ollama: init package (#23615)

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/partners/ollama/langchain_ollama/chat_models.py

@@ -0,0 +1,719 @@
+"""Ollama chat models."""
+
+from typing import (
+    Any,
+    AsyncIterator,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Literal,
+    Mapping,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+    cast,
+)
+from uuid import uuid4
+
+import ollama
+from langchain_core.callbacks import (
+    CallbackManagerForLLMRun,
+)
+from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    ToolCall,
+    ToolMessage,
+)
+from langchain_core.messages.ai import UsageMetadata
+from langchain_core.messages.tool import tool_call
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
+from langchain_core.utils.function_calling import convert_to_openai_tool
+from ollama import AsyncClient, Message, Options
+
+
+def _get_usage_metadata_from_generation_info(
+    generation_info: Optional[Mapping[str, Any]],
+) -> Optional[UsageMetadata]:
+    """Get usage metadata from ollama generation info mapping."""
+    if generation_info is None:
+        return None
+    input_tokens: Optional[int] = generation_info.get("prompt_eval_count")
+    output_tokens: Optional[int] = generation_info.get("eval_count")
+    if input_tokens is not None and output_tokens is not None:
+        return UsageMetadata(
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            total_tokens=input_tokens + output_tokens,
+        )
+    return None
+
+
+def _get_tool_calls_from_response(
+    response: Mapping[str, Any],
+) -> List[ToolCall]:
+    """Get tool calls from ollama response."""
+    tool_calls = []
+    if "message" in response:
+        if "tool_calls" in response["message"]:
+            for tc in response["message"]["tool_calls"]:
+                tool_calls.append(
+                    tool_call(
+                        id=str(uuid4()),
+                        name=tc["function"]["name"],
+                        args=tc["function"]["arguments"],
+                    )
+                )
+    return tool_calls
+
+
+def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
+    return {
+        "type": "function",
+        "id": tool_call["id"],
+        "function": {
+            "name": tool_call["name"],
+            "arguments": tool_call["args"],
+        },
+    }
+
+
+class ChatOllama(BaseChatModel):
+    """Ollama chat model integration.
+
+    Setup:
+        Install ``langchain-ollama`` and download any models you want to use from ollama.
+
+        .. code-block:: bash
+
+            ollama pull mistral:v0.3
+            pip install -U langchain-ollama
+
+    Key init args — completion params:
+        model: str
+            Name of Ollama model to use.
+        temperature: float
+            Sampling temperature. Ranges from 0.0 to 1.0.
+        num_predict: Optional[int]
+            Max number of tokens to generate.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_ollama import ChatOllama
+
+            llm = ChatOllama(
+                model = "llama3",
+                temperature = 0.8,
+                num_predict = 256,
+                # other params ...
+            )
+
+    Invoke:
+        .. code-block:: python
+
+            messages = [
+                ("system", "You are a helpful translator. Translate the user sentence to French."),
+                ("human", "I love programming."),
+            ]
+            llm.invoke(messages)
+
+        .. code-block:: python
+
+            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')
+
+    Stream:
+        .. code-block:: python
+
+            messages = [
+                ("human", "Return the words Hello World!"),
+            ]
+            for chunk in llm.stream(messages):
+                print(chunk)
+
+
+        .. code-block:: python
+
+            content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
+            content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
+            content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
+            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'
+
+
+        .. code-block:: python
+
+            stream = llm.stream(messages)
+            full = next(stream)
+            for chunk in stream:
+                full += chunk
+            full
+
+        .. code-block:: python
+
+            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')
+
+    Async:
+        .. code-block:: python
+
+            messages = [
+                ("human", "Hello how are you!"),
+            ]
+            await llm.ainvoke(messages)
+
+        .. code-block:: python
+
+            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')
+
+        .. code-block:: python
+
+            messages = [
+                ("human", "Say hello world!"),
+            ]
+            async for chunk in llm.astream(messages):
+                print(chunk.content)
+
+        .. code-block:: python
+
+            HEL
+            LO
+            WORLD
+            !
