ex in sample) + + +@pytest.mark.integration_test +class TestDeepSearchQADatasetIntegration: + """Integration tests for DeepSearchQADataset. + + These tests download the actual dataset from Kaggle. + """ + + def test_load_real_dataset(self): + """Test loading the real dataset.""" + dataset = DeepSearchQADataset() + + # Dataset may have fewer than 900 examples after filtering NaN answers + assert len(dataset) > 800 # Should have most examples + assert dataset[0].example_id == 0 + assert len(dataset.get_categories()) > 0 diff --git a/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_grounding_tool.py b/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_grounding_tool.py new file mode 100644 index 00000000..385e31f9 --- /dev/null +++ b/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_grounding_tool.py @@ -0,0 +1,131 @@ +"""Tests for Gemini grounding tool.""" + +from unittest.mock import MagicMock, patch + +import pytest +from aieng.agent_evals.knowledge_agent.grounding_tool import ( + GeminiGroundingTool, + GroundedResponse, + GroundingChunk, +) + + +class TestGroundingChunk: + """Tests for the GroundingChunk model.""" + + def test_grounding_chunk_creation(self): + """Test creating a grounding chunk.""" + chunk = GroundingChunk(title="Test Title", uri="https://example.com") + assert chunk.title == "Test Title" + assert chunk.uri == "https://example.com" + + def test_grounding_chunk_defaults(self): + """Test default values for grounding chunk.""" + chunk = GroundingChunk() + assert chunk.title == "" + assert chunk.uri == "" + + +class TestGroundedResponse: + """Tests for the GroundedResponse model.""" + + def test_grounded_response_creation(self): + """Test creating a grounded response.""" + response = GroundedResponse( + text="Test response", + search_queries=["query1", "query2"], + sources=[ + GroundingChunk(title="Source 1", uri="https://source1.com"), + ], + ) + assert response.text == "Test response" + assert len(response.search_queries) == 2 + assert len(response.sources) == 1 + + def test_grounded_response_defaults(self): + """Test default values for grounded response.""" + response = GroundedResponse(text="Just text") + assert response.text == "Just text" + assert response.search_queries == [] + assert response.sources == [] + + +class TestGeminiGroundingTool: + """Tests for the GeminiGroundingTool class.""" + + @pytest.fixture + def mock_config(self): + """Create a mock config for testing.""" + config = MagicMock() + config.openai_api_key = "test-api-key" + config.default_worker_model = "gemini-2.5-flash" + return config + + @patch("aieng.agent_evals.knowledge_agent.grounding_tool.genai.Client") + def test_tool_initialization(self, mock_client_class, mock_config): + """Test initializing the grounding tool.""" + tool = GeminiGroundingTool(config=mock_config) + + assert tool.config is mock_config + assert tool.model == "gemini-2.5-flash" + mock_client_class.assert_called_once_with(api_key="test-api-key") + + @patch("aieng.agent_evals.knowledge_agent.grounding_tool.genai.Client") + def test_tool_with_custom_model(self, mock_client_class, mock_config): + """Test initializing with a custom model.""" + tool = GeminiGroundingTool(config=mock_config, model="gemini-2.5-pro") + assert tool.model == "gemini-2.5-pro" + + def test_format_response_with_citations(self, mock_config): + """Test formatting response with citations.""" + with patch("aieng.agent_evals.knowledge_agent.grounding_tool.genai.Client"): + tool = GeminiGroundingTool(config=mock_config) + + response = GroundedResponse( + text="The answer is 42.", + search_queries=["meaning of life"], + sources=[ + GroundingChunk(title="Wikipedia", uri="https://en.wikipedia.org/wiki/42"), + GroundingChunk(title="Guide", uri="https://example.com/guide"), + ], + ) + + formatted = tool.format_response_with_citations(response) + + assert "The answer is 42." in formatted + assert "**Sources:**" in formatted + assert "[Wikipedia](https://en.wikipedia.org/wiki/42)" in formatted + assert "[Guide](https://example.com/guide)" in formatted + + def test_format_response_without_sources(self, mock_config): + """Test formatting response without sources.""" + with patch("aieng.agent_evals.knowledge_agent.grounding_tool.genai.Client"): + tool = GeminiGroundingTool(config=mock_config) + + response = GroundedResponse(text="Simple answer.") + + formatted = tool.format_response_with_citations(response) + + assert formatted == "Simple answer." + assert "Sources" not in formatted + + +@pytest.mark.integration_test +class TestGeminiGroundingToolIntegration: + """Integration tests for the GeminiGroundingTool. + + These tests require a valid GOOGLE_API_KEY environment variable. + """ + + def test_search_real_query(self): + """Test a real search query.""" + from aieng.agent_evals.knowledge_agent import ( # noqa: PLC0415 + GeminiGroundingTool, + ) + + tool = GeminiGroundingTool() + response = tool.search("What is the capital of France?") + + assert response.text + assert "Paris" in response.text + # May or may not have search queries depending on model decision diff --git a/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_session.py b/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_session.py new file mode 100644 index 00000000..9622fef9 --- /dev/null +++ b/aieng-eval-agents/tests/aieng/agent_evals/knowledge_agent/test_session.py @@ -0,0 +1,164 @@ +"""Tests for session management utilities.""" + +from aieng.agent_evals.knowledge_agent.session import ( + ConversationSession, + Message, + get_or_create_session, +) + + +class TestMessage: + """Tests for the Message class.""" + + def test_message_creation(self): + """Test creating a message.""" + msg = Message(role="user", content="Hello") + assert msg.role == "user" + assert msg.content == "Hello" + + def test_message_roles(self): + """Test different message roles.""" + user_msg = Message(role="user", content="Hi") + assistant_msg = Message(role="assistant", content="Hello!") + + assert user_msg.role == "user" + assert assistant_msg.role == "assistant" + + +class TestConversationSession: + """Tests for the ConversationSession class.""" + + def test_session_creation(self): + """Test creating a new session.""" + session = ConversationSession() + assert session.session_id is not None + assert len(session) == 0 + assert session.history == [] + + def test_session_with_custom_id(self): + """Test creating a session with a custom ID.""" + session = ConversationSession(session_id="test-123") + assert session.session_id == "test-123" + + def test_add_message(self): + """Test adding messages to a session.""" + session = ConversationSession() + session.add_message("user", "Hello") + session.add_message("assistant", "Hi there!") + + assert len(session) == 2 + assert session.history[0].role == "user" + assert session.history[0].content == "Hello" + assert session.history[1].role == "assistant" + assert session.history[1].content == "Hi there!" + + def test_add_user_message(self): + """Test adding a user message.""" + session = ConversationSession() + session.add_user_message("What is AI?") + + assert len(session) == 1 + assert session.history[0].role == "user" + assert session.history[0].content == "What is AI?" + + def test_add_assistant_message(self): + """Test adding an assistant message.""" + session = ConversationSession() + session.add_assistant_message("AI stands for Artificial Intelligence.") + + assert len(session) == 1 + assert session.history[0].role == "assistant" + + def test_get_history(self): + """Test getting history as dictionaries.""" + session = ConversationSession() + session.add_user_message("Question?") + session.add_assistant_message("Answer!") + + history = session.get_history() + assert len(history) == 2 + assert history[0] == {"role": "user", "content": "Question?"} + assert history[1] == {"role": "assistant", "content": "Answer!"} + + def test_get_history_as_text(self): + """Test getting history as formatted text.""" + session = ConversationSession() + session.add_user_message("Hello") + session.add_assistant_message("Hi!") + + text = session.get_history_as_text() + assert "User: Hello" in text + assert "Assistant: Hi!" in text + + def test_get_last_n_messages(self): + """Test getting last n messages.""" + session = ConversationSession() + for i in range(5): + session.add_user_message(f"Message {i}") + + last_2 = session.get_last_n_messages(2) + assert len(last_2) == 2 + assert last_2[0].content == "Message 3" + assert last_2[1].content == "Message 4" + + def test_get_last_n_messages_when_fewer_messages(self): + """Test getting more messages than available.""" + session = ConversationSession() + session.add_user_message("Only message") + + last_5 = session.get_last_n_messages(5) + assert len(last_5) == 1 + + def test_clear(self): + """Test clearing a session.""" + session = ConversationSession(session_id="original-id") + session.add_user_message("Test") + session.metadata["key"] = "value" + + old_id = session.session_id + session.clear() + + assert len(session) == 0 + assert session.metadata == {} + assert session.session_id != old_id + + def test_repr(self): + """Test string representation.""" + session = ConversationSession(session_id="test-id") + session.add_user_message("Hello") + + repr_str = repr(session) + assert "test-id" in repr_str + assert "1" in repr_str # 1 message + + +class TestGetOrCreateSession: + """Tests for the get_or_create_session function.""" + + def test_creates_new_session(self): + """Test creating a new session when none exists.""" + state: dict = {} + session = get_or_create_session(state) + + assert isinstance(session, ConversationSession) + assert "session" in state + assert state["session"] is session + + def test_returns_existing_session(self): + """Test returning existing session.""" + state: dict = {} + session1 = get_or_create_session(state) + session1.add_user_message("Hello") + + session2 = get_or_create_session(state) + + assert session1 is session2 + assert len(session2) == 1 + + def test_custom_session_key(self): + """Test using a custom session key.""" + state: dict = {} + session = get_or_create_session(state, session_key="my_session") + + assert "my_session" in state + assert state["my_session"] is session diff --git a/implementations/knowledge_agent/01_grounding_basics.ipynb b/implementations/knowledge_agent/01_grounding_basics.ipynb new file mode 100644 index 00000000..1068cb3f --- /dev/null +++ b/implementations/knowledge_agent/01_grounding_basics.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 01: Gemini Google Search Grounding Basics\n", + "\n", + "This notebook introduces Gemini's built-in Google Search grounding capability,\n", + "which allows the model to search the web and ground its responses in real-time information.