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Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM.
https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html
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dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed
https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html
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**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html
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first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_environment  »  all fields[source]¶ Validate that api key and python package exists in environment. property lc_attributes: Dict¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html
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Validate that api key and python package exists in environment. property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. extra = 'forbid'¶ Examples using NLPCloud¶ NLPCloud NLP Cloud
https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html
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langchain.llms.azureml_endpoint.GPT2ContentFormatter¶ class langchain.llms.azureml_endpoint.GPT2ContentFormatter[source]¶ Bases: ContentFormatterBase Content handler for GPT2 Methods __init__() escape_special_characters(prompt) Escapes any special characters in prompt format_request_payload(prompt, model_kwargs) Formats the request body according to the input schema of the model. format_response_payload(output) Formats the response body according to the output schema of the model. Attributes accepts The MIME type of the response data returned from the endpoint content_type The MIME type of the input data passed to the endpoint static escape_special_characters(prompt: str) → str¶ Escapes any special characters in prompt format_request_payload(prompt: str, model_kwargs: Dict) → bytes[source]¶ Formats the request body according to the input schema of the model. Returns bytes or seekable file like object in the format specified in the content_type request header. format_response_payload(output: bytes) → str[source]¶ Formats the response body according to the output schema of the model. Returns the data type that is received from the response. accepts: Optional[str] = 'application/json'¶ The MIME type of the response data returned from the endpoint content_type: Optional[str] = 'application/json'¶ The MIME type of the input data passed to the endpoint
https://api.python.langchain.com/en/latest/llms/langchain.llms.azureml_endpoint.GPT2ContentFormatter.html
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langchain.llms.google_palm.GooglePalm¶ class langchain.llms.google_palm.GooglePalm(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, client: Any = None, google_api_key: Optional[str] = None, model_name: str = 'models/text-bison-001', temperature: float = 0.7, top_p: Optional[float] = None, top_k: Optional[int] = None, max_output_tokens: Optional[int] = None, n: int = 1)[source]¶ Bases: BaseLLM, BaseModel Google PaLM models. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param google_api_key: Optional[str] = None¶ param max_output_tokens: Optional[int] = None¶ Maximum number of tokens to include in a candidate. Must be greater than zero. If unset, will default to 64. param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model_name: str = 'models/text-bison-001'¶ Model name to use. param n: int = 1¶ Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated. param tags: Optional[List[str]] = None¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.GooglePalm.html
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param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param temperature: float = 0.7¶ Run inference with this temperature. Must by in the closed interval [0.0, 1.0]. param top_k: Optional[int] = None¶ Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive. param top_p: Optional[float] = None¶ Decode using nucleus sampling: consider the smallest set of tokens whose probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.GooglePalm.html
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Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.GooglePalm.html
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Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM.
https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.GooglePalm.html
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dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed
https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.GooglePalm.html
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**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.GooglePalm.html
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first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_environment  »  all fields[source]¶ Validate api key, python package exists. property lc_attributes: Dict¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.GooglePalm.html
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Validate api key, python package exists. property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.GooglePalm.html
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langchain.llms.ai21.AI21¶ class langchain.llms.ai21.AI21(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, model: str = 'j2-jumbo-instruct', temperature: float = 0.7, maxTokens: int = 256, minTokens: int = 0, topP: float = 1.0, presencePenalty: AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True), countPenalty: AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True), frequencyPenalty: AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True), numResults: int = 1, logitBias: Optional[Dict[str, float]] = None, ai21_api_key: Optional[str] = None, stop: Optional[List[str]] = None, base_url: Optional[str] = None)[source]¶ Bases: LLM AI21 large language models. To use, you should have the environment variable AI21_API_KEY set with your API key. Example from langchain.llms import AI21 ai21 = AI21(model="j2-jumbo-instruct")
https://api.python.langchain.com/en/latest/llms/langchain.llms.ai21.AI21.html
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ai21 = AI21(model="j2-jumbo-instruct") Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param ai21_api_key: Optional[str] = None¶ param base_url: Optional[str] = None¶ Base url to use, if None decides based on model name. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param countPenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)¶ Penalizes repeated tokens according to count. param frequencyPenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)¶ Penalizes repeated tokens according to frequency. param logitBias: Optional[Dict[str, float]] = None¶ Adjust the probability of specific tokens being generated. param maxTokens: int = 256¶ The maximum number of tokens to generate in the completion. param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param minTokens: int = 0¶ The minimum number of tokens to generate in the completion. param model: str = 'j2-jumbo-instruct'¶ Model name to use. param numResults: int = 1¶ How many completions to generate for each prompt.
