LLM Providers

from akhara import Akhara

client = Akhara(api_key="gr_live_xxxxxxxx")

# Configure OpenAI integration
client.integrations.llm.configure(
    provider="openai",
    api_key="sk-xxxxxxxx",  # Stored encrypted

    # Optional: Organization ID
    organization_id="org-xxxxxxxx",

    # Optional: Custom base URL for proxies
    base_url="https://api.openai.com/v1"
)

# Log calls with automatic model detection
client.calls.log(
    project="patient-triage",
    model="gpt-4-turbo",
    input={"messages": [...]},
    output={"response": "..."}
)

from akhara import Akhara

client = Akhara(api_key="gr_live_xxxxxxxx")

# Configure Anthropic integration
client.integrations.llm.configure(
    provider="anthropic",
    api_key="sk-ant-xxxxxxxx"
)

# Log Claude calls
client.calls.log(
    project="symptom-checker",
    model="claude-3-opus",
    input={"prompt": "..."},
    output={"completion": "..."}
)

from akhara import Akhara

client = Akhara(api_key="gr_live_xxxxxxxx")

# Configure Azure OpenAI
client.integrations.llm.configure(
    provider="azure_openai",

    # Azure-specific settings
    azure_endpoint="https://your-resource.openai.azure.com",
    api_version="2024-02-15-preview",

    # Authentication (choose one)
    api_key="xxxxxxxx",
    # Or use Azure AD
    # use_azure_ad=True,
    # tenant_id="your-tenant-id"
)

from akhara import Akhara

client = Akhara(api_key="gr_live_xxxxxxxx")

# Configure Vertex AI
client.integrations.llm.configure(
    provider="vertex_ai",

    # GCP settings
    project_id="your-gcp-project",
    location="us-central1",

    # Authentication
    credentials_path="/path/to/service-account.json"
    # Or use default credentials
    # use_default_credentials=True
)

from akhara import Akhara

client = Akhara(api_key="gr_live_xxxxxxxx")

# Configure AWS Bedrock
client.integrations.llm.configure(
    provider="bedrock",

    # AWS settings
    region="us-east-1",

    # Authentication
    aws_access_key_id="AKIA...",
    aws_secret_access_key="...",
    # Or use IAM role
    # use_iam_role=True
)

from akhara import Akhara

client = Akhara(api_key="gr_live_xxxxxxxx")

# Configure custom LLM endpoint
client.integrations.llm.configure(
    provider="custom",
    name="internal-medical-llm",

    # Endpoint configuration
    base_url="https://llm.internal.company.com/v1",

    # Authentication
    auth_type="bearer",
    api_key="your-internal-key",

    # Optional: Custom headers
    headers={
        "X-Internal-Service": "akhara-eval"
    },

    # Model mapping
    model_aliases={
        "medical-gpt": "internal-medical-v2"
    }
)