id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
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7bb8c64566f1-3 | **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 ... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.OpenAIChat.html |
7bb8c64566f1-4 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.OpenAIChat.html |
7bb8c64566f1-5 | 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 agno... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.OpenAIChat.html |
7bb8c64566f1-6 | Get the token IDs using the tiktoken package.
invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetInt... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.OpenAIChat.html |
7bb8c64566f1-7 | 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], *, sto... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.OpenAIChat.html |
7bb8c64566f1-8 | to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.OpenAIChat.html |
54b92f32ff71-0 | langchain.llms.fake.FakeListLLM¶
class langchain.llms.fake.FakeListLLM[source]¶
Bases: LLM
Fake LLM for testing purposes.
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¶
p... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
54b92f32ff71-1 | 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]... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
54b92f32ff71-2 | 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... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
54b92f32ff71-3 | batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod cons... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
54b92f32ff71-4 | classmethod from_orm(obj: Any) → Model¶
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, metada... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
54b92f32ff71-5 | 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.
Pa... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
54b92f32ff71-6 | Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
54b92f32ff71-7 | **kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
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/... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
54b92f32ff71-8 | 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 s... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fake.FakeListLLM.html |
b3895c751cef-0 | langchain.llms.google_palm.generate_with_retry¶
langchain.llms.google_palm.generate_with_retry(llm: GooglePalm, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call. | https://api.python.langchain.com/en/latest/llms/langchain.llms.google_palm.generate_with_retry.html |
7ba7dec8a00a-0 | langchain.llms.fireworks.update_token_usage¶
langchain.llms.fireworks.update_token_usage(keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]) → None[source]¶
Update token usage. | https://api.python.langchain.com/en/latest/llms/langchain.llms.fireworks.update_token_usage.html |
714d629d841e-0 | langchain.llms.bananadev.Banana¶
class langchain.llms.bananadev.Banana[source]¶
Bases: LLM
Banana large language models.
To use, you should have the banana-dev python package installed,
and the environment variable BANANA_API_KEY set with your API key.
Any parameters that are valid to be passed to the call can be passe... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
714d629d841e-1 | 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[Li... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
714d629d841e-2 | 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.
**kwarg... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
714d629d841e-3 | 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[... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
714d629d841e-4 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Return a dictionary of the LLM.
classmethod from_orm(obj: Any) → Model¶
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHa... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
714d629d841e-5 | 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 co... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
714d629d841e-6 | json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Cal... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
714d629d841e-7 | 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 ... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
714d629d841e-8 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/llms/langchain.llms.bananadev.Banana.html |
4249d3f3a120-0 | langchain.llms.openlm.OpenLM¶
class langchain.llms.openlm.OpenLM[source]¶
Bases: BaseOpenAI
OpenLM 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 allowed_special: Union[Literal['all'], Abstrac... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-1 | Model name to use.
param n: int = 1¶
How many completions to generate for each prompt.
param openai_api_base: Optional[str] = None¶
param openai_api_key: Optional[str] = None¶
param openai_organization: Optional[str] = None¶
param openai_proxy: Optional[str] = None¶
param presence_penalty: float = 0¶
Penalizes repeated... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-2 | 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... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-3 | 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 languag... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-4 | 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 a... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-5 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-6 | 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 PromptVal... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-7 | get_token_ids(text: str) → List[int]¶
Get the token IDs using the tiktoken package.
invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, ... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-8 | max_tokens = openai.modelname_to_contextsize("text-davinci-003")
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-9 | to the model provider API call.
Returns
Top model prediction as a message.
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”)
classmethod schema(by_alias: bool = True, ref_template: unicode =... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
4249d3f3a120-10 | 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.
property max_context_size: int¶
Get max context size for this model... | https://api.python.langchain.com/en/latest/llms/langchain.llms.openlm.OpenLM.html |
2c5fc75f3c7e-0 | langchain_experimental.llms.jsonformer_decoder.import_jsonformer¶
langchain_experimental.llms.jsonformer_decoder.import_jsonformer() → jsonformer[source]¶
Lazily import jsonformer. | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.jsonformer_decoder.import_jsonformer.html |
7c76a3bccd22-0 | langchain.llms.anthropic.Anthropic¶
class langchain.llms.anthropic.Anthropic[source]¶
Bases: LLM, _AnthropicCommon
Anthropic large language models.
To use, you should have the anthropic python package installed, and the
environment variable ANTHROPIC_API_KEY set with your API key, or pass
it as a named parameter to the... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-1 | Timeout for requests to Anthropic Completion API. Default is 600 seconds.
param max_tokens_to_sample: int = 256¶
Denotes the number of tokens to predict per generation.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model: str = 'claude-2'¶
Model name to use.
param streaming: b... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-2 | 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]... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-3 | 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... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-4 | batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod cons... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-5 | classmethod from_orm(obj: Any) → Model¶
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, metada... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-6 | 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[source]¶
Calculate number of tokens.
get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶
Get the number of... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-7 | classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-8 | 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”)
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/... | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
7c76a3bccd22-9 | 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. | https://api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
3683cfffc57a-0 | langchain_experimental.llms.anthropic_functions.TagParser¶
class langchain_experimental.llms.anthropic_functions.TagParser[source]¶
A heavy-handed solution, but it’s fast for prototyping.
Might be re-implemented later to restrict scope to the limited grammar, and
more efficiency.
Uses an HTML parser to parse a limited ... | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.anthropic_functions.TagParser.html |
3683cfffc57a-1 | parse_declaration(i)
parse_endtag(i)
parse_html_declaration(i)
parse_marked_section(i[, report])
parse_pi(i)
parse_starttag(i)
reset()
Reset this instance.
set_cdata_mode(elem)
unknown_decl(data)
updatepos(i, j)
__init__() → None[source]¶
A heavy-handed solution, but it’s fast for prototyping.
Might be re-implemented l... | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.anthropic_functions.TagParser.html |
3683cfffc57a-2 | Hook when a tag is closed.
handle_entityref(name)¶
handle_pi(data)¶
handle_startendtag(tag, attrs)¶
handle_starttag(tag: str, attrs: Any) → None[source]¶
Hook when a new tag is encountered.
parse_bogus_comment(i, report=1)¶
parse_comment(i, report=1)¶
parse_declaration(i)¶
parse_endtag(i)¶
parse_html_declaration(i)¶
pa... | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.anthropic_functions.TagParser.html |
dad2962b0d75-0 | langchain.llms.cohere.acompletion_with_retry¶
langchain.llms.cohere.acompletion_with_retry(llm: Cohere, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call. | https://api.python.langchain.com/en/latest/llms/langchain.llms.cohere.acompletion_with_retry.html |
bc42c6a2bb85-0 | langchain.llms.azureml_endpoint.OSSContentFormatter¶
class langchain.llms.azureml_endpoint.OSSContentFormatter[source]¶
Deprecated: Kept for backwards compatibility
Content handler for LLMs from the OSS catalog.
Attributes
accepts
The MIME type of the response data returned from the endpoint
content_formatter
content_t... | https://api.python.langchain.com/en/latest/llms/langchain.llms.azureml_endpoint.OSSContentFormatter.html |
e274631c9935-0 | langchain.llms.vertexai.VertexAI¶
class langchain.llms.vertexai.VertexAI[source]¶
Bases: _VertexAICommon, LLM
Google Vertex AI large language 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 cac... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
e274631c9935-1 | param top_p: float = 0.95¶
Tokens are selected from most probable to least until the sum of their
param tuned_model_name: Optional[str] = None¶
The name of a tuned model. If provided, model_name is ignored.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
e274631c9935-2 | 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 ag... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
e274631c9935-3 | **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 ... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
e274631c9935-4 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
e274631c9935-5 | 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 agno... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
e274631c9935-6 | 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]] =... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
e274631c9935-7 | 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
fir... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
e274631c9935-8 | 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¶
classmethod update_forward_ref... | https://api.python.langchain.com/en/latest/llms/langchain.llms.vertexai.VertexAI.html |
628aef77ed2a-0 | langchain.llms.predictionguard.PredictionGuard¶
class langchain.llms.predictionguard.PredictionGuard[source]¶
Bases: LLM
Prediction Guard large language models.
To use, you should have the predictionguard python package installed, and the
environment variable PREDICTIONGUARD_TOKEN set with your access token, or pass
it... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
628aef77ed2a-1 | param token: Optional[str] = None¶
Your Prediction Guard access token.
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, metad... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
628aef77ed2a-2 | 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 PromptVal... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
628aef77ed2a-3 | 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 on... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
628aef77ed2a-4 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
628aef77ed2a-5 | 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 agno... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
628aef77ed2a-6 | 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]] =... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
628aef77ed2a-7 | 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
fir... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
628aef77ed2a-8 | 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¶
classmethod update_forward_ref... | https://api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
20ebbc4d8106-0 | langchain.llms.databricks.get_default_api_token¶
langchain.llms.databricks.get_default_api_token() → str[source]¶
Gets the default Databricks personal access token.
