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https://github.com/hwchase17/langchain.git
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181bb91ce0
The Ollama chat model adapter does not support all of the possible message content formats. That leads to Ollama model adapter crashing on some messages from different models (e.g. Gemini 2.5 Flash). These changes should fix one known scenario - when `content` is a list containing a string.
1392 lines
57 KiB
Python
1392 lines
57 KiB
Python
"""Ollama chat models."""
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from __future__ import annotations
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import ast
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import json
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import logging
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from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
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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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Literal,
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Optional,
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Union,
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cast,
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)
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from uuid import uuid4
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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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AIMessage,
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AIMessageChunk,
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BaseMessage,
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ChatMessage,
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HumanMessage,
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SystemMessage,
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ToolCall,
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ToolMessage,
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is_data_content_block,
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)
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from langchain_core.messages.ai import UsageMetadata
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from langchain_core.messages.tool import tool_call
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from langchain_core.output_parsers import (
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JsonOutputKeyToolsParser,
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JsonOutputParser,
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PydanticOutputParser,
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PydanticToolsParser,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
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from langchain_core.tools import BaseTool
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from langchain_core.utils.function_calling import (
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convert_to_json_schema,
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convert_to_openai_tool,
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)
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from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
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from ollama import AsyncClient, Client, Message, Options
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from pydantic import BaseModel, PrivateAttr, model_validator
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from pydantic.json_schema import JsonSchemaValue
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from pydantic.v1 import BaseModel as BaseModelV1
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from typing_extensions import Self, is_typeddict
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from ._utils import validate_model
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log = logging.getLogger(__name__)
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def _get_usage_metadata_from_generation_info(
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generation_info: Optional[Mapping[str, Any]],
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) -> Optional[UsageMetadata]:
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"""Get usage metadata from ollama generation info mapping."""
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if generation_info is None:
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return None
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input_tokens: Optional[int] = generation_info.get("prompt_eval_count")
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output_tokens: Optional[int] = generation_info.get("eval_count")
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if input_tokens is not None and output_tokens is not None:
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return UsageMetadata(
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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total_tokens=input_tokens + output_tokens,
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)
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return None
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def _parse_json_string(
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json_string: str,
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*,
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raw_tool_call: dict[str, Any],
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skip: bool,
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) -> Any:
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"""Attempt to parse a JSON string for tool calling.
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It first tries to use the standard ``json.loads``. If that fails, it falls
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back to ``ast.literal_eval`` to safely parse Python literals, which is more
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robust against models using single quotes or containing apostrophes.
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Args:
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json_string: JSON string to parse.
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raw_tool_call: Raw tool call to include in error message.
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skip: Whether to ignore parsing errors and return the value anyways.
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Returns:
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The parsed JSON string or Python literal.
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Raises:
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OutputParserException: If the string is 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:
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try:
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# Use ast.literal_eval to safely parse Python-style dicts
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# (e.g. with single quotes)
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return ast.literal_eval(json_string)
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except (SyntaxError, ValueError) as e:
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# If both fail, and we're not skipping, raise an informative error.
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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']}"
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"\n\nare not valid JSON or a Python literal. "
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f"Received error: {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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function_name = raw_tool_call["function"]["name"]
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arguments = raw_tool_call["function"]["arguments"]
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parsed_arguments: dict = {}
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if isinstance(arguments, dict):
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for key, value in arguments.items():
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# Filter out metadata fields like 'functionName' that echo function name
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if key == "functionName" and value == function_name:
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continue
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if isinstance(value, str):
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parsed_value = _parse_json_string(
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value, skip=True, raw_tool_call=raw_tool_call
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)
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if isinstance(parsed_value, (dict, list)):
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parsed_arguments[key] = parsed_value
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else:
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parsed_arguments[key] = value
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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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"""Get tool calls from ollama response."""
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tool_calls = []
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if "message" in response and (
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raw_tool_calls := response["message"].get("tool_calls")
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):
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tool_calls.extend(
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[
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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=_parse_arguments_from_tool_call(tc) or {},
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)
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for tc in raw_tool_calls
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]
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)
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return tool_calls
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def _lc_tool_call_to_openai_tool_call(tool_call_: ToolCall) -> dict:
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"""Convert a LangChain tool call to an OpenAI tool call format."""
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return {
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"type": "function",
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"id": tool_call_["id"],
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"function": {
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"name": tool_call_["name"],
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"arguments": tool_call_["args"],
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},
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}
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def _get_image_from_data_content_block(block: dict) -> str:
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"""Format standard data content block to format expected by Ollama."""
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if block["type"] == "image":
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if block["source_type"] == "base64":
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return block["data"]
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error_message = "Image data only supported through in-line base64 format."
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raise ValueError(error_message)
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error_message = f"Blocks of type {block['type']} not supported."
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raise ValueError(error_message)
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def _is_pydantic_class(obj: Any) -> bool:
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return isinstance(obj, type) and is_basemodel_subclass(obj)
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class ChatOllama(BaseChatModel):
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r"""Ollama chat model integration.
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.. dropdown:: Setup
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:open:
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Install ``langchain-ollama`` and download any models you want to use from ollama.
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.. code-block:: bash
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ollama pull gpt-oss:20b
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pip install -U langchain-ollama
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Key init args — completion params:
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model: str
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Name of Ollama model to use.
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reasoning: Optional[bool]
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Controls the reasoning/thinking mode for
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`supported models <https://ollama.com/search?c=thinking>`__.
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- ``True``: Enables reasoning mode. The model's reasoning process will be
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captured and returned separately in the ``additional_kwargs`` of the
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response message, under ``reasoning_content``. The main response
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content will not include the reasoning tags.
