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10 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Sébastien Han
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02e5e8a633
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fix: only print routes that match the runtime config (#2226)
# What does this PR do? We now only print the 'active' routes, not all the possible routes. This is based on the distribution server config by looking at enabled APIs and their respective providers. Signed-off-by: Sébastien Han <seb@redhat.com> |
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Sébastien Han
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69554158fa
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feat: add health to all providers through providers endpoint (#1418)
The `/v1/providers` now reports the health status of each provider when implemented. ``` curl -L http://127.0.0.1:8321/v1/providers|jq % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 4072 100 4072 0 0 246k 0 --:--:-- --:--:-- --:--:-- 248k { "data": [ { "api": "inference", "provider_id": "ollama", "provider_type": "remote::ollama", "config": { "url": "http://localhost:11434" }, "health": { "status": "OK" } }, { "api": "vector_io", "provider_id": "faiss", "provider_type": "inline::faiss", "config": { "kvstore": { "type": "sqlite", "namespace": null, "db_path": "/Users/leseb/.llama/distributions/ollama/faiss_store.db" } }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "safety", "provider_id": "llama-guard", "provider_type": "inline::llama-guard", "config": { "excluded_categories": [] }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "agents", "provider_id": "meta-reference", "provider_type": "inline::meta-reference", "config": { "persistence_store": { "type": "sqlite", "namespace": null, "db_path": "/Users/leseb/.llama/distributions/ollama/agents_store.db" } }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "telemetry", "provider_id": "meta-reference", "provider_type": "inline::meta-reference", "config": { "service_name": "llama-stack", "sinks": "console,sqlite", "sqlite_db_path": "/Users/leseb/.llama/distributions/ollama/trace_store.db" }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "eval", "provider_id": "meta-reference", "provider_type": "inline::meta-reference", "config": { "kvstore": { "type": "sqlite", "namespace": null, "db_path": "/Users/leseb/.llama/distributions/ollama/meta_reference_eval.db" } }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "datasetio", "provider_id": "huggingface", "provider_type": "remote::huggingface", "config": { "kvstore": { "type": "sqlite", "namespace": null, "db_path": "/Users/leseb/.llama/distributions/ollama/huggingface_datasetio.db" } }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "datasetio", "provider_id": "localfs", "provider_type": "inline::localfs", "config": { "kvstore": { "type": "sqlite", "namespace": null, "db_path": "/Users/leseb/.llama/distributions/ollama/localfs_datasetio.db" } }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "scoring", "provider_id": "basic", "provider_type": "inline::basic", "config": {}, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "scoring", "provider_id": "llm-as-judge", "provider_type": "inline::llm-as-judge", "config": {}, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "scoring", "provider_id": "braintrust", "provider_type": "inline::braintrust", "config": { "openai_api_key": "********" }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "tool_runtime", "provider_id": "brave-search", "provider_type": "remote::brave-search", "config": { "api_key": "********", "max_results": 3 }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "tool_runtime", "provider_id": "tavily-search", "provider_type": "remote::tavily-search", "config": { "api_key": "********", "max_results": 3 }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "tool_runtime", "provider_id": "code-interpreter", "provider_type": "inline::code-interpreter", "config": {}, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "tool_runtime", "provider_id": "rag-runtime", "provider_type": "inline::rag-runtime", "config": {}, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "tool_runtime", "provider_id": "model-context-protocol", "provider_type": "remote::model-context-protocol", "config": {}, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } }, { "api": "tool_runtime", "provider_id": "wolfram-alpha", "provider_type": "remote::wolfram-alpha", "config": { "api_key": "********" }, "health": { "status": "Not Implemented", "message": "Provider does not implement health check" } } ] } ``` Per providers too: ``` curl -L http://127.0.0.1:8321/v1/providers/ollama {"api":"inference","provider_id":"ollama","provider_type":"remote::ollama","config":{"url":"http://localhost:11434"},"health":{"status":"OK"}} ``` Signed-off-by: Sébastien Han <seb@redhat.com> |
