phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-10-08 13:00:52 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

fix: update pyproject.toml dependencies for vector processing (#3555)

Browse source 
What does this PR do?

Updates pyproject.toml dependencies to fix vector processing
compatibility issues.

closes: #3495 

  Test Plan

  Tested llama stack server with faiss vector database:

1. Built and ran server: llama stack build --distro starter --image-type
venv --image-name llamastack-faiss
3. Tested file upload: Successfully uploaded PDF via /v1/openai/v1/files
  4. Tested vector operations:
    - Created vector store with faiss backend
    - Added PDF to vector store
    - Performed semantic search queries
This commit is contained in:
Sumanth Kamenani 2025-10-07 09:01:36 -04:00 • committed by GitHub
parent 509ac4a659
commit 1fcde5fc2f
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 18 additions and 15 deletions
Download patch file Download diff file Expand all files Collapse all files

6
llama_stack/providers/registry/tool_runtime.py
View file

@ -11,6 +11,7 @@ from llama_stack.providers.datatypes import (
ProviderSpec,
RemoteProviderSpec,
)
from llama_stack.providers.registry.vector_io import DEFAULT_VECTOR_IO_DEPS
def available_providers() -> list[ProviderSpec]:
@ -18,9 +19,8 @@ def available_providers() -> list[ProviderSpec]:
InlineProviderSpec(
api=Api.tool_runtime,
provider_type="inline::rag-runtime",
pip_packages=[
"chardet",
"pypdf",
pip_packages=DEFAULT_VECTOR_IO_DEPS
+ [
"tqdm",
"numpy",
"scikit-learn",

27
llama_stack/providers/registry/vector_io.py
View file

@ -12,13 +12,16 @@ from llama_stack.providers.datatypes import (
RemoteProviderSpec,
)
# Common dependencies for all vector IO providers that support document processing
DEFAULT_VECTOR_IO_DEPS = ["chardet", "pypdf"]
def available_providers() -> list[ProviderSpec]:
return [
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::meta-reference",
pip_packages=["faiss-cpu"],
pip_packages=["faiss-cpu"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.inline.vector_io.faiss",
config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
deprecation_warning="Please use the `inline::faiss` provider instead.",
@ -29,7 +32,7 @@ def available_providers() -> list[ProviderSpec]:
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::faiss",
pip_packages=["faiss-cpu"],
pip_packages=["faiss-cpu"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.inline.vector_io.faiss",
config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
api_dependencies=[Api.inference],
@ -82,7 +85,7 @@ more details about Faiss in general.
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::sqlite-vec",
pip_packages=["sqlite-vec"],
pip_packages=["sqlite-vec"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.inline.vector_io.sqlite_vec",
config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
api_dependencies=[Api.inference],
@ -289,7 +292,7 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::sqlite_vec",
pip_packages=["sqlite-vec"],
pip_packages=["sqlite-vec"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.inline.vector_io.sqlite_vec",
config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
deprecation_warning="Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.",
@ -303,7 +306,7 @@ Please refer to the sqlite-vec provider documentation.
api=Api.vector_io,
adapter_type="chromadb",
provider_type="remote::chromadb",
pip_packages=["chromadb-client"],
pip_packages=["chromadb-client"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.remote.vector_io.chroma",
config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig",
api_dependencies=[Api.inference],
@ -345,7 +348,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::chromadb",
pip_packages=["chromadb"],
pip_packages=["chromadb"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.inline.vector_io.chroma",
config_class="llama_stack.providers.inline.vector_io.chroma.ChromaVectorIOConfig",
api_dependencies=[Api.inference],
@ -389,7 +392,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
api=Api.vector_io,
adapter_type="pgvector",
provider_type="remote::pgvector",
pip_packages=["psycopg2-binary"],
pip_packages=["psycopg2-binary"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.remote.vector_io.pgvector",
config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig",
api_dependencies=[Api.inference],
@ -500,7 +503,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
api=Api.vector_io,
adapter_type="weaviate",
provider_type="remote::weaviate",
pip_packages=["weaviate-client>=4.16.5"],
pip_packages=["weaviate-client>=4.16.5"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.remote.vector_io.weaviate",
config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
@ -541,7 +544,7 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::qdrant",
pip_packages=["qdrant-client"],
pip_packages=["qdrant-client"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.inline.vector_io.qdrant",
config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
api_dependencies=[Api.inference],
@ -594,7 +597,7 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
api=Api.vector_io,
adapter_type="qdrant",
provider_type="remote::qdrant",
pip_packages=["qdrant-client"],
pip_packages=["qdrant-client"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.remote.vector_io.qdrant",
config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig",
api_dependencies=[Api.inference],
@ -607,7 +610,7 @@ Please refer to the inline provider documentation.
api=Api.vector_io,
adapter_type="milvus",
provider_type="remote::milvus",
pip_packages=["pymilvus>=2.4.10"],
pip_packages=["pymilvus>=2.4.10"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.remote.vector_io.milvus",
config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
api_dependencies=[Api.inference],
@ -813,7 +816,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::milvus",
pip_packages=["pymilvus[milvus-lite]>=2.4.10"],
pip_packages=["pymilvus[milvus-lite]>=2.4.10"] + DEFAULT_VECTOR_IO_DEPS,
module="llama_stack.providers.inline.vector_io.milvus",
config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
api_dependencies=[Api.inference],