Merge branch 'main' into fix-base64

.github/workflows/tests.yml

@@ -0,0 +1,69 @@
+name: auto-tests
+
+on:
+  # pull_request:
+  workflow_dispatch:
+    inputs:
+      commit_sha:
+        description: 'Specific Commit SHA to trigger on'
+        required: false
+        default: $GITHUB_SHA # default to the last commit of $GITHUB_REF branch
+
+jobs:
+  test-llama-stack-as-library:
+    runs-on: ubuntu-latest
+    env:
+      TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
+      FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
+      TAVILY_SEARCH_API_KEY: ${{ secrets.TAVILY_SEARCH_API_KEY }}
+    strategy:
+      matrix:
+        provider: [fireworks, together]
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event.inputs.commit_sha }}
+
+      - name: Echo commit SHA
+        run: |
+          echo "Triggered on commit SHA: ${{ github.event.inputs.commit_sha }}"
+          git rev-parse HEAD
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt pytest
+          pip install -e .
+
+      - name: Build providers
+        run: |
+          llama stack build --template ${{ matrix.provider }} --image-type venv
+
+      - name: Install the latest llama-stack-client & llama-models packages
+        run: |
+          pip install -e git+https://github.com/meta-llama/llama-stack-client-python.git#egg=llama-stack-client
+          pip install -e git+https://github.com/meta-llama/llama-models.git#egg=llama-models
+
+      - name: Run client-sdk test
+        working-directory: "${{ github.workspace }}"
+        env:
+          REPORT_OUTPUT: md_report.md
+        shell: bash
+        run: |
+          pip install --upgrade pytest-md-report
+          echo "REPORT_FILE=${REPORT_OUTPUT}" >> "$GITHUB_ENV"
+
+          export INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
+          LLAMA_STACK_CONFIG=./llama_stack/templates/${{ matrix.provider }}/run.yaml pytest --md-report --md-report-verbose=1 ./tests/client-sdk/inference/test_inference.py --md-report-output "$REPORT_OUTPUT"
+
+      - name: Output reports to the job summary
+        if: always()
+        shell: bash
+        run: |
+          if [ -f "$REPORT_FILE" ]; then
+            echo "<details><summary> Test Report for ${{ matrix.provider }} </summary>" >> $GITHUB_STEP_SUMMARY
+            echo "" >> $GITHUB_STEP_SUMMARY
+            cat "$REPORT_FILE" >> $GITHUB_STEP_SUMMARY
+            echo "" >> $GITHUB_STEP_SUMMARY
+            echo "</details>" >> $GITHUB_STEP_SUMMARY
+          fi

docs/source/distributions/index.md

@@ -7,9 +7,9 @@ You can run a Llama Stack server in one of the following ways:
 This is the simplest way to get started. Using Llama Stack as a library means you do not need to start a server. This is especially useful when you are not running inference locally and relying on an external inference service (eg. fireworks, together, groq, etc.) See [Using Llama Stack as a Library](importing_as_library)
 
 
-**Docker**:
+**Container**:
 
-Another simple way to start interacting with Llama Stack is to just spin up docker which is pre-built with all the providers you need. We provide a number of pre-built Docker containers so you can start a Llama Stack server instantly. You can also build your own custom Docker container. Which distribution to choose depends on the hardware you have. See [Selection of a Distribution](distributions/selection) for more details.
+Another simple way to start interacting with Llama Stack is to just spin up a container (via Docker or Podman) which is pre-built with all the providers you need. We provide a number of pre-built images so you can start a Llama Stack server instantly. You can also build your own custom container. Which distribution to choose depends on the hardware you have. See [Selection of a Distribution](selection) for more details.
 
