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MLflow AI Gateway

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MLflow AI Gateway has been deprecated. Please use MLflow Deployments for LLMs instead.

The MLflow AI Gateway service 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 AI Gateway dependencies:

pip install 'mlflow[gateway]'

Set the OpenAI API key as an environment variable:

export OPENAI_API_KEY=...

Create a configuration file:

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

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

Start the Gateway server:

mlflow gateway start --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.

Completions Example​

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

gateway = MlflowAIGateway(
    gateway_uri="http://127.0.0.1:5000",
    route="completions",
    params={
        "temperature": 0.0,
        "top_p": 0.1,
    },
)

llm_chain = LLMChain(
    llm=gateway,
    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"}]))

Embeddings Example​

from langchain_community.embeddings import MlflowAIGatewayEmbeddings

embeddings = MlflowAIGatewayEmbeddings(
    gateway_uri="http://127.0.0.1:5000",
    route="embeddings",
)

print(embeddings.embed_query("hello"))
print(embeddings.embed_documents(["hello"]))

Chat Example​

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

chat = ChatMLflowAIGateway(
    gateway_uri="http://127.0.0.1:5000",
    route="chat",
    params={
        "temperature": 0.1
    }
)

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))

Databricks MLflow AI Gateway​

Databricks MLflow AI Gateway is in private preview. Please contact a Databricks representative to enroll in the preview.

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_community.llms import MlflowAIGateway

gateway = MlflowAIGateway(
    gateway_uri="databricks",
    route="completions",
)

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

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