v0.3.0

package.json

@@ -1,6 +1,6 @@
 {
   "name": "identitydb",
-  "version": "0.2.1",
+  "version": "0.3.0",
   "description": "TypeScript memory graph database wrapper for topics, facts, and AI-assisted ingestion.",
   "license": "MIT",
   "type": "module",

src/ingestion/llm-extractor.ts

@@ -1,25 +1,22 @@
-import { IdentityDBError } from '../core/errors';
-import type { TopicCategory, TopicGranularity } from '../types/domain';
 import type {
   ExtractedFact,
   FactExtractor,
   LlmFactExtractorOptions,
-} from './types';
+} from "./types";
 
 const DEFAULT_INSTRUCTIONS = [
-  'Extract one structured fact from the user input.',
-  'Return JSON only. Do not include markdown, explanations, or prose outside the JSON object.',
+  "Extract one structured fact from the user input.",
+  "Return JSON only. Do not include markdown, explanations, or prose outside the JSON object.",
   'Use this shape: {"statement": string?, "summary": string|null, "source": string|null, "confidence": number|null, "metadata": object|null, "topics": Array<{"name": string, "category": "entity"|"concept"|"temporal"|"custom"?, "granularity": "abstract"|"concrete"|"mixed"?, "role": string|null, "description": string|null, "metadata": object|null}>}.',
-  'Only include topics that are explicitly supported by the input.',
-].join('\n');
+  'Only include topics that are explicitly in the input as-is. For example, topic "I started TypeScript since 2015" can be "I", "TypeScript", "2015".',
+].join("\n");
 
 export class LlmFactExtractor implements FactExtractor {
   constructor(private readonly options: LlmFactExtractorOptions) {}
 
   async extract(input: string): Promise<ExtractedFact> {
     const prompt = this.buildPrompt(input);
-    const response = await this.options.model.generateText(prompt);
-    return parseLlmExtractedFactResponse(response);
+    return this.options.model.generateText(prompt);
   }
 
   private buildPrompt(input: string): string {
@@ -31,243 +28,12 @@ export class LlmFactExtractor implements FactExtractor {
 
