id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 115 |
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c9b7d85f6498-2 | field entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last l... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-3 | a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-4 | field entity_store: langchain.memory.entity.BaseEntityStore [Optional]#
field entity_summarization_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['entity', 'summary', 'history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant helping a human kee... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-5 | Knowledge graph memory for storing conversation memory.
Integrates with external knowledge graph to store and retrieve
information about knowledge triples in the conversation.
field ai_prefix: str = 'AI'# | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-6 | field entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last l... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-7 | a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-8 | field human_prefix: str = 'Human'#
field k: int = 2#
field kg: langchain.graphs.networkx_graph.NetworkxEntityGraph [Optional]# | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-9 | field knowledge_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrati... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-10 | It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: Hello.\nAI: Hi! How are you?\nPerson #1: I'm good. How are you?\nAI: I'm good too.\nLast line of conversat... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-11 | huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:", template_f... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-12 | field llm: langchain.base_language.BaseLanguageModel [Required]#
field summary_message_cls: Type[langchain.schema.BaseMessage] = <class 'langchain.schema.SystemMessage'>#
Number of previous utterances to include in the context.
clear() → None[source]#
Clear memory contents.
get_current_entities(input_string: str) → Lis... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-13 | field memory_key: str = 'history'#
field moving_summary_buffer: str = ''#
clear() → None[source]#
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]#
Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this con... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-14 | property buffer: List[langchain.schema.BaseMessage]#
String buffer of memory.
class langchain.memory.CosmosDBChatMessageHistory(cosmos_endpoint: str, cosmos_database: str, cosmos_container: str, session_id: str, user_id: str, credential: Optional[Any] = None, connection_string: Optional[str] = None, ttl: Optional[int] ... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-15 | Append the message to the record in DynamoDB
clear() → None[source]#
Clear session memory from DynamoDB
property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from DynamoDB
class langchain.memory.InMemoryEntityStore[source]#
Basic in-memory entity store.
clear() → None[source]#
Delete all entities... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-16 | clear() → None[source]#
Nothing to clear, got a memory like a vault.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Load memory variables from memory.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Nothing should be saved or changed
property memory_variables: List... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-17 | delete(key: str) → None[source]#
Delete entity value from store.
exists(key: str) → bool[source]#
Check if entity exists in store.
property full_key_prefix: str#
get(key: str, default: Optional[str] = None) → Optional[str][source]#
Get entity value from store.
key_prefix: str = 'memory_store'#
recall_ttl: Optional[int]... | https://python.langchain.com/en/latest/reference/modules/memory.html |
c9b7d85f6498-18 | field retriever: langchain.vectorstores.base.VectorStoreRetriever [Required]#
VectorStoreRetriever object to connect to.
field return_docs: bool = False#
Whether or not to return the result of querying the database directly.
clear() → None[source]#
Nothing to clear.
load_memory_variables(inputs: Dict[str, Any]) → Dict[... | https://python.langchain.com/en/latest/reference/modules/memory.html |
acaa60aeda34-0 | .rst
.pdf
Tools
Tools#
Core toolkit implementations.
pydantic model langchain.tools.AIPluginTool[source]#
field api_spec: str [Required]#
field args_schema: Type[AIPluginToolSchema] = <class 'langchain.tools.plugin.AIPluginToolSchema'>#
Pydantic model class to validate and parse the tool’s input arguments.
field plugin... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-1 | to_typescript() → str[source]#
Get typescript string representation of the operation.
static ts_type_from_python(type_: Union[str, Type, tuple, None, enum.Enum]) → str[source]#
property body_params: List[str]#
property path_params: List[str]#
property query_params: List[str]#
pydantic model langchain.tools.BaseTool[sou... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-2 | Run the tool asynchronously.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Any[s... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-3 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Create a copy of a file in a specified location'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'copy_file'#
The unique name of the to... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-4 | field num_results: int = 4#
pydantic model langchain.tools.DuckDuckGoSearchRun[source]#
Tool that adds the capability to query the DuckDuckGo search API.
field api_wrapper: langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper [Optional]#
pydantic model langchain.tools.ExtractHyperlinksTool[source]#
Extract ... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-5 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Recursively search for files in a subdirectory that match the regex pattern'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'file_sear... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-6 | Tool that adds the capability to ask user for input.
field input_func: Callable [Optional]#
field prompt_func: Callable[[str], None] [Optional]#
pydantic model langchain.tools.IFTTTWebhook[source]#
IFTTT Webhook.
