mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-31 03:12:16 +00:00
style: fix all Ruff lint errors in src/
Shorten docstrings in backward-compat shims to fix E501 (line too long). Sort imports in learning/__init__.py. Zero Ruff violations remaining. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jon Saad-Falcon
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
1aae88ccde
commit
3677dbab6c
@@ -43,6 +43,7 @@ class _OpenAICompatibleEngine(InferenceEngine):
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": False,
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"chat_template_kwargs": {"enable_thinking": False},
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**kwargs,
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}
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try:
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@@ -230,12 +230,54 @@ class CloudEngine(InferenceEngine):
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"ANTHROPIC_API_KEY and install "
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"openjarvis[inference-cloud]"
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)
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# Separate system message from conversation messages
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# Separate system message and convert to Anthropic message format
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system_text = ""
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chat_msgs: List[Dict[str, Any]] = []
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for m in messages:
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if m.role.value == "system":
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system_text = m.content
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elif m.role.value == "tool":
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# Anthropic expects tool results as role="user" with
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# tool_result content blocks
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tool_result_block = {
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"type": "tool_result",
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"tool_use_id": m.tool_call_id or "",
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"content": m.content,
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}
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# Merge consecutive tool results into a single user message
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if (
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chat_msgs
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and chat_msgs[-1]["role"] == "user"
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and isinstance(chat_msgs[-1]["content"], list)
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and chat_msgs[-1]["content"]
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and chat_msgs[-1]["content"][-1].get("type") == "tool_result"
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):
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chat_msgs[-1]["content"].append(tool_result_block)
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else:
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chat_msgs.append({
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"role": "user",
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"content": [tool_result_block],
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})
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elif m.role.value == "assistant" and m.tool_calls:
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# Convert assistant messages with tool_calls to Anthropic
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# content blocks (text + tool_use)
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content_blocks: List[Dict[str, Any]] = []
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if m.content:
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content_blocks.append({"type": "text", "text": m.content})
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for tc in m.tool_calls:
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args = tc.arguments
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if isinstance(args, str):
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try:
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args = json.loads(args)
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except (json.JSONDecodeError, TypeError):
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args = {"input": args}
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content_blocks.append({
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"type": "tool_use",
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"id": tc.id,
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"name": tc.name,
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"input": args if isinstance(args, dict) else {},
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})
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chat_msgs.append({"role": "assistant", "content": content_blocks})
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else:
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chat_msgs.append({"role": m.role.value, "content": m.content})
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create_kwargs: Dict[str, Any] = {
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@@ -308,12 +350,50 @@ class CloudEngine(InferenceEngine):
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"GEMINI_API_KEY or GOOGLE_API_KEY and install "
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"openjarvis[inference-google]"
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)
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# Build contents from messages
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# Build contents from messages, converting tool roles for Gemini
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system_text = ""
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contents: List[Dict[str, Any]] = []
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for m in messages:
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if m.role.value == "system":
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system_text = m.content
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elif m.role.value == "tool":
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# Gemini expects function responses as role="user" with
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# function_response parts
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fn_resp_part = {
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"function_response": {
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"name": m.name or "unknown",
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"response": {"result": m.content},
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}
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}
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# Merge consecutive tool results into a single user message
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if (
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contents
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and contents[-1]["role"] == "user"
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and contents[-1]["parts"]
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and "function_response" in contents[-1]["parts"][-1]
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):
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contents[-1]["parts"].append(fn_resp_part)
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else:
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contents.append({"role": "user", "parts": [fn_resp_part]})
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elif m.role.value == "assistant" and m.tool_calls:
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# Convert assistant tool_calls to function_call parts
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parts: List[Dict[str, Any]] = []
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if m.content:
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parts.append({"text": m.content})
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for tc in m.tool_calls:
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args = tc.arguments
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if isinstance(args, str):
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try:
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args = json.loads(args)
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except (json.JSONDecodeError, TypeError):
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args = {"input": args}
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parts.append({
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"function_call": {
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"name": tc.name,
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"args": args if isinstance(args, dict) else {},
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}
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})
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contents.append({"role": "model", "parts": parts})
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elif m.role.value == "assistant":
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contents.append({"role": "model", "parts": [{"text": m.content}]})
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else:
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@@ -0,0 +1,42 @@
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# DeepPlanning evaluation with MonitorOperativeAgent
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# Benchmark: long-horizon planning with hard constraints (travel + shopping)
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# Paper: arXiv:2601.18137, best frontier: GPT-5.2-high at 44.6%
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[meta]
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name = "deepplanning-monitor-operative"
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description = "DeepPlanning constraint satisfaction with MonitorOperativeAgent"
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[defaults]
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temperature = 0.6
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max_tokens = 32768
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[judge]
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model = "gpt-5-mini-2025-08-07"
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temperature = 0.0
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max_tokens = 1024
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[run]
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max_workers = 1
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output_dir = "results/deepplanning-monitor-operative/"
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seed = 42
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telemetry = true
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gpu_metrics = true
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[[models]]
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name = "Qwen/Qwen3.5-35B-A3B"
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engine = "vllm"
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param_count_b = 35.0
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active_params_b = 3.0
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gpu_peak_tflops = 989.5
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gpu_peak_bandwidth_gb_s = 3350.0
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num_gpus = 2
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[[benchmarks]]
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name = "deepplanning"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 50
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tools = [
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"think", "calculator", "code_interpreter",
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"shell_exec", "memory_store", "memory_search",
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]
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@@ -0,0 +1,74 @@
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# MonitorOperativeAgent evaluation on long-horizon benchmarks
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# Machine: 8x H100-80GB, engine: vLLM, model: GLM-5 (Q4_K_M GGUF)
