NVIDIA's LLM Text Completion API¶

Extending the NVIDIA class to support /completion API's for below models:

• bigcode/starcoder2-7b
• bigcode/starcoder2-15b

Installation¶

In [ ]:

!pip install --force-reinstall llama_index-llms-nvidia

!pip install --force-reinstall llama_index-llms-nvidia

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.

In [ ]:

!which python

!which python

In [ ]:

import getpass
import os

# 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

import getpass import os # 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

In [ ]:

os.environ["NVIDIA_API_KEY"]

os.environ["NVIDIA_API_KEY"]

In [ ]:

# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio
import nest_asyncio

nest_asyncio.apply()

# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio import nest_asyncio nest_asyncio.apply()

Working with NVIDIA API Catalog¶

In [ ]:

from llama_index.llms.nvidia import NVIDIA

llm = NVIDIA(model="bigcode/starcoder2-15b", use_chat_completions=False)

from llama_index.llms.nvidia import NVIDIA llm = NVIDIA(model="bigcode/starcoder2-15b", use_chat_completions=False)

In [ ]:

llm.available_models

llm.available_models

Working with NVIDIA NIMs¶

In addition to connecting to hosted NVIDIA NIMs, this connector can be used to connect to local microservice instances. This helps you take your applications local when necessary.

For instructions on how to setup local microservice instances, see https://developer.nvidia.com/blog/nvidia-nim-offers-optimized-inference-microservices-for-deploying-ai-models-at-scale/

In [ ]:

from llama_index.llms.nvidia import NVIDIA

# connect to an chat NIM running at localhost:8080, spcecifying a specific model
llm = NVIDIA(base_url="http://localhost:8080/v1")

from llama_index.llms.nvidia import NVIDIA # connect to an chat NIM running at localhost:8080, spcecifying a specific model llm = NVIDIA(base_url="http://localhost:8080/v1")

Complete: .complete()¶

We can use .complete()/.acomplete() (which takes a string) to prompt a response from the selected model.

Let's use our default model for this task.

In [ ]:

print(llm.complete("# Function that does quicksort:"))

print(llm.complete("# Function that does quicksort:"))

As is expected by LlamaIndex - we get a CompletionResponse in response.

Async Complete: .acomplete()¶

There is also an async implementation which can be leveraged in the same way!

In [ ]:

await llm.acomplete("# Function that does quicksort:")

await llm.acomplete("# Function that does quicksort:")

Streaming¶

In [ ]:

x = llm.stream_complete(prompt="# Reverse string in python:", max_tokens=512)

x = llm.stream_complete(prompt="# Reverse string in python:", max_tokens=512)

Async Streaming¶

In [ ]:

x = await llm.astream_complete(
    prompt="# Reverse program in python:", max_tokens=512
)

x = await llm.astream_complete( prompt="# Reverse program in python:", max_tokens=512 )

In [ ]:

async for t in x:
    print(t.delta, end="")

async for t in x: print(t.delta, end="")