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¶ Ollama MCP Provider
The ObjAiMcpOllama class provides an interface to a local Ollama server, allowing you to run open-source language models on your own hardware. It is part of the Multi-Cloud Provider (MCP) framework and inherits from the ObjAiMcpBase class.
¶ Initialization
The class is initialized with the following parameters:
def __init__(self, db=0, api_key: str = "", model: str = "mistral", max_turns: int = 50):
db: The database connection object.api_key: Not used for Ollama.model: The name of the Ollama model to use (e.g., "mistral", "llama2").max_turns: Maximum number of conversation turns sent to the model in eachchat()call. Oldest turns beyond this limit are excluded from the request (but remain in_contextfor reference). Defaults to 50.
¶ Methods
¶ prompt
Sends a one-shot prompt to the Ollama model and returns the response. Retries up to MAX_RETRIES times on transient failures with RETRY_DELAY_S seconds between attempts.
def prompt(self, role: str = "", prompt: str = "", image_base64: str = "", options: dict | None = None) -> str:
role: The system role for the AI (e.g., "You are a helpful assistant.").prompt: The user's prompt or question.image_base64: A base64-encoded image for multimodal (vision) prompts.options: Optional dict of Ollama generation options (e.g.,{"temperature": 0.7, "seed": 42}).
¶ embed
Generates an embedding vector for text using the local model. Use a dedicated embedding model such as nomic-embed-text or mxbai-embed-large for best results.
def embed(self, text: str = "") -> list:
¶ chat
Sends a message in a multi-turn conversation, accumulating history in self._context. Subsequent calls include the full conversation history automatically.
def chat(self, message: str, role: str = "", options: dict | None = None) -> str:
¶ reset_context
Clears the accumulated multi-turn conversation history.
def reset_context() -> None:
¶ stream_prompt
Streams a prompt response token-by-token. Yields text chunks as they arrive from the model — useful for TUI or CLI interfaces that display responses in real time.
def stream_prompt(self, role: str = "", prompt: str = "", options: dict | None = None):
for chunk in provider.stream_prompt("You are helpful.", "Tell me a story."):
print(chunk, end="", flush=True)
¶ get_model_registry
Returns the curated model list from resource.config/ollama-models.yaml. Each entry has name, use, tags, and default keys. Returns [] if the file is absent.
registry = ObjAiMcpOllama.get_model_registry()
for m in registry:
flag = "✅" if m["default"] else "➖"
print(f"{flag} {m['name']:25} {m['use']}")
¶ pull_model_set
Pulls models from the registry. When default_only=True (the default), only models marked default: true are pulled. Returns the list of model names that were successfully pulled. Failed pulls are logged via debug() and skipped.
# Pull mistral + nomic-embed-text + llava (the default set)
pulled = provider.pull_model_set()
# Pull the full registry including optional models
pulled = provider.pull_model_set(default_only=False)
¶ smoke_test
Sends a minimal prompt to verify the model actually responds. Returns True when a non-empty response is received.
¶ warm_model
Pre-loads the model into VRAM using ollama.generate(prompt="", keep_alive="5m"). Returns True on success.
¶ get_loaded_models
Returns the names of models currently loaded into VRAM via ollama.ps().
¶ get_capabilities
Inspects model metadata via ollama.show() and returns {"vision": bool, "context_length": int | None}.
¶ Usage
To use the Ollama provider, you must have an Ollama server running. No API key is required.
# One-shot prompt
ai_obj = ObjAI(db=0, model="mcp:ollama:mistral")
response = ai_obj.prompt("You are a helpful assistant.", "What is the speed of light?")
print(response)
# Multi-turn conversation
from ObjAiMcpOllama import ObjAiMcpOllama
provider = ObjAiMcpOllama(model="mistral")
provider.chat("Hello! What can you help me with?")
provider.chat("Tell me more about the first topic.")
provider.reset_context() # start fresh
# Vision (image analysis)
ai_obj = ObjAI(db=0, model="mcp:ollama:llava")
response = ai_obj.prompt("Describe what you see.", image_base64=base64_str)
# Embeddings
provider = ObjAiMcpOllama(model="nomic-embed-text")
vector = provider.embed("some text to embed")