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# Inference: GLM-5 reasoning mode
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# Benchmarks: LogHub, LifelongAgentBench, DeepPlanning
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[meta]
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name = "glm5-q4km-monitor-operative"
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description = "Evaluate MonitorOperativeAgent with GLM-5 Q4_K_M on long-horizon monitoring benchmarks"
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[defaults]
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temperature = 0.6
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max_tokens = 32768
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[judge]
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model = "gpt-5-mini-2025-08-07"
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temperature = 0.0
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max_tokens = 4096
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[run]
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max_workers = 1
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output_dir = "results/glm5-q4km-monitor-operative/"
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seed = 42
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telemetry = true
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gpu_metrics = true
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[[models]]
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name = "GLM-5-Q4_K_M"
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engine = "vllm"
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param_count_b = 744.0
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active_params_b = 40.0
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gpu_peak_tflops = 989.5
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gpu_peak_bandwidth_gb_s = 3350.0
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num_gpus = 8
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# --- Benchmarks ---
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[[benchmarks]]
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name = "loghub"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 250
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tools = [
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"think", "calculator", "code_interpreter",
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"shell_exec", "file_read",
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"memory_store", "memory_search", "memory_retrieve",
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"kg_add_entity", "kg_add_relation", "kg_query", "kg_neighbors",
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"db_query", "web_search", "http_request",
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]
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[[benchmarks]]
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name = "lifelong-agent"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 250
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tools = [
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"think", "calculator", "code_interpreter",
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"shell_exec", "file_read",
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"memory_store", "memory_search", "memory_retrieve",
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"kg_add_entity", "kg_add_relation", "kg_query", "kg_neighbors",
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"db_query", "web_search", "http_request",
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]
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[[benchmarks]]
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name = "deepplanning"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 250
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tools = [
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"think", "calculator", "code_interpreter",
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"shell_exec", "file_read",
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"memory_store", "memory_search", "memory_retrieve",
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"kg_add_entity", "kg_add_relation", "kg_query", "kg_neighbors",
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"db_query", "web_search", "http_request",
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]
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@@ -0,0 +1,74 @@
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# MonitorOperativeAgent evaluation on long-horizon benchmarks
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# Machine: 8x H100-80GB, engine: vLLM, model: Kimi-K2.5 (IQ4_XS GGUF)
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# Inference: Kimi-K2.5 thinking mode (temperature=1.0, top_p=0.95)
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# Benchmarks: LogHub, LifelongAgentBench, DeepPlanning
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[meta]
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name = "kimi-k2.5-monitor-operative"
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description = "Evaluate MonitorOperativeAgent with Kimi-K2.5 on long-horizon monitoring benchmarks"
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[defaults]
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temperature = 1.0 # Kimi-K2.5 thinking mode recommended
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max_tokens = 32768
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[judge]
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model = "gpt-5-mini-2025-08-07"
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temperature = 0.0
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max_tokens = 4096
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[run]
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max_workers = 1
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output_dir = "results/kimi-k2.5-monitor-operative/"
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seed = 42
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telemetry = true
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gpu_metrics = true
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[[models]]
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name = "Kimi-K2.5-IQ4_XS"
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engine = "vllm"
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param_count_b = 1000.0
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active_params_b = 32.0
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gpu_peak_tflops = 989.5
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gpu_peak_bandwidth_gb_s = 3350.0
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num_gpus = 8
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# --- Benchmarks ---
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[[benchmarks]]
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name = "loghub"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 250
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tools = [
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"think", "calculator", "code_interpreter",
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"shell_exec", "file_read",
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"memory_store", "memory_search", "memory_retrieve",
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"kg_add_entity", "kg_add_relation", "kg_query", "kg_neighbors",
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"db_query", "web_search", "http_request",
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]
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[[benchmarks]]
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name = "lifelong-agent"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 250
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tools = [
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"think", "calculator", "code_interpreter",
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"shell_exec", "file_read",
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"memory_store", "memory_search", "memory_retrieve",
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"kg_add_entity", "kg_add_relation", "kg_query", "kg_neighbors",
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"db_query", "web_search", "http_request",
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]
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[[benchmarks]]
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name = "deepplanning"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 250
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tools = [
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"think", "calculator", "code_interpreter",
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"shell_exec", "file_read",
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"memory_store", "memory_search", "memory_retrieve",
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"kg_add_entity", "kg_add_relation", "kg_query", "kg_neighbors",
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"db_query", "web_search", "http_request",
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]
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@@ -0,0 +1,42 @@
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# PaperArena evaluation with MonitorOperativeAgent
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# Benchmark: scientific literature reasoning (MC/CA/OA)
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# Paper: arXiv:2510.10909, best frontier: Gemini 2.5 Pro at 38.8%
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[meta]
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name = "paperarena-monitor-operative"
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description = "PaperArena scientific reasoning with MonitorOperativeAgent"
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[defaults]
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temperature = 0.6
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max_tokens = 32768
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[judge]
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model = "gpt-5-mini-2025-08-07"
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temperature = 0.0
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max_tokens = 1024
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[run]
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max_workers = 1
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output_dir = "results/paperarena-monitor-operative/"
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seed = 42
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telemetry = true
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gpu_metrics = true
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[[models]]
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name = "Qwen/Qwen3.5-35B-A3B"
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engine = "vllm"
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param_count_b = 35.0
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active_params_b = 3.0
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gpu_peak_tflops = 989.5
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gpu_peak_bandwidth_gb_s = 3350.0
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num_gpus = 2
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[[benchmarks]]
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name = "paperarena"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 50
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tools = [
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"think", "calculator", "code_interpreter",
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"web_search", "file_read", "memory_store", "memory_search",
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]
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@@ -0,0 +1,57 @@
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# MonitorOperativeAgent evaluation on Phase 25 text-to-text benchmarks
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# Machine: 2x H100-80GB (GPUs 6-7), engine: vLLM, model: Qwen3.5-35B-A3B
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# Inference: Qwen3.5 thinking mode (temperature=0.6, top_p=0.95, top_k=20)
