Optional fields: Partial<OpenAIChatInput> & Partial<AzureOpenAIInput> & BaseLanguageModelParams & { Optional configuration: ClientOptions & LegacyOpenAIInputThe async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
Penalizes repeated tokens according to frequency
Model name to use
Number of completions to generate for each prompt
Penalizes repeated tokens
Whether to stream the results or not. Enabling disables tokenUsage reporting
Sampling temperature to use
Total probability mass of tokens to consider at each step
Whether to print out response text.
Optional azureAzure OpenAI API deployment name to use for completions when making requests to Azure OpenAI. This is the name of the deployment you created in the Azure portal. e.g. "my-openai-deployment" this will be used in the endpoint URL: https://{InstanceName}.openai.azure.com/openai/deployments/my-openai-deployment/
Optional azureAzure OpenAI API instance name to use when making requests to Azure OpenAI. this is the name of the instance you created in the Azure portal. e.g. "my-openai-instance" this will be used in the endpoint URL: https://my-openai-instance.openai.azure.com/openai/deployments/{DeploymentName}/
Optional azureAPI key to use when making requests to Azure OpenAI.
Optional azureAPI version to use when making requests to Azure OpenAI.
Optional azureCustom endpoint for Azure OpenAI API. This is useful in case you have a deployment in another region. e.g. setting this value to "https://westeurope.api.cognitive.microsoft.com/openai/deployments" will be result in the endpoint URL: https://westeurope.api.cognitive.microsoft.com/openai/deployments/{DeploymentName}/
Optional cacheOptional callbacksOptional logitDictionary used to adjust the probability of specific tokens being generated
Optional maxMaximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the model's maximum context size.
Optional metadataOptional modelHolds any additional parameters that are valid to pass to openai.createCompletion that are not explicitly specified on this class.
Optional nameOptional openAIApiAPI key to use when making requests to OpenAI. Defaults to the value of
OPENAI_API_KEY environment variable.
Optional organizationOptional stopList of stop words to use when generating
Optional tagsOptional timeoutTimeout to use when making requests to OpenAI.
Optional userUnique string identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
Keys that the language model accepts as call options.
Assigns new fields to the dict output of this runnable. Returns a new runnable.
Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.
Array of inputs to each batch call.
Optional options: Partial<CallOptions> | Partial<CallOptions>[]Either a single call options object to apply to each batch call or an array for each call.
Optional batchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optional options: Partial<CallOptions> | Partial<CallOptions>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<CallOptions> | Partial<CallOptions>[]Optional batchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Makes a single call to the chat model.
An array of BaseMessage instances.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Makes a single call to the chat model with a prompt value.
The value of the prompt.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Calls the OpenAI API with retry logic in case of failures.
The request to send to the OpenAI API.
Optional options: OpenAICoreRequestOptionsOptional configuration for the API call.
The response from the OpenAI API.
Optional options: OpenAICoreRequestOptionsGenerates chat based on the input messages.
An array of arrays of BaseMessage instances.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to an LLMResult.
Generates a prompt based on the input prompt values.
An array of BasePromptValue instances.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to an LLMResult.
Invokes the chat model with a single input.
The input for the language model.
Optional options: CallOptionsThe call options.
A Promise that resolves to a BaseMessageChunk.
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
Pick keys from the dict output of this runnable. Returns a new runnable.
Create a new runnable sequence that runs each individual runnable in series, piping the output of one runnable into another runnable or runnable-like.
A runnable, function, or object whose values are functions or runnables.
A new runnable sequence.
Predicts the next message based on a text input.
The text input.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a string.
Predicts the next message based on the input messages.
An array of BaseMessage instances.
Optional options: string[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Return a json-like object representing this LLM.
Stream output in chunks.
Optional options: Partial<CallOptions>A readable stream that is also an iterable.
Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.
Optional options: Partial<CallOptions>Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
Bind config to a Runnable, returning a new Runnable.
New configuration parameters to attach to the new runnable.
A new RunnableBinding with a config matching what's passed.
Create a new runnable from the current one that will try invoking other passed fallback runnables if the initial invocation fails.
Other runnables to call if the runnable errors.
A new RunnableWithFallbacks.
Bind lifecycle listeners to a Runnable, returning a new Runnable. The Run object contains information about the run, including its id, type, input, output, error, startTime, endTime, and any tags or metadata added to the run.
The object containing the callback functions.
Optional onCalled after the runnable finishes running, with the Run object.
Optional config: RunnableConfigOptional onCalled if the runnable throws an error, with the Run object.
Optional config: RunnableConfigOptional onCalled before the runnable starts running, with the Run object.
Optional config: RunnableConfigAdd retry logic to an existing runnable.
Optional fields: { Optional onOptional stopA new RunnableRetry that, when invoked, will retry according to the parameters.
Static deserializeLoad an LLM from a json-like object describing it.
Static isGenerated using TypeDoc
Wrapper around OpenAI large language models that use the Chat endpoint.
To use you should have the
openaipackage installed, with theOPENAI_API_KEYenvironment variable set.To use with Azure you should have the
openaipackage installed, with theAZURE_OPENAI_API_KEY,AZURE_OPENAI_API_INSTANCE_NAME,AZURE_OPENAI_API_DEPLOYMENT_NAMEandAZURE_OPENAI_API_VERSIONenvironment variable set.AZURE_OPENAI_BASE_PATHis optional and will overrideAZURE_OPENAI_API_INSTANCE_NAMEif you need to use a custom endpoint.Remarks
Any parameters that are valid to be passed to
openai.createChatCompletioncan be passed through modelKwargs, even if not explicitly available on this class.Example