# 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>
# What does this PR do?
Add support for the temperature to the responses API
## Test Plan
Manually tested simple case
unit tests added for simple case and tool calls
Signed-off-by: Derek Higgins <derekh@redhat.com>
# What does this PR do?
This provides an initial [OpenAI Responses
API](https://platform.openai.com/docs/api-reference/responses)
implementation. The API is not yet complete, and this is more a
proof-of-concept to show how we can store responses in our key-value
stores and use them to support the Responses API concepts like
`previous_response_id`.
## Test Plan
I've added a new
`tests/integration/openai_responses/test_openai_responses.py` as part of
a test-driven development for this new API. I'm only testing this
locally with the remote-vllm provider for now, but it should work with
any of our inference providers since the only API it requires out of the
inference provider is the `openai_chat_completion` endpoint.
```
VLLM_URL="http://localhost:8000/v1" \
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack build --template remote-vllm --image-type venv --run
```
```
LLAMA_STACK_CONFIG="http://localhost:8321" \
python -m pytest -v \
tests/integration/openai_responses/test_openai_responses.py \
--text-model "meta-llama/Llama-3.2-3B-Instruct"
```
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
- recent merge https://github.com/meta-llama/llama-stack/pull/1410
introduce error
```
ValueError: Provider meta-reference (Api.agents) does not implement the following methods:
[('list_agent_sessions', 'not_actually_implemented'), ('list_agents', 'not_actually_implemented')]
```
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```
llama stack run
```
```
LLAMA_STACK_CONFIG=fireworks pytest -v tests/integration/agents/test_agents.py --text-model meta-llama/Llama-3.3-70B-Instruct
```
1379530386
[//]: # (## Documentation)
# Summary:
Client side change in
https://github.com/meta-llama/llama-stack-client-python/pull/180
Changes the resume_turn API to accept `ToolResponse` instead of
`ToolResponseMessage`:
1. `ToolResponse` contains `metadata`
2. `ToolResponseMessage` is a concept for model inputs. Here we are just
submitting the outputs of tool execution.
# Test Plan:
Ran integration tests with newly added test using client tool with
metadata
LLAMA_STACK_CONFIG=fireworks pytest -s -v
tests/integration/agents/test_agents.py --safety-shield
meta-llama/Llama-Guard-3-8B --record-responses
# What does this PR do?
- Deprecate allow_turn_resume flag as this is used for staying backward
compat.
- Closes https://github.com/meta-llama/llama-stack/issues/1363
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```
LLAMA_STACK_CONFIG=fireworks pytest -v tests/api/agents/test_agents.py --inference-model "meta-llama/Llama-3.3-70B-Instruct" --record-responses
```
<img width="1054" alt="image"
src="https://github.com/user-attachments/assets/d31de2d4-0953-41e1-a71a-7e1579fa351a"
/>
[//]: # (## Documentation)
# Problem
Our current Agent framework has discrepancies in definition on how we
handle server side and client side tools.
1. Server Tools: a single Turn is returned including `ToolExecutionStep`
in agenst
2. Client Tools: `create_agent_turn` is called in loop with client agent
lib yielding the agent chunk
ad6ffc63df/src/llama_stack_client/lib/agents/agent.py (L186-L211)
This makes it inconsistent to work with server & client tools. It also
complicates the logs to telemetry to get information about agents turn /
history for observability.
#### Principle
The same `turn_id` should be used to represent the steps required to
complete a user message including client tools.
## Solution
1. `AgentTurnResponseEventType.turn_awaiting_input` status to indicate
that the current turn is not completed, and awaiting tool input
2. `continue_agent_turn` endpoint to update agent turn with client's
tool response.
# What does this PR do?
- Skeleton API as example
## 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.*]
- Just API update, no functionality change
```
llama stack run + client-sdk test
```
<img width="842" alt="image"
src="https://github.com/user-attachments/assets/7ac56b5f-f424-4632-9476-7e0f57555bc3"
/>
[//]: # (## Documentation)
# 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>
# What does this PR do?
The current default system prompt for llama3.2 tends to overindex on
tool calling and doesn't work well when the prompt does not require tool
calling.
This PR adds an option to override the default system prompt, and
organizes tool-related configs into a new config object.
