feat: use openai embedding client instead of llama-index (#1313)

letta/embeddings.py

@@ -3,6 +3,7 @@ from typing import Any, List, Optional
 
 import numpy as np
 import tiktoken
+from openai import OpenAI
 
 from letta.constants import EMBEDDING_TO_TOKENIZER_DEFAULT, EMBEDDING_TO_TOKENIZER_MAP, MAX_EMBEDDING_DIM
 from letta.schemas.embedding_config import EmbeddingConfig
@@ -201,6 +202,21 @@ class GoogleVertexEmbeddings:
         return response.embeddings[0].embedding
 
 
+class OpenAIEmbeddings:
+
+    def __init__(self, api_key: str, model: str, base_url: str):
+        if base_url:
+            self.client = OpenAI(api_key=api_key, base_url=base_url)
+        else:
+            self.client = OpenAI(api_key=api_key)
+        self.model = model
+
+    def get_text_embedding(self, text: str):
+        response = self.client.embeddings.create(input=text, model=self.model)
+
+        return response.data[0].embedding
+
+
 def query_embedding(embedding_model, query_text: str):
     """Generate padded embedding for querying database"""
     query_vec = embedding_model.get_text_embedding(query_text)
@@ -218,15 +234,9 @@ def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] = None
     from letta.settings import model_settings
 
     if endpoint_type == "openai":
-        from llama_index.embeddings.openai import OpenAIEmbedding
-
-        additional_kwargs = {"user_id": user_id} if user_id else {}
-        model = OpenAIEmbedding(
-            api_base=config.embedding_endpoint,
-            api_key=model_settings.openai_api_key,
-            additional_kwargs=additional_kwargs,
+        return OpenAIEmbeddings(
+            api_key=model_settings.openai_api_key, model=config.embedding_model, base_url=model_settings.openai_api_base
         )
-        return model
 
     elif endpoint_type == "azure":
         assert all(