File size: 12,814 Bytes
2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 2091a1b c0a7163 a0c0f61 c0a7163 a0c0f61 c0a7163 a0c0f61 c0a7163 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 | """Orchestrator agent: function-calling loop over a list of Tools.
No agent framework — uses the openai SDK's chat-completions function-calling
interface directly. This is the same SDK already used by src/llm/explainer.py,
keeping the dependency surface minimal.
Public entry: `Orchestrator(llm_client, tools, system_prompt, model).run(user_input)`.
Returns an `AgentResult` with synthesized text + full tool-call trace.
"""
from __future__ import annotations
import json
from collections.abc import Callable
from typing import Any
from src.agents.schemas import AgentResult, ToolTraceItem
from src.agents.tools import Tool
from src.core.logger import get_logger
logger = get_logger(__name__)
WorkflowRouter = Callable[[str, dict[str, Any] | None], tuple[str, dict[str, Any]] | None]
WorkflowQueryBuilder = Callable[[str, ToolTraceItem, dict[str, Any] | None], str]
class Orchestrator:
"""Single-agent function-calling loop. Stops on (a) text response, (b) max steps."""
def __init__(
self,
llm_client: Any,
tools: list[Tool],
system_prompt: str,
model: str,
max_steps: int = 5,
temperature: float = 0.0,
enforce_workflow: bool = False,
workflow_pipeline_tools: set[str] | None = None,
workflow_retrieval_tool: str | None = None,
workflow_router: WorkflowRouter | None = None,
workflow_query_builder: WorkflowQueryBuilder | None = None,
) -> None:
self._client = llm_client
self._tools_by_name = {t.name: t for t in tools}
self._tool_schemas = [t.openai_schema() for t in tools]
self._tool_schemas_by_name = {
t.name: t.openai_schema()
for t in tools
}
self._system_prompt = system_prompt
self._model = model
self._max_steps = max_steps
self._temperature = temperature
self._enforce_workflow = enforce_workflow
self._workflow_pipeline_tools = workflow_pipeline_tools or set()
self._workflow_retrieval_tool = workflow_retrieval_tool
self._workflow_router = workflow_router
self._workflow_query_builder = workflow_query_builder
def run(
self,
user_input: str,
context: dict[str, Any] | None = None,
) -> AgentResult:
messages: list[dict[str, Any]] = [
{"role": "system", "content": self._system_prompt},
{"role": "user", "content": user_input},
]
trace: list[ToolTraceItem] = []
for _step in range(self._max_steps):
stage = self._workflow_stage(trace)
request_kwargs = self._completion_kwargs(messages, stage)
response = self._client.chat.completions.create(**request_kwargs)
msg = response.choices[0].message
if not getattr(msg, "tool_calls", None):
if self._enforce_workflow and stage == "pipeline":
if self._invoke_routed_pipeline(user_input, context, trace, messages):
continue
return AgentResult(
text=(
"Cannot identify modality. Provide a SMILES, .fif/.edf "
"path, or NIfTI directory."
),
trace=trace,
model=self._model,
finish_reason="error",
)
if self._enforce_workflow and stage == "retrieve":
if self._invoke_fallback_retrieval(user_input, context, trace, messages):
continue
return AgentResult(
text="Pipeline completed, but retrieval could not be executed.",
trace=trace,
model=self._model,
finish_reason="error",
)
return AgentResult(
text=(msg.content or "").strip(),
trace=trace,
model=self._model,
finish_reason="complete",
)
selected_tool_calls = self._select_tool_calls(msg.tool_calls, stage)
if self._enforce_workflow and not selected_tool_calls:
if stage == "pipeline":
if self._invoke_routed_pipeline(user_input, context, trace, messages):
continue
return AgentResult(
text=(
"Cannot identify modality. Provide a SMILES, .fif/.edf "
"path, or NIfTI directory."
