mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +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
72 lines
2.3 KiB
Python
72 lines
2.3 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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from typing import (
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Callable,
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Iterator,
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Optional,
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TypeVar,
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)
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from openai.types.chat import ChatCompletionChunk
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from pydantic import BaseModel
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from dapr_agents.types.message import LLMChatCandidateChunk
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T = TypeVar("T", bound=BaseModel)
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class StreamHandler:
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"""
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Handles streaming of chat completion responses, delegating to the
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provider-specific stream processor and optionally validating output
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against Pydantic models.
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"""
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@staticmethod
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def process_stream(
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stream: Iterator[ChatCompletionChunk],
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llm_provider: str,
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on_chunk: Optional[Callable],
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) -> Iterator[LLMChatCandidateChunk]:
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"""
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Process a streaming chat completion.
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Args:
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stream: Iterator of ChatCompletionChunk from OpenAI SDK.
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llm_provider: Name of the LLM provider (e.g., "openai").
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on_chunk: Callback fired on every partial LLMChatCandidateChunk.
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Yields:
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LLMChatCandidateChunk: fully-typed chunks, partial and final.
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"""
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if llm_provider in ("openai", "nvidia"):
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from dapr_agents.llm.openai.utils import process_openai_stream
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yield from process_openai_stream(
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raw_stream=stream,
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enrich_metadata={"provider": llm_provider},
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on_chunk=on_chunk,
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)
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elif llm_provider == "huggingface":
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from dapr_agents.llm.huggingface.utils import process_hf_stream
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yield from process_hf_stream(
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raw_stream=stream,
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enrich_metadata={"provider": llm_provider},
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on_chunk=on_chunk,
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)
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else:
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raise ValueError(f"Streaming not supported for provider: {llm_provider}")
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