llama-index-llms-vercel-ai-gateway 0.2.0

llama-index llms vercel ai gateway integration

LlamaIndex Llms Integration: Vercel AI Gateway

Installation

To install the required packages, run:

%pip install llama-index-llms-vercel-ai-gateway
!pip install llama-index

Setup

Initialize Vercel AI Gateway

You need to set either the environment variable VERCEL_AI_GATEWAY_API_KEY, VERCEL_OIDC_TOKEN, or pass your API key directly in the class constructor. Replace <your-api-key> with your actual API key:

from llama_index.llms.vercel_ai_gateway import VercelAIGateway
from llama_index.core.llms import ChatMessage

llm = VercelAIGateway(
    api_key="<your-api-key>",
    max_tokens=200000,
    context_window=64000,
    model="anthropic/claude-4-sonnet",
)

Generate Chat Responses

You can generate a chat response by sending a list of ChatMessage instances:

message = ChatMessage(role="user", content="Tell me a joke")
resp = llm.chat([message])
print(resp)

Streaming Responses

To stream responses, use the stream_chat method:

message = ChatMessage(role="user", content="Tell me a story in 250 words")
resp = llm.stream_chat([message])
for r in resp:
    print(r.delta, end="")

Complete with Prompt

You can also generate completions with a prompt using the complete method:

resp = llm.complete("Tell me a joke")
print(resp)

Streaming Completion

To stream completions, use the stream_complete method:

resp = llm.stream_complete("Tell me a story in 250 words")
for r in resp:
    print(r.delta, end="")

Model Configuration

To use a specific model, you can specify it during initialization. For example, to use Anthropic's Claude 3 Sonnet model, you can set it like this:

llm = VercelAIGateway(model="anthropic/claude-4-sonnet")
resp = llm.complete("Write a story about a dragon who can code in Rust")
print(resp)

LLM Implementation example

https://docs.llamaindex.ai/en/stable/examples/llm/vercel-ai-gateway/