     return [
       DEFAULT_INSTRUCTIONS,
-      instructions && instructions.length > 0 ? `Additional instructions:\n${instructions}` : null,
+      instructions && instructions.length > 0
+        ? `Additional instructions:\n${instructions}`
+        : null,
       `Input:\n${input.trim()}`,
     ]
       .filter((value): value is string => value !== null)
-      .join('\n\n');
+      .join("\n\n");
   }
 }
-
-export function parseLlmExtractedFactResponse(response: string): ExtractedFact {
-  const payload = parseJsonCandidate(response);
-
-  if (!isRecord(payload)) {
-    throw new IdentityDBError('LLM extractor response must be a JSON object.');
-  }
-
-  const topics = parseTopics(payload.topics);
-  const extracted: ExtractedFact = { topics };
-
-  const statement = optionalString(payload.statement);
-  if (statement !== undefined) {
-    extracted.statement = statement;
-  }
-
-  const summary = optionalNullableString(payload.summary);
-  if (summary !== undefined) {
-    extracted.summary = summary;
-  }
-
-  const source = optionalNullableString(payload.source);
-  if (source !== undefined) {
-    extracted.source = source;
-  }
-
-  const confidence = optionalNullableNumber(payload.confidence);
-  if (confidence !== undefined) {
-    extracted.confidence = confidence;
-  }
-
-  const metadata = optionalMetadata(payload.metadata);
-  if (metadata !== undefined) {
-    extracted.metadata = metadata;
-  }
-
-  return extracted;
-}
-
-function parseJsonCandidate(response: string): unknown {
-  const trimmed = response.trim();
-
-  for (const candidate of collectJsonCandidates(trimmed)) {
-    try {
-      return JSON.parse(candidate);
-    } catch {
-      continue;
-    }
-  }
-
-  throw new IdentityDBError('LLM extractor returned invalid JSON.');
-}
-
-function collectJsonCandidates(response: string): string[] {
-  const candidates = new Set<string>();
-  candidates.add(response);
-
-  const fencePattern = /```(?:json)?\s*([\s\S]*?)```/gi;
-  let match: RegExpExecArray | null = fencePattern.exec(response);
-
-  while (match) {
-    const candidate = match[1]?.trim();
-    if (candidate) {
-      candidates.add(candidate);
-    }
-
-    match = fencePattern.exec(response);
-  }
-
-  const firstBrace = response.indexOf('{');
-  const lastBrace = response.lastIndexOf('}');
-  if (firstBrace >= 0 && lastBrace > firstBrace) {
-    candidates.add(response.slice(firstBrace, lastBrace + 1));
-  }
-
-  return Array.from(candidates);
-}
-
-function parseTopics(value: unknown): ExtractedFact['topics'] {
-  if (!Array.isArray(value)) {
-    throw new IdentityDBError('LLM extractor response must include a topics array.');
-  }
-
-  return value.map((entry) => parseTopic(entry));
-}
-
-function parseTopic(value: unknown): ExtractedFact['topics'][number] {
-  if (!isRecord(value)) {
-    throw new IdentityDBError('LLM extractor topics must be JSON objects.');
-  }
-
-  const name = optionalString(value.name)?.trim();
-  if (!name) {
-    throw new IdentityDBError('LLM extractor topics must include a non-empty name.');
-  }
-
-  const topic: ExtractedFact['topics'][number] = { name };
-
-  const category = optionalTopicCategory(value.category);
-  if (category !== undefined) {
-    topic.category = category;
-  }
-
-  const granularity = optionalTopicGranularity(value.granularity);
-  if (granularity !== undefined) {
-    topic.granularity = granularity;
-  }
-
-  const role = optionalNullableString(value.role);
-  if (role !== undefined) {
-    topic.role = role;
-  }
-
-  const description = optionalNullableString(value.description);
-  if (description !== undefined) {
-    topic.description = description;
-  }
-
-  const metadata = optionalMetadata(value.metadata);
-  if (metadata !== undefined) {
-    topic.metadata = metadata;
-  }
-
-  return topic;
-}
-
-function optionalString(value: unknown): string | undefined {
-  if (value === undefined) {
-    return undefined;
-  }
-
-  if (typeof value !== 'string') {
-    throw new IdentityDBError('LLM extractor expected a string field.');
-  }
-
-  return value;
-}
-
-function optionalNullableString(value: unknown): string | null | undefined {
-  if (value === undefined) {
-    return undefined;
-  }
-
-  if (value === null) {
-    return null;
-  }
-
-  if (typeof value !== 'string') {
-    throw new IdentityDBError('LLM extractor expected a nullable string field.');
-  }
-
-  return value;
-}
-
-function optionalNullableNumber(value: unknown): number | null | undefined {
-  if (value === undefined) {
-    return undefined;
-  }
-
-  if (value === null) {
-    return null;
-  }
-
-  if (typeof value !== 'number' || Number.isNaN(value)) {
-    throw new IdentityDBError('LLM extractor expected confidence to be a number or null.');
-  }
-
-  return value;
-}
-
-function optionalMetadata(value: unknown): ExtractedFact['metadata'] | undefined {
-  if (value === undefined) {
-    return undefined;
-  }
-
-  if (value === null) {
-    return null;
-  }
-
-  if (!isJsonLike(value)) {
-    throw new IdentityDBError('LLM extractor metadata must be valid JSON-compatible data.');
-  }
-
-  return value as ExtractedFact['metadata'];
-}
-
-function optionalTopicCategory(value: unknown): TopicCategory | undefined {
-  if (value === undefined) {
-    return undefined;
-  }
-
-  if (value === 'entity' || value === 'concept' || value === 'temporal' || value === 'custom') {
-    return value;
-  }
-
-  throw new IdentityDBError('LLM extractor returned an unsupported topic category.');
-}
-
-function optionalTopicGranularity(value: unknown): TopicGranularity | undefined {
-  if (value === undefined) {
-    return undefined;
-  }
-
-  if (value === 'abstract' || value === 'concrete' || value === 'mixed') {
-    return value;
-  }
-
-  throw new IdentityDBError('LLM extractor returned an unsupported topic granularity.');
-}
-
-function isRecord(value: unknown): value is Record<string, unknown> {
-  return typeof value === 'object' && value !== null && !Array.isArray(value);
-}
-
-function isJsonLike(value: unknown): boolean {
-  if (value === null) {
-    return true;
-  }
-
-  if (typeof value === 'string' || typeof value === 'number' || typeof value === 'boolean') {
-    return true;
-  }
-
-  if (Array.isArray(value)) {
-    return value.every((entry) => isJsonLike(entry));
-  }
-
-  if (isRecord(value)) {
-    return Object.values(value).every((entry) => isJsonLike(entry));
-  }
-
-  return false;
-}

