InterleavedTextAttachment -> InterleavedTextMedia, introduce memory tool

llama_toolchain/agentic_system/api/datatypes.py

@@ -10,7 +10,7 @@ from typing import Any, Dict, List, Literal, Optional, Union
 
 from llama_models.schema_utils import json_schema_type
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, validator
 from typing_extensions import Annotated
 
 from llama_toolchain.common.deployment_types import *  # noqa: F403
@@ -20,7 +20,31 @@ from llama_toolchain.memory.api.datatypes import *  # noqa: F403
 
 
 @json_schema_type
-class AgenticSystemToolDefinition(ToolDefinition):
+class Attachment(BaseModel):
+    url: URL
+    mime_type: str
+
+
+class AgenticSystemBuiltinTool(BuiltinTool):
+    memory = "memory"
+
+
+@json_schema_type
+class AgenticSystemToolDefinition(BaseModel):
+    tool_name: Union[AgenticSystemBuiltinTool, str]
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, ToolParamDefinition]] = None
+
+    @validator("tool_name", pre=True)
+    @classmethod
+    def validate_field(cls, v):
+        if isinstance(v, str):
+            try:
+                return AgenticSystemBuiltinTool(v)
+            except ValueError:
+                return v
+        return v
+
     execution_config: Optional[RestAPIExecutionConfig] = None
     input_shields: Optional[List[ShieldDefinition]] = Field(default_factory=list)
     output_shields: Optional[List[ShieldDefinition]] = Field(default_factory=list)
@@ -124,11 +148,7 @@ class MemoryConfig(BaseModel):
 
 class AgentConfigCommon(BaseModel):
     sampling_params: Optional[SamplingParams] = SamplingParams()
-    # zero-shot or built-in tool configurations as input to the model
+    memory_configs: Optional[List[MemoryConfig]] = Field(default_factory=list)
-    available_tools: Optional[List[AgenticSystemToolDefinition]] = Field(
-        default_factory=list
-    )
-    memory: Optional[List[MemoryConfig]] = Field(default_factory=list)
 
     input_shields: Optional[List[ShieldDefinition]] = Field(default_factory=list)
     output_shields: Optional[List[ShieldDefinition]] = Field(default_factory=list)
@@ -136,6 +156,9 @@ class AgentConfigCommon(BaseModel):
     # if you completely want to replace the messages prefixed by the system,
     # this is debug only
     debug_prefix_messages: Optional[List[Message]] = Field(default_factory=list)
+
+    tools: Optional[List[AgenticSystemToolDefinition]] = Field(default_factory=list)
+    tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
     tool_prompt_format: Optional[ToolPromptFormat] = Field(
         default=ToolPromptFormat.json
     )

llama_toolchain/agentic_system/api/endpoints.py

@@ -46,7 +46,7 @@ class AgenticSystemTurnCreateRequest(BaseModel, AgentConfigOverridablePerTurn):
             ToolResponseMessage,
         ]
     ]
-    attachments: List[Attachment]
+    attachments: Optional[List[Attachment]] = None
 
     stream: Optional[bool] = False
 

llama_toolchain/agentic_system/meta_reference/agent_instance.py

@@ -33,7 +33,6 @@ class ChatAgent(ShieldRunnerMixin):
         memory_api: Memory,
         safety_api: Safety,
         builtin_tools: List[SingleMessageBuiltinTool],
-        custom_tool_definitions: List[ToolDefinition],
         max_infer_iters: int = 10,
     ):
         self.agent_config = agent_config
@@ -108,7 +107,6 @@ class ChatAgent(ShieldRunnerMixin):
         # print_dialog(messages)
 
         turn_id = str(uuid.uuid4())
-        params = self.agent_config.sampling_params
         start_time = datetime.now()
         yield AgenticSystemTurnResponseStreamChunk(
             event=AgenticSystemTurnResponseEvent(
@@ -123,10 +121,9 @@ class ChatAgent(ShieldRunnerMixin):
         async for chunk in self.run(
             turn_id=turn_id,
             input_messages=messages,
-            temperature=params.temperature,
+            attachments=request.attachments or [],
-            top_p=params.top_p,
+            sampling_params=self.agent_config.sampling_params,
             stream=request.stream,
-            max_gen_len=params.max_tokens,
         ):
             if isinstance(chunk, CompletionMessage):
                 cprint(
@@ -175,10 +172,9 @@ class ChatAgent(ShieldRunnerMixin):
         self,
         turn_id: str,
         input_messages: List[Message],
-        temperature: float,
+        attachments: List[Attachment],
-        top_p: float,
+        sampling_params: SamplingParams,
         stream: bool = False,
-        max_gen_len: Optional[int] = None,
     ) -> AsyncGenerator:
         # Doing async generators makes downstream code much simpler and everything amenable to
         # streaming. However, it also makes things complicated here because AsyncGenerators cannot
@@ -194,7 +190,7 @@ class ChatAgent(ShieldRunnerMixin):
                 yield res
 
         async for res in self._run(
-            turn_id, input_messages, temperature, top_p, stream, max_gen_len
+            turn_id, input_messages, attachments, sampling_params, stream
         ):
             if isinstance(res, bool):
                 return
@@ -279,20 +275,27 @@ class ChatAgent(ShieldRunnerMixin):
             )
         )
 