+
+        .. code-block:: python
+
+            messages = [
+                ("human", "Say hello world!"),
+                ("human","Say goodbye world!")
+            ]
+            await llm.abatch(messages)
+
+        .. code-block:: python
+
+            [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'),
+            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')]
+
+    JSON mode:
+        .. code-block:: python
+
+
+            json_llm = ChatOllama(format="json")
+            messages = [
+                ("human", "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."),
+            ]
+            llm.invoke(messages).content
+
+        .. code-block:: python
+
+            '{"location": "Pune, India", "time_of_day": "morning"}'
+
+    Tool Calling:
+        .. warning::
+            Ollama currently does not support streaming for tools
+
+        .. code-block:: python
+
+            from langchain_ollama import ChatOllama
+            from langchain_core.pydantic_v1 import BaseModel, Field
+
+            class Multiply(BaseModel):
+                a: int = Field(..., description="First integer")
+                b: int = Field(..., description="Second integer")
+
+            ans = await chat.invoke("What is 45*67")
+            ans.tool_calls
+
+        .. code-block:: python
+
+            [{'name': 'Multiply',
+            'args': {'a': 45, 'b': 67},
+            'id': '420c3f3b-df10-4188-945f-eb3abdb40622',
+            'type': 'tool_call'}]
+    """  # noqa: E501
+
+    model: str
+    """Model name to use."""
+
+    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)"""
+
+    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: Literal["", "json"] = ""
+    """Specify the format of the output (options: json)"""
+
+    keep_alive: Optional[Union[int, str]] = None
+    """How long the model will stay loaded into memory."""
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Ollama."""
+        return {
+            "model": self.model,
+            "format": self.format,
+            "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,
+                "stop": self.stop,
+                "tfs_z": self.tfs_z,
+                "top_k": self.top_k,
+                "top_p": self.top_p,
+            },
+            "keep_alive": self.keep_alive,
+        }
+
+    def _convert_messages_to_ollama_messages(
+        self, messages: List[BaseMessage]
+    ) -> Sequence[Message]:
+        ollama_messages: List = []
+        for message in messages:
+            role = ""
+            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, ToolMessage):
+                role = "tool"
+                tool_call_id = message.tool_call_id
+            else:
+                raise ValueError("Received unsupported message type for Ollama.")
+
+            content = ""
+            images = []
+            if isinstance(message.content, str):
+                content = message.content
+            else:
+                for content_part in cast(List[Dict], message.content):
+                    if 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 = content_part["image_url"]
+                        elif (
+                            isinstance(temp_image_url, dict) and "url" in temp_image_url
+                        ):
+                            image_url = temp_image_url
+                        else:
+                            raise ValueError(
+                                "Only string image_url or dict with string 'url' "
+                                "inside content parts are supported."
+                            )
+
+                        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])
+
+                    else:
+                        raise ValueError(
+                            "Unsupported message content type. "
+                            "Must either have type 'text' or type 'image_url' "
+                            "with a string 'image_url' field."