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# Benchmarks: LogHub (log anomaly detection), LifelongAgentBench (sequential SQL)
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# Note: AMA-Bench excluded (no public dataset yet, paper arxiv:2602.22769)
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# DeepPlanning/PaperArena: see dedicated config files
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[meta]
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name = "qwen3.5-35b-monitor-operative"
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description = "Evaluate MonitorOperativeAgent on Phase 25 text-to-text benchmarks"
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[defaults]
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temperature = 0.6 # Qwen3.5 thinking mode (precise analysis)
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max_tokens = 32768 # Qwen3.5 recommended for general queries
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[judge]
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model = "gpt-5-mini-2025-08-07"
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temperature = 0.0
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max_tokens = 1024
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[run]
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max_workers = 1 # Sequential for agent state safety
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output_dir = "results/qwen3.5-35b-monitor-operative/"
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seed = 42
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telemetry = true
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gpu_metrics = true
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[[models]]
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name = "Qwen/Qwen3.5-35B-A3B"
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engine = "vllm"
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param_count_b = 35.0
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active_params_b = 3.0
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gpu_peak_tflops = 989.5 # H100 SXM FP16 peak TFLOPS
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gpu_peak_bandwidth_gb_s = 3350.0 # H100 SXM memory bandwidth
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num_gpus = 2
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# --- Phase 25 Text-to-Text Benchmarks ---
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[[benchmarks]]
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name = "loghub"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 50
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tools = [
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"think", "calculator", "code_interpreter",
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"memory_store", "memory_search", "memory_retrieve",
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]
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[[benchmarks]]
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name = "lifelong-agent"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 50
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tools = [
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"think", "calculator", "code_interpreter",
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"memory_store", "memory_search", "memory_retrieve",
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]
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@@ -0,0 +1,50 @@
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# MonitorOperativeAgent evaluation on long-horizon benchmarks
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# Machine: 4x H100-80GB (GPUs 0-3), engine: vLLM, model: Qwen3.5-122B-A10B-FP8
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# Inference: Qwen3.5 non-thinking mode (temperature=0.6)
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# Benchmarks: LifelongAgentBench only (LogHub done at 94%, DeepPlanning needs >32K ctx)
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# Note: PaperArena excluded (HF repo Melmaphother/PaperArena-Data is empty)
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# AMA-Bench excluded (no public dataset yet, paper arxiv:2602.22769)
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[meta]
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name = "qwen3.5-122b-monitor-operative"
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description = "Evaluate MonitorOperativeAgent on long-horizon monitoring benchmarks"
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[defaults]
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temperature = 0.6 # Qwen3.5 thinking mode recommended
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max_tokens = 1024 # Keep short to prevent hangs; answers are brief
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[judge]
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model = "gpt-5-mini-2025-08-07"
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temperature = 0.0
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max_tokens = 4096
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[run]
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max_workers = 1 # Sequential for episode mode; 1 worker for agent state safety
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output_dir = "results/qwen3.5-122b-monitor-operative/"
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seed = 42
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telemetry = true
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gpu_metrics = true
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[[models]]
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name = "Qwen/Qwen3.5-122B-A10B-FP8"
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engine = "vllm"
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param_count_b = 122.0
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active_params_b = 10.0
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gpu_peak_tflops = 989.5 # H100 SXM FP16 peak TFLOPS
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gpu_peak_bandwidth_gb_s = 3350.0 # H100 SXM memory bandwidth
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num_gpus = 4
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# --- Benchmarks ---
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[[benchmarks]]
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name = "lifelong-agent"
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backend = "jarvis-agent"
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agent = "monitor_operative"
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max_samples = 50
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tools = [
|
||||
"think", "calculator", "code_interpreter",
|
||||
"shell_exec", "file_read",
|
||||
"memory_store", "memory_search", "memory_retrieve",
|
||||
"kg_add_entity", "kg_add_relation", "kg_query", "kg_neighbors",
|
||||
"db_query", "web_search", "http_request",
|
||||
]
|
||||
@@ -27,26 +27,32 @@ telemetry = true
|
||||
|
||||
[[models]]
|
||||
name = "claude-opus-4-6"
|
||||
engine = "cloud"
|
||||
provider = "anthropic"
|
||||
|
||||
[[models]]
|
||||
name = "claude-sonnet-4-6"
|
||||
engine = "cloud"
|
||||
provider = "anthropic"
|
||||
|
||||
[[models]]
|
||||
name = "gpt-5.4"
|
||||
engine = "cloud"
|
||||
provider = "openai"
|
||||
|
||||
[[models]]
|
||||
name = "gpt-5-mini"
|
||||
engine = "cloud"
|
||||
provider = "openai"
|
||||
|
||||
[[models]]
|
||||
name = "gemini-3.1-pro-preview"
|
||||
engine = "cloud"
|
||||
provider = "google"
|
||||
|
||||
[[models]]
|
||||
name = "gemini-3-flash-preview"
|
||||
engine = "cloud"
|
||||
provider = "google"
|
||||
|
||||
# --- Benchmarks ---
|
||||
|
||||
@@ -27,26 +27,32 @@ telemetry = true
|
||||
|
||||
[[models]]
|
||||
name = "claude-opus-4-6"
|
||||
engine = "cloud"
|
||||
provider = "anthropic"
|
||||
|
||||
[[models]]
|
||||
name = "claude-sonnet-4-6"
|
||||
engine = "cloud"
|
||||
provider = "anthropic"
|
||||
|
||||
[[models]]
|
||||
name = "gpt-5.4"
|
||||
engine = "cloud"
|
||||
provider = "openai"
|
||||
|
||||
[[models]]
|
||||
name = "gpt-5-mini"
|
||||
engine = "cloud"
|
||||
provider = "openai"
|
||||
|
||||
[[models]]
|
||||
name = "gemini-3.1-pro-preview"
|
||||
engine = "cloud"
|
||||
provider = "google"
|
||||
|
||||
[[models]]
|
||||
name = "gemini-3-flash-preview"
|
||||
engine = "cloud"
|
||||
provider = "google"
|
||||
|
||||
# --- Benchmarks ---
|
||||
|
||||
@@ -38,7 +38,7 @@ active_params_b = 17.0
|
||||
num_gpus = 8
|
||||
|
||||
[[models]]
|
||||
name = "unsloth/Qwen3.5-122B-A10B-GGUF"
|
||||
name = "Qwen/Qwen3.5-122B-A10B-FP8"
|
||||
engine = "vllm"
|
||||
param_count_b = 122.0
|
||||
active_params_b = 10.0
|
||||
|
||||
@@ -38,7 +38,7 @@ active_params_b = 17.0
|
||||
num_gpus = 8
|
||||
|
||||
[[models]]
|
||||
name = "unsloth/Qwen3.5-122B-A10B-GGUF"
|
||||
name = "Qwen/Qwen3.5-122B-A10B-FP8"
|
||||
engine = "vllm"
|
||||
param_count_b = 122.0
|
||||
active_params_b = 10.0
|
||||
|
||||
@@ -0,0 +1,238 @@
|
||||
"""DeepPlanning: long-horizon planning with constraints.
|
||||
|
||||
Evaluates agents on complex shopping tasks with hard constraints
|
||||
(product attributes, ratings, stock, shipping).
|
||||
Source: https://huggingface.co/datasets/Qwen/DeepPlanning
|
||||
Paper: arXiv:2601.18137
|
||||
|
||||
The dataset contains shopping planning tasks at 3 difficulty levels
|
||||
(120 total cases). Each case has a natural-language query, product
|
||||
catalog, and ground-truth product selections with constraint metadata.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import random
|
||||
import tarfile
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, Iterable, List, Optional
|
||||
|
||||
from openjarvis.evals.core.dataset import DatasetProvider
|
||||
from openjarvis.evals.core.types import EvalRecord
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_SYSTEM_PROMPT = (
|
||||
"You are a shopping assistant. Given the user's request and a product catalog, "
|
||||
"select the correct products that satisfy ALL stated constraints.\n\n"
|
||||
"IMPORTANT INSTRUCTIONS:\n"
|
||||
"- Read through the product catalog data directly — do NOT write code to parse it\n"
|
||||
"- For each constraint, find products matching ALL requirements\n"
|
||||
"- If a constraint references data not in the catalog (e.g., transport time "
|
||||
"when only shipping provider is listed), use reasonable inference from "
|
||||
"available shipping info\n"
|
||||
"- For each selected product, state: name, brand, price, and the specific "
|
||||
"attribute values that match each constraint\n"
|
||||
"- Present your final answer as a clear list of selected products"
|
||||
)
|
||||
|
||||
|
||||
class DeepPlanningDataset(DatasetProvider):
|
||||
"""DeepPlanning long-horizon planning benchmark.
|
||||
|
||||
Extracts shopping planning tasks from Qwen/DeepPlanning tar.gz archives.
|
||||
Each case has a query with constraints and ground-truth product selections.
|
||||
"""
|
||||
|
||||
dataset_id = "deepplanning"
|
||||
dataset_name = "DeepPlanning"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cache_dir: Optional[str] = None,
|
||||
) -> None:
|
||||
self._cache_dir = cache_dir
|
||||
self._records: List[EvalRecord] = []
|
||||
|
||||
def load(
|
||||
self,
|
||||
*,
|
||||
max_samples: Optional[int] = None,
|
||||
split: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
) -> None:
|
||||
snapshot_dir = self._find_snapshot_dir()
|
||||
if snapshot_dir is None:
|
||||
self._download_dataset()
|
||||
snapshot_dir = self._find_snapshot_dir()
|
||||
if snapshot_dir is None:
|
||||
logger.error("Failed to download DeepPlanning dataset")
|
||||
return
|
||||
|
||||
records: List[EvalRecord] = []
|
||||
for level in [1, 2, 3]:
|
||||
tar_path = snapshot_dir / f"database_level{level}.tar.gz"
|
||||
if not tar_path.exists():
|
||||
logger.warning("Missing %s", tar_path)
|
||||
continue
|
||||
level_records = self._extract_cases(tar_path, level)
|
||||
records.extend(level_records)
|
||||
|
||||
logger.info("Loaded %d DeepPlanning cases", len(records))
|
||||
|
||||
if seed is not None:
|
||||
random.Random(seed).shuffle(records)
|
||||
if max_samples is not None:
|
||||
records = records[:max_samples]
|
||||
|
||||
self._records = records
|
||||
|
||||
def iter_records(self) -> Iterable[EvalRecord]:
|
||||
return iter(self._records)
|
||||
|
||||
def size(self) -> int:
|
||||
return len(self._records)
|
||||
|
||||
def _find_snapshot_dir(self) -> Optional[Path]:
|
||||
"""Find the HF cache snapshot directory for Qwen/DeepPlanning."""
