Sporing af store sprogmodeller (LLM) med MLflow: En komplet vejledning

pip install mlflow>=2.8.1
pip install openai
pip install chromadb==0.4.15
pip install langchain==0.0.348
pip install tiktoken
pip install 'mlflow[genai]'
pip install databricks-sdk --upgrade

import mlflow
import chromadb

print(f"MLflow version: {mlflow.__version__}")
print(f"ChromaDB version: {chromadb.__version__}")

import mlflow
import openai

def query_llm(prompt, max_tokens=100):
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=max_tokens
    )
    return response.choices[0].text.strip()

with mlflow.start_run():
    prompt = "Explain the concept of machine learning in simple terms."
    
    # Log parameters
    mlflow.log_param("model", "text-davinci-002")
    mlflow.log_param("max_tokens", 100)
    
    # Query the LLM and log the result
    result = query_llm(prompt)
    mlflow.log_metric("response_length", len(result))
    
    # Log the prompt and response
    mlflow.log_table("prompt_responses", {"prompt": [prompt], "response": [result]})
    
    print(f"Response: {result}")

import mlflow
from mlflow.deployments import get_deploy_client

# Initialize the deployment client
client = get_deploy_client("databricks")

# Define the endpoint configuration
endpoint_name = "llm-endpoint"
endpoint_config = {
    "served_entities": [{
        "name": "gpt-model",
        "external_model": {
            "name": "gpt-3.5-turbo",
            "provider": "openai",
            "task": "llm/v1/completions",
            "openai_config": {
                "openai_api_type": "azure",
                "openai_api_key": "{{secrets/scope/openai_api_key}}",
                "openai_api_base": "{{secrets/scope/openai_api_base}}",
                "openai_deployment_name": "gpt-35-turbo",
                "openai_api_version": "2023-05-15",
            },
        },
    }],
}

# Create the endpoint
client.create_endpoint(name=endpoint_name, config=endpoint_config)

<div class="relative flex flex-col rounded-lg">

response = client.predict(
endpoint=endpoint_name,
inputs={"prompt": "Explain the concept of neural networks briefly.","max_tokens": 100,},)

print(response)

Dette vil sende en prompt til vores implementerede model og returnere det genererede svar.

Evaluering af LLM'er med MLflow

Evaluering er afgørende for at forstå ydeevnen og adfærden af ​​dine LLM'er. MLflow leverer omfattende værktøjer til evaluering af LLM'er, herunder både indbyggede og tilpassede metrics.

Forberedelse af din LLM til evaluering

At evaluere din LLM med mlflow.evaluate(), skal din model være i en af ​​disse former:

1. An mlflow.pyfunc.PyFuncModel instans eller en URI, der peger på en logget MLflow-model.
2. En Python-funktion, der tager strenginput og udsender en enkelt streng.
3. En MLflow Deployments-endepunkts-URI.
4. sæt model=None og inkludere modeloutput i evalueringsdataene.

Lad os se på et eksempel ved hjælp af en logget MLflow-model:

import mlflow
import openai

with mlflow.start_run():
    system_prompt = "Answer the following question concisely."
    logged_model_info = mlflow.openai.log_model(
        model="gpt-3.5-turbo",
        task=openai.chat.completions,
        artifact_path="model",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": "{question}"},
        ],
    )

# Prepare evaluation data
eval_data = pd.DataFrame({
    "question": ["What is machine learning?", "Explain neural networks."],
    "ground_truth": [
        "Machine learning is a subset of AI that enables systems to learn and improve from experience without explicit programming.",
        "Neural networks are computing systems inspired by biological neural networks, consisting of interconnected nodes that process and transmit information."
    ]
})

# Evaluate the model
results = mlflow.evaluate(
    logged_model_info.model_uri,
    eval_data,
    targets="ground_truth",
    model_type="question-answering",
)

print(f"Evaluation metrics: {results.metrics}")

Dette eksempel logger en OpenAI-model, forbereder evalueringsdata og evaluerer derefter modellen ved hjælp af MLflows indbyggede metrikker til besvarelse af spørgsmål.

Brugerdefinerede evalueringsmetrics

MLflow giver dig mulighed for at definere brugerdefinerede metrikker til LLM-evaluering. Her er et eksempel på oprettelse af en brugerdefineret metrik til evaluering af svarenes professionalisme:

from mlflow.metrics.genai import EvaluationExample, make_genai_metric

professionalism = make_genai_metric(
    name="professionalism",
    definition="Measure of formal and appropriate communication style.",
    grading_prompt=(
        "Score the professionalism of the answer on a scale of 0-4:\n"
        "0: Extremely casual or inappropriate\n"
        "1: Casual but respectful\n"
        "2: Moderately formal\n"
        "3: Professional and appropriate\n"
        "4: Highly formal and expertly crafted"
    ),
    examples=[
        EvaluationExample(
            input="What is MLflow?",
            output="MLflow is like your friendly neighborhood toolkit for managing ML projects. It's super cool!",
            score=1,
            justification="The response is casual and uses informal language."
        ),
        EvaluationExample(
            input="What is MLflow?",
            output="MLflow is an open-source platform for the machine learning lifecycle, including experimentation, reproducibility, and deployment.",
            score=4,
            justification="The response is formal, concise, and professionally worded."
        )
    ],
    model="openai:/gpt-3.5-turbo-16k",
    parameters={"temperature": 0.0},
    aggregations=["mean", "variance"],
    greater_is_better=True,
)

# Use the custom metric in evaluation
results = mlflow.evaluate(
    logged_model_info.model_uri,
    eval_data,
    targets="ground_truth",
    model_type="question-answering",
    extra_metrics=[professionalism]
)

print(f"Professionalism score: {results.metrics['professionalism_mean']}")

Denne brugerdefinerede metric bruger GPT-3.5-turbo til at score professionalismen i svarene og demonstrerer, hvordan du kan udnytte LLM'er selv til evaluering.

Avancerede LLM evalueringsteknikker

Efterhånden som LLM'er bliver mere sofistikerede, bliver teknikkerne til at evaluere dem også mere sofistikerede. Lad os udforske nogle avancerede evalueringsmetoder ved hjælp af MLflow.

Retrieval-Augmented Generation (RAG) evaluering

RAG-systemer kombinerer kraften i hentningsbaserede og generative modeller. Evaluering af RAG-systemer kræver vurdering af både hentnings- og genereringskomponenterne. Sådan kan du opsætte et RAG-system og evaluere det ved hjælp af MLflow:

from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# Load and preprocess documents
loader = WebBaseLoader(["https://mlflow.org/docs/latest/index.html"])
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)

# Create RAG chain
llm = OpenAI(temperature=0)
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
    return_source_documents=True
)

# Evaluation function
def evaluate_rag(question):
    result = qa_chain({"query": question})
    return result["result"], [doc.page_content for doc in result["source_documents"]]

# Prepare evaluation data
eval_questions = [
    "What is MLflow?",
    "How does MLflow handle experiment tracking?",
    "What are the main components of MLflow?"
]

# Evaluate using MLflow
with mlflow.start_run():
    for question in eval_questions:
        answer, sources = evaluate_rag(question)
        
        mlflow.log_param(f"question", question)
        mlflow.log_metric("num_sources", len(sources))
        mlflow.log_text(answer, f"answer_{question}.txt")
        
        for i, source in enumerate(sources):
            mlflow.log_text(source, f"source_{question}_{i}.txt")

    # Log custom metrics
    mlflow.log_metric("avg_sources_per_question", sum(len(evaluate_rag(q)[1]) for q in eval_questions) / len(eval_questions))