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- ``False``: Disables reasoning mode. The model will not perform any reasoning,
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and the response will not include any reasoning content.
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- ``None`` (Default): The model will use its default reasoning behavior. Note
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however, if the model's default behavior *is* to perform reasoning, think tags
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(``<think>`` and ``</think>``) will be present within the main response content
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unless you set ``reasoning`` to ``True``.
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temperature: float
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Sampling temperature. Ranges from ``0.0`` to ``1.0``.
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num_predict: Optional[int]
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Max number of tokens to generate.
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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.. code-block:: python
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from langchain_ollama import ChatOllama
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llm = ChatOllama(
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model = "gpt-oss:20b",
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validate_model_on_init = True,
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temperature = 0.8,
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num_predict = 256,
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# other params ...
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)
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Invoke:
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.. code-block:: python
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messages = [
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("system", "You are a helpful translator. Translate the user sentence to French."),
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("human", "I love programming."),
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]
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llm.invoke(messages)
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.. code-block:: python
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AIMessage(content='J'adore le programmation. (Note: "programming" can also refer to the act of writing code, so if you meant that, I could translate it as "J'adore programmer". But since you didn\'t specify, I assumed you were talking about the activity itself, which is what "le programmation" usually refers to.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:37:50.182604Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 3576619666, 'load_duration': 788524916, 'prompt_eval_count': 32, 'prompt_eval_duration': 128125000, 'eval_count': 71, 'eval_duration': 2656556000}, id='run-ba48f958-6402-41a5-b461-5e250a4ebd36-0')
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Stream:
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.. code-block:: python
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for chunk in llm.stream("Return the words Hello World!"):
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print(chunk.text(), end="")
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.. code-block:: python
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content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
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content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
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content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
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content='' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:39:42.274449Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 411875125, 'load_duration': 1898166, 'prompt_eval_count': 14, 'prompt_eval_duration': 297320000, 'eval_count': 4, 'eval_duration': 111099000} id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
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.. code-block:: python
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stream = llm.stream(messages)
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full = next(stream)
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for chunk in stream:
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full += chunk
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full
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.. code-block:: python
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AIMessageChunk(content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:38:54.933154Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1977300042, 'load_duration': 1345709, 'prompt_eval_duration': 159343000, 'eval_count': 47, 'eval_duration': 1815123000}, id='run-3c81a3ed-3e79-4dd3-a796-04064d804890')
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Async:
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.. code-block:: python
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await llm.ainvoke("Hello how are you!")
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.. code-block:: python
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AIMessage(content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:52:08.165478Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2138492875, 'load_duration': 1364000, 'prompt_eval_count': 10, 'prompt_eval_duration': 297081000, 'eval_count': 47, 'eval_duration': 1838524000}, id='run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0')
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.. code-block:: python
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async for chunk in llm.astream("Say hello world!"):
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print(chunk.content)
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.. code-block:: python
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HEL
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LO
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WORLD
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!
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.. code-block:: python
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messages = [
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("human", "Say hello world!"),
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("human","Say goodbye world!")
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]
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await llm.abatch(messages)
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.. code-block:: python
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[AIMessage(content='HELLO, WORLD!', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.315396Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1696745458, 'load_duration': 1505000, 'prompt_eval_count': 8, 'prompt_eval_duration': 111627000, 'eval_count': 6, 'eval_duration': 185181000}, id='run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0'),
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AIMessage(content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.018076Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1399391083, 'load_duration': 1187417, 'prompt_eval_count': 20, 'prompt_eval_duration': 230349000, 'eval_count': 31, 'eval_duration': 1166047000}, id='run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0')]
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JSON mode:
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.. code-block:: python
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json_llm = ChatOllama(format="json")
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llm.invoke("Return a query for the weather in a random location and time of day with two keys: location and time_of_day. Respond using JSON only.").content
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.. code-block:: python
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'{"location": "Pune, India", "time_of_day": "morning"}'
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Tool Calling:
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.. code-block:: python
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from langchain_ollama import ChatOllama
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from pydantic import BaseModel, Field
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class Multiply(BaseModel):
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a: int = Field(..., description="First integer")
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b: int = Field(..., description="Second integer")
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ans = await chat.invoke("What is 45*67")
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ans.tool_calls
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.. code-block:: python
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[{'name': 'Multiply',
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'args': {'a': 45, 'b': 67},
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'id': '420c3f3b-df10-4188-945f-eb3abdb40622',
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'type': 'tool_call'}]
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Thinking / Reasoning:
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You can enable reasoning mode for models that support it by setting
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the ``reasoning`` parameter to ``True`` in either the constructor or
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the ``invoke``/``stream`` methods. This will enable the model to think
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through the problem and return the reasoning process separately in the
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``additional_kwargs`` of the response message, under ``reasoning_content``.
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If ``reasoning`` is set to ``None``, the model will use its default reasoning
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behavior, and any reasoning content will *not* be captured under the
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``reasoning_content`` key, but will be present within the main response content
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as think tags (``<think>`` and ``</think>``).
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.. note::
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This feature is only available for `models that support reasoning <https://ollama.com/search?c=thinking>`__.
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.. code-block:: python
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from langchain_ollama import ChatOllama
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llm = ChatOllama(
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model = "deepseek-r1:8b",
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validate_model_on_init = True,
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reasoning= True,
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)
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llm.invoke("how many r in the word strawberry?")
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# or, on an invocation basis:
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llm.invoke("how many r in the word strawberry?", reasoning=True)
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# or llm.stream("how many r in the word strawberry?", reasoning=True)
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# If not provided, the invocation will default to the ChatOllama reasoning
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# param provided (None by default).
