优化了提示词
This commit is contained in:
chentianrui
@@ -0,0 +1,253 @@
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import logging
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import os
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from typing import Any, Dict, List, Literal, Optional, Set
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from llama_index.core.llms import ChatMessage, MessageRole
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from llama_index.core.schema import NodeWithScore
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from pydantic import BaseModel, Field, validator, field_validator
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from pydantic.alias_generators import to_camel
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logger = logging.getLogger("uvicorn")
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class FileContent(BaseModel):
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type: Literal["text", "ref"]
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# If the file is pure text then the value is be a string
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# otherwise, it's a list of document IDs
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value: str | List[str]
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class File(BaseModel):
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id: str
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content: FileContent
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filename: str
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filesize: int
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filetype: str
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class AnnotationFileData(BaseModel):
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files: List[File] = Field(
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default=[],
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description="List of files",
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)
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class Config:
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json_schema_extra = {
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"example": {
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"csvFiles": [
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{
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"content": "Name, Age\nAlice, 25\nBob, 30",
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"filename": "example.csv",
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"filesize": 123,
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"id": "123",
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"type": "text/csv",
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}
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]
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}
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}
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alias_generator = to_camel
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class Annotation(BaseModel):
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type: str
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data: AnnotationFileData | List[str]
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def to_content(self) -> str | None:
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if self.type == "document_file":
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# We only support generating context content for CSV files for now
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csv_files = [file for file in self.data.files if file.filetype == "csv"]
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if len(csv_files) > 0:
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return "Use data from following CSV raw content\n" + "\n".join(
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[f"```csv\n{csv_file.content.value}\n```" for csv_file in csv_files]
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)
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else:
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logger.warning(
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f"The annotation {self.type} is not supported for generating context content"
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)
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return None
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class Message(BaseModel):
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role: MessageRole
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content: str
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annotations: List[Annotation] | None = None
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class ChatData(BaseModel):
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messages: List[Message]
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data: Any = None
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class Config:
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json_schema_extra = {
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"example": {
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"messages": [
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{
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"role": "user",
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"content": "What standards for letters exist?",
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}
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]
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}
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}
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@field_validator("messages")
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def messages_must_not_be_empty(cls, v):
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if len(v) == 0:
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raise ValueError("Messages must not be empty")
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return v
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def get_last_message_content(self) -> str:
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"""
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Get the content of the last message along with the data content if available.
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Fallback to use data content from previous messages
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"""
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if len(self.messages) == 0:
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raise ValueError("There is not any message in the chat")
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last_message = self.messages[-1]
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message_content = last_message.content
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for message in reversed(self.messages):
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if message.role == MessageRole.USER and message.annotations is not None:
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annotation_contents = filter(
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None,
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[annotation.to_content() for annotation in message.annotations],
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)
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if not annotation_contents:
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continue
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annotation_text = "\n".join(annotation_contents)
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message_content = f"{message_content}\n{annotation_text}"
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break
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return message_content
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def get_history_messages(self) -> List[ChatMessage]:
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"""
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Get the history messages
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"""
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return [
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ChatMessage(role=message.role, content=message.content)
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for message in self.messages[:-1]
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]
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def is_last_message_from_user(self) -> bool:
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return self.messages[-1].role == MessageRole.USER
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def get_chat_document_ids(self) -> List[str]:
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"""
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Get the document IDs from the chat messages
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"""
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document_ids: List[str] = []
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for message in self.messages:
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if message.role == MessageRole.USER and message.annotations is not None:
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for annotation in message.annotations:
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if (
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annotation.type == "document_file"
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and annotation.data.files is not None
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):
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for fi in annotation.data.files:
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if fi.content.type == "ref":
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document_ids += fi.content.value
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return list(set(document_ids))
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class LlamaCloudFile(BaseModel):
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file_name: str
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pipeline_id: str
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def __eq__(self, other):
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if not isinstance(other, LlamaCloudFile):
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return NotImplemented
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return (
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self.file_name == other.file_name and self.pipeline_id == other.pipeline_id
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)
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def __hash__(self):
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return hash((self.file_name, self.pipeline_id))
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class SourceNodes(BaseModel):
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id: str
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metadata: Dict[str, Any]
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score: Optional[float]
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text: str
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url: Optional[str]
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@classmethod
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def from_source_node(cls, source_node: NodeWithScore):
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metadata = source_node.node.metadata
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url = cls.get_url_from_metadata(metadata)
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#text = 'filename' in metadata and metadata['filename'] or source_node.node.node_id
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text = source_node.node.text
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return cls(
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id=source_node.node.node_id,
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metadata=metadata,
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score=source_node.score,
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text=text, # type: ignore
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url=url,
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)
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@classmethod
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def get_url_from_metadata(cls, metadata: Dict[str, Any]) -> str:
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url_prefix = os.getenv("FILESERVER_URL_PREFIX")
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if not url_prefix:
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logger.warning(
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"Warning: FILESERVER_URL_PREFIX not set in environment variables. Can't use file server"
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)
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file_name = metadata.get("file_name")
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if file_name and url_prefix:
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# file_name exists and file server is configured
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pipeline_id = metadata.get("pipeline_id")
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if pipeline_id and metadata.get("private") is None:
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# file is from LlamaCloud and was not ingested locally
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file_name = f"{pipeline_id}${file_name}"
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return f"{url_prefix}/output/llamacloud/{file_name}"
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is_private = metadata.get("private", "false") == "true"
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if is_private:
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return f"{url_prefix}/output/uploaded/{file_name}"
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return f"{url_prefix}/data/{file_name}"
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else:
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# fallback to URL in metadata (e.g. for websites)
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return metadata.get("URL")
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@classmethod
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def from_source_nodes(cls, source_nodes: List[NodeWithScore]):
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return [cls.from_source_node(node) for node in source_nodes]
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@staticmethod
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def get_download_files(nodes: List[NodeWithScore]) -> Set[LlamaCloudFile]:
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source_nodes = SourceNodes.from_source_nodes(nodes)
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llama_cloud_files = [
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LlamaCloudFile(
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file_name=node.metadata.get("file_name"),
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pipeline_id=node.metadata.get("pipeline_id"),
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)
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for node in source_nodes
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if (
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node.metadata.get("private")
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is None # Only download files are from LlamaCloud and were not ingested locally
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and node.metadata.get("pipeline_id") is not None
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and node.metadata.get("file_name") is not None
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)
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]
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# Remove duplicates and return
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return set(llama_cloud_files)
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class Result(BaseModel):
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result: Message
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nodes: List[SourceNodes]
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class ChatConfig(BaseModel):
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starter_questions: Optional[List[str]] = Field(
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default=None,
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description="List of starter questions",
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serialization_alias="starterQuestions",
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)
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class Config:
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json_schema_extra = {
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"example": {
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"starterQuestions": [
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"What standards for letters exist?",
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"What are the requirements for a letter to be considered a letter?",
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]
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}
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}
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