NVIDIA AI Foundation Endpoints

NVIDIA AI Foundation Endpoints gives users easy access to hosted endpoints for generative AI models like Llama-2, SteerLM, Mistral, etc. Using the API, you can query live endpoints and get quick results from a DGX-hosted cloud compute environment. All models are source-accessible and can be deployed on your own compute cluster.

This example goes over how to use LangChain to interact with the supported NVIDIA Retrieval QA Embedding Model for retrieval-augmented generation via the NVIDIAEmbeddings class.

For more information on accessing the chat models through this api, check out the ChatNVIDIA documentation.

Installation

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

Setup

To get started: 1. Create a free account with the NVIDIA GPU Cloud service, which hosts AI solution catalogs, containers, models, etc. 2. Navigate to Catalog > AI Foundation Models > (Model with API endpoint). 3. Select the API option and click Generate Key. 4. Save the generated key as NVIDIA_API_KEY. From there, you should have access to the endpoints.

import getpass
import os

## API Key can be found by going to NVIDIA NGC -> AI Foundation Models -> (some model) -> Get API Code or similar.
## 10K free queries to any endpoint (which is a lot actually).

# del os.environ['NVIDIA_API_KEY']  ## delete key and reset
if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
    print("Valid NVIDIA_API_KEY already in environment. Delete to reset")
else:
    nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ")
    assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is not a valid key"
    os.environ["NVIDIA_API_KEY"] = nvapi_key

We should be able to see an embedding model among that list which can be used in conjunction with an LLM for effective RAG solutions. We can interface with this model pretty easily with the help of the NVIDIAEmbeddings model.

Initialization

The main requirement when initializing an embedding model is to provide the model name. An example is nvolveqa_40k below.

For nvovleqa_40k, you can also specify the model_type as passage or query. When doing retrieval, you will get best results if you embed the source documents with the passage type and the user queries with the query type.

If not provided, the embed_query method will default to the query type, and the embed_documents mehod will default to the passage type.

from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings

embedder = NVIDIAEmbeddings(model="nvolveqa_40k")

# Alternatively, if you want to specify whether it will use the query or passage type
# embedder = NVIDIAEmbeddings(model="nvolveqa_40k", model_type="passage")

This model is a fine-tuned E5-large model which supports the expected Embeddings methods including: - embed_query: Generate query embedding for a query sample. - embed_documents: Generate passage embeddings for a list of documents which you would like to search over. - aembed_quey/embed_documents: Asynchronous versions of the above.

Similarity/Speed Test

The following is a quick test of the methods in terms of usage, format, and speed for the use case of embedding the following data points:

Queries: - What’s the weather like in Komchatka? - What kinds of food is Italy known for? - What’s my name? I bet you don’t remember… - What’s the point of life anyways? - The point of life is to have fun :D

Documents: - Komchatka’s weather is cold, with long, severe winters. - Italy is famous for pasta, pizza, gelato, and espresso. - I can’t recall personal names, only provide information. - Life’s purpose varies, often seen as personal fulfillment. - Enjoying life’s moments is indeed a wonderful approach.

Embedding Runtimes

import time

print("Single Query Embedding: ")
s = time.perf_counter()
q_embedding = embedder.embed_query("What's the weather like in Komchatka?")
elapsed = time.perf_counter() - s
print("\033[1m" + f"Executed in {elapsed:0.2f} seconds." + "\033[0m")
print("Shape:", (len(q_embedding),))

print("\nSequential Embedding: ")
s = time.perf_counter()
q_embeddings = [
    embedder.embed_query("What's the weather like in Komchatka?"),
    embedder.embed_query("What kinds of food is Italy known for?"),
    embedder.embed_query("What's my name? I bet you don't remember..."),
    embedder.embed_query("What's the point of life anyways?"),
    embedder.embed_query("The point of life is to have fun :D"),
]
elapsed = time.perf_counter() - s
print("\033[1m" + f"Executed in {elapsed:0.2f} seconds." + "\033[0m")
print("Shape:", (len(q_embeddings), len(q_embeddings[0])))

print("\nBatch Query Embedding: ")
s = time.perf_counter()
# To use the "query" mode, we have to add it as an instance arg
q_embeddings = NVIDIAEmbeddings(
    model="nvolveqa_40k", model_type="query"
).embed_documents(
    [
        "What's the weather like in Komchatka?",
        "What kinds of food is Italy known for?",
        "What's my name? I bet you don't remember...",
        "What's the point of life anyways?",
        "The point of life is to have fun :D",
    ]
)
elapsed = time.perf_counter() - s
print("\033[1m" + f"Executed in {elapsed:0.2f} seconds." + "\033[0m")
print("Shape:", (len(q_embeddings), len(q_embeddings[0])))

