import type { CommitRecord, CommitMetadata, GitMemoryConfig } from './types.js';
import type { IEmbeddingFunction } from './embeddings.js';
export declare class ContextStore {
    private col;
    private llm;
    private llmModel;
    private constructor();
    /**
     * Async factory — opens (or creates) the local SQLite context store and detects LLM availability.
     * LLM detection order (matches Python _default_mem0_config() priority):
     *   1. OPENAI_API_KEY set → use OpenAI gpt-4o-mini
     *   2. otherwise → attempt Ollama at OLLAMA_URL
     *   3. Ollama fails → llm = null (graceful degradation)
     */
    static create(config: GitMemoryConfig, embedFn: IEmbeddingFunction): Promise<ContextStore>;
    /**
     * Extract LLM interpretation and store it.
     * Mirrors Python: memory.add(messages=[{"role":"user","content":summary}], metadata)
     *
     * Flow:
     *   1. If llm is null → no-op (graceful degradation)
     *   2. Dedup check — skip if hash already stored
     *   3. Extract 1–2 sentence interpretation via LLM
     *   4. Store extracted text + metadata in collection
     */
    addCommit(params: {
        hash: string;
        summary: string;
        metadata: CommitMetadata;
        userId: string;
    }): Promise<void>;
    /**
     * Semantic search on extracted context collection.
     * Returns CommitRecord[] with the LLM interpretation as the summary field.
     */
    search(params: {
        query: string;
        userId: string;
        limit: number;
    }): Promise<CommitRecord[]>;
    /**
     * Get all stored context entries (for latest_commits enrichment map).
     * Matches Python: mem.get_all(user_id=USER_ID)
     */
    getAll(_userId: string): Promise<CommitRecord[]>;
    /** Whether LLM context extraction is available. */
    get isActive(): boolean;
    /**
     * Call LLM to extract a concise interpretation of a commit.
     * Prompt matches spirit of mem0's extraction: brief, why-focused.
     */
    private extractContext;
}
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