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
109 lines
4.3 KiB
Python
109 lines
4.3 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 openai.types.create_embedding_response import CreateEmbeddingResponse
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from dapr_agents.llm.openai.client.base import OpenAIClientBase
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from typing import Union, Dict, Any, Literal, List, Optional
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from pydantic import Field, model_validator
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import logging
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logger = logging.getLogger(__name__)
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class OpenAIEmbeddingClient(OpenAIClientBase):
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"""
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Client for handling OpenAI's embedding functionalities, supporting both OpenAI and Azure OpenAI configurations.
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Attributes:
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model (str): The ID of the model to use for embedding. Defaults to `text-embedding-ada-002` if not specified.
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encoding_format (Optional[Literal["float", "base64"]]): The format of the embeddings. Defaults to 'float'.
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dimensions (Optional[int]): Number of dimensions for the output embeddings. Only supported in specific models like `text-embedding-3`.
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user (Optional[str]): A unique identifier representing the end-user.
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"""
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model: str = Field(
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default=None, description="ID of the model to use for embedding."
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)
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encoding_format: Optional[Literal["float", "base64"]] = Field(
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"float", description="Format for the embeddings. Defaults to 'float'."
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)
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dimensions: Optional[int] = Field(
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None,
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description="Number of dimensions for the output embeddings. Supported in text-embedding-3 and later models.",
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)
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user: Optional[str] = Field(
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None, description="Unique identifier representing the end-user."
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)
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@model_validator(mode="before")
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def validate_and_initialize(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Ensures that the 'model' attribute is set during validation.
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If 'model' is not provided, defaults to 'text-embedding-ada-002' or uses 'azure_deployment' if available.
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Args:
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values (Dict[str, Any]): Dictionary of model attributes to validate and initialize.
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Returns:
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Dict[str, Any]: Updated dictionary of validated attributes.
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"""
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if "model" not in values or values["model"] is None:
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values["model"] = values.get("azure_deployment", "text-embedding-ada-002")
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return values
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def model_post_init(self, __context: Any) -> None:
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"""
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Post-initialization setup for private attributes.
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This method configures the API endpoint for embedding operations.
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Args:
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__context (Any): Context provided during model initialization.
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"""
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self._api = "embeddings"
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return super().model_post_init(__context)
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def create_embedding(
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self,
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input: Union[str, List[Union[str, List[int]]]],
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model: Optional[str] = None,
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) -> CreateEmbeddingResponse:
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"""
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Generate embeddings for the given input text(s).
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Args:
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input (Union[str, List[Union[str, List[int]]]]): Input text(s) or tokenized input(s) to generate embeddings for.
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- A single string for one input.
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- A list of strings or tokenized lists for multiple inputs.
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model (Optional[str]): Model to use for embedding. Overrides the default model if provided.
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Returns:
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CreateEmbeddingResponse: A response object containing the generated embeddings and associated metadata.
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Raises:
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ValueError: If the client fails to generate embeddings.
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"""
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logger.info(f"Using model '{self.model}' for embedding generation.")
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# If a model is provided, override the default model
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model = model or self.model
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response = self.client.embeddings.create(
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model=model,
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input=input,
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encoding_format=self.encoding_format,
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dimensions=self.dimensions,
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user=self.user,
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)
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return response
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