https://api.python.langchain.com/en/latest/llms/langchain.llms.ai21.AI21.html
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How many completions to generate for each prompt. param presencePenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)¶ Penalizes repeated tokens. param stop: Optional[List[str]] = None¶ param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param temperature: float = 0.7¶ What sampling temperature to use. param topP: float = 1.0¶ Total probability mass of tokens to consider at each step. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.ai21.AI21.html
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async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns
https://api.python.langchain.com/en/latest/llms/langchain.llms.ai21.AI21.html
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to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.ai21.AI21.html
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batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
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text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction.
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Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting.
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This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_environment  »  all fields[source]¶ Validate that api key exists in environment. property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. extra = 'forbid'¶ Examples using AI21¶ AI21 Labs AI21
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langchain.llms.tongyi.Tongyi¶ class langchain.llms.tongyi.Tongyi(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, client: Any = None, model_name: str = 'qwen-plus-v1', model_kwargs: Dict[str, Any] = None, top_p: float = 0.8, dashscope_api_key: Optional[str] = None, n: int = 1, streaming: bool = False, max_retries: int = 10, prefix_messages: List = None)[source]¶ Bases: LLM Tongyi Qwen large language models. To use, you should have the dashscope python package installed, and the environment variable DASHSCOPE_API_KEY set with your API key, or pass it as a named parameter to the constructor. Example from langchain.llms import Tongyi Tongyi = tongyi() Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param dashscope_api_key: Optional[str] = None¶ Dashscope api key provide by alicloud. param max_retries: int = 10¶ Maximum number of retries to make when generating. param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model_kwargs: Dict[str, Any] [Optional]¶
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param model_kwargs: Dict[str, Any] [Optional]¶ param model_name: str = 'qwen-plus-v1'¶ Model name to use. param n: int = 1¶ How many completions to generate for each prompt. param prefix_messages: List [Optional]¶ Series of messages for Chat input. param streaming: bool = False¶ Whether to stream the results or not. param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param top_p: float = 0.8¶ Total probability mass of tokens to consider at each step. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶
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Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
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Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM.
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dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed
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**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
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first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_environment  »  all fields[source]¶ Validate that api key and python package exists in environment. property lc_attributes: Dict¶
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Validate that api key and python package exists in environment. property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ Examples using Tongyi¶ Tongyi Qwen
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langchain.llms.self_hosted.SelfHostedPipeline¶ class langchain.llms.self_hosted.SelfHostedPipeline(*, cache: ~typing.Optional[bool] = None, verbose: bool = None, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, pipeline_ref: ~typing.Any = None, client: ~typing.Any = None, inference_fn: ~typing.Callable = <function _generate_text>, hardware: ~typing.Any = None, model_load_fn: ~typing.Callable, load_fn_kwargs: ~typing.Optional[dict] = None, model_reqs: ~typing.List[str] = ['./', 'torch'])[source]¶ Bases: LLM Model inference on self-hosted remote hardware. Supported hardware includes auto-launched instances on AWS, GCP, Azure, and Lambda, as well as servers specified by IP address and SSH credentials (such as on-prem, or another cloud like Paperspace, Coreweave, etc.). To use, you should have the runhouse python package installed. Example for custom pipeline and inference functions:from langchain.llms import SelfHostedPipeline from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import runhouse as rh def load_pipeline(): tokenizer = AutoTokenizer.from_pretrained("gpt2") model = AutoModelForCausalLM.from_pretrained("gpt2") return pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10 )