\n", + "\n", + "## Learning Objectives\n", + "\n", + "- Understand how Google Search grounding works\n", + "- Use the `GeminiGroundingTool` to make grounded queries\n", + "- Extract and display source citations\n", + "- Compare grounded vs non-grounded responses" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup: Load environment and configure rich console\n", + "from aieng.agent_evals import (\n", + " create_console,\n", + " display_comparison,\n", + " display_response,\n", + " display_source_table,\n", + ")\n", + "from dotenv import load_dotenv\n", + "\n", + "\n", + "console = create_console()\n", + "load_dotenv(verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Understanding Google Search Grounding\n", + "\n", + "When you enable Google Search grounding, Gemini can:\n", + "\n", + "1. Analyze your query and determine if web search would help\n", + "2. Automatically generate and execute search queries\n", + "3. Process search results and synthesize information\n", + "4. Return a response grounded in the search results with citations\n", + "\n", + "This is different from traditional RAG because:\n", + "- No need to pre-index documents\n", + "- Access to real-time information\n", + "- The model decides when and what to search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent import GeminiGroundingTool\n", + "from rich.panel import Panel\n", + "\n", + "\n", + "# Initialize the grounding tool\n", + "tool = GeminiGroundingTool()\n", + "\n", + "console.print(\n", + " Panel(\n", + " f\"[green]✓[/green] Grounding tool initialized\\n[cyan]Model:[/cyan] {tool.model}\",\n", + " title=\"🔧 Setup Complete\",\n", + " border_style=\"green\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Making Your First Grounded Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ask a question that requires current information\n", + "query = \"What is the current population of Tokyo?\"\n", + "\n", + "console.print(f\"\\n[cyan]📝 Query:[/cyan] {query}\\n\")\n", + "\n", + "with console.status(\"[cyan]Searching...[/cyan]\", spinner=\"dots\"):\n", + " response = tool.search(query)\n", + "\n", + "display_response(response, console=console, title=\"Tokyo Population\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display sources in a detailed table format\n", + "display_source_table(response, console=console)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 3. Comparing Grounded vs Non-Grounded Responses\n\nThis is where grounding truly shines. We'll ask about Toronto's record single-day snowfall.\n\n**Why this example works:**\n- The record was set on **January 25, 2026** - after the model's training data cutoff\n- Without grounding, the model can only guess based on historical data it was trained on\n- With grounding, the model searches the web and finds the recent news about this event\n\nThis clearly demonstrates that grounding enables access to information the model couldn't possibly know from training alone." + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent import KnowledgeAgentConfig\n", + "from google import genai\n", + "\n", + "\n", + "config = KnowledgeAgentConfig()\n", + "client = genai.Client(api_key=config.openai_api_key)\n", + "\n", + "# This question requires very recent information (Jan 2026)\n", + "# The non-grounded model will fail since its training data doesn't include this event\n", + "question = \"Which day had the highest recorded snowfall in a single day in Toronto?\"\n", + "expected_answer = \"January 25, 2026\"\n", + "\n", + "console.print(f\"\\n[bold]Question:[/bold] {question}\")\n", + "console.print(f\"[dim]Expected Answer: {expected_answer}[/dim]\\n\")\n", + "\n", + "# Without grounding - model relies on training data (cutoff before Jan 2026)\n", + "console.print(\"[dim]Generating without grounding...[/dim]\")\n", + "response_no_grounding = client.models.generate_content(\n", + " model=config.default_worker_model,\n", + " contents=question,\n", + ")\n", + "\n", + "# With grounding - model can search for current information\n", + "console.print(\"[dim]Generating with grounding...[/dim]\")\n", + "response_grounded = tool.search(question)\n", + "\n", + "# Side-by-side comparison using our display utility\n", + "display_comparison(response_no_grounding.text, response_grounded, console=console)\n", + "\n", + "# Check if the grounded response contains the correct answer\n", + "if expected_answer.lower() in response_grounded.text.lower() or \"january 25\" in response_grounded.text.lower():\n", + " console.print(\"\\n[green]✓ Grounded response contains the correct answer![/green]\")\n", + "else:\n", + " console.print(\"\\n[yellow]⚠ Check the grounded response for accuracy[/yellow]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Exercise: Try Your Own Queries\n", + "\n", + "Try asking questions that:\n", + "- Require recent information (news, events, statistics)\n", + "- Need multiple facts combined\n", + "- Are about specific domains (sports, science, politics)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try your own query\n", + "my_query = \"What are the latest developments in fusion energy?\"\n", + "\n", + "console.print(f\"[bold cyan]🔍 Query:[/bold cyan] {my_query}\\n\")\n", + "\n", + "with console.status(\"[cyan]Searching the web...[/cyan]\", spinner=\"dots\"):\n", + " my_response = tool.search(my_query)\n", + "\n", + "display_response(my_response, console=console, title=\"Fusion Energy Developments\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, you learned:\n", + "\n", + "1. How Google Search grounding works with Gemini\n", + "2. How to use the `GeminiGroundingTool` for grounded queries\n", + "3. How to extract and display source citations\n", + "4. The difference between grounded and non-grounded responses\n", + "\n", + "**Next**: In the next notebook, we'll build a full knowledge agent that uses this grounding capability." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "console.print(\n", + " Panel(\n", + " \"[green]✓[/green] Notebook complete!\\n\\n\"\n", + " \"[cyan]Next:[/cyan] Open [bold]02_agent_basics.ipynb[/bold] to learn about the Knowledge Agent.\",\n", + " title=\"🎉 Done\",\n", + " border_style=\"green\",\n", + " )\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/implementations/knowledge_agent/02_agent_basics.ipynb b/implementations/knowledge_agent/02_agent_basics.ipynb new file mode 100644 index 00000000..a0cf2bc2 --- /dev/null +++ b/implementations/knowledge_agent/02_agent_basics.