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https://github.com/danswer-ai/danswer.git
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06624a988d
* WIP rebased * style * WIP, testing theory * fix type issue * fixed filtering bug * fix silliness * correct serialization and validation of threadsafedict * concurrent drive access * nits * nit * oauth bug fix * testing fix * fix slim retrieval * fix integration tests * fix testing change * CW comments * nit * guarantee completion stage existence * fix default values
47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
import json
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from typing import Any
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from langchain_core.messages.ai import AIMessage
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from langchain_core.messages.tool import ToolCall
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from langchain_core.messages.tool import ToolMessage
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from pydantic import BaseModel
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from onyx.natural_language_processing.utils import BaseTokenizer
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# Langchain has their own version of pydantic which is version 1
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def build_tool_message(
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tool_call: ToolCall, tool_content: str | list[str | dict[str, Any]]
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) -> ToolMessage:
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return ToolMessage(
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tool_call_id=tool_call["id"] or "",
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name=tool_call["name"],
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content=tool_content,
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)
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class ToolCallSummary(BaseModel):
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tool_call_request: AIMessage
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tool_call_result: ToolMessage
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# This is a workaround to allow arbitrary types in the model
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# TODO: Remove this once we have a better solution
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class Config:
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arbitrary_types_allowed = True
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def tool_call_tokens(
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tool_call_summary: ToolCallSummary, llm_tokenizer: BaseTokenizer
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) -> int:
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request_tokens = len(
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llm_tokenizer.encode(
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json.dumps(tool_call_summary.tool_call_request.tool_calls[0]["args"])
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)
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)
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result_tokens = len(
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llm_tokenizer.encode(json.dumps(tool_call_summary.tool_call_result.content))
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)
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return request_tokens + result_tokens
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