DeepInfra

DeepInfra is a serverless inference as a service that provides access to a variety of LLMs and embeddings models. This notebook goes over how to use LangChain with DeepInfra for chat models.

Set the Environment API Key

Make sure to get your API key from DeepInfra. You have to Login and get a new token.

You are given a 1 hour free of serverless GPU compute to test different models. (see here) You can print your token with deepctl auth token

# get a new token: https://deepinfra.com/login?from=%2Fdash

import os
from getpass import getpass

from langchain_community.chat_models import ChatDeepInfra
from langchain_core.messages import HumanMessage

DEEPINFRA_API_TOKEN = getpass()

# or pass deepinfra_api_token parameter to the ChatDeepInfra constructor
os.environ["DEEPINFRA_API_TOKEN"] = DEEPINFRA_API_TOKEN

chat = ChatDeepInfra(model="meta-llama/Llama-2-7b-chat-hf")

messages = [
    HumanMessage(
        content="Translate this sentence from English to French. I love programming."
    )
]
chat.invoke(messages)

API Reference:ChatDeepInfra | HumanMessage

ChatDeepInfra also supports async and streaming functionality:

from langchain_core.callbacks import StreamingStdOutCallbackHandler

API Reference:StreamingStdOutCallbackHandler

await chat.agenerate([messages])

chat = ChatDeepInfra(
    streaming=True,
    verbose=True,
    callbacks=[StreamingStdOutCallbackHandler()],
)
chat.invoke(messages)

Tool Calling

DeepInfra currently supports only invoke and async invoke tool calling.

For a complete list of models that support tool calling, please refer to our tool calling documentation.

import asyncio

from dotenv import find_dotenv, load_dotenv
from langchain_community.chat_models import ChatDeepInfra
from langchain_core.messages import HumanMessage
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.tools import tool

model_name = "meta-llama/Meta-Llama-3-70B-Instruct"

_ = load_dotenv(find_dotenv())


# Langchain tool
@tool
def foo(something):
    """
    Called when foo
    """
    pass


# Pydantic class
class Bar(BaseModel):
    """
    Called when Bar
    """

    pass


llm = ChatDeepInfra(model=model_name)
tools = [foo, Bar]
llm_with_tools = llm.bind_tools(tools)
messages = [
    HumanMessage("Foo and bar, please."),
]

response = llm_with_tools.invoke(messages)
print(response.tool_calls)
# [{'name': 'foo', 'args': {'something': None}, 'id': 'call_Mi4N4wAtW89OlbizFE1aDxDj'}, {'name': 'Bar', 'args': {}, 'id': 'call_daiE0mW454j2O1KVbmET4s2r'}]


async def call_ainvoke():
    result = await llm_with_tools.ainvoke(messages)
    print(result.tool_calls)


# Async call
asyncio.run(call_ainvoke())
# [{'name': 'foo', 'args': {'something': None}, 'id': 'call_ZH7FetmgSot4LHcMU6CEb8tI'}, {'name': 'Bar', 'args': {}, 'id': 'call_2MQhDifAJVoijZEvH8PeFSVB'}]

API Reference:ChatDeepInfra | HumanMessage | tool