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max_new_tokens=10 ) def inference_fn(pipeline, prompt, stop = None): return pipeline(prompt)[0]["generated_text"] gpu = rh.cluster(name="rh-a10x", instance_type="A100:1") llm = SelfHostedPipeline( model_load_fn=load_pipeline, hardware=gpu, model_reqs=model_reqs, inference_fn=inference_fn ) Example for <2GB model (can be serialized and sent directly to the server):from langchain.llms import SelfHostedPipeline import runhouse as rh gpu = rh.cluster(name="rh-a10x", instance_type="A100:1") my_model = ... llm = SelfHostedPipeline.from_pipeline( pipeline=my_model, hardware=gpu, model_reqs=["./", "torch", "transformers"], ) Example passing model path for larger models:from langchain.llms import SelfHostedPipeline import runhouse as rh import pickle from transformers import pipeline generator = pipeline(model="gpt2") rh.blob(pickle.dumps(generator), path="models/pipeline.pkl" ).save().to(gpu, path="models") llm = SelfHostedPipeline.from_pipeline( pipeline="models/pipeline.pkl", hardware=gpu, model_reqs=["./", "torch", "transformers"], ) Init the pipeline with an auxiliary function. The load function must be in global scope to be imported and run on the server, i.e. in a module and not a REPL or closure. Then, initialize the remote inference function. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param hardware: Any = None¶
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param callbacks: Callbacks = None¶ param hardware: Any = None¶ Remote hardware to send the inference function to. param inference_fn: Callable = <function _generate_text>¶ Inference function to send to the remote hardware. param load_fn_kwargs: Optional[dict] = None¶ Key word arguments to pass to the model load function. param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model_load_fn: Callable [Required]¶ Function to load the model remotely on the server. param model_reqs: List[str] = ['./', 'torch']¶ Requirements to install on hardware to inference the model. param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶
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async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns
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to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶
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batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → LLM[source]¶ Init the SelfHostedPipeline from a pipeline object or string. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
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Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted.SelfHostedPipeline.html
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predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting.
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted.SelfHostedPipeline.html
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This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. extra = 'forbid'¶ Examples using SelfHostedPipeline¶ Runhouse
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted.SelfHostedPipeline.html
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langchain.llms.amazon_api_gateway.AmazonAPIGateway¶ class langchain.llms.amazon_api_gateway.AmazonAPIGateway(*, cache: ~typing.Optional[bool] = None, verbose: bool = None, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, api_url: str, headers: ~typing.Optional[~typing.Dict] = None, model_kwargs: ~typing.Optional[~typing.Dict] = None, content_handler: ~langchain.llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway = <langchain.llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway object>)[source]¶ Bases: LLM Amazon API Gateway to access LLM models hosted on AWS. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param api_url: str [Required]¶ API Gateway URL param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param content_handler: langchain.llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway = <langchain.llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway object>¶ The content handler class that provides an input and output transform functions to handle formats between LLM and the endpoint. param headers: Optional[Dict] = None¶ API Gateway HTTP Headers to send, e.g. for authentication param metadata: Optional[Dict[str, Any]] = None¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html
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param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model_kwargs: Optional[Dict] = None¶ Key word arguments to pass to the model. param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input.
https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html
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Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html
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Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM.
