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https://github.com/hwchase17/langchain.git
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experimental[minor]: Add bind_tools and with_structured_output functions to OllamaFunctions (#20881)
Implemented bind_tools for OllamaFunctions. Made OllamaFunctions sub class of ChatOllama. Implemented with_structured_output for OllamaFunctions. integration unit test has been updated. notebook has been updated. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Karim Lalani
GitHub
@@ -1,14 +1,34 @@
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import json
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from typing import Any, Dict, List, Optional
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from operator import itemgetter
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from typing import (
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Any,
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Callable,
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Dict,
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List,
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Literal,
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Optional,
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Sequence,
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Type,
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TypedDict,
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TypeVar,
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Union,
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overload,
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)
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from langchain_community.chat_models.ollama import ChatOllama
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models import BaseChatModel
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.messages import AIMessage, BaseMessage
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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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from langchain_core.outputs import ChatGeneration, ChatResult
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from langchain_core.prompts import SystemMessagePromptTemplate
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from langchain_experimental.pydantic_v1 import root_validator
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import Runnable, RunnableLambda
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from langchain_core.runnables.base import RunnableMap
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from langchain_core.runnables.passthrough import RunnablePassthrough
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from langchain_core.tools import BaseTool
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DEFAULT_SYSTEM_TEMPLATE = """You have access to the following tools:
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@@ -22,7 +42,6 @@ You must always select one of the above tools and respond with only a JSON objec
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}}
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""" # noqa: E501
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DEFAULT_RESPONSE_FUNCTION = {
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"name": "__conversational_response",
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"description": (
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@@ -40,26 +59,219 @@ DEFAULT_RESPONSE_FUNCTION = {
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},
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}
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_BM = TypeVar("_BM", bound=BaseModel)
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_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM]]
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_DictOrPydantic = Union[Dict, _BM]
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class OllamaFunctions(BaseChatModel):
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def _is_pydantic_class(obj: Any) -> bool:
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return isinstance(obj, type) and (
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issubclass(obj, BaseModel) or BaseModel in obj.__bases__
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)
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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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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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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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class _AllReturnType(TypedDict):
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raw: BaseMessage
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parsed: Optional[_DictOrPydantic]
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parsing_error: Optional[BaseException]
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def parse_response(message: BaseMessage) -> str:
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"""Extract `function_call` from `AIMessage`."""
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if isinstance(message, AIMessage):
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kwargs = message.additional_kwargs
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if "function_call" in kwargs:
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if "arguments" in kwargs["function_call"]:
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return kwargs["function_call"]["arguments"]
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raise ValueError(
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f"`arguments` missing from `function_call` within AIMessage: {message}"
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)
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raise ValueError(
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"`function_call` missing from `additional_kwargs` "
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f"within AIMessage: {message}"
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)
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raise ValueError(f"`message` is not an instance of `AIMessage`: {message}")
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class OllamaFunctions(ChatOllama):
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"""Function chat model that uses Ollama API."""
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llm: ChatOllama
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tool_system_prompt_template: str = DEFAULT_SYSTEM_TEMPLATE
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tool_system_prompt_template: str
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def __init__(self, **kwargs: Any) -> None:
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super().__init__(**kwargs)
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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values["llm"] = values.get("llm") or ChatOllama(**values, format="json")
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values["tool_system_prompt_template"] = (
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values.get("tool_system_prompt_template") or DEFAULT_SYSTEM_TEMPLATE
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)
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return values
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def bind_tools(
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self,
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tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, BaseMessage]:
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return self.bind(functions=tools, **kwargs)
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@property
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def model(self) -> BaseChatModel:
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"""For backwards compatibility."""
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return self.llm
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@overload
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def with_structured_output(
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self,
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schema: Optional[_DictOrPydanticClass] = None,
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*,
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include_raw: Literal[True] = True,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _AllReturnType]:
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...
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@overload
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def with_structured_output(
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self,
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schema: Optional[_DictOrPydanticClass] = None,
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*,
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include_raw: Literal[False] = False,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _DictOrPydantic]:
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...
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def with_structured_output(
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self,
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schema: Optional[_DictOrPydanticClass] = None,
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*,
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include_raw: bool = False,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _DictOrPydantic]:
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"""Model wrapper that returns outputs formatted to match the given schema.
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Args:
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schema: The output schema as a dict or a Pydantic class. If a Pydantic class
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then the model output will be an object of that class. If a dict then
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the model output will be a dict. With a Pydantic class the returned
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attributes will be validated, whereas with a dict they will not be.
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include_raw: If False then only the parsed structured output is returned. If
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an error occurs during model output parsing it will be raised. If True
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then both the raw model response (a BaseMessage) and the parsed model
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response will be returned. If an error occurs during output parsing it
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will be caught and returned as well. The final output is always a dict
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with keys "raw", "parsed", and "parsing_error".