 
 **Conda**:
@@ -24,4 +24,5 @@ Lastly, if you have a custom or an advanced setup or you are developing on Llama
 importing_as_library
 building_distro
 configuration
+selection
 ```

docs/source/distributions/self_hosted_distro/sambanova.md

@@ -44,7 +44,7 @@ The following models are available by default:
 
 ### Prerequisite: API Keys
 
-Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaBova.ai](https://sambanova.ai/).
+Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://cloud.sambanova.ai/).
 
 
 ## Running Llama Stack with SambaNova

llama_stack/cli/stack/run.py

@@ -140,6 +140,10 @@ class StackRun(Subcommand):
                 return
 
             def get_conda_prefix(env_name):
+                # Conda "base" environment does not end with "base" in the
+                # prefix, so should be handled separately.
+                if env_name == "base":
+                    return os.environ.get("CONDA_PREFIX")
                 # Get conda environments info
                 conda_env_info = json.loads(
                     subprocess.check_output(

llama_stack/providers/remote/inference/groq/groq_utils.py

@@ -6,7 +6,7 @@
 
 import json
 import warnings
-from typing import AsyncGenerator, Literal
+from typing import AsyncGenerator, Literal, Union
 
 from groq import Stream
 from groq.types.chat.chat_completion import ChatCompletion
@@ -30,6 +30,8 @@ from groq.types.shared.function_definition import FunctionDefinition
 
 from llama_models.llama3.api.datatypes import ToolParamDefinition
 
+from pydantic import BaseModel
+
 from llama_stack.apis.common.content_types import (
     TextDelta,
     ToolCallDelta,
@@ -150,15 +152,26 @@ def convert_chat_completion_response(
             _convert_groq_tool_call(tool_call)
             for tool_call in choice.message.tool_calls
         ]
-        return ChatCompletionResponse(
-            completion_message=CompletionMessage(
-                tool_calls=tool_calls,
-                stop_reason=StopReason.end_of_message,
-                # Content is not optional
-                content="",
-            ),
-            logprobs=None,
-        )
+        if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
+            # If we couldn't parse a tool call, jsonify the tool calls and return them
+            return ChatCompletionResponse(
+                completion_message=CompletionMessage(
+                    stop_reason=StopReason.end_of_message,
+                    content=json.dumps(tool_calls, default=lambda x: x.model_dump()),
+                ),
+                logprobs=None,
+            )
+        else:
+            # Otherwise, return tool calls as normal
+            return ChatCompletionResponse(
+                completion_message=CompletionMessage(
+                    tool_calls=tool_calls,
+                    stop_reason=StopReason.end_of_message,
+                    # Content is not optional
+                    content="",
+                ),
+                logprobs=None,
+            )
     else:
         return ChatCompletionResponse(
             completion_message=CompletionMessage(
@@ -214,15 +227,27 @@ async def convert_chat_completion_response_stream(
 
             # We assume Groq produces fully formed tool calls for each chunk
             tool_call = _convert_groq_tool_call(choice.delta.tool_calls[0])
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=event_type,
-                    delta=ToolCallDelta(
-                        tool_call=tool_call,
-                        parse_status=ToolCallParseStatus.succeeded,
-                    ),
+            if isinstance(tool_call, ToolCall):
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=event_type,
+                        delta=ToolCallDelta(
+                            tool_call=tool_call,
+                            parse_status=ToolCallParseStatus.succeeded,
+                        ),
+                    )
+                )
+            else:
+                # Otherwise it's an UnparseableToolCall - return the raw tool call
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=event_type,
+                        delta=ToolCallDelta(
+                            tool_call=tool_call.model_dump_json(),
+                            parse_status=ToolCallParseStatus.failed,
+                        ),
+                    )
                 )
-            )
         else:
             yield ChatCompletionResponseStreamChunk(
