6 commits

Author	SHA1	Message	Date
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
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
Ashwin Bharambe	2665f00102	chore(rename): move llama_stack.distribution to llama_stack.core (#2975) ... We would like to rename the term `template` to `distribution`. To prepare for that, this is a precursor. cc @leseb	2025-07-30 23:30:53 -07:00
Nathan Weinberg	b3d86ca926	fix: stop image_name from being cast to an integer (#2759) ... # What does this PR do? https://github.com/meta-llama/llama-stack/pull/2490 introduced a new function for type conversion of strings. However, a side effect of this is that it will cast any string that can be cast to an integer if possible, which for something like `image_name` is not desired as we only accept strings for this field in the `StackRunConfig` This PR introduces logic to ensure that `image_name` remains a string Closes #2749 ## Test Plan You can run the original step to reproduce from the bug to verify this manually ```bash OPENAI_API_KEY=bogus llama stack build --image-type venv --image-name 2745 --providers inference=remote::openai --run ``` I have also added an additional unit test to prevent any future regression here Signed-off-by: Nathan Weinberg <nweinber@redhat.com>	2025-07-15 09:44:21 -07:00
Ihar Hrachyshka	9e6561a1ec	chore: enable pyupgrade fixes (#1806) ... # What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>	2025-05-01 14:23:50 -07:00
Ashwin Bharambe	4ca58eb987	refactor: tests/unittests -> tests/unit; tests/api -> tests/integration	2025-03-04 09:57:00 -08:00

Renamed from tests/unittests/cli/test_stack_config.py (Browse further)