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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,101 @@ | ||
| import { SupabaseClient } from "@supabase/supabase-js"; | ||
| import { logger } from "../../utils/logger"; | ||
| import { VoyageAIClient } from "voyageai"; | ||
| import { CommentSimilaritySearchResult, DatabaseItem, IssueSimilaritySearchResult } from "../../types/ai"; | ||
|
|
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| export class Embeddings { | ||
| protected supabase: SupabaseClient; | ||
| protected voyage: VoyageAIClient; | ||
|
|
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| constructor(supabase: SupabaseClient | void, client: VoyageAIClient) { | ||
| if (!supabase) { | ||
| throw new Error("Supabase client is required to use Embeddings"); | ||
| } | ||
| this.supabase = supabase; | ||
| this.voyage = client; | ||
| } | ||
|
|
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| async getIssue(issueNodeId: string): Promise<DatabaseItem[] | null> { | ||
| const { data, error } = await this.supabase.from("issues").select("*").eq("id", issueNodeId).returns<DatabaseItem[]>(); | ||
| if (error) { | ||
| logger.error("Error getting issue", { error }); | ||
| return null; | ||
| } | ||
| return data; | ||
| } | ||
|
|
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| async getComment(commentNodeId: string): Promise<DatabaseItem[] | null> { | ||
| const { data, error } = await this.supabase.from("issue_comments").select("*").eq("id", commentNodeId); | ||
| if (error) { | ||
| logger.error("Error getting comment", { error }); | ||
| } | ||
| return data; | ||
| } | ||
|
|
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| async findSimilarIssues(plaintext: string, threshold: number): Promise<IssueSimilaritySearchResult[] | null> { | ||
| const embedding = await this.createEmbedding({ text: plaintext, prompt: "This is a query for the stored documents:" }); | ||
| plaintext = plaintext.replace(/'/g, "''").replace(/\\/g, "\\\\").replace(/"/g, '\\"').replace(/%/g, "\\%").replace(/_/g, "\\_"); | ||
| const { data, error } = await this.supabase.rpc("find_similar_issue_ftse", { | ||
| current_id: "", | ||
| query_text: plaintext, | ||
| query_embedding: embedding, | ||
| threshold: threshold, | ||
| max_results: 10, | ||
| }); | ||
| if (error) { | ||
| logger.error("Error finding similar issues", { error }); | ||
| throw new Error("Error finding similar issues"); | ||
| } | ||
| return data; | ||
| } | ||
|
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| async findSimilarComments(query: string, threshold: number): Promise<CommentSimilaritySearchResult[] | null> { | ||
| const embedding = await this.createEmbedding({ text: query, prompt: "This is a query for the stored documents:" }); | ||
| query = query.replace(/'/g, "''").replace(/\\/g, "\\\\").replace(/"/g, '\\"').replace(/%/g, "\\%").replace(/_/g, "\\_"); | ||
| logger.info(`Query: ${query}`); | ||
| const { data, error } = await this.supabase.rpc("find_similar_comments", { | ||
| current_id: "", | ||
| query_text: query, | ||
| query_embedding: embedding, | ||
| threshold: threshold, | ||
| max_results: 10, | ||
| }); | ||
| if (error) { | ||
| logger.error("Error finding similar comments", { error }); | ||
| throw new Error("Error finding similar comments"); | ||
| } | ||
| return data; | ||
| } | ||
|
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| async createEmbedding(input: { text?: string; prompt?: string } = {}): Promise<number[]> { | ||
| const VECTOR_SIZE = 1024; | ||
| const { text = null, prompt = null } = input; | ||
| if (text === null) { | ||
| return new Array(VECTOR_SIZE).fill(0); | ||
| } else { | ||
| const response = await this.voyage.embed({ | ||
| input: prompt ? `${prompt} ${text}` : text, | ||
| model: "voyage-large-2-instruct", | ||
| }); | ||
| return response.data?.[0]?.embedding || []; | ||
| } | ||
| } | ||
|
|
||
| async reRankResults(results: string[], query: string, topK: number = 5): Promise<string[]> { | ||
| let response; | ||
| try { | ||
| response = await this.voyage.rerank({ | ||
| query, | ||
| documents: results, | ||
| model: "rerank-2", | ||
| returnDocuments: true, | ||
| topK, | ||
| }); | ||
| } catch (e: unknown) { | ||
| logger.error("Reranking failed!", { e }); | ||
| return results; | ||
| } | ||
| const rerankedResults = response.data || []; | ||
| return rerankedResults.map((result) => result.document).filter((document): document is string => document !== undefined); | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,69 @@ | ||
| import { chatAction } from "@grammyjs/auto-chat-action"; | ||
| import { Composer } from "grammy"; | ||
| import { GrammyContext } from "../../../helpers/grammy-context"; | ||
| import { logHandle } from "../../../helpers/logging"; | ||
| import { logger } from "../../../../utils/logger"; | ||
| import { PluginContext } from "../../../../types/plugin-context-single"; | ||
| import { CommentSimilaritySearchResult, IssueSimilaritySearchResult } from "../../../../types/ai"; | ||
|
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| const composer = new Composer<GrammyContext>(); | ||
| const feature = composer.chatType(["group", "private", "supergroup", "channel"]); | ||
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| feature.command("ubiquityos", logHandle("command-ubiquityos"), chatAction("typing"), async (ctx) => { | ||
| const { | ||
| adapters: { ai, embeddings }, | ||
| } = ctx; | ||
|
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| const directives = [ | ||
| "Extract Relevant Information: Identify key pieces of information, even if they are incomplete, from the available corpus.", | ||
| "Apply Knowledge: Use the extracted information and relevant documentation to construct an informed response.", | ||
| "Draft Response: Compile the gathered insights into a coherent and concise response, ensuring it's clear and directly addresses the user's query.", | ||
| "Review and Refine: Check for accuracy and completeness, filling any gaps with logical assumptions where necessary.", | ||
| ]; | ||
|
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| const constraints = [ | ||
| "Ensure the response is crafted from the corpus provided, without introducing information outside of what's available or relevant to the query.", | ||
| "Consider edge cases where the corpus might lack explicit answers, and justify responses with logical reasoning based on the existing information.", | ||
| "Replies MUST be in Markdown V1 format but do not wrap in code blocks.", | ||
| ]; | ||
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| const outputStyle = "Concise and coherent responses in paragraphs that directly address the user's question."; | ||
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| const question = ctx.message?.text.replace("/ubiquityos", "").trim(); | ||
|
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| if (!question) { | ||
| return ctx.reply("Please provide a question to ask UbiquityOS."); | ||
| } | ||
|
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| const { similarityThreshold, model } = PluginContext.getInstance().config.aiConfig; | ||
| const similarText = await Promise.all([ | ||
| embeddings.findSimilarComments(question, 1 - similarityThreshold), | ||
| embeddings.findSimilarIssues(question, 1 - similarityThreshold), | ||
| ]).then(([comments, issues]) => { | ||
| return [ | ||
| ...(comments?.map((comment: CommentSimilaritySearchResult) => comment.comment_plaintext) || []), | ||
| ...(issues?.map((issue: IssueSimilaritySearchResult) => issue.issue_plaintext) || []), | ||
| ]; | ||
| }); | ||
|
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| logger.info("Similar Text:\n\n", { similarText }); | ||
| const rerankedText = similarText.length > 0 ? await embeddings.reRankResults(similarText, question) : []; | ||
| logger.info("Reranked Text:\n\n", { rerankedText }); | ||
|
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| return ctx.reply( | ||
| await ai.createCompletion({ | ||
| directives, | ||
| constraints, | ||
| query: question, | ||
| embeddingsSearch: rerankedText, | ||
| additionalContext: [], | ||
| outputStyle, | ||
| model, | ||
| }), | ||
| { | ||
| parse_mode: "Markdown", | ||
| } | ||
| ); | ||
| }); | ||
|
|
||
| export { composer as askFeature }; |
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