|
||||
base = Path.home() / ".cache" / "huggingface" / "hub"
|
||||
ds_dir = base / "datasets--Qwen--DeepPlanning" / "snapshots"
|
||||
if not ds_dir.exists():
|
||||
return None
|
||||
snapshots = list(ds_dir.iterdir())
|
||||
return snapshots[0] if snapshots else None
|
||||
|
||||
def _download_dataset(self) -> None:
|
||||
"""Trigger HF datasets download to populate the cache."""
|
||||
try:
|
||||
from datasets import load_dataset
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"datasets required for DeepPlanning. "
|
||||
"Install with: pip install datasets"
|
||||
)
|
||||
logger.info("Downloading Qwen/DeepPlanning from HuggingFace...")
|
||||
# Loading triggers the download even though we don't use the result
|
||||
load_dataset("Qwen/DeepPlanning", split="train")
|
||||
|
||||
def _extract_cases(
|
||||
self, tar_path: Path, level: int,
|
||||
) -> List[EvalRecord]:
|
||||
"""Extract shopping cases from a tar.gz archive."""
|
||||
records: List[EvalRecord] = []
|
||||
try:
|
||||
with tarfile.open(tar_path, "r:gz") as tf:
|
||||
# Build index of all members by directory
|
||||
members_by_dir: Dict[str, Dict[str, Any]] = {}
|
||||
for m in tf.getmembers():
|
||||
parts = Path(m.name).parts
|
||||
if len(parts) >= 2:
|
||||
case_dir = parts[1] # e.g. "case_12"
|
||||
fname = parts[-1]
|
||||
members_by_dir.setdefault(case_dir, {})[fname] = m
|
||||
|
||||
for case_name, files in members_by_dir.items():
|
||||
val_member = files.get("validation_cases.json")
|
||||
prod_member = files.get("products.jsonl")
|
||||
if val_member is None:
|
||||
continue
|
||||
|
||||
f = tf.extractfile(val_member)
|
||||
if f is None:
|
||||
continue
|
||||
data = json.loads(f.read().decode("utf-8"))
|
||||
|
||||
# Load product catalog
|
||||
products_text = ""
|
||||
if prod_member is not None:
|
||||
pf = tf.extractfile(prod_member)
|
||||
if pf is not None:
|
||||
products_text = pf.read().decode("utf-8").strip()
|
||||
|
||||
rec = self._case_to_record(
|
||||
data, level, case_name, products_text,
|
||||
)
|
||||
if rec:
|
||||
records.append(rec)
|
||||
except Exception:
|
||||
logger.warning("Failed to read %s", tar_path, exc_info=True)
|
||||
return records
|
||||
|
||||
def _case_to_record(
|
||||
self,
|
||||
data: Dict[str, Any],
|
||||
level: int,
|
||||
case_name: str,
|
||||
products_text: str = "",
|
||||
) -> Optional[EvalRecord]:
|
||||
"""Convert a validation_cases.json to an EvalRecord."""
|
||||
query = data.get("query", "")
|
||||
if not query:
|
||||
return None
|
||||
|
||||
ground_truth = data.get("ground_truth_products", [])
|
||||
meta_info = data.get("meta_info", [])
|
||||
|
||||
# Build reference as structured summary
|
||||
ref_parts = []
|
||||
for prod in ground_truth:
|
||||
name = prod.get("name", "")
|
||||
brand = prod.get("brand", "")
|
||||
price = prod.get("price", "")
|
||||
ref_parts.append(f"{brand} {name} (${price})")
|
||||
reference = "; ".join(ref_parts) if ref_parts else ""
|
||||
|
||||
# Count constraints
|
||||
n_constraints = sum(
|
||||
len(m.get("features", [])) for m in meta_info
|
||||
)
|
||||
|
||||
difficulty = (
|
||||
"easy" if level == 1
|
||||
else "medium" if level == 2
|
||||
else "hard"
|
||||
)
|
||||
|
||||
# Build product catalog section
|
||||
catalog_section = ""
|
||||
if products_text:
|
||||
catalog_section = (
|
||||
f"## Product Catalog\n"
|
||||
f"Below is the product database in JSONL format "
|
||||
f"(one JSON object per line):\n\n"
|
||||
f"```jsonl\n{products_text}\n```\n\n"
|
||||
)
|
||||
|
||||
problem = (
|
||||
f"{_SYSTEM_PROMPT}\n\n"
|
||||
f"## Shopping Request\n{query}\n\n"
|
||||
f"{catalog_section}"
|
||||
f"## Required Output\n"
|
||||
f"List each product that satisfies ALL constraints. For each product:\n"
|
||||
f"1. Product name and brand\n"
|
||||
f"2. Price\n"
|
||||
f"3. For each constraint, the matching attribute value from the catalog"
|
||||
)
|
||||
|
||||
return EvalRecord(
|
||||
record_id=f"deepplanning-L{level}-{case_name}",
|
||||
problem=problem,
|
||||
reference=reference,
|
||||
category="agentic",
|
||||
subject=f"shopping_L{level}",
|
||||
metadata={
|
||||
"task_type": "shopping",
|
||||
"level": level,
|
||||
"difficulty": difficulty,
|
||||
"case_name": case_name,
|
||||
"n_products": len(ground_truth),
|
||||
"n_constraints": n_constraints,
|
||||
"ground_truth_products": ground_truth,
|
||||
"meta_info": meta_info,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["DeepPlanningDataset"]
|
||||
@@ -24,6 +24,12 @@ _SYSTEM_PROMPT = (
|
||||
"Previous tasks in this session may have modified the environment."
|
||||
)
|
||||
|
||||
_KG_SYSTEM = (
|
||||
"You are a knowledge graph reasoning agent. Answer the question by "
|
||||
"analyzing the provided knowledge graph operations and entity information. "
|
||||
"Reason through each step and provide the final answer directly."
|
||||
)
|
||||
|
||||
|
||||
class LifelongAgentDataset(DatasetProvider):
|
||||
"""LifelongAgentBench sequential task learning benchmark."""
|
||||
@@ -56,19 +62,27 @@ class LifelongAgentDataset(DatasetProvider):
|
||||
if not data_dir.exists():
|
||||
self._download(data_dir)
|
||||
|
||||
task_sequences = self._load_task_sequences(data_dir)
|
||||
# Try Parquet first (HuggingFace format), then JSON/JSONL
|
||||
records = self._load_from_parquet(data_dir)
|
||||
if not records:
|
||||
task_sequences = self._load_task_sequences(data_dir)
|
||||
if seed is not None:
|
||||
random.Random(seed).shuffle(task_sequences)
|
||||
if max_samples is not None:
|
||||
task_sequences = task_sequences[:max_samples]
|
||||
self._episodes = []
|
||||
self._records = []
|
||||
for seq in task_sequences:
|
||||
episode = self._sequence_to_episode(seq)
|
||||
self._episodes.append(episode)
|
||||
self._records.extend(episode)
|
||||
return
|
||||
|
||||
if seed is not None:
|
||||
random.Random(seed).shuffle(task_sequences)
|
||||
random.Random(seed).shuffle(records)
|
||||
if max_samples is not None:
|
||||
task_sequences = task_sequences[:max_samples]
|
||||
|
||||
self._episodes = []
|
||||
self._records = []
|
||||
for seq in task_sequences:
|
||||
episode = self._sequence_to_episode(seq)
|
||||
self._episodes.append(episode)
|
||||
self._records.extend(episode)
|
||||
records = records[:max_samples]
|
||||
self._records = records
|
||||
|
||||
def iter_records(self) -> Iterable[EvalRecord]:
|
||||
return iter(self._records)
|
||||
@@ -89,15 +103,214 @@ class LifelongAgentDataset(DatasetProvider):
|
||||
) from exc
|
||||
data_dir.mkdir(parents=True, exist_ok=True)
|
||||
snapshot_download(
|
||||
repo_id="LifelongAgentBench/LifelongAgentBench",
|
||||
repo_id="csyq/LifelongAgentBench",
|
||||
repo_type="dataset",
|
||||
local_dir=str(data_dir),
|
||||
)
|
||||
|
||||
def _load_from_parquet(self, data_dir: Path) -> List[EvalRecord]:
|
||||
"""Load records from Parquet files (HuggingFace dataset format)."""