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 02: Knowledge-Grounded Agent Basics\n", + "\n", + "This notebook introduces the `KnowledgeGroundedAgent` class, which wraps Gemini\n", + "with Google Search grounding into a full-featured QA agent.\n", + "\n", + "## Learning Objectives\n", + "\n", + "- Create and configure a `KnowledgeGroundedAgent`\n", + "- Understand the agent's system instructions\n", + "- Use the agent for single-turn Q&A\n", + "- Explore the DeepSearchQA evaluation dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup: Load environment and configure rich console\n", + "from aieng.agent_evals import (\n", + " create_console,\n", + " display_example,\n", + " display_info,\n", + " display_response,\n", + " display_success,\n", + ")\n", + "from dotenv import load_dotenv\n", + "\n", + "\n", + "console = create_console()\n", + "load_dotenv(verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Creating a Knowledge Agent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent import KnowledgeGroundedAgent\n", + "from rich.panel import Panel\n", + "from rich.table import Table\n", + "\n", + "\n", + "# Create the agent\n", + "agent = KnowledgeGroundedAgent()\n", + "\n", + "# Display configuration\n", + "config_table = Table(title=\"🤖 Agent Configuration\", show_header=True, header_style=\"bold cyan\")\n", + "config_table.add_column(\"Setting\", style=\"cyan\")\n", + "config_table.add_column(\"Value\", style=\"white\")\n", + "config_table.add_row(\"Model\", agent.model)\n", + "config_table.add_row(\"Planner Model\", agent.config.default_planner_model)\n", + "config_table.add_row(\"Worker Model\", agent.config.default_worker_model)\n", + "\n", + "console.print(config_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Understanding System Instructions\n", + "\n", + "The agent uses carefully crafted system instructions to guide its behavior." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent.agent import KNOWLEDGE_AGENT_INSTRUCTIONS\n", + "from rich.markdown import Markdown\n", + "\n", + "\n", + "console.print(\n", + " Panel(\n", + " Markdown(KNOWLEDGE_AGENT_INSTRUCTIONS),\n", + " title=\"📜 System Instructions\",\n", + " border_style=\"blue\",\n", + " subtitle=\"[dim]Guides agent behavior[/dim]\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Single-Turn Q&A\n", + "\n", + "Let's use the agent to answer some questions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example 1: Current events\n", + "question = \"What are the most significant AI developments in January 2026?\"\n", + "\n", + "console.print(\n", + " Panel(\n", + " f\"[bold green]{question}[/bold green]\",\n", + " title=\"❓ Question\",\n", + " border_style=\"green\",\n", + " )\n", + ")\n", + "\n", + "with console.status(\"[cyan]🔍 Agent is researching...[/cyan]\", spinner=\"dots\"):\n", + " response = agent.answer(question)\n", + "\n", + "display_response(response, console=console, title=\"AI Developments January 2026\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example 2: Factual question\n", + "question = \"What countries have successfully landed spacecraft on the Moon?\"\n", + "\n", + "console.print(\n", + " Panel(\n", + " f\"[bold green]{question}[/bold green]\",\n", + " title=\"❓ Question\",\n", + " border_style=\"green\",\n", + " )\n", + ")\n", + "\n", + "with console.status(\"[cyan]🔍 Agent is researching...[/cyan]\", spinner=\"dots\"):\n", + " response = agent.answer(question)\n", + "\n", + "display_response(response, console=console, title=\"Moon Landing Countries\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Using the AsyncClientManager\n", + "\n", + "For applications that need to manage client lifecycle, use `AsyncClientManager`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent import AsyncClientManager\n", + "\n", + "\n", + "# Create a manager (lazy initialization)\n", + "manager = AsyncClientManager()\n", + "\n", + "display_info(f\"Initialized: {manager.is_initialized()}\", console=console)\n", + "\n", + "# Access the agent (triggers initialization)\n", + "managed_agent = manager.agent\n", + "display_info(f\"After access: {manager.is_initialized()}\", console=console)\n", + "\n", + "# Use the agent\n", + "with console.status(\"[cyan]Querying...[/cyan]\", spinner=\"dots\"):\n", + " response = managed_agent.answer(\"What is the speed of light?\")\n", + "\n", + "display_response(response, console=console, title=\"Quick Answer\", show_queries=False)\n", + "\n", + "# Cleanup\n", + "manager.close()\n", + "display_success(\"Manager closed\", console=console)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Exploring the DeepSearchQA Dataset\n", + "\n", + "The DeepSearchQA dataset contains 896 research questions for evaluating knowledge agents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent import DeepSearchQADataset\n", + "\n", + "\n", + "# Load the dataset\n", + "with console.status(\"[cyan]Loading DeepSearchQA dataset...