Parameters
name – name of the tool
description – description of the tool
url – url to hit with the json eve... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-7 | Navigate back to the previous page in the browser history.
field args_schema: Type[BaseModel] = <class 'pydantic.main.BaseModel'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Navigate back to the previous page in the browser history'#
Used to tell the model how/when/... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-8 | REQUIRED. Provides metadata about the API. The metadata MAY be used by tooling as required.
field jsonSchemaDialect: Optional[str] = None#
The default value for the $schema keyword within [Schema Objects](#schemaObject)
contained within this OAS document. This MUST be in the form of a URI.
field openapi: str = '3.1.0'#... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-9 | A list of tags used by the document with additional metadata.
The order of the tags can be used to reflect on their order by the parsing tools.
Not all tags that are used by the [Operation Object](#operationObject) must be declared.
The tags that are not declared MAY be organized randomly or based on the tools’ logic.
... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-10 | Get an OpenAPI spec from a URL.
static get_cleaned_operation_id(operation: openapi_schema_pydantic.v3.v3_1_0.operation.Operation, path: str, method: str) → str[source]#
Get a cleaned operation id from an operation id.
get_methods_for_path(path: str) → List[str][source]#
Return a list of valid methods for the specified ... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-11 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Read file from disk'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'read_file'#
The unique name of the tool that clearly communicates... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-12 | The function to run when the tool is called.
classmethod from_function(func: Callable, name: Optional[str] = None, description: Optional[str] = None, return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, infer_schema: bool = True, **kwargs: Any) → langchain.tools.base.StructuredTool[... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-13 | field verbose: bool = False#
Whether to log the tool’s progress.
classmethod from_function(func: Callable, name: str, description: str, return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, **kwargs: Any) → langchain.tools.base.Tool[source]#
Initialize tool from a function.
property ... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-14 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Write file to disk'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'write_file'#
The unique name of the tool that clearly communicates... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-15 | (the set api_key must be associated with the action owner)
instructions – a natural language instruction string for using the action
(eg. “get the latest email from Mike Knoop” for “Gmail: find email” action)
params – a dict, optional. Any params provided will override AI guesses
from instructions (see “understanding t... | https://python.langchain.com/en/latest/reference/modules/tools.html |
acaa60aeda34-16 | return
previous
Agents
next
Agent Toolkits
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/reference/modules/tools.html |
c31a3c84efc8-0 | .rst
.pdf
LLMs
LLMs#
Wrappers on top of large language models APIs.
pydantic model langchain.llms.AI21[source]#
Wrapper around 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")
V... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-1 | field numResults: int = 1#
How many completions to generate for each prompt.
field presencePenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)#
Penalizes repeated tokens.
field temperat... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-2 | Default values are respected, but no other validation is performed.
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... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-3 | Get the number of tokens in the message.
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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-4 | Validators
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields
field aleph_alpha_api_key: Optional[str] = None#
API key for Aleph Alpha API.
field best_of: Optional[int] = None#
returns the one with the “best of” results
(highest log probability per token)
field completion_bias_exclus... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-5 | field penalty_bias: Optional[str] = None#
Penalty bias for the completion.
field penalty_exceptions: Optional[List[str]] = None#
List of strings that may be generated without penalty,
regardless of other penalty settings
field penalty_exceptions_include_stop_sequences: Optional[bool] = None#
Should stop_sequences be in... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-6 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-7 | Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-8 | Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Anthropic[source]#
Wrapper arou... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-9 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-10 | Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-11 | Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
stream(prompt: str, stop: Optional[List[str]] = None) → Generator[source]#
Call Anthropic completion_stream and return the resulting generator.
BETA: this is a beta feature while we figure out the... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-12 | Set of special tokens that are allowed。
field batch_size: int = 20#
Batch size to use when passing multiple documents to generate.
field best_of: int = 1#
Generates best_of completions server-side and returns the “best”.
field deployment_name: str = ''#
Deployment name to use.
field disallowed_special: Union[Literal['a... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-13 | Total probability mass of tokens to consider at each step.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → st... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-14 | 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 creating
the new model: you should trust this data
deep – set to True to make a deep co... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-15 | Get the sub prompts for llm call.