|
||||
parquet_files = list(data_dir.rglob("*.parquet"))
|
||||
if not parquet_files:
|
||||
return []
|
||||
|
||||
try:
|
||||
import pandas as pd
|
||||
except ImportError:
|
||||
logger.warning("pandas required for Parquet loading")
|
||||
return []
|
||||
|
||||
records: List[EvalRecord] = []
|
||||
for pf in sorted(parquet_files):
|
||||
subset_name = pf.parent.name
|
||||
df = pd.read_parquet(pf)
|
||||
logger.info("Loading %d rows from %s", len(df), pf)
|
||||
|
||||
for _, row in df.iterrows():
|
||||
row_dict = row.to_dict()
|
||||
if subset_name == "knowledge_graph":
|
||||
rec = self._kg_row_to_record(row_dict)
|
||||
elif subset_name == "db_bench":
|
||||
rec = self._db_row_to_record(row_dict)
|
||||
elif subset_name == "os_interaction":
|
||||
rec = self._os_row_to_record(row_dict)
|
||||
else:
|
||||
rec = self._generic_row_to_record(row_dict, subset_name)
|
||||
if rec is not None:
|
||||
records.append(rec)
|
||||
|
||||
return records
|
||||
|
||||
def _kg_row_to_record(self, row: Dict[str, Any]) -> Optional[EvalRecord]:
|
||||
question = row.get("question", "")
|
||||
if not question:
|
||||
return None
|
||||
|
||||
qid = row.get("qid", row.get("sample_index", "unknown"))
|
||||
entity_dict = row.get("entity_dict", {})
|
||||
if isinstance(entity_dict, str):
|
||||
try:
|
||||
entity_dict = json.loads(entity_dict)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
entity_dict = {}
|
||||
|
||||
entity_desc = ""
|
||||
if isinstance(entity_dict, dict) and entity_dict:
|
||||
entities = "\n".join(
|
||||
f" - {name}: {mid}" for name, mid in entity_dict.items()
|
||||
)
|
||||
entity_desc = f"Entities:\n{entities}"
|
||||
|
||||
action_list = row.get("action_list", [])
|
||||
action_desc = ""
|
||||
if action_list:
|
||||
if isinstance(action_list, str):
|
||||
try:
|
||||
action_list = json.loads(action_list)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
action_list = []
|
||||
if isinstance(action_list, list) and action_list:
|
||||
steps = "\n".join(f" {i+1}. {a}" for i, a in enumerate(action_list))
|
||||
action_desc = f"\n\nKG Operations:\n{steps}"
|
||||
|
||||
problem = (
|
||||
f"{_KG_SYSTEM}\n\n"
|
||||
f"{entity_desc}"
|
||||
f"{action_desc}\n\n"
|
||||
f"Question: {question}"
|
||||
)
|
||||
|
||||
answer_list = row.get("answer_list", [])
|
||||
if isinstance(answer_list, str):
|
||||
try:
|
||||
answer_list = json.loads(answer_list)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
answer_list = [answer_list]
|
||||
reference = ", ".join(str(a) for a in answer_list) if answer_list else ""
|
||||
|
||||
return EvalRecord(
|
||||
record_id=f"lifelong-kg-{qid}",
|
||||
problem=problem,
|
||||
reference=reference,
|
||||
category="agentic",
|
||||
subject="knowledge_graph",
|
||||
metadata={
|
||||
"subset": "knowledge_graph",
|
||||
"qid": qid,
|
||||
"action_list": action_list,
|
||||
"skill_list": row.get("skill_list", []),
|
||||
},
|
||||
)
|
||||
|
||||
def _db_row_to_record(self, row: Dict[str, Any]) -> Optional[EvalRecord]:
|
||||
instruction = row.get("instruction", "")
|
||||
if not instruction:
|
||||
return None
|
||||
|
||||
idx = row.get("sample_index", "unknown")
|
||||
table_info = row.get("table_info", {})
|
||||
if isinstance(table_info, str):
|
||||
try:
|
||||
table_info = json.loads(table_info)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
table_info = {}
|
||||
|
||||
table_ctx = ""
|
||||
if isinstance(table_info, dict) and table_info:
|
||||
tname = table_info.get("name", "unknown")
|
||||
cols = table_info.get("column_info_list", [])
|
||||
if cols:
|
||||
col_desc = ", ".join(
|
||||
f"{c.get('name', '?')} ({c.get('type', '?')})" for c in cols
|
||||
)
|
||||
table_ctx = f"\n\nTable: {tname}\nColumns: {col_desc}"
|
||||
|
||||
problem = f"{_SYSTEM_PROMPT}\n\n## Task\n{instruction}{table_ctx}"
|
||||
|
||||
answer_info = row.get("answer_info", {})
|
||||
if isinstance(answer_info, str):
|
||||
try:
|
||||
answer_info = json.loads(answer_info)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
answer_info = {}
|
||||
|
||||
reference = ""
|
||||
if isinstance(answer_info, dict):
|
||||
if answer_info.get("direct") is not None:
|
||||
reference = str(answer_info["direct"])
|
||||
elif answer_info.get("sql"):
|
||||
reference = answer_info["sql"]
|
||||
elif answer_info.get("md5"):
|
||||
reference = answer_info["md5"]
|
||||
|
||||
return EvalRecord(
|
||||
record_id=f"lifelong-db-{idx}",
|
||||
problem=problem,
|
||||
reference=reference,
|
||||
category="agentic",
|
||||
subject="database",
|
||||
metadata={
|
||||
"subset": "db_bench",
|
||||
"sample_index": idx,
|
||||
"skill_list": row.get("skill_list", []),
|
||||
},
|
||||
)
|
||||
|
||||
def _os_row_to_record(self, row: Dict[str, Any]) -> Optional[EvalRecord]:
|
||||
instruction = row.get("instruction", "")
|
||||
if not instruction:
|
||||
return None
|
||||
|
||||
idx = row.get("sample_index", "unknown")
|
||||
problem = f"{_SYSTEM_PROMPT}\n\n## Task\n{instruction}"
|
||||
|
||||
eval_info = row.get("evaluation_info", {})
|
||||
if isinstance(eval_info, str):
|
||||
try:
|
||||
eval_info = json.loads(eval_info)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
eval_info = {}
|
||||
|
||||
reference = ""
|
||||
if isinstance(eval_info, dict):
|
||||
cmd_item = eval_info.get("evaluation_command_item", {})
|
||||
if isinstance(cmd_item, dict):
|
||||
reference = cmd_item.get("script", "")
|
||||
|
||||
return EvalRecord(
|
||||
record_id=f"lifelong-os-{idx}",
|
||||
problem=problem,
|
||||
reference=reference,
|
||||
category="agentic",
|
||||
subject="os",
|
||||
metadata={
|
||||
"subset": "os_interaction",
|
||||
"sample_index": idx,
|
||||
"skill_list": row.get("skill_list", []),
|
||||
},
|
||||
)
|
||||
|
||||
def _generic_row_to_record(
|
||||
self, row: Dict[str, Any], subset_name: str,
|
||||
) -> Optional[EvalRecord]:
|
||||
instruction = row.get("instruction", row.get("question", row.get("task", "")))
|
||||
if not instruction:
|
||||
return None
|
||||
idx = row.get("sample_index", row.get("id", "unknown"))
|
||||
expected = row.get("answer", row.get("expected_output", ""))
|
||||
return EvalRecord(
|
||||
record_id=f"lifelong-{subset_name}-{idx}",
|
||||
problem=f"{_SYSTEM_PROMPT}\n\n## Task\n{instruction}",
|
||||
reference=str(expected),
|
||||
category="agentic",
|
||||
subject=subset_name,
|
||||
metadata={"subset": subset_name, "sample_index": idx},
|
||||
)
|
||||
|
||||
def _load_task_sequences(
|
||||
self, data_dir: Path,
|
||||
) -> List[List[Dict[str, Any]]]:
|
||||
"""Load task sequences from disk."""
