feat(llms)!: Move all model config options to OpenAIOptions (#232)

docs/modules/chains/chains.md

@@ -38,7 +38,10 @@ and returns the response from an LLM.
 To use the `LLMChain`, first create a prompt template.
 
 ```dart
-final llm = OpenAI(apiKey: openaiApiKey, temperature: 0.9);
+final llm = OpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: const OpenAIOptions(temperature: 0.9),
+);
 final prompt = PromptTemplate.fromTemplate(
   'What is a good name for a company that makes {product}?',
 );

docs/modules/chains/foundational/llm.md

@@ -12,7 +12,10 @@ to LLM and returns the LLM output.
 ## Get started
 
 ```dart
-final llm = OpenAI(apiKey: openaiApiKey, temperature: 0.9);
+final llm = OpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: const OpenAIOptions(temperature: 0.9),
+);
 final prompt = PromptTemplate.fromTemplate(
   'What is a good name for a company that makes {product}?',
 );

docs/modules/chains/getting_started.md

@@ -137,7 +137,10 @@ Now, we can try running the chain that we called:
 
 ```dart
 final openaiApiKey = Platform.environment['OPENAI_API_KEY'];
-final llm = OpenAI(apiKey: openaiApiKey, temperature: 0.9);
+final llm = OpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: const OpenAIOptions(temperature: 0.9),
+);
 
 final prompt1 = PromptTemplate.fromTemplate(
 'What is a good name for a company that makes {product}?',

docs/modules/chains/how_to/custom_chain.md

@@ -50,7 +50,10 @@ Now, we can try running the chain that we called:
 
 ```dart
 final openaiApiKey = Platform.environment['OPENAI_API_KEY'];
-final llm = OpenAI(apiKey: openaiApiKey, temperature: 0.9);
+final llm = OpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: const OpenAIOptions(temperature: 0.9),
+);
 
 final prompt1 = PromptTemplate.fromTemplate(
 'What is a good name for a company that makes {product}?',

docs/modules/memory/memory.md

@@ -85,7 +85,10 @@ print(await memory.loadMemoryVariables());
 Finally, let’s take a look at using this in a chain:
 
 ```dart
-final llm = OpenAI(apiKey: openaiApiKey, temperature: 0);
+final llm = OpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: const OpenAIOptions(temperature: 0),
+);
 final conversation = ConversationChain(
   llm: llm,
   memory: ConversationBufferMemory(),

docs/modules/model_io/models/llms/how_to/token_usage_tracking.md

@@ -4,7 +4,10 @@ This tutorial goes over how to track your token usage for specific calls. It is
 currently only implemented for the OpenAI API.
 
 ```dart
-final openai = OpenAI(apiKey: openaiApiKey, temperature: 0.9);
+final openai = OpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: const OpenAIOptions(temperature: 0.9),
+);
 final result = await openai.generate('Tell me a joke');
 final usage = result.usage;
 print(usage?.promptTokens);   // 4

