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This PR introduces several enhancements and refactorings to the AI Persona and RAG (Retrieval-Augmented Generation) functionalities within the discourse-ai plugin. Here's a breakdown of the changes: **1. LLM Model Association for RAG and Personas:** - **New Database Columns:** Adds `rag_llm_model_id` to both `ai_personas` and `ai_tools` tables. This allows specifying a dedicated LLM for RAG indexing, separate from the persona's primary LLM. Adds `default_llm_id` and `question_consolidator_llm_id` to `ai_personas`. - **Migration:** Includes a migration (`20250210032345_migrate_persona_to_llm_model_id.rb`) to populate the new `default_llm_id` and `question_consolidator_llm_id` columns in `ai_personas` based on the existing `default_llm` and `question_consolidator_llm` string columns, and a post migration to remove the latter. - **Model Changes:** The `AiPersona` and `AiTool` models now `belong_to` an `LlmModel` via `rag_llm_model_id`. The `LlmModel.proxy` method now accepts an `LlmModel` instance instead of just an identifier. `AiPersona` now has `default_llm_id` and `question_consolidator_llm_id` attributes. - **UI Updates:** The AI Persona and AI Tool editors in the admin panel now allow selecting an LLM for RAG indexing (if PDF/image support is enabled). The RAG options component displays an LLM selector. - **Serialization:** The serializers (`AiCustomToolSerializer`, `AiCustomToolListSerializer`, `LocalizedAiPersonaSerializer`) have been updated to include the new `rag_llm_model_id`, `default_llm_id` and `question_consolidator_llm_id` attributes. **2. PDF and Image Support for RAG:** - **Site Setting:** Introduces a new hidden site setting, `ai_rag_pdf_images_enabled`, to control whether PDF and image files can be indexed for RAG. This defaults to `false`. - **File Upload Validation:** The `RagDocumentFragmentsController` now checks the `ai_rag_pdf_images_enabled` setting and allows PDF, PNG, JPG, and JPEG files if enabled. Error handling is included for cases where PDF/image indexing is attempted with the setting disabled. - **PDF Processing:** Adds a new utility class, `DiscourseAi::Utils::PdfToImages`, which uses ImageMagick (`magick`) to convert PDF pages into individual PNG images. A maximum PDF size and conversion timeout are enforced. - **Image Processing:** A new utility class, `DiscourseAi::Utils::ImageToText`, is included to handle OCR for the images and PDFs. - **RAG Digestion Job:** The `DigestRagUpload` job now handles PDF and image uploads. It uses `PdfToImages` and `ImageToText` to extract text and create document fragments. - **UI Updates:** The RAG uploader component now accepts PDF and image file types if `ai_rag_pdf_images_enabled` is true. The UI text is adjusted to indicate supported file types. **3. Refactoring and Improvements:** - **LLM Enumeration:** The `DiscourseAi::Configuration::LlmEnumerator` now provides a `values_for_serialization` method, which returns a simplified array of LLM data (id, name, vision_enabled) suitable for use in serializers. This avoids exposing unnecessary details to the frontend. - **AI Helper:** The `AiHelper::Assistant` now takes optional `helper_llm` and `image_caption_llm` parameters in its constructor, allowing for greater flexibility. - **Bot and Persona Updates:** Several updates were made across the codebase, changing the string based association to a LLM to the new model based. - **Audit Logs:** The `DiscourseAi::Completions::Endpoints::Base` now formats raw request payloads as pretty JSON for easier auditing. - **Eval Script:** An evaluation script is included. **4. Testing:** - The PR introduces a new eval system for LLMs, this allows us to test how functionality works across various LLM providers. This lives in `/evals`
171 lines
4.2 KiB
JavaScript
171 lines
4.2 KiB
JavaScript
import { tracked } from "@glimmer/tracking";
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import { ajax } from "discourse/lib/ajax";
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import RestModel from "discourse/models/rest";
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const CREATE_ATTRIBUTES = [
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"id",
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"name",
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"description",
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"tools",
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"system_prompt",
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"allowed_group_ids",
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"enabled",
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"system",
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"priority",
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"top_p",
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"temperature",
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"user_id",
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"default_llm_id",
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"force_default_llm",
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"user",
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"max_context_posts",
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"vision_enabled",
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"vision_max_pixels",
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"rag_uploads",
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"rag_chunk_tokens",
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"rag_chunk_overlap_tokens",
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"rag_conversation_chunks",
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"rag_llm_model_id",
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"question_consolidator_llm_id",
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"allow_chat",
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"tool_details",
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"forced_tool_count",
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"allow_personal_messages",
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"allow_topic_mentions",
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"allow_chat_channel_mentions",
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"allow_chat_direct_messages",
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];
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const SYSTEM_ATTRIBUTES = [
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"id",
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"allowed_group_ids",
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"enabled",
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"system",
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"priority",
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"tools",
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"user_id",
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"default_llm_id",
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"force_default_llm",
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"user",
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"max_context_posts",
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"vision_enabled",
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"vision_max_pixels",
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"rag_uploads",
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"rag_chunk_tokens",
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"rag_chunk_overlap_tokens",
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"rag_conversation_chunks",
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"rag_llm_model_id",
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"question_consolidator_llm_id",
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"tool_details",
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"allow_personal_messages",