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Returns:
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A Runnable that takes any ChatModel input and returns as output:
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If include_raw is True then a dict with keys:
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raw: BaseMessage
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parsed: Optional[_DictOrPydantic]
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parsing_error: Optional[BaseException]
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If include_raw is False then just _DictOrPydantic is returned,
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where _DictOrPydantic depends on the schema:
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If schema is a Pydantic class then _DictOrPydantic is the Pydantic
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class.
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If schema is a dict then _DictOrPydantic is a dict.
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Example: Pydantic schema (include_raw=False):
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.. code-block:: python
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from langchain_experimental.llms import OllamaFunctions
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from langchain_core.pydantic_v1 import BaseModel
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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llm = OllamaFunctions(model="phi3", format="json", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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# -> AnswerWithJustification(
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# answer='They weigh the same',
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# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
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# )
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Example: Pydantic schema (include_raw=True):
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.. code-block:: python
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from langchain_experimental.llms import OllamaFunctions
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from langchain_core.pydantic_v1 import BaseModel
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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llm = OllamaFunctions(model="phi3", format="json", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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# -> {
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# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
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# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
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# 'parsing_error': None
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# }
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Example: dict schema (method="include_raw=False):
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.. code-block:: python
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from langchain_experimental.llms import OllamaFunctions, convert_to_ollama_tool
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from langchain_core.pydantic_v1 import BaseModel
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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dict_schema = convert_to_ollama_tool(AnswerWithJustification)
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llm = OllamaFunctions(model="phi3", format="json", temperature=0)
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structured_llm = llm.with_structured_output(dict_schema)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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# -> {
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# 'answer': 'They weigh the same',
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# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
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# }
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""" # noqa: E501
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if kwargs:
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raise ValueError(f"Received unsupported arguments {kwargs}")
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is_pydantic_schema = _is_pydantic_class(schema)
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if schema is None:
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raise ValueError(
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"schema must be specified when method is 'function_calling'. "
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"Received None."
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)
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llm = self.bind_tools(tools=[schema], format="json")
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if is_pydantic_schema:
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output_parser: OutputParserLike = PydanticOutputParser(
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pydantic_object=schema
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)
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else:
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output_parser = JsonOutputParser()
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parser_chain = RunnableLambda(parse_response) | output_parser
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if include_raw:
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parser_assign = RunnablePassthrough.assign(
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parsed=itemgetter("raw") | parser_chain, parsing_error=lambda _: None
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)
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parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
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parser_with_fallback = parser_assign.with_fallbacks(
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[parser_none], exception_key="parsing_error"
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)
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return RunnableMap(raw=llm) | parser_with_fallback
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else:
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return llm | parser_chain
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def _generate(
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self,
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@@ -69,37 +281,41 @@ class OllamaFunctions(BaseChatModel):
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**kwargs: Any,
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) -> ChatResult:
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functions = kwargs.get("functions", [])
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if "functions" in kwargs:
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del kwargs["functions"]
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if "function_call" in kwargs:
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functions = [
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fn for fn in functions if fn["name"] == kwargs["function_call"]["name"]
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]
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if not functions:
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raise ValueError(
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'If "function_call" is specified, you must also pass a matching \
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function in "functions".'
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"If `function_call` is specified, you must also pass a "
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"matching function in `functions`."
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)
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del kwargs["function_call"]
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elif not functions:
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functions.append(DEFAULT_RESPONSE_FUNCTION)
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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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system_message_prompt_template = SystemMessagePromptTemplate.from_template(
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self.tool_system_prompt_template
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)
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system_message = system_message_prompt_template.format(
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tools=json.dumps(functions, indent=2)
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)
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if "functions" in kwargs:
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del kwargs["functions"]
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response_message = self.llm.invoke(
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[system_message] + messages, stop=stop, callbacks=run_manager, **kwargs
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response_message = super()._generate(
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[system_message] + messages, stop=stop, run_manager=run_manager, **kwargs
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)
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chat_generation_content = response_message.content
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chat_generation_content = response_message.generations[0].text
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if not isinstance(chat_generation_content, str):
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raise ValueError("OllamaFunctions does not support non-string output.")
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try:
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parsed_chat_result = json.loads(chat_generation_content)
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except json.JSONDecodeError:
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raise ValueError(
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f'"{self.llm.model}" did not respond with valid JSON. Please try again.'
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f"""'{self.model}' did not respond with valid JSON.
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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_arguments = parsed_chat_result["tool_input"]
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@@ -108,8 +324,8 @@ function in "functions".'
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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.llm.model} \
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output: {chat_generation_content}"
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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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return ChatResult(
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