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Charlie Doern
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a483a58c6e
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chore: deprecate /v1/inspect/providers (#1678)
# What does this PR do? with the new /v1/providers API, /v1/inspect/providers is duplicative, deprecate it by removing the route, and add a test for the full /v1/providers API resolves #1623 ## Test Plan `uv run pytest -v tests/integration/providers --stack-config=ollama --text-model="meta-llama/Llama-3.2-3B-Instruct" --embedding-model=all-MiniLM-L6-v2` <img width="1512" alt="Screenshot 2025-03-18 at 9 18 38 AM" src="https://github.com/user-attachments/assets/2db30f25-3ff6-4374-b39d-0047f093fe36" /> Signed-off-by: Charlie Doern <cdoern@redhat.com> |
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Sébastien Han
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418645696a
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fix: improve signal handling and update dependencies (#1044)
# What does this PR do? This commit enhances the signal handling mechanism in the server by improving the `handle_signal` (previously handle_sigint) function. It now properly retrieves the signal name, ensuring clearer logging when a termination signal is received. Additionally, it cancels all running tasks and waits for their completion before stopping the event loop, allowing for a more graceful shutdown. Support for handling SIGTERM has also been added alongside SIGINT. Before the changes, handle_sigint used asyncio.run(run_shutdown()). However, asyncio.run() is meant to start a new event loop, and calling it inside an existing one (like when running Uvicorn) raises an error. The fix replaces asyncio.run(run_shutdown()) with an async function scheduled on the existing loop using loop.create_task(shutdown()). This ensures that the shutdown coroutine runs within the current event loop instead of trying to create a new one. Furthermore, this commit updates the project dependencies. `fastapi` and `uvicorn` have been added to the development dependencies in `pyproject.toml` and `uv.lock`, ensuring that the necessary packages are available for development and execution. Closes: https://github.com/meta-llama/llama-stack/issues/1043 Signed-off-by: Sébastien Han <seb@redhat.com> [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan Run a server and send SIGINT: ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" python -m llama_stack.distribution.server.server --yaml-config ./llama_stack/templates/ollama/run.yaml Using config file: llama_stack/templates/ollama/run.yaml Run configuration: apis: - agents - datasetio - eval - inference - safety - scoring - telemetry - tool_runtime - vector_io container_image: null datasets: [] eval_tasks: [] image_name: ollama metadata_store: db_path: /Users/leseb/.llama/distributions/ollama/registry.db namespace: null type: sqlite models: - metadata: {} model_id: meta-llama/Llama-3.2-3B-Instruct model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType - llm provider_id: ollama provider_model_id: null - metadata: embedding_dimension: 384 model_id: all-MiniLM-L6-v2 model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType - embedding provider_id: sentence-transformers provider_model_id: null providers: agents: - config: persistence_store: db_path: /Users/leseb/.llama/distributions/ollama/agents_store.db namespace: null type: sqlite provider_id: meta-reference provider_type: inline::meta-reference datasetio: - config: {} provider_id: huggingface provider_type: remote::huggingface - config: {} provider_id: localfs provider_type: inline::localfs eval: - config: {} provider_id: meta-reference provider_type: inline::meta-reference inference: - config: url: http://localhost:11434 provider_id: ollama provider_type: remote::ollama - config: {} provider_id: sentence-transformers provider_type: inline::sentence-transformers safety: - config: {} provider_id: llama-guard provider_type: inline::llama-guard 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 telemetry: - config: service_name: llama-stack sinks: console,sqlite sqlite_db_path: /Users/leseb/.llama/distributions/ollama/trace_store.db provider_id: meta-reference provider_type: inline::meta-reference 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: code-interpreter provider_type: inline::code-interpreter - config: {} provider_id: rag-runtime provider_type: inline::rag-runtime vector_io: - config: kvstore: db_path: /Users/leseb/.llama/distributions/ollama/faiss_store.db namespace: null type: sqlite provider_id: faiss provider_type: inline::faiss scoring_fns: [] server: port: 8321 