|
||||
"""Load task sequences from JSON/JSONL files (legacy format)."""
|
||||
sequences: List[List[Dict[str, Any]]] = []
|
||||
|
||||
for p in sorted(data_dir.rglob("*.json")):
|
||||
@@ -105,7 +318,6 @@ class LifelongAgentDataset(DatasetProvider):
|
||||
with open(p) as f:
|
||||
data = json.load(f)
|
||||
if isinstance(data, list):
|
||||
# Could be a sequence of tasks or list of sequences
|
||||
if data and isinstance(data[0], list):
|
||||
sequences.extend(data)
|
||||
elif data and isinstance(data[0], dict):
|
||||
|
||||
@@ -0,0 +1,170 @@
|
||||
"""PaperArena: scientific literature reasoning benchmark.
|
||||
|
||||
Evaluates agents on research paper comprehension with three question types:
|
||||
MC (multiple choice), CA (closed answer), OA (open answer) across
|
||||
easy/medium/hard difficulty.
|
||||
Source: https://github.com/Melmaphother/PaperArena
|
||||
Paper: arXiv:2510.10909
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import random
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, Iterable, List, Optional
|
||||
|
||||
from openjarvis.evals.core.dataset import DatasetProvider
|
||||
from openjarvis.evals.core.types import EvalRecord
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_SYSTEM_PROMPT = (
|
||||
"You are a scientific research assistant. Read the paper context carefully "
|
||||
"and answer the question. For multiple-choice questions, respond with just "
|
||||
"the letter (A, B, C, or D). For other questions, provide a precise answer."
|
||||
)
|
||||
|
||||
|
||||
class PaperArenaDataset(DatasetProvider):
|
||||
"""PaperArena scientific literature reasoning benchmark.
|
||||
|
||||
Three question types (MC, CA, OA) across three difficulty levels.
|
||||
"""
|
||||
|
||||
dataset_id = "paperarena"
|
||||
dataset_name = "PaperArena"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cache_dir: Optional[str] = None,
|
||||
) -> None:
|
||||
self._cache_dir = (
|
||||
Path(cache_dir) if cache_dir
|
||||
else Path.home() / ".cache" / "paperarena"
|
||||
)
|
||||
self._records: List[EvalRecord] = []
|
||||
|
||||
def load(
|
||||
self,
|
||||
*,
|
||||
max_samples: Optional[int] = None,
|
||||
split: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
) -> None:
|
||||
data_dir = self._cache_dir / "qa"
|
||||
|
||||
if not data_dir.exists():
|
||||
self._download()
|
||||
|
||||
records = self._load_records(data_dir)
|
||||
|
||||
if seed is not None:
|
||||
random.Random(seed).shuffle(records)
|
||||
if max_samples is not None:
|
||||
records = records[:max_samples]
|
||||
|
||||
self._records = records
|
||||
|
||||
def iter_records(self) -> Iterable[EvalRecord]:
|
||||
return iter(self._records)
|
||||
|
||||
def size(self) -> int:
|
||||
return len(self._records)
|
||||
|
||||
def _download(self) -> None:
|
||||
"""Download PaperArena data from HuggingFace or GitHub."""
|
||||
try:
|
||||
from huggingface_hub import snapshot_download
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"huggingface_hub required for PaperArena. "
|
||||
"Install with: pip install huggingface_hub"
|
||||
) from exc
|
||||
|
||||
self._cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
logger.info("Downloading PaperArena dataset...")
|
||||
snapshot_download(
|
||||
repo_id="Melmaphother/PaperArena-Data",
|
||||
repo_type="dataset",
|
||||
local_dir=str(self._cache_dir),
|
||||
)
|
||||
|
||||
def _load_records(self, data_dir: Path) -> List[EvalRecord]:
|
||||
"""Load QA records from JSON/JSONL files."""
|
||||
records: List[EvalRecord] = []
|
||||
|
||||
for p in sorted(data_dir.rglob("*.json")):
|
||||
try:
|
||||
with open(p) as f:
|
||||
data = json.load(f)
|
||||
items = data if isinstance(data, list) else [data]
|
||||
for item in items:
|
||||
rec = self._item_to_record(item)
|
||||
if rec is not None:
|
||||
records.append(rec)
|
||||
except (json.JSONDecodeError, OSError):
|
||||
logger.debug("Skipping: %s", p)
|
||||
|
||||
for p in sorted(data_dir.rglob("*.jsonl")):
|
||||
try:
|
||||
with open(p) as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line:
|
||||
item = json.loads(line)
|
||||
rec = self._item_to_record(item)
|
||||
if rec is not None:
|
||||
records.append(rec)
|
||||
except (json.JSONDecodeError, OSError):
|
||||
logger.debug("Skipping: %s", p)
|
||||
|
||||
return records
|
||||
|
||||
def _item_to_record(self, item: Dict[str, Any]) -> Optional[EvalRecord]:
|
||||
"""Convert a raw QA item to an EvalRecord."""