-    async def _should_retrieve_context(self, messages: List[Message]) -> bool: ...
+    async def _should_retrieve_context(
+        self, messages: List[Message], attachments: List[Attachment]
+    ) -> bool:
+        return self.agent_config.memory_configs or len(attachments) > 0
+
+    async def _retrieve_context(
+        self, messages: List[Message], attachments: List[Attachment]
+    ) -> List[Message]:
+        return []
 
     async def _run(
         self,
         turn_id: str,
         input_messages: List[Message],
-        temperature: float,
+        attachments: List[Attachment],
-        top_p: float,
+        sampling_params: SamplingParams,
         stream: bool = False,
-        max_gen_len: Optional[int] = None,
     ) -> AsyncGenerator:
-        need_context = await self._should_retrieve_context(input_messages)
+        need_context = await self._should_retrieve_context(input_messages, attachments)
         if need_context:
-            context = await self._retrieve_context(input_messages)
+            context_messages = await self._retrieve_context(input_messages)
             # input_messages = preprocess_dialog(input_messages, self.prefix_messages)
             # input_messages = input_messages + context
         input_messages = preprocess_dialog(input_messages)
@@ -320,18 +323,13 @@ class ChatAgent(ShieldRunnerMixin):
                 )
             )
 
-            # where are the available tools?
             req = ChatCompletionRequest(
                 model=self.agent_config.model,
                 messages=input_messages,
                 tools=self.agent_config.available_tools,
                 tool_prompt_format=self.agent_config.tool_prompt_format,
                 stream=True,
-                sampling_params=SamplingParams(
+                sampling_params=sampling_params,
-                    temperature=temperature,
-                    top_p=top_p,
-                    max_tokens=max_gen_len,
-                ),
             )
 
             tool_calls = []
@@ -554,6 +552,7 @@ def attachment_message(url: URL) -> ToolResponseMessage:
 
 
 def preprocess_dialog(messages: List[Message]) -> List[Message]:
+    # remove system message since those are
     """
     Preprocesses the dialog by removing the system message and
     adding the system message to the beginning of the dialog.
@@ -565,7 +564,7 @@ def preprocess_dialog(messages: List[Message]) -> List[Message]:
             continue
 
         # NOTE: the ideal behavior is to use `file_path = ...` but that
-        # means we need to have stateful execution o    f code which we currently
+        # means we need to have stateful execution of code which we currently
         # do not have.
         if isinstance(m.content, Attachment):
             ret.append(attachment_message(m.content.url))

llama_toolchain/agentic_system/meta_reference/agentic_system.py

@@ -16,9 +16,6 @@ from llama_toolchain.inference.api.datatypes import BuiltinTool
 from llama_toolchain.memory.api import Memory
 from llama_toolchain.safety.api import Safety
 from llama_toolchain.agentic_system.api import *  # noqa: F403
-from .agent_instance import ChatAgent
-from .config import MetaReferenceImplConfig
-
 from llama_toolchain.tools.builtin import (
     BraveSearchTool,
     CodeInterpreterTool,
@@ -27,7 +24,8 @@ from llama_toolchain.tools.builtin import (
 )
 from llama_toolchain.tools.safety import with_safety
 
-from .agent_instance import AgentInstance
+from .agent_instance import AgentInstance, ChatAgent
+from .config import MetaReferenceImplConfig
 
 
 logger = logging.getLogger()
@@ -76,7 +74,6 @@ class MetaReferenceAgenticSystemImpl(AgenticSystem):
         system_id = str(uuid.uuid4())
 
         builtin_tools = []
-        custom_tool_definitions = []
         cfg = request.agent_config
         for dfn in cfg.available_tools:
             if isinstance(dfn.tool_name, BuiltinTool):
@@ -104,8 +101,6 @@ class MetaReferenceAgenticSystemImpl(AgenticSystem):
                         tool, self.safety_api, dfn.input_shields, dfn.output_shields
                     )
                 )
-            else:
-                custom_tool_definitions.append(dfn)
 
         AGENT_INSTANCES_BY_ID[system_id] = ChatAgent(
             agent_config=cfg,
@@ -113,7 +108,6 @@ class MetaReferenceAgenticSystemImpl(AgenticSystem):
             safety_api=self.safety_api,
             memory_api=self.memory_api,
             builtin_tools=builtin_tools,
-            custom_tool_definitions=custom_tool_definitions,
         )
 
         return AgenticSystemCreateResponse(

llama_toolchain/memory/api/endpoints.py

@@ -6,7 +6,7 @@
 
 from typing import List, Protocol
 
-from llama_models.llama3_1.api.datatypes import InterleavedTextAttachment
+from llama_models.llama3.api.datatypes import InterleavedTextMedia
 
 from llama_models.schema_utils import webmethod
 from .datatypes import *  # noqa: F403
@@ -14,7 +14,7 @@ from .datatypes import *  # noqa: F403
 
 @json_schema_type
 class RetrieveMemoryDocumentsRequest(BaseModel):
-    query: InterleavedTextAttachment
+    query: InterleavedTextMedia
     bank_ids: str