- [ ] Addresses issue (#issue)
## Test Plan
LLAMA_STACK_CONFIG=together pytest
\-\-inference\-model=meta\-llama/Llama\-3\.3\-70B\-Instruct -s -v
tests/client-sdk/agents/test_agents.py::test_override_system_message_behavior
## 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.
Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.
Third part:
- we need to make `tool_runtime.rag_tool.query_context()` and
`tool_runtime.rag_tool.insert_documents()` methods work smoothly with
complete type safety. To that end, we introduce a sub-resource path
`tool-runtime/rag-tool/` and make changes to the resolver to make things
work.
- the PR updates the agents implementation to directly call these typed
APIs for memory accesses rather than going through the complex, untyped
"invoke_tool" API. the code looks much nicer and simpler (expectedly.)
- there are a number of hacks in the server resolver implementation
still, we will live with some and fix some
Note that we must make sure the client SDKs are able to handle this
subresource complexity also. Stainless has support for subresources, so
this should be possible but beware.
## Test Plan
Our RAG test is sad (doesn't actually test for actual RAG output) but I
verified that the implementation works. I will work on fixing the RAG
test afterwards.
```bash
pytest -s -v tests/agents/test_agents.py -k "rag and together" --safety-shield=meta-llama/Llama-Guard-3-8B
```
# What does this PR do?
This PR changes our API to follow more idiomatic REST API approaches of
having paths being resources and methods indicating the action being
performed.
Changes made to generator:
1) removed the prefix check of "get" as its not required and is actually
needed for other method types too
2) removed _ check on path since variables can have "_"
## Test Plan
LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v
tests/client-sdk/agents/test_agents.py
# What does this PR do?
PR #639 introduced the notion of Tools API and ability to invoke tools
through API just as any resource. This PR changes the Agents to start
using the Tools API to invoke tools. Major changes include:
1) Ability to specify tool groups with AgentConfig
2) Agent gets the corresponding tool definitions for the specified tools
and pass along to the model
3) Attachements are now named as Documents and their behavior is mostly
unchanged from user perspective
4) You can specify args that can be injected to a tool call through
Agent config. This is especially useful in case of memory tool, where
you want the tool to operate on a specific memory bank.
5) You can also register tool groups with args, which lets the agent
inject these as well into the tool call.
6) All tests have been migrated to use new tools API and fixtures
including client SDK tests
7) Telemetry just works with tools API because of our trace protocol
decorator
## Test Plan
```
pytest -s -v -k fireworks llama_stack/providers/tests/agents/test_agents.py \
--safety-shield=meta-llama/Llama-Guard-3-8B \
--inference-model=meta-llama/Llama-3.1-8B-Instruct
pytest -s -v -k together llama_stack/providers/tests/tools/test_tools.py \
--safety-shield=meta-llama/Llama-Guard-3-8B \
--inference-model=meta-llama/Llama-3.1-8B-Instruct
LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml" pytest -v tests/client-sdk/agents/test_agents.py
```
run.yaml:
https://gist.github.com/dineshyv/0365845ad325e1c2cab755788ccc5994
Notebook:
https://colab.research.google.com/drive/1ck7hXQxRl6UvT-ijNRZ-gMZxH1G3cN2d?usp=sharing
This brings an interesting aspect -- we need to maintain session-level
tempdir state (!) since the model was told there was some resource at a
given location that it needs to maintain
# What does this PR do?
This PR fixes some of the issues with our telemetry setup to enable logs
to be delivered to opentelemetry and jaeger. Main fixes
1) Updates the open telemetry provider to use the latest oltp exports
instead of deprected ones.
2) Adds a tracing middleware, which injects traces into each HTTP
request that the server recieves and this is going to be the root trace.
Previously, we did this in the create_dynamic_route method, which is
actually not the actual exectuion flow, but more of a config and this
causes the traces to end prematurely. Through middleware, we plugin the
trace start and end at the right location.
3) We manage our own methods to create traces and spans and this does
not fit well with Opentelemetry SDK since it does not support provide a
way to take in traces and spans that are already created. it expects us
to use the SDK to create them. For now, I have a hacky approach of just
maintaining a map from our internal telemetry objects to the open
telemetry specfic ones. This is not the ideal solution. I will explore
other ways to get around this issue. for now, to have something that
works, i am going to keep this as is.
Addresses: #509