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.. code-block:: python
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AIMessage(content='The word "strawberry" contains **three \'r\' letters**. Here\'s a breakdown for clarity:\n\n- The spelling of "strawberry" has two parts ... be 3.\n\nTo be thorough, let\'s confirm with an online source or common knowledge.\n\nI can recall that "strawberry" has: s-t-r-a-w-b-e-r-r-y — yes, three r\'s.\n\nPerhaps it\'s misspelled by some, but standard is correct.\n\nSo I think the response should be 3.\n'}, response_metadata={'model': 'deepseek-r1:8b', 'created_at': '2025-07-08T19:33:55.891269Z', 'done': True, 'done_reason': 'stop', 'total_duration': 98232561292, 'load_duration': 28036792, 'prompt_eval_count': 10, 'prompt_eval_duration': 40171834, 'eval_count': 3615, 'eval_duration': 98163832416, 'model_name': 'deepseek-r1:8b'}, id='run--18f8269f-6a35-4a7c-826d-b89d52c753b3-0', usage_metadata={'input_tokens': 10, 'output_tokens': 3615, 'total_tokens': 3625})
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""" # noqa: E501, pylint: disable=line-too-long
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model: str
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"""Model name to use."""
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reasoning: Optional[Union[bool, str]] = None
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"""Controls the reasoning/thinking mode for
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`supported models <https://ollama.com/search?c=thinking>`__.
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- ``True``: Enables reasoning mode. The model's reasoning process will be
|
|
captured and returned separately in the ``additional_kwargs`` of the
|
|
response message, under ``reasoning_content``. The main response
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|
content will not include the reasoning tags.
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- ``False``: Disables reasoning mode. The model will not perform any reasoning,
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|
and the response will not include any reasoning content.
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|
- ``None`` (Default): The model will use its default reasoning behavior. Note
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|
however, if the model's default behavior *is* to perform reasoning, think tags
|
|
()``<think>`` and ``</think>``) will be present within the main response content
|
|
unless you set ``reasoning`` to ``True``.
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- ``str``: e.g. ``'low'``, ``'medium'``, ``'high'``. Enables reasoning with a custom
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intensity level. Currently, this is only supported ``gpt-oss``. See the
|
|
`Ollama docs <https://github.com/ollama/ollama-python/blob/da79e987f0ac0a4986bf396f043b36ef840370bc/ollama/_types.py#L210>`__
|
|
for more information.
|
|
|
|
"""
|
|
|
|
validate_model_on_init: bool = False
|
|
"""Whether to validate the model exists in Ollama locally on initialization.
|
|
|
|
.. versionadded:: 0.3.4
|
|
"""
|
|
|
|
mirostat: Optional[int] = None
|
|
"""Enable Mirostat sampling for controlling perplexity.
|
|
(default: ``0``, ``0`` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)"""
|
|
|
|
mirostat_eta: Optional[float] = None
|
|
"""Influences how quickly the algorithm responds to feedback
|
|
from the generated text. A lower learning rate will result in
|
|
slower adjustments, while a higher learning rate will make
|
|
the algorithm more responsive. (Default: ``0.1``)"""
|
|
|
|
mirostat_tau: Optional[float] = None
|
|
"""Controls the balance between coherence and diversity
|
|
of the output. A lower value will result in more focused and
|
|
coherent text. (Default: ``5.0``)"""
|
|
|
|
num_ctx: Optional[int] = None
|
|
"""Sets the size of the context window used to generate the
|
|
next token. (Default: ``2048``) """
|
|
|
|
num_gpu: Optional[int] = None
|
|
"""The number of GPUs to use. On macOS it defaults to ``1`` to
|
|
enable metal support, ``0`` to disable."""
|
|
|
|
num_thread: Optional[int] = None
|
|
"""Sets the number of threads to use during computation.
|
|
By default, Ollama will detect this for optimal performance.
|
|
It is recommended to set this value to the number of physical
|
|
CPU cores your system has (as opposed to the logical number of cores)."""
|
|
|
|
num_predict: Optional[int] = None
|
|
"""Maximum number of tokens to predict when generating text.
|
|
(Default: ``128``, ``-1`` = infinite generation, ``-2`` = fill context)"""
|
|
|
|
repeat_last_n: Optional[int] = None
|
|
"""Sets how far back for the model to look back to prevent
|
|
repetition. (Default: ``64``, ``0`` = disabled, ``-1`` = ``num_ctx``)"""
|
|
|
|
repeat_penalty: Optional[float] = None
|
|
"""Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``)
|
|
will penalize repetitions more strongly, while a lower value (e.g., ``0.9``)
|
|
will be more lenient. (Default: ``1.1``)"""
|
|
|
|
temperature: Optional[float] = None
|
|
"""The temperature of the model. Increasing the temperature will
|
|
make the model answer more creatively. (Default: ``0.8``)"""
|
|
|
|
seed: Optional[int] = None
|
|
"""Sets the random number seed to use for generation. Setting this
|
|
to a specific number will make the model generate the same text for
|
|
the same prompt."""
|
|
|
|
stop: Optional[list[str]] = None
|
|
"""Sets the stop tokens to use."""
