docs(examples): add ollama example

examples/ollama/.gitignore

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+local.db

examples/ollama/README.md

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+# Ollama + Vector Search Example
+
+This example demonstrates how to use libSQL vector search with a local database and Ollama.
+
+## Install Dependencies
+
+```bash
+npm i
+```
+
+## Install Ollama
+
+[Download Ollama](https://ollama.com/download) and install it.
+
+## Running
+
+Make sure Ollama is running with the model `mistral`:
+
+```bash
+ollama run mistral
+```
+
+Execute the example:
+
+```bash
+node index.mjs
+```
+
+This will setup a local SQLite database, generate embeddings using Ollama, and insert the data with embeddings, and then query the results using the vector similarity search function.

examples/ollama/index.mjs

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+import { createClient } from "@libsql/client";
+import ollama from "ollama";
+
+const client = createClient({
+    url: "file:local.db",
+});
+
+await client.batch(
+    [
+        "CREATE TABLE IF NOT EXISTS movies (id INTEGER PRIMARY KEY, title TEXT NOT NULL, description TEXT NOT NULL, embedding F32_BLOB(4096))",
+        "CREATE INDEX IF NOT EXISTS movies_embedding_idx ON movies(libsql_vector_idx(embedding))",
+    ],
+    "write",
+);
+
+async function getEmbedding(prompt) {
+    const response = await ollama.embeddings({
+        model: "mistral",
+        prompt,
+    });
+
+    return response.embedding;
+}
+
+async function insertMovie(title, description) {
+    const embedding = await getEmbedding(description);
+
+    await client.execute({
+        sql: `
+      INSERT INTO movies (title, description, embedding)
+      VALUES (?, ?, vector(?))
+    `,
+        args: [title, description, JSON.stringify(embedding)],
+    });
+}
+
+async function findSimilarMovies(description, limit = 3) {
+    const queryEmbedding = await getEmbedding(description);
+
+    const results = await client.execute({
+        sql: `
+          WITH vector_scores AS (
+            SELECT
+              rowid as id,
+              title,
+              description,
+              embedding,
+              1 - vector_distance_cos(embedding, vector32(?)) AS similarity
+            FROM movies
+            ORDER BY similarity DESC
+            LIMIT ?
+          )
+          SELECT id, title, description, similarity FROM vector_scores
+        `,
+        args: [JSON.stringify(queryEmbedding), limit],
+    });
+
+    return results.rows;
+}
+
+try {
+    const sampleMovies = [
+        {
+            title: "Inception",
+            description:
+                "A thief who enters the dreams of others to steal secrets from their subconscious.",
+        },
+        {
+            title: "The Matrix",
+            description:
+                "A computer programmer discovers that reality as he knows it is a simulation created by machines.",
+        },
+        {
+            title: "Interstellar",
+            description:
+                "Astronauts travel through a wormhole in search of a new habitable planet for humanity.",
+        },
+    ];
+
+    for (const movie of sampleMovies) {
+        await insertMovie(movie.title, movie.description);
+        console.log(`Inserted: ${movie.title}`);
+    }
+
+    const query =
+        "A sci-fi movie about virtual reality and artificial intelligence";
+    console.log("\nSearching for movies similar to:", query);
+
+    const similarMovies = await findSimilarMovies(query);
+    console.log("\nSimilar movies found:");
+    similarMovies.forEach((movie) => {
+        console.log(`\nTitle: ${movie.title}`);
+        console.log(`Description: ${movie.description}`);
+    });
+} catch (error) {
+    console.error("Error:", error);
+}