viruPVS/langchain-docs huggingface dataset:
Infino
Comet
Aim
Weights & Biases
SageMaker Tracking
OpenAI
Rebuff
MLflow
Helicone
Shale Protocol
WhyLabs
WandB Tracing
ClearML
Ray Serve
Log, Trace, and Monitor
Portkey
Chat Over Documents with Vectara
Vectara Text Generation
CSV
Xorbits
Jira
Spark Dataframe
Python
SQL Database
Natural Language APIs
JSON
Github
Pandas Dataframe
OpenAPI
Gitlab
Vectara
Psychic
Docugami
Amazon Textract 
OpaquePrompts
LLM Caching integrations
Fallbacks
Removing logical fallacies from model output
Set env var OPENAI_API_KEY or load from a .env file:
Perform context-aware text splitting
Retrieve from vector stores directly
Retrieve as you generate with FLARE
Improve document indexing with HyDE
QA using Activeloop’s DeepLake
Graph QA
Tree of Thought (ToT) example
HuggingGPT
SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base
Agent Debates with Tools
Bash chain
LLM Symbolic Math 
Summarization checker chain
Self-checking chain
Set env var OPENAI_API_KEY or load from a .env file
Vector SQL Retriever with MyScale
SQL
Milvus
Weaviate
Elasticsearch
Chroma
Pinecone
Supabase
Redis
MyScale
Deep Lake
Qdrant
Lost in the middle: The problem with long contexts
Memory in the Multi-Input Chain
Memory in LLMChain
Multiple Memory classes
Customizing Conversational Memory
Conversation Knowledge Graph
Conversation Token Buffer
Conversation Summary Buffer
Multiple callback handlers
Token counting
Logging to file
Multi-Input Tools
Defining Custom Tools
Tool Input Schema
Human-in-the-loop Tool Validation
langchain_community.llms.openllm.OpenLLM¶
class langchain_community.llms.openllm.OpenLLM[source]¶
Bases: LLM
OpenLLM, supporting both in-process model
instance and remote OpenLLM servers.
To use, you should have the openllm library installed:
pip install openllm
Learn more at: https://github.com/bentoml/openllm
Example running an LLM model locally managed by OpenLLM:from langchain_community.llms import OpenLLM
llm = OpenLLM(
    model_name='flan-t5',
    model_id='google/flan-t5-large',
)
llm("What is the difference between a duck and a goose?")
For all available supported models, you can run ‘openllm models’.
If you have a OpenLLM server running, you can also use it remotely:from langchain_community.llms import OpenLLM
llm = OpenLLM(server_url='http://localhost:3000')
llm("What is the difference between a duck and a goose?")
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 embedded: bool = True¶
Initialize this LLM instance in current process by default. Should
only set to False when using in conjunction with BentoML Service.
param llm_kwargs: Dict[str, Any] [Required]¶
Keyword arguments to be passed to openllm.LLM
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_id: Optional[str] = None¶
Metadata to add to the run trace.
param model_id: Optional[str] = None¶
Model Id to use. If not provided, will use the default model for the model name.
See ‘openllm models’ for all available model variants.
param model_name: Optional[str] = None¶
Model name to use. See ‘openllm models’ for all available models.
param server_type: ServerType = 'http'¶
Optional server type. Either ‘http’ or ‘grpc’.
param server_url: Optional[str] = None¶
Optional server URL that currently runs a LLMServer with ‘openllm start’.
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, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
property runner: openllm.LLMRunner¶
Get the underlying openllm.LLMRunner instance for integration with BentoML.
Example:
.. code-block:: python
llm = OpenLLM(model_name=’flan-t5’,
model_id=’google/flan-t5-large’,
embedded=False,
)
tools = load_tools([“serpapi”, “llm-math”], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)
svc = bentoml.Service(“langchain-openllm”, runners=[llm.runner])
@svc.api(input=Text(), output=Text())
def chat(input_text: str):
return agent.run(input_text)
Examples using OpenLLM¶
OpenLLM
langchain_community.llms.titan_takeoff.TitanTakeoff¶
class langchain_community.llms.titan_takeoff.TitanTakeoff[source]¶
Bases: LLM
Wrapper around Titan Takeoff APIs.
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 base_url: str = 'http://localhost:8000'¶
Specifies the baseURL to use for the Titan Takeoff API.
Default = http://localhost:8000.
param cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param generate_max_length: int = 128¶
Maximum generation length. Default = 128.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param no_repeat_ngram_size: int = 0¶
Prevent repetitions of ngrams of this size. Default = 0 (turned off).
param repetition_penalty: float = 1.0¶
Penalise the generation of tokens that have been generated before.
Set to > 1 to penalize. Default = 1 (no penalty).
param sampling_temperature: float = 1.0¶
Sample with randomness. Bigger temperatures are associated with
more randomness and ‘creativity’. Default = 1.0.
param sampling_topk: int = 1¶
Sample predictions from the top K most probable candidates. Default = 1.
param sampling_topp: float = 1.0¶
Sample from predictions whose cumulative probability exceeds this value.