Single Query Embedding: 
Executed in 1.39 seconds.
Shape: (1024,)

Sequential Embedding: 
Executed in 3.20 seconds.
Shape: (5, 1024)

Batch Query Embedding: 
Executed in 1.52 seconds.
Shape: (5, 1024)

Document Embedding

import time

print("Single Document Embedding: ")
s = time.perf_counter()
d_embeddings = embedder.embed_documents(
    [
        "Komchatka's weather is cold, with long, severe winters.",
    ]
)
elapsed = time.perf_counter() - s
print("\033[1m" + f"Executed in {elapsed:0.2f} seconds." + "\033[0m")
print("Shape:", (len(q_embedding),))

print("\nBatch Document Embedding: ")
s = time.perf_counter()
d_embeddings = embedder.embed_documents(
    [
        "Komchatka's weather is cold, with long, severe winters.",
        "Italy is famous for pasta, pizza, gelato, and espresso.",
        "I can't recall personal names, only provide information.",
        "Life's purpose varies, often seen as personal fulfillment.",
        "Enjoying life's moments is indeed a wonderful approach.",
    ]
)
elapsed = time.perf_counter() - s
print("\033[1m" + f"Executed in {elapsed:0.2f} seconds." + "\033[0m")
print("Shape:", (len(q_embeddings), len(q_embeddings[0])))

Single Document Embedding: 
Executed in 0.76 seconds.
Shape: (1024,)

Batch Document Embedding: 
Executed in 0.86 seconds.
Shape: (5, 1024)

Now that we’ve generated our embeddings, we can do a simple similarity check on the results to see which documents would have triggered as reasonable answers in a retrieval task:

%pip install --quiet matplotlib scikit-learn

Note: you may need to restart the kernel to use updated packages.

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# Assuming embeddings1 and embeddings2 are your two sets of vectors
# Compute the similarity matrix between embeddings1 and embeddings2
cross_similarity_matrix = cosine_similarity(
    np.array(q_embeddings),
    np.array(d_embeddings),
)

# Plotting the cross-similarity matrix
plt.figure(figsize=(8, 6))
plt.imshow(cross_similarity_matrix, cmap="Greens", interpolation="nearest")
plt.colorbar()
plt.title("Cross-Similarity Matrix")
plt.xlabel("Query Embeddings")
plt.ylabel("Document Embeddings")
plt.grid(True)
plt.show()

As a reminder, the queries and documents sent to our system were:

RAG Retrieval:

The following is a repurposing of the initial example of the LangChain Expression Language Retrieval Cookbook entry, but executed with the AI Foundation Models’ Mixtral 8x7B Instruct and NVIDIA Retrieval QA Embedding models available in their playground environments. The subsequent examples in the cookbook also run as expected, and we encourage you to explore with these options.

TIP: We would recommend using Mixtral for internal reasoning (i.e. instruction following for data extraction, tool selection, etc.) and Llama-Chat for a single final “wrap-up by making a simple response that works for this user based on the history and context” response.

%pip install --quiet langchain faiss-cpu tiktoken

from operator import itemgetter

from langchain.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_nvidia_ai_endpoints import ChatNVIDIA

Note: you may need to restart the kernel to use updated packages.

vectorstore = FAISS.from_texts(
    ["harrison worked at kensho"],
    embedding=NVIDIAEmbeddings(model="nvolveqa_40k"),
)
retriever = vectorstore.as_retriever()

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "Answer solely based on the following context:\n<Documents>\n{context}\n</Documents>",
        ),
        ("user", "{question}"),
    ]
)

model = ChatNVIDIA(model="mixtral_8x7b")

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

chain.invoke("where did harrison work?")

'Based on the document provided, Harrison worked at Kensho.'

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "Answer using information solely based on the following context:\n<Documents>\n{context}\n</Documents>"
            "\nSpeak only in the following language: {language}",
        ),
        ("user", "{question}"),
    ]
)

chain = (
    {
        "context": itemgetter("question") | retriever,
        "question": itemgetter("question"),
        "language": itemgetter("language"),
    }
    | prompt
    | model
    | StrOutputParser()
)

chain.invoke({"question": "where did harrison work", "language": "italian"})

'Harrison ha lavorato presso Kensho.\n\n(In English: Harrison worked at Kensho.)'

Installation​

Setup​

Initialization​

Similarity/Speed Test​

Embedding Runtimes​

Document Embedding​

RAG Retrieval:​