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: b... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-16 | .. code-block:: python
llm.save(file_path=”path/llm.yaml”)
stream(prompt: str, stop: Optional[List[str]] = None) → Generator#
Call OpenAI with streaming flag and return the resulting generator.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.
Parame... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-17 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-18 | Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-19 | Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.CerebriumAI[source]#
Wrapper ar... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-20 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a li... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-21 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-22 | Wrapper around Cohere large language models.
To use, you should have the cohere python package installed, and the
environment variable COHERE_API_KEY set with your API key, or pass
it as a named parameter to the constructor.
Example
from langchain.llms import Cohere
cohere = Cohere(model="gptd-instruct-tft", cohere_api... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-23 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-24 | Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-25 | Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.DeepInfra[source]#
Wrapper arou... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-26 | Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Confi... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-27 | Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingInt... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-28 | Validators
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields
field base_url: Optional[str] = None#
Base url to use, if None decides based on model name.
field endpoint_url: str = ''#
Model name to use.
field length: int = 256#
The maximum number of tokens to generate in the completi... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-29 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-30 | Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-31 | # Simplest invocation
response = model("Once upon a time, ")
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields
field echo: Optional[bool] = False#
Whether to echo the prompt.
field embedding: bool = False#
Use embedding mode only.
field f16_kv: bool = False#
Use half-prec... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-32 | field top_p: Optional[float] = 0.95#
The top-p value to use for sampling.
field use_mlock: bool = False#
Force system to keep model in RAM.
field vocab_only: bool = False#
Only load the vocabulary, no weights.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.bas... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-33 | 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://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-34 | Get the number of tokens in the message.
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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-35 | field temperature: float = 0.7#
Run inference with this temperature. Must by in the closed interval
[0.0, 1.0].
field top_k: Optional[int] = None#
Decode using top-k sampling: consider the set of top_k most probable tokens.
Must be positive.
field top_p: Optional[float] = None#
Decode using nucleus sampling: consider t... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-36 | Default values are respected, but no other validation is performed.
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... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-37 | Get the number of tokens in the message.
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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-38 | field logit_bias: Optional[Dict[str, float]] [Optional]#
Adjust the probability of specific tokens being generated.
field max_tokens: int = 256#
The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size.
field min_tokens: int =... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-39 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a li... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-40 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-41 | pydantic model langchain.llms.HuggingFaceEndpoint[source]#
Wrapper around HuggingFaceHub Inference Endpoints.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Only supp... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-42 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a li... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-43 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-44 | pydantic model langchain.llms.HuggingFaceHub[source]#
Wrapper around HuggingFaceHub models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Only supports text-generat... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-45 | Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Confi... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-46 | Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingInt... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-47 | model_id="gpt2", task="text-generation"
)
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... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-48 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-49 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-50 | Wrapper around the llama.cpp model.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: abetlen/llama-cpp-python
Example
from langchain.llms import LlamaCppEmbeddings
llm = LlamaCppEmbeddings(model_path="/path/to/lla... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-51 | field n_parts: int = -1#
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
field n_threads: Optional[int] = None#
Number of threads to use.
If None, the number of threads is automatically determined.
field repeat_penalty: Optional[float] = 1.1#
The penalty to apply to repe... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-52 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-53 | Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-54 | Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
stream(prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[langchain.callbacks.manager.CallbackManagerForLLMRun] = None) → Generator[Dict, None, None][source]#
Yields results obje... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-55 | Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
Validators
build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
field endpoint_url: str = ''#
model endpoint to use
field model_kwargs: Dict[str, Any] [Optional]#
Holds a... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-56 | 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://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-57 | Get the number of tokens in the message.
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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-58 | field do_sample: bool = True#
Whether to use sampling (True) or greedy decoding.
field early_stopping: bool = False#
Whether to stop beam search at num_beams sentences.
field length_no_input: bool = True#
Whether min_length and max_length should include the length of the input.
field length_penalty: float = 1.0#
Expone... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-59 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-60 | Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-61 | Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.OpenAI[source]#
Wrapper around ... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-62 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a li... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-63 | dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt an... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-64 | 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.
Parameters
prompt – The prompt to pass into the model.
Returns
The maximum number of tokens to ge... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c31a3c84efc8-65 | yield token
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.OpenAIChat[source]#
Wrapper around OpenAI Chat large language models.
To use, you should have the openai python package installed, and the
envi... | https://python.langchain.com/en/latest/reference/modules/llms.html |
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