feat: add RAG_EMBEDDING_OPENAI_BATCH_SIZE to batch multiple embeddings

backend/apps/rag/utils.py

@@ -2,7 +2,7 @@ import os
 import logging
 import requests
 
-from typing import List
+from typing import List, Union
 
 from apps.ollama.main import (
     generate_ollama_embeddings,
@@ -21,17 +21,7 @@ from langchain.retrievers import (
 from typing import Optional
 
 
-from config import (
-    SRC_LOG_LEVELS,
-    CHROMA_CLIENT,
-    SEARXNG_QUERY_URL,
-    GOOGLE_PSE_API_KEY,
-    GOOGLE_PSE_ENGINE_ID,
-    BRAVE_SEARCH_API_KEY,
-    SERPSTACK_API_KEY,
-    SERPSTACK_HTTPS,
-    SERPER_API_KEY,
-)
+from config import SRC_LOG_LEVELS, CHROMA_CLIENT
 
 log = logging.getLogger(__name__)
 log.setLevel(SRC_LOG_LEVELS["RAG"])
@@ -209,6 +199,7 @@ def get_embedding_function(
     embedding_function,
     openai_key,
     openai_url,
+    batch_size,
 ):
     if embedding_engine == "":
         return lambda query: embedding_function.encode(query).tolist()
@@ -232,7 +223,13 @@ def get_embedding_function(
 
         def generate_multiple(query, f):
             if isinstance(query, list):
-                return [f(q) for q in query]
+                if embedding_engine == "openai":
+                    embeddings = []
+                    for i in range(0, len(query), batch_size):
+                        embeddings.extend(f(query[i : i + batch_size]))
+                    return embeddings
+                else:
+                    return [f(q) for q in query]
             else:
                 return f(query)
 
@@ -413,8 +410,22 @@ def get_model_path(model: str, update_model: bool = False):
 
 
 def generate_openai_embeddings(
-    model: str, text: str, key: str, url: str = "https://api.openai.com/v1"
+    model: str,
+    text: Union[str, list[str]],
+    key: str,
+    url: str = "https://api.openai.com/v1",
 ):
+    if isinstance(text, list):
+        embeddings = generate_openai_batch_embeddings(model, text, key, url)
+    else:
+        embeddings = generate_openai_batch_embeddings(model, [text], key, url)
+
+    return embeddings[0] if isinstance(text, str) else embeddings
+
+
+def generate_openai_batch_embeddings(
+    model: str, texts: list[str], key: str, url: str = "https://api.openai.com/v1"
+) -> Optional[list[list[float]]]:
     try:
         r = requests.post(
             f"{url}/embeddings",
@@ -422,12 +433,12 @@ def generate_openai_embeddings(
                 "Content-Type": "application/json",
                 "Authorization": f"Bearer {key}",
             },
-            json={"input": text, "model": model},
+            json={"input": texts, "model": model},
         )
         r.raise_for_status()
         data = r.json()
         if "data" in data:
-            return data["data"][0]["embedding"]
+            return [elem["embedding"] for elem in data["data"]]
         else:
             raise "Something went wrong :/"
     except Exception as e: