Add Local Ollama Model Support to Langchain Handler

[tmichaeldb]

Description

Fixes RAG-54

Will follow this up with another PR for Langchain Refactor (link)

This allows users to use their own local Ollama models with Langchain. See here for a list of all supported models.

It's important to note that many local Ollama models (i.e. mistral) are substantially slower than using other LLM APIs. Depending on the specs of the machine, you can expect anywhere from 4-10+ output tokens/second. For higher tokens/second output, users can experiment with smaller models

Type of change

(Please delete options that are not relevant)

• ⚡ New feature (non-breaking change which adds functionality)
• 📄 This change requires a documentation update

Verification Process

To ensure the changes are working as expected:

• Test Location: ./tests/unit/ml_handlers/test_langchain.py
• Verification Steps:
  1. pip install ollama to install Ollama
  2. ollama pull mistral to download the mistral model locally.
  3. Create & Query Langchain model

CREATE MODEL local_mistral_model
    PREDICT answer
    USING
        engine = 'langchain',
        model_name = 'mistral',
        user_column = 'question',
        assistant_column = 'answer',
        mode = 'conversational',
        prompt_template = 'You are a helpful assistant. Make sure to NEVER capitalize your letters. Always reply in all lowercase no matter what. Here is the user input: {{question}}';

SELECT answer FROM local_mistral_model WHERE question = 'What is your name?';

Additional Media:

• I have attached a brief loom video or screenshots showcasing the new functionality or change.

Checklist:

• My code follows the style guidelines(PEP 8) of MindsDB.
• I have appropriately commented on my code, especially in complex areas.
• Necessary documentation updates are either made or tracked in issues.
• Relevant unit and integration tests are updated or added.

[tmichaeldb]

cc @QuantumPlumber

[dusvyat]

LGTM. Not a blocker for this merge but it would be good to use pydantic for user input parameters in future