|
|
|
|
tfs_z: Optional[float] = None
|
|
"""Tail free sampling is used to reduce the impact of less probable
|
|
tokens from the output. A higher value (e.g., ``2.0``) will reduce the
|
|
impact more, while a value of ``1.0`` disables this setting. (default: ``1``)"""
|
|
|
|
top_k: Optional[int] = None
|
|
"""Reduces the probability of generating nonsense. A higher value (e.g. ``100``)
|
|
will give more diverse answers, while a lower value (e.g. ``10``)
|
|
will be more conservative. (Default: ``40``)"""
|
|
|
|
top_p: Optional[float] = None
|
|
"""Works together with top-k. A higher value (e.g., ``0.95``) will lead
|
|
to more diverse text, while a lower value (e.g., ``0.5``) will
|
|
generate more focused and conservative text. (Default: ``0.9``)"""
|
|
|
|
format: Optional[Union[Literal["", "json"], JsonSchemaValue]] = None
|
|
"""Specify the format of the output (options: ``'json'``, JSON schema)."""
|
|
|
|
keep_alive: Optional[Union[int, str]] = None
|
|
"""How long the model will stay loaded into memory."""
|
|
|
|
base_url: Optional[str] = None
|
|
"""Base url the model is hosted under."""
|
|
|
|
client_kwargs: Optional[dict] = {}
|
|
"""Additional kwargs to pass to the httpx clients.
|
|
|
|
These arguments are passed to both synchronous and async clients.
|
|
|
|
Use ``sync_client_kwargs`` and ``async_client_kwargs`` to pass different arguments
|
|
to synchronous and asynchronous clients.
|
|
|
|
"""
|
|
|
|
async_client_kwargs: Optional[dict] = {}
|
|
"""Additional kwargs to merge with ``client_kwargs`` before
|
|
passing to the httpx AsyncClient.
|
|
|
|
`Full list of params. <https://www.python-httpx.org/api/#asyncclient>`__
|
|
|
|
"""
|
|
|
|
sync_client_kwargs: Optional[dict] = {}
|
|
"""Additional kwargs to merge with ``client_kwargs`` before
|
|
passing to the httpx Client.
|
|
|
|
`Full list of params. <https://www.python-httpx.org/api/#client>`__
|
|
|
|
"""
|
|
|
|
_client: Client = PrivateAttr()
|
|
"""The client to use for making requests."""
|
|
|
|
_async_client: AsyncClient = PrivateAttr()
|
|
"""The async client to use for making requests."""
|
|
|
|
def _chat_params(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
**kwargs: Any,
|
|
) -> dict[str, Any]:
|
|
ollama_messages = self._convert_messages_to_ollama_messages(messages)
|
|
|
|
if self.stop is not None and stop is not None:
|
|
msg = "`stop` found in both the input and default params."
|
|
raise ValueError(msg)
|
|
if self.stop is not None:
|
|
stop = self.stop
|
|
|
|
options_dict = kwargs.pop(
|
|
"options",
|
|
{
|
|
"mirostat": self.mirostat,
|
|
"mirostat_eta": self.mirostat_eta,
|
|
"mirostat_tau": self.mirostat_tau,
|
|
"num_ctx": self.num_ctx,
|
|
"num_gpu": self.num_gpu,
|
|
"num_thread": self.num_thread,
|
|
"num_predict": self.num_predict,
|
|
"repeat_last_n": self.repeat_last_n,
|
|
"repeat_penalty": self.repeat_penalty,
|
|
"temperature": self.temperature,
|
|
"seed": self.seed,
|
|
"stop": self.stop if stop is None else stop,
|
|
"tfs_z": self.tfs_z,
|
|
"top_k": self.top_k,
|
|
"top_p": self.top_p,
|
|
},
|
|
)
|
|
|
|
params = {
|
|
"messages": ollama_messages,
|
|
"stream": kwargs.pop("stream", True),
|
|
"model": kwargs.pop("model", self.model),
|
|
"think": kwargs.pop("reasoning", self.reasoning),
|
|
"format": kwargs.pop("format", self.format),
|
|
"options": Options(**options_dict),
|
|
"keep_alive": kwargs.pop("keep_alive", self.keep_alive),
|
|
**kwargs,
|
|
}
|
|
|
|
if tools := kwargs.get("tools"):
|
|
params["tools"] = tools
|
|
|
|
return params
|
|
|
|
@model_validator(mode="after")
|
|
def _set_clients(self) -> Self:
|
|
"""Set clients to use for ollama."""
|
|
client_kwargs = self.client_kwargs or {}
|
|
|
|
sync_client_kwargs = client_kwargs
|
|
if self.sync_client_kwargs:
|
|
sync_client_kwargs = {**sync_client_kwargs, **self.sync_client_kwargs}
|
|
|
|
async_client_kwargs = client_kwargs
|
|
if self.async_client_kwargs:
|
|
async_client_kwargs = {**async_client_kwargs, **self.async_client_kwargs}
|
|
|
|
self._client = Client(host=self.base_url, **sync_client_kwargs)
|
|
self._async_client = AsyncClient(host=self.base_url, **async_client_kwargs)
|
|
if self.validate_model_on_init:
|
|
validate_model(self._client, self.model)
|
|
return self
|
|
|
|
def _convert_messages_to_ollama_messages(
|
|
self, messages: list[BaseMessage]
|
|
) -> Sequence[Message]:
|
|
ollama_messages: list = []
|
|
for message in messages:
|
|
role: str
|
|
tool_call_id: Optional[str] = None
|
|
tool_calls: Optional[list[dict[str, Any]]] = None
|
|
if isinstance(message, HumanMessage):
|
|
role = "user"
|
|
elif isinstance(message, AIMessage):
|
|
role = "assistant"
|
|
tool_calls = (
|
|
[
|
|
_lc_tool_call_to_openai_tool_call(tool_call)
|
|
for tool_call in message.tool_calls
|
|
]
|
|
if message.tool_calls
|
|
else None
|
|
)
|
|
elif isinstance(message, SystemMessage):
|
|
role = "system"
|
|
elif isinstance(message, ChatMessage):
|
|
role = message.role
|
|
elif isinstance(message, ToolMessage):
|
|
role = "tool"
|
|
tool_call_id = message.tool_call_id
|
|
else:
|
|
msg = "Received unsupported message type for Ollama."