[/cyan]\", spinner=\"dots\"):\n", + " dataset = DeepSearchQADataset()\n", + "\n", + "# Display dataset info\n", + "info_table = Table(title=\"📊 DeepSearchQA Dataset\", show_header=True, header_style=\"bold cyan\")\n", + "info_table.add_column(\"Metric\", style=\"cyan\")\n", + "info_table.add_column(\"Value\", style=\"white\")\n", + "info_table.add_row(\"Total Examples\", str(len(dataset)))\n", + "info_table.add_row(\"Categories\", str(len(dataset.get_categories())))\n", + "\n", + "console.print(info_table)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display categories\n", + "categories = dataset.get_categories()\n", + "\n", + "cat_table = Table(title=\"📁 Problem Categories\", show_header=True, header_style=\"bold green\")\n", + "cat_table.add_column(\"Category\", style=\"white\")\n", + "cat_table.add_column(\"Count\", style=\"cyan\", justify=\"right\")\n", + "\n", + "for cat in sorted(categories):\n", + " count = len(dataset.get_by_category(cat))\n", + " cat_table.add_row(cat, str(count))\n", + "\n", + "console.print(cat_table)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show a sample example using the shared display utility\n", + "example = dataset[0]\n", + "\n", + "display_example(\n", + " example_id=example.example_id,\n", + " problem=example.problem,\n", + " category=example.problem_category,\n", + " answer=example.answer,\n", + " answer_type=example.answer_type,\n", + " console=console,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get random samples\n", + "samples = dataset.sample(n=3, random_state=42)\n", + "\n", + "console.print(\"[bold]📚 Random Samples from Dataset[/bold]\\n\")\n", + "\n", + "for ex in samples:\n", + " display_example(\n", + " example_id=ex.example_id,\n", + " problem=ex.problem[:300] + \"...\" if len(ex.problem) > 300 else ex.problem,\n", + " category=ex.problem_category,\n", + " answer=ex.answer,\n", + " console=console,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Testing the Agent on DeepSearchQA\n", + "\n", + "Let's test the agent on a sample question from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pick an example\n", + "test_example = samples[0]\n", + "\n", + "console.print(\n", + " Panel(\n", + " f\"[bold]Testing on Example {test_example.example_id}[/bold]\\n\\n\"\n", + " f\"[cyan]Category:[/cyan] {test_example.problem_category}\\n\"\n", + " f\"[cyan]Expected Answer:[/cyan] {test_example.answer}\",\n", + " title=\"🧪 Test Setup\",\n", + " border_style=\"yellow\",\n", + " )\n", + ")\n", + "\n", + "# Ask the agent\n", + "console.print(\n", + " Panel(\n", + " f\"[bold green]{test_example.problem}[/bold green]\",\n", + " title=\"❓ Question\",\n", + " border_style=\"green\",\n", + " )\n", + ")\n", + "\n", + "with console.status(\"[cyan]🔍 Agent is researching...[/cyan]\", spinner=\"dots\"):\n", + " response = agent.answer(test_example.problem)\n", + "\n", + "display_response(response, console=console, title=\"Agent Response\")\n", + "\n", + "# Compare\n", + "contains_answer = test_example.answer.lower() in response.text.lower()\n", + "if contains_answer:\n", + " console.print(\"\\n[green]✓ CONTAINS EXPECTED ANSWER[/green]\")\n", + "else:\n", + " console.print(\"\\n[yellow]⚠ Answer may differ[/yellow]\")\n", + "console.print(f\"[dim]Expected: {test_example.answer}[/dim]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, you learned:\n", + "\n", + "1. How to create and configure a `KnowledgeGroundedAgent`\n", + "2. The system instructions that guide agent behavior\n", + "3. How to use the agent for single-turn Q&A\n", + "4. How to explore the DeepSearchQA evaluation dataset\n", + "\n", + "**Next**: In the next notebook, we'll explore multi-turn conversations and run systematic evaluations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "console.print(\n", + " Panel(\n", + " \"[green]✓[/green] Notebook complete!\\n\\n\"\n", + " \"[cyan]Next:[/cyan] Open [bold]03_multi_turn.ipynb[/bold] to learn about multi-turn conversations and evaluation.\",\n", + " title=\"🎉 Done\",\n", + " border_style=\"green\",\n", + " )\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/implementations/knowledge_agent/03_multi_turn.ipynb b/implementations/knowledge_agent/03_multi_turn.ipynb new file mode 100644 index 00000000..129f4fc1 --- /dev/null +++ b/implementations/knowledge_agent/03_multi_turn.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 03: Multi-Turn Conversations & Evaluation\n", + "\n", + "This notebook demonstrates multi-turn conversation management and\n", + "how to evaluate the agent on the DeepSearchQA benchmark.\n", + "\n", + "## Learning Objectives\n", + "\n", + "- Manage multi-turn conversations with session state\n", + "- Use the `DeepSearchQAEvaluator` for systematic evaluation\n", + "- Analyze evaluation results with rich visualizations\n", + "- Understand evaluation metrics for research agents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup: Load environment and configure rich console\n", + "from aieng.agent_evals import (\n", + " create_console,\n", + " display_evaluation_result,\n", + " display_info,\n", + " display_metrics_table,\n", + " display_success,\n", + ")\n", + "from dotenv import load_dotenv\n", + "\n", + "\n", + "console = create_console()\n", + "load_dotenv(verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Multi-Turn Session Management" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent import ConversationSession\n", + "from rich.panel import Panel\n", + "from rich.table import Table\n", + "\n", + "\n", + "# Create a session\n", + "session = ConversationSession()\n", + "\n", + "console.print(\n", + " Panel(\n", + " f\"[cyan]Session ID:[/cyan] {session.session_id}\",\n", + " title=\"🗨️ New Session Created\",\n", + " border_style=\"green\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate a multi-turn conversation\n", + "session.add_user_message(\"What is the capital of France?\")\n", + "session.add_assistant_message(\"The capital of France is Paris.\")\n", + "\n", + "session.add_user_message(\"What is its population?\")\n", + "session.add_assistant_message(\"Paris has a population of about 2.1 million in the city proper.