+                        )
+            msg = {
+                "role": role,
+                "content": content,
+                "images": images,
+            }
+            if tool_call_id:
+                msg["tool_call_id"] = tool_call_id
+            if tool_calls:
+                msg["tool_calls"] = tool_calls
+            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]]:
+        ollama_messages = self._convert_messages_to_ollama_messages(messages)
+
+        stop = stop if stop is not None else self.stop
+
+        params = self._default_params
+
+        for key in self._default_params:
+            if key in kwargs:
+                params[key] = kwargs[key]
+
+        params["options"]["stop"] = stop
+        if "tools" in kwargs:
+            yield await AsyncClient().chat(
+                model=params["model"],
+                messages=ollama_messages,
+                stream=False,
+                options=Options(**params["options"]),
+                keep_alive=params["keep_alive"],
+                format=params["format"],
+                tools=kwargs["tools"],
+            )  # type:ignore
+        else:
+            async for part in await AsyncClient().chat(
+                model=params["model"],
+                messages=ollama_messages,
+                stream=True,
+                options=Options(**params["options"]),
+                keep_alive=params["keep_alive"],
+                format=params["format"],
+            ):  # type:ignore
+                yield part
+
+    def _create_chat_stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> Iterator[Union[Mapping[str, Any], str]]:
+        ollama_messages = self._convert_messages_to_ollama_messages(messages)
+
+        stop = stop if stop is not None else self.stop
+
+        params = self._default_params
+
+        for key in self._default_params:
+            if key in kwargs:
+                params[key] = kwargs[key]
+
+        params["options"]["stop"] = stop
+        if "tools" in kwargs:
+            yield ollama.chat(
+                model=params["model"],
+                messages=ollama_messages,
+                stream=False,
+                options=Options(**params["options"]),
+                keep_alive=params["keep_alive"],
+                format=params["format"],
+                tools=kwargs["tools"],
+            )
+        else:
+            yield from ollama.chat(
+                model=params["model"],
+                messages=ollama_messages,
+                stream=True,
+                options=Options(**params["options"]),
+                keep_alive=params["keep_alive"],
+                format=params["format"],
+            )
+
+    def _chat_stream_with_aggregation(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        verbose: bool = False,
+        **kwargs: Any,
+    ) -> ChatGenerationChunk:
+        final_chunk = None
+        for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = ChatGenerationChunk(
+                    message=AIMessageChunk(
+                        content=(
+                            stream_resp["message"]["content"]
+                            if "message" in stream_resp
+                            and "content" in stream_resp["message"]
+                            else ""
+                        ),
+                        usage_metadata=_get_usage_metadata_from_generation_info(
+                            stream_resp
+                        ),
+                        tool_calls=_get_tool_calls_from_response(stream_resp),
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                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:
+            raise ValueError("No data received from Ollama stream.")
+
+        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,
+        **kwargs: Any,
+    ) -> ChatGenerationChunk:
+        final_chunk = None
+        async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = ChatGenerationChunk(
+                    message=AIMessageChunk(
+                        content=(
+                            stream_resp["message"]["content"]
+                            if "message" in stream_resp
+                            and "content" in stream_resp["message"]
+                            else ""
+                        ),
+                        usage_metadata=_get_usage_metadata_from_generation_info(
+                            stream_resp
+                        ),
+                        tool_calls=_get_tool_calls_from_response(stream_resp),
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                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:
+            raise ValueError("No data received from Ollama stream.")
+
+        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,
+            ),
+            generation_info=generation_info,
+        )
+        return ChatResult(generations=[chat_generation])
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = ChatGenerationChunk(
+                    message=AIMessageChunk(
+                        content=(
+                            stream_resp["message"]["content"]
+                            if "message" in stream_resp
+                            and "content" in stream_resp["message"]
+                            else ""
+                        ),
+                        usage_metadata=_get_usage_metadata_from_generation_info(
+                            stream_resp
+                        ),
+                        tool_calls=_get_tool_calls_from_response(stream_resp),
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if run_manager:
+                    run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )
+                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 stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = ChatGenerationChunk(
+                    message=AIMessageChunk(
+                        content=(
+                            stream_resp["message"]["content"]
+                            if "message" in stream_resp
+                            and "content" in stream_resp["message"]
+                            else ""
+                        ),
+                        usage_metadata=_get_usage_metadata_from_generation_info(
+                            stream_resp
+                        ),
+                        tool_calls=_get_tool_calls_from_response(stream_resp),
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                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,
+            ),
+            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[BaseModel], Callable, BaseTool]],
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
+        return super().bind(tools=formatted_tools, **kwargs)