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10 Commits
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@@ -0,0 +1,80 @@
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# The Llama Cloud API key.
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# LLAMA_CLOUD_API_KEY=
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SQL_DATABASE_URL=mysql+pymysql://zjinfo1:Dy2Bcr53Hm5xRkba@110.42.234.166:3306/zjinfo1
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#SQL_DATABASE_URL=mysql+pymysql://zjinfo2:GSKcziSdBixDXwcd@110.42.234.166:3306/zjinfo2
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DASHSCOPE_API_KEY=sk-02c8540e86d84b7ca0e6f4f51bac6e60
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# The provider for the AI models to use.
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MODEL_PROVIDER=dashscope
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# The name of LLM model to use.
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MODEL=qwen-max
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# 是否启用检索重排功能
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ENABLE_RERANK=true
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# Name of the embedding model to use.
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EMBEDDING_MODEL=text-embedding-v2
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# Dimension of the embedding model to use.
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EMBEDDING_DIM=1024
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# The questions to help users get started (multi-line).
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CONVERSATION_STARTERS=本工程指什么?\n总算表有哪些费用?\n项目划分哪些内容构成?\n其他费用表有哪些内容?
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# The OpenAI API key to use.
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# OPENAI_API_KEY=
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# Temperature for sampling from the model.
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# LLM_TEMPERATURE=
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# Maximum number of tokens to generate.
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# LLM_MAX_TOKENS=
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# The number of similar embeddings to return when retrieving documents.
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TOP_K=5
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# The time in milliseconds to wait for the stream to return a response.
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STREAM_TIMEOUT=60000
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# 向量存储数据库类型,目前可选:chroma、qdrant
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VECTOR_STORE_TYPE=chroma
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# The name of the collection in your vector database
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VECTOR_STORE_COLLECTION=default
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# The API endpoint for your vector database
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# VECTOR_STORE_HOST=
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# The port for your vector database
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# VECTOR_STORE_PORT=
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# The local path to the vector database.
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# Specify this if you are using a local vector database.
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# Otherwise, use VECTOR_STORE__HOST and VECTOR_STORE__PORT config above
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VECTOR_STORE_PATH=./storage_vector
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PHOENIX_API_KEY=123456
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PHOENIX_URL=http://localhost:6006/v1/traces
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PHOENIX_PROJECT_NAME=ly_zjapp
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#OTEL_SERVICE_NAME=ly_zjapp
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#OTEL_RESOURCE_ATTRIBUTES=openinference.project.name=ly_zjapp
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# The address to start the backend app.
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APP_HOST=0.0.0.0
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# The port to start the backend app.
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APP_PORT=8000
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FILESERVER_URL_PREFIX=/api/files
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# E2B_API_KEY key is required to run code interpreter tool. Get it here: https://e2b.dev/docs/getting-started/api-key
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# E2B_API_KEY=
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# The system prompt for the AI model.
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SYSTEM_PROMPT="You are a weather forecast agent. You help users to get the weather forecast for a given location.
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-You are a Python interpreter that can run any python code in a secure environment.
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- The python code runs in a Jupyter notebook. Every time you call the 'interpreter' tool, the python code is executed in a separate cell.
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- You are given tasks to complete and you run python code to solve them.
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- It's okay to make multiple calls to interpreter tool. If you get an error or the result is not what you expected, you can call the tool again. Don't give up too soon!
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- Plot visualizations using matplotlib or any other visualization library directly in the notebook.
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- You can install any pip package (if it exists) by running a cell with pip install.
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"
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@@ -2,3 +2,6 @@ __pycache__
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storage
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storage
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.env
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.env
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output
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output
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/storage_vector/
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/.idea/
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/.python-version
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@@ -124,7 +124,7 @@ async def chat_config() -> ChatConfig:
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starter_questions = None
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starter_questions = None
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conversation_starters = os.getenv("CONVERSATION_STARTERS")
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conversation_starters = os.getenv("CONVERSATION_STARTERS")
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if conversation_starters and conversation_starters.strip():
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if conversation_starters and conversation_starters.strip():
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starter_questions = conversation_starters.strip().split("\n")
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starter_questions = conversation_starters.strip().split("\\n")
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return ChatConfig(starter_questions=starter_questions)
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return ChatConfig(starter_questions=starter_questions)
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@@ -4,7 +4,7 @@ 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.llms import ChatMessage, MessageRole
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from llama_index.core.schema import NodeWithScore
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from llama_index.core.schema import NodeWithScore
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from pydantic import BaseModel, Field, validator
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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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from pydantic.alias_generators import to_camel
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logger = logging.getLogger("uvicorn")
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logger = logging.getLogger("uvicorn")
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@@ -89,7 +89,7 @@ class ChatData(BaseModel):
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}
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}
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}
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}
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@validator("messages")
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@field_validator("messages")
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def messages_must_not_be_empty(cls, v):
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def messages_must_not_be_empty(cls, v):
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if len(v) == 0:
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if len(v) == 0:
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raise ValueError("Messages must not be empty")
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raise ValueError("Messages must not be empty")
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@@ -173,7 +173,8 @@ class SourceNodes(BaseModel):
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def from_source_node(cls, source_node: NodeWithScore):
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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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metadata = source_node.node.metadata
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url = cls.get_url_from_metadata(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 = '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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return cls(
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id=source_node.node.node_id,
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id=source_node.node.node_id,
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metadata=metadata,
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metadata=metadata,
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@@ -6,10 +6,10 @@ from llama_index.core.settings import Settings
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from pydantic import BaseModel
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from pydantic import BaseModel
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NEXT_QUESTIONS_SUGGESTION_PROMPT = PromptTemplate(
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NEXT_QUESTIONS_SUGGESTION_PROMPT = PromptTemplate(
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"You're a helpful assistant! Your task is to suggest the next question that user might ask. "