mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-27 07:54:19 +00:00
feat: make agent runner config schema driven
This commit is contained in:
huanghuoguoguo
committed by
huanghuoguoguo
huanghuoguoguo
parent
651e28113e
commit
d8d98b0838
@@ -0,0 +1,208 @@
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"""Helpers for interpreting AgentRunner DynamicForm configuration."""
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from __future__ import annotations
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import typing
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from .descriptor import AgentRunnerDescriptor
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LLM_MODEL_SELECTOR_TYPES = {'model-fallback-selector', 'llm-model-selector'}
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KB_SELECTOR_TYPES = {'knowledge-base-multi-selector'}
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PROMPT_EDITOR_TYPES = {'prompt-editor'}
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NONE_SENTINELS = {'', '__none__', '__none'}
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def iter_schema_items(
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descriptor: AgentRunnerDescriptor | None,
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field_types: set[str],
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) -> typing.Iterator[dict[str, typing.Any]]:
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"""Yield descriptor config schema items whose type is in field_types."""
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if descriptor is None:
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return
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for item in descriptor.config_schema or []:
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if not isinstance(item, dict):
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continue
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if item.get('type') in field_types:
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yield item
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def has_permission(
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descriptor: AgentRunnerDescriptor | None,
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name: str,
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actions: set[str],
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) -> bool:
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"""Return whether a runner descriptor requests one of the given actions."""
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if descriptor is None:
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return False
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configured_actions = descriptor.permissions.get(name, [])
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return any(action in configured_actions for action in actions)
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def uses_host_models(descriptor: AgentRunnerDescriptor | None) -> bool:
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"""Return whether LangBot should resolve model resources for this runner."""
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return (
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has_permission(descriptor, 'models', {'invoke', 'stream', 'list'})
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and any(True for _ in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES))
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)
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def uses_host_tools(descriptor: AgentRunnerDescriptor | None) -> bool:
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"""Return whether LangBot should expose tool resources to this runner."""
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return (
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descriptor is not None
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and descriptor.supports_tool_calling()
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and has_permission(descriptor, 'tools', {'list', 'detail', 'call'})
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)
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def uses_host_knowledge_bases(descriptor: AgentRunnerDescriptor | None) -> bool:
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"""Return whether LangBot should expose knowledge-base resources to this runner."""
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return (
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descriptor is not None
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and descriptor.supports_knowledge_retrieval()
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and has_permission(descriptor, 'knowledge_bases', {'list', 'retrieve'})
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)
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def extract_prompt_config(
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descriptor: AgentRunnerDescriptor | None,
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runner_config: dict[str, typing.Any],
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default_prompt: list[dict[str, typing.Any]],
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) -> list[dict[str, typing.Any]]:
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"""Extract the prompt-editor value selected by the runner schema."""
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for item in iter_schema_items(descriptor, PROMPT_EDITOR_TYPES):
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field_name = item.get('name')
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if field_name and field_name in runner_config:
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configured_prompt = runner_config[field_name]
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if isinstance(configured_prompt, list):
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return configured_prompt
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default_value = item.get('default')
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if isinstance(default_value, list):
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return default_value
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return default_prompt
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def extract_model_selection(
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descriptor: AgentRunnerDescriptor | None,
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runner_config: dict[str, typing.Any],
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) -> tuple[str, list[str]]:
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"""Extract primary/fallback LLM selections from schema-defined fields."""
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primary_uuid = ''
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fallback_uuids: list[str] = []
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for item in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES):
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field_name = item.get('name')
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if not field_name:
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continue
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value = runner_config.get(field_name, item.get('default'))
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if item.get('type') == 'model-fallback-selector':
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if isinstance(value, str):
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primary_uuid = value
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elif isinstance(value, dict):
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primary_uuid = value.get('primary') or ''
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fallbacks = value.get('fallbacks', [])
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if isinstance(fallbacks, list):
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fallback_uuids = [fallback for fallback in fallbacks if isinstance(fallback, str)]
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break
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if item.get('type') == 'llm-model-selector' and isinstance(value, str):
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primary_uuid = value
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break
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return primary_uuid, fallback_uuids
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def extract_knowledge_base_uuids(
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descriptor: AgentRunnerDescriptor | None,
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runner_config: dict[str, typing.Any],
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) -> list[str]:
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"""Extract configured knowledge-base UUIDs from schema-defined fields."""
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if not uses_host_knowledge_bases(descriptor):
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return []
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kb_uuids: list[str] = []
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for item in iter_schema_items(descriptor, KB_SELECTOR_TYPES):
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field_name = item.get('name')
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if not field_name:
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continue
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value = runner_config.get(field_name, item.get('default', []))
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if isinstance(value, list):
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kb_uuids.extend(
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kb_uuid for kb_uuid in value if isinstance(kb_uuid, str) and kb_uuid not in NONE_SENTINELS
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)
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return list(dict.fromkeys(kb_uuids))
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def iter_config_model_refs(
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descriptor: AgentRunnerDescriptor,
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runner_config: dict[str, typing.Any],
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) -> typing.Iterator[tuple[str, str]]:
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"""Yield model references declared by schema-defined model selector fields."""
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for item in descriptor.config_schema or []:
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if not isinstance(item, dict):
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continue
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field_name = item.get('name')
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field_type = item.get('type')
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if not field_name or field_name not in runner_config:
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continue
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value = runner_config.get(field_name)
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if field_type == 'model-fallback-selector':
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if isinstance(value, str) and value not in NONE_SENTINELS:
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yield 'llm', value
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elif isinstance(value, dict):
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primary = value.get('primary')
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if isinstance(primary, str) and primary not in NONE_SENTINELS:
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yield 'llm', primary
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fallbacks = value.get('fallbacks', [])
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if isinstance(fallbacks, list):
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for fallback_uuid in fallbacks:
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if isinstance(fallback_uuid, str) and fallback_uuid not in NONE_SENTINELS:
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yield 'llm', fallback_uuid
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elif field_type == 'llm-model-selector':
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if isinstance(value, str) and value not in NONE_SENTINELS:
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yield 'llm', value
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elif field_type == 'rerank-model-selector':
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if isinstance(value, str) and value not in NONE_SENTINELS:
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yield 'rerank', value
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def set_empty_llm_model_selection(
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descriptor: AgentRunnerDescriptor,
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runner_config: dict[str, typing.Any],
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model_uuid: str,
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) -> bool:
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"""Set the first empty schema-defined LLM selector to model_uuid."""
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for item in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES):
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field_name = item.get('name')
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field_type = item.get('type')
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if not field_name:
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continue
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value = runner_config.get(field_name, item.get('default'))
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if field_type == 'model-fallback-selector':
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if isinstance(value, dict):
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primary = value.get('primary') or ''
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if primary not in NONE_SENTINELS:
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return False
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fallbacks = value.get('fallbacks', [])
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runner_config[field_name] = {
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'primary': model_uuid,
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'fallbacks': fallbacks if isinstance(fallbacks, list) else [],
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}
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return True
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if isinstance(value, str) and value not in NONE_SENTINELS:
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return False
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runner_config[field_name] = {'primary': model_uuid, 'fallbacks': []}
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return True
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if field_type == 'llm-model-selector':
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if isinstance(value, str) and value not in NONE_SENTINELS:
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return False
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runner_config[field_name] = model_uuid
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return True
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return False
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