\")\n", + "\n", + "# Display the conversation\n", + "console.print(\"[bold]📝 Conversation History[/bold]\\n\")\n", + "console.print(Panel(session.get_history_as_text(), border_style=\"blue\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get history as structured data\n", + "history = session.get_history()\n", + "\n", + "history_table = Table(title=\"📊 Structured History\", show_header=True, header_style=\"bold cyan\")\n", + "history_table.add_column(\"Role\", style=\"cyan\")\n", + "history_table.add_column(\"Content\", style=\"white\")\n", + "\n", + "for msg in history:\n", + " role_style = \"green\" if msg[\"role\"] == \"user\" else \"blue\"\n", + " history_table.add_row(\n", + " f\"[{role_style}]{msg['role'].upper()}[/{role_style}]\",\n", + " msg[\"content\"][:50] + \"...\" if len(msg[\"content\"]) > 50 else msg[\"content\"],\n", + " )\n", + "\n", + "console.print(history_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Using Sessions with Gradio State\n", + "\n", + "In Gradio apps, use `get_or_create_session` to manage sessions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent import get_or_create_session\n", + "\n", + "\n", + "# Simulate Gradio state\n", + "gradio_state = {}\n", + "\n", + "# First turn - creates a new session\n", + "session1 = get_or_create_session(gradio_state)\n", + "session1.add_user_message(\"Hello!\")\n", + "display_info(f\"Created session: {session1.session_id}\", console=console)\n", + "\n", + "# Second turn - retrieves existing session\n", + "session2 = get_or_create_session(gradio_state)\n", + "display_info(f\"Retrieved session: {session2.session_id}\", console=console)\n", + "display_success(f\"Same session: {session1 is session2}\", console=console)\n", + "display_info(f\"Messages in session: {len(session2)}\", console=console)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Running DeepSearchQA Evaluation\n", + "\n", + "The `DeepSearchQAEvaluator` provides a systematic way to evaluate the agent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.agent_evals.knowledge_agent import (\n", + " DeepSearchQADataset,\n", + " DeepSearchQAEvaluator,\n", + " KnowledgeGroundedAgent,\n", + ")\n", + "\n", + "\n", + "# Create agent and evaluator\n", + "with console.status(\"[cyan]Initializing agent and evaluator...[/cyan]\", spinner=\"dots\"):\n", + " agent = KnowledgeGroundedAgent()\n", + " evaluator = DeepSearchQAEvaluator(agent)\n", + "\n", + "display_success(f\"Dataset size: {len(evaluator.dataset)} examples\", console=console)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate a small sample\n", + "console.print(\"[bold]🔬 Running evaluation on 3 examples...[/bold]\\n\")\n", + "\n", + "with console.status(\"[cyan]Evaluating...[/cyan]\", spinner=\"dots\"):\n", + " results = evaluator.evaluate_sample(n=3, random_state=42)\n", + "\n", + "display_success(f\"Completed {len(results)} evaluations\", console=console)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# View results using the display utility\n", + "console.print(\"\\n[bold]📋 Evaluation Results[/bold]\\n\")\n", + "\n", + "for result in results:\n", + " contains_answer = result.ground_truth.lower() in result.prediction.lower()\n", + " display_evaluation_result(\n", + " example_id=result.example_id,\n", + " problem=result.problem,\n", + " ground_truth=result.ground_truth,\n", + " prediction=result.prediction,\n", + " sources_used=result.sources_used,\n", + " search_queries=result.search_queries,\n", + " contains_answer=contains_answer,\n", + " console=console,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Analyzing Evaluation Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert to DataFrame for analysis\n", + "df = evaluator.results_to_dataframe(results)\n", + "\n", + "# Calculate metrics\n", + "containment_correct = sum(1 for r in results if r.ground_truth.lower() in r.prediction.lower())\n", + "containment_accuracy = containment_correct / len(results) * 100\n", + "\n", + "metrics = {\n", + " \"Total Examples\": len(results),\n", + " \"Containment Accuracy\": f\"{containment_accuracy:.1f}%\",\n", + " \"Avg Sources Used\": df[\"sources_used\"].mean(),\n", + " \"Avg Search Queries\": df[\"search_queries\"].apply(len).mean(),\n", + "}\n", + "\n", + "display_metrics_table(metrics, title=\"Evaluation Metrics\", console=console)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Understanding Evaluation Metrics\n", + "\n", + "For research agents, we care about:\n", + "\n", + "1. **Answer Correctness**: Does the prediction match the ground truth?\n", + "2. **Source Quality**: Are the sources relevant and authoritative?\n", + "3. **Comprehensiveness**: Did the agent find all necessary information?\n", + "4. **Search Efficiency**: How many searches were needed?\n", + "\n", + "DeepSearchQA specifically measures:\n", + "- **Precision**: Quality of the answer\n", + "- **Recall**: Completeness of the answer (for list-type questions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Manual correctness check with better display\n", + "def check_answer_contains_ground_truth(prediction: str, ground_truth: str) -> bool:\n", + " \"\"\"Check if prediction contains the ground truth answer.\"\"\"\n", + " return ground_truth.lower() in prediction.lower()\n", + "\n", + "\n", + "# Check our results\n", + "console.print(\"\\n[bold]📊 Correctness Check[/bold]\\n\")\n", + "\n", + "result_table = Table(show_header=True, header_style=\"bold cyan\")\n", + "result_table.add_column(\"Example\", style=\"cyan\")\n", + "result_table.add_column(\"Status\", style=\"white\")\n", + "result_table.add_column(\"Expected\", style=\"dim\")\n", + "\n", + "for result in results:\n", + " contains = check_answer_contains_ground_truth(result.prediction, result.ground_truth)\n", + " status = \"[green]✓ MATCH[/green]\" if contains else \"[yellow]✗ NO MATCH[/yellow]\"\n", + " result_table.add_row(\n", + " str(result.example_id),\n", + " status,\n", + " result.ground_truth[:40] + \"...