added optional openai base_url argument

Author: dutchfarao

CHANGELOG.md

@@ -4,6 +4,7 @@
 
 * **Migrate Jira Source connector from V1 to V2**
 * **Add Jira Source connector integration and unit tests**
+* **Added option for custom OpenAI baseurl in EmbedderConfig**
 
 ## 0.5.9
 

unstructured_ingest/embed/openai.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
 
 from pydantic import Field, SecretStr
 
@@ -26,6 +26,7 @@
 class OpenAIEmbeddingConfig(EmbeddingConfig):
     api_key: SecretStr
     embedder_model_name: str = Field(default="text-embedding-ada-002", alias="model_name")
+    base_url: Optional[str] = Field(default=None)
 
     def wrap_error(self, e: Exception) -> Exception:
         if is_internal_error(e=e):
@@ -57,13 +58,13 @@ def wrap_error(self, e: Exception) -> Exception:
     def get_client(self) -> "OpenAI":
         from openai import OpenAI
 
-        return OpenAI(api_key=self.api_key.get_secret_value())
+        return OpenAI(api_key=self.api_key.get_secret_value(), base_url=self.base_url)
 
     @requires_dependencies(["openai"], extras="openai")
     def get_async_client(self) -> "AsyncOpenAI":
         from openai import AsyncOpenAI
 
-        return AsyncOpenAI(api_key=self.api_key.get_secret_value())
+        return AsyncOpenAI(api_key=self.api_key.get_secret_value(), base_url=self.base_url)
 
 
 @dataclass

unstructured_ingest/v2/processes/embedder.py

@@ -52,6 +52,11 @@ class EmbedderConfig(BaseModel):
     embedding_azure_api_version: Optional[str] = Field(
         description="Azure API version", default=None
     )
+    embedding_openai_endpoint: Optional[str] = Field(
+        default=None,
+        description="Your custom OpenAI base url, "
+        "e.g. `https://custom-openai-deployment.com/`",
+    )
 
     def get_huggingface_embedder(self, embedding_kwargs: dict) -> "BaseEmbeddingEncoder":
         from unstructured_ingest.embed.huggingface import (
@@ -66,7 +71,16 @@ def get_huggingface_embedder(self, embedding_kwargs: dict) -> "BaseEmbeddingEnco
     def get_openai_embedder(self, embedding_kwargs: dict) -> "BaseEmbeddingEncoder":
         from unstructured_ingest.embed.openai import OpenAIEmbeddingConfig, OpenAIEmbeddingEncoder
 
-        return OpenAIEmbeddingEncoder(config=OpenAIEmbeddingConfig.model_validate(embedding_kwargs))
+        config_kwargs = {
+            "api_key": self.embedding_api_key,
+            "base_url": self.embedding_openai_endpoint,
+        }
+        if model_name := self.embedding_model_name:
+            config_kwargs["model_name"] = model_name
+
+        return OpenAIEmbeddingEncoder(
+            config=OpenAIEmbeddingConfig.model_validate(config_kwargs)
+            )
 
     def get_azure_openai_embedder(self, embedding_kwargs: dict) -> "BaseEmbeddingEncoder":
         from unstructured_ingest.embed.azure_openai import (