mirror of
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262 lines
10 KiB
Python
262 lines
10 KiB
Python
from typing import cast
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from uuid import UUID
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from pydantic import BaseModel
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from pydantic import Field
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from sqlalchemy.orm import Session
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from danswer.chat.models import AnswerStyleConfig
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from danswer.chat.models import CitationConfig
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from danswer.chat.models import DocumentPruningConfig
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from danswer.chat.models import PromptConfig
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from danswer.configs.app_configs import AZURE_DALLE_API_BASE
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from danswer.configs.app_configs import AZURE_DALLE_API_KEY
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from danswer.configs.app_configs import AZURE_DALLE_API_VERSION
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from danswer.configs.app_configs import AZURE_DALLE_DEPLOYMENT_NAME
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from danswer.configs.chat_configs import BING_API_KEY
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from danswer.configs.model_configs import GEN_AI_TEMPERATURE
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from danswer.context.search.enums import LLMEvaluationType
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from danswer.context.search.models import InferenceSection
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from danswer.context.search.models import RerankingDetails
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from danswer.context.search.models import RetrievalDetails
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from danswer.db.llm import fetch_existing_llm_providers
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from danswer.db.models import Persona
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from danswer.db.models import User
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from danswer.file_store.models import InMemoryChatFile
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from danswer.llm.interfaces import LLM
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from danswer.llm.interfaces import LLMConfig
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from danswer.natural_language_processing.utils import get_tokenizer
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from danswer.tools.built_in_tools import get_built_in_tool_by_id
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from danswer.tools.models import DynamicSchemaInfo
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from danswer.tools.tool import Tool
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from danswer.tools.tool_implementations.custom.custom_tool import (
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build_custom_tools_from_openapi_schema_and_headers,
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)
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from danswer.tools.tool_implementations.images.image_generation_tool import (
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ImageGenerationTool,
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)
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from danswer.tools.tool_implementations.internet_search.internet_search_tool import (
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InternetSearchTool,
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)
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from danswer.tools.tool_implementations.search.search_tool import SearchTool
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from danswer.tools.utils import compute_all_tool_tokens
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from danswer.tools.utils import explicit_tool_calling_supported
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from danswer.utils.headers import header_dict_to_header_list
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from danswer.utils.logger import setup_logger
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logger = setup_logger()
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def _get_image_generation_config(llm: LLM, db_session: Session) -> LLMConfig:
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"""Helper function to get image generation LLM config based on available providers"""
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if llm and llm.config.api_key and llm.config.model_provider == "openai":
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return LLMConfig(
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model_provider=llm.config.model_provider,
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model_name="dall-e-3",
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temperature=GEN_AI_TEMPERATURE,
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api_key=llm.config.api_key,
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api_base=llm.config.api_base,
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api_version=llm.config.api_version,
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)
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if llm.config.model_provider == "azure" and AZURE_DALLE_API_KEY is not None:
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return LLMConfig(
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model_provider="azure",
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model_name=f"azure/{AZURE_DALLE_DEPLOYMENT_NAME}",
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temperature=GEN_AI_TEMPERATURE,
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api_key=AZURE_DALLE_API_KEY,
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api_base=AZURE_DALLE_API_BASE,
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api_version=AZURE_DALLE_API_VERSION,
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)
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# Fallback to checking for OpenAI provider in database
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llm_providers = fetch_existing_llm_providers(db_session)
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openai_provider = next(
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iter(
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[
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llm_provider
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for llm_provider in llm_providers
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if llm_provider.provider == "openai"
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]
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),
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None,
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)
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if not openai_provider or not openai_provider.api_key:
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raise ValueError("Image generation tool requires an OpenAI API key")
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return LLMConfig(
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model_provider=openai_provider.provider,
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model_name="dall-e-3",
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temperature=GEN_AI_TEMPERATURE,
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api_key=openai_provider.api_key,
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api_base=openai_provider.api_base,
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api_version=openai_provider.api_version,
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)
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class SearchToolConfig(BaseModel):
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answer_style_config: AnswerStyleConfig = Field(
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default_factory=lambda: AnswerStyleConfig(citation_config=CitationConfig())
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)
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document_pruning_config: DocumentPruningConfig = Field(
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default_factory=DocumentPruningConfig
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)
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retrieval_options: RetrievalDetails = Field(default_factory=RetrievalDetails)
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rerank_settings: RerankingDetails | None = None
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selected_sections: list[InferenceSection] | None = None
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chunks_above: int = 0
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chunks_below: int = 0
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full_doc: bool = False
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latest_query_files: list[InMemoryChatFile] | None = None
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# Use with care, should only be used for DanswerBot in channels with multiple users
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bypass_acl: bool = False
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class InternetSearchToolConfig(BaseModel):
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answer_style_config: AnswerStyleConfig = Field(
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default_factory=lambda: AnswerStyleConfig(
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citation_config=CitationConfig(all_docs_useful=True)
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)
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)
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class ImageGenerationToolConfig(BaseModel):
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additional_headers: dict[str, str] | None = None
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class CustomToolConfig(BaseModel):
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chat_session_id: UUID | None = None
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message_id: int | None = None
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additional_headers: dict[str, str] | None = None
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def construct_tools(
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persona: Persona,
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prompt_config: PromptConfig,
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db_session: Session,
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user: User | None,
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llm: LLM,
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fast_llm: LLM,
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search_tool_config: SearchToolConfig | None = None,
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internet_search_tool_config: InternetSearchToolConfig | None = None,
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image_generation_tool_config: ImageGenerationToolConfig | None = None,
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custom_tool_config: CustomToolConfig | None = None,
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) -> dict[int, list[Tool]]:
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"""Constructs tools based on persona configuration and available APIs"""
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tool_dict: dict[int, list[Tool]] = {}
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for db_tool_model in persona.tools:
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if db_tool_model.in_code_tool_id:
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tool_cls = get_built_in_tool_by_id(db_tool_model.id, db_session)
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# Handle Search Tool
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if tool_cls.__name__ == SearchTool.__name__:
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if not search_tool_config:
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search_tool_config = SearchToolConfig()
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search_tool = SearchTool(
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db_session=db_session,
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user=user,
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persona=persona,
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retrieval_options=search_tool_config.retrieval_options,
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prompt_config=prompt_config,
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llm=llm,
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fast_llm=fast_llm,
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pruning_config=search_tool_config.document_pruning_config,
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answer_style_config=search_tool_config.answer_style_config,
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selected_sections=search_tool_config.selected_sections,
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chunks_above=search_tool_config.chunks_above,
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chunks_below=search_tool_config.chunks_below,
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full_doc=search_tool_config.full_doc,
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evaluation_type=(
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LLMEvaluationType.BASIC
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if persona.llm_relevance_filter
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else LLMEvaluationType.SKIP
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),
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rerank_settings=search_tool_config.rerank_settings,
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bypass_acl=search_tool_config.bypass_acl,
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)
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tool_dict[db_tool_model.id] = [search_tool]
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# Handle Image Generation Tool
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elif tool_cls.__name__ == ImageGenerationTool.__name__:
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if not image_generation_tool_config:
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image_generation_tool_config = ImageGenerationToolConfig()
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img_generation_llm_config = _get_image_generation_config(
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llm, db_session
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)
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tool_dict[db_tool_model.id] = [
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ImageGenerationTool(
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api_key=cast(str, img_generation_llm_config.api_key),
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api_base=img_generation_llm_config.api_base,
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api_version=img_generation_llm_config.api_version,
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additional_headers=image_generation_tool_config.additional_headers,
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model=img_generation_llm_config.model_name,
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)
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]
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# Handle Internet Search Tool
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elif tool_cls.__name__ == InternetSearchTool.__name__:
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if not internet_search_tool_config:
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internet_search_tool_config = InternetSearchToolConfig()
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if not BING_API_KEY:
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raise ValueError(
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"Internet search tool requires a Bing API key, please contact your Danswer admin to get it added!"
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)
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tool_dict[db_tool_model.id] = [
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InternetSearchTool(
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api_key=BING_API_KEY,
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answer_style_config=internet_search_tool_config.answer_style_config,
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prompt_config=prompt_config,
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)
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]
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# Handle custom tools
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elif db_tool_model.openapi_schema:
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if not custom_tool_config:
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custom_tool_config = CustomToolConfig()
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tool_dict[db_tool_model.id] = cast(
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list[Tool],
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build_custom_tools_from_openapi_schema_and_headers(
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db_tool_model.openapi_schema,
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dynamic_schema_info=DynamicSchemaInfo(
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chat_session_id=custom_tool_config.chat_session_id,
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message_id=custom_tool_config.message_id,
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),
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custom_headers=(db_tool_model.custom_headers or [])
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+ (
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header_dict_to_header_list(
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custom_tool_config.additional_headers or {}
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)
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),
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),
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)
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tools: list[Tool] = []
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for tool_list in tool_dict.values():
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tools.extend(tool_list)
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# factor in tool definition size when pruning
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if search_tool_config:
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search_tool_config.document_pruning_config.tool_num_tokens = (
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compute_all_tool_tokens(
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tools,
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get_tokenizer(
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model_name=llm.config.model_name,
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provider_type=llm.config.model_provider,
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),
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)
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)
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search_tool_config.document_pruning_config.using_tool_message = (
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explicit_tool_calling_supported(
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llm.config.model_provider, llm.config.model_name
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)
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)
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return tool_dict
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