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josvdwandhagen-danswer
50c17438d5
* ran bump-pydantic * replace root_validator with model_validator * mostly working. some alternate assistant error. changed root_validator and typing_extensions * working generation chat. changed type * replacing .dict with .model_dump * argument needed to bring model_dump up to parity with dict() * fix a fewremaining issues -- working with llama and gpt * updating requirements file * more requirement updates * more requirement updates * fix to make search work * return type fix: * half way tpyes change * fixes for mypy and pydantic: * endpoint fix * fix pydantic protected namespaces * it works! * removed unecessary None initializations * better logging * changed default values to empty lists * mypy fixes * fixed array defaulting --------- Co-authored-by: hagen-danswer <hagen@danswer.ai>
42 lines
1.1 KiB
Python
42 lines
1.1 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.v1 import BaseModel as BaseModel__v1
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from danswer.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__v1):
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tool_call_request: AIMessage
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tool_call_result: ToolMessage
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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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