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fix typo

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Xi Yan 2025-02-18 11:05:10 -08:00
parent 081f245772
commit 117a3601de
1 changed files with 8 additions and 4 deletions
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12
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
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@ -63,7 +63,11 @@ from llama_stack.apis.models import Models
from llama_stack.apis.safety import Safety from llama_stack.apis.safety import Safety
from llama_stack.apis.tools import RAGDocument, RAGQueryConfig, ToolGroups, ToolRuntime from llama_stack.apis.tools import RAGDocument, RAGQueryConfig, ToolGroups, ToolRuntime
from llama_stack.apis.vector_io import VectorIO from llama_stack.apis.vector_io import VectorIO
from llama_stack.models.llama.datatypes import BuiltinTool, ToolCall, ToolParamDefinition from llama_stack.models.llama.datatypes import (
BuiltinTool,
ToolCall,
ToolParamDefinition,
)
from llama_stack.providers.utils.kvstore import KVStore from llama_stack.providers.utils.kvstore import KVStore
from llama_stack.providers.utils.memory.vector_store import concat_interleaved_content from llama_stack.providers.utils.memory.vector_store import concat_interleaved_content
from llama_stack.providers.utils.telemetry import tracing from llama_stack.providers.utils.telemetry import tracing
@ -829,11 +833,11 @@ class ChatAgent(ShieldRunnerMixin):
# so we need to fix it if we expect the agent to create a new vector db # so we need to fix it if we expect the agent to create a new vector db
# for each session # for each session
list_models_response = await self.models_api.list_models() list_models_response = await self.models_api.list_models()
embdding_models = [x for x in list_models_response.data if x.model_type == "embedding"] embedding_models = [x for x in list_models_response.data if x.model_type == "embedding"]
await self.vector_io_api.register_vector_db( await self.vector_io_api.register_vector_db(
vector_db_id=vector_db_id, vector_db_id=vector_db_id,
embedding_model=embdding_models[0].identifier, embedding_model=embedding_models[0].identifier,
embedding_dimension=embdding_models[0].metadata["embedding_dimension"], embedding_dimension=embedding_models[0].metadata["embedding_dimension"],
) )
await self.storage.add_vector_db_to_session(session_id, vector_db_id) await self.storage.add_vector_db_to_session(session_id, vector_db_id)
else: else: