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Documentation Index

Fetch the complete documentation index at: https://docs.adaptive-ml.com/llms.txt

Use this file to discover all available pages before exploring further.

Connect external models or use Adaptive with LangChain.

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External Models

Connect proprietary models (OpenAI, Azure, Google, Anthropic, NVIDIA NIM) to use them through the Adaptive API with interaction and feedback logging.

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OpenAI (Direct)

external_model = adaptive.models.add_external(
    provider="open_ai",
    external_model_id="GPT4O",
    name="GPT-4o",
    api_key="OPENAI_API_KEY"
)
Supported model IDs: GPT4O, GPT4O_MINI, GPT4, GPT4_TURBO, GPT3_5_TURBO

​
Azure OpenAI

external_model = adaptive.models.add_external(
    provider="azure",
    external_model_id="DEPLOYMENT_NAME",
    name="Azure GPT-4o",
    api_key="AZURE_API_KEY",
    endpoint="https://aoairesource.openai.azure.com"
)
The external_model_id is your deployment name, and endpoint is your Azure OpenAI subscription endpoint.

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Google

external_model = adaptive.models.add_external(
    provider="google",
    external_model_id="gemini-1.5-pro",
    name="Gemini 1.5 Pro",
    api_key="GOOGLE_API_KEY"
)
Supported models: Gemini model variations (excluding embeddings).

​
Anthropic

external_model = adaptive.models.add_external(
    provider="anthropic",
    external_model_id="claude-sonnet-4-5-20250929",
    name="Claude Sonnet 4.5",
    api_key="ANTHROPIC_API_KEY"
)
Use the model ID from Anthropic’s API documentation as the external_model_id. Once connected, attach the model to a use case and make inference requests like any other model.

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LangChain

Adaptive is compatible with LangChain chat model classes.

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ChatOpenAI

from langchain_openai import ChatOpenAI
import os

os.environ["OPENAI_API_KEY"] = "ADAPTIVE_API_KEY"

llm = ChatOpenAI(
    model="use_case_key/model_key",  # model_key is optional
    base_url="ADAPTIVE_URL/api/v1",
)

messages = [
    ("system", "You are a helpful assistant that translates English to French."),
    ("human", "I love programming."),
]
response = llm.invoke(messages)

​
ChatGoogleGenerativeAI

from langchain_google_genai import ChatGoogleGenerativeAI
import os

os.environ["GOOGLE_API_KEY"] = "ADAPTIVE_API_KEY"

llm = ChatGoogleGenerativeAI(
    model="use_case_key",
    client_options={"api_endpoint": "ADAPTIVE_URL/api/v1/google"},
    transport="rest",
)

messages = [
    ("system", "You are a helpful assistant that translates English to French."),
    ("human", "I love programming."),
]
response = llm.invoke(messages)