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partners/ollama: fix tool calling with nested schemas (#28225)
## Description This PR addresses the following: **Fixes Issue #25343:** - Adds additional logic to parse shallowly nested JSON-encoded strings in tool call arguments, allowing for proper parsing of responses like that of Llama3.1 and 3.2 with nested schemas. **Adds Integration Test for Fix:** - Adds a Ollama specific integration test to ensure the issue is resolved and to prevent regressions in the future. **Fixes Failing Integration Tests:** - Fixes failing integration tests (even prior to changes) caused by `llama3-groq-tool-use` model. Previously, tests`test_structured_output_async` and `test_structured_output_optional_param` failed due to the model not issuing a tool call in the response. Resolved by switching to `llama3.1`. ## Issue Fixes #25343. ## Dependencies No dependencies. ____ Done in collaboration with @ishaan-upadhyay @mirajismail @ZackSteine.
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@ -1,5 +1,6 @@
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"""Ollama chat models."""
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import json
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from typing import (
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Any,
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AsyncIterator,
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@ -21,6 +22,7 @@ from langchain_core.callbacks import (
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CallbackManagerForLLMRun,
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)
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from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun
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from langchain_core.exceptions import OutputParserException
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
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from langchain_core.messages import (
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@ -60,6 +62,72 @@ def _get_usage_metadata_from_generation_info(
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return None
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def _parse_json_string(
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json_string: str, raw_tool_call: dict[str, Any], skip: bool
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) -> Any:
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"""Attempt to parse a JSON string for tool calling.
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Args:
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json_string: JSON string to parse.
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skip: Whether to ignore parsing errors and return the value anyways.
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raw_tool_call: Raw tool call to include in error message.
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Returns:
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The parsed JSON string.
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Raises:
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OutputParserException: If the JSON string wrong invalid and skip=False.
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"""
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try:
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return json.loads(json_string)
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except json.JSONDecodeError as e:
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if skip:
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return json_string
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msg = (
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f"Function {raw_tool_call['function']['name']} arguments:\n\n"
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f"{raw_tool_call['function']['arguments']}\n\nare not valid JSON. "
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f"Received JSONDecodeError {e}"
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)
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raise OutputParserException(msg) from e
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except TypeError as e:
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if skip:
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return json_string
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msg = (
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f"Function {raw_tool_call['function']['name']} arguments:\n\n"
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f"{raw_tool_call['function']['arguments']}\n\nare not a string or a "
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f"dictionary. Received TypeError {e}"
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)
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raise OutputParserException(msg) from e
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def _parse_arguments_from_tool_call(
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raw_tool_call: dict[str, Any],
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) -> Optional[dict[str, Any]]:
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"""Parse arguments by trying to parse any shallowly nested string-encoded JSON.
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Band-aid fix for issue in Ollama with inconsistent tool call argument structure.
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Should be removed/changed if fixed upstream.
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See https://github.com/ollama/ollama/issues/6155
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"""
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if "function" not in raw_tool_call:
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return None
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arguments = raw_tool_call["function"]["arguments"]
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parsed_arguments = {}
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if isinstance(arguments, dict):
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for key, value in arguments.items():
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if isinstance(value, str):
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parsed_arguments[key] = _parse_json_string(
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value, skip=True, raw_tool_call=raw_tool_call
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)
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else:
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parsed_arguments[key] = value
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else:
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parsed_arguments = _parse_json_string(
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arguments, skip=False, raw_tool_call=raw_tool_call
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)
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return parsed_arguments
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def _get_tool_calls_from_response(
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response: Mapping[str, Any],
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) -> List[ToolCall]:
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@ -72,7 +140,7 @@ def _get_tool_calls_from_response(
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tool_call(
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id=str(uuid4()),
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name=tc["function"]["name"],
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args=tc["function"]["arguments"],
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args=_parse_arguments_from_tool_call(tc) or {},
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)
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)
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return tool_calls
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@ -0,0 +1,41 @@
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"""Ollama specific chat model integration tests"""
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from typing import List, Optional
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import pytest
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from pydantic import BaseModel, Field
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from langchain_ollama import ChatOllama
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@pytest.mark.parametrize(("model"), [("llama3.1")])
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def test_structured_output_deeply_nested(model: str) -> None:
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"""Test to verify structured output with a nested objects."""
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llm = ChatOllama(model=model, temperature=0)
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class Person(BaseModel):
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"""Information about a person."""
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name: Optional[str] = Field(default=None, description="The name of the person")
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hair_color: Optional[str] = Field(
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default=None, description="The color of the person's hair if known"
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)
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height_in_meters: Optional[str] = Field(
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default=None, description="Height measured in meters"
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)
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class Data(BaseModel):
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"""Extracted data about people."""
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people: List[Person]
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chat = llm.with_structured_output(Data) # type: ignore[arg-type]
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text = (
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"Alan Smith is 6 feet tall and has blond hair."
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"Alan Poe is 3 feet tall and has grey hair."
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)
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result = chat.invoke(text)
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assert isinstance(result, Data)
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for chunk in chat.stream(text):
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assert isinstance(chunk, Data)
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@ -1,4 +1,4 @@
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"""Test chat model integration."""
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"""Test chat model integration using standard integration tests."""
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from typing import Type
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@ -16,7 +16,7 @@ class TestChatOllama(ChatModelIntegrationTests):
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@property
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def chat_model_params(self) -> dict:
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return {"model": "llama3-groq-tool-use"}
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return {"model": "llama3.1"}
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@property
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def supports_image_inputs(self) -> bool:
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