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# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
The purpose of this task is to implement
`openai/v1/vector_stores/{vector_store_id}/search` for PGVector
provider. It involves implementing vector similarity search, keyword
search and hybrid search for `PGVectorIndex`.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes #3006
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Run unit tests:
` ./scripts/unit-tests.sh `
Run integration tests for openai vector stores:
1. Export env vars:
```
export ENABLE_PGVECTOR=true
export PGVECTOR_HOST=localhost
export PGVECTOR_PORT=5432
export PGVECTOR_DB=llamastack
export PGVECTOR_USER=llamastack
export PGVECTOR_PASSWORD=llamastack
```
2. Create DB:
```
psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';"
psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;"
psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;"
```
3. Install sentence-transformers:
` uv pip install sentence-transformers `
4. Run:
```
uv run --group test pytest -s -v --stack-config="inference=inline::sentence-transformers,vector_io=remote::pgvector" --embedding-model sentence-transformers/all-MiniLM-L6-v2 tests/integration/vector_io/test_openai_vector_stores.py
```
Inspect PGVector vector stores (optional):
```
psql llamastack
psql (14.18 (Homebrew))
Type "help" for help.
llamastack=# \z
Access privileges
Schema | Name | Type | Access privileges | Column privileges | Policies
--------+------------------------------------------------------+-------+-------------------+-------------------+----------
public | llamastack_kvstore | table | | |
public | metadata_store | table | | |
public | vector_store_pgvector_main | table | | |
public | vector_store_vs_1dfbc061_1f4d_4497_9165_ecba2622ba3a | table | | |
public | vector_store_vs_2085a9fb_1822_4e42_a277_c6a685843fa7 | table | | |
public | vector_store_vs_2b3dae46_38be_462a_afd6_37ee5fe661b1 | table | | |
public | vector_store_vs_2f438de6_f606_4561_9d50_ef9160eb9060 | table | | |
public | vector_store_vs_3eeca564_2580_4c68_bfea_83dc57e31214 | table | | |
public | vector_store_vs_53942163_05f3_40e0_83c0_0997c64613da | table | | |
public | vector_store_vs_545bac75_8950_4ff1_b084_e221192d4709 | table | | |
public | vector_store_vs_688a37d8_35b2_4298_a035_bfedf5b21f86 | table | | |
public | vector_store_vs_70624d9a_f6ac_4c42_b8ab_0649473c6600 | table | | |
public | vector_store_vs_73fc1dd2_e942_4972_afb1_1e177b591ac2 | table | | |
public | vector_store_vs_9d464949_d51f_49db_9f87_e033b8b84ac9 | table | | |
public | vector_store_vs_a1e4d724_5162_4d6d_a6c0_bdafaf6b76ec | table | | |
public | vector_store_vs_a328fb1b_1a21_480f_9624_ffaa60fb6672 | table | | |
public | vector_store_vs_a8981bf0_2e66_4445_a267_a8fff442db53 | table | | |
public | vector_store_vs_ccd4b6a4_1efd_4984_ad03_e7ff8eadb296 | table | | |
public | vector_store_vs_cd6420a4_a1fc_4cec_948c_1413a26281c9 | table | | |
public | vector_store_vs_cd709284_e5cf_4a88_aba5_dc76a35364bd | table | | |
public | vector_store_vs_d7a4548e_fbc1_44d7_b2ec_b664417f2a46 | table | | |
public | vector_store_vs_e7f73231_414c_4523_886c_d1174eee836e | table | | |
public | vector_store_vs_ffd53588_819f_47e8_bb9d_954af6f7833d | table | | |
(23 rows)
llamastack=#
```
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
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|---|---|---|
| .. | ||
| _static | ||
| notebooks | ||
| openapi_generator | ||
| resources | ||
| source | ||
| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
| make.bat | ||
| Makefile | ||
| original_rfc.md | ||
| quick_start.ipynb | ||
| README.md | ||
Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Render locally
From the llama-stack root directory, run the following command to render the docs locally:
uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
You can open up the docs in your browser at http://localhost:8000
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack