static/js/gen.js

import zod from 'https://cdn.jsdelivr.net/npm/[email protected]/+esm'

/**
 * Met en forme le prompt template passé en paramètres avec les arguments
 * @param {String} template 
 * @param {Object} args 
 */
export function formatTemplate(template, args) {
    return template.replace(/\{\{(\w+)\}\}/g, (_, key) => {
        // 'match' est la correspondance complète (ex: "{nom}")
        // 'key' est le contenu du premier groupe capturé (ex: "nom")

        if (key in args)
            return args[key];

        // Si la clé n'est pas trouvée dans args, on laisse le placeholder tel quel.
        return "";
    });
}

/**
 * Recupère le prompt pour la tâche spécifiée.
 * @param {String} task 
 */
export async function retrieveTemplate(task) {
    const req = await fetch(`/prompt/${task}`)
    return await req.text();
}

/**
 * Lance un deep search sur le serveur pour les topics donnés.
 * @param {Array} topics 
 */
export async function performDeepSearch(topics) {
    const response = await fetch('/solutions/search_prior_art', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ topics: topics })
    });
    const results = await response.json();
    return results.content;
}

/**
 * Genère une completion avec le LLM specifié
 * @param {String} providerUrl - URL du provider du LLM
 * @param {String} modelName - Nom du modèle à appeler
 * @param {String} apiKey - API key a utiliser
 * @param {Array<{role: string, content: string}>} messages - Liste de messages à passer au modèle
 * @param {Number} temperature - Température à utiliser pour la génération
 */
export async function generateCompletion(providerUrl, modelName, apiKey, messages, temperature = 0.5) {
    const genEndpoint = providerUrl + "/chat/completions"

    try {
        const response = await fetch(genEndpoint, {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${apiKey}`, // OpenAI-like authorization header
            },
            body: JSON.stringify({
                model: modelName,
                messages: messages,
                temperature: temperature,
            }),
        });

        if (!response.ok) {
            const errorData = await response.json();
            throw new Error(`API request failed with status ${response.status}: ${errorData.error?.message || 'Unknown error'}`);
        }

        const data = await response.json();

        if (data.choices && data.choices.length > 0 && data.choices[0].message && data.choices[0].message.content)
            return data.choices[0].message.content;

    } catch (error) {
        console.error("Error calling private LLM :", error);
        throw error;
    }
}



/**
 * Genère une completion structurée avec le LLM specifié
 * @param {String} providerUrl - URL du provider du LLM
 * @param {String} modelName - Nom du modèle à appeler
 * @param {String} apiKey - API key a utiliser
 * @param {Array<{role: string, content: string}>} messages - Liste de messages à passer au modèle
 * @param {Object} schema - Zod schema to use for structured generation
 * @param {Number} temperature - Température à utiliser pour la génération
 */
//TODO: Find the correct args to constrain the LLM to the json schema instead of enforcing json correct parsing
export async function generateStructuredCompletion(providerUrl, modelName, apiKey, messages, schema, temperature = 0.5) {
    const genEndpoint = providerUrl + "/chat/completions";
    try {
        const response = await fetch(genEndpoint, {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${apiKey}`,
            },
            body: JSON.stringify({
                model: modelName,
                messages: messages,
                temperature: temperature,
                response_format: { type: "json_object" }
            }),
        });

        if (!response.ok) {
            const errorData = await response.json();
            throw new Error(`API request failed with status ${response.status}: ${errorData.error?.message || 'Unknown error'}`);
        }

        const data = await response.json();

        console.log(data.choices[0].message.content);

        // parse json output
        const parsedJSON = JSON.parse(data.choices[0].message.content.replace('```json', '').replace("```", ""));

        // validate output with zod
        const validatedSchema = schema.parse(parsedJSON);

        return validatedSchema;
    } catch (error) {
        console.error("Error calling private LLM :", error);
        throw error;
    }
}

/**
 * Retrieves a list of available models from an OpenAI-compatible API using fetch.
 *
 * @param {string} providerUrl The base URL of the OpenAI-compatible API endpoint (e.g., "http://localhost:8000/v1").
 * @param {string} apiKey The API key for authentication.
 * @returns {Promise<Array<string>>} A promise that resolves with an array of model names, or rejects with an error.
 */
export async function getModelList(providerUrl, apiKey) {
    try {
        // Construct the full URL for the models endpoint
        const modelsUrl = `${providerUrl}/models`;

        console.log(modelsUrl);

        // Make a GET request to the models endpoint using fetch
        const response = await fetch(modelsUrl, {
            method: 'GET', // Explicitly state the method
            headers: {
                'Authorization': `Bearer ${apiKey}`, // OpenAI-compatible authorization header
                'Content-Type': 'application/json',
            },
        });

        // Check if the request was successful (status code 200-299)
        if (!response.ok) {
            // If the response is not OK, try to get more error details
            const errorData = await response.json().catch(() => ({})); // Attempt to parse JSON error, fallback to empty object
            throw new Error(`HTTP error! Status: ${response.status}, Message: ${errorData.message || response.statusText}`);
        }

        // Parse the JSON response body
        const data = await response.json();

        // The response data structure for OpenAI-compatible APIs usually contains a 'data' array
        // where each item represents a model and has an 'id' property.
        if (data && Array.isArray(data.data)) {
            const allModelNames = data.data.map(model => model.id);

            // Filter out models containing "embedding" (case-insensitive)
            const filteredModelNames = allModelNames.filter(modelName =>
                !modelName.toLowerCase().includes('embedding')
            );

            return filteredModelNames;
        } else {
            // Handle cases where the response format is unexpected
            throw new Error('Unexpected response format from the API. Could not find model list.');
        }

    } catch (error) {
        console.error('Error fetching model list:', error.message);
        // Re-throw the error to allow the caller to handle it
        throw error;
    }
}

// # ========================================================================================== Idea assessment logic ==================================================================

// JS schema for the assessment output.
// keep in sync with contents of "extract" prompt
const StructuredAssessmentOutput = zod.object({
    final_verdict: zod.string(),
    summary: zod.string(),
    insights: zod.array(zod.string()),
});

export async function assessSolution(providerUrl, modelName, apiKey, solution, assessment_rules, portfolio_info) {
    const template = await retrieveTemplate("assess");

    const assessment_template = formatTemplate(template, {
        notation_criterias: assessment_rules,
        business: portfolio_info,
        problem_description: solution.problem_description,
        solution_description: solution.solution_description,
    });

    const assessment_full = await generateCompletion(providerUrl, modelName, apiKey, [
        { role: "user", content: assessment_template }
    ]);

    const structured_template = await retrieveTemplate("extract");
    const structured_filled_template = formatTemplate(structured_template, {
        "report": assessment_full,
        "response_schema": zod.toJSONSchema(StructuredAssessmentOutput)
    })

    const extracted_info = await generateStructuredCompletion(providerUrl, modelName, apiKey, [{ role: "user", content: structured_filled_template }], StructuredAssessmentOutput);

    return { assessment_full, extracted_info };
}

export async function refineSolution(providerUrl, modelName, apiKey, solution, insights, user_insights, assessment_rules, portfolio_info) {
    const template = await retrieveTemplate("refine");

    const refine_template = formatTemplate(template, {
        "problem_description": solution.problem_description,
        "solution_description": solution.solution_description,
        "insights": insights.join("\n -"),
        "user_insights": user_insights,
        "business_info": portfolio_info,
    });

    console.log(refine_template);

    const refined_idea = await generateCompletion(providerUrl, modelName, apiKey, [{ role: "user", content: refine_template }]);

    const newSolution = structuredClone(solution);
    newSolution.solution_description = refined_idea;

    return newSolution;
}


// FTO analysis

// JS schema for FTO analysis topic extraction
const FTOAnalysisTopicsSchema = zod.object({
    topics: zod.array(zod.string())
});

/**
 * Extract the topics to search for FTO
 */
async function getFtoAnalysisTopics(providerUrl, modelName, apiKey, idea, count) {
    const template = await retrieveTemplate("fto_topics");

    const structured_template = formatTemplate(template, {
        "problem_description": idea.problem_description,
        "solution_description": idea.solution_description,
        "response_schema": zod.toJSONSchema(FTOAnalysisTopicsSchema),
        "max_topic_count": count
    });

    const topics = await generateStructuredCompletion(providerUrl, modelName, apiKey, [{ role: "user", content: structured_template }], FTOAnalysisTopicsSchema);

    return topics;
}

/*
 * Assess the infringement of the idea wrt 
*/
async function assessFTOReport(providerUrl, modelName, apiKey, solution, fto_report, portfolio_info) {
    const template = await retrieveTemplate("fto_assess");

    const assessment_template = formatTemplate(template, {
        business: portfolio_info,
        fto_report: fto_report,
        problem_description: solution.problem_description,
        solution_description: solution.solution_description,
    });

    console.log("FTO Length: " + assessment_template.length);

    const assessment_full = await generateCompletion(providerUrl, modelName, apiKey, [
        { role: "user", content: assessment_template }
    ]);

    const structured_template = await retrieveTemplate("extract");
    const structured_filled_template = formatTemplate(structured_template, {
        "report": assessment_full,
        "response_schema": zod.toJSONSchema(StructuredAssessmentOutput)
    })

    const extracted_info = await generateStructuredCompletion(providerUrl, modelName, apiKey, [{ role: "user", content: structured_filled_template }], StructuredAssessmentOutput);

    return { assessment_full, extracted_info };
}

export async function runFTOAnalysis(providerUrl, providerModel, apiKey, solution, portfolio_info, ftoTopicCount) {
    const fto_topics = await getFtoAnalysisTopics(providerUrl, providerModel, apiKey, solution, ftoTopicCount);
    console.log(fto_topics);

    const fto_report = await performDeepSearch(fto_topics.topics);

    console.log(fto_report);

    const assess_results = await assessFTOReport(providerUrl, providerModel, apiKey, solution, fto_report, portfolio_info);
    console.log(assess_results.extracted_info);

    return {
        fto_topics: fto_topics,
        fto_report: fto_report,
        assessment_full: assess_results.assessment_full,
        extracted_info: assess_results.extracted_info,
    };

}