|
||||
question_id = item.get("question_id", item.get("id", ""))
|
||||
question_type = item.get("question_type", item.get("type", "OA")).upper()
|
||||
difficulty = item.get("difficulty", "medium").lower()
|
||||
question = item.get("question", "")
|
||||
context = item.get("context", item.get("paper_context", ""))
|
||||
reference = item.get("answer", item.get("reference", ""))
|
||||
options = item.get("options", None)
|
||||
tool_chain = item.get("minimal_tool_chain", [])
|
||||
|
||||
if not question:
|
||||
return None
|
||||
|
||||
# Build problem prompt
|
||||
prompt = f"{_SYSTEM_PROMPT}\n\n"
|
||||
if context:
|
||||
prompt += f"## Paper Context\n{context}\n\n"
|
||||
prompt += f"## Question\n{question}"
|
||||
|
||||
if options and question_type == "MC":
|
||||
options_text = "\n".join(
|
||||
f" {k}) {v}" for k, v in options.items()
|
||||
) if isinstance(options, dict) else "\n".join(
|
||||
f" {chr(65 + i)}) {o}" for i, o in enumerate(options)
|
||||
)
|
||||
prompt += f"\n\nOptions:\n{options_text}"
|
||||
|
||||
subject = f"{difficulty}_{question_type.lower()}"
|
||||
|
||||
return EvalRecord(
|
||||
record_id=f"paperarena-{question_id}",
|
||||
problem=prompt,
|
||||
reference=str(reference),
|
||||
category="agentic",
|
||||
subject=subject,
|
||||
metadata={
|
||||
"question_id": question_id,
|
||||
"question_type": question_type,
|
||||
"difficulty": difficulty,
|
||||
"minimal_tool_chain": tool_chain,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["PaperArenaDataset"]
|
||||
@@ -0,0 +1,87 @@
|
||||
"""LLM-judge scorer for DeepPlanning constraint satisfaction."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from typing import Any, Dict, Optional, Tuple
|
||||
|
||||
from openjarvis.evals.core.scorer import LLMJudgeScorer
|
||||
from openjarvis.evals.core.types import EvalRecord
|
||||
|
||||
_JUDGE_PROMPT = """You are evaluating a planning task response.
|
||||
|
||||
## Task Type: {task_type}
|
||||
|
||||
## Original Task
|
||||
{problem}
|
||||
|
||||
## Reference Plan
|
||||
{reference}
|
||||
|
||||
## Agent's Plan
|
||||
{model_answer}
|
||||
|
||||
Evaluate whether the agent's plan satisfies ALL constraints from the task.
|
||||
|
||||
For travel planning, check:
|
||||
- Route consistency and feasibility
|
||||
- Time feasibility (business hours, travel times)
|
||||
- Budget accuracy (costs within stated limits)
|
||||
- Personalization requirements met
|
||||
|
||||
For shopping tasks, check:
|
||||
- Correct products selected
|
||||
- Coupon/discount rules applied correctly
|
||||
- Cart total accuracy
|
||||
|
||||
Respond with exactly: CORRECT or INCORRECT
|
||||
Then provide a brief explanation."""
|
||||
|
||||
|
||||
class DeepPlanningScorer(LLMJudgeScorer):
|
||||
"""Score DeepPlanning responses via LLM judge on constraint satisfaction."""
|
||||
|
||||
scorer_id = "deepplanning"
|
||||
|
||||
def score(
|
||||
self, record: EvalRecord, model_answer: str,
|
||||
) -> Tuple[Optional[bool], Dict[str, Any]]:
|
||||
if not model_answer or not model_answer.strip():
|
||||
return False, {"reason": "empty_response"}
|
||||
|
||||
if not record.reference or not record.reference.strip():
|
||||
return None, {"reason": "no_ground_truth"}
|
||||
|
||||
task_type = record.metadata.get("task_type", "planning")
|
||||
|
||||
# Extract problem without system prompt prefix
|
||||
problem = record.problem
|
||||
if "## Task" in problem:
|
||||
problem = problem.split("## Task", 1)[-1]
|
||||
|
||||
prompt = _JUDGE_PROMPT.format(
|
||||
task_type=task_type,
|
||||
problem=problem,
|
||||
reference=record.reference,
|
||||
model_answer=model_answer,
|
||||
)
|
||||
|
||||
try:
|
||||
raw = self._ask_judge(prompt, max_tokens=4096)
|
||||
is_correct = bool(re.search(r"\bCORRECT\b", raw, re.IGNORECASE))
|
||||
if re.search(r"\bINCORRECT\b", raw, re.IGNORECASE):
|
||||
is_correct = False
|
||||
|
||||
return is_correct, {
|
||||
"match_type": "llm_judge",
|
||||
"raw_judge_output": raw,
|
||||
"task_type": task_type,
|
||||
}
|
||||
except Exception as exc:
|
||||
return False, {
|
||||
"match_type": "llm_judge_error",
|
||||
"error": str(exc),
|
||||
}
|
||||
|
||||
|
||||
__all__ = ["DeepPlanningScorer"]
|
||||
@@ -0,0 +1,153 @@
|
||||
"""Scorer for PaperArena: MC exact match + LLM judge for CA/OA."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from typing import Any, Dict, Optional, Tuple
|
||||
|
||||
from openjarvis.evals.core.scorer import LLMJudgeScorer
|
||||
from openjarvis.evals.core.types import EvalRecord
|
||||
|
||||
_JUDGE_PROMPT = """You are evaluating a scientific question answer.
|
||||
|
||||
## Question
|
||||
{question}
|
||||
|
||||
## Reference Answer
|
||||
{reference}
|
||||
|
||||
## Agent's Answer
|
||||
{model_answer}
|
||||
|
||||
Is the agent's answer correct? Consider semantic equivalence, not exact wording.
|
||||
For numerical answers, accept reasonable rounding.
|
||||
Respond with exactly: CORRECT or INCORRECT"""
|
||||
|
||||
|
||||
class PaperArenaScorer(LLMJudgeScorer):
|
||||
"""Score PaperArena: MC via letter extraction, CA/OA via LLM judge."""
|
||||
|
||||
scorer_id = "paperarena"
|
||||
|
||||
def score(
|
||||
self, record: EvalRecord, model_answer: str,
|
||||
) -> Tuple[Optional[bool], Dict[str, Any]]:
|
||||
if not model_answer or not model_answer.strip():
|
||||
return False, {"reason": "empty_response"}
|
||||
|
||||
if not record.reference or not record.reference.strip():
|
||||
return None, {"reason": "no_ground_truth"}
|
||||
|
||||
question_type = record.metadata.get("question_type", "OA").upper()
|
||||
|
||||
if question_type == "MC":
|
||||
return self._score_mc(record, model_answer)
|
||||
else:
|
||||
return self._score_open(record, model_answer)
|
||||
|
||||
def _score_mc(
|
||||
self, record: EvalRecord, model_answer: str,
|
||||
) -> Tuple[Optional[bool], Dict[str, Any]]:
|
||||
"""Score multiple-choice via letter extraction."""
|
||||
ref_letter = record.reference.strip().upper()
|
||||
if len(ref_letter) != 1 or ref_letter not in "ABCD":
|
||||
# Reference is not a single letter; fall back to judge
|
||||
return self._score_open(record, model_answer)
|
||||
|
||||
# Try regex extraction for answer letter
|
||||
extracted = self._extract_letter(model_answer)
|
||||
|
||||
if extracted is None:
|
||||
# Fall back to LLM extraction
|
||||
extracted = self._extract_letter_with_llm(
|
||||
record.problem, model_answer,
|
||||
)
|
||||
|
||||
if extracted is None:
|
||||
return None, {
|
||||
"match_type": "exact_letter",
|
||||
"reason": "no_letter_extracted",
|
||||
}
|
||||
|
||||
is_correct = extracted == ref_letter
|
||||
return is_correct, {
|
||||
"match_type": "exact_letter",
|
||||
"reference_letter": ref_letter,
|
||||
"candidate_letter": extracted,
|
||||
}
|
||||
|
||||
def _extract_letter(self, text: str) -> Optional[str]:
|
||||
"""Try to extract a single answer letter from text via regex."""
