diff --git a/examples/custom_http_embedding.py b/examples/custom_http_embedding.py
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+"""
+Custom HTTP Embedding Example for zvec
+======================================
+
+Demonstrates how to use any OpenAI-compatible embedding endpoint
+(LM Studio, Ollama, vLLM, LocalAI, …) as an embedding source in zvec.
+
+Usage
+-----
+1. Start your local inference server:
+
+   **LM Studio** (https://lmstudio.ai/):
+       - Open LM Studio → go to "Local Server" tab
+       - Load an embedding model (e.g. nomic-embed-text, all-minilm-l6-v2)
+       - Click "Start Server" (default: http://localhost:1234)
+       - Enable "Allow external connections" if accessing from another machine
+
+   **Ollama** (https://ollama.com/):
+       $ ollama serve                      # starts on http://localhost:11434
+       $ ollama pull nomic-embed-text      # pull the model first
+
+2. Install zvec:
+       $ pip install zvec
+
+3. Run the example:
+       # LM Studio (default)
+       $ python examples/custom_http_embedding.py
+
+       # Ollama
+       $ python examples/custom_http_embedding.py \\
+             --base-url http://localhost:11434 \\
+             --model nomic-embed-text
+
+       # Remote / custom server
+       $ python examples/custom_http_embedding.py \\
+             --base-url http://192.168.1.10:1234 \\
+             --model text-embedding-nomic-embed-text-v1.5@f16
+
+Notes
+-----
+- The embedding dimension is detected automatically on the first call.
+- No API key is required for local servers; pass ``--api-key`` if yours needs one.
+- The collection is stored under ``/tmp/zvec_http_embedding_example`` and is
+  destroyed at the end of the script.  Remove the ``collection.destroy()`` call
+  at the bottom to keep the data across runs.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import urllib.request
+from functools import lru_cache
+from typing import List, Optional
+
+
+# ---------------------------------------------------------------------------
+# HTTPEmbeddingFunction
+# ---------------------------------------------------------------------------
+
+class HTTPEmbeddingFunction:
+    """Dense embedding function that calls an OpenAI-compatible /v1/embeddings
+    endpoint.
+
+    This class satisfies the :class:`zvec.DenseEmbeddingFunction` protocol and
+    can therefore be used anywhere zvec accepts a custom embedding function.
+
+    Parameters
+    ----------
+    base_url:
+        Base URL of the OpenAI-compatible inference server.
+        Examples:
+        - ``http://localhost:1234``   (LM Studio default)
+        - ``http://localhost:11434``  (Ollama default)
+    model:
+        Name / identifier of the embedding model as expected by the server.
+    api_key:
+        Optional API key sent as ``Authorization: Bearer <key>``.
+        Leave as ``None`` for servers that do not require authentication.
+    timeout:
+        HTTP request timeout in seconds (default: 30).
+    """
+
+    ENDPOINT = "/v1/embeddings"
+
+    def __init__(
+        self,
+        base_url: str = "http://localhost:1234",
+        model: str = "text-embedding-nomic-embed-text-v1.5@f16",
+        api_key: Optional[str] = None,
+        timeout: int = 30,
+    ) -> None:
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        self.api_key = api_key or os.environ.get("OPENAI_API_KEY", "")
+        self.timeout = timeout
+
+        # Detect dimension on first use (lazy)
+        self._dimension: Optional[int] = None
+
+    # ------------------------------------------------------------------
+    # DenseEmbeddingFunction protocol
+    # ------------------------------------------------------------------
+
+    @property
+    def dimension(self) -> int:
+        """Return the embedding dimension (detected lazily)."""
+        if self._dimension is None:
+            # Trigger a probe call to learn the dimension
+            self._dimension = len(self.embed("dimension probe"))
+        return self._dimension
+
+    def __call__(self, text: str) -> List[float]:
+        return self.embed(text)
+
+    @lru_cache(maxsize=256)
+    def embed(self, text: str) -> List[float]:
+        """Embed *text* and return a ``list[float]``.
+
+        Results are cached (LRU, up to 256 entries) to avoid redundant
+        network calls when the same string is encountered more than once.
+
+        Parameters
+        ----------
+        text:
+            The input string to embed.  Must be non-empty.
+
+        Returns
+        -------
+        list[float]
+            The dense embedding vector produced by the server.
+
+        Raises
+        ------
+        ValueError
+            If *text* is empty or the server returns an unexpected response.
+        RuntimeError
+            If the HTTP request fails.
+        """
+        if not isinstance(text, str):
+            raise TypeError(f"Expected str, got {type(text).__name__}")
+        text = text.strip()
+        if not text:
+            raise ValueError("Input text must not be empty or whitespace only.")
+
+        url = self.base_url + self.ENDPOINT
+        payload = json.dumps({"model": self.model, "input": text}).encode()
+
+        headers = {"Content-Type": "application/json"}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+
+        req = urllib.request.Request(url, data=payload, headers=headers, method="POST")
+        try:
+            with urllib.request.urlopen(req, timeout=self.timeout) as resp:
+                body = json.loads(resp.read())
+        except urllib.error.HTTPError as exc:
+            raise RuntimeError(
+                f"Embedding server returned HTTP {exc.code}: {exc.read().decode()}"
+            ) from exc
+        except OSError as exc:
+            raise RuntimeError(
+                f"Could not reach embedding server at {url}: {exc}"
+            ) from exc
+
+        try:
+            vector: List[float] = body["data"][0]["embedding"]
+        except (KeyError, IndexError) as exc:
+            raise ValueError(
+                f"Unexpected response format from embedding server: {body}"
+            ) from exc
+
+        return vector
+
+
+# ---------------------------------------------------------------------------
+# Demo
+# ---------------------------------------------------------------------------
+
+SAMPLE_DOCUMENTS = [
+    {
+        "id": "doc_1",
+        "text": "LM Studio lets you run large language models locally on your computer.",
+        "topic": "local AI",
+    },
+    {
+        "id": "doc_2",
+        "text": "Ollama is an open-source tool for running language models on-device.",
+        "topic": "local AI",
+    },
+    {
+        "id": "doc_3",
+        "text": "zvec is a lightweight, in-process vector database built on Proxima.",
+        "topic": "vector database",
+    },
+    {
+        "id": "doc_4",
+        "text": "HNSW is a graph-based algorithm for approximate nearest-neighbor search.",
+        "topic": "ANN algorithms",
+    },
+    {
+        "id": "doc_5",
+        "text": "Cosine similarity measures the angle between two vectors, ignoring magnitude.",
+        "topic": "math",
+    },
+]
+
+QUERY = "How do I run an embedding model on my laptop?"
+
+
+def run_demo(
+    base_url: str,
+    model: str,
+    api_key: Optional[str],
+    collection_path: str,
+) -> None:
+    import zvec
+    from zvec import (
+        CollectionSchema,
+        DataType,
+        Doc,
+        HnswIndexParam,
+        MetricType,
+        VectorQuery,
+        VectorSchema,
+        create_and_open,
+    )
+
+    # ------------------------------------------------------------------ #
+    # 1.  Embedding function                                               #
+    # ------------------------------------------------------------------ #
+    print(f"[1/4] Connecting to embedding server at {base_url} …")
+    emb = HTTPEmbeddingFunction(base_url=base_url, model=model, api_key=api_key)
+
+    # Probe dimension
+    dim = emb.dimension
+    print(f"      Model: {model!r}  |  Dimension: {dim}")
+
+    # ------------------------------------------------------------------ #
+    # 2.  Create collection with HNSW + cosine                            #
+    # ------------------------------------------------------------------ #
+    print("[2/4] Creating zvec collection (HNSW / cosine) …")
+    if os.path.exists(collection_path):
+        shutil.rmtree(collection_path)
+
+    schema = CollectionSchema(
+        name="http_embedding_demo",
+        vectors=VectorSchema(
+            name="embedding",
+            data_type=DataType.VECTOR_FP32,
+            dimension=dim,
+            index_param=HnswIndexParam(
+                metric_type=MetricType.COSINE,
+                m=16,
+                ef_construction=200,
+            ),
+        ),
+    )
+    collection = create_and_open(path=collection_path, schema=schema)
+
+    # ------------------------------------------------------------------ #
+    # 3.  Insert documents                                                 #
+    # ------------------------------------------------------------------ #
+    print(f"[3/4] Embedding and inserting {len(SAMPLE_DOCUMENTS)} documents …")
+    docs = []
+    for item in SAMPLE_DOCUMENTS:
+        vector = emb.embed(item["text"])
+        doc = Doc(
+            id=item["id"],
+            vectors={"embedding": vector},
+            fields={
+                "text": item["text"],
+                "topic": item["topic"],
+            },
+        )
+        docs.append(doc)
+
+    collection.insert(docs)
+    collection.flush()
+    print(f"      Inserted {collection.stats.total_doc_count} documents.")
+
+    # ------------------------------------------------------------------ #
+    # 4.  Search                                                           #
+    # ------------------------------------------------------------------ #
+    print(f"[4/4] Searching for: {QUERY!r}\n")
+    query_vector = emb.embed(QUERY)
+
+    results = collection.query(
+        VectorQuery("embedding", vector=query_vector),
+        topk=3,
+    )
+
+    print("Top-3 results:")
+    print("-" * 60)
+    for rank, result in enumerate(results, start=1):
+        # Retrieve stored fields if available
+        doc_id = result.id
+        score = result.score
+        # Find original text for display
+        original = next((d for d in SAMPLE_DOCUMENTS if d["id"] == doc_id), {})
+        print(f"  #{rank}  id={doc_id}  score={score:.4f}")
+        print(f"       {original.get('text', '(text not stored)')}")
+    print("-" * 60)
+
+    # ------------------------------------------------------------------ #
+    # Cleanup                                                              #
+    # ------------------------------------------------------------------ #
+    collection.destroy()
+    print("\nCollection destroyed.  Done!")
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="zvec custom HTTP embedding demo (LM Studio / Ollama)",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--base-url",
+        default="http://localhost:1234",
+        help="Base URL of the OpenAI-compatible embedding server.",
+    )
+    parser.add_argument(
+        "--model",
+        default="text-embedding-nomic-embed-text-v1.5@f16",
+        help="Embedding model name as expected by the server.",
+    )
+    parser.add_argument(
+        "--api-key",
+        default=None,
+        help="Optional API key (leave blank for local servers).",
+    )
+    parser.add_argument(
+        "--collection-path",
+        default="/tmp/zvec_http_embedding_example",
+        help="Filesystem path for the zvec collection.",
+    )
+    args = parser.parse_args()
+
+    run_demo(
+        base_url=args.base_url,
+        model=args.model,
+        api_key=args.api_key,
+        collection_path=args.collection_path,
+    )
+
+
+if __name__ == "__main__":
+    main()