refactor: Update GenerateEmbeddingsForm to support batch processing

refactor: Update embedding batch size handling in RAG configuration

refactor: add query_doc query caching

refactor: update logging statements in generate_chat_completion function

change embedding_batch_size to Optional

backend/open_webui/apps/retrieval/utils.py

@@ -12,8 +12,8 @@ from langchain_core.documents import Document
 
 
 from open_webui.apps.ollama.main import (
-    GenerateEmbeddingsForm,
-    generate_ollama_embeddings,
+    GenerateEmbedForm,
+    generate_ollama_batch_embeddings,
 )
 from open_webui.apps.retrieval.vector.connector import VECTOR_DB_CLIENT
 from open_webui.utils.misc import get_last_user_message
@@ -71,7 +71,7 @@ def query_doc(
     try:
         result = VECTOR_DB_CLIENT.search(
             collection_name=collection_name,
-            vectors=[query_embedding],
+            vectors=query_embedding,
             limit=k,
         )
 
@@ -265,19 +265,15 @@ def get_embedding_function(
     embedding_function,
     openai_key,
     openai_url,
-    batch_size,
+    embedding_batch_size,
 ):
     if embedding_engine == "":
         return lambda query: embedding_function.encode(query).tolist()
     elif embedding_engine in ["ollama", "openai"]:
         if embedding_engine == "ollama":
             func = lambda query: generate_ollama_embeddings(
-                GenerateEmbeddingsForm(
-                    **{
-                        "model": embedding_model,
-                        "prompt": query,
-                    }
-                )
+                model=embedding_model,
+                input=query,
             )
         elif embedding_engine == "openai":
             func = lambda query: generate_openai_embeddings(
@@ -289,13 +285,10 @@ def get_embedding_function(
 
         def generate_multiple(query, f):
             if isinstance(query, list):
-                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]
+                embeddings = []
+                for i in range(0, len(query), embedding_batch_size):
+                    embeddings.extend(f(query[i : i + embedding_batch_size]))
+                return embeddings
             else:
                 return f(query)
 
@@ -481,6 +474,21 @@ def generate_openai_batch_embeddings(
         return None
 
 
+def generate_ollama_embeddings(
+    model: str, input: list[str]
+) -> Optional[list[list[float]]]:
+    if isinstance(input, list):
+        embeddings = generate_ollama_batch_embeddings(
+            GenerateEmbedForm(**{"model": model, "input": input})
+        )
+    else:
+        embeddings = generate_ollama_batch_embeddings(
+            GenerateEmbedForm(**{"model": model, "input": [input]})
+        )
+
+    return embeddings["embeddings"]
+
+
 import operator
 from typing import Optional, Sequence