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LlamaIndex LLMs Integration: AI Badgr

llama-index-integrations/llms/llama-index-llms-aibadgr/README.md

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LlamaIndex LLMs Integration: AI Badgr

AI Badgr (Budget/Utility, OpenAI-compatible)

Installation

To install the required packages, run:

bash
pip install llama-index-llms-aibadgr

Setup

Initialize AI Badgr

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

python
from llama_index.llms.aibadgr import AIBadgr
from llama_index.core.llms import ChatMessage

llm = AIBadgr(
    api_key="<your-api-key>",
    model="premium",
)

Generate Chat Responses

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

python
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:

python
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:

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

Streaming Completion

To stream completions, use the stream_complete method:

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

Model Configuration

AI Badgr supports tier-based model names for easy selection:

  • basic - Budget tier model (maps to phi-3-mini)
  • normal - Standard tier model (maps to mistral-7b)
  • premium - Premium tier model (maps to llama3-8b-instruct, recommended default)
python
# Using tier names (recommended)
llm = AIBadgr(model="premium", api_key="your_api_key")
resp = llm.complete("Write a story about a dragon who can code in Rust")
print(resp)

Advanced: Power-User Model Names

You can also use specific model names directly:

python
llm = AIBadgr(model="llama3-8b-instruct", api_key="your_api_key")
resp = llm.complete("Explain quantum computing")
print(resp)

OpenAI model names are accepted and mapped automatically.

Environment Variables

You can configure AI Badgr using environment variables:

bash
export AIBADGR_API_KEY="your_api_key"
export AIBADGR_BASE_URL="https://aibadgr.com/api/v1"