How to use azureopenai embeddings in lancedb

azureopenai embeddings working solution

Introduction

• I was unable to use azure openai embeddings inside lancedb
• However, lancedb has fixed in their recent commit
• Still I cannot able to install the dev version in my project, hence copied the changes from the commit, did an minor change on top of that, like added attribute api_version which worked for me.
• let’s go.

References

• https://github.com/lancedb/lancedb/commit/bd82e1f66d184bc13869030f03783cb4cb71da18

# !pip install lancedb openai -Uq

Implementation

from functools import cached_property
from typing import TYPE_CHECKING, List, Optional, Union
import logging
import os 
from lancedb.util import attempt_import_or_raise
from lancedb.embeddings.base import TextEmbeddingFunction
from lancedb.embeddings.registry import register, EmbeddingFunctionRegistry

if TYPE_CHECKING:
    import numpy as np


@register("azure-openai")
class AzureOpenAIEmbeddings(TextEmbeddingFunction):
    """
    An embedding function that uses the OpenAI API
    https://platform.openai.com/docs/guides/embeddings
    This can also be used for open source models that
    are compatible with the OpenAI API.
    Notes
    -----
    If you're running an Ollama server locally,
    you can just override the `base_url` parameter
    and provide the Ollama embedding model you want
    to use (https://ollama.com/library):
    ```python
    from lancedb.embeddings import get_registry
    openai = get_registry().get("openai")
    embedding_function = openai.create(
        name="<ollama-embedding-model-name>",
        base_url="http://localhost:11434",
        )
    ```
    """

    name: str = "text-embedding-ada-002"
    dim: Optional[int] = None
    azure_endpoint: Optional[str] = None
    default_headers: Optional[dict] = None
    azure_deployment: Optional[str] = None
    api_key: Optional[str] = None
    api_version: Optional[str] = None 
    use_azure: bool = False
    
    def ndims(self):
        return self._ndims

    @staticmethod
    def model_names():
        return [
            "text-embedding-ada-002",
            "text-embedding-3-large",
            "text-embedding-3-small",
        ]

    @cached_property
    def _ndims(self):
        if self.name == "text-embedding-ada-002":
            return 1536
        elif self.name == "text-embedding-3-large":
            return self.dim or 3072
        elif self.name == "text-embedding-3-small":
            return self.dim or 1536
        else:
            raise ValueError(f"Unknown model name {self.name}")

    def generate_embeddings(
        self, texts: Union[List[str], "np.ndarray"]
    ) -> List["np.array"]:
        """
        Get the embeddings for the given texts
        Parameters
        ----------
        texts: list[str] or np.ndarray (of str)
            The texts to embed
        """
        openai = attempt_import_or_raise("openai")

        valid_texts = []
        valid_indices = []
        for idx, text in enumerate(texts):
            if text:
                valid_texts.append(text)
                valid_indices.append(idx)

        # TODO retry, rate limit, token limit
        try:
            kwargs = {
                "input": valid_texts,
                "model": self.name,
            }
            if self.name != "text-embedding-ada-002":
                kwargs["dimensions"] = self.ndims()

            rs = self._openai_client.embeddings.create(**kwargs)
            valid_embeddings = {
                idx: v.embedding for v, idx in zip(rs.data, valid_indices)
            }
        except openai.BadRequestError:
            logging.exception("Bad request: %s", texts)
            return [None] * len(texts)
        except Exception:
            logging.exception("OpenAI embeddings error")
            raise
        return [valid_embeddings.get(idx, None) for idx in range(len(texts))]

    @cached_property
    def _openai_client(self):
        openai = attempt_import_or_raise("openai")
        kwargs = {}
        if self.azure_endpoint:
            kwargs["azure_endpoint"] = self.azure_endpoint
        if self.default_headers:
            kwargs["default_headers"] = self.default_headers
        if self.azure_deployment:
            kwargs["azure_deployment"] = self.azure_deployment
        if self.api_key:
            kwargs["api_key"] = self.api_key
        if self.api_version: 
            kwargs['api_version'] = self.api_version 
        if self.use_azure:
            return openai.AzureOpenAI(**kwargs)
        else:
            return openai.OpenAI(**kwargs)

# REQUIRED ENV VARIABLES, ALWAYS LOAD IT FROM .env

AZURE_EMBEDDING_MODEL= os.environ['AZURE_EMBEDDING_MODEL']
AZURE_EMBEDDING_ENDPOINT=os.environ['AZURE_EMBEDDING_ENDPOINT']
AZURE_EMBEDDING_DEPLOYMENT=os.environ['AZURE_EMBEDDING_DEPLOYMENT']
AZURE_EMBEDDING_API_KEY=os.environ['AZURE_EMBEDDING_API_KEY']
AZURE_API_VERSION=os.environ['AZURE_API_VERSION']

reg = EmbeddingFunctionRegistry.get_instance() 
azure_openai = reg.get('azure-openai')

• Here by default, organization, base_url parameter is not working as expected, so I am passing azure_endpoint, azure_deployment and api_version parameters which are required for openai.AzureOpenAI API’s

embed_fn = azure_openai.create(
    api_version=AZURE_API_VERSION,
    name=AZURE_EMBEDDING_MODEL,
    azure_endpoint=AZURE_EMBEDDING_ENDPOINT,
    azure_deployment=AZURE_EMBEDDING_DEPLOYMENT,
    api_key=AZURE_EMBEDDING_API_KEY,
    use_azure=True
)

embed_fn.generate_embeddings('what is 2+3')

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