chore: import upstream snapshot with attribution

python/zvec/extension/http_embedding_function.py

@@ -0,0 +1,162 @@
+# Copyright 2025-present the zvec project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import json
+import os
+import urllib.request
+from functools import lru_cache
+from typing import Optional
+
+from ..common.constants import TEXT, DenseVectorType
+from .embedding_function import DenseEmbeddingFunction
+
+
+class HTTPDenseEmbedding(DenseEmbeddingFunction[TEXT]):
+    """Dense text embedding function using any OpenAI-compatible HTTP endpoint.
+
+    This class calls any server that implements the ``/v1/embeddings`` API
+    (LM Studio, Ollama, vLLM, LocalAI, etc.) using only the Python standard
+    library — no extra dependencies are required.
+
+    The embedding dimension is detected automatically from the first server
+    response.
+
+    Args:
+        base_url (str, optional): Base URL of the embedding server.
+            Defaults to ``"http://localhost:1234"`` (LM Studio).
+            Common values:
+
+            - ``"http://localhost:1234"``  — LM Studio
+            - ``"http://localhost:11434"`` — Ollama
+        model (str, optional): Model identifier as expected by the server.
+            Defaults to ``"text-embedding-nomic-embed-text-v1.5@f16"``.
+        api_key (Optional[str], optional): Bearer token for authenticated
+            endpoints.  Falls back to the ``OPENAI_API_KEY`` environment
+            variable.  Leave as ``None`` for local servers that do not
+            require authentication.
+        timeout (int, optional): HTTP request timeout in seconds.
+            Defaults to 30.
+
+    Attributes:
+        dimension (int): Embedding vector dimensionality (auto-detected).
+
+    Raises:
+        TypeError: If ``embed()`` receives a non-string input.
+        ValueError: If input is empty/whitespace-only or the server returns
+            an unexpected response format.
+        RuntimeError: If the HTTP request fails or the server is unreachable.
+
+    Examples:
+        >>> from zvec.extension import HTTPDenseEmbedding
+        >>>
+        >>> # LM Studio (default)
+        >>> emb = HTTPDenseEmbedding()
+        >>> vector = emb.embed("Hello, world!")
+        >>> len(vector)
+        768
+        >>>
+        >>> # Ollama
+        >>> emb = HTTPDenseEmbedding(
+        ...     base_url="http://localhost:11434",
+        ...     model="nomic-embed-text",
+        ... )
+        >>> vector = emb.embed("Semantic search with local models")
+
+    See Also:
+        - ``DenseEmbeddingFunction``: Protocol for dense embeddings.
+        - ``OpenAIDenseEmbedding``: Cloud embedding via the OpenAI API.
+    """
+
+    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
+        self._dimension: Optional[int] = None
+
+    @property
+    def dimension(self) -> int:
+        """int: Embedding vector dimensionality (auto-detected on first call)."""
+        if self._dimension is None:
+            self._dimension = len(self.embed("dimension probe"))
+        return self._dimension
+
+    def __call__(self, input: TEXT) -> DenseVectorType:
+        """Make the embedding function callable."""
+        return self.embed(input)
+
+    @lru_cache(maxsize=256)
+    def embed(self, input: TEXT) -> DenseVectorType:
+        """Generate a dense embedding vector for the input text.
+
+        Results are cached (LRU, up to 256 entries) so repeated strings
+        do not trigger extra HTTP requests.
+
+        Args:
+            input (TEXT): Input text string to embed.  Must be non-empty
+                after stripping whitespace.
+
+        Returns:
+            DenseVectorType: A list of floats representing the embedding.
+
+        Raises:
+            TypeError: If *input* is not a string.
+            ValueError: If *input* is empty/whitespace-only or the server
+                returns an unexpected response format.
+            RuntimeError: If the HTTP request fails.
+        """
+        if not isinstance(input, TEXT):
+            raise TypeError(f"Expected 'input' to be str, got {type(input).__name__}")
+
+        input = input.strip()
+        if not input:
+            raise ValueError("Input text cannot be empty or whitespace only")
+
+        url = self._base_url + self.ENDPOINT
+        payload = json.dumps({"model": self._model, "input": input}).encode()
+
+        headers: dict[str, str] = {"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