import argparse import concurrent.futures import json import logging import os import time from datetime import datetime from io import StringIO from textwrap import dedent from threading import Lock from typing import Any, Dict import pandas as pd import requests from datasets import Dataset, load_dataset from khoj.utils.helpers import get_cost_of_chat_message, is_none_or_empty, timer # Configure root logger logging.basicConfig(level=logging.INFO, format="%(message)s") logger = logging.getLogger(__name__) # Configuration KHOJ_URL = os.getenv("KHOJ_URL", "http://localhost:42110") KHOJ_CHAT_API_URL = f"{KHOJ_URL}/api/chat" KHOJ_API_KEY = os.getenv("KHOJ_API_KEY") KHOJ_MODE = os.getenv("KHOJ_MODE", "default") # E.g research, general, notes etc. GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") GEMINI_EVAL_MODEL = os.getenv("GEMINI_EVAL_MODEL", "gemini-1.5-pro-002") GEMINI_API_URL = ( f"https://generativelanguage.googleapis.com/v1beta/models/{GEMINI_EVAL_MODEL}:generateContent?key={GEMINI_API_KEY}" ) SAMPLE_SIZE = os.getenv("SAMPLE_SIZE") # Number of examples to evaluate RANDOMIZE = os.getenv("RANDOMIZE", "false").lower() == "true" # Randomize examples BATCH_SIZE = int( os.getenv("BATCH_SIZE", int(SAMPLE_SIZE) / 10 if SAMPLE_SIZE else 10) ) # Examples to evaluate in each batch SLEEP_SECONDS = 3 if KHOJ_MODE == "general" else 1 # Sleep between API calls to avoid rate limiting class Counter: """Thread-safe counter for tracking metrics""" def __init__(self, value=0.0): self.value = value self.lock = Lock() def add(self, amount): with self.lock: self.value += amount def get(self): with self.lock: return self.value # Track running metrics while evaluating running_cost = Counter() running_true_count = Counter(0) running_false_count = Counter(0) def load_frames_dataset(): """ Load the Google FRAMES benchmark dataset from HuggingFace FRAMES is a benchmark dataset to evaluate retrieval and answering capabilities of agents. It contains ~800 requiring multi-hop retrieval and reasoning across various topics. ### Data Fields - Prompt: The question to be answered - Answer: The ground truth answer - reasoning_types: The type of reasoning required to answer the question """ try: dataset = load_dataset("google/frames-benchmark") # Use test split for evaluation. Sample and shuffle dataset if configured dataset = dataset.shuffle() if RANDOMIZE else dataset return dataset["test"][: int(SAMPLE_SIZE)] if SAMPLE_SIZE else dataset["test"] except Exception as e: logger.error(f"Error loading dataset: {e}") return None def load_simpleqa_dataset(): """ Load the OpenAI SimpleQA benchmark dataset from their public bucket. SimpleQA is a dataset of moderately difficult q&a for 2024 models to answer across various topics. It contains ~4000 human vetted questions and answers with additional metadata. Its usage can be seen in openai/simple-evals github repository as well. ### Data Fields - problem: The question to be answered - answer: The ground truth answer - metadata: Additional metadata including topic information """ try: # Load SimpleQA benchmark from OpenAI public bucket raw_url = "https://openaipublic.blob.core.windows.net/simple-evals/simple_qa_test_set.csv" response = requests.get(raw_url) response.raise_for_status() # Parse benchmark from raw CSV response csv_data = pd.read_csv(StringIO(response.text)) # Normalize it into FRAMES format formatted_data = [ { "Prompt": d["problem"], "Answer": d["answer"], "reasoning_types": json.loads(csv_data.to_dict("records")[0]["metadata"].replace("'", '"'))["topic"], } for d in csv_data.to_dict("records") ] # Convert benchmark to HF Dataset dataset = Dataset.from_list(formatted_data) dataset = dataset.shuffle() if RANDOMIZE else dataset dataset = dataset.select(range(int(SAMPLE_SIZE))) if SAMPLE_SIZE else dataset return dataset except Exception as e: logger.error(f"Error loading simpleqa dataset: {e}") return None def get_agent_response(prompt: str) -> Dict[str, Any]: """Get response from the Khoj API""" # Set headers headers = {"Content-Type": "application/json"} if not