https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html
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dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed
https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html
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**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html
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first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the
https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html
f2a4dbb69ec7-7
serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. extra = 'forbid'¶ Examples using AmazonAPIGateway¶ Amazon API Gateway
https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html
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langchain.llms.mlflow_ai_gateway.MlflowAIGateway¶ class langchain.llms.mlflow_ai_gateway.MlflowAIGateway(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, route: str, gateway_uri: Optional[str] = None, params: Optional[Params] = None)[source]¶ Bases: LLM Wrapper around completions LLMs in the MLflow AI Gateway. To use, you should have the mlflow[gateway] python package installed. For more information, see https://mlflow.org/docs/latest/gateway/index.html. Example from langchain.llms import MlflowAIGateway completions = MlflowAIGateway( gateway_uri="<your-mlflow-ai-gateway-uri>", route="<your-mlflow-ai-gateway-completions-route>", params={ "temperature": 0.1 } ) Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param gateway_uri: Optional[str] = None¶ param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param params: Optional[Params] = None¶ param route: str [Required]¶ param tags: Optional[List[str]] = None¶ Tags to add to the run trace.
https://api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html
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param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to:
https://api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html
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API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string.
https://api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html
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to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input.
https://api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html
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Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html
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get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input.
https://api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html
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Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html
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Return whether or not the class is serializable. model Config¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ Examples using MlflowAIGateway¶ MLflow AI Gateway
https://api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html
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langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM¶ class langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM(*, cache: ~typing.Optional[bool] = None, verbose: bool = None, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, pipeline_ref: ~typing.Any = None, client: ~typing.Any = None, inference_fn: ~typing.Callable = <function _generate_text>, hardware: ~typing.Any = None, model_load_fn: ~typing.Callable = <function _load_transformer>, load_fn_kwargs: ~typing.Optional[dict] = None, model_reqs: ~typing.List[str] = ['./', 'transformers', 'torch'], model_id: str = 'gpt2', task: str = 'text-generation', device: int = 0, model_kwargs: ~typing.Optional[dict] = None)[source]¶ Bases: SelfHostedPipeline HuggingFace Pipeline API to run on self-hosted remote hardware. Supported hardware includes auto-launched instances on AWS, GCP, Azure, and Lambda, as well as servers specified by IP address and SSH credentials (such as on-prem, or another cloud like Paperspace, Coreweave, etc.). To use, you should have the runhouse python package installed. Only supports text-generation, text2text-generation and summarization for now.
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
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Only supports text-generation, text2text-generation and summarization for now. Example using from_model_id:from langchain.llms import SelfHostedHuggingFaceLLM import runhouse as rh gpu = rh.cluster(name="rh-a10x", instance_type="A100:1") hf = SelfHostedHuggingFaceLLM( model_id="google/flan-t5-large", task="text2text-generation", hardware=gpu ) Example passing fn that generates a pipeline (bc the pipeline is not serializable):from langchain.llms import SelfHostedHuggingFaceLLM from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import runhouse as rh def get_pipeline(): model_id = "gpt2" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer ) return pipe hf = SelfHostedHuggingFaceLLM( model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu) Construct the pipeline remotely using an auxiliary function. The load function needs to be importable to be imported and run on the server, i.e. in a module and not a REPL or closure. Then, initialize the remote inference function. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param device: int = 0¶ Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc. param hardware: Any = None¶ Remote hardware to send the inference function to.
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
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param hardware: Any = None¶ Remote hardware to send the inference function to. param inference_fn: Callable = <function _generate_text>¶ Inference function to send to the remote hardware. param load_fn_kwargs: Optional[dict] = None¶ Key word arguments to pass to the model load function. param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model_id: str = 'gpt2'¶ Hugging Face model_id to load the model. param model_kwargs: Optional[dict] = None¶ Key word arguments to pass to the model. param model_load_fn: Callable = <function _load_transformer>¶ Function to load the model remotely on the server. param model_reqs: List[str] = ['./', 'transformers', 'torch']¶ Requirements to install on hardware to inference the model. param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param task: str = 'text-generation'¶ Hugging Face task (“text-generation”, “text2text-generation” or “summarization”). param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input.
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
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Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
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text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
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first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → LLM¶ Init the SelfHostedPipeline from a pipeline object or string. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
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Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
1fed060b3e12-7
Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used.
