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docs: standardize OllamaLLM and BaseOpenAI docstrings (#32758)
- Add comprehensive docstring following LangChain standards - Include Setup, Key init args, Instantiate, Invoke, Stream, and Async sections - Provide detailed parameter descriptions and code examples - Fix linting issues for code formatting compliance Contributes to #24803 --------- Co-authored-by: Mason Daugherty <github@mdrxy.com>
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Ravirajsingh Sodha
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@@ -24,15 +24,93 @@ from ._utils import validate_model
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class OllamaLLM(BaseLLM):
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"""OllamaLLM large language models.
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"""Ollama large language models.
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Example:
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Setup:
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Install ``langchain-ollama`` and install/run the Ollama server locally:
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.. code-block:: bash
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pip install -U langchain-ollama
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# Visit https://ollama.com/download to download and install Ollama
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# (Linux users): start the server with ``ollama serve``
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Download a model to use:
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.. code-block:: bash
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ollama pull llama3.1
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Key init args — generation params:
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model: str
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Name of the Ollama model to use (e.g. ``'llama4'``).
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temperature: Optional[float]
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Sampling temperature. Higher values make output more creative.
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num_predict: Optional[int]
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Maximum number of tokens to predict.
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top_k: Optional[int]
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Limits the next token selection to the K most probable tokens.
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top_p: Optional[float]
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Nucleus sampling parameter. Higher values lead to more diverse text.
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mirostat: Optional[int]
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Enable Mirostat sampling for controlling perplexity.
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seed: Optional[int]
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Random number seed for generation reproducibility.
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Key init args — client params:
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base_url: Optional[str]
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Base URL where Ollama server is hosted.
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keep_alive: Optional[Union[int, str]]
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How long the model stays loaded into memory.
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format: Literal["", "json"]
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Specify the format of the output.
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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 OllamaLLM
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model = OllamaLLM(model="llama3")
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print(model.invoke("Come up with 10 names for a song about parrots"))
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llm = OllamaLLM(
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model="llama3.1",
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temperature=0.7,
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num_predict=256,
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# base_url="http://localhost:11434",
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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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input_text = "The meaning of life is "
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response = llm.invoke(input_text)
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print(response)
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.. code-block:: none
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"a philosophical question that has been contemplated by humans for
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centuries..."
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Stream:
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.. code-block:: python
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for chunk in llm.stream(input_text):
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print(chunk, end="")
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.. code-block:: none
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a philosophical question that has been contemplated by humans for
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centuries...
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Async:
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.. code-block:: python
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response = await llm.ainvoke(input_text)
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# stream:
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# async for chunk in llm.astream(input_text):
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# print(chunk, end="")
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"""
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@@ -49,7 +49,120 @@ def _stream_response_to_generation_chunk(
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class BaseOpenAI(BaseLLM):
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"""Base OpenAI large language model class."""
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"""Base OpenAI large language model class.
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Setup:
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Install ``langchain-openai`` and set environment variable ``OPENAI_API_KEY``.
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.. code-block:: bash
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pip install -U langchain-openai
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export OPENAI_API_KEY="your-api-key"
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Key init args — completion params:
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model_name: str
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Name of OpenAI model to use.
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temperature: float
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Sampling temperature.
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max_tokens: int
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Max number of tokens to generate.
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top_p: float
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Total probability mass of tokens to consider at each step.
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frequency_penalty: float
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Penalizes repeated tokens according to frequency.
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presence_penalty: float
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Penalizes repeated tokens.
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n: int
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How many completions to generate for each prompt.
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best_of: int
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Generates best_of completions server-side and returns the "best".
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logit_bias: Optional[dict[str, float]]
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Adjust the probability of specific tokens being generated.
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seed: Optional[int]
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Seed for generation.
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logprobs: Optional[int]
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Include the log probabilities on the logprobs most likely output tokens.
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streaming: bool
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Whether to stream the results or not.
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Key init args — client params:
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openai_api_key: Optional[SecretStr]
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OpenAI API key. If not passed in will be read from env var
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``OPENAI_API_KEY``.