tls_certfile: null tls_keyfile: null shields: [] tool_groups: - args: null mcp_endpoint: null provider_id: tavily-search toolgroup_id: builtin::websearch - args: null mcp_endpoint: null provider_id: rag-runtime toolgroup_id: builtin::rag - args: null mcp_endpoint: null provider_id: code-interpreter toolgroup_id: builtin::code_interpreter vector_dbs: [] version: '2' INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:213: Resolved 31 providers INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-inference => ollama INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-inference => sentence-transformers INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: models => __routing_table__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inference => __autorouted__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-vector_io => faiss INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-safety => llama-guard INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: shields => __routing_table__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: safety => __autorouted__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: vector_dbs => __routing_table__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: vector_io => __autorouted__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-tool_runtime => brave-search INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-tool_runtime => tavily-search INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-tool_runtime => code-interpreter INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-tool_runtime => rag-runtime INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: tool_groups => __routing_table__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: tool_runtime => __autorouted__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: agents => meta-reference INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-datasetio => huggingface INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-datasetio => localfs INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: datasets => __routing_table__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: datasetio => __autorouted__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: telemetry => meta-reference INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-scoring => basic INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-scoring => llm-as-judge INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-scoring => braintrust INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: scoring_functions => __routing_table__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: scoring => __autorouted__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inner-eval => meta-reference INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: eval_tasks => __routing_table__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: eval => __autorouted__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:215: inspect => __builtin__ INFO 2025-02-12 10:21:03,540 llama_stack.distribution.resolver:216: INFO 2025-02-12 10:21:03,723 llama_stack.providers.remote.inference.ollama.ollama:148: checking connectivity to Ollama at `http://localhost:11434`... INFO 2025-02-12 10:21:03,734 httpx:1740: HTTP Request: GET http://localhost:11434/api/ps "HTTP/1.1 200 OK" INFO 2025-02-12 10:21:03,843 faiss.loader:148: Loading faiss. INFO 2025-02-12 10:21:03,865 faiss.loader:150: Successfully loaded faiss. INFO 2025-02-12 10:21:03,868 faiss:173: Failed to load GPU Faiss: name 'GpuIndexIVFFlat' is not defined. Will not load constructor refs for GPU indexes. Warning: `bwrap` is not available. Code interpreter tool will not work correctly. INFO 2025-02-12 10:21:04,315 datasets:54: PyTorch version 2.6.0 available. INFO 2025-02-12 10:21:04,556 httpx:1740: HTTP Request: GET http://localhost:11434/api/ps "HTTP/1.1 200 OK" INFO 2025-02-12 10:21:04,557 llama_stack.providers.utils.inference.embedding_mixin:42: Loading sentence transformer for all-MiniLM-L6-v2... INFO 2025-02-12 10:21:07,202 sentence_transformers.SentenceTransformer:210: Use pytorch device_name: mps INFO 2025-02-12 10:21:07,202 sentence_transformers.SentenceTransformer:218: Load pretrained SentenceTransformer: all-MiniLM-L6-v2 INFO 2025-02-12 10:21:09,500 llama_stack.distribution.stack:102: Models: all-MiniLM-L6-v2 served by sentence-transformers INFO 2025-02-12 10:21:09,500 llama_stack.distribution.stack:102: Models: meta-llama/Llama-3.2-3B-Instruct served by ollama INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: basic::equality served by basic INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: basic::regex_parser_multiple_choice_answer served by basic INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: basic::subset_of served by basic INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::answer-correctness served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::answer-relevancy served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::answer-similarity served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::context-entity-recall served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::context-precision served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::context-recall served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::context-relevancy served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::factuality served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: braintrust::faithfulness served by braintrust INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: llm-as-judge::405b-simpleqa served by llm-as-judge INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Scoring_fns: llm-as-judge::base served by llm-as-judge INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Tool_groups: builtin::code_interpreter served by code-interpreter INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Tool_groups: builtin::rag served by rag-runtime INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:102: Tool_groups: builtin::websearch served by tavily-search INFO 2025-02-12 10:21:09,501 llama_stack.distribution.stack:106: Serving API eval POST /v1/eval/tasks/{task_id}/evaluations DELETE /v1/eval/tasks/{task_id}/jobs/{job_id} GET /v1/eval/tasks/{task_id}/jobs/{job_id}/result GET /v1/eval/tasks/{task_id}/jobs/{job_id} POST /v1/eval/tasks/{task_id}/jobs Serving API agents POST /v1/agents POST /v1/agents/{agent_id}/session POST /v1/agents/{agent_id}/session/{session_id}/turn DELETE /v1/agents/{agent_id} DELETE /v1/agents/{agent_id}/session/{session_id} GET /v1/agents/{agent_id}/session/{session_id} GET /v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}/step/{step_id} GET /v1/agents/{agent_id}/session/{session_id}/turn/{turn_id} Serving API scoring_functions GET /v1/scoring-functions/{scoring_fn_id} GET /v1/scoring-functions POST /v1/scoring-functions Serving API safety POST /v1/safety/run-shield Serving API inspect GET /v1/health GET /v1/inspect/providers GET /v1/inspect/routes GET /v1/version Serving API tool_runtime POST /v1/tool-runtime/invoke GET /v1/tool-runtime/list-tools POST /v1/tool-runtime/rag-tool/insert POST /v1/tool-runtime/rag-tool/query Serving API datasetio POST /v1/datasetio/rows GET /v1/datasetio/rows Serving API shields GET /v1/shields/{identifier} GET /v1/shields POST /v1/shields Serving API eval_tasks GET /v1/eval-tasks/{eval_task_id} GET /v1/eval-tasks POST /v1/eval-tasks Serving API models GET /v1/models/{model_id} GET /v1/models POST /v1/models DELETE /v1/models/{model_id} Serving API datasets GET /v1/datasets/{dataset_id} GET /v1/datasets POST /v1/datasets DELETE /v1/datasets/{dataset_id} Serving API vector_io POST /v1/vector-io/insert POST /v1/vector-io/query Serving API inference POST /v1/inference/chat-completion POST /v1/inference/completion POST /v1/inference/embeddings Serving API tool_groups GET /v1/tools/{tool_name} GET /v1/toolgroups/{toolgroup_id} GET /v1/toolgroups GET /v1/tools POST /v1/toolgroups DELETE /v1/toolgroups/{toolgroup_id} Serving API vector_dbs GET /v1/vector-dbs/{vector_db_id} GET /v1/vector-dbs POST /v1/vector-dbs DELETE /v1/vector-dbs/{vector_db_id} Serving API scoring POST /v1/scoring/score POST /v1/scoring/score-batch Serving API telemetry GET /v1/telemetry/traces/{trace_id}/spans/{span_id} GET /v1/telemetry/spans/{span_id}/tree GET /v1/telemetry/traces/{trace_id} POST /v1/telemetry/events GET /v1/telemetry/spans GET /v1/telemetry/traces POST /v1/telemetry/spans/export Listening on ['::', '0.0.0.0']:5001 INFO: Started server process [65372] INFO: Waiting for application startup. INFO: ASGI 'lifespan' protocol appears unsupported. INFO: Application startup complete. INFO: Uvicorn running on http://['::', '0.0.0.0']:5001 (Press CTRL+C to quit) ^CINFO: Shutting down INFO: Finished server process [65372] Received signal SIGINT (2). Exiting gracefully... INFO 2025-02-12 10:21:11,215 __main__:151: Shutting down ModelsRoutingTable INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down InferenceRouter INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ShieldsRoutingTable INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down SafetyRouter INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down VectorDBsRoutingTable INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down VectorIORouter INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ToolGroupsRoutingTable INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ToolRuntimeRouter INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down MetaReferenceAgentsImpl INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down DatasetsRoutingTable INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down DatasetIORouter INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down TelemetryAdapter INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ScoringFunctionsRoutingTable INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down ScoringRouter INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down EvalTasksRoutingTable INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down EvalRouter INFO 2025-02-12 10:21:11,216 __main__:151: Shutting down DistributionInspectImpl ``` [//]: # (## Documentation) [//]: # (- [ ] Added a Changelog entry if the change is significant) Signed-off-by: Sébastien Han <seb@redhat.com> |
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Dinesh Yeduguru
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12c994b5b2
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REST API fixes (#789)
# What does this PR do? Client SDK fixes ## Test Plan LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-fireworks/fireworks-run.yaml" pytest -v tests/client-sdk/safety/test_safety.py LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-fireworks/fireworks-run.yaml" pytest -v tests/client-sdk/memory/test_memory.py |
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Dinesh Yeduguru
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678ab29129
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Idiomatic REST API: Inspect (#779)
# What does this PR do? Since provider list returns a map grouping providers by API, we should not be using data. This PR fixes the types to just be the plain dict, basically reverting back to previous behavior ## Test Plan llama-stack on fix-provider-list [$] 🅒 stack❯ LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml" pytest -v tests/client-sdk/safety/test_safety.py |
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Xi Yan
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596afc6497
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add --version to llama stack CLI & /version endpoint (#732)
# What does this PR do? - add --version to llama stack CLI - add /version endpoint - run OpenAPI generator for the new endpoint ## Test Plan **CLI** <img width="184" alt="image" src="https://github.com/user-attachments/assets/3acb1d22-453e-4b79-baf6-e98e88d0671c" /> **endpoint** <img width="430" alt="image" src="https://github.com/user-attachments/assets/79cdd670-493b-40cf-8f9e-28a4ac0988ac" /> ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests. |
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Xi Yan
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3c72c034e6
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[remove import *] clean up import *'s (#689)
# What does this PR do? - as title, cleaning up `import *`'s - upgrade tests to make them more robust to bad model outputs - remove import *'s in llama_stack/apis/* (skip __init__ modules) <img width="465" alt="image" src="https://github.com/user-attachments/assets/d8339c13-3b40-4ba5-9c53-0d2329726ee2" /> - run `sh run_openapi_generator.sh`, no types gets affected ## Test Plan ### Providers Tests **agents** ``` pytest -v -s llama_stack/providers/tests/agents/test_agents.py -m "together" --safety-shield meta-llama/Llama-Guard-3-8B --inference-model meta-llama/Llama-3.1-405B-Instruct-FP8 ``` **inference** ```bash # meta-reference torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py # together pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py pytest ./llama_stack/providers/tests/inference/test_prompt_adapter.py ``` **safety** ``` pytest -v -s llama_stack/providers/tests/safety/test_safety.py -m together --safety-shield meta-llama/Llama-Guard-3-8B ``` **memory** ``` pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m "sentence_transformers" --env EMBEDDING_DIMENSION=384 ``` **scoring** ``` pytest -v -s -m llm_as_judge_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct pytest -v -s -m basic_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py pytest -v -s -m braintrust_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py ``` **datasetio** ``` pytest -v -s -m localfs llama_stack/providers/tests/datasetio/test_datasetio.py pytest -v -s -m huggingface llama_stack/providers/tests/datasetio/test_datasetio.py ``` **eval** ``` pytest -v -s -m meta_reference_eval_together_inference llama_stack/providers/tests/eval/test_eval.py pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio llama_stack/providers/tests/eval/test_eval.py ``` ### Client-SDK Tests ``` LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v ./tests/client-sdk ``` ### llama-stack-apps ``` PORT=5000 LOCALHOST=localhost python -m examples.agents.hello $LOCALHOST $PORT