llama-stack-mirror/llama_stack/providers/registry/tool_runtime.py
Francisco Arceo 7cd1c2c238
Some checks failed
SqlStore Integration Tests / test-postgres (3.13) (push) Failing after 1s
Python Package Build Test / build (3.12) (push) Failing after 1s
Test External Providers Installed via Module / test-external-providers-from-module (venv) (push) Has been skipped
SqlStore Integration Tests / test-postgres (3.12) (push) Failing after 18s
Update ReadTheDocs / update-readthedocs (push) Failing after 15s
Python Package Build Test / build (3.13) (push) Failing after 19s
Test External API and Providers / test-external (venv) (push) Failing after 17s
Integration Auth Tests / test-matrix (oauth2_token) (push) Failing after 23s
Integration Tests (Replay) / Integration Tests (, , , client=, ) (push) Failing after 22s
Unit Tests / unit-tests (3.12) (push) Failing after 19s
Unit Tests / unit-tests (3.13) (push) Failing after 19s
Vector IO Integration Tests / test-matrix (push) Failing after 23s
UI Tests / ui-tests (22) (push) Successful in 44s
Pre-commit / pre-commit (push) Successful in 1m32s
feat: Updating Rag Tool to use Files API and Vector Stores API (#3344)
2025-09-06 07:26:34 -06:00

93 lines
4.4 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_stack.providers.datatypes import (
AdapterSpec,
Api,
InlineProviderSpec,
ProviderSpec,
remote_provider_spec,
)
def available_providers() -> list[ProviderSpec]:
return [
InlineProviderSpec(
api=Api.tool_runtime,
provider_type="inline::rag-runtime",
pip_packages=[
"chardet",
"pypdf",
"tqdm",
"numpy",
"scikit-learn",
"scipy",
"nltk",
"sentencepiece",
"transformers",
],
module="llama_stack.providers.inline.tool_runtime.rag",
config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig",
api_dependencies=[Api.vector_io, Api.inference, Api.files],
description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.",
),
remote_provider_spec(
api=Api.tool_runtime,
adapter=AdapterSpec(
adapter_type="brave-search",
module="llama_stack.providers.remote.tool_runtime.brave_search",
config_class="llama_stack.providers.remote.tool_runtime.brave_search.config.BraveSearchToolConfig",
pip_packages=["requests"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.brave_search.BraveSearchToolProviderDataValidator",
description="Brave Search tool for web search capabilities with privacy-focused results.",
),
),
remote_provider_spec(
api=Api.tool_runtime,
adapter=AdapterSpec(
adapter_type="bing-search",
module="llama_stack.providers.remote.tool_runtime.bing_search",
config_class="llama_stack.providers.remote.tool_runtime.bing_search.config.BingSearchToolConfig",
pip_packages=["requests"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.bing_search.BingSearchToolProviderDataValidator",
description="Bing Search tool for web search capabilities using Microsoft's search engine.",
),
),
remote_provider_spec(
api=Api.tool_runtime,
adapter=AdapterSpec(
adapter_type="tavily-search",
module="llama_stack.providers.remote.tool_runtime.tavily_search",
config_class="llama_stack.providers.remote.tool_runtime.tavily_search.config.TavilySearchToolConfig",
pip_packages=["requests"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.tavily_search.TavilySearchToolProviderDataValidator",
description="Tavily Search tool for AI-optimized web search with structured results.",
),
),
remote_provider_spec(
api=Api.tool_runtime,
adapter=AdapterSpec(
adapter_type="wolfram-alpha",
module="llama_stack.providers.remote.tool_runtime.wolfram_alpha",
config_class="llama_stack.providers.remote.tool_runtime.wolfram_alpha.config.WolframAlphaToolConfig",
pip_packages=["requests"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.wolfram_alpha.WolframAlphaToolProviderDataValidator",
description="Wolfram Alpha tool for computational knowledge and mathematical calculations.",
),
),
remote_provider_spec(
api=Api.tool_runtime,
adapter=AdapterSpec(
adapter_type="model-context-protocol",
module="llama_stack.providers.remote.tool_runtime.model_context_protocol",
config_class="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderConfig",
pip_packages=["mcp>=1.8.1"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderDataValidator",
description="Model Context Protocol (MCP) tool for standardized tool calling and context management.",
),
),
]