feat(llm): Ollama LLM-Embeddings decouple + longer keep_alive settings (

#1800)

private_gpt/components/embedding/embedding_component.py

@@ -70,7 +70,7 @@ def __init__(self, settings: Settings) -> None:
                 ollama_settings = settings.ollama
                 self.embedding_model = OllamaEmbedding(
                     model_name=ollama_settings.embedding_model,
-                    base_url=ollama_settings.api_base,
+                    base_url=ollama_settings.embedding_api_base,
                 )
             case "azopenai":
                 try:

private_gpt/components/llm/llm_component.py

@@ -1,4 +1,6 @@
 import logging
+from collections.abc import Callable
+from typing import Any
 
 from injector import inject, singleton
 from llama_index.core.llms import LLM, MockLLM
@@ -133,6 +135,24 @@ def __init__(self, settings: Settings) -> None:
                     additional_kwargs=settings_kwargs,
                     request_timeout=ollama_settings.request_timeout,
                 )
+
+                if (
+                    ollama_settings.keep_alive
+                    != ollama_settings.model_fields["keep_alive"].default
+                ):
+                    # Modify Ollama methods to use the "keep_alive" field.
+                    def add_keep_alive(func: Callable[..., Any]) -> Callable[..., Any]:
+                        def wrapper(*args: Any, **kwargs: Any) -> Any:
+                            kwargs["keep_alive"] = ollama_settings.keep_alive
+                            return func(*args, **kwargs)
+
+                        return wrapper
+
+                    Ollama.chat = add_keep_alive(Ollama.chat)
+                    Ollama.stream_chat = add_keep_alive(Ollama.stream_chat)
+                    Ollama.complete = add_keep_alive(Ollama.complete)
+                    Ollama.stream_complete = add_keep_alive(Ollama.stream_complete)
+
             case "azopenai":
                 try:
                     from llama_index.llms.azure_openai import (  # type: ignore

private_gpt/settings/settings.py

@@ -209,6 +209,10 @@ class OllamaSettings(BaseModel):
         "http://localhost:11434",
         description="Base URL of Ollama API. Example: 'https://localhost:11434'.",
     )
+    embedding_api_base: str = Field(
+        api_base,  # default is same as api_base, unless specified differently
+        description="Base URL of Ollama embedding API. Defaults to the same value as api_base",
+    )
     llm_model: str = Field(
         None,
         description="Model to use. Example: 'llama2-uncensored'.",
@@ -217,6 +221,10 @@ class OllamaSettings(BaseModel):
         None,
         description="Model to use. Example: 'nomic-embed-text'.",
     )
+    keep_alive: str = Field(
+        "5m",
+        description="Time the model will stay loaded in memory after a request. examples: 5m, 5h, '-1' ",
+    )
     tfs_z: float = Field(
         1.0,
         description="Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.",

settings-ollama.yaml

@@ -14,6 +14,8 @@ ollama:
   llm_model: mistral
   embedding_model: nomic-embed-text
   api_base: http://localhost:11434
+  keep_alive: 5m
+  # embedding_api_base: http://ollama_embedding:11434  # uncomment if your embedding model runs on another ollama
   tfs_z: 1.0              # Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.
   top_k: 40               # Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)
   top_p: 0.9              # Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)

settings.yaml

@@ -99,6 +99,8 @@ ollama:
   llm_model: llama2
   embedding_model: nomic-embed-text
   api_base: http://localhost:11434
+  keep_alive: 5m
+  # embedding_api_base: http://ollama_embedding:11434  # uncomment if your embedding model runs on another ollama
   request_timeout: 120.0
 
 azopenai: