fix(ollama): robustly parse single-quoted JSON in tool calls (#32109)

**Description:**
This PR makes argument parsing for Ollama tool calls more robust. Some
LLMs—including Ollama—may return arguments as Python-style dictionaries
with single quotes (e.g., `{'a': 1}`), which are not valid JSON and
previously caused parsing to fail.
The updated `_parse_json_string` method in
`langchain_ollama.chat_models` now attempts standard JSON parsing and,
if that fails, falls back to `ast.literal_eval` for safe evaluation of
Python-style dictionaries. This improves interoperability with LLMs and
fixes a common usability issue for tool-based agents.

**Issue:**
Closes #30910

**Dependencies:**
None

**Tests:**
- Added new unit tests for double-quoted JSON, single-quoted dicts,
mixed quoting, and malformed/failure cases.
- All tests pass locally, including new coverage for single-quoted
inputs.

**Notes:**
- No breaking changes.
- No new dependencies introduced.
- Code is formatted and linted (`ruff format`, `ruff check`).
- If maintainers have suggestions for further improvements, I’m happy to
revise!

Thank you for maintaining LangChain! Looking forward to your feedback.
This commit is contained in:
diego-coder 2025-07-21 09:11:22 -07:00 committed by GitHub
parent 6794422b85
commit 8e4396bb32
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GPG Key ID: B5690EEEBB952194
3 changed files with 88 additions and 18 deletions

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@ -5,7 +5,7 @@ build-backend = "pdm.backend"
[project]
authors = []
license = { text = "MIT" }
requires-python = ">=3.9"
requires-python = ">=3.9, <4.0"
dependencies = [
"langchain-core<1.0.0,>=0.3.66",
"langchain-text-splitters<1.0.0,>=0.3.8",

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@ -2,6 +2,7 @@
from __future__ import annotations
import ast
import json
from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
from operator import itemgetter
@ -77,31 +78,43 @@ def _get_usage_metadata_from_generation_info(
def _parse_json_string(
json_string: str,
*,
raw_tool_call: dict[str, Any],
skip: bool, # noqa: FBT001
skip: bool,
) -> Any:
"""Attempt to parse a JSON string for tool calling.
It first tries to use the standard json.loads. If that fails, it falls
back to ast.literal_eval to safely parse Python literals, which is more
robust against models using single quotes or containing apostrophes.
Args:
json_string: JSON string to parse.
skip: Whether to ignore parsing errors and return the value anyways.
raw_tool_call: Raw tool call to include in error message.
skip: Whether to ignore parsing errors and return the value anyways.
Returns:
The parsed JSON string.
The parsed JSON string or Python literal.
Raises:
OutputParserException: If the JSON string wrong invalid and skip=False.
OutputParserException: If the string is invalid and skip=False.
"""
try:
return json.loads(json_string)
except json.JSONDecodeError as e:
except json.JSONDecodeError:
try:
# Use ast.literal_eval to safely parse Python-style dicts
# (e.g. with single quotes)
return ast.literal_eval(json_string)
except (SyntaxError, ValueError) as e:
# If both fail, and we're not skipping, raise an informative error.
if skip:
return json_string
msg = (
f"Function {raw_tool_call['function']['name']} arguments:\n\n"
f"{raw_tool_call['function']['arguments']}\n\nare not valid JSON. "
f"Received JSONDecodeError {e}"
f"{raw_tool_call['function']['arguments']}"
"\n\nare not valid JSON or a Python literal. "
f"Received error: {e}"
)
raise OutputParserException(msg) from e
except TypeError as e:

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@ -8,10 +8,15 @@ from unittest.mock import patch
import pytest
from httpx import Client, Request, Response
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import ChatMessage
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_ollama.chat_models import ChatOllama, _parse_arguments_from_tool_call
from langchain_ollama.chat_models import (
ChatOllama,
_parse_arguments_from_tool_call,
_parse_json_string,
)
MODEL_NAME = "llama3.1"
@ -49,13 +54,11 @@ def test_arbitrary_roles_accepted_in_chatmessages(
monkeypatch: pytest.MonkeyPatch,
) -> None:
monkeypatch.setattr(Client, "stream", _mock_httpx_client_stream)
llm = ChatOllama(
model=MODEL_NAME,
verbose=True,
format=None,
)
messages = [
ChatMessage(
role="somerandomrole",
@ -64,7 +67,6 @@ def test_arbitrary_roles_accepted_in_chatmessages(
ChatMessage(role="control", content="thinking"),
ChatMessage(role="user", content="What is the meaning of life?"),
]
llm.invoke(messages)
@ -83,3 +85,58 @@ def test_validate_model_on_init(mock_validate_model: Any) -> None:
# Test that validate_model is NOT called by default
ChatOllama(model=MODEL_NAME)
mock_validate_model.assert_not_called()
# Define a dummy raw_tool_call for the function signature
dummy_raw_tool_call = {
"function": {"name": "test_func", "arguments": ""},
}
# --- Regression tests for tool-call argument parsing (see #30910) ---
@pytest.mark.parametrize(
"input_string, expected_output",
[
# Case 1: Standard double-quoted JSON
('{"key": "value", "number": 123}', {"key": "value", "number": 123}),
# Case 2: Single-quoted string (the original bug)
("{'key': 'value', 'number': 123}", {"key": "value", "number": 123}),
# Case 3: String with an internal apostrophe
('{"text": "It\'s a great test!"}', {"text": "It's a great test!"}),
# Case 4: Mixed quotes that ast can handle
("{'text': \"It's a great test!\"}", {"text": "It's a great test!"}),
],
)
def test_parse_json_string_success_cases(
input_string: str, expected_output: Any
) -> None:
"""Tests that _parse_json_string correctly parses valid and fixable strings."""
raw_tool_call = {"function": {"name": "test_func", "arguments": input_string}}
result = _parse_json_string(input_string, raw_tool_call=raw_tool_call, skip=False)
assert result == expected_output
def test_parse_json_string_failure_case_raises_exception() -> None:
"""Tests that _parse_json_string raises an exception for truly malformed strings."""
malformed_string = "{'key': 'value',,}"
raw_tool_call = {"function": {"name": "test_func", "arguments": malformed_string}}
with pytest.raises(OutputParserException):
_parse_json_string(
malformed_string,
raw_tool_call=raw_tool_call,
skip=False,
)
def test_parse_json_string_skip_returns_input_on_failure() -> None:
"""Tests that skip=True returns the original string on parse failure."""
malformed_string = "{'not': valid,,,}"
raw_tool_call = {"function": {"name": "test_func", "arguments": malformed_string}}
result = _parse_json_string(
malformed_string,
raw_tool_call=raw_tool_call,
skip=True,
)
assert result == malformed_string