docs/modules/model_io/prompts/prompt_templates/custom_prompt_template.md

@@ -98,7 +98,10 @@ Now that we have created a custom prompt template, we can use it to generate
 prompts for our task.
 
 ```dart
-final openai = OpenAI(apiKey: openaiApiKey, temperature: 0.9);
+final openai = OpenAI(
+  apiKey: openaiApiKey,
+  defaultOptions: const OpenAIOptions(temperature: 0.9),
+);
 
 const fnExplainer = FunctionExplainerPromptTemplate();
 final prompt = fnExplainer.formatPrompt({

packages/langchain/lib/src/model_io/chat_models/fake.dart

@@ -29,7 +29,10 @@ class FakeChatModel extends SimpleChatModel {
   }
 
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final ChatModelOptions? options,
+  }) async {
     return promptValue
         .toString()
         .split(' ')
@@ -77,7 +80,10 @@ class FakeEchoChatModel extends SimpleChatModel {
   }
 
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final ChatModelOptions? options,
+  }) async {
     return promptValue
         .toString()
         .split(' ')

packages/langchain/lib/src/model_io/language_models/base.dart

@@ -54,7 +54,10 @@ abstract class BaseLanguageModel<Input extends Object,
   /// model.
   ///
   /// - [promptValue] The prompt to tokenize.
-  Future<List<int>> tokenize(final PromptValue promptValue);
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final Options? options,
+  });
 
   /// Returns the number of tokens resulting from [tokenize] the given prompt.
   ///

packages/langchain/lib/src/model_io/llms/fake.dart

@@ -29,7 +29,10 @@ class FakeListLLM extends SimpleLLM {
   }
 
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final LLMOptions? options,
+  }) async {
     return promptValue
         .toString()
         .split(' ')
@@ -76,7 +79,10 @@ class FakeEchoLLM extends SimpleLLM {
   }
 
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final LLMOptions? options,
+  }) async {
     return promptValue
         .toString()
         .split(' ')
@@ -116,7 +122,10 @@ class FakeHandlerLLM extends SimpleLLM {
   }
 
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final LLMOptions? options,
+  }) async {
     return promptValue
         .toString()
         .split(' ')

packages/langchain/test/core/runnable/binding_test.dart

@@ -76,7 +76,10 @@ class _FakeOptionsChatModel
   }
 
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final _FakeOptionsChatModelOptions? options,
+  }) async {
     return promptValue
         .toString()
         .split(' ')

packages/langchain_google/lib/src/chat_models/vertex_ai.dart

@@ -206,7 +206,10 @@ class ChatVertexAI extends BaseChatModel<ChatVertexAIOptions> {
   ///
   /// - [promptValue] The prompt to tokenize.
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final ChatVertexAIOptions? options,
+  }) async {
     final encoding = encodingForModel('text-davinci-003');
     return encoding.encode(promptValue.toString());
   }

packages/langchain_google/lib/src/llms/vertex_ai.dart

@@ -187,7 +187,10 @@ class VertexAI extends BaseLLM<VertexAIOptions> {
   ///
   /// - [promptValue] The prompt to tokenize.
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final VertexAIOptions? options,
+  }) async {
     final encoding = encodingForModel('text-davinci-003');
     return encoding.encode(promptValue.toString());
   }

packages/langchain_openai/example/langchain_openai_example.dart

@@ -13,7 +13,10 @@ void main() async {
 /// The most basic building block of LangChain is calling an LLM on some input.
 Future<void> _example1() async {
   final openaiApiKey = Platform.environment['OPENAI_API_KEY'];
-  final openai = OpenAI(apiKey: openaiApiKey, temperature: 0.9);
+  final openai = OpenAI(
+    apiKey: openaiApiKey,
+    defaultOptions: const OpenAIOptions(temperature: 0.9),
+  );
   final result = await openai('Tell me a joke');
   print(result);
 }

packages/langchain_openai/lib/langchain_openai.dart

@@ -1,6 +1,8 @@
 /// LangChain.dart integration module for OpenAI (GPT-3, GPT-4, Functions, etc.).
 library;
 
+export 'package:openai_dart/openai_dart.dart' show OpenAIClientException;
+
 export 'src/agents/agents.dart';
 export 'src/chains/chains.dart';
 export 'src/chat_models/chat_models.dart';

packages/langchain_openai/lib/src/chat_models/openai.dart

@@ -353,7 +353,10 @@ class ChatOpenAI extends BaseChatModel<ChatOpenAIOptions> {
   ///
   /// - [promptValue] The prompt to tokenize.
   @override
-  Future<List<int>> tokenize(final PromptValue promptValue) async {
+  Future<List<int>> tokenize(
+    final PromptValue promptValue, {
+    final ChatOpenAIOptions? options,
+  }) async {