|
|
raise ValueError(msg)
|
|
|
|
content = ""
|
|
images = []
|
|
if isinstance(message.content, str):
|
|
content = message.content
|
|
else:
|
|
for content_part in message.content:
|
|
if isinstance(content_part, str):
|
|
content += f"\n{content_part}"
|
|
elif content_part.get("type") == "text":
|
|
content += f"\n{content_part['text']}"
|
|
elif content_part.get("type") == "tool_use":
|
|
continue
|
|
elif content_part.get("type") == "image_url":
|
|
image_url = None
|
|
temp_image_url = content_part.get("image_url")
|
|
if isinstance(temp_image_url, str):
|
|
image_url = temp_image_url
|
|
elif (
|
|
isinstance(temp_image_url, dict)
|
|
and "url" in temp_image_url
|
|
and isinstance(temp_image_url["url"], str)
|
|
):
|
|
image_url = temp_image_url["url"]
|
|
else:
|
|
msg = (
|
|
"Only string image_url or dict with string 'url' "
|
|
"inside content parts are supported."
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
image_url_components = image_url.split(",")
|
|
# Support data:image/jpeg;base64,<image> format
|
|
# and base64 strings
|
|
if len(image_url_components) > 1:
|
|
images.append(image_url_components[1])
|
|
else:
|
|
images.append(image_url_components[0])
|
|
elif is_data_content_block(content_part):
|
|
image = _get_image_from_data_content_block(content_part)
|
|
images.append(image)
|
|
else:
|
|
msg = (
|
|
"Unsupported message content type. "
|
|
"Must either have type 'text' or type 'image_url' "
|
|
"with a string 'image_url' field."
|
|
)
|
|
raise ValueError(msg)
|
|
# Should convert to ollama.Message once role includes tool,
|
|
# and tool_call_id is in Message
|
|
msg_: dict = {
|
|
"role": role,
|
|
"content": content,
|
|
"images": images,
|
|
}
|
|
if tool_calls:
|
|
msg_["tool_calls"] = tool_calls
|
|
if tool_call_id:
|
|
msg_["tool_call_id"] = tool_call_id
|
|
ollama_messages.append(msg_)
|
|
|
|
return ollama_messages
|
|
|
|
async def _acreate_chat_stream(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
|
|
chat_params = self._chat_params(messages, stop, **kwargs)
|
|
|
|
if chat_params["stream"]:
|
|
async for part in await self._async_client.chat(**chat_params):
|
|
yield part
|
|
else:
|
|
yield await self._async_client.chat(**chat_params)
|
|
|
|
def _create_chat_stream(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[Union[Mapping[str, Any], str]]:
|
|
chat_params = self._chat_params(messages, stop, **kwargs)
|
|
|
|
if chat_params["stream"]:
|
|
if self._client:
|
|
yield from self._client.chat(**chat_params)
|
|
else:
|
|
if self._client:
|
|
yield self._client.chat(**chat_params)
|
|
|
|
def _chat_stream_with_aggregation(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
verbose: bool = False, # noqa: FBT001, FBT002
|
|
**kwargs: Any,
|
|
) -> ChatGenerationChunk:
|
|
final_chunk = None
|
|
for chunk in self._iterate_over_stream(messages, stop, **kwargs):
|
|
if final_chunk is None:
|
|
final_chunk = chunk
|
|
else:
|
|
final_chunk += chunk
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(
|
|
chunk.text,
|
|
chunk=chunk,
|
|
verbose=verbose,
|
|
)
|
|
if final_chunk is None:
|
|
msg = "No data received from Ollama stream."
|
|
raise ValueError(msg)
|
|
|
|
return final_chunk
|
|
|
|
async def _achat_stream_with_aggregation(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
verbose: bool = False, # noqa: FBT001, FBT002
|
|
**kwargs: Any,
|
|
) -> ChatGenerationChunk:
|
|
final_chunk = None
|
|
async for chunk in self._aiterate_over_stream(messages, stop, **kwargs):
|
|
if final_chunk is None:
|
|
final_chunk = chunk
|
|
else:
|
|
final_chunk += chunk
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(
|
|
chunk.text,
|
|
chunk=chunk,
|
|
verbose=verbose,
|
|
)
|
|
if final_chunk is None:
|
|
msg = "No data received from Ollama stream."
|
|
raise ValueError(msg)
|
|
|
|
return final_chunk
|
|
|
|
def _get_ls_params(
|
|
self, stop: Optional[list[str]] = None, **kwargs: Any
|
|
) -> LangSmithParams:
|
|
"""Get standard params for tracing."""