is_none_or_empty(KHOJ_API_KEY): headers["Authorization"] = f"Bearer {KHOJ_API_KEY}" try: response = requests.post( KHOJ_CHAT_API_URL, headers=headers, json={ "q": prompt, "create_new": True, }, ) response.raise_for_status() response_json = response.json() return {"response": response_json.get("response", ""), "usage": response_json.get("usage", {})} except Exception as e: logger.error(f"Error getting agent response: {e}") return {"response": "", "usage": {}} def evaluate_response(query: str, agent_response: str, ground_truth: str) -> tuple[bool | None, str, float]: """Evaluate Khoj response against benchmark ground truth using Gemini""" evaluation_prompt = f""" Compare the following agent response with the ground truth answer. Determine if the agent response contains the key information from the ground truth. Focus on factual correctness rather than exact wording. Query: {query} Agent Response: {agent_response} Ground Truth: {ground_truth} Provide your evaluation in the following json format: {"explanation:" "[How you made the decision?)", "decision:" "(TRUE if response contains key information, FALSE otherwise)"} """ try: response = requests.post( GEMINI_API_URL, headers={"Content-Type": "application/json"}, json={ "contents": [{"parts": [{"text": evaluation_prompt}]}], "generationConfig": {"response_mime_type": "application/json"}, }, ) response.raise_for_status() response_json = response.json() # Update cost of evaluation input_tokens = response_json["usageMetadata"]["promptTokenCount"] ouput_tokens = response_json["usageMetadata"]["candidatesTokenCount"] cost = get_cost_of_chat_message(GEMINI_EVAL_MODEL, input_tokens, ouput_tokens) # Parse evaluation response eval_response: dict[str, str] = json.loads( clean_json(response_json["candidates"][0]["content"]["parts"][0]["text"]) ) decision = str(eval_response.get("decision", "")).upper() == "TRUE" explanation = eval_response.get("explanation", "") # Handle evaluation service errors if "503 Service Error" in explanation: decision = None # Extract decision and explanation from structured response return decision, explanation, cost except Exception as e: logger.error(f"Error in evaluation: {e}") return None, f"Evaluation failed: {str(e)}", 0.0 def process_batch(batch, batch_start, results, dataset_length): global running_cost for idx, (prompt, answer, reasoning_type) in enumerate(batch): current_index = batch_start + idx logger.info(f"Processing example: {current_index}/{dataset_length}") # Trigger research mode if enabled prompt = f"/{KHOJ_MODE} {prompt}" if KHOJ_MODE and not prompt.startswith(f"/{KHOJ_MODE}") else prompt # Get agent response response = get_agent_response(prompt) agent_response = response["response"] agent_usage = response["usage"] # Evaluate response if is_none_or_empty(agent_response): decision = None explanation = "Agent response is empty. This maybe due to a service error." else: decision, explanation, eval_cost = evaluate_response(prompt, agent_response, answer) # Store results results.append( { "index": current_index, "prompt": prompt, "ground_truth": answer, "agent_response": agent_response, "evaluation_decision": decision, "evaluation_explanation": explanation, "reasoning_type": reasoning_type, "usage": agent_usage, } ) # Update running cost query_cost = float(agent_usage.get("cost", 0.0)) running_cost.add(query_cost + eval_cost) # Update running accuracy running_accuracy = 0.0 if decision is not None: running_true_count.add(1) if decision == True else running_false_count.add(1) running_accuracy = running_true_count.get() / (running_true_count.get() + running_false_count.get()) ## Log results decision_color = {True: "green", None: "blue", False: "red"}[decision] colored_decision = color_text(str(decision), decision_color) result_to_print = f""" --------- Decision: {colored_decision} Accuracy: {running_accuracy:.2%} Question: {prompt} Expected Answer: {answer} Agent Answer: {agent_response} Explanation: {explanation} Cost: ${running_cost.get():.5f} (Query: ${query_cost:.5f}, Eval: ${eval_cost:.5f}) --------- """ logger.info(dedent(result_to_print).lstrip()) # Sleep