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
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Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. extra = 'forbid'¶ Examples using SelfHostedHuggingFaceLLM¶ Runhouse
https://api.python.langchain.com/en/latest/llms/langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM.html
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langchain.llms.aviary.get_completions¶ langchain.llms.aviary.get_completions(model: str, prompt: str, use_prompt_format: bool = True, version: str = '') → Dict[str, Union[str, float, int]][source]¶ Get completions from Aviary models.
https://api.python.langchain.com/en/latest/llms/langchain.llms.aviary.get_completions.html
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langchain.llms.ctransformers.CTransformers¶ class langchain.llms.ctransformers.CTransformers(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, client: Any = None, model: str, model_type: Optional[str] = None, model_file: Optional[str] = None, config: Optional[Dict[str, Any]] = None, lib: Optional[str] = None)[source]¶ Bases: LLM C Transformers LLM models. To use, you should have the ctransformers python package installed. See https://github.com/marella/ctransformers Example from langchain.llms import CTransformers llm = CTransformers(model="/path/to/ggml-gpt-2.bin", model_type="gpt2") Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param config: Optional[Dict[str, Any]] = None¶ The config parameters. See https://github.com/marella/ctransformers#config param lib: Optional[str] = None¶ The path to a shared library or one of avx2, avx, basic. param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model: str [Required]¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.ctransformers.CTransformers.html
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Metadata to add to the run trace. param model: str [Required]¶ The path to a model file or directory or the name of a Hugging Face Hub model repo. param model_file: Optional[str] = None¶ The name of the model file in repo or directory. param model_type: Optional[str] = None¶ The model type. param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input.
https://api.python.langchain.com/en/latest/llms/langchain.llms.ctransformers.CTransformers.html
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Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.ctransformers.CTransformers.html
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Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM.
https://api.python.langchain.com/en/latest/llms/langchain.llms.ctransformers.CTransformers.html
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dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed
https://api.python.langchain.com/en/latest/llms/langchain.llms.ctransformers.CTransformers.html
1247f5eeb6e6-5
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
https://api.python.langchain.com/en/latest/llms/langchain.llms.ctransformers.CTransformers.html
1247f5eeb6e6-6
first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_environment  »  all fields[source]¶ Validate that ctransformers package is installed. property lc_attributes: Dict¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.ctransformers.CTransformers.html
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Validate that ctransformers package is installed. property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ Examples using CTransformers¶ C Transformers
https://api.python.langchain.com/en/latest/llms/langchain.llms.ctransformers.CTransformers.html
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langchain.llms.gpt4all.GPT4All¶ class langchain.llms.gpt4all.GPT4All(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, model: str, backend: Optional[str] = None, max_tokens: int = 200, n_parts: int = - 1, seed: int = 0, f16_kv: bool = False, logits_all: bool = False, vocab_only: bool = False, use_mlock: bool = False, embedding: bool = False, n_threads: Optional[int] = 4, n_predict: Optional[int] = 256, temp: Optional[float] = 0.7, top_p: Optional[float] = 0.1, top_k: Optional[int] = 40, echo: Optional[bool] = False, stop: Optional[List[str]] = [], repeat_last_n: Optional[int] = 64, repeat_penalty: Optional[float] = 1.18, n_batch: int = 8, streaming: bool = False, allow_download: bool = False, client: Any = None)[source]¶ Bases: LLM GPT4All language models. To use, you should have the gpt4all python package installed, the pre-trained model file, and the model’s config information. Example from langchain.llms import GPT4All model = GPT4All(model="./models/gpt4all-model.bin", n_threads=8) # Simplest invocation response = model("Once upon a time, ") Create a new model by parsing and validating input data from keyword arguments.
