agentfootprint/dist/memory/pipeline/fact.js

The explainable agent framework — backtrack a wrong answer to the exact context that caused it (evidence, not guesses). Built on footprintjs.

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.factPipeline = void 0;
/**
 * factPipeline — key/value fact memory preset.
 *
 * Distills stable user claims out of each turn and recalls them as a
 * compact key/value block. Complements `narrativePipeline` (beats are
 * "what happened") — facts are "what's true right now."
 *
 *   READ   :  LoadFacts → FormatFacts
 *   WRITE  :  LoadFacts → ExtractFacts → WriteFacts
 *
 * Why `LoadFacts` on BOTH sides?
 *   - Read side: obvious — inject what we know.
 *   - Write side: surfaces existing facts to the extractor via
 *     `scope.loadedFacts`, so LLM-based extractors can UPDATE rather
 *     than duplicate. Costs one extra `store.list` per turn; saves
 *     many LLM-emitted duplicate facts.
 *
 * **Default extractor**: `patternFactExtractor()` — zero-dep, zero-cost
 * regex heuristics for common identity / contact disclosures. Opt into
 * `llmFactExtractor({ provider })` when you want semantic-quality
 * extraction (preferences, commitments, task statuses).
 *
 * **Stable ids**: every fact is stored under `fact:${key}`, so writing
 * `user.name = "Alice"` twice in two turns produces ONE entry in the
 * store, not two. Updates overwrite in place — there's no
 * accumulation, no "fact history" by default. Consumers who need fact
 * history should run `narrativePipeline` alongside.
 *
 * Most consumers reach for `factPipeline` indirectly through
 * `defineMemory({ type: MEMORY_TYPES.SEMANTIC, strategy: { kind:
 * MEMORY_STRATEGIES.EXTRACT, extractor: 'pattern' | 'llm' }, store })`.
 *
 * @example Direct usage (low-level — custom flowchart composition):
 * ```ts
 * import { factPipeline, llmFactExtractor, InMemoryStore } from 'agentfootprint/memory';
 *
 * // Cheap default — regex heuristics, no LLM cost.
 * const pipeline = factPipeline({ store: new InMemoryStore() });
 *
 * // Or opt into LLM-backed extraction (pass any LLMProvider):
 * const hqPipeline = factPipeline({
 *   store: new InMemoryStore(),
 *   extractor: llmFactExtractor({ provider: yourLLMProvider }),
 * });
 * ```
 */
const footprintjs_1 = require("footprintjs");
const extractFacts_js_1 = require("../facts/extractFacts.js");
const writeFacts_js_1 = require("../facts/writeFacts.js");
const loadFacts_js_1 = require("../facts/loadFacts.js");
const formatFacts_js_1 = require("../facts/formatFacts.js");
const patternFactExtractor_js_1 = require("../facts/patternFactExtractor.js");
function factPipeline(config) {
    const extractor = config.extractor ?? (0, patternFactExtractor_js_1.patternFactExtractor)();
    const loadConfig = {
        store: config.store,
        ...(config.loadLimit !== undefined && { limit: config.loadLimit }),
        ...(config.tiers && { tiers: config.tiers }),
    };
    const formatConfig = {
        ...(config.formatHeader !== undefined && { header: config.formatHeader }),
        ...(config.formatFooter !== undefined && { footer: config.formatFooter }),
        ...(config.formatShowConfidence !== undefined && {
            showConfidence: config.formatShowConfidence,
        }),
    };
    const extractConfig = {
        extractor,
        ...(config.writeTier && { tier: config.writeTier }),
        ...(config.writeTtlMs !== undefined && { ttlMs: config.writeTtlMs }),
    };
    // ── Read subflow: LoadFacts → FormatFacts
    const read = (0, footprintjs_1.flowChart)('LoadFacts', (0, loadFacts_js_1.loadFacts)(loadConfig), 'load-facts', {
        description: 'Load stored Fact entries (ids starting with `fact:`) into scope.loadedFacts',
    })
        .addFunction('FormatFacts', (0, formatFacts_js_1.formatFacts)(formatConfig), 'format-facts', 'Render loaded facts as one system message; writes scope.formatted')
        .build();
    // ── Write subflow: LoadFacts → ExtractFacts → WriteFacts
    // LoadFacts first so llmFactExtractor can see existing facts and
    // update rather than duplicate.
    const write = (0, footprintjs_1.flowChart)('LoadFacts', (0, loadFacts_js_1.loadFacts)(loadConfig), 'load-facts-for-extract', { description: 'Surface existing facts to the extractor for update-awareness' })
        .addFunction('ExtractFacts', (0, extractFacts_js_1.extractFacts)(extractConfig), 'extract-facts', 'Distill scope.newMessages into Fact entries')
        .addFunction('WriteFacts', (0, writeFacts_js_1.writeFacts)({ store: config.store }), 'write-facts', 'Batch-persist extracted facts via store.putMany (overwrite on key collision)')
        .build();
    return { read, write };
}
exports.factPipeline = factPipeline;
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