between API calls to avoid rate limiting time.sleep(SLEEP_SECONDS) def color_text(text, color): colors = { "red": "\033[91m", # Bright red "green": "\033[32m", # Standard green "blue": "\033[34m", # Bright blue "reset": "\033[0m", } return f"{colors[color]}{text}{colors['reset']}" def clean_json(response: str): """Remove any markdown json codeblock and newline formatting if present. Useful for non schema enforceable models""" return response.strip().replace("\n", "").removeprefix("```json").removesuffix("```") def parse_args(): parser = argparse.ArgumentParser(description="Evaluate Khoj on a supported benchmark.") parser.add_argument( "--output", "-o", default=None, help="Path to store evaluation results CSV (default: [benchmark]_evaluation_results_[datetime].csv)", ) parser.add_argument( "--dataset", "-d", default="frames", choices=["frames", "simpleqa"], help="Dataset to use for evaluation (default: frames)", ) return parser.parse_args() def main(): # Initialize variables args = parse_args() dataset = None # Load dataset with timer(f"Loaded {args.dataset} dataset in", logger, log_level=logging.INFO): if args.dataset == "frames": dataset = load_frames_dataset() elif args.dataset == "simpleqa": dataset = load_simpleqa_dataset() if dataset is None: return # Initialize variables results = [] dataset_length = len(dataset["Prompt"]) # Process examples in batches with concurrent.futures.ThreadPoolExecutor() as executor: futures = [] for i in range(0, dataset_length, BATCH_SIZE): batch_start = i batch = zip( dataset["Prompt"][i : i + BATCH_SIZE], dataset["Answer"][i : i + BATCH_SIZE], dataset["reasoning_types"][i : i + BATCH_SIZE], ) futures.append(executor.submit(process_batch, batch, batch_start, results, dataset_length)) # Wait for all futures to complete concurrent.futures.wait(futures) # Calculate metrics df = pd.DataFrame(results) eval_df = df.dropna(subset=["evaluation_decision"]) # Exclude rows with missing evaluation decision accuracy = (eval_df["evaluation_decision"] == True).mean() # Calculate accuracy by reasoning type reasoning_type_accuracy = eval_df.groupby("reasoning_type")["evaluation_decision"].apply( lambda x: (x == True).mean() ) # Collect summary colored_accuracy = color_text(f"{accuracy:.2%}", "blue") colored_accuracy_str = f"Overall Accuracy: {colored_accuracy} on {args.dataset.title()} dataset." accuracy_str = f"Overall Accuracy: {accuracy:.2%} on {args.dataset}." accuracy_by_reasoning = f"Accuracy by Reasoning Type:\n{reasoning_type_accuracy}" cost = f"Total Cost: ${running_cost.get():.5f}." sample_type = f"Sampling Type: {SAMPLE_SIZE} samples." if SAMPLE_SIZE else "Whole dataset." sample_type += " Randomized." if RANDOMIZE else "" logger.info(f"\n{colored_accuracy_str}\n\n{accuracy_by_reasoning}\n\n{cost}\n\n{sample_type}\n") # Save summary to file summary = f"{accuracy_str}\n\n{accuracy_by_reasoning}\n\n{cost}\n\n{sample_type}\n" summary_file = args.output.replace(".csv", ".txt") if args.output else None summary_file = ( summary_file or f"{args.dataset}_evaluation_summary_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt" ) with open(summary_file, "w") as f: f.write(summary) # Save raw results to file output_file = args.output or f"{args.dataset}_evaluation_results_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.csv" df.to_csv(output_file, index=False) logger.info(f"Results saved to {summary_file}, {output_file}") if __name__ == "__main__": """ Evaluate Khoj on supported benchmarks. Response are evaluated by GEMINI_EVAL_MODEL (default: gemini-pro-1.5-002). Khoj should be running at KHOJ_URL (default: http://localhost:42110). The Gemini judge model is accessed via the Gemini API with your GEMINI_API_KEY. To evaluate Khoj in research mode, set the KHOJ_MODE environment variable to "research". Run the script using the following command: KHOJ_MODE="research" GEMINI_API_KEY="" python eval_frames.py """ logger.info(f"{datetime.now()} - Begin Quizzing Khoj.") with timer("Ran eval script in", logger, log_level=logging.INFO): main() logger.info(f"{datetime.now()} - End Quizzing Khoj.")