Embedding Utility class to use with a Local LM Studio embedding model

embeddings.py

import requests
from langchain.embeddings.base import Embeddings


class LMStudioEmbeddings(Embeddings):
    def __init__(
        self,
        endpoint,
        model,
    ):
        self.endpoint = endpoint
        self.model = model

    def embed_documents(self, texts):
        # Convert LangChain Documents to plain strings
        string_texts = [
            text.page_content if hasattr(text, "page_content") else text
            for text in texts
        ]
        return [self._embed(text) for text in string_texts]

    def embed_query(self, text):
        return self._embed(text)

    def _embed(self, text):
        response = requests.post(
            self.endpoint,
            headers={"Content-Type": "application/json"},
            json={"model": self.model, "input": text},
        )
        response.raise_for_status()
        return response.json()["data"][0]["embedding"]