あなたの Telegram ミームをセマンティック検索します。ロシア語または英語で質問してください — ぼんやり覚えているミームを見つけます。
パイプライン: Telegram エクスポート → ビジョンキャプション（バイリンガル）+ OCR → 多言語埋め込み（bge-m3、ローカル）→ PostgreSQL/pgvector ハイブリッド検索（dense + full-text、RRF）→ cross-encoder 再ランキング。CLI、Web UI、MCP サーバーとして公開されているため、AI アシスタントもあなたのミームを検索できます。
完全なアーキテクチャについては 設計仕様書 を参照してください。
cp .env.example .env # fill in OpenRouter API key
make up # PostgreSQL + pgvector on :5433
make test
uv run memgrep ingest # load data/result.json + photos into the db
uv run memgrep caption --limit 400 # describe images via a vision model (pilot)
uv run memgrep embed # local bge-m3 embeddings
uv run memgrep search "this is fine" # CLI search; --open shows files in Preview
uv run memgrep serve # web UI on localhost:8000
uv run memgrep evals # golden-set metrics for the current config
uv run memgrep sync # ingest + caption + embed for new exports
uv run memgrep watch # follow the channel via Bot APITelegram のエクスポートは data/ に出力されます（Telegram Desktop → チャット履歴をエクスポート → 写真、JSON 形式）。ミームとエクスポートされたデータはローカルに留まります — data/ は gitignore されており、公開されるのはコードのみです。
Related MCP server: local-docs-mcp
21 クエリのゴールデンセット（UI のフィードバックボタン「это он ✓」で収集）と 400 枚の画像からなるパイロットコーパスで測定されました。各検索レイヤーは、その効果が実証された後にのみ追加されました:
Layer | hit@5 | hit@10 | MRR |
dense ベクトルのみ | 0.857 | 0.905 | 0.768 |
+ full-text 検索、RRF 融合 | 0.905 | 0.952 | 0.815 |
+ cross-encoder 再ランキング | 0.905 | 1.000 | 0.839 |
同じコーパスとゴールデンセットでのキャプショニングモデルの比較: qwen3-vl-8b は、検索メトリクスで qwen3-vl-235b に匹敵するかそれを上回り、コストは 3 分の 1 でした。そのため、フルコーパスは 8b モデルでインデックス化されています（約 11k 枚の画像で約 $3.6）。evals 履歴に記録されている注意点: ゴールデンセットは 8b インデックスの上で収集されたため、比較がそちらに偏ります。
検索設定は環境変数で駆動されます: MEMGREP_SEARCH_MODE=vector|hybrid、MEMGREP_RERANK_ENABLED=true|false。memgrep evals は、設定ごとにタイムスタンプ付きの JSON を evals/results/ に書き込むため、実行結果を比較可能に保てます。
Claude（または任意の MCP クライアント）にあなたのミームを検索させることができます:
claude mcp add memgrep -- uv run --directory /absolute/path/to/memgrep python -m memgrep.mcp_serverツール: search_memes(query, k) はファイルパスとキャプション付きの一致結果を返します。get_meme(sha256) は画像自体を返します。
memgrep watch コマンドは、Bot API のロングポーリングを介して Telegram チャンネルをリアルタイムでフォローします。ボットをチャンネル管理者として設定し、MEMGREP_TG_BOT_TOKEN と MEMGREP_TG_CHANNEL_ID を構成して、ウォッチャーサービスを実行します。チャンネルに投稿された新しい写真は自動的にインデックス化され、数分以内にキャプションが付けられ、埋め込まれ、検索可能になります。最近の投稿へのリアクションもエンゲージメント追跡のためにデータベースに同期されます。
Python 3.12+、uv、PostgreSQL 17 + pgvector、sentence-transformers（BAAI/bge-m3 embeddings、BAAI/bge-reranker-v2-m3 reranker、いずれもローカル）、キャプショニング用の任意の OpenAI 互換ビジョンエンドポイント（デフォルト: OpenRouter、qwen3-vl）、FastAPI、typer、MCP Python SDK。テスト 49 件、テストスイートでのネットワークやモデルのダウンロードはありません。
Return the meme image itself by sha256 from a search_memes result.
| Name | Required | Description | Default |
|---|---|---|---|
| sha256 | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral disclosure burden. It states the core behavior (returning the image binary for a given hash) and implies a read-only action, but it does not disclose output format (e.g., binary, base64), error conditions, or whether any authentication is required. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler words. Every phrase contributes meaning: 'meme image itself' clarifies the return value, 'by sha256' identifies the key parameter, and 'from a search_memes result' indicates the source workflow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description is largely complete: it identifies the input provenance, the operation, and the result. It does not mention potential errors or the exact binary format, but given the tool's simplicity the missing details are minor and unlikely to cause incorrect invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only the parameter name and type (string) with 0% description coverage, so the description must add meaning. It does this by explaining that the sha256 parameter comes from a search_memes result, giving the agent crucial context on how to obtain a valid value. It could specify the expected format (e.g., hex-encoded SHA-256), but the guidance is already helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Return') and names a precise resource ('the meme image itself') with a clear retrieval mechanism ('by sha256'). Referencing 'from a search_memes result' distinguishes it from its sibling tool search_memes, which presumably returns metadata or search results rather than the image content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies the intended usage: call this tool with a sha256 obtained from search_memes to fetch the actual image. It doesn't explicitly state exclusions or when to prefer alternatives, but for a simple single-purpose tool the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Semantic search over the meme collection. Query in Russian or English.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | ||
| query | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden; it does convey that matching is semantic rather than exact and that the query language is flexible. It does not state read-only behavior, result ordering, or limitations, but for a simple search tool the disclosed traits are non-trivial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences carry the essential message, with the main action stated first and no filler. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter search with an output schema, the description is nearly sufficient, but it leaves two gaps: the meaning/behavior of 'k' and the relationship to the sibling get_meme tool. This is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It adds meaning to 'query' by explaining the search is semantic and accepts Russian or English, but it remains silent on the 'k' parameter. The compensation is partial, making this minimum viable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a specific verb and resource ('Semantic search over the meme collection') and the 'semantic' qualifier distinguishes it from a direct fetch like get_meme. It is clear, but it does not explicitly name or contrast the sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Semantic search over the meme collection' implies when the tool is relevant, and 'Query in Russian or English' gives practical input guidance. However, it offers no explicit direction on when to choose this over get_meme or what types of queries are not appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two tools have clearly distinct purposes: one performs semantic search over the collection, the other retrieves a specific image by hash. There is no overlap or ambiguity between them.
Both tools follow the same verb_noun pattern in snake_case: search_memes and get_meme. The singular/plural variation is natural for resource action and does not break consistency.
With only two tools, the server feels thin and sits at the low end of the borderline range. However, the narrow purpose of search and retrieval justifies a minimal surface, so it is not unreasonable.
For a read-only meme search and retrieval server, the surface is complete: search returns hashes and get_meme fetches the image. There are no dead ends or missing core operations within this defined scope.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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