\" if len(result.ground_truth) > 40 else result.ground_truth,\n", + " )\n", + "\n", + "console.print(result_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Exploring Categories" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get examples from a specific category\n", + "dataset = DeepSearchQADataset()\n", + "categories = dataset.get_categories()\n", + "\n", + "cat_table = Table(title=\"📁 Available Categories\", show_header=True, header_style=\"bold green\")\n", + "cat_table.add_column(\"Category\", style=\"white\")\n", + "cat_table.add_column(\"Count\", style=\"cyan\", justify=\"right\")\n", + "\n", + "for cat in sorted(categories):\n", + " count = len(dataset.get_by_category(cat))\n", + " cat_table.add_row(cat, str(count))\n", + "\n", + "console.print(cat_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, you learned:\n", + "\n", + "1. How to manage multi-turn conversations with `ConversationSession`\n", + "2. How to use `get_or_create_session` for Gradio integration\n", + "3. How to run systematic evaluations with `DeepSearchQAEvaluator`\n", + "4. How to analyze evaluation results with rich visualizations\n", + "5. Key metrics for evaluating research agents\n", + "\n", + "## Next Steps\n", + "\n", + "- Run the Gradio app for interactive testing\n", + "- Experiment with different models (gemini-2.5-pro vs flash)\n", + "- Try the async evaluator for larger-scale evaluation\n", + "- Implement LLM-as-judge evaluation for more nuanced correctness checking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "console.print(\n", + " Panel(\n", + " \"[green]✓[/green] Notebook complete!\\n\\n\"\n", + " \"[cyan]Next:[/cyan] Run [bold]gradio_app.py[/bold] for interactive testing.\",\n", + " title=\"🎉 Done\",\n", + " border_style=\"green\",\n", + " )\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/implementations/knowledge_agent/README.md b/implementations/knowledge_agent/README.md new file mode 100644 index 00000000..ec797d8d --- /dev/null +++ b/implementations/knowledge_agent/README.md @@ -0,0 +1,123 @@ +# Knowledge-Grounded QA Agent + +This implementation demonstrates a knowledge-grounded question answering agent using **Gemini's Google Search grounding** capability, evaluated on the **DeepSearchQA** benchmark. + +## Overview + +The knowledge agent uses Gemini's built-in Google Search tool to answer questions that require real-time information. Unlike traditional RAG systems that rely on pre-indexed documents, this approach searches the live web to find relevant information. + +## Features + +- **Google Search Grounding**: Uses Gemini's native search capability for real-time information retrieval +- **Source Citation**: Automatically includes source URLs in responses +- **DeepSearchQA Evaluation**: Built-in evaluation on the DeepSearchQA benchmark (900 research tasks) +- **Multi-turn Conversations**: Session management for follow-up questions +- **Gradio Interface**: Interactive chat UI for testing + +## Setup + +1. **Configure environment variables** in `.env`: + +```bash +# Required: Google API key (get from https://aistudio.google.com/apikey) +GOOGLE_API_KEY="your-api-key" + +# Optional: Langfuse for tracing +LANGFUSE_PUBLIC_KEY="pk-lf-..." +LANGFUSE_SECRET_KEY="sk-lf-..." +``` + +2. **Install dependencies**: + +```bash +uv sync +``` + +## Usage + +### Interactive Chat + +Run the Gradio app: + +```bash +uv run --env-file .env gradio implementations/knowledge_agent/gradio_app.py +``` + +### Programmatic Usage + +```python +from aieng.agent_evals.knowledge_agent import KnowledgeGroundedAgent + +agent = KnowledgeGroundedAgent() +response = agent.answer("What is the current population of Tokyo?") + +print(response.text) +print(f"Sources: {[s.uri for s in response.sources]}") +``` + +### Evaluation on DeepSearchQA + +```python +from aieng.agent_evals.knowledge_agent import ( + KnowledgeGroundedAgent, + DeepSearchQADataset, + DeepSearchQAEvaluator, +) + +agent = KnowledgeGroundedAgent() +evaluator = DeepSearchQAEvaluator(agent) + +# Evaluate a sample +results = evaluator.evaluate_sample(n=10, random_state=42) + +# Convert to DataFrame for analysis +df = evaluator.results_to_dataframe(results) +print(df[["example_id", "ground_truth", "prediction", "sources_used"]]) +``` + +## Notebooks + +1. **01_grounding_basics.ipynb**: Introduction to Gemini's Google Search grounding +2. **02_agent_basics.ipynb**: Creating and using the knowledge agent +3. **03_multi_turn.ipynb**: Multi-turn conversations and DeepSearchQA evaluation + +## Architecture + +``` +aieng.agent_evals.knowledge_agent/ +├── config.py # Configuration (Pydantic settings) +├── grounding_tool.py # Gemini Google Search grounding tool +├── agent.py # KnowledgeGroundedAgent class +├── session.py # Multi-turn session management +├── evaluation.py # DeepSearchQA dataset and evaluator +└── wikipedia_tool.py # Alternative Wikipedia search tool +``` + +## DeepSearchQA Dataset + +The [DeepSearchQA](https://www.kaggle.com/datasets/deepmind/deepsearchqa) benchmark consists of 900 "causal chain" research tasks across 17 categories. These questions require: + +- Multi-source lookups +- Statistical comparisons +- Real-time web search + +Example question: +> "Consider the OECD countries whose total population was composed of at least 20% of foreign-born populations as of 2023. Amongst them, which country saw their overall criminality score increase by at least +0.2 point between 2021 and 2023?" + +## Models + +The agent supports Gemini models with Google Search grounding: + +| Model | Best For | +|-------|----------| +| `gemini-2.5-flash` (default) | Fast, cost-effective | +| `gemini-2.5-pro` | Complex reasoning | +| `gemini-3-flash-preview` | Latest capabilities (preview) | + +See [Gemini models documentation](https://ai.google.dev/gemini-api/docs/models) for the full list. + +## References + +- [Grounding with Google Search - Gemini API](https://ai.google.dev/gemini-api/docs/google-search) +- [DeepSearchQA Dataset - Kaggle](https://www.kaggle.com/datasets/deepmind/deepsearchqa) +- [Google GenAI Python SDK](https://github.com/googleapis/python-genai) diff --git a/implementations/knowledge_agent/gradio_app.py b/implementations/knowledge_agent/gradio_app.py new file mode 100644 index 00000000..96c22b12 --- /dev/null +++ b/implementations/knowledge_agent/gradio_app.py @@ -0,0 +1,158 @@ +"""Gradio app for the Knowledge-Grounded QA Agent. + +This app provides an interactive chat interface for testing the +knowledge-grounded QA agent with Gemini's Google Search grounding. + +Run with: + uv run --env-file .env gradio implementations/knowledge_agent/gradio_app.py +""" + +import logging +from typing import Any, Generator + +import gradio as gr +from aieng.agent_evals.knowledge_agent import ( + AsyncClientManager, + DeepSearchQADataset, + get_or_create_session, +) +from dotenv import load_dotenv +from gradio.components.chatbot import ChatMessage + + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def format_sources(sources: list) -> str: + """Format sources for display.""" + if not sources: + return "" + + lines = ["\n\n---\n**Sources:**"] + for i, source in enumerate(sources, 1): + if source.uri: + lines.append(f"[{i}] [{source.title or 'Source'}]({source.uri})") + return "\n".join(lines) + + +def chat( + query: str, + history: list[ChatMessage], + session_state: dict[str, Any], +) -> Generator[list[ChatMessage], None, None]: + """Handle chat interactions with the agent. + + Parameters + ---------- + query : str + The user's question. + history : list[ChatMessage] + The conversation history. + session_state : dict[str, Any] + Gradio session state for maintaining conversation context. + + Yields + ------ + list[ChatMessage] + Updated conversation history with agent response. + """ + # Get or create session for multi-turn context + session = get_or_create_session(session_state) + session.add_user_message(query) + + # Show thinking indicator + thinking_message = ChatMessage( + role="assistant", + content="Searching...", + metadata={"title": "Searching the web"}, + ) + yield [thinking_message] + + try: + # Get response from agent + response = client_manager.agent.answer(query) + + # Format response with sources + formatted_response = response.text + if response.sources: + formatted_response += format_sources(response.sources) + + # Add search queries as metadata if available + metadata: dict[str, str] | None = None + if response.search_queries: + title = f"Searched: {', '.join(response.search_queries[:2])}" + if len(response.search_queries) > 2: + title += f" (+{len(response.search_queries) - 2} more)" + metadata = {"title": title} + + # Create final message + final_message = ChatMessage( + role="assistant", + content=formatted_response, + metadata=metadata, # type: ignore[arg-type] + ) + + session.add_assistant_message(response.text) + yield [final_message] + + except Exception as e: + logger.error(f"Error processing query: {e}") + error_message = ChatMessage( + role="assistant", + content=f"Sorry, I encountered an error: {str(e)}", + metadata={"title": "Error"}, + ) + yield [error_message] + + +def load_example_questions() -> list[list[str]]: + """Load example questions from DeepSearchQA dataset.""" + try: + dataset = DeepSearchQADataset() + # Get a diverse sample of questions + sample = dataset.sample(n=5, random_state=42) + return [[ex.problem] for ex in sample] + except Exception as e: + logger.warning(f"Could not load DeepSearchQA examples: {e}") + return [ + ["What is the current population of Tokyo?"], + ["Who won the Nobel Prize in Physics in 2024?"], + ["What are the latest developments in fusion energy?"], + ] + + +if __name__ == "__main__": + load_dotenv(verbose=True) + + # Initialize client manager + client_manager = AsyncClientManager() + logger.info(f"Using model: {client_manager.config.default_worker_model}") + + # Load example questions + examples = load_example_questions() + + # Create Gradio interface + demo = gr.ChatInterface( + chat, + chatbot=gr.Chatbot( + height=600, + render_markdown=True, + ), + textbox=gr.Textbox( + lines=2, + placeholder="Ask a question that requires current information...", + ), + examples=examples, + additional_inputs=gr.State(value={}, render=False), + title="Knowledge-Grounded QA Agent", + description=( + "Ask questions that require up-to-date information. " + "The agent uses Gemini with Google Search grounding to find answers." + ), + ) + + try: + demo.launch(share=True) + finally: + client_manager.close() diff --git a/pyproject.toml b/pyproject.toml index ef71b33f..92b9cde5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,6 +21,7 @@ dependencies = [ "openai-agents>=0.6.1", "plotly>=6.5.0", "pydantic>=2.12.4", + "pydantic-settings>=2.7.0", "pydantic-ai-slim[logfire]>=1.26.0", "scikit-learn>=1.7.0", "weaviate-client>=4.18.3", @@ -124,8 +125,10 @@ ignore = [ # Ignore import violations in all `__init__.py` files. # Ignore missing docstring in `__init__.py` files +# Ignore E402 in notebooks (imports not at top) as this is expected [tool.ruff.lint.per-file-ignores] "__init__.py" = ["E402", "F401", "F403", "F811", "D104"] +"*.ipynb" = ["E402", "D100", "D103"] diff --git a/uv.lock b/uv.lock index 81256e97..3e63b2eb 100644 --- a/uv.lock +++ b/uv.lock @@ -37,6 +37,7 @@ dependencies = [ { name = "pyasn1" }, { name = "pydantic" }, { name = 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