src/ingestion/types.ts

@@ -18,7 +18,7 @@ export interface FactExtractor {
 }
 
 export interface LlmTextGenerationModel {
-  generateText(prompt: string): Promise<string>;
+  generateText(prompt: string): Promise<ExtractedFact>;
 }
 
 export interface LlmFactExtractorOptions {

tests/ingestion.test.ts

@@ -53,7 +53,7 @@ describe('IdentityDB ingestion', () => {
     expect(topic?.facts).toHaveLength(1);
   });
 
-  it('ships an LLM extractor adapter that turns structured JSON responses into facts', async () => {
+  it('ships an LLM extractor adapter that returns structured facts from the model', async () => {
     let prompt = '';
 
     const extractor = new LlmFactExtractor({
@@ -61,7 +61,7 @@ describe('IdentityDB ingestion', () => {
         async generateText(input) {
           prompt = input;
 
-          return JSON.stringify({
+          return {
             statement: 'I have worked with Bun and TypeScript since 2025.',
             summary: 'The speaker has Bun and TypeScript experience.',
             source: 'chat',
@@ -73,7 +73,7 @@ describe('IdentityDB ingestion', () => {
               { name: 'TypeScript', category: 'entity', granularity: 'concrete', role: 'object' },
               { name: '2025', category: 'temporal', granularity: 'concrete', role: 'time' },
             ],
-          });
+          };
         },
       },
       instructions: 'Prefer technology and time topics.',
@@ -91,47 +91,4 @@ describe('IdentityDB ingestion', () => {
     expect(fact.metadata).toEqual({ channel: 'telegram' });
     expect(fact.topics.map((topic) => topic.name)).toEqual(['I', 'Bun', 'TypeScript', '2025']);
   });
-
-  it('parses JSON responses wrapped in markdown code fences', async () => {
-    const extractor = new LlmFactExtractor({
-      model: {
-        async generateText() {
-          return [
-            'Here is the extracted fact:',
-            '```json',
-            JSON.stringify({
-              statement: 'Bun powers TypeScript tooling.',
-              topics: [
-                { name: 'Bun', category: 'entity', granularity: 'concrete' },
-                { name: 'TypeScript', category: 'entity', granularity: 'concrete' },
-              ],
-            }),
-            '```',
-          ].join('\n');
-        },
-      },
-    });
-
-    const fact = await db.ingestStatement('Bun powers TypeScript tooling.', {
-      extractor,
-    });
-
-    expect(fact.topics.map((topic) => topic.name)).toEqual(['Bun', 'TypeScript']);
-  });
-
-  it('rejects invalid LLM responses before writing facts', async () => {
-    const extractor = new LlmFactExtractor({
-      model: {
-        async generateText() {
-          return 'not json at all';
-        },
-      },
-    });
-
-    await expect(
-      db.ingestStatement('Bun powers TypeScript tooling.', {
-        extractor,
-      }),
-    ).rejects.toThrow('LLM extractor returned invalid JSON.');
-  });
 });