Skip to main content

MLflow Deployments for LLMs

The MLflow Deployments for LLMs is a powerful tool designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests.

Installation and Setup​

Install mlflow with MLflow Deployments dependencies:

pip install 'mlflow[genai]'

Set the OpenAI API key as an environment variable:

export OPENAI_API_KEY=...

Create a configuration file:

endpoints:
- name: completions
endpoint_type: llm/v1/completions
model:
provider: openai
name: text-davinci-003
config:
openai_api_key: $OPENAI_API_KEY

- name: embeddings
endpoint_type: llm/v1/embeddings
model:
provider: openai
name: text-embedding-ada-002
config:
openai_api_key: $OPENAI_API_KEY

Start the deployments server:

mlflow deployments start-server --config-path /path/to/config.yaml

Example provided by MLflow​

The mlflow.langchain module provides an API for logging and loading LangChain models. This module exports multivariate LangChain models in the langchain flavor and univariate LangChain models in the pyfunc flavor.

See the API documentation and examples for more information.

Completions Example​

import mlflow
from langchain.chains import LLMChain, PromptTemplate
from langchain_community.llms import Mlflow

llm = Mlflow(
target_uri="http://127.0.0.1:5000",
endpoint="completions",
)

llm_chain = LLMChain(
llm=Mlflow,
prompt=PromptTemplate(
input_variables=["adjective"],
template="Tell me a {adjective} joke",
),
)
result = llm_chain.run(adjective="funny")
print(result)

with mlflow.start_run():
model_info = mlflow.langchain.log_model(chain, "model")

model = mlflow.pyfunc.load_model(model_info.model_uri)
print(model.predict([{"adjective": "funny"}]))
API Reference:LLMChain | Mlflow

Embeddings Example​

from langchain_community.embeddings import MlflowEmbeddings

embeddings = MlflowEmbeddings(
target_uri="http://127.0.0.1:5000",
endpoint="embeddings",
)

print(embeddings.embed_query("hello"))
print(embeddings.embed_documents(["hello"]))
API Reference:MlflowEmbeddings

Chat Example​

from langchain_community.chat_models import ChatMlflow
from langchain_core.messages import HumanMessage, SystemMessage

chat = ChatMlflow(
target_uri="http://127.0.0.1:5000",
endpoint="chat",
)

messages = [
SystemMessage(
content="You are a helpful assistant that translates English to French."
),
HumanMessage(
content="Translate this sentence from English to French: I love programming."
),
]
print(chat(messages))

Was this page helpful?


You can leave detailed feedback on GitHub.