APIDash AI Request Feature Implementation #850
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There was a problem hiding this comment. There is currently no distinction between LLM models and providers need to revisit the class design. Also, create a new package inside packages folder called genai where all LLM models, services and utilities must reside. There was a problem hiding this comment. Each model provider has their own api and params. And model is one of the parameters which is selected by the end user. |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,36 @@ | ||
| import 'package:apidash/models/llm_models/google/gemini_20_flash.dart'; | ||
| import 'package:apidash/models/llm_models/llm_model.dart'; | ||
| import 'package:apidash/models/llm_models/ollama/llama3.dart'; | ||
| import 'package:apidash/models/llm_models/openai/azure_openai.dart'; | ||
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| // Exports | ||
| export 'package:apidash/models/llm_models/google/gemini_20_flash.dart'; | ||
| export 'package:apidash/models/llm_models/ollama/llama3.dart'; | ||
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| Map<String, (LLMModel instance, LLMModel Function())> availableModels = { | ||
| Gemini20FlashModel.instance.modelIdentifier: ( | ||
| Gemini20FlashModel.instance, | ||
| () => Gemini20FlashModel() | ||
| ), | ||
| LLama3LocalModel.instance.modelIdentifier: ( | ||
| LLama3LocalModel.instance, | ||
| () => LLama3LocalModel() | ||
| ), | ||
| AzureOpenAIModel.instance.modelIdentifier: ( | ||
| AzureOpenAIModel.instance, | ||
| () => AzureOpenAIModel() | ||
| ), | ||
| }; | ||
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| LLMModel? getLLMModelFromID(String modelID, [Map? configMap]) { | ||
| for (final entry in availableModels.entries) { | ||
| if (entry.key == modelID) { | ||
| final m = entry.value.$2(); | ||
| if (configMap != null) { | ||
| m.loadConfigurations(configMap); | ||
| } | ||
| return m; | ||
| } | ||
| } | ||
| return null; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,146 @@ | ||
| import 'dart:convert'; | ||
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| import 'package:apidash/models/llm_models/llm_config.dart'; | ||
| import 'package:apidash/models/llm_models/llm_model.dart'; | ||
| import 'package:apidash_core/apidash_core.dart' as http; | ||
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| class Gemini20FlashModel extends LLMModel { | ||
| static Gemini20FlashModel instance = Gemini20FlashModel(); | ||
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| @override | ||
| String provider = 'Google'; | ||
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| @override | ||
| String modelName = 'Gemini 2.0 Flash'; | ||
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| @override | ||
| String modelIdentifier = 'gemini_20_flash'; | ||
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| @override | ||
| LLMModelAuthorizationType authorizationType = | ||
| LLMModelAuthorizationType.apiKey; | ||
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| @override | ||
| Map<String, LLMModelConfiguration> configurations = { | ||
| 'temperature': LLMModelConfiguration( | ||
| configId: 'temperature', | ||
| configName: 'Temperature', | ||
| configDescription: | ||
| 'Higher values mean greater variability and lesser values mean more deterministic responses', | ||
| configType: LLMModelConfigurationType.slider, | ||
| configValue: LLMConfigSliderValue(value: (0.0, 0.5, 1.0)), | ||
| ), | ||
| 'top_p': LLMModelConfiguration( | ||
| configId: 'top_p', | ||
| configName: 'Top P', | ||
| configDescription: 'Controls the randomness of the LLM Response', | ||
| configType: LLMModelConfigurationType.slider, | ||
| configValue: LLMConfigSliderValue(value: (0.0, 0.95, 1.0)), | ||
| ), | ||
| 'max_tokens': LLMModelConfiguration( | ||
| configId: 'max_tokens', | ||
| configName: 'Max Tokens', | ||
| configDescription: | ||
| 'The maximum number of tokens to generate. -1 means no limit', | ||
| configType: LLMModelConfigurationType.numeric, | ||
| configValue: LLMConfigNumericValue(value: -1), | ||
| ), | ||
| }; | ||
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| @override | ||
| String providerIcon = 'https://img.icons8.com/color/48/google-logo.png'; | ||
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There was a problem hiding this comment. No external hardcoded image URL is allowed. |
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| @override | ||
| LLMModelSpecifics specifics = LLMModelSpecifics( | ||
| endpoint: | ||
| 'https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent', | ||
| method: 'POST', | ||
| headers: {}, | ||
| outputFormatter: (resp) { | ||
| if (resp == null) return null; | ||
| return resp['candidates']?[0]?['content']?['parts']?[0]?['text']; | ||
| }, | ||
| ); | ||
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| @override | ||
| Map getRequestPayload({ | ||
| required String systemPrompt, | ||
| required String userPrompt, | ||
| required String credential, | ||
| }) { | ||
| final temp = configurations['temperature']!.configValue.serialize(); | ||
| final top_p = configurations['top_p']!.configValue.serialize(); | ||
| final mT = configurations['max_tokens']!.configValue.serialize(); | ||
| final payload = { | ||
| "model": "gemini-2.0-flash", | ||
| "contents": [ | ||
| { | ||
| "role": "user", | ||
| "parts": [ | ||
| {"text": userPrompt} | ||
| ] | ||
| } | ||
| ], | ||
| "systemInstruction": { | ||
| "role": "system", | ||
| "parts": [ | ||
| {"text": systemPrompt} | ||
| ] | ||
| }, | ||
| "generationConfig": { | ||
| "temperature": (jsonDecode(temp) as List)[1].toString(), | ||
| "topP": (jsonDecode(top_p) as List)[1].toString(), | ||
| if (mT != '-1') ...{ | ||
| "maxOutputTokens": mT, | ||
| } | ||
| } | ||
| }; | ||
| final endpoint = specifics.endpoint; | ||
| final url = "$endpoint?key=$credential"; | ||
| return { | ||
| 'url': url, | ||
| 'payload': payload, | ||
| }; | ||
| } | ||
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| @override | ||
| loadConfigurations(Map configMap) { | ||
| print('Loaded Gemini Configurations'); | ||
| final double? temperature = configMap['temperature']; | ||
| final double? top_p = configMap['top_p']; | ||
| final int? max_tokens = configMap['max_tokens']; | ||
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| //print('loading configs => $temperature, $top_p, $max_tokens'); | ||
| if (temperature != null) { | ||
| final config = configurations['temperature']!; | ||
| configurations['temperature'] = | ||
| configurations['temperature']!.updateValue( | ||
| LLMConfigSliderValue(value: ( | ||
| config.configValue.value.$1, | ||
| temperature, | ||
| config.configValue.value.$3 | ||
| )), | ||
| ); | ||
| } | ||
| if (top_p != null) { | ||
| final config = configurations['top_p']!; | ||
| configurations['top_p'] = configurations['top_p']!.updateValue( | ||
| LLMConfigSliderValue(value: ( | ||
| config.configValue.value.$1, | ||
| top_p, | ||
| config.configValue.value.$3 | ||
| )), | ||
| ); | ||
| } | ||
| if (max_tokens != null) { | ||
| configurations['max_tokens'] = configurations['max_tokens']!.updateValue( | ||
| LLMConfigNumericValue(value: max_tokens), | ||
| ); | ||
| } | ||
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| // Load Modified Endpoint | ||
| if (configMap['modifed_endpoint'] != null) { | ||
| specifics.endpoint = configMap['modifed_endpoint']!; | ||
| } | ||
| } | ||
| } | ||
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