11 commits

Author	SHA1	Message	Date
Emilio Garcia	0d11cb1cb8	Revert "fix: remove parts of trace_protocol and telemetry that were overlooked" ... This reverts commit 9d24211d9d.	2025-11-20 15:07:50 -05:00
Emilio Garcia	9d24211d9d	fix: remove parts of trace_protocol and telemetry that were overlooked	2025-11-20 13:48:36 -05:00
IAN MILLER	98a5047f9d	feat(prompts): attach prompts to storage stores in run configs (#3893) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> This PR is responsible for attaching prompts to storage stores in run configs. It allows to specify prompts as stores in different distributions. The need of this functionality was initiated in #3514 > Note, #3514 is divided on three separate PRs. Current PR is the first of three. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## 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.* --> Manual testing and updated CI unit tests Prerequisites: 1. `uv run --with llama-stack llama stack list-deps starter | xargs -L1 uv pip install` 2. `llama stack run starter ` ``` INFO 2025-10-23 15:36:17,387 llama_stack.cli.stack.run:100 cli: Using run configuration: /Users/ianmiller/llama-stack/llama_stack/distributions/starter/run.yaml INFO 2025-10-23 15:36:17,423 llama_stack.cli.stack.run:157 cli: HTTPS enabled with certificates: Key: None Cert: None INFO 2025-10-23 15:36:17,424 llama_stack.cli.stack.run:159 cli: Listening on ['::', '0.0.0.0']:8321 INFO 2025-10-23 15:36:17,749 llama_stack.core.server.server:521 core::server: Run configuration: INFO 2025-10-23 15:36:17,756 llama_stack.core.server.server:524 core::server: apis: - agents - batches - datasetio - eval - files - inference - post_training - safety - scoring - tool_runtime - vector_io image_name: starter providers: agents: - config: persistence: agent_state: backend: kv_default namespace: agents responses: backend: sql_default max_write_queue_size: 10000 num_writers: 4 table_name: responses provider_id: meta-reference provider_type: inline::meta-reference batches: - config: kvstore: backend: kv_default namespace: batches provider_id: reference provider_type: inline::reference datasetio: - config: kvstore: backend: kv_default namespace: datasetio::huggingface provider_id: huggingface provider_type: remote::huggingface - config: kvstore: backend: kv_default namespace: datasetio::localfs provider_id: localfs provider_type: inline::localfs eval: - config: kvstore: backend: kv_default namespace: eval provider_id: meta-reference provider_type: inline::meta-reference files: - config: metadata_store: backend: sql_default table_name: files_metadata storage_dir: /Users/ianmiller/.llama/distributions/starter/files provider_id: meta-reference-files provider_type: inline::localfs inference: - config: api_key: '********' url: https://api.fireworks.ai/inference/v1 provider_id: fireworks provider_type: remote::fireworks - config: api_key: '********' url: https://api.together.xyz/v1 provider_id: together provider_type: remote::together - config: {} provider_id: bedrock provider_type: remote::bedrock - config: api_key: '********' base_url: https://api.openai.com/v1 provider_id: openai provider_type: remote::openai - config: api_key: '********' provider_id: anthropic provider_type: remote::anthropic - config: api_key: '********' provider_id: gemini provider_type: remote::gemini - config: api_key: '********' url: https://api.groq.com provider_id: groq provider_type: remote::groq - config: api_key: '********' url: https://api.sambanova.ai/v1 provider_id: sambanova provider_type: remote::sambanova - config: {} provider_id: sentence-transformers provider_type: inline::sentence-transformers post_training: - config: checkpoint_format: meta provider_id: torchtune-cpu provider_type: inline::torchtune-cpu safety: - config: excluded_categories: [] provider_id: llama-guard provider_type: inline::llama-guard - config: {} provider_id: code-scanner provider_type: inline::code-scanner scoring: - config: {} provider_id: basic provider_type: inline::basic - config: {} provider_id: llm-as-judge provider_type: inline::llm-as-judge - config: openai_api_key: '********' provider_id: braintrust provider_type: inline::braintrust tool_runtime: - config: api_key: '********' max_results: 3 provider_id: brave-search provider_type: remote::brave-search - config: api_key: '********' max_results: 3 provider_id: tavily-search provider_type: remote::tavily-search - config: {} provider_id: rag-runtime provider_type: inline::rag-runtime - config: {} provider_id: model-context-protocol provider_type: remote::model-context-protocol vector_io: - config: persistence: backend: kv_default namespace: vector_io::faiss provider_id: faiss provider_type: inline::faiss - config: db_path: /Users/ianmiller/.llama/distributions/starter/sqlite_vec.db persistence: backend: kv_default namespace: vector_io::sqlite_vec provider_id: sqlite-vec provider_type: inline::sqlite-vec registered_resources: benchmarks: [] datasets: [] models: [] scoring_fns: [] shields: [] tool_groups: - provider_id: tavily-search toolgroup_id: builtin::websearch - provider_id: rag-runtime toolgroup_id: builtin::rag vector_stores: [] server: port: 8321 storage: backends: kv_default: db_path: /Users/ianmiller/.llama/distributions/starter/kvstore.db type: kv_sqlite sql_default: db_path: /Users/ianmiller/.llama/distributions/starter/sql_store.db type: sql_sqlite stores: conversations: backend: sql_default table_name: openai_conversations inference: backend: sql_default max_write_queue_size: 10000 num_writers: 4 table_name: inference_store metadata: backend: kv_default namespace: registry prompts: backend: kv_default namespace: prompts telemetry: enabled: true vector_stores: default_embedding_model: model_id: nomic-ai/nomic-embed-text-v1.5 provider_id: sentence-transformers default_provider_id: faiss version: 2 INFO 2025-10-23 15:36:20,032 llama_stack.providers.utils.inference.inference_store:74 inference: Write queue disabled for SQLite to avoid concurrency issues WARNING 2025-10-23 15:36:20,422 llama_stack.providers.inline.telemetry.meta_reference.telemetry:84 telemetry: OTEL_EXPORTER_OTLP_ENDPOINT is not set, skipping telemetry INFO 2025-10-23 15:36:22,379 llama_stack.providers.utils.inference.openai_mixin:436 providers::utils: OpenAIInferenceAdapter.list_provider_model_ids() returned 105 models INFO 2025-10-23 15:36:22,703 uvicorn.error:84 uncategorized: Started server process [17328] INFO 2025-10-23 15:36:22,704 uvicorn.error:48 uncategorized: Waiting for application startup. INFO 2025-10-23 15:36:22,706 llama_stack.core.server.server:179 core::server: Starting up Llama Stack server (version: 0.3.0) INFO 2025-10-23 15:36:22,707 llama_stack.core.stack:470 core: starting registry refresh task INFO 2025-10-23 15:36:22,708 uvicorn.error:62 uncategorized: Application startup complete. INFO 2025-10-23 15:36:22,708 uvicorn.error:216 uncategorized: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit) ``` As you can see, prompts are attached to stores in config Testing: 1. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.", "variables": ["name", "company", "role", "tone"] }' ``` `{"prompt":"Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.","version":1,"prompt_id":"pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e163f","variables":["name","company","role","tone"],"is_default":false}% ` 2. Get prompt: `curl -X GET http://localhost:8321/v1/prompts/pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e163f` `{"prompt":"Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.","version":1,"prompt_id":"pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e163f","variables":["name","company","role","tone"],"is_default":false}% ` 3. Query sqlite KV storage to check created prompt: ``` sqlite> .mode column sqlite> .headers on sqlite> SELECT * FROM kvstore WHERE key LIKE 'prompts:v1:%'; key value expiration ------------------------------------------------------------ ------------------------------------------------------------ ---------- prompts:v1:pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e {"prompt_id": "pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab 163f:1 5f6e163f", "prompt": "Hello {{name}}! You are working at {{c ompany}}. Your role is {{role}} at {{company}}. Remember, {{ name}}, to be {{tone}}.", "version": 1, "variables": ["name" , "company", "role", "tone"], "is_default": false} prompts:v1:pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e 1 163f:default sqlite> ```	2025-10-27 11:12:12 -07:00
Sébastien Han	7c0e43424d	chore: remove duplicate provider definition (#3917) ... # What does this PR do? Files was present twice. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-10-27 09:19:04 -07:00
ehhuang	509676641a	chore: update run configs (#3902) ... # What does this PR do? telemetry was deprecated ## Test Plan	2025-10-24 15:03:06 -07:00
Ashwin Bharambe	658fb2c777	refactor(k8s): update run configs to v2 storage and registered_resources structure ... Migrates k8s run configs to match the updated run configs - Replace storage.references with storage.stores - Wrap resources under registered_resources section - Update provider configs to use persistence with namespace/backend - Add telemetry and vector_stores top-level sections - Simplify agent/files metadata store configuration	2025-10-22 15:33:50 -07:00
