zrb-ollama 0.0.34

Zrb LLM plugin

Zrb Ollama

Zrb Ollama is a Pypi package that acts as LiteLLM's wrapper, allowing you to incorporate LLM into your workflow.

Zrb Ollama is a part of the Zrb ecosystem, but you can install it independently from Zrb.

Installation

You can install Zrb Ollama by invoking any of the following commands:

# From pypi
pip install zrb-ollama[chromadb,aws]

# From github
pip install git+https://github.com/state-alchemists/zrb-ollama.git@main

# From directory
pip install --use-feature=in-tree-build path/to/this/directory

By default, Zrb Ollama uses Ollama-based LLM. You can install Ollama by visiting the official website: https://ollama.ai/.

The default LLM is ollama/mistral:7b-instruct, while the default embedding LLM is ollama/nomic-embed-text.

You can change this by setting the model parameter on LLMTask or the create_rag function. See LiteLLM provider to use custom LLM.

CLI Command

Zrb Ollama provides a simple CLI command so you can interact with the LLM immediately. The LLM has two tools:

• query_internet
• run_shell_command

To interact with the LLM, you can invoke the following command.

zrb-ollama

Using LLMTask

Zrb Ollama provides a task named LLMTask, allowing you to create a Zrb Task with a custom model or tools.

from zrb import runner, StrInput
from zrb_ollama import LLMTask
from zrb_ollama.tools import query_internet, create_rag

import os

_CURRENT_DIR = os.path.dirname(__file__)
with open(os.path.join(_CURRENT_DIR, "john-titor.md")) as f:
    john_titor_article = f.read()

ask = LLMTask(
    name="ask",
    inputs=[
        StrInput(name="user-prompt", default="How John Titor introduce himself?"),
    ],
    model="gpt-4o",
    user_message="{{input.user_prompt}}",
    tools=[
        create_rag(
            documents=[john_titor_article],
            model="text-embedding-ada-002",
            rag_description="Look for anything related to John Titor"
        ),
        query_internet,
    ]
)
runner.register(ask)

Assuming there is a file named john-titor.md, you can invoke the Task by invoking the following command.

zrb ask

The LLM can browse the article or look for anything on the internet.

Using Agent

Under the hood, LLMTask makes use of Agent. You can create and interact with the agent programmatically as follows.

from zrb_ollama import agent
from zrb_ollama.tools import create_rag, query_internet

import asyncio
import os

_CURRENT_DIR = os.path.dirname(__file__)
with open(os.path.join(_CURRENT_DIR, "john-titor.md")) as f:
    john_titor_article = f.read()


agent = Agent(
    model="gpt-4o",
    tools=[
        create_rag(
            documents=[john_titor_article],
            model="text-embedding-ada-002",
            rag_description="Look for anything related to John Titor"
        ),
        query_internet,
    ]
)
result = asyncio.run(agent.add_user_message("How John Titor introduce himself?"))
print(result)