python -m examples.agents.inflation $LOCALHOST $PORT python -m examples.agents.podcast_transcript $LOCALHOST $PORT python -m examples.agents.rag_as_attachments $LOCALHOST $PORT python -m examples.agents.rag_with_memory_bank $LOCALHOST $PORT python -m examples.safety.llama_guard_demo_mm $LOCALHOST $PORT python -m examples.agents.e2e_loop_with_custom_tools $LOCALHOST $PORT # Vision model python -m examples.interior_design_assistant.app python -m examples.agent_store.app $LOCALHOST $PORT ``` ### CLI ``` which llama llama model prompt-format -m Llama3.2-11B-Vision-Instruct llama model list llama stack list-apis llama stack list-providers inference llama stack build --template ollama --image-type conda ``` ### Distributions Tests **ollama** ``` llama stack build --template ollama --image-type conda ollama run llama3.2:1b-instruct-fp16 llama stack run ./llama_stack/templates/ollama/run.yaml --env INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct ``` **fireworks** ``` llama stack build --template fireworks --image-type conda llama stack run ./llama_stack/templates/fireworks/run.yaml ``` **together** ``` llama stack build --template together --image-type conda llama stack run ./llama_stack/templates/together/run.yaml ``` **tgi** ``` llama stack run ./llama_stack/templates/tgi/run.yaml --env TGI_URL=http://0.0.0.0:5009 --env INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct ``` ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests. |
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Ashwin Bharambe
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6bb57e72a7
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Remove "routing_table" and "routing_key" concepts for the user (#201)
This PR makes several core changes to the developer experience surrounding Llama Stack. Background: PR #92 introduced the notion of "routing" to the Llama Stack. It introduces three object types: (1) models, (2) shields and (3) memory banks. Each of these objects can be associated with a distinct provider. So you can get model A to be inferenced locally while model B, C can be inference remotely (e.g.) However, this had a few drawbacks: you could not address the provider instances -- i.e., if you configured "meta-reference" with a given model, you could not assign an identifier to this instance which you could re-use later. the above meant that you could not register a "routing_key" (e.g. model) dynamically and say "please use this existing provider I have already configured" for a new model. the terms "routing_table" and "routing_key" were exposed directly to the user. in my view, this is way too much overhead for a new user (which almost everyone is.) people come to the stack wanting to do ML and encounter a completely unexpected term. What this PR does: This PR structures the run config with only a single prominent key: - providers Providers are instances of configured provider types. Here's an example which shows two instances of the remote::tgi provider which are serving two different models. providers: inference: - provider_id: foo provider_type: remote::tgi config: { ... } - provider_id: bar provider_type: remote::tgi config: { ... } Secondly, the PR adds dynamic registration of { models | shields | memory_banks } to the API surface. The distribution still acts like a "routing table" (as previously) except that it asks the backing providers for a listing of these objects. For example it asks a TGI or Ollama inference adapter what models it is serving. Only the models that are being actually served can be requested by the user for inference. Otherwise, the Stack server will throw an error. When dynamically registering these objects, you can use the provider IDs shown above. Info about providers can be obtained using the Api.inspect set of endpoints (/providers, /routes, etc.) The above examples shows the correspondence between inference providers and models registry items. Things work similarly for the safety <=> shields and memory <=> memory_banks pairs. Registry: This PR also makes it so that Providers need to implement additional methods for registering and listing objects. For example, each Inference provider is now expected to implement the ModelsProtocolPrivate protocol (naming is not great!) which consists of two methods register_model list_models The goal is to inform the provider that a certain model needs to be supported so the provider can make any relevant backend changes if needed (or throw an error if the model cannot be supported.) There are many other cleanups included some of which are detailed in a follow-up comment. |
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Ashwin Bharambe
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8d049000e3 | Add an introspection "Api.inspect" API |