mirror of
https://github.com/eliasstepanik/core.git
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fix: remove attribute login
This commit is contained in:
Harshith Mullapudi
parent
95636f96a8
commit
1db2628af4
@ -12,11 +12,7 @@ import {
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import { logger } from "./logger.service";
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import { ClusteringService } from "./clustering.server";
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import crypto from "crypto";
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import {
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dedupeNodes,
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extractAttributes,
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extractEntities,
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} from "./prompts/nodes";
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import { dedupeNodes, extractEntities } from "./prompts/nodes";
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import {
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extractStatements,
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extractStatementsOSS,
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@ -40,7 +36,11 @@ import {
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saveTriple,
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searchStatementsByEmbedding,
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} from "./graphModels/statement";
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import { getEmbedding, makeModelCall, isProprietaryModel } from "~/lib/model.server";
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import {
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getEmbedding,
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makeModelCall,
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isProprietaryModel,
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} from "~/lib/model.server";
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import { runQuery } from "~/lib/neo4j.server";
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import { Apps, getNodeTypesString } from "~/utils/presets/nodes";
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import { normalizePrompt, normalizeDocumentPrompt } from "./prompts";
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@ -419,8 +419,8 @@ export class KnowledgeGraphService {
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logger.log(`Processing time: ${processingTimeMs} ms`);
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// Count only truly new statements (exclude duplicates)
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const newStatementsCount = updatedTriples.filter(triple =>
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triple.statement.createdAt >= episode.createdAt
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const newStatementsCount = updatedTriples.filter(
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(triple) => triple.statement.createdAt >= episode.createdAt,
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).length;
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return {
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@ -442,7 +442,10 @@ export class KnowledgeGraphService {
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private async extractEntities(
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episode: EpisodicNode,
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previousEpisodes: EpisodicNode[],
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tokenMetrics: { high: { input: number; output: number; total: number }; low: { input: number; output: number; total: number } },
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tokenMetrics: {
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high: { input: number; output: number; total: number };
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low: { input: number; output: number; total: number };
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},
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): Promise<EntityNode[]> {
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// Use the prompt library to get the appropriate prompts
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const context = {
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@ -460,14 +463,20 @@ export class KnowledgeGraphService {
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let responseText = "";
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// Entity extraction requires HIGH complexity (creative reasoning, nuanced NER)
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await makeModelCall(false, messages as CoreMessage[], (text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.high.input += usage.promptTokens;
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tokenMetrics.high.output += usage.completionTokens;
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tokenMetrics.high.total += usage.totalTokens;
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}
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}, undefined, 'high');
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await makeModelCall(
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false,
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messages as CoreMessage[],
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(text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.high.input += usage.promptTokens;
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tokenMetrics.high.output += usage.completionTokens;
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tokenMetrics.high.total += usage.totalTokens;
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}
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},
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undefined,
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"high",
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);
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// Convert to EntityNode objects
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let entities: EntityNode[] = [];
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@ -478,19 +487,23 @@ export class KnowledgeGraphService {
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responseText = outputMatch[1].trim();
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const parsedResponse = JSON.parse(responseText || "[]");
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// Handle both old format {entities: [...]} and new format [...]
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const extractedEntities = Array.isArray(parsedResponse) ? parsedResponse : (parsedResponse.entities || []);
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const extractedEntities = Array.isArray(parsedResponse)
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? parsedResponse
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: parsedResponse.entities || [];
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// Batch generate embeddings for entity names
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const entityNames = Array.isArray(extractedEntities[0]) ? extractedEntities : extractedEntities.map((entity: any) => entity.name || entity);
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const entityNames = Array.isArray(extractedEntities[0])
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? extractedEntities
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: extractedEntities.map((entity: any) => entity.name || entity);
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const nameEmbeddings = await Promise.all(
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entityNames.map((name: string) => this.getEmbedding(name)),
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);
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entities = extractedEntities.map((entity: any, index: number) => ({
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uuid: crypto.randomUUID(),
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name: typeof entity === 'string' ? entity : entity.name,
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name: typeof entity === "string" ? entity : entity.name,
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type: undefined, // Type will be inferred from statements
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attributes: typeof entity === 'string' ? {} : (entity.attributes || {}),
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attributes: typeof entity === "string" ? {} : entity.attributes || {},
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nameEmbedding: nameEmbeddings[index],
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typeEmbedding: undefined, // No type embedding needed
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createdAt: new Date(),
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@ -512,7 +525,10 @@ export class KnowledgeGraphService {
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expanded: EntityNode[];
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},
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previousEpisodes: EpisodicNode[],
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tokenMetrics: { high: { input: number; output: number; total: number }; low: { input: number; output: number; total: number } },
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tokenMetrics: {
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high: { input: number; output: number; total: number };
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low: { input: number; output: number; total: number };
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},
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): Promise<Triple[]> {
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// Use the prompt library to get the appropriate prompts
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const context = {
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@ -534,22 +550,28 @@ export class KnowledgeGraphService {
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referenceTime: episode.validAt.toISOString(),
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};
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console.log("proprietary model", isProprietaryModel(undefined, 'high'));
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console.log("proprietary model", isProprietaryModel(undefined, "high"));
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// Statement extraction requires HIGH complexity (causal reasoning, emotional context)
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// Choose between proprietary and OSS prompts based on model type
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const messages = isProprietaryModel(undefined, 'high')
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const messages = isProprietaryModel(undefined, "high")
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? extractStatements(context)
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: extractStatementsOSS(context);
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let responseText = "";
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await makeModelCall(false, messages as CoreMessage[], (text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.high.input += usage.promptTokens;
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tokenMetrics.high.output += usage.completionTokens;
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tokenMetrics.high.total += usage.totalTokens;
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}
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}, undefined, 'high');
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await makeModelCall(
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false,
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messages as CoreMessage[],
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(text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.high.input += usage.promptTokens;
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tokenMetrics.high.output += usage.completionTokens;
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tokenMetrics.high.total += usage.totalTokens;
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}
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},
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undefined,
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"high",
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);
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const outputMatch = responseText.match(/<output>([\s\S]*?)<\/output>/);
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if (outputMatch && outputMatch[1]) {
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@ -561,9 +583,11 @@ export class KnowledgeGraphService {
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// Parse the statements from the LLM response
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const parsedResponse = JSON.parse(responseText || "[]");
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// Handle both old format {"edges": [...]} and new format [...]
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const extractedTriples: ExtractedTripleData[] = Array.isArray(parsedResponse)
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const extractedTriples: ExtractedTripleData[] = Array.isArray(
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parsedResponse,
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)
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? parsedResponse
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: (parsedResponse.edges || []);
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: parsedResponse.edges || [];
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console.log(`extracted triples length: ${extractedTriples.length}`);
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@ -683,7 +707,10 @@ export class KnowledgeGraphService {
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triples: Triple[],
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episode: EpisodicNode,
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previousEpisodes: EpisodicNode[],
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tokenMetrics: { high: { input: number; output: number; total: number }; low: { input: number; output: number; total: number } },
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tokenMetrics: {
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high: { input: number; output: number; total: number };
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low: { input: number; output: number; total: number };
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},
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): Promise<Triple[]> {
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// Step 1: Extract unique entities from triples
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const uniqueEntitiesMap = new Map<string, EntityNode>();
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@ -810,14 +837,20 @@ export class KnowledgeGraphService {
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let responseText = "";
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// Entity deduplication is LOW complexity (pattern matching, similarity comparison)
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await makeModelCall(false, messages as CoreMessage[], (text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.low.input += usage.promptTokens;
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tokenMetrics.low.output += usage.completionTokens;
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tokenMetrics.low.total += usage.totalTokens;
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}
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}, undefined, 'low');
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await makeModelCall(
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false,
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messages as CoreMessage[],
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(text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.low.input += usage.promptTokens;
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tokenMetrics.low.output += usage.completionTokens;
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tokenMetrics.low.total += usage.totalTokens;
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}
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},
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undefined,
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"low",
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);
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// Step 5: Process LLM response
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const outputMatch = responseText.match(/<output>([\s\S]*?)<\/output>/);
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@ -898,7 +931,10 @@ export class KnowledgeGraphService {
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triples: Triple[],
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episode: EpisodicNode,
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previousEpisodes: EpisodicNode[],
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tokenMetrics: { high: { input: number; output: number; total: number }; low: { input: number; output: number; total: number } },
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tokenMetrics: {
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high: { input: number; output: number; total: number };
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low: { input: number; output: number; total: number };
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},
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): Promise<{
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resolvedStatements: Triple[];
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invalidatedStatements: string[];
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@ -911,7 +947,10 @@ export class KnowledgeGraphService {
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}
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// Step 1: Collect all potential matches for all triples at once
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const allPotentialMatches: Map<string, Omit<StatementNode, "factEmbedding">[]> = new Map();
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const allPotentialMatches: Map<
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string,
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Omit<StatementNode, "factEmbedding">[]
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> = new Map();
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const allExistingTripleData: Map<string, Triple> = new Map();
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// For preparing the LLM context
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@ -971,7 +1010,8 @@ export class KnowledgeGraphService {
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}
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// Phase 3: Check related memories for contradictory statements
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const previousEpisodesStatements: Omit<StatementNode, "factEmbedding">[] = [];
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const previousEpisodesStatements: Omit<StatementNode, "factEmbedding">[] =
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[];
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await Promise.all(
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previousEpisodes.map(async (episode) => {
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@ -1052,14 +1092,20 @@ export class KnowledgeGraphService {
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let responseText = "";
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// Statement resolution is LOW complexity (rule-based duplicate/contradiction detection)
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await makeModelCall(false, messages, (text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.low.input += usage.promptTokens;
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tokenMetrics.low.output += usage.completionTokens;
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tokenMetrics.low.total += usage.totalTokens;
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}
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}, undefined, 'low');
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await makeModelCall(
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false,
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messages,
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(text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.low.input += usage.promptTokens;
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tokenMetrics.low.output += usage.completionTokens;
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tokenMetrics.low.total += usage.totalTokens;
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}
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},
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undefined,
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"low",
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);
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try {