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param allow_download: bool = False¶ If model does not exist in ~/.cache/gpt4all/, download it. param backend: Optional[str] = None¶ param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param echo: Optional[bool] = False¶ Whether to echo the prompt. param embedding: bool = False¶ Use embedding mode only. param f16_kv: bool = False¶ Use half-precision for key/value cache. param logits_all: bool = False¶ Return logits for all tokens, not just the last token. param max_tokens: int = 200¶ Token context window. param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model: str [Required]¶ Path to the pre-trained GPT4All model file. param n_batch: int = 8¶ Batch size for prompt processing. param n_parts: int = -1¶ Number of parts to split the model into. If -1, the number of parts is automatically determined. param n_predict: Optional[int] = 256¶ The maximum number of tokens to generate. param n_threads: Optional[int] = 4¶ Number of threads to use. param repeat_last_n: Optional[int] = 64¶ Last n tokens to penalize param repeat_penalty: Optional[float] = 1.18¶ The penalty to apply to repeated tokens. param seed: int = 0¶ Seed. If -1, a random seed is used.
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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Seed. If -1, a random seed is used. param stop: Optional[List[str]] = []¶ A list of strings to stop generation when encountered. param streaming: bool = False¶ Whether to stream the results or not. param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param temp: Optional[float] = 0.7¶ The temperature to use for sampling. param top_k: Optional[int] = 40¶ The top-k value to use for sampling. param top_p: Optional[float] = 0.1¶ The top-p value to use for sampling. param use_mlock: bool = False¶ Force system to keep model in RAM. param verbose: bool [Optional]¶ Whether to print out response text. param vocab_only: bool = False¶ Only load the vocabulary, no weights. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction.
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting.
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_environment  »  all fields[source]¶ Validate that the python package exists in the environment. property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. extra = 'forbid'¶ Examples using GPT4All¶ PromptLayer GPT4All Running LLMs locally Use local LLMs
https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html
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langchain.llms.huggingface_pipeline.HuggingFacePipeline¶ class langchain.llms.huggingface_pipeline.HuggingFacePipeline(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, pipeline: Any = None, model_id: str = 'gpt2', model_kwargs: Optional[dict] = None, pipeline_kwargs: Optional[dict] = None)[source]¶ Bases: LLM HuggingFace Pipeline API. To use, you should have the transformers python package installed. Only supports text-generation, text2text-generation and summarization for now. Example using from_model_id:from langchain.llms import HuggingFacePipeline hf = HuggingFacePipeline.from_model_id( model_id="gpt2", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}, ) Example passing pipeline in directly:from langchain.llms import HuggingFacePipeline from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_id = "gpt2" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10 ) hf = HuggingFacePipeline(pipeline=pipe) Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_pipeline.HuggingFacePipeline.html
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param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model_id: str = 'gpt2'¶ Model name to use. param model_kwargs: Optional[dict] = None¶ Key word arguments passed to the model. param pipeline_kwargs: Optional[dict] = None¶ Key word arguments passed to the pipeline. param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input.
https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_pipeline.HuggingFacePipeline.html
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Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_pipeline.HuggingFacePipeline.html
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Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM.
https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_pipeline.HuggingFacePipeline.html
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dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. classmethod from_model_id(model_id: str, task: str, device: int = - 1, model_kwargs: Optional[dict] = None, pipeline_kwargs: Optional[dict] = None, **kwargs: Any) → LLM[source]¶ Construct the pipeline object from model_id and task. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_pipeline.HuggingFacePipeline.html
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text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction.
https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_pipeline.HuggingFacePipeline.html
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Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting.
https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_pipeline.HuggingFacePipeline.html
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This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. extra = 'forbid'¶ Examples using HuggingFacePipeline¶ Hugging Face RELLM JSONFormer
https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_pipeline.HuggingFacePipeline.html
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langchain.llms.mosaicml.MosaicML¶ class langchain.llms.mosaicml.MosaicML(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, endpoint_url: str = 'https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict', inject_instruction_format: bool = False, model_kwargs: Optional[dict] = None, retry_sleep: float = 1.0, mosaicml_api_token: Optional[str] = None)[source]¶ Bases: LLM MosaicML LLM service. To use, you should have the environment variable MOSAICML_API_TOKEN set with your API token, or pass it as a named parameter to the constructor. Example from langchain.llms import MosaicML endpoint_url = ( "https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict" ) mosaic_llm = MosaicML( endpoint_url=endpoint_url, mosaicml_api_token="my-api-key" ) Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param cache: Optional[bool] = None¶ param callback_manager: Optional[BaseCallbackManager] = None¶ param callbacks: Callbacks = None¶ param endpoint_url: str = 'https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict'¶ Endpoint URL to use. param inject_instruction_format: bool = False¶
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Endpoint URL to use. param inject_instruction_format: bool = False¶ Whether to inject the instruction format into the prompt. param metadata: Optional[Dict[str, Any]] = None¶ Metadata to add to the run trace. param model_kwargs: Optional[dict] = None¶ Key word arguments to pass to the model. param mosaicml_api_token: Optional[str] = None¶ param retry_sleep: float = 1.0¶ How long to try sleeping for if a rate limit is encountered param tags: Optional[List[str]] = None¶ Tags to add to the run trace. param verbose: bool [Optional]¶ Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶ Check Cache and run the LLM on the given prompt and input. async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input.
https://api.python.langchain.com/en/latest/llms/langchain.llms.mosaicml.MosaicML.html
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Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Asynchronously pass a sequence of prompts and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.mosaicml.MosaicML.html
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Asynchronously pass a string to the model and return a string prediction. Use this method when calling pure text generation models and only the topcandidate generation is needed. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Asynchronously pass messages to the model and return a message prediction. Use this method when calling chat models and only the topcandidate generation is needed. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶ batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶ dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM.
https://api.python.langchain.com/en/latest/llms/langchain.llms.mosaicml.MosaicML.html
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dict(**kwargs: Any) → Dict¶ Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶ Run the LLM on the given prompt and input. generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶ Pass a sequence of prompts to the model and return model generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: take advantage of batched calls, need more output from the model than just the top generated value, are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models). Parameters prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models). stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs – Arbitrary additional keyword arguments. These are usually passed
https://api.python.langchain.com/en/latest/llms/langchain.llms.mosaicml.MosaicML.html
1fcb78608d78-5
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output. get_num_tokens(text: str) → int¶ Get the number of tokens present in the text. Useful for checking if an input will fit in a model’s context window. Parameters text – The string input to tokenize. Returns The integer number of tokens in the text. get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶ Get the number of tokens in the messages. Useful for checking if an input will fit in a model’s context window. Parameters messages – The message inputs to tokenize. Returns The sum of the number of tokens across the messages. get_token_ids(text: str) → List[int]¶ Return the ordered ids of the tokens in a text. Parameters text – The string input to tokenize. Returns A list of ids corresponding to the tokens in the text, in order they occurin the text. invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶ predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶ Pass a single string input to the model and return a string prediction. Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages. Parameters text – String input to pass to the model. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
https://api.python.langchain.com/en/latest/llms/langchain.llms.mosaicml.MosaicML.html
1fcb78608d78-6
first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a string. predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶ Pass a message sequence to the model and return a message prediction. Use this method when passing in chat messages. If you want to pass in raw text,use predict. Parameters messages – A sequence of chat messages corresponding to a single model input. stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns Top model prediction as a message. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. save(file_path: Union[Path, str]) → None¶ Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶ to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_environment  »  all fields[source]¶ Validate that api key and python package exists in environment. property lc_attributes: Dict¶
https://api.python.langchain.com/en/latest/llms/langchain.llms.mosaicml.MosaicML.html
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Validate that api key and python package exists in environment. property lc_attributes: Dict¶ Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str]¶ Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str]¶ Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool¶ Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. extra = 'forbid'¶ Examples using MosaicML¶ MosaicML
https://api.python.langchain.com/en/latest/llms/langchain.llms.mosaicml.MosaicML.html
2fc408704723-0
langchain.llms.openai.acompletion_with_retry¶ async langchain.llms.openai.acompletion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any) → Any[source]¶ Use tenacity to retry the async completion call.
https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.acompletion_with_retry.html