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NVIDIA LLM Text Completion API

docs/examples/llm/nvidia_text_completion.ipynb

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NVIDIA LLM Text Completion API

The llama-index-llms-nvidia package extends the NVIDIA class to support the /completions API for code completion models such as:

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

Installation

python
%pip install --upgrade --quiet 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.

python
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
python
# 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 the NVIDIA API Catalog

Usage of the use_chat_completions argument

Set None (default) to decide per-invocation whether to use /chat/completions or /completions endpoints with query keyword arguments.

  • Set False to use the /completions endpoint.
  • Set True to use the /chat/completions endpoint.
python
from llama_index.llms.nvidia import NVIDIA

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

Available models

Use is_chat_model to filter available text completion models:

python
print([model for model in llm.available_models if model.is_chat_model])

Working with NVIDIA NIMs

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

For instructions on how to set up local NIM instances, refer to NVIDIA NIM.

python
from llama_index.llms.nvidia import NVIDIA

# Connect to a NIM running at localhost:8080
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.

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

As expected, LlamaIndex returns a CompletionResponse.

Async Complete: .acomplete()

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

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

Streaming

python
x = llm.stream_complete(prompt="# Reverse string in python:", max_tokens=512)
python
for t in x:
    print(t.delta, end="")

Async Streaming

python
x = await llm.astream_complete(
    prompt="# Reverse program in python:", max_tokens=512
)
python
async for t in x:
    print(t.delta, end="")