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
87 lines
3.2 KiB
Python
87 lines
3.2 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 List, Dict, Any
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from pydantic import BaseModel, Field, ConfigDict
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from dapr_agents.storage.vectorstores import VectorStoreBase
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import logging
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logger = logging.getLogger(__name__)
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class VectorToolStore(BaseModel):
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"""
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Manages tool information within a vector store, providing methods for adding tools and
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retrieving similar tools based on queries.
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"""
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vector_store: VectorStoreBase = Field(
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..., description="The vector store instance for tool data storage."
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)
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model_config = ConfigDict(arbitrary_types_allowed=True)
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def add_tools(self, tools: List[Dict[str, Any]]):
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"""
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Adds tool information to the vector store.
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Args:
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tools (List[Dict[str, Any]]): A list of dictionaries representing tools, each containing
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definitions and optional metadata for each tool.
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"""
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logger.info("Adding tools to Vector Tool Store.")
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documents = []
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metadatas = []
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for tool in tools:
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func_name = tool["definition"]["function"]["name"]
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description = tool["definition"]["function"]["description"]
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parameters = tool["definition"]["function"]["parameters"]
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# Prepare the document for each tool
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documents.append(f"{func_name}: {description}. Args schema: {parameters}")
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# Prepare metadata, ensuring 'name' is always set
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metadata = tool.get("metadata", {}).copy()
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metadata.setdefault("name", func_name) # Ensure name is set in metadata
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metadatas.append(metadata)
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self.vector_store.add(documents=documents, metadatas=metadatas)
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def get_similar_tools(self, query_texts: str, k: int = 4) -> List[Dict[str, Any]]:
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"""
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Retrieves tools from the vector store similar to the query text.
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Args:
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query_texts (str): The query string to find similar tools.
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k (int): The number of similar results to return. Defaults to 4.
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Returns:
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List[Dict[str, Any]]: List of similar tool entries based on the query.
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"""
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logger.info(f"Searching for tools similar to query: {query_texts}")
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similar_docs = self.vector_store.search_similar(query_texts=query_texts, k=k)
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return similar_docs
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def get_tool_names(self) -> List[str]:
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"""
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Retrieves the names of all tools stored in the vector store.
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Returns:
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List[str]: A list of tool names.
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"""
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logger.info("Retrieving all tool names from Vector Tool Store.")
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tools = self.vector_store.get()
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return [tool["metadata"]["name"] for tool in tools]
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