ollama+DSPy using OpenAI APIs.

ollama_dspy.py

# install DSPy: pip install dspy
import dspy

# Ollam is now compatible with OpenAI APIs
# 
# To get this to work you must include `model_type='chat'` in the `dspy.OpenAI` call. 
# If you do not include this you will get an error. 
# 
# I have also found that `stop='\n\n'` is required to get the model to stop generating text after the ansewr is complete. 
# At least with mistral.

ollama_model = dspy.OpenAI(api_base='http://localhost:11434/v1/', api_key='ollama', model='mistral:7b-instruct-v0.2-q6_K', stop='\n\n', model_type='chat')

# This sets the language model for DSPy.
dspy.settings.configure(lm=ollama_model)

# This is not required but it helps to understand what is happening
my_example = {
    "question": "What game was Super Mario Bros. 2 based on?",
    "answer": "Doki Doki Panic",
}

# This is the signature for the predictor. It is a simple question and answer model.
class BasicQA(dspy.Signature):
    """Answer questions about classic video games."""

    question = dspy.InputField(desc="a question about classic video games")
    answer = dspy.OutputField(desc="often between 1 and 5 words")

# Define the predictor.
generate_answer = dspy.Predict(BasicQA)

# Call the predictor on a particular input.
pred = generate_answer(question=my_example['question'])

# Print the answer...profit :)
print(pred.answer)

With latest dspy 3.0.3 we can as well use dspy.LM().

import dspy

# Configure DSPy to use local Ollama model
lm = dspy.LM('ollama_chat/mistral', api_base='http://localhost:11434', api_key='')
dspy.configure(lm=lm)
class BasicQA(dspy.Signature):
    question = dspy.InputField()
    answer = dspy.OutputField()

generate_answer = dspy.Predict(BasicQA)
answer = generate_answer(question="What is the secret of life?").answer
print(answer)