                 event=ChatCompletionResponseEvent(
@@ -234,12 +259,35 @@ async def convert_chat_completion_response_stream(
         event_type = ChatCompletionResponseEventType.progress
 
 
-def _convert_groq_tool_call(tool_call: ChatCompletionMessageToolCall) -> ToolCall:
+class UnparseableToolCall(BaseModel):
+    """
+    A ToolCall with arguments that are not valid JSON.
+    Mirrors the ToolCall schema, but with arguments as a string.
+    """
+
+    call_id: str
+    tool_name: str
+    arguments: str
+
+
+def _convert_groq_tool_call(
+    tool_call: ChatCompletionMessageToolCall,
+) -> Union[ToolCall, UnparseableToolCall]:
+    """
+    Convert a Groq tool call to a ToolCall.
+    Returns an UnparseableToolCall if the tool call is not valid JSON.
+    """
+    try:
+        arguments = json.loads(tool_call.function.arguments)
+    except Exception as e:
+        return UnparseableToolCall(
+            call_id=tool_call.id,
+            tool_name=tool_call.function.name,
+            arguments=tool_call.function.arguments,
+        )
+
     return ToolCall(
         call_id=tool_call.id,
         tool_name=tool_call.function.name,
-        # Note that Groq may return a string that is not valid JSON here
-        # So this may raise a 500 error. Going to leave this as is to see
-        # how big of an issue this is and what we can do about it.
-        arguments=json.loads(tool_call.function.arguments),
+        arguments=arguments,
     )

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -57,6 +57,10 @@ MODEL_ALIASES = [
         "Meta-Llama-3.2-3B-Instruct",
         CoreModelId.llama3_2_3b_instruct.value,
     ),
+    build_model_alias(
+        "Meta-Llama-3.3-70B-Instruct",
+        CoreModelId.llama3_3_70b_instruct.value,
+    ),
     build_model_alias(
         "Llama-3.2-11B-Vision-Instruct",
         CoreModelId.llama3_2_11b_vision_instruct.value,

llama_stack/providers/remote/inference/together/together.py

@@ -161,7 +161,10 @@ class TogetherInferenceAdapter(
             yield chunk
 
     def _build_options(
-        self, sampling_params: Optional[SamplingParams], fmt: ResponseFormat
+        self,
+        sampling_params: Optional[SamplingParams],
+        logprobs: Optional[LogProbConfig],
+        fmt: ResponseFormat,
     ) -> dict:
         options = get_sampling_options(sampling_params)
         if fmt:
@@ -175,6 +178,13 @@ class TogetherInferenceAdapter(
             else:
                 raise ValueError(f"Unknown response format {fmt.type}")
 
+        if logprobs and logprobs.top_k:
+            if logprobs.top_k != 1:
+                raise ValueError(
+                    f"Unsupported value: Together only supports logprobs top_k=1. {logprobs.top_k} was provided",
+                )
+            options["logprobs"] = 1
+
         return options
 
     async def chat_completion(
@@ -263,7 +273,9 @@ class TogetherInferenceAdapter(
             "model": request.model,
             **input_dict,
             "stream": request.stream,
-            **self._build_options(request.sampling_params, request.response_format),
+            **self._build_options(
+                request.sampling_params, request.logprobs, request.response_format
+            ),
         }
 
     async def embeddings(

llama_stack/providers/tests/inference/groq/test_groq_utils.py

@@ -23,6 +23,7 @@ from groq.types.chat.chat_completion_message_tool_call import (
 from groq.types.shared.function_definition import FunctionDefinition
 from llama_models.datatypes import GreedySamplingStrategy, TopPSamplingStrategy
 from llama_models.llama3.api.datatypes import ToolParamDefinition
+from llama_stack.apis.common.content_types import ToolCallParseStatus
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponseEventType,
@@ -347,6 +348,26 @@ class TestConvertNonStreamChatCompletionResponse:
             ),
         ]
 
+    def test_converts_unparseable_tool_calls(self):
+        response = self._dummy_chat_completion_response_with_tool_call()
+        response.choices[0].message.tool_calls = [
+            ChatCompletionMessageToolCall(
+                id="tool_call_id",
+                type="function",
+                function=Function(
+                    name="log",
+                    arguments="(number=10, base=2)",
+                ),
+            ),
+        ]
+
+        converted = convert_chat_completion_response(response)