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openai_api_base: Optional[str]
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Base URL path for API requests, leave blank if not using a proxy or
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service emulator.
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openai_organization: Optional[str]
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OpenAI organization ID. If not passed in will be read from env
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var ``OPENAI_ORG_ID``.
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request_timeout: Union[float, tuple[float, float], Any, None]
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Timeout for requests to OpenAI completion API.
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max_retries: int
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Maximum number of retries to make when generating.
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batch_size: int
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Batch size to use when passing multiple documents 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_openai.llms.base import BaseOpenAI
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llm = BaseOpenAI(
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model_name="gpt-3.5-turbo-instruct",
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temperature=0.7,
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max_tokens=256,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0,
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# openai_api_key="...",
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# openai_api_base="...",
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# openai_organization="...",
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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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input_text = "The meaning of life is "
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response = llm.invoke(input_text)
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print(response)
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.. code-block:: none
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"a philosophical question that has been debated by thinkers and
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scholars for centuries."
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Stream:
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.. code-block:: python
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for chunk in llm.stream(input_text):
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print(chunk, end="")
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.. code-block:: none
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a philosophical question that has been debated by thinkers and
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scholars for centuries.
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Async:
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.. code-block:: python
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response = await llm.ainvoke(input_text)
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# stream:
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# async for chunk in llm.astream(input_text):
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# print(chunk, end="")
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# batch:
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# await llm.abatch([input_text])
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.. code-block:: none
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"a philosophical question that has been debated by thinkers and
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scholars for centuries."
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"""
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client: Any = Field(default=None, exclude=True) #: :meta private:
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async_client: Any = Field(default=None, exclude=True) #: :meta private:
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@@ -80,7 +193,7 @@ class BaseOpenAI(BaseLLM):
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openai_api_base: Optional[str] = Field(
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alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
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)
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"""Base URL path for API requests, leave blank if not using a proxy or service
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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openai_organization: Optional[str] = Field(
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alias="organization",
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@@ -116,26 +229,26 @@ class BaseOpenAI(BaseLLM):
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disallowed_special: Union[Literal["all"], Collection[str]] = "all"
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"""Set of special tokens that are not allowed。"""
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tiktoken_model_name: Optional[str] = None
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"""The model name to pass to tiktoken when using this class.
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Tiktoken is used to count the number of tokens in documents to constrain
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them to be under a certain limit. By default, when set to None, this will
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be the same as the embedding model name. However, there are some cases
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where you may want to use this Embedding class with a model name not
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supported by tiktoken. This can include when using Azure embeddings or
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when using one of the many model providers that expose an OpenAI-like
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API but with different models. In those cases, in order to avoid erroring
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"""The model name to pass to tiktoken when using this class.
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Tiktoken is used to count the number of tokens in documents to constrain
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them to be under a certain limit. By default, when set to None, this will
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be the same as the embedding model name. However, there are some cases
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where you may want to use this Embedding class with a model name not
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supported by tiktoken. This can include when using Azure embeddings or
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when using one of the many model providers that expose an OpenAI-like
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API but with different models. In those cases, in order to avoid erroring
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when tiktoken is called, you can specify a model name to use here."""
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default_headers: Union[Mapping[str, str], None] = None
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default_query: Union[Mapping[str, object], None] = None
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# Configure a custom httpx client. See the
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# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
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http_client: Union[Any, None] = None
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"""Optional ``httpx.Client``. Only used for sync invocations. Must specify
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"""Optional ``httpx.Client``. Only used for sync invocations. Must specify
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``http_async_client`` as well if you'd like a custom client for async
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invocations.
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"""
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http_async_client: Union[Any, None] = None
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"""Optional ``httpx.AsyncClient``. Only used for async invocations. Must specify
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"""Optional ``httpx.AsyncClient``. Only used for async invocations. Must specify
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``http_client`` as well if you'd like a custom client for sync invocations."""
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extra_body: Optional[Mapping[str, Any]] = None
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"""Optional additional JSON properties to include in the request parameters when
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