     return _getTiktoken().encode(promptValue.toString());
   }
 

packages/langchain_openai/lib/src/llms/models/models.dart

@@ -1,30 +1,116 @@
 import 'package:langchain/langchain.dart';
-import '../openai.dart';
 
 /// {@template openai_options}
 /// Options to pass into the OpenAI LLM.
 /// {@endtemplate}
 class OpenAIOptions extends LLMOptions {
   /// {@macro openai_options}
   const OpenAIOptions({
+    this.model = 'gpt-3.5-turbo-instruct',
+    this.bestOf = 1,
+    this.frequencyPenalty = 0,
+    this.logitBias,
+    this.logprobs,
+    this.maxTokens = 256,
+    this.n = 1,
+    this.presencePenalty = 0,
+    this.seed,
     this.stop,
+    this.suffix,
+    this.temperature = 1,
+    this.topP = 1,
     this.user,
   });
 
+  /// ID of the model to use (e.g. 'gpt-3.5-turbo-instruct').
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-model
+  final String model;
+
+  /// Generates best_of completions server-side and returns the "best"
+  /// (the one with the highest log probability per token).
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-best_of
+  final int bestOf;
+
+  /// Number between -2.0 and 2.0. Positive values penalize new tokens based on
+  /// their existing frequency in the text so far, decreasing the model's
+  /// likelihood to repeat the same line verbatim.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-frequency_penalty
+  final double frequencyPenalty;
+
+  /// Modify the likelihood of specified tokens appearing in the completion.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-logit_bias
+  final Map<String, int>? logitBias;
+
+  /// Include the log probabilities on the `logprobs` most likely tokens, as
+  /// well the chosen tokens. For example, if `logprobs` is 5, the API will
+  /// return a list of the 5 most likely tokens. The API will always return the
+  /// `logprob` of the sampled token, so there may be up to `logprobs+1`
+  /// elements in the response.
+  ///
+  /// The maximum value for logprobs is 5.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-logprobs
+  final int? logprobs;
+
+  /// The maximum number of tokens to generate in the completion.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-max_tokens
+  final int? maxTokens;
+
+  /// How many completions to generate for each prompt.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-n
+  final int n;
+
+  /// Number between -2.0 and 2.0. Positive values penalize new tokens based on
+  /// whether they appear in the text so far, increasing the model's likelihood
+  /// to talk about new topics.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-presence_penalty
+  final double presencePenalty;
+
+  /// If specified, our system will make a best effort to sample
+  /// deterministically, such that repeated requests with the same seed and
+  /// parameters should return the same result.
+  ///
+  /// Determinism is not guaranteed, and you should refer to the
+  /// `system_fingerprint` response parameter to monitor changes in the backend.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-seed
+  final int? seed;
+
   /// Up to 4 sequences where the API will stop generating further tokens.
   /// The returned text will not contain the stop sequence.
   ///
-  /// Ref: https://platform.openai.com/docs/api-reference/completions/create#stop
+  /// Ref: https://platform.openai.com/docs/api-reference/completions/create#completions-create-stop
   final List<String>? stop;
 
+  /// The suffix that comes after a completion of inserted text.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-suffix
+  final String? suffix;
+
+  /// What sampling temperature to use, between 0 and 2.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-temperature
+  final double temperature;
+
+  /// An alternative to sampling with temperature, called nucleus sampling,
+  /// where the model considers the results of the tokens with top_p
+  /// probability mass.
+  ///
+  /// See https://platform.openai.com/docs/api-reference/completions/create#completions-create-top_p
+  final double topP;
+
   /// A unique identifier representing your end-user, which can help OpenAI to
   /// monitor and detect abuse.
   ///
-  /// If the user does not change between requests, you can set this field in
-  /// [OpenAI.user] instead.
-  ///
-  /// If you specify it in both places, the value in [OpenAIOptions.user] will
-  /// be used.
+  /// If you need to send different users in different requests, you can set
+  /// this field in [OpenAIOptions.user] instead.
   ///
   /// Ref: https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids
   final String? user;