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https://github.com/hwchase17/langchain.git
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[experimental][llms][OllamaFunctions] tool calling related fixes (#22339)
Fixes issues with tool calling to handle tool objects correctly. Added support to handle ToolMessage correctly. Added additional checks for error conditions. --------- Co-authored-by: ccurme <chester.curme@gmail.com>
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parent
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commit
276be6cdd4
@ -13,6 +13,7 @@ from typing import (
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TypedDict,
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TypeVar,
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Union,
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cast,
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overload,
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)
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@ -22,7 +23,14 @@ from langchain_core.callbacks import (
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.messages import AIMessage, BaseMessage, ToolCall
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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HumanMessage,
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SystemMessage,
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ToolCall,
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ToolMessage,
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)
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from langchain_core.output_parsers.base import OutputParserLike
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from langchain_core.output_parsers.json import JsonOutputParser
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from langchain_core.output_parsers.pydantic import PydanticOutputParser
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@ -74,18 +82,32 @@ def _is_pydantic_class(obj: Any) -> bool:
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)
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def _is_pydantic_object(obj: Any) -> bool:
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return isinstance(obj, BaseModel)
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def convert_to_ollama_tool(tool: Any) -> Dict:
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"""Convert a tool to an Ollama tool."""
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description = None
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if _is_pydantic_class(tool):
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schema = tool.construct().schema()
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definition = {"name": schema["title"], "properties": schema["properties"]}
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if "required" in schema:
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definition["required"] = schema["required"]
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name = schema["title"]
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elif _is_pydantic_object(tool):
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schema = tool.get_input_schema().schema()
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name = tool.get_name()
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description = tool.description
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elif isinstance(tool, dict) and "name" in tool and "parameters" in tool:
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return tool.copy()
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else:
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raise ValueError(
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f"""Cannot convert {tool} to an Ollama tool.
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{tool} needs to be a Pydantic class, model, or a dict."""
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)
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definition = {"name": name, "parameters": schema}
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if description:
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definition["description"] = description
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return definition
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raise ValueError(
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f"Cannot convert {tool} to an Ollama tool. {tool} needs to be a Pydantic model."
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)
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return definition
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class _AllReturnType(TypedDict):
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@ -280,6 +302,59 @@ class OllamaFunctions(ChatOllama):
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else:
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return llm | parser_chain
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def _convert_messages_to_ollama_messages(
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self, messages: List[BaseMessage]
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) -> List[Dict[str, Union[str, List[str]]]]:
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ollama_messages: List = []
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for message in messages:
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role = ""
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if isinstance(message, HumanMessage):
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role = "user"
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elif isinstance(message, AIMessage) or isinstance(message, ToolMessage):
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role = "assistant"
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elif isinstance(message, SystemMessage):
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role = "system"
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else:
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raise ValueError("Received unsupported message type for Ollama.")
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content = ""
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images = []
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if isinstance(message.content, str):
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content = message.content
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else:
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for content_part in cast(List[Dict], message.content):
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if content_part.get("type") == "text":
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content += f"\n{content_part['text']}"
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elif content_part.get("type") == "image_url":
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if isinstance(content_part.get("image_url"), str):
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image_url_components = content_part["image_url"].split(",")
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# Support data:image/jpeg;base64,<image> format
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# and base64 strings
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if len(image_url_components) > 1:
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images.append(image_url_components[1])
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else:
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images.append(image_url_components[0])
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else:
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raise ValueError(
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"Only string image_url content parts are supported."
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)
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else:
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raise ValueError(
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"Unsupported message content type. "
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"Must either have type 'text' or type 'image_url' "
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"with a string 'image_url' field."
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)
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ollama_messages.append(
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{
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"role": role,
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"content": content,
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"images": images,
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}
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)
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return ollama_messages
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def _generate(
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self,
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messages: List[BaseMessage],
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@ -300,9 +375,8 @@ class OllamaFunctions(ChatOllama):
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"matching function in `functions`."
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)
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del kwargs["function_call"]
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if _is_pydantic_class(functions[0]):
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functions = [convert_to_ollama_tool(fn) for fn in functions]
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functions.insert(0, DEFAULT_RESPONSE_FUNCTION)
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functions = [convert_to_ollama_tool(fn) for fn in functions]
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functions.append(DEFAULT_RESPONSE_FUNCTION)
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system_message_prompt_template = SystemMessagePromptTemplate.from_template(
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self.tool_system_prompt_template
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)
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@ -323,16 +397,16 @@ class OllamaFunctions(ChatOllama):
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Please try again.
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Response: {chat_generation_content}"""
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)
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called_tool_name = parsed_chat_result["tool"]
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called_tool_name = (
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parsed_chat_result["tool"] if "tool" in parsed_chat_result else None
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)
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called_tool = next(
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(fn for fn in functions if fn["name"] == called_tool_name), None
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)
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if called_tool is None:
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raise ValueError(
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f"Failed to parse a function call from {self.model} output: "
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f"{chat_generation_content}"
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)
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if called_tool["name"] == DEFAULT_RESPONSE_FUNCTION["name"]:
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if (
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called_tool is None
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or called_tool["name"] == DEFAULT_RESPONSE_FUNCTION["name"]
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):
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if (
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"tool_input" in parsed_chat_result
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and "response" in parsed_chat_result["tool_input"]
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@ -355,7 +429,11 @@ class OllamaFunctions(ChatOllama):
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]
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)
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called_tool_arguments = parsed_chat_result["tool_input"]
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called_tool_arguments = (
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parsed_chat_result["tool_input"]
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if "tool_input" in parsed_chat_result
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else {}
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)
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response_message_with_functions = AIMessage(
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content="",
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@ -2,6 +2,8 @@
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import unittest
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from langchain_community.tools import DuckDuckGoSearchResults
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from langchain_community.tools.pubmed.tool import PubmedQueryRun
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from langchain_core.messages import AIMessage
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from langchain_core.pydantic_v1 import BaseModel, Field
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@ -22,7 +24,7 @@ class TestOllamaFunctions(unittest.TestCase):
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"""
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def test_default_ollama_functions(self) -> None:
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base_model = OllamaFunctions(model="llama3", format="json")
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base_model = OllamaFunctions(model="phi3", format="json")
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# bind functions
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model = base_model.bind_tools(
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@ -60,8 +62,22 @@ class TestOllamaFunctions(unittest.TestCase):
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assert tool_call
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self.assertEqual("get_current_weather", tool_call.get("name"))
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def test_ollama_functions_tools(self) -> None:
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base_model = OllamaFunctions(model="phi3", format="json")
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model = base_model.bind_tools(
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tools=[PubmedQueryRun(), DuckDuckGoSearchResults(max_results=2)]
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)
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res = model.invoke("What causes lung cancer?")
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self.assertIsInstance(res, AIMessage)
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res = AIMessage(**res.__dict__)
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tool_calls = res.tool_calls
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assert tool_calls
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tool_call = tool_calls[0]
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assert tool_call
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self.assertEqual("pub_med", tool_call.get("name"))
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def test_default_ollama_functions_default_response(self) -> None:
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base_model = OllamaFunctions(model="llama3", format="json")
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base_model = OllamaFunctions(model="phi3", format="json")
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# bind functions
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model = base_model.bind_tools(
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