NVIDIA NIM Microservices

The langchain-nvidia-ai-endpoints package contains LangChain integrations for chat models and embeddings powered by NVIDIA AI Foundation Models, and hosted on NVIDIA API Catalog.

NVIDIA AI Foundation models are community and NVIDIA-built models and are NVIDIA-optimized to deliver the best performance on NVIDIA accelerated infrastructure.  Using the API, you can query live endpoints available on the NVIDIA API Catalog to get quick results from a DGX-hosted cloud compute environment. All models are source-accessible and can be deployed on your own compute cluster using NVIDIA NIM™ microservices which is part of NVIDIA AI Enterprise.

Models can be exported from NVIDIA’s API catalog with NVIDIA NIM, which is included with the NVIDIA AI Enterprise license, and run them on-premises, giving Enterprises ownership of their customizations and full control of their IP and AI application. NIM microservices are packaged as container images on a per model/model family basis and are distributed as NGC container images through the NVIDIA NGC Catalog. At their core, NIM microservices are containers that provide interactive APIs for running inference on an AI Model. 

Below is an example on how to use some common functionality surrounding text-generative and embedding models.

Installation

%pip install -U --quiet langchain-nvidia-ai-endpoints

Setup

To get started:

1. Create a free account with NVIDIA, which hosts NVIDIA AI Foundation models.
2. Click on your model of choice.
3. Under Input select the Python tab, and click Get API Key. Then click Generate Key.
4. Copy and save the generated key as NVIDIA_API_KEY. From there, you should have access to the endpoints.

import getpass
import os

if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
    nvidia_api_key = getpass.getpass("Enter your NVIDIA API key: ")
    assert nvidia_api_key.startswith("nvapi-"), f"{nvidia_api_key[:5]}... is not a valid key"
    os.environ["NVIDIA_API_KEY"] = nvidia_api_key

Working with NVIDIA API Catalog

## Core LC Chat Interface
from langchain_nvidia_ai_endpoints import ChatNVIDIA

llm = ChatNVIDIA(model="meta/llama3-70b-instruct", max_tokens=419)
result = llm.invoke("Write a ballad about LangChain.")
print(result.content)

Working with NVIDIA NIM Microservices

When ready to deploy, you can self-host models with NVIDIA NIM—which is included with the NVIDIA AI Enterprise software license—and run them anywhere, giving you ownership of your customizations and full control of your intellectual property (IP) and AI applications.

Learn more about NIM microservices

from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings, NVIDIARerank

# connect to an chat NIM running at localhost:8000, specifying a specific model
llm = ChatNVIDIA(base_url="http://localhost:8000/v1", model="meta-llama3-8b-instruct")

# connect to an embedding NIM running at localhost:8080
embedder = NVIDIAEmbeddings(base_url="http://localhost:8080/v1")

# connect to a reranking NIM running at localhost:2016
ranker = NVIDIARerank(base_url="http://localhost:2016/v1")

Stream, Batch, and Async

These models natively support streaming, and as is the case with all LangChain LLMs they expose a batch method to handle concurrent requests, as well as async methods for invoke, stream, and batch. Below are a few examples.

print(llm.batch(["What's 2*3?", "What's 2*6?"]))
# Or via the async API
# await llm.abatch(["What's 2*3?", "What's 2*6?"])

for chunk in llm.stream("How far can a seagull fly in one day?"):
    # Show the token separations
    print(chunk.content, end="|")

async for chunk in llm.astream("How long does it take for monarch butterflies to migrate?"):
    print(chunk.content, end="|")

Supported models

Querying available_models will still give you all of the other models offered by your API credentials.

[model.id for model in llm.available_models if model.model_type]

#[
# ...
# 'databricks/dbrx-instruct',
# 'google/codegemma-7b',
# 'google/gemma-2b',
# 'google/gemma-7b',
# 'google/recurrentgemma-2b',
# 'meta/codellama-70b',
# 'meta/llama2-70b',
# 'meta/llama3-70b-instruct',
# 'meta/llama3-8b-instruct',
# 'microsoft/phi-3-mini-128k-instruct',
# 'mistralai/mistral-7b-instruct-v0.2',
# 'mistralai/mistral-large',
# 'mistralai/mixtral-8x22b-instruct-v0.1',
# 'mistralai/mixtral-8x7b-instruct-v0.1',
# 'snowflake/arctic',
# ...
#]

Model types

All of these models above are supported and can be accessed via ChatNVIDIA.

Some model types support unique prompting techniques and chat messages. We will review a few important ones below.

To find out more about a specific model, please navigate to the NVIDIA NIM section of ai.nvidia.com as linked here.

General Chat

Models such as meta/llama3-8b-instruct and mistralai/mixtral-8x22b-instruct-v0.1 are good all-around models that you can use for with any LangChain chat messages. Example below.

from langchain_nvidia_ai_endpoints import ChatNVIDIA
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful AI assistant named Fred."),
        ("user", "{input}")
    ]
)
chain = (
    prompt
    | ChatNVIDIA(model="meta/llama3-8b-instruct")
    | StrOutputParser()
)

for txt in chain.stream({"input": "What's your name?"}):
    print(txt, end="")

Code Generation

These models accept the same arguments and input structure as regular chat models, but they tend to perform better on code-genreation and structured code tasks. An example of this is meta/codellama-70b and google/codegemma-7b.

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are an expert coding AI. Respond only in valid python; no narration whatsoever."),
        ("user", "{input}")
    ]
)
chain = (
    prompt
    | ChatNVIDIA(model="meta/codellama-70b", max_tokens=419)
    | StrOutputParser()
)

for txt in chain.stream({"input": "How do I solve this fizz buzz problem?"}):
    print(txt, end="")

Multimodal

NVIDIA also supports multimodal inputs, meaning you can provide both images and text for the model to reason over.

An example model supporting multimodal inputs is nvidia/neva-22b.

These models accept LangChain's standard image formats. Below are examples.

import requests

image_url = "https://picsum.photos/seed/kitten/300/200"
image_content = requests.get(image_url).content

Initialize the model like so:

from langchain_nvidia_ai_endpoints import ChatNVIDIA

llm = ChatNVIDIA(model="nvidia/neva-22b")

Passing an image as a URL

from langchain_core.messages import HumanMessage

llm.invoke(
    [
        HumanMessage(content=[
            {"type": "text", "text": "Describe this image:"},
            {"type": "image_url", "image_url": {"url": image_url}},
        ])
    ])

Passing an image as a base64 encoded string

import base64
b64_string = base64.b64encode(image_content).decode('utf-8')
llm.invoke(
    [
        HumanMessage(content=[
            {"type": "text", "text": "Describe this image:"},
            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64_string}"}},
        ])
    ])

Directly within the string

The NVIDIA API uniquely accepts images as base64 images inlined within HTML tags. While this isn't interoperable with other LLMs, you can directly prompt the model accordingly.

base64_with_mime_type = f"data:image/png;base64,{b64_string}"
llm.invoke(
    f'What\'s in this image?\n<img src="{base64_with_mime_type}" />'
)

Completions

You can also work with models that support the Completions API. These models accept a prompt instead of messages.

completions_llm = NVIDIA().bind(max_tokens=512)
[model.id for model in completions_llm.get_available_models()]

# [
#   ...
#   'bigcode/starcoder2-7b',
#   'bigcode/starcoder2-15b',
#   ...
# ]

prompt = "# Function that does quicksort written in Rust without comments:"
for chunk in completions_llm.stream(prompt):
    print(chunk, end="", flush=True)

Embeddings

You can also connect to embeddings models through this package. Below is an example:

from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings

embedder = NVIDIAEmbeddings(model="NV-Embed-QA")
embedder.embed_query("What's the temperature today?")
embedder.embed_documents([
    "The temperature is 42 degrees.",
    "Class is dismissed at 9 PM."
])

Ranking

You can connect to ranking models. Below is an example:

from langchain_nvidia_ai_endpoints import NVIDIARerank
from langchain_core.documents import Document

query = "What is the GPU memory bandwidth of H100 SXM?"
passages = [
    "The Hopper GPU is paired with the Grace CPU using NVIDIA's ultra-fast chip-to-chip interconnect, delivering 900GB/s of bandwidth, 7X faster than PCIe Gen5. This innovative design will deliver up to 30X higher aggregate system memory bandwidth to the GPU compared to today's fastest servers and up to 10X higher performance for applications running terabytes of data.",
    "A100 provides up to 20X higher performance over the prior generation and can be partitioned into seven GPU instances to dynamically adjust to shifting demands. The A100 80GB debuts the world's fastest memory bandwidth at over 2 terabytes per second (TB/s) to run the largest models and datasets.",
    "Accelerated servers with H100 deliver the compute power—along with 3 terabytes per second (TB/s) of memory bandwidth per GPU and scalability with NVLink and NVSwitch™.",
]

client = NVIDIARerank(model="nvidia/llama-3.2-nv-rerankqa-1b-v1")

response = client.compress_documents(
  query=query,
  documents=[Document(page_content=passage) for passage in passages]
)

print(f"Most relevant: {response[0].page_content}\nLeast relevant: {response[-1].page_content}")