Ashwin Bharambe	2c43285e22	feat(stores)!: use backend storage references instead of configs (#3697) ... **This PR changes configurations in a backward incompatible way.** Run configs today repeat full SQLite/Postgres snippets everywhere a store is needed, which means duplicated credentials, extra connection pools, and lots of drift between files. This PR introduces named storage backends so the stack and providers can share a single catalog and reference those backends by name. ## Key Changes - Add `storage.backends` to `StackRunConfig`, register each KV/SQL backend once at startup, and validate that references point to the right family. - Move server stores under `storage.stores` with lightweight references (backend + namespace/table) instead of full configs. - Update every provider/config/doc to use the new reference style; docs/codegen now surface the simplified YAML. ## Migration Before: ```yaml metadata_store: type: sqlite db_path: ~/.llama/distributions/foo/registry.db inference_store: type: postgres host: ${env.POSTGRES_HOST} port: ${env.POSTGRES_PORT} db: ${env.POSTGRES_DB} user: ${env.POSTGRES_USER} password: ${env.POSTGRES_PASSWORD} conversations_store: type: postgres host: ${env.POSTGRES_HOST} port: ${env.POSTGRES_PORT} db: ${env.POSTGRES_DB} user: ${env.POSTGRES_USER} password: ${env.POSTGRES_PASSWORD} ``` After: ```yaml storage: backends: kv_default: type: kv_sqlite db_path: ~/.llama/distributions/foo/kvstore.db sql_default: type: sql_postgres host: ${env.POSTGRES_HOST} port: ${env.POSTGRES_PORT} db: ${env.POSTGRES_DB} user: ${env.POSTGRES_USER} password: ${env.POSTGRES_PASSWORD} stores: metadata: backend: kv_default namespace: registry inference: backend: sql_default table_name: inference_store max_write_queue_size: 10000 num_writers: 4 conversations: backend: sql_default table_name: openai_conversations ``` Provider configs follow the same pattern—for example, a Chroma vector adapter switches from: ```yaml providers: vector_io: - provider_id: chromadb provider_type: remote::chromadb config: url: ${env.CHROMADB_URL} kvstore: type: sqlite db_path: ~/.llama/distributions/foo/chroma.db ``` to: ```yaml providers: vector_io: - provider_id: chromadb provider_type: remote::chromadb config: url: ${env.CHROMADB_URL} persistence: backend: kv_default namespace: vector_io::chroma_remote ``` Once the backends are declared, everything else just points at them, so rotating credentials or swapping to Postgres happens in one place and the stack reuses a single connection pool.	2025-10-20 13:20:09 -07:00
IAN MILLER	007efa6eb5	refactor: replace default all-MiniLM-L6-v2 embedding model by nomic-embed-text-v1.5 in Llama Stack (#3183) ... # 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 PR is to replace the Llama Stack's default embedding model by nomic-embed-text-v1.5. These are the key reasons why Llama Stack community decided to switch from all-MiniLM-L6-v2 to nomic-embed-text-v1.5: 1. The training data for [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2#training-data) includes a lot of data sets with various licensing terms, so it is tricky to know when/whether it is appropriate to use this model for commercial applications. 2. The model is not particularly competitive on major benchmarks. For example, if you look at the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) and click on Miscellaneous/BEIR to see English information retrieval accuracy, you see that the top of the leaderboard is dominated by enormous models but also that there are many, many models of relatively modest size whith much higher Retrieval scores. If you want to look closely at the data, I recommend clicking "Download Table" because it is easier to browse that way. More discussion info can be founded [here](https://github.com/llamastack/llama-stack/issues/2418) <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Closes #2418 ## 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.* --> 1. Run `./scripts/unit-tests.sh` 2. Integration tests via CI wokrflow --------- Signed-off-by: Sébastien Han <seb@redhat.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com> Co-authored-by: Sébastien Han <seb@redhat.com>	2025-10-14 10:44:20 -04:00
ehhuang	48a551ecbc	chore(perf): run guidellm benchmarks (#3421) ... # What does this PR do? - Mostly AI-generated scripts to run guidellm (https://github.com/vllm-project/guidellm) benchmarks on k8s setup - Stack is using image built from main on 9/11 ## Test Plan See updated README.md	2025-09-24 10:18:33 -07:00
ehhuang	4c2fcb6b51	chore: refactor server.main (#3462) ... # What does this PR do? As shown in #3421, we can scale stack to handle more RPS with k8s replicas. This PR enables multi process stack with uvicorn --workers so that we can achieve the same scaling without being in k8s. To achieve that we refactor main to split out the app construction logic. This method needs to be non-async. We created a new `Stack` class to house impls and have a `start()` method to be called in lifespan to start background tasks instead of starting them in the old `construct_stack`. This way we avoid having to manage an event loop manually. ## Test Plan CI > uv run --with llama-stack python -m llama_stack.core.server.server benchmarking/k8s-benchmark/stack_run_config.yaml works. > LLAMA_STACK_CONFIG=benchmarking/k8s-benchmark/stack_run_config.yaml uv run uvicorn llama_stack.core.server.server:create_app --port 8321 --workers 4 works.	2025-09-18 21:11:13 -07:00
ehhuang	c04f1c1e8c	chore: move benchmarking related code (#3406) ... # What does this PR do? - moving things and some formatting changes ## Test Plan	2025-09-10 13:19:44 -07:00