Default = 1.0.
param streaming: bool = False¶
Whether to stream the output. Default = False.
param tags: Optional[List[str]] = None¶
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using TitanTakeoff¶
Titan Takeoff
langchain_community.llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway¶
class langchain_community.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.
Methods
__init__()
transform_input(prompt, model_kwargs)
transform_output(response)
__init__()¶
classmethod transform_input(prompt: str, model_kwargs: Dict[str, Any]) → Dict[str, Any][source]¶
classmethod transform_output(response: Any) → str[source]¶
langchain_community.llms.chatglm.ChatGLM¶
class langchain_community.llms.chatglm.ChatGLM[source]¶
Bases: LLM
ChatGLM LLM service.
Example
from langchain_community.llms import ChatGLM
endpoint_url = (
    "http://127.0.0.1:8000"
)
ChatGLM_llm = ChatGLM(
    endpoint_url=endpoint_url
)
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 = 'http://127.0.0.1:8000/'¶
Endpoint URL to use.
param history: List[List] = []¶
History of the conversation
param max_token: int = 20000¶
Max token allowed to pass to the model.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Optional[dict] = None¶
Keyword arguments to pass to the model.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.1¶
LLM model temperature from 0 to 10.
param top_p: float = 0.7¶
Top P for nucleus sampling from 0 to 1
param verbose: bool [Optional]¶
Whether to print out response text.
param with_history: bool = False¶
Whether to use history or not
param with_history: bool = False¶
Whether to use history or not
__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, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
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, run_name: Optional[Union[str, List[str]]] = None, **kwargs: Any) → LLMResult¶
Run the LLM on the given prompt and input.
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using ChatGLM¶
ChatGLM
langchain_community.llms.minimax.Minimax¶
class langchain_community.llms.minimax.Minimax[source]¶
Bases: MinimaxCommon, LLM
Wrapper around Minimax large language models.
To use, you should have the environment variable
MINIMAX_API_KEY and MINIMAX_GROUP_ID set with your API key,
or pass them as a named parameter to the constructor.
.. rubric:: Example
. code-block:: pythonfrom langchain_community.llms.minimax import Minimax
minimax = Minimax(model=”<model_name>”, minimax_api_key=”my-api-key”,
minimax_group_id=”my-group-id”)
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 max_tokens: 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 minimax_api_host: Optional[str] = None¶
param minimax_api_key: Optional[SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
param minimax_group_id: Optional[str] = None¶
param model: str = 'abab5.5-chat'¶
Model name to use.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.7¶
A non-negative float that tunes the degree of randomness in generation.
A non-negative float that tunes the degree of randomness in generation.
param top_p: float = 0.95¶
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, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
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, run_name: Optional[Union[str, List[str]]] = None, **kwargs: Any) → LLMResult¶
Run the LLM on the given prompt and input.
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using Minimax¶
Minimax
langchain_community.llms.openlm.OpenLM¶
class langchain_community.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'], AbstractSet[str]] = {}¶
Set of special tokens that are allowed。
param batch_size: int = 20¶
Batch size to use when passing multiple documents to generate.
param best_of: int = 1¶
Generates best_of completions server-side and returns the “best”.
param cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param default_headers: Union[Mapping[str, str], None] = None¶
param default_query: Union[Mapping[str, object], None] = None¶
param disallowed_special: Union[Literal['all'], Collection[str]] = 'all'¶
Set of special tokens that are not allowed。
param frequency_penalty: float = 0¶
Penalizes repeated tokens according to frequency.
param http_client: Union[Any, None] = None¶
Optional httpx.Client.
param logit_bias: Optional[Dict[str, float]] [Optional]¶
Adjust the probability of specific tokens being generated.
param max_retries: int = 2¶
Maximum number of retries to make when generating.
param 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.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
Metadata to add to the run trace.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param model_name: str = 'text-davinci-003' (alias 'model')¶
Model name to use.
param n: int = 1¶
How many completions to generate for each prompt.
param openai_api_base: Optional[str] = None (alias 'base_url')¶
Base URL path for API requests, leave blank if not using a proxy or service
emulator.
param openai_api_key: Optional[str] = None (alias 'api_key')¶
Automatically inferred from env var OPENAI_API_KEY if not provided.
param openai_organization: Optional[str] = None (alias 'organization')¶
Automatically inferred from env var OPENAI_ORG_ID if not provided.
param openai_proxy: Optional[str] = None¶
param presence_penalty: float = 0¶
Penalizes repeated tokens.
param request_timeout: Union[float, Tuple[float, float], Any, None] = None (alias 'timeout')¶
Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None.
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 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
be the same as the embedding model name. However, there are some cases
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here.
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: 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, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
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_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
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_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.