Raises an error if the token cannot be automatically determined. | https://api.python.langchain.com/en/latest/llms/langchain.llms.databricks.get_default_api_token.html |
2fbe595783c0-0 | langchain.llms.nlpcloud.NLPCloud¶
class langchain.llms.nlpcloud.NLPCloud[source]¶
Bases: LLM
NLPCloud large language models.
To use, you should have the nlpcloud python package installed, and the
environment variable NLPCLOUD_API_KEY set with your API key.
Example
from langchain.llms import NLPCloud
nlpcloud = NLPCloud... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-1 | The minimum number of tokens to generate in the completion.
param model_name: str = 'finetuned-gpt-neox-20b'¶
Model name to use.
param nlpcloud_api_key: Optional[str] = None¶
param num_beams: int = 1¶
Number of beams for beam search.
param num_return_sequences: int = 1¶
How many completions to generate for each prompt.... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-2 | 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]... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-3 | 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... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-4 | batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod cons... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-5 | classmethod from_orm(obj: Any) → Model¶
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, metada... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-6 | 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.
Pa... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-7 | Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-8 | **kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
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/... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
2fbe595783c0-9 | 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 s... | https://api.python.langchain.com/en/latest/llms/langchain.llms.nlpcloud.NLPCloud.html |
174625e744c6-0 | langchain.llms.openai.update_token_usage¶
langchain.llms.openai.update_token_usage(keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]) → None[source]¶
Update token usage. | https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.update_token_usage.html |
e6bd29f58aeb-0 | langchain.llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway¶
class langchain.llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway[source]¶
Adapter to prepare the inputs from Langchain to a format
that LLM model expects.
It also provides helper function to extract
the generated text from the model response.
Metho... | https://api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway.html |
0483aa9a5f18-0 | langchain.llms.openai.completion_with_retry¶
langchain.llms.openai.completion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call. | https://api.python.langchain.com/en/latest/llms/langchain.llms.openai.completion_with_retry.html |
6a1260f3197e-0 | langchain.llms.databricks.get_default_host¶
langchain.llms.databricks.get_default_host() → str[source]¶
Gets the default Databricks workspace hostname.
Raises an error if the hostname cannot be automatically determined. | https://api.python.langchain.com/en/latest/llms/langchain.llms.databricks.get_default_host.html |
b6693cb02454-0 | langchain.llms.promptlayer_openai.PromptLayerOpenAI¶
class langchain.llms.promptlayer_openai.PromptLayerOpenAI[source]¶
Bases: OpenAI
PromptLayer OpenAI large language models.
To use, you should have the openai and promptlayer python
package installed, and the environment variable OPENAI_API_KEY
and PROMPTLAYER_API_KEY... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-1 | param frequency_penalty: float = 0¶
Penalizes repeated tokens according to frequency.
param logit_bias: Optional[Dict[str, float]] [Optional]¶
Adjust the probability of specific tokens being generated.
param max_retries: int = 6¶
Maximum number of retries to make when generating.
param max_tokens: int = 256¶
The maximu... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-2 | param temperature: float = 0.7¶
What sampling temperature to use.
param tiktoken_model_name: Optional[str] = None¶
The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-3 | 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]... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-4 | 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... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-5 | batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod cons... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-6 | classmethod from_orm(obj: Any) → Model¶
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, metada... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-7 | 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.
Pa... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-8 | Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
max_tokens_for_prompt(prompt: str) → int¶
Calculate the maximum number of tokens possible to generate for a prompt.
Paramet... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-9 | 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 m... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
b6693cb02454-10 | classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Out... | https://api.python.langchain.com/en/latest/llms/langchain.llms.promptlayer_openai.PromptLayerOpenAI.html |
9de7165eddac-0 | langchain.llms.fireworks.Fireworks¶
class langchain.llms.fireworks.Fireworks[source]¶
Bases: BaseFireworks
Wrapper around Fireworks large language models.
To use, you should have the fireworks python package installed, and the
environment variable FIREWORKS_API_KEY set with your API key.
Any parameters that are valid t... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fireworks.Fireworks.html |
9de7165eddac-1 | Tags to add to the run trace.
param temperature: float = 0.7¶
What sampling temperature to use.
param top_p: float = 1¶
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: Option... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fireworks.Fireworks.html |
9de7165eddac-2 | 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 ag... | https://api.python.langchain.com/en/latest/llms/langchain.llms.fireworks.Fireworks.html |
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