mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-08-06 02:32:40 +00:00
clean up
This commit is contained in:
Xi Yan
parent
8fa487bdc3
commit
9cf356ccce
1 changed files with 16 additions and 154 deletions
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@ -466,21 +466,6 @@ class ChatAgent(ShieldRunnerMixin):
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)
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span.set_attribute("output", "no violations")
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async def get_raw_document_text(self, document: Document) -> str:
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if isinstance(document.content, URL):
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return await load_data_from_url(document.content.uri)
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elif isinstance(document.content, str):
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if document.content.startswith("http"):
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return await load_data_from_url(document.content)
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else:
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return document.content
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elif isinstance(document.content, TextContentItem):
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return document.content.text
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else:
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raise ValueError(
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f"Unexpected document content type: {type(document.content)}"
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)
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async def _run(
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self,
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session_id: str,
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@ -490,25 +475,14 @@ class ChatAgent(ShieldRunnerMixin):
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stream: bool = False,
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documents: Optional[List[Document]] = None,
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) -> AsyncGenerator:
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# if documents:
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# await self.handle_documents(session_id, documents, input_messages)
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# if document is passed in a turn, we parse the raw text of the document
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# and sent it as a user message
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if documents:
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contexts = []
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for document in documents:
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raw_document_text = await self.get_raw_document_text(document)
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raw_document_text = await get_raw_document_text(document)
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contexts.append(raw_document_text)
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print("documents", contexts)
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input_messages[-1].context = "\n".join(contexts)
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# modify the last user message to include the document
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# input_messages.append(
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# ToolResponseMessage(
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# call_id=str(uuid.uuid4()),
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# content=contexts,
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# )
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# )
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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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@ -982,95 +956,6 @@ class ChatAgent(ShieldRunnerMixin):
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logger.debug(f"tool call {tool_name_str} completed with result: {result}")
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return result
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# async def handle_documents(
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# self,
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# session_id: str,
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# documents: List[Document],
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# input_messages: List[Message],
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# ) -> None:
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# memory_tool = any(tool_def.tool_name == MEMORY_QUERY_TOOL for tool_def in self.tool_defs)
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# code_interpreter_tool = any(tool_def.tool_name == BuiltinTool.code_interpreter for tool_def in self.tool_defs)
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# content_items = []
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# url_items = []
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# pattern = re.compile("^(https?://|file://|data:)")
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# for d in documents:
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# if isinstance(d.content, URL):
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# url_items.append(d.content)
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# elif pattern.match(d.content):
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# url_items.append(URL(uri=d.content))
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# else:
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# content_items.append(d)
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# # Save the contents to a tempdir and use its path as a URL if code interpreter is present
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# if code_interpreter_tool:
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# for c in content_items:
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# temp_file_path = os.path.join(self.tempdir, f"{make_random_string()}.txt")
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# with open(temp_file_path, "w") as temp_file:
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# temp_file.write(c.content)
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# url_items.append(URL(uri=f"file://{temp_file_path}"))
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# if memory_tool and code_interpreter_tool:
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# # if both memory and code_interpreter are available, we download the URLs
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# # and attach the data to the last message.
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# await attachment_message(self.tempdir, url_items, input_messages[-1])
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# # Since memory is present, add all the data to the memory bank
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# await self.add_to_session_vector_db(session_id, documents)
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# elif code_interpreter_tool:
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# # if only code_interpreter is available, we download the URLs to a tempdir
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# # and attach the path to them as a message to inference with the
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# # assumption that the model invokes the code_interpreter tool with the path
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# await attachment_message(self.tempdir, url_items, input_messages[-1])
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# elif memory_tool:
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# # if only memory is available, we load the data from the URLs and content items to the memory bank
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# await self.add_to_session_vector_db(session_id, documents)
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# else:
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# # if no memory or code_interpreter tool is available,
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# # we try to load the data from the URLs and content items as a message to inference
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# # and add it to the last message's context
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# input_messages[-1].context = "\n".join(
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# [doc.content for doc in content_items] + await load_data_from_urls(url_items)
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# )
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# async def _ensure_vector_db(self, session_id: str) -> str:
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# session_info = await self.storage.get_session_info(session_id)
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# if session_info is None:
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# raise ValueError(f"Session {session_id} not found")
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# if session_info.vector_db_id is None:
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# vector_db_id = f"vector_db_{session_id}"
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# # TODO: the semantic for registration is definitely not "creation"
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# # so we need to fix it if we expect the agent to create a new vector db
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# # for each session
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# await self.vector_io_api.register_vector_db(
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# vector_db_id=vector_db_id,
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# embedding_model="all-MiniLM-L6-v2",
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# )
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# await self.storage.add_vector_db_to_session(session_id, vector_db_id)
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# else:
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# vector_db_id = session_info.vector_db_id
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# return vector_db_id
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# async def add_to_session_vector_db(
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# self, session_id: str, data: List[Document]
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# ) -> None:
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# vector_db_id = await self._ensure_vector_db(session_id)
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# documents = [
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# RAGDocument(
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# document_id=str(uuid.uuid4()),
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# content=a.content,
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# mime_type=a.mime_type,
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# metadata={},
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# )
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# for a in data
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# ]
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# await self.tool_runtime_api.rag_tool.insert(
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# documents=documents,
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# vector_db_id=vector_db_id,
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# chunk_size_in_tokens=512,
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# )
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async def load_data_from_url(url: str) -> str:
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if url.startswith("http"):
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@ -1080,43 +965,20 @@ async def load_data_from_url(url: str) -> str:
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return resp
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return ""
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# async def attachment_message(
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# tempdir: str, urls: List[URL], message: UserMessage
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# ) -> None:
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# contents = []
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# for url in urls:
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# uri = url.uri
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# if uri.startswith("file://"):
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# filepath = uri[len("file://") :]
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# elif uri.startswith("http"):
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# path = urlparse(uri).path
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# basename = os.path.basename(path)
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# filepath = f"{tempdir}/{make_random_string() + basename}"
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# logger.info(f"Downloading {url} -> {filepath}")
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# async with httpx.AsyncClient() as client:
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# r = await client.get(uri)
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# resp = r.text
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# with open(filepath, "w") as fp:
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# fp.write(resp)
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# else:
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# raise ValueError(f"Unsupported URL {url}")
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# contents.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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# if isinstance(message.content, list):
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# message.content.extend(contents)
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# else:
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# if isinstance(message.content, str):
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# message.content = [TextContentItem(text=message.content)] + contents
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# else:
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# message.content = [message.content] + contents
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async def get_raw_document_text(document: Document) -> str:
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if isinstance(document.content, URL):
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return await load_data_from_url(document.content.uri)
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elif isinstance(document.content, str):
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if document.content.startswith("http"):
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return await load_data_from_url(document.content)
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else:
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return document.content
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elif isinstance(document.content, TextContentItem):
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return document.content.text
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else:
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raise ValueError(
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f"Unexpected document content type: {type(document.content)}"
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)
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def _interpret_content_as_attachment(
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@ -1131,4 +993,4 @@ def _interpret_content_as_attachment(
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mime_type=data["mimetype"],
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)
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return None
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return None
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