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openai[patch]: use tool_calls in request (#20272)

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This commit is contained in:
Bagatur
2024-04-11 03:55:52 -07:00
committed by GitHub
parent e936fba428
commit c706689413
3 changed files with 93 additions and 12 deletions
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48
libs/partners/openai/langchain_openai/chat_models/base.py
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@@ -2,6 +2,7 @@
from __future__ import annotations
import json
import logging
import os
import sys
@@ -50,8 +51,10 @@ from langchain_core.messages import (
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
InvalidToolCall,
SystemMessage,
SystemMessageChunk,
ToolCall,
ToolMessage,
ToolMessageChunk,
)
@@ -169,20 +172,25 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
message_dict["role"] = "assistant"
if "function_call" in message.additional_kwargs:
message_dict["function_call"] = message.additional_kwargs["function_call"]
# If function call only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
if "tool_calls" in message.additional_kwargs:
if message.tool_calls or message.invalid_tool_calls:
message_dict["tool_calls"] = [
_lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls
] + [
_lc_invalid_tool_call_to_openai_tool_call(tc)
for tc in message.invalid_tool_calls
]
elif "tool_calls" in message.additional_kwargs:
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
# If tool calls only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
tool_call_supported_props = {"id", "type", "function"}
message_dict["tool_calls"] = [
{k: v for k, v in tool_call.items() if k in tool_call_supported_props}
for tool_call in message_dict["tool_calls"]
]
else:
pass
# If tool calls present, content null value should be None not empty string.
if "function_call" in message_dict or "tool_calls" in message_dict:
message_dict["content"] = message_dict["content"] or None
elif isinstance(message, SystemMessage):
message_dict["role"] = "system"
elif isinstance(message, FunctionMessage):
@@ -1067,3 +1075,27 @@ class ChatOpenAI(BaseChatModel):
def _is_pydantic_class(obj: Any) -> bool:
return isinstance(obj, type) and issubclass(obj, BaseModel)
def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
return {
"type": "function",
"id": tool_call["id"],
"function": {
"name": tool_call["name"],
"arguments": json.dumps(tool_call["args"]),
},
}
def _lc_invalid_tool_call_to_openai_tool_call(
invalid_tool_call: InvalidToolCall,
) -> dict:
return {
"type": "function",
"id": invalid_tool_call["id"],
"function": {
"name": invalid_tool_call["name"],
"arguments": invalid_tool_call["args"],
},
}