),
trace=trace,
model=self._model,
finish_reason="error",
)
if stage == "retrieve":
if self._invoke_fallback_retrieval(user_input, context, trace, messages):
continue
return AgentResult(
text="Pipeline completed, but retrieval could not be executed.",
trace=trace,
model=self._model,
finish_reason="error",
)
messages.append({
"role": "assistant",
"content": msg.content,
"tool_calls": [tc.model_dump() for tc in selected_tool_calls],
})
for tc in selected_tool_calls:
name = tc.function.name
tool = self._tools_by_name.get(name)
if tool is None:
err = f"unknown tool: {name}"
trace.append(ToolTraceItem(name=name, args={}, error=err))
messages.append({
"role": "tool",
"tool_call_id": tc.id,
"content": json.dumps({"error": err}),
})
continue
try:
args = json.loads(tc.function.arguments or "{}")
result = tool.invoke(args)
trace.append(ToolTraceItem(name=name, args=args, result=result))
messages.append({
"role": "tool",
"tool_call_id": tc.id,
"content": json.dumps({"result": result}, default=str),
})
except Exception as e:
err = str(e)
trace.append(ToolTraceItem(name=name, args={}, error=err))
messages.append({
"role": "tool",
"tool_call_id": tc.id,
"content": json.dumps({"error": err}),
})
return AgentResult(
text="Max steps reached without a final answer.",
trace=trace,
model=self._model,
finish_reason="max_steps",
)
def _completion_kwargs(
self,
messages: list[dict[str, Any]],
stage: str,
) -> dict[str, Any]:
kwargs: dict[str, Any] = {
"model": self._model,
"messages": messages,
"temperature": self._temperature,
}
if not self._enforce_workflow:
kwargs["tools"] = self._tool_schemas
kwargs["tool_choice"] = "auto"
return kwargs
schemas = self._schemas_for_stage(stage)
if schemas:
kwargs["tools"] = schemas
kwargs["tool_choice"] = "auto"
return kwargs
def _schemas_for_stage(self, stage: str) -> list[dict[str, Any]]:
if stage == "pipeline":
return [
self._tool_schemas_by_name[name]
for name in sorted(self._workflow_pipeline_tools)
if name in self._tool_schemas_by_name
]
if stage == "retrieve" and self._workflow_retrieval_tool:
schema = self._tool_schemas_by_name.get(self._workflow_retrieval_tool)
return [schema] if schema else []
return []
def _workflow_stage(self, trace: list[ToolTraceItem]) -> str:
if not self._enforce_workflow:
return "open"
has_pipeline = any(
t.name in self._workflow_pipeline_tools and t.result is not None and t.error is None
for t in trace
)
if not has_pipeline:
return "pipeline"
has_retrieval = any(
t.name == self._workflow_retrieval_tool and t.result is not None and t.error is None
for t in trace
)
return "final" if has_retrieval else "retrieve"
def _select_tool_calls(self, tool_calls: list[Any], stage: str) -> list[Any]:
if not self._enforce_workflow:
return list(tool_calls)
if stage == "pipeline":
for tc in tool_calls:
if tc.function.name in self._workflow_pipeline_tools:
return [tc]
for tc in tool_calls:
logger.info(
"dropped out-of-stage tool call: name=%s stage=%s",
tc.function.name,
stage,
)
return []
if stage == "retrieve":
for tc in tool_calls:
if tc.function.name == self._workflow_retrieval_tool:
return [tc]
for tc in tool_calls:
logger.info(
"dropped out-of-stage tool call: name=%s stage=%s",
tc.function.name,
stage,
)
return []
for tc in tool_calls:
logger.info(
"dropped out-of-stage tool call: name=%s stage=%s",
tc.function.name,
stage,
)
return []
def _invoke_routed_pipeline(
self,
user_input: str,
context: dict[str, Any] | None,
trace: list[ToolTraceItem],
messages: list[dict[str, Any]],
) -> bool:
if self._workflow_router is None:
return False
routed = self._workflow_router(user_input, context)
if routed is None:
return False
name, args = routed
tool = self._tools_by_name.get(name)
if tool is None:
trace.append(ToolTraceItem(name=name, args=args, error=f"unknown tool: {name}"))
return False
try:
result = tool.invoke(args)
trace.append(ToolTraceItem(name=name, args=args, result=result))
messages.append({
"role": "user",
"content": (
"Workflow guard executed the required pipeline tool. "
f"Tool: {name}. Result: {json.dumps(result, default=str)}. "
"Now call retrieve_context with a focused scientific query."
),
})
return True
except Exception as e:
trace.append(ToolTraceItem(name=name, args=args, error=str(e)))
return False
def _invoke_fallback_retrieval(
self,
user_input: str,
context: dict[str, Any] | None,
trace: list[ToolTraceItem],
messages: list[dict[str, Any]],
) -> bool:
if self._workflow_retrieval_tool is None or self._workflow_query_builder is None:
return False
pipeline_trace = next(
(
t for t in trace
if t.name in self._workflow_pipeline_tools and t.result is not None and t.error is None
),
None,
)
if pipeline_trace is None:
return False
tool = self._tools_by_name.get(self._workflow_retrieval_tool)
if tool is None:
return False
query = self._workflow_query_builder(user_input, pipeline_trace, context)
args = {"query": query, "k": 4}
try:
result = tool.invoke(args)
trace.append(ToolTraceItem(
name=self._workflow_retrieval_tool,
args=args,
result=result,
))
messages.append({
"role": "user",
"content": (
"Workflow guard executed retrieve_context. "
f"Result: {json.dumps(result, default=str)}. "
"Now synthesize the final answer in the user's language."
),
})
return True
except Exception as e:
trace.append(ToolTraceItem(
name=self._workflow_retrieval_tool,
args=args,
error=str(e),
))
return False
|