mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 00:23:15 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
#
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# Copyright 2026 The Dapr Authors
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import time
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from typing import Any, Dict, Optional, List
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from dapr_agents.types.message import (
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AssistantMessage,
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LLMChatCandidate,
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LLMChatResponse,
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ToolCall,
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FunctionCall,
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)
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logger = logging.getLogger(__name__)
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def process_dapr_chat_response(response: Dict[str, Any]) -> LLMChatResponse:
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"""
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Convert a Dapr-normalized chat dict (with OpenAI-style 'choices') into a unified LLMChatResponse.
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Args:
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response: The dict returned by `DaprChatClient.translate_response`.
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Returns:
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LLMChatResponse: Contains a list of candidates and metadata.
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"""
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# 1) Extract each choice → build AssistantMessage + LLMChatCandidate
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candidates: List[LLMChatCandidate] = []
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for choice in response.get("choices", []):
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msg = choice.get("message", {})
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# Build tool_calls if present (OpenAI-like)
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tool_calls: Optional[List[ToolCall]] = None
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if msg.get("tool_calls"):
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tool_calls = []
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for tc in msg["tool_calls"]:
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try:
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tool_calls.append(
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ToolCall(
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id=tc.get("id", ""),
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type=tc.get("type", "function"),
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function=FunctionCall(
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name=tc.get("function", {}).get("name", ""),
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arguments=tc.get("function", {}).get("arguments", ""),
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),
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)
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)
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except Exception:
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logger.exception(f"Invalid tool_call entry: {tc}")
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function_call = (
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None # there is no openai "function_call" in dapr only tool calls
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)
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content = msg.get("content")
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if isinstance(content, dict):
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try:
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import json
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content = json.dumps(content)
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except Exception as e:
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logger.warning(f"Failed to serialize dictionary content: {e}")
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assistant_message = AssistantMessage(
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content=content,
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tool_calls=tool_calls,
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function_call=function_call,
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)
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candidate = LLMChatCandidate(
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message=assistant_message,
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finish_reason=choice.get("finish_reason"),
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index=choice.get("index"),
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logprobs=choice.get("logprobs"),
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)
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candidates.append(candidate)
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# 2) Build metadata from the top‐level fields
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metadata: Dict[str, Any] = {
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"provider": "dapr",
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"id": response.get("id", None),
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"model": response.get("model", None),
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"object": response.get("object", None),
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"usage": response.get("usage", {}),
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"created": response.get("created", int(time.time())),
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}
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return LLMChatResponse(results=candidates, metadata=metadata)
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