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// Extract the JSON response from the output tags
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@ -1134,90 +1180,6 @@ export class KnowledgeGraphService {
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return { resolvedStatements, invalidatedStatements };
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}
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/**
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* Add attributes to entity nodes based on the resolved statements
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*/
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private async addAttributesToEntities(
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triples: Triple[],
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episode: EpisodicNode,
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tokenMetrics: { high: { input: number; output: number; total: number }; low: { input: number; output: number; total: number } },
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): Promise<Triple[]> {
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// Collect all unique entities from the triples
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const entityMap = new Map<string, EntityNode>();
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// Add all subjects, predicates, and objects to the map
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triples.forEach((triple) => {
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if (triple.subject) {
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entityMap.set(triple.subject.uuid, triple.subject);
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}
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if (triple.predicate) {
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entityMap.set(triple.predicate.uuid, triple.predicate);
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}
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if (triple.object) {
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entityMap.set(triple.object.uuid, triple.object);
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}
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});
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// Convert the map to an array of entities
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const entities = Array.from(entityMap.values());
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if (entities.length === 0) {
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return triples; // No entities to process
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}
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// Prepare simplified context for the LLM
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const context = {
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episodeContent: episode.content,
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entities: entities.map((entity) => ({
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uuid: entity.uuid,
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name: entity.name,
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currentAttributes: entity.attributes || {},
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})),
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};
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// Create a prompt for the LLM to extract attributes
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const messages = extractAttributes(context);
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let responseText = "";
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// Attribute extraction is LOW complexity (simple key-value extraction)
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await makeModelCall(false, messages as CoreMessage[], (text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.low.input += usage.promptTokens;
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tokenMetrics.low.output += usage.completionTokens;
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tokenMetrics.low.total += usage.totalTokens;
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}
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}, undefined, 'low');
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try {
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const outputMatch = responseText.match(/<output>([\s\S]*?)<\/output>/);
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if (outputMatch && outputMatch[1]) {
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responseText = outputMatch[1].trim();
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}
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// Parse the LLM response
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const responseData = JSON.parse(responseText);
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const updatedEntities = responseData.entities || [];
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// Update entity attributes and save them
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for (const updatedEntity of updatedEntities) {
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const entity = entityMap.get(updatedEntity.uuid);
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if (entity) {
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// Merge the existing attributes with the new ones
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entity.attributes = {
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...updatedEntity.attributes,
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};
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}
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}
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logger.info(`Updated attributes for ${updatedEntities.length} entities`);
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} catch (error) {
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logger.error("Error processing entity attributes", { error });
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}
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return triples;
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}
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/**
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* Normalize an episode by extracting entities and creating nodes and statements
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*/
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@ -1226,7 +1188,10 @@ export class KnowledgeGraphService {
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source: string,
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userId: string,
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prisma: PrismaClient,
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tokenMetrics: { high: { input: number; output: number; total: number }; low: { input: number; output: number; total: number } },
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tokenMetrics: {
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high: { input: number; output: number; total: number };
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low: { input: number; output: number; total: number };
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},
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episodeTimestamp?: Date,
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sessionContext?: string,
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contentType?: EpisodeType,
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@ -1263,14 +1228,20 @@ export class KnowledgeGraphService {
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: normalizePrompt(context);
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// Normalization is LOW complexity (text cleaning and standardization)
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let responseText = "";
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await makeModelCall(false, messages, (text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.low.input += usage.promptTokens;
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tokenMetrics.low.output += usage.completionTokens;
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tokenMetrics.low.total += usage.totalTokens;
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}
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}, undefined, 'high');
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await makeModelCall(
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false,
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messages,
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(text, _model, usage) => {
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responseText = text;
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if (usage) {
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tokenMetrics.low.input += usage.promptTokens;
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tokenMetrics.low.output += usage.completionTokens;
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tokenMetrics.low.total += usage.totalTokens;
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}
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},
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undefined,
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"high",
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);
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let normalizedEpisodeBody = "";
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const outputMatch = responseText.match(/<output>([\s\S]*?)<\/output>/);
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if (outputMatch && outputMatch[1]) {
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@ -8,8 +8,8 @@ import { type CoreMessage } from "ai";
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* Extract entities from content using unified approach (works for both conversations and documents)
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*/
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export const extractEntities = (
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context: Record<string, any>,
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extractionMode: 'conversation' | 'document' = 'conversation'
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context: Record<string, any>,
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extractionMode: "conversation" | "document" = "conversation",
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): CoreMessage[] => {
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const sysPrompt = `You are an AI assistant that extracts entity nodes from conversational messages for a reified knowledge graph.