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"allow_topic_mentions",
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"allow_chat_channel_mentions",
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"allow_chat_direct_messages",
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];
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class ToolOption {
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@tracked value = null;
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}
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export default class AiPersona extends RestModel {
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// this code is here to convert the wire schema to easier to work with object
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// on the wire we pass in/out tools as an Array.
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// [[ToolName, {option1: value, option2: value}, force], ToolName2, ToolName3]
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// So we rework this into a "tools" property and nested toolOptions
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init(properties) {
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this.forcedTools = [];
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if (properties.tools) {
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properties.tools = properties.tools.map((tool) => {
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if (typeof tool === "string") {
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return tool;
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} else {
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let [toolId, options, force] = tool;
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for (let optionId in options) {
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if (!options.hasOwnProperty(optionId)) {
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continue;
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}
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this.getToolOption(toolId, optionId).value = options[optionId];
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}
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if (force) {
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this.forcedTools.push(toolId);
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}
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return toolId;
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}
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});
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}
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super.init(properties);
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this.tools = properties.tools;
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}
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async createUser() {
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const result = await ajax(
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`/admin/plugins/discourse-ai/ai-personas/${this.id}/create-user.json`,
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{
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type: "POST",
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}
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);
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this.user = result.user;
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this.user_id = this.user.id;
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return this.user;
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}
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getToolOption(toolId, optionId) {
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this.toolOptions ||= {};
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this.toolOptions[toolId] ||= {};
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return (this.toolOptions[toolId][optionId] ||= new ToolOption());
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}
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populateToolOptions(attrs) {
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if (!attrs.tools) {
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return;
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}
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let toolsWithOptions = [];
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attrs.tools.forEach((toolId) => {
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if (typeof toolId !== "string") {
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toolId = toolId[0];
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}
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let force = this.forcedTools.includes(toolId);
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if (this.toolOptions && this.toolOptions[toolId]) {
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let options = this.toolOptions[toolId];
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let optionsWithValues = {};
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for (let optionId in options) {
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if (!options.hasOwnProperty(optionId)) {
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continue;
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}
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let option = options[optionId];
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optionsWithValues[optionId] = option.value;
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}
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toolsWithOptions.push([toolId, optionsWithValues, force]);
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} else {
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toolsWithOptions.push([toolId, {}, force]);
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}
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});
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attrs.tools = toolsWithOptions;
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}
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updateProperties() {
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let attrs = this.system
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? this.getProperties(SYSTEM_ATTRIBUTES)
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: this.getProperties(CREATE_ATTRIBUTES);
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attrs.id = this.id;
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this.populateToolOptions(attrs);
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return attrs;
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}
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createProperties() {
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let attrs = this.getProperties(CREATE_ATTRIBUTES);
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this.populateToolOptions(attrs);
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return attrs;
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}
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workingCopy() {
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let attrs = this.getProperties(CREATE_ATTRIBUTES);
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this.populateToolOptions(attrs);
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const persona = AiPersona.create(attrs);
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persona.forcedTools = (this.forcedTools || []).slice();
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persona.forced_tool_count = this.forced_tool_count || -1;
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return persona;
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}
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}
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