from llama_stack_client.types import (
Attachment,
BatchCompletion,
CompletionMessage,
SamplingParams,
SystemMessage,
ToolCall,
ToolResponseMessage,
UserMessage,
)Types:
from llama_stack_client.types import TelemetryGetTraceResponseMethods:
client.telemetry.get_trace(**params) -> TelemetryGetTraceResponseclient.telemetry.log(**params) -> None
Types:
from llama_stack_client.types import (
InferenceStep,
MemoryRetrievalStep,
RestAPIExecutionConfig,
ShieldCallStep,
ToolExecutionStep,
ToolParamDefinition,
AgentCreateResponse,
)Methods:
client.agents.create(**params) -> AgentCreateResponseclient.agents.delete(**params) -> None
Types:
from llama_stack_client.types.agents import Session, SessionCreateResponseMethods:
client.agents.sessions.create(**params) -> SessionCreateResponseclient.agents.sessions.retrieve(**params) -> Sessionclient.agents.sessions.delete(**params) -> None
Types:
from llama_stack_client.types.agents import AgentsStepMethods:
client.agents.steps.retrieve(**params) -> AgentsStep
Types:
from llama_stack_client.types.agents import AgentsTurnStreamChunk, Turn, TurnStreamEventMethods:
client.agents.turns.create(**params) -> AgentsTurnStreamChunkclient.agents.turns.retrieve(**params) -> Turn
Types:
from llama_stack_client.types import TrainEvalDatasetMethods:
client.datasets.create(**params) -> Noneclient.datasets.delete(**params) -> Noneclient.datasets.get(**params) -> TrainEvalDataset
Types:
from llama_stack_client.types import EvaluationJobTypes:
from llama_stack_client.types.evaluate import (
EvaluationJobArtifacts,
EvaluationJobLogStream,
EvaluationJobStatus,
)Methods:
client.evaluate.jobs.list() -> EvaluationJobclient.evaluate.jobs.cancel(**params) -> None
Methods:
client.evaluate.jobs.artifacts.list(**params) -> EvaluationJobArtifacts
Methods:
client.evaluate.jobs.logs.list(**params) -> EvaluationJobLogStream
Methods:
client.evaluate.jobs.status.list(**params) -> EvaluationJobStatus
Methods:
client.evaluate.question_answering.create(**params) -> EvaluationJob
Methods:
client.evaluations.summarization(**params) -> EvaluationJobclient.evaluations.text_generation(**params) -> EvaluationJob
Types:
from llama_stack_client.types import (
ChatCompletionStreamChunk,
CompletionStreamChunk,
TokenLogProbs,
InferenceChatCompletionResponse,
InferenceCompletionResponse,
)Methods:
client.inference.chat_completion(**params) -> InferenceChatCompletionResponseclient.inference.completion(**params) -> InferenceCompletionResponse
Types:
from llama_stack_client.types.inference import EmbeddingsMethods:
client.inference.embeddings.create(**params) -> Embeddings
Types:
from llama_stack_client.types import RunSheidResponseMethods:
client.safety.run_shield(**params) -> RunSheidResponse
Types:
from llama_stack_client.types import (
QueryDocuments,
MemoryCreateResponse,
MemoryRetrieveResponse,
MemoryListResponse,
MemoryDropResponse,
)Methods:
client.memory.create(**params) -> objectclient.memory.retrieve(**params) -> objectclient.memory.update(**params) -> Noneclient.memory.list() -> objectclient.memory.drop(**params) -> strclient.memory.insert(**params) -> Noneclient.memory.query(**params) -> QueryDocuments
Types:
from llama_stack_client.types.memory import DocumentRetrieveResponseMethods:
client.memory.documents.retrieve(**params) -> DocumentRetrieveResponseclient.memory.documents.delete(**params) -> None
Types:
from llama_stack_client.types import PostTrainingJobMethods:
client.post_training.preference_optimize(**params) -> PostTrainingJobclient.post_training.supervised_fine_tune(**params) -> PostTrainingJob
Types:
from llama_stack_client.types.post_training import (
PostTrainingJobArtifacts,
PostTrainingJobLogStream,
PostTrainingJobStatus,
)Methods:
client.post_training.jobs.list() -> PostTrainingJobclient.post_training.jobs.artifacts(**params) -> PostTrainingJobArtifactsclient.post_training.jobs.cancel(**params) -> Noneclient.post_training.jobs.logs(**params) -> PostTrainingJobLogStreamclient.post_training.jobs.status(**params) -> PostTrainingJobStatus
Types:
from llama_stack_client.types import RewardScoring, ScoredDialogGenerationsMethods:
client.reward_scoring.score(**params) -> RewardScoring
Types:
from llama_stack_client.types import SyntheticDataGenerationMethods:
client.synthetic_data_generation.generate(**params) -> SyntheticDataGeneration
Types:
from llama_stack_client.types import BatchChatCompletionMethods:
client.batch_inference.chat_completion(**params) -> BatchChatCompletionclient.batch_inference.completion(**params) -> BatchCompletion
Types:
from llama_stack_client.types import ModelServingSpecMethods:
client.models.list() -> ModelServingSpecclient.models.get(**params) -> Optional
Types:
from llama_stack_client.types import MemoryBankSpecMethods:
client.memory_banks.list() -> MemoryBankSpecclient.memory_banks.get(**params) -> Optional
Types:
from llama_stack_client.types import ShieldSpecMethods:
client.shields.list() -> ShieldSpecclient.shields.get(**params) -> Optional