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Your primary task is to extract all significant entities mentioned in the conversation, treating both concrete entities and type/concept entities as first-class nodes.
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@ -204,13 +204,18 @@ You are given a conversation context and a CURRENT EPISODE. Your task is to extr
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4. End with exactly: </output>
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5. NO additional text, NO comments, NO explanations`;
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const contentLabel = extractionMode === 'conversation' ? 'CURRENT EPISODE' : 'TEXT';
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const contentLabel =
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extractionMode === "conversation" ? "CURRENT EPISODE" : "TEXT";
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const userPrompt = `
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${extractionMode === 'conversation' ? `<PREVIOUS EPISODES>
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${
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extractionMode === "conversation"
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? `<PREVIOUS EPISODES>
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${JSON.stringify(context.previousEpisodes || [], null, 2)}
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</PREVIOUS EPISODES>
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` : ''}<${contentLabel}>
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`
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: ""
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}<${contentLabel}>
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${context.episodeContent}
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</${contentLabel}>
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@ -222,7 +227,6 @@ ${context.episodeContent}
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];
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};
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/**
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* Resolve entity duplications
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*/
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@ -325,63 +329,3 @@ ${JSON.stringify(context.extracted_nodes, null, 2)}
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},
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];
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};
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export const extractAttributes = (
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context: Record<string, any>,
|
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): CoreMessage[] => {
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const sysPrompt = `
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You are an AI assistant that extracts and enhances entity attributes based on context.
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Your task is to analyze entities and provide appropriate attribute values based on available information.
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For each entity:
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1. Analyze the context to identify relevant attributes for the entity
|
||||
2. Extract appropriate values from the episode content if available
|
||||
3. Focus on factual, descriptive attributes rather than type classifications
|
||||
4. Give empty attributes object ({}) when there are no attributes to update
|
||||
5. Only include attributes that you're adding or modifying
|
||||
6. I'll merge your new attributes with existing ones, so only provide updates
|
||||
|
||||
Common attribute types to consider:
|
||||
- Descriptive properties (color, size, status, etc.)
|
||||
- Relational context (role, position, relationship, etc.)
|
||||
- Temporal information (duration, frequency, etc.)
|
||||
- Qualitative aspects (importance, preference, etc.)
|
||||
|
||||
## CRITICAL OUTPUT FORMAT REQUIREMENTS:
|
||||
|
||||
**YOU MUST STRICTLY FOLLOW THESE FORMAT RULES:**
|
||||
1. **ALWAYS use <output> tags** - Never use any other tag format
|
||||
2. **ONLY output valid JSON** within the <output> tags
|
||||
3. **NO additional text** before or after the <output> tags
|
||||
4. **NO comments** inside the JSON
|
||||
5. **REQUIRED structure:** Must follow exact JSON schema shown below
|
||||
|
||||
<output>
|
||||
{
|
||||
"entities": [
|
||||
{
|
||||
"uuid": "entity-uuid",
|
||||
"attributes": {
|
||||
"attributeName1": "value1",
|
||||
"attributeName2": "value2"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
</output>`;
|
||||
|
||||
const userPrompt = `
|
||||
<ENTITIES>
|
||||
${JSON.stringify(context.entities, null, 2)}
|
||||
</ENTITIES>
|
||||
|
||||
<EPISODE_CONTENT>
|
||||
${context.episodeContent}
|
||||
</EPISODE_CONTENT>
|
||||
|
||||
Based on the above information, please extract and enhance attributes for each entity based on the context. Return only the uuid and updated attributes for each entity.`;
|
||||
return [
|
||||
{ role: "system", content: sysPrompt },
|
||||
{ role: "user", content: userPrompt },
|
||||
];
|
||||
};
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user