forked from phoenix-oss/llama-stack-mirror
feat: remove special handling of builtin::rag tool (#1015)
Summary: Lets the model decide which tool it needs to call to respond to a query. Test Plan: ``` LLAMA_STACK_CONFIG=fireworks pytest -s -v tests/client-sdk/ --safety-shield meta-llama/Llama-Guard-3-8B ``` Also evaluated on a small benchmark with 20 questions from HotpotQA. With this PR and some prompting, the performance is 77% recall compared to 50% currently. --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/meta-llama/llama-stack/pull/1015). * #1268 * #1239 * __->__ #1015
This commit is contained in:
ehhuang
GitHub
parent
c64f0d5888
commit
bb2690f176
4 changed files with 94 additions and 133 deletions
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@ -441,7 +441,7 @@ class ToolRuntimeRouter(ToolRuntime):
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vector_db_ids: List[str],
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query_config: Optional[RAGQueryConfig] = None,
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) -> RAGQueryResult:
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return await self.routing_table.get_provider_impl("query_from_memory").query(
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return await self.routing_table.get_provider_impl("knowledge_search").query(
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content, vector_db_ids, query_config
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)
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@ -17,7 +17,6 @@ from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple
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from urllib.parse import urlparse
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import httpx
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from pydantic import TypeAdapter
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from llama_stack.apis.agents import (
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AgentConfig,
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@ -62,7 +61,7 @@ from llama_stack.apis.inference import (
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UserMessage,
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)
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from llama_stack.apis.safety import Safety
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from llama_stack.apis.tools import RAGDocument, RAGQueryConfig, ToolGroups, ToolInvocationResult, ToolRuntime
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from llama_stack.apis.tools import RAGDocument, ToolGroups, ToolInvocationResult, ToolRuntime
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from llama_stack.apis.vector_io import VectorIO
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from llama_stack.models.llama.datatypes import (
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BuiltinTool,
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@ -70,7 +69,6 @@ from llama_stack.models.llama.datatypes import (
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ToolParamDefinition,
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)
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from llama_stack.providers.utils.kvstore import KVStore
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from llama_stack.providers.utils.memory.vector_store import concat_interleaved_content
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from llama_stack.providers.utils.telemetry import tracing
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from .persistence import AgentPersistence
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@ -84,7 +82,7 @@ def make_random_string(length: int = 8):
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TOOLS_ATTACHMENT_KEY_REGEX = re.compile(r"__tools_attachment__=(\{.*?\})")
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MEMORY_QUERY_TOOL = "query_from_memory"
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MEMORY_QUERY_TOOL = "knowledge_search"
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WEB_SEARCH_TOOL = "web_search"
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RAG_TOOL_GROUP = "builtin::rag"
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@ -517,93 +515,6 @@ class ChatAgent(ShieldRunnerMixin):
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if documents:
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await self.handle_documents(session_id, documents, input_messages, tool_defs)
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if RAG_TOOL_GROUP in toolgroups and len(input_messages) > 0:
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with tracing.span(MEMORY_QUERY_TOOL) as span:
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step_id = str(uuid.uuid4())
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yield AgentTurnResponseStreamChunk(
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event=AgentTurnResponseEvent(
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payload=AgentTurnResponseStepStartPayload(
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step_type=StepType.tool_execution.value,
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step_id=step_id,
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)
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)
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)
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args = toolgroup_args.get(RAG_TOOL_GROUP, {})
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vector_db_ids = args.get("vector_db_ids", [])
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query_config = args.get("query_config")
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if query_config:
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query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config)
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else:
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# handle someone passing an empty dict
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query_config = RAGQueryConfig()
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session_info = await self.storage.get_session_info(session_id)
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# if the session has a memory bank id, let the memory tool use it
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if session_info.vector_db_id:
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vector_db_ids.append(session_info.vector_db_id)
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yield AgentTurnResponseStreamChunk(
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event=AgentTurnResponseEvent(
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payload=AgentTurnResponseStepProgressPayload(
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step_type=StepType.tool_execution.value,
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step_id=step_id,
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delta=ToolCallDelta(
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parse_status=ToolCallParseStatus.succeeded,
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tool_call=ToolCall(
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call_id="",
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tool_name=MEMORY_QUERY_TOOL,
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arguments={},
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),
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),
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)
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)
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)
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result = await self.tool_runtime_api.rag_tool.query(
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content=concat_interleaved_content([msg.content for msg in input_messages]),
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vector_db_ids=vector_db_ids,
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query_config=query_config,
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)
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retrieved_context = result.content
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yield AgentTurnResponseStreamChunk(
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event=AgentTurnResponseEvent(
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payload=AgentTurnResponseStepCompletePayload(
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step_type=StepType.tool_execution.value,
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step_id=step_id,
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step_details=ToolExecutionStep(
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step_id=step_id,
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turn_id=turn_id,
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tool_calls=[
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ToolCall(
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call_id="",
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tool_name=MEMORY_QUERY_TOOL,
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arguments={},
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)
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],
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tool_responses=[
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ToolResponse(
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call_id="",
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tool_name=MEMORY_QUERY_TOOL,
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content=retrieved_context or [],
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metadata=result.metadata,
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)
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],
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),
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)
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)
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)
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span.set_attribute("input", [m.model_dump_json() for m in input_messages])
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span.set_attribute("output", retrieved_context)
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span.set_attribute("tool_name", MEMORY_QUERY_TOOL)
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# append retrieved_context to the last user message
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for message in input_messages[::-1]:
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if isinstance(message, UserMessage):
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message.context = retrieved_context
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break
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output_attachments = []
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n_iter = 0
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@ -631,9 +542,7 @@ class ChatAgent(ShieldRunnerMixin):
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async for chunk in await self.inference_api.chat_completion(
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self.agent_config.model,
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input_messages,
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tools=[
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tool for tool in tool_defs.values() if tool_to_group.get(tool.tool_name, None) != RAG_TOOL_GROUP
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],
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tools=[tool for tool in tool_defs.values()],
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tool_prompt_format=self.agent_config.tool_config.tool_prompt_format,
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response_format=self.agent_config.response_format,
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stream=True,
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@ -845,8 +754,9 @@ class ChatAgent(ShieldRunnerMixin):
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# TODO: add tool-input touchpoint and a "start" event for this step also
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# but that needs a lot more refactoring of Tool code potentially
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if out_attachment := _interpret_content_as_attachment(result_message.content):
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if (type(result_message.content) is str) and (
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out_attachment := _interpret_content_as_attachment(result_message.content)
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):
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# NOTE: when we push this message back to the model, the model may ignore the
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# attached file path etc. since the model is trained to only provide a user message
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# with the summary. We keep all generated attachments and then attach them to final message
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@ -1072,7 +982,11 @@ async def attachment_message(tempdir: str, urls: List[URL]) -> ToolResponseMessa
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else:
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raise ValueError(f"Unsupported URL {url}")
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content.append(TextContentItem(text=f'# There is a file accessible to you at "{filepath}"\n'))
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content.append(
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TextContentItem(
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text=f'# User provided a file accessible to you at "{filepath}"\nYou can use code_interpreter to load and inspect it.'
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)
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)
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return ToolResponseMessage(
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call_id="",
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@ -10,6 +10,8 @@ import secrets
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import string
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from typing import Any, Dict, List, Optional
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from pydantic import TypeAdapter
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from llama_stack.apis.common.content_types import (
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URL,
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InterleavedContent,
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@ -23,6 +25,7 @@ from llama_stack.apis.tools import (
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RAGToolRuntime,
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ToolDef,
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ToolInvocationResult,
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ToolParameter,
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ToolRuntime,
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)
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from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO
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@ -120,9 +123,14 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
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# sort by score
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chunks, scores = zip(*sorted(zip(chunks, scores, strict=False), key=lambda x: x[1], reverse=True), strict=False)
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chunks = chunks[: query_config.max_chunks]
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tokens = 0
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picked = []
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for c in chunks:
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picked = [
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TextContentItem(
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text=f"knowledge_search tool found {len(chunks)} chunks:\nBEGIN of knowledge_search tool results.\n"
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)
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]
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for i, c in enumerate(chunks):
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metadata = c.metadata
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tokens += metadata["token_count"]
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if tokens > query_config.max_tokens_in_context:
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@ -132,20 +140,13 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
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break
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picked.append(
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TextContentItem(
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text=f"id:{metadata['document_id']}; content:{c.content}",
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text=f"Result {i + 1}:\nDocument_id:{metadata['document_id'][:5]}\nContent: {c.content}\n",
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)
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)
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picked.append(TextContentItem(text="END of knowledge_search tool results.\n"))
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return RAGQueryResult(
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content=[
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TextContentItem(
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text="Here are the retrieved documents for relevant context:\n=== START-RETRIEVED-CONTEXT ===\n",
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),
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*picked,
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TextContentItem(
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text="\n=== END-RETRIEVED-CONTEXT ===\n",
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),
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],
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content=picked,
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metadata={
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"document_ids": [c.metadata["document_id"] for c in chunks[: len(picked)]],
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},
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@ -158,17 +159,40 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
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# by the LLM. The method is only implemented so things like /tools can list without
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# encountering fatals.
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return [
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ToolDef(
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name="query_from_memory",
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description="Retrieve context from memory",
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),
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ToolDef(
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name="insert_into_memory",
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description="Insert documents into memory",
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),
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ToolDef(
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name="knowledge_search",
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description="Search for information in a database.",
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parameters=[
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ToolParameter(
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name="query",
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description="The query to search for. Can be a natural language sentence or keywords.",
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parameter_type="string",
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),
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],
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),
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]
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async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult:
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raise RuntimeError(
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"This toolgroup should not be called generically but only through specific methods of the RAGToolRuntime protocol"
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vector_db_ids = kwargs.get("vector_db_ids", [])
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query_config = kwargs.get("query_config")
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if query_config:
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query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config)
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else:
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# handle someone passing an empty dict
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query_config = RAGQueryConfig()
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query = kwargs["query"]
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result = await self.query(
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content=query,
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vector_db_ids=vector_db_ids,
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query_config=query_config,
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)
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return ToolInvocationResult(
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content=result.content,
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metadata=result.metadata,
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)
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