forked from phoenix-oss/llama-stack-mirror
dc84bc755a
This is unfortunate because `sqlite-vec` seems promising. But its PIP
package is not quite complete. It does not have binary for arm64 (I
think, or maybe it even lacks 64 bit builds?) which results in the arm64
container resulting in
```
File "/usr/local/lib/python3.10/site-packages/sqlite_vec/init.py", line 17, in load
conn.load_extension(loadable_path())
sqlite3.OperationalError: /usr/local/lib/python3.10/site-packages/sqlite_vec/vec0.so: wrong ELF class: ELFCLASS32
```
To get around I tried to install from source via `uv pip install
sqlite-vec --no-binary=sqlite-vec` however it even lacks a source
distribution which makes that impossible.
## Test Plan
Build the container locally using:
```bash
LLAMA_STACK_DIR=. llama stack build --template ollama --image-type container
```
Run the container as:
```
podman run --privileged -it -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.containers.internal:11434 \
-v ~/local/llama-stack:/app/llama-stack-source
localhost/distribution-ollama:dev --port $LLAMA_STACK_PORT
```
Verify the container starts up correctly. Without this patch, it would
encounter the ELFCLASS32 error.
133 lines
5.5 KiB
Python
133 lines
5.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import List
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from llama_stack.providers.datatypes import (
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AdapterSpec,
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Api,
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InlineProviderSpec,
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ProviderSpec,
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remote_provider_spec,
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)
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def available_providers() -> List[ProviderSpec]:
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return [
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InlineProviderSpec(
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api=Api.vector_io,
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provider_type="inline::meta-reference",
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pip_packages=["faiss-cpu"],
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module="llama_stack.providers.inline.vector_io.faiss",
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config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
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deprecation_warning="Please use the `inline::faiss` provider instead.",
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api_dependencies=[Api.inference],
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),
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InlineProviderSpec(
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api=Api.vector_io,
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provider_type="inline::faiss",
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pip_packages=["faiss-cpu"],
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module="llama_stack.providers.inline.vector_io.faiss",
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config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
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api_dependencies=[Api.inference],
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),
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# NOTE: sqlite-vec cannot be bundled into the container image because it does not have a
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# source distribution and the wheels are not available for all platforms.
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InlineProviderSpec(
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api=Api.vector_io,
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provider_type="inline::sqlite-vec",
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pip_packages=["sqlite-vec"],
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module="llama_stack.providers.inline.vector_io.sqlite_vec",
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config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
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api_dependencies=[Api.inference],
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),
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InlineProviderSpec(
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api=Api.vector_io,
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provider_type="inline::sqlite_vec",
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pip_packages=["sqlite-vec"],
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module="llama_stack.providers.inline.vector_io.sqlite_vec",
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config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
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deprecation_warning="Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.",
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.vector_io,
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AdapterSpec(
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adapter_type="chromadb",
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pip_packages=["chromadb-client"],
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module="llama_stack.providers.remote.vector_io.chroma",
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config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig",
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),
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api_dependencies=[Api.inference],
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),
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InlineProviderSpec(
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api=Api.vector_io,
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provider_type="inline::chromadb",
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pip_packages=["chromadb"],
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module="llama_stack.providers.inline.vector_io.chroma",
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config_class="llama_stack.providers.inline.vector_io.chroma.ChromaVectorIOConfig",
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.vector_io,
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AdapterSpec(
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adapter_type="pgvector",
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pip_packages=["psycopg2-binary"],
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module="llama_stack.providers.remote.vector_io.pgvector",
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config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig",
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),
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.vector_io,
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AdapterSpec(
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adapter_type="weaviate",
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pip_packages=["weaviate-client"],
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module="llama_stack.providers.remote.vector_io.weaviate",
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config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
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provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
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),
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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api=Api.vector_io,
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adapter=AdapterSpec(
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adapter_type="sample",
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pip_packages=[],
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module="llama_stack.providers.remote.vector_io.sample",
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config_class="llama_stack.providers.remote.vector_io.sample.SampleVectorIOConfig",
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),
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api_dependencies=[],
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),
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remote_provider_spec(
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Api.vector_io,
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AdapterSpec(
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adapter_type="qdrant",
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pip_packages=["qdrant-client"],
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module="llama_stack.providers.remote.vector_io.qdrant",
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config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig",
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),
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.vector_io,
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AdapterSpec(
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adapter_type="milvus",
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pip_packages=["pymilvus"],
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module="llama_stack.providers.remote.vector_io.milvus",
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config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
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),
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api_dependencies=[Api.inference],
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),
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InlineProviderSpec(
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api=Api.vector_io,
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provider_type="inline::milvus",
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pip_packages=["pymilvus"],
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module="llama_stack.providers.inline.vector_io.milvus",
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config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
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api_dependencies=[Api.inference],
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),
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]
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