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4371cfd48e
* Use regex for parsing CF RAG results and log full response when it is partial * Refactor & add test
71 lines
4.0 KiB
C++
71 lines
4.0 KiB
C++
#pragma once
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#include <string>
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#include <condition_variable>
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#include <mutex>
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#include <json.hpp>
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#include "option.h"
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struct async_conversation_t {
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std::string response;
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std::mutex mutex;
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std::condition_variable cv;
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};
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class ConversationModel {
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public:
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virtual ~ConversationModel() {};
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static Option<std::string> get_answer(const std::string& context, const std::string& prompt, const nlohmann::json& model_config);
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static Option<bool> validate_model(const nlohmann::json& model_config);
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static Option<std::string> get_standalone_question(const nlohmann::json& conversation_history, const std::string& question, const nlohmann::json& model_config);
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static Option<nlohmann::json> format_question(const std::string& message, const nlohmann::json& model_config);
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static Option<nlohmann::json> format_answer(const std::string& message, const nlohmann::json& model_config);
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// max token count for the context search results
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static Option<size_t> max_context_tokens(const nlohmann::json& model_config);
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private:
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};
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class OpenAIConversationModel : public ConversationModel {
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public:
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static Option<std::string> get_answer(const std::string& context, const std::string& prompt, const std::string& system_prompt, const nlohmann::json& model_config);
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static Option<bool> validate_model(const nlohmann::json& model_config);
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static Option<std::string> get_standalone_question(const nlohmann::json& conversation_history, const std::string& question, const nlohmann::json& model_config);
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static Option<nlohmann::json> format_question(const std::string& message);
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static Option<nlohmann::json> format_answer(const std::string& message);
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static const inline std::string STANDALONE_QUESTION_PROMPT = R"(
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Rewrite the follow-up question on top of a human-assistant conversation history as a standalone question that encompasses all pertinent context.
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)";
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// prevent instantiation
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OpenAIConversationModel() = delete;
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static size_t max_context_tokens() {
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return 3000;
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}
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private:
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static constexpr char* OPENAI_LIST_MODELS = "https://api.openai.com/v1/models";
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static constexpr char* OPENAI_CHAT_COMPLETION = "https://api.openai.com/v1/chat/completions";
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};
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class CFConversationModel : public ConversationModel {
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public:
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static Option<std::string> get_answer(const std::string& context, const std::string& prompt, const std::string& system_prompt, const nlohmann::json& model_config);
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static Option<bool> validate_model(const nlohmann::json& model_config);
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static Option<std::string> get_standalone_question(const nlohmann::json& conversation_history, const std::string& question, const nlohmann::json& model_config);
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static Option<nlohmann::json> format_question(const std::string& message);
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static Option<nlohmann::json> format_answer(const std::string& message);
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static Option<std::string> parse_stream_response(const std::string& response);
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static const inline std::string STANDALONE_QUESTION_PROMPT = R"(
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Rewrite the follow-up question on top of a human-assistant conversation history as a standalone question that encompasses all pertinent context.Use 1024 characters maximum.
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)";
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static const inline std::string INFO_PROMPT = "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and do not mention provided context directly, act like already knowing the context.";
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// prevent instantiation
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CFConversationModel() = delete;
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static size_t max_context_tokens() {
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return 768;
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}
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private:
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static const inline std::vector<std::string> CF_MODEL_NAMES{"mistral/mistral-7b-instruct-v0.1"};
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static const std::string get_model_url(const std::string& model_name, const std::string& account_id);
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};
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