+
+        assert (
+            converted.completion_message.content
+            == '[{"call_id": "tool_call_id", "tool_name": "log", "arguments": "(number=10, base=2)"}]'
+        )
+
     def _dummy_chat_completion_response(self):
         return ChatCompletion(
             id="chatcmpl-123",
@@ -478,6 +499,40 @@ class TestConvertStreamChatCompletionResponse:
             arguments={"origin": "AU", "destination": "LAX"},
         )
 
+    @pytest.mark.asyncio
+    async def test_returns_tool_calls_stream_with_unparseable_tool_calls(self):
+        def tool_call_stream():
+            chunk = self._dummy_chat_completion_chunk_with_tool_call()
+            chunk.choices[0].delta.tool_calls = [
+                ChoiceDeltaToolCall(
+                    index=0,
+                    type="function",
+                    id="tool_call_id",
+                    function=ChoiceDeltaToolCallFunction(
+                        name="get_flight_info",
+                        arguments="(origin=AU, destination=LAX)",
+                    ),
+                ),
+            ]
+            yield chunk
+
+            chunk = self._dummy_chat_completion_chunk_with_tool_call()
+            chunk.choices[0].delta.content = None
+            chunk.choices[0].finish_reason = "stop"
+            yield chunk
+
+        stream = tool_call_stream()
+        converted = convert_chat_completion_response_stream(stream)
+
+        iter = converted.__aiter__()
+        chunk = await iter.__anext__()
+        assert chunk.event.event_type == ChatCompletionResponseEventType.start
+        assert (
+            chunk.event.delta.content
+            == '{"call_id":"tool_call_id","tool_name":"get_flight_info","arguments":"(origin=AU, destination=LAX)"}'
+        )
+        assert chunk.event.delta.parse_status == ToolCallParseStatus.failed
+
     def _dummy_chat_completion_chunk(self):
         return ChatCompletionChunk(
             id="chatcmpl-123",

llama_stack/providers/utils/inference/openai_compat.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import AsyncGenerator, Dict, List, Optional
+from typing import AsyncGenerator, Dict, List, Optional, Union
 
 from llama_models.datatypes import (
     GreedySamplingStrategy,
@@ -121,7 +121,31 @@ def convert_openai_completion_logprobs(
 ) -> Optional[List[TokenLogProbs]]:
     if not logprobs:
         return None
-    return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs]
+    if hasattr(logprobs, "top_logprobs"):
+        return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs]
+
+    # Together supports logprobs with top_k=1 only. This means for each token position,
+    # they return only the logprobs for the selected token (vs. the top n most likely tokens).
+    # Here we construct the response by matching the selected token with the logprobs.
+    if logprobs.tokens and logprobs.token_logprobs:
+        return [
+            TokenLogProbs(logprobs_by_token={token: token_lp})
+            for token, token_lp in zip(logprobs.tokens, logprobs.token_logprobs)
+        ]
+    return None
+
+
+def convert_openai_completion_logprobs_stream(
+    text: str, logprobs: Optional[Union[float, OpenAICompatLogprobs]]
+):
+    if logprobs is None:
+        return None
+    if isinstance(logprobs, float):
+        # Adapt response from Together CompletionChoicesChunk
+        return [TokenLogProbs(logprobs_by_token={text: logprobs})]
+    if hasattr(logprobs, "top_logprobs"):
+        return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs]
+    return None
 
 
 def process_completion_response(
@@ -188,7 +212,7 @@ async def process_completion_stream_response(
         yield CompletionResponseStreamChunk(
             delta=text,
             stop_reason=stop_reason,
-            logprobs=convert_openai_completion_logprobs(choice.logprobs),
+            logprobs=convert_openai_completion_logprobs_stream(text, choice.logprobs),
         )
         if finish_reason:
             if finish_reason in ["stop", "eos", "eos_token"]:

llama_stack/templates/sambanova/run.yaml

@@ -116,6 +116,11 @@ models:
   provider_id: sambanova
   provider_model_id: Meta-Llama-3.2-3B-Instruct
   model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.3-70B-Instruct
+  provider_id: sambanova
+  provider_model_id: Meta-Llama-3.3-70B-Instruct
+  model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
   provider_id: sambanova