JPonsa
commented
4 months ago The issue seems to be around Ollama and mistral and how the stop is handled; impacting n>5 processing. I get different behaviours using OllamaLocal(model="mistral") or dspy.OpenAI(api_key="ollama", model="mistral"). However, only gpt3.5 sees to work for ChainOfThought(n>1)
import dspy
from pydantic import BaseModel, Field
from dspy.teleprompt import LabeledFewShot
from dspy.functional import TypedPredictor
class SytheticFactBaseModel(BaseModel):
fact: str = Field(..., description="a statement")
veracity: bool = Field(..., description="is the statement trie or false")
class ExampleSignature(dspy.Signature):
"""Generate an example of a synthetic fact. Be creative"""
fact: SytheticFactBaseModel = dspy.OutputField()
# lm = dspy.OpenAI(
# api_base="http://localhost:11434/v1/",
# api_key="ollama",
# model="mistral",
# stop="\n\n",
# model_type="chat",
# )
# lm = dspy.OpenAI(model="gpt-3.5-turbo", stop="\n\n")
lm = dspy.OllamaLocal(model="mistral", stop=["\n\n"])
dspy.settings.configure(lm=lm, temperature=0.7)
generator = TypedPredictor(ExampleSignature)
examples = generator(config=dict(n=5)).completions.fact
existing_examples = [
dspy.Example(fact="The ski is green", veracity=False),
dspy.Example(fact="Typed Dspy is cool!", veracity=True),
]
trained = LabeledFewShot().compile(student=generator, trainset=existing_examples)
augmented_examples = trained(config=dict(n=5)).completions.fact-------- GTP3.5 turbo output --------
['{"fact": "Synthetic facts must be in a JSON format", "veracity": true}',
'{"fact": "Synthetic facts should always be wrapped in a JSON object", "veracity": true}',
'{"fact": "Synthetic facts should always be presented in a JSON object format", "veracity": true}',
'{"fact": "Synthetic facts should always be formatted as a JSON object", "veracity": true}',
'{"fact": "Synthetic facts should always be in the format of a JSON object", "veracity": true}']-------- .OpenAI(api_key="ollama",model="mistral) output --------
['{\n"fact": "Artificial intelligence can create original compositions of music based on predefined parameters and styles.",\n"veracity": true\n}']-------- OllamaLocal(model="mistral") output --------
['{\n"fact": "In the future, advanced AI systems will be able to generate creative and complex synthetic facts in a JSON format without human intervention.",\n"veracity": true\n}',
'{\n"fact": "The mythical creature known as the \'Chupacabra\' is a cryptid that is said to inhabit parts of the Americas, particularly Puerto Rico and Mexico. It is described as having reddish-purple skin, large black eyes, and fangs or spines along its back.",\n"veracity": false\n}',
'{\n"fact": "In the future, all JSON objects will have consistent formatting and begin and end with \'{\' and \'}\' respectively.",\n"veracity": true\n}',
'{\n"fact": "In the future, all JSON objects must be properly formatted and enclosed within curly braces {}",\n"veracity": true\n}',
'{\n"fact": "Artificial intelligence systems can\'t create or experience emotions, despite generating creative outputs.",\n"veracity": true\n}']
chris-cortner
RamXX
mikeedjones
I am running the example shown in https://dspy-docs.vercel.app/docs/building-blocks/modules for ChainOfThought. Instead of getting 5 answers, I got only one answer.
Moreover, when running on a jupyter notebook. Rerunning the same cell produces the same outcome. I need to restart the kernel to get a different response.
lm = dspy.OpenAI( api_base="http://localhost:11434/v1/", api_key="ollama", model="mistral", stop="\n\n", model_type="chat", ) dspy.settings.configure(lm=lm, temperature=0.7)
question = "What's something great about the ColBERT retrieval model?"
1) Declare with a signature, and pass some config.
classify = dspy.cChainOfThought("question -> answer", n=5)
2) Call with input argument.
response = classify(question=question)
3) Access the outputs.
response.completions.answer # len(response.completions.answer) -> 1 expected 5
Output: ['The ColBERT retrieval model is great because it combines the power of BERT, a pre-trained language model, with information retrieval techniques. This allows it to understand the context and meaning behind queries, and retrieve relevant documents more effectively compared to traditional retrieval models.']