# MLflow AI Gateway

>[The MLflow AI Gateway](https://www.mlflow.org/docs/latest/gateway/index) 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.
> See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index) for more details.

## Installation and Setup

Install `mlflow` with MLflow AI Gateway dependencies:

```sh
pip install 'mlflow[gateway]'
```

Set the OpenAI API key as an environment variable:

```sh
export OPENAI_API_KEY=...
```

Create a configuration file:

```yaml
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:

```sh
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](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain).



## Completions Example

```python
import mlflow
from langchain.chains import LLMChain, PromptTemplate
from langchain.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

```python
from langchain.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

```python
from langchain.chat_models import ChatMLflowAIGateway
from langchain.schema 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.

```python
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.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)
```
