feat: allow to autopull ollama models

private_gpt/components/embedding/embedding_component.py

@@ -68,6 +68,7 @@ class EmbeddingComponent:
                 )
             case "ollama":
                 try:
+                    from ollama import Client, AsyncClient # type: ignore
                     from llama_index.embeddings.ollama import (  # type: ignore
                         OllamaEmbedding,
                     )
@@ -77,10 +78,36 @@ class EmbeddingComponent:
                     ) from e
 
                 ollama_settings = settings.ollama
+
+                # calculate embedding model. If not provided tag, it will be use latest model
+                model_name = (
+                    ollama_settings.embedding_model + ':latest'
+                    if ":" not in ollama_settings.embedding_model
+                    else ollama_settings.embedding_model
+                )
+
                 self.embedding_model = OllamaEmbedding(
-                    model_name=ollama_settings.embedding_model,
+                    model_name=model_name,
                     base_url=ollama_settings.embedding_api_base,
                 )
+
+                if ollama_settings.autopull_models:
+                    try:
+                        # TODO: Reuse llama-index client when llama-index is updated
+                        client = Client(
+                            host=ollama_settings.embedding_api_base,
+                            timeout=ollama_settings.request_timeout,
+                        )
+                        installed_models = [model['name'] for model in client.list().get("models", {})]
+                        if model_name not in installed_models:
+                            logger.info(
+                                f"Pulling model {model_name}. Please wait..."
+                            )
+                            client.pull(model_name)
+                            logger.info(f"Model {model_name} pulled successfully")
+                    except Exception as e:
+                        logger.error(f"Failed to pull model {model_name}: {e!s}")
+
             case "azopenai":
                 try:
                     from llama_index.embeddings.azure_openai import (  # type: ignore

private_gpt/components/llm/llm_component.py

@@ -146,8 +146,15 @@ class LLMComponent:
                     "repeat_penalty": ollama_settings.repeat_penalty,  # ollama llama-cpp
                 }
 
-                self.llm = Ollama(
-                    model=ollama_settings.llm_model,
+                # calculate llm model. If not provided tag, it will be use latest model
+                model_name = (
+                    ollama_settings.llm_model + ':latest'
+                    if ":" not in ollama_settings.llm_model
+                    else ollama_settings.llm_model
+                )
+
+                llm = Ollama(
+                    model=model_name,
                     base_url=ollama_settings.api_base,
                     temperature=settings.llm.temperature,
                     context_window=settings.llm.context_window,
@@ -155,6 +162,18 @@ class LLMComponent:
                     request_timeout=ollama_settings.request_timeout,
                 )
 
+                if ollama_settings.autopull_models:
+                    try:
+                        installed_models = [model['name'] for model in llm.client.list().get("models", {})]
+                        if model_name not in installed_models:
+                            logger.info(
+                                f"Pulling model {model_name}. Please wait..."
+                            )
+                            llm.client.pull(model_name)
+                            logger.info(f"Model {model_name} pulled successfully")
+                    except Exception as e:
+                        logger.error(f"Failed to pull model {model_name}: {e!s}")
+
                 if (
                     ollama_settings.keep_alive
                     != ollama_settings.model_fields["keep_alive"].default
@@ -172,6 +191,8 @@ class LLMComponent:
                     Ollama.complete = add_keep_alive(Ollama.complete)
                     Ollama.stream_complete = add_keep_alive(Ollama.stream_complete)
 
+                self.llm = llm
+
             case "azopenai":
                 try:
                     from llama_index.llms.azure_openai import (  # type: ignore

private_gpt/settings/settings.py

@@ -290,6 +290,10 @@ class OllamaSettings(BaseModel):
         120.0,
         description="Time elapsed until ollama times out the request. Default is 120s. Format is float. ",
     )
+    autopull_models: bool = Field(
+        False,
+        description="If set to True, the Ollama will automatically pull the models from the API base.",
+    )
 
 
 class AzureOpenAISettings(BaseModel):

settings.yaml

@@ -117,6 +117,7 @@ ollama:
   embedding_api_base: http://localhost:11434  # change if your embedding model runs on another ollama
   keep_alive: 5m
   request_timeout: 120.0
+  autopull_models: true
 
 azopenai:
   api_key: ${AZ_OPENAI_API_KEY:}