|
|
params = self._get_invocation_params(stop=stop, **kwargs)
|
|
ls_params = LangSmithParams(
|
|
ls_provider="ollama",
|
|
ls_model_name=self.model,
|
|
ls_model_type="chat",
|
|
ls_temperature=params.get("temperature", self.temperature),
|
|
)
|
|
if ls_stop := stop or params.get("stop", None) or self.stop:
|
|
ls_params["ls_stop"] = ls_stop
|
|
return ls_params
|
|
|
|
def _generate(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
final_chunk = self._chat_stream_with_aggregation(
|
|
messages, stop, run_manager, verbose=self.verbose, **kwargs
|
|
)
|
|
generation_info = final_chunk.generation_info
|
|
chat_generation = ChatGeneration(
|
|
message=AIMessage(
|
|
content=final_chunk.text,
|
|
usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
|
|
tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
|
|
additional_kwargs=final_chunk.message.additional_kwargs,
|
|
),
|
|
generation_info=generation_info,
|
|
)
|
|
return ChatResult(generations=[chat_generation])
|
|
|
|
def _iterate_over_stream(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[ChatGenerationChunk]:
|
|
reasoning = kwargs.get("reasoning", self.reasoning)
|
|
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
|
|
if not isinstance(stream_resp, str):
|
|
content = (
|
|
stream_resp["message"]["content"]
|
|
if "message" in stream_resp and "content" in stream_resp["message"]
|
|
else ""
|
|
)
|
|
|
|
# Warn and skip responses with done_reason: 'load' and empty content
|
|
# These indicate the model was loaded but no actual generation occurred
|
|
is_load_response_with_empty_content = (
|
|
stream_resp.get("done") is True
|
|
and stream_resp.get("done_reason") == "load"
|
|
and not content.strip()
|
|
)
|
|
|
|
if is_load_response_with_empty_content:
|
|
log.warning(
|
|
"Ollama returned empty response with done_reason='load'."
|
|
"This typically indicates the model was loaded but no content "
|
|
"was generated. Skipping this response."
|
|
)
|
|
continue
|
|
|
|
if stream_resp.get("done") is True:
|
|
generation_info = dict(stream_resp)
|
|
if "model" in generation_info:
|
|
generation_info["model_name"] = generation_info["model"]
|
|
_ = generation_info.pop("message", None)
|
|
else:
|
|
generation_info = None
|
|
|
|
additional_kwargs = {}
|
|
if (
|
|
reasoning
|
|
and "message" in stream_resp
|
|
and (thinking_content := stream_resp["message"].get("thinking"))
|
|
):
|
|
additional_kwargs["reasoning_content"] = thinking_content
|
|
|
|
chunk = ChatGenerationChunk(
|
|
message=AIMessageChunk(
|
|
content=content,
|
|
additional_kwargs=additional_kwargs,
|
|
usage_metadata=_get_usage_metadata_from_generation_info(
|
|
stream_resp
|
|
),
|
|
tool_calls=_get_tool_calls_from_response(stream_resp),
|
|
),
|
|
generation_info=generation_info,
|
|
)
|
|
|
|
yield chunk
|
|
|
|
def _stream(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[ChatGenerationChunk]:
|
|
for chunk in self._iterate_over_stream(messages, stop, **kwargs):
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(
|
|
chunk.text,
|
|
verbose=self.verbose,
|
|
)
|
|
yield chunk
|
|
|
|
async def _aiterate_over_stream(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[ChatGenerationChunk]:
|
|
reasoning = kwargs.get("reasoning", self.reasoning)
|
|
async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
|
|
if not isinstance(stream_resp, str):
|
|
content = (
|
|
stream_resp["message"]["content"]
|
|
if "message" in stream_resp and "content" in stream_resp["message"]
|
|
else ""
|
|
)
|
|
|
|
# Warn and skip responses with done_reason: 'load' and empty content
|
|
# These indicate the model was loaded but no actual generation occurred
|
|
is_load_response_with_empty_content = (
|
|
stream_resp.get("done") is True
|
|
and stream_resp.get("done_reason") == "load"
|
|
and not content.strip()
|
|
)
|
|
|
|
if is_load_response_with_empty_content:
|
|
log.warning(
|
|
"Ollama returned empty response with done_reason='load'. "
|
|
"This typically indicates the model was loaded but no content "
|
|
"was generated. Skipping this response."
|
|
)
|
|
continue
|
|
|
|
if stream_resp.get("done") is True:
|
|
generation_info = dict(stream_resp)
|
|
if "model" in generation_info:
|
|
generation_info["model_name"] = generation_info["model"]
|
|
_ = generation_info.pop("message", None)
|
|
else:
|
|
generation_info = None
|
|
|
|
additional_kwargs = {}
|
|
if (
|
|
reasoning
|
|
and "message" in stream_resp
|
|
and (thinking_content := stream_resp["message"].get("thinking"))
|
|
):
|
|
additional_kwargs["reasoning_content"] = thinking_content
|
|
|
|
chunk = ChatGenerationChunk(
|
|
message=AIMessageChunk(
|
|
content=content,
|
|
additional_kwargs=additional_kwargs,
|
|
usage_metadata=_get_usage_metadata_from_generation_info(
|
|
stream_resp
|
|
),
|
|
tool_calls=_get_tool_calls_from_response(stream_resp),
|
|
),
|
|
generation_info=generation_info,
|
|
)
|
|
|
|
yield chunk
|
|
|
|
async def _astream(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[ChatGenerationChunk]:
|
|
async for chunk in self._aiterate_over_stream(messages, stop, **kwargs):
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(
|
|
chunk.text,
|
|
verbose=self.verbose,
|
|
)
|
|
yield chunk
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: list[BaseMessage],
|
|
stop: Optional[list[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
final_chunk = await self._achat_stream_with_aggregation(
|
|
messages, stop, run_manager, verbose=self.verbose, **kwargs
|
|
)
|
|
generation_info = final_chunk.generation_info
|
|
chat_generation = ChatGeneration(
|
|
message=AIMessage(
|
|
content=final_chunk.text,
|
|
usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
|
|
tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
|
|
additional_kwargs=final_chunk.message.additional_kwargs,
|
|
),
|
|
generation_info=generation_info,
|
|
)
|
|
return ChatResult(generations=[chat_generation])
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of chat model."""
|
|
return "chat-ollama"
|
|
|
|
def bind_tools(
|
|
self,
|
|
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
|
|
*,
|
|
tool_choice: Optional[Union[dict, str, Literal["auto", "any"], bool]] = None, # noqa: PYI051
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind tool-like objects to this chat model.
|
|
|
|
Assumes model is compatible with OpenAI tool-calling API.
|
|
|
|
Args:
|
|
tools: A list of tool definitions to bind to this chat model.
|
|
Supports any tool definition handled by
|
|
:meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
|
|
tool_choice: If provided, which tool for model to call. **This parameter
|
|
is currently ignored as it is not supported by Ollama.**
|
|
kwargs: Any additional parameters are passed directly to
|
|
``self.bind(**kwargs)``.
|
|
"""
|
|
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
|
return super().bind(tools=formatted_tools, **kwargs)
|
|
|
|
def with_structured_output(
|
|
self,
|
|
schema: Union[dict, type],
|
|
*,
|
|
method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
|
|
include_raw: bool = False,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
|
|
"""Model wrapper that returns outputs formatted to match the given schema.
|
|
|
|
Args:
|
|
schema: The output schema. Can be passed in as:
|
|
|
|
- a Pydantic class,
|
|
- a JSON schema
|
|
- a TypedDict class
|
|
- an OpenAI function/tool schema.
|
|
|
|
If ``schema`` is a Pydantic class then the model output will be a
|
|
Pydantic instance of that class, and the model-generated fields will be
|
|
validated by the Pydantic class. Otherwise the model output will be a
|
|
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
|
|
for more on how to properly specify types and descriptions of
|
|
schema fields when specifying a Pydantic or TypedDict class.
|
|
|
|
method: The method for steering model generation, one of:
|
|
|
|
- ``'json_schema'``:
|
|
Uses Ollama's `structured output API <https://ollama.com/blog/structured-outputs>`__
|
|
- ``'function_calling'``:
|
|
Uses Ollama's tool-calling API
|
|
- ``'json_mode'``:
|
|
Specifies ``format='json'``. Note that if using JSON mode then you
|
|
must include instructions for formatting the output into the
|
|
desired schema into the model call.
|
|
|
|
include_raw:
|
|
If False then only the parsed structured output is returned. If
|
|
an error occurs during model output parsing it will be raised. If True
|
|
then both the raw model response (a ``BaseMessage``) and the parsed model
|
|
response will be returned. If an error occurs during output parsing it
|
|
will be caught and returned as well. The final output is always a dict
|
|
with keys ``'raw'``, ``'parsed'``, and ``'parsing_error'``.
|
|
|
|
kwargs: Additional keyword args aren't supported.
|
|
|
|
Returns:
|
|
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
|
|
|
|
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
|
|
|
|
If ``include_raw`` is True, then Runnable outputs a dict with keys:
|
|
|
|
- ``'raw'``: ``BaseMessage``
|
|
- ``'parsed'``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
|
|
- ``'parsing_error'``: Optional[BaseException]
|
|
|
|
.. versionchanged:: 0.2.2
|
|
|
|
Added support for structured output API via ``format`` parameter.
|
|
|
|
.. versionchanged:: 0.3.0
|
|
|
|
Updated default ``method`` to ``'json_schema'``.
|
|
|
|
.. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=False
|
|
|
|
.. code-block:: python
|
|
|
|
from typing import Optional
|
|
|
|
from langchain_ollama import ChatOllama
|
|
from pydantic import BaseModel, Field
|
|
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
|
|
answer: str
|
|
justification: Optional[str] = Field(
|
|
default=..., description="A justification for the answer."
|
|
)
|
|
|
|
|
|
llm = ChatOllama(model="llama3.1", temperature=0)
|
|
structured_llm = llm.with_structured_output(
|
|
AnswerWithJustification
|
|
)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
|
|
# -> 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.'
|
|
# )
|
|
|
|
.. dropdown:: Example: ``schema=Pydantic`` class, ``method='json_schema'``, ``include_raw=True``
|
|
|
|
.. code-block:: python
|
|
|
|
from langchain_ollama import ChatOllama
|
|
from pydantic import BaseModel
|
|
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
|
|
answer: str
|
|
justification: str
|
|
|
|
|
|
llm = ChatOllama(model="llama3.1", temperature=0)
|
|
structured_llm = llm.with_structured_output(
|
|
AnswerWithJustification, include_raw=True
|
|
)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
# -> {
|
|
# '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'}]}),
|
|
# '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.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
.. dropdown:: Example: ``schema=Pydantic`` class, ``method='function_calling'``, ``include_raw=False``
|
|
|
|
.. code-block:: python
|
|
|
|
from typing import Optional
|
|
|
|
from langchain_ollama import ChatOllama
|
|
from pydantic import BaseModel, Field
|
|
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
|
|
answer: str
|
|
justification: Optional[str] = Field(
|
|
default=..., description="A justification for the answer."
|
|
)
|
|
|
|
|
|
llm = ChatOllama(model="llama3.1", temperature=0)
|
|
structured_llm = llm.with_structured_output(
|
|
AnswerWithJustification, method="function_calling"
|
|
)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
|
|
# -> 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.'
|
|
# )
|
|
|
|
.. dropdown:: Example: schema=TypedDict class, method="function_calling", include_raw=False
|
|
|
|
.. code-block:: python
|
|
|
|
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
|
|
# from typing_extensions, not from typing.
|
|
from typing_extensions import Annotated, TypedDict
|
|
|
|
from langchain_ollama import ChatOllama
|
|
|
|
|
|
class AnswerWithJustification(TypedDict):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
|
|
answer: str
|
|
justification: Annotated[
|
|
Optional[str], None, "A justification for the answer."
