Search

  - `imageRecognition.labels`: Visual elements detected (people, objects, logos,
    etc.)
  - `vectors`: Text embeddings for semantic similarity (using Basilica method)
- **Content metadata fields** (may not yet be generally populated):
  - `description`: Manual content descriptions

  - `imageRecognition.labels`: Visual elements detected (people, objects, logos,
    etc.)
  - `vectors`: Text embeddings for semantic similarity (using Basilica method)
- **Content metadata fields** (may not yet be generally populated):
  - `description`: Manual content descriptions

    "slug": "val-vibes",
    "link": "/blog/val-vibes",
    "description": "How to build semantic search with embeddings for Val Town within Val Town itself",
    "pubDate": "Tue, 18 Jun 2024 00:00:00 GMT",
    "author": "JP Posma",

        contents: [query],
      });
      const values = result.embeddings[0].values;
      const queryResult = (await qdrant.search("wdl", {
        vector: values,

          return handleCompletions(await request.json(), apiKey)
            .catch(errHandler);
        case pathname.endsWith("/embeddings"):
          assert(request.method === "POST");
          return handleEmbeddings(await request.json(), apiKey)
            .catch(errHandler);
        case pathname.endsWith("/models"):
}
const DEFAULT_EMBEDDINGS_MODEL = "gemini-embedding-001";
async function handleEmbeddings (req, apiKey) {
  let modelFull, model;
  switch (true) {
      break;
    default:
      model = DEFAULT_EMBEDDINGS_MODEL;
  }
  modelFull = modelFull ?? "models/" + model;
  let { body } = response;
  if (response.ok) {
    const { embeddings } = JSON.parse(await response.text());
    body = JSON.stringify({
      object: "list",
      data: embeddings.map(({ values }, index) => ({
        object: "embedding",
        index,

transactions	Shows transaction usage: starting, performing operations, and committing/rolling back.
memory	Uses an in-memory SQLite database for temporary storage or fast access.
vector	Works with vector embeddings, storing and querying for similarity search.
encryption	Creates and uses an encrypted SQLite database, demonstrating setup and data operations.
ollama	Similarity search with Ollama and Mistral.

transactions	Shows transaction usage: starting, performing operations, and committing/rolling back.
memory	Uses an in-memory SQLite database for temporary storage or fast access.
vector	Works with vector embeddings, storing and querying for similarity search.
encryption	Creates and uses an encrypted SQLite database, demonstrating setup and data operations.
ollama	Similarity search with Ollama and Mistral.

      "Connectors stream data from IoT/APIs/DBs into JetStream.",
      "Persist to Streams + mirror across regions for locality.",
      "Use KV/Object Store for embeddings & RAG artifacts.",
    ],
  },

    "slug": "val-vibes",
    "link": "/blog/val-vibes",
    "description": "How to build semantic search with embeddings for Val Town within Val Town itself",
    "pubDate": "Tue, 18 Jun 2024 00:00:00 GMT",
    "author": "JP Posma",

    "slug": "val-vibes",
    "link": "/blog/val-vibes",
    "description": "How to build semantic search with embeddings for Val Town within Val Town itself",
    "pubDate": "Tue, 18 Jun 2024 00:00:00 GMT",
    "author": "JP Posma",