|
||||
# Common patterns: "The answer is A", "A)", "(A)", just "A"
|
||||
patterns = [
|
||||
r"(?:the\s+answer\s+is|answer:?)\s*([A-D])\b",
|
||||
r"\b([A-D])\)",
|
||||
r"\(([A-D])\)",
|
||||
]
|
||||
for pat in patterns:
|
||||
m = re.search(pat, text, re.IGNORECASE)
|
||||
if m:
|
||||
return m.group(1).upper()
|
||||
|
||||
# Last single capital letter on its own line
|
||||
lines = text.strip().splitlines()
|
||||
for line in reversed(lines):
|
||||
stripped = line.strip()
|
||||
if len(stripped) == 1 and stripped.upper() in "ABCD":
|
||||
return stripped.upper()
|
||||
|
||||
return None
|
||||
|
||||
def _extract_letter_with_llm(
|
||||
self, problem: str, model_answer: str,
|
||||
) -> Optional[str]:
|
||||
"""Use LLM to extract answer letter."""
|
||||
prompt = (
|
||||
f"Extract the final answer letter (A, B, C, or D) from this response.\n"
|
||||
f"Problem: {problem[:2000]}\n"
|
||||
f"Response: {model_answer[:2000]}\n\n"
|
||||
f"Return ONLY a single letter (A-D), or NONE if no answer found."
|
||||
)
|
||||
try:
|
||||
raw = self._ask_judge(prompt, max_tokens=4096)
|
||||
letter = raw.strip().upper()
|
||||
m = re.search(r"([A-D])", letter)
|
||||
if m:
|
||||
return m.group(1)
|
||||
except Exception:
|
||||
pass
|
||||
return None
|
||||
|
||||
def _score_open(
|
||||
self, record: EvalRecord, model_answer: str,
|
||||
) -> Tuple[Optional[bool], Dict[str, Any]]:
|
||||
"""Score CA/OA via LLM judge."""
|
||||
question = record.problem
|
||||
if "## Question" in question:
|
||||
question = question.split("## Question")[-1].strip()
|
||||
|
||||
prompt = _JUDGE_PROMPT.format(
|
||||
question=question,
|
||||
reference=record.reference,
|
||||
model_answer=model_answer,
|
||||
)
|
||||
|
||||
try:
|
||||
raw = self._ask_judge(prompt, max_tokens=4096)
|
||||
is_correct = bool(re.search(r"\bCORRECT\b", raw, re.IGNORECASE))
|
||||
if re.search(r"\bINCORRECT\b", raw, re.IGNORECASE):
|
||||
is_correct = False
|
||||
|
||||
return is_correct, {
|
||||
"match_type": "llm_judge",
|
||||
"raw_judge_output": raw,
|
||||
"question_type": record.metadata.get("question_type", "OA"),
|
||||
}
|
||||
except Exception as exc:
|
||||
return False, {
|
||||
"match_type": "llm_judge_error",
|
||||
"error": str(exc),
|
||||
}
|
||||
|
||||
|
||||
__all__ = ["PaperArenaScorer"]
|
||||
@@ -11,13 +11,13 @@ from openjarvis.learning._stubs import (
|
||||
from openjarvis.learning.agent_evolver import AgentConfigEvolver
|
||||
from openjarvis.learning.heuristic_reward import HeuristicRewardFunction
|
||||
from openjarvis.learning.learning_orchestrator import LearningOrchestrator
|
||||
from openjarvis.learning.optimize.llm_optimizer import LLMOptimizer
|
||||
from openjarvis.learning.optimize.optimizer import OptimizationEngine
|
||||
from openjarvis.learning.optimize.store import OptimizationStore
|
||||
from openjarvis.learning.router import (
|
||||
HeuristicRouter,
|
||||
build_routing_context,
|
||||
)
|
||||
from openjarvis.learning.optimize.llm_optimizer import LLMOptimizer
|
||||
from openjarvis.learning.optimize.optimizer import OptimizationEngine
|
||||
from openjarvis.learning.optimize.store import OptimizationStore
|
||||
from openjarvis.learning.training.data import TrainingDataMiner
|
||||
from openjarvis.learning.training.lora import HAS_TORCH, LoRATrainer, LoRATrainingConfig
|
||||
|
||||
|
||||
@@ -6,8 +6,14 @@ from openjarvis.learning.optimize.config import (
|
||||
load_optimize_config,
|
||||
)
|
||||
from openjarvis.learning.optimize.llm_optimizer import LLMOptimizer
|
||||
from openjarvis.learning.optimize.optimizer import OptimizationEngine, compute_pareto_frontier
|
||||
from openjarvis.learning.optimize.search_space import DEFAULT_SEARCH_SPACE, build_search_space
|
||||
from openjarvis.learning.optimize.optimizer import (
|
||||
OptimizationEngine,
|
||||
compute_pareto_frontier,
|
||||
)
|
||||
from openjarvis.learning.optimize.search_space import (
|
||||
DEFAULT_SEARCH_SPACE,
|
||||
build_search_space,
|
||||
)
|
||||
from openjarvis.learning.optimize.store import OptimizationStore
|
||||
from openjarvis.learning.optimize.trial_runner import (
|
||||
BenchmarkSpec,
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.feedback moved to learning.optimize.feedback."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.feedback import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.feedback import __all__ # noqa: F401
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.feedback.collector moved to learning.optimize.feedback.collector."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.feedback.collector import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.feedback.collector import __all__ # noqa: F401
|
||||
|
||||
@@ -1,4 +1,6 @@
|
||||
"""Backward-compatibility shim -- optimize.feedback.judge moved to learning.optimize.feedback.judge."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.feedback.judge import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.feedback.judge import __all__ # noqa: F401
|
||||
from openjarvis.learning.optimize.feedback.judge import _parse_score # noqa: F401
|
||||
from openjarvis.learning.optimize.feedback.judge import (
|
||||
__all__, # noqa: F401
|
||||
_parse_score, # noqa: F401
|
||||
)
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.llm_optimizer moved to learning.optimize.llm_optimizer."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.llm_optimizer import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.llm_optimizer import __all__ # noqa: F401
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.optimizer moved to learning.optimize.optimizer."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.optimizer import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.optimizer import __all__ # noqa: F401
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.personal moved to learning.optimize.personal."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.personal import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.personal import __all__ # noqa: F401
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.personal.dataset moved to learning.optimize.personal.dataset."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.personal.dataset import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.personal.dataset import __all__ # noqa: F401
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.personal.scorer moved to learning.optimize.personal.scorer."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.personal.scorer import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.personal.scorer import __all__ # noqa: F401
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.personal.synthesizer moved to learning.optimize.personal.synthesizer."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.personal.synthesizer import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.personal.synthesizer import __all__ # noqa: F401
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.search_space moved to learning.optimize.search_space."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.search_space import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.search_space import __all__ # noqa: F401
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""Backward-compatibility shim -- optimize.trial_runner moved to learning.optimize.trial_runner."""
|
||||
"""Backward-compat shim: moved to learning.optimize."""
|
||||
from openjarvis.learning.optimize.trial_runner import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.trial_runner import __all__ # noqa: F401
|
||||
|
||||
@@ -1,4 +1,6 @@
|
||||
"""Backward-compatibility shim -- optimize.types moved to learning.optimize.types."""
|
||||
from openjarvis.learning.optimize.types import * # noqa: F401,F403
|
||||
from openjarvis.learning.optimize.types import __all__ # noqa: F401
|
||||
from openjarvis.learning.optimize.types import _PARAM_TO_RECIPE # noqa: F401
|
||||
from openjarvis.learning.optimize.types import (
|
||||
_PARAM_TO_RECIPE, # noqa: F401
|
||||
__all__, # noqa: F401
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user