|
|
]
|
|
|
|
|
|
llm = ChatOllama(model="llama3.1", temperature=0)
|
|
structured_llm = llm.with_structured_output(AnswerWithJustification)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
# -> {
|
|
# '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 and density of the two substances differ.'
|
|
# }
|
|
|
|
.. dropdown:: Example: ``schema=OpenAI`` function schema, ``method='function_calling'``, ``include_raw=False``
|
|
|
|
.. code-block:: python
|
|
|
|
from langchain_ollama import ChatOllama
|
|
|
|
oai_schema = {
|
|
'name': 'AnswerWithJustification',
|
|
'description': 'An answer to the user question along with justification for the answer.',
|
|
'parameters': {
|
|
'type': 'object',
|
|
'properties': {
|
|
'answer': {'type': 'string'},
|
|
'justification': {'description': 'A justification for the answer.', 'type': 'string'}
|
|
},
|
|
'required': ['answer']
|
|
}
|
|
}
|
|
|
|
llm = ChatOllama(model="llama3.1", temperature=0)
|
|
structured_llm = llm.with_structured_output(oai_schema)
|
|
|
|
structured_llm.invoke(
|
|
"What weighs more a pound of bricks or a pound of feathers"
|
|
)
|
|
# -> {
|
|
# '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 and density of the two substances differ.'
|
|
# }
|
|
|
|
.. dropdown:: Example: ``schema=Pydantic`` class, ``method='json_mode'``, ``include_raw=True``
|
|
|
|
.. code-block::
|
|
|
|
from langchain_ollama import ChatOllama
|
|
from pydantic import BaseModel
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
answer: str
|
|
justification: str
|
|
|
|
llm = ChatOllama(model="llama3.1", temperature=0)
|
|
structured_llm = llm.with_structured_output(
|
|
AnswerWithJustification,
|
|
method="json_mode",
|
|
include_raw=True
|
|
)
|
|
|
|
structured_llm.invoke(
|
|
"Answer the following question. "
|
|
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
|
|
"What's heavier a pound of bricks or a pound of feathers?"
|
|
)
|
|
# -> {
|
|
# 'raw': AIMessage(content='{\\n "answer": "They are both the same weight.",\\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
|
|
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
""" # noqa: E501, D301
|
|
_ = kwargs.pop("strict", None)
|
|
if kwargs:
|
|
msg = f"Received unsupported arguments {kwargs}"
|
|
raise ValueError(msg)
|
|
is_pydantic_schema = _is_pydantic_class(schema)
|
|
if method == "function_calling":
|
|
if schema is None:
|
|
msg = (
|
|
"schema must be specified when method is not 'json_mode'. "
|
|
"Received None."
|
|
)
|
|
raise ValueError(msg)
|
|
formatted_tool = convert_to_openai_tool(schema)
|
|
tool_name = formatted_tool["function"]["name"]
|
|
llm = self.bind_tools(
|
|
[schema],
|
|
tool_choice=tool_name,
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": method},
|
|
"schema": formatted_tool,
|
|
},
|
|
)
|
|
if is_pydantic_schema:
|
|
output_parser: Runnable = PydanticToolsParser(
|
|
tools=[schema], # type: ignore[list-item]
|
|
first_tool_only=True,
|
|
)
|
|
else:
|
|
output_parser = JsonOutputKeyToolsParser(
|
|
key_name=tool_name, first_tool_only=True
|
|
)
|
|
elif method == "json_mode":
|
|
llm = self.bind(
|
|
format="json",
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": method},
|
|
"schema": schema,
|
|
},
|
|
)
|
|
output_parser = (
|
|
PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
|
|
if is_pydantic_schema
|
|
else JsonOutputParser()
|
|
)
|
|
elif method == "json_schema":
|
|
if schema is None:
|
|
msg = (
|
|
"schema must be specified when method is not 'json_mode'. "
|
|
"Received None."
|
|
)
|
|
raise ValueError(msg)
|
|
if is_pydantic_schema:
|
|
schema = cast(TypeBaseModel, schema)
|
|
if issubclass(schema, BaseModelV1):
|
|
response_format = schema.schema()
|
|
else:
|
|
response_format = schema.model_json_schema()
|
|
llm = self.bind(
|
|
format=response_format,
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": method},
|
|
"schema": schema,
|
|
},
|
|
)
|
|
output_parser = PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
|
|
else:
|
|
if is_typeddict(schema):
|
|
response_format = convert_to_json_schema(schema)
|
|
if "required" not in response_format:
|
|
response_format["required"] = list(
|
|
response_format["properties"].keys()
|
|
)
|
|
else:
|
|
# is JSON schema
|
|
response_format = cast(dict, schema)
|
|
llm = self.bind(
|
|
format=response_format,
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": method},
|
|
"schema": response_format,
|
|
},
|
|
)
|
|
output_parser = JsonOutputParser()
|
|
else:
|
|
msg = (
|
|
f"Unrecognized method argument. Expected one of 'function_calling', "
|
|
f"'json_schema', or 'json_mode'. Received: '{method}'"
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
if include_raw:
|
|
parser_assign = RunnablePassthrough.assign(
|
|
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
|
|
)
|
|
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
|
|
parser_with_fallback = parser_assign.with_fallbacks(
|
|
[parser_none], exception_key="parsing_error"
|
|
)
|
|
return RunnableMap(raw=llm) | parser_with_fallback
|
|
return llm | output_parser
|