Optional fields: Partial<OpenAIInput> & Partial<AzureOpenAIInput> & BaseLLMParams & { Optional configuration: ClientOptions & LegacyOpenAIInputBatch size to use when passing multiple documents to generate
The 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
Maximum 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.
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 bestGenerates bestOf completions server side and returns the "best"
Optional cacheOptional callbacksOptional logitDictionary used to adjust the probability of specific tokens being generated
Optional metadataOptional modelHolds any additional parameters that are valid to pass to openai.createCompletion that are not explicitly specified on this class.
Optional 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.
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.
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: OpenAICoreRequestOptionsThis method takes prompt values, options, and callbacks, and generates a result based on the prompts.
Prompt values for the LLM.
Optional options: string[] | CallOptionsOptions for the LLM call.
Optional callbacks: CallbacksCallbacks for the LLM call.
An LLMResult based on the prompts.
This method takes an input and options, and returns a string. It converts the input to a prompt value and generates a result based on the prompt.
Input for the LLM.
Optional options: CallOptionsOptions for the LLM call.
A string result based on the prompt.
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
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.
This method is similar to call, but it's used for making predictions
based on the input text.
Input text for the prediction.
Optional options: string[] | CallOptionsOptions for the LLM call.
Optional callbacks: CallbacksCallbacks for the LLM call.
A prediction based on the input text.
This method takes a list of messages, options, and callbacks, and returns a predicted message.
A list of messages for the prediction.
Optional options: string[] | CallOptionsOptions for the LLM call.
Optional callbacks: CallbacksCallbacks for the LLM call.
A predicted message based on the list of messages.
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.
Add 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.
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.Remarks
Any parameters that are valid to be passed to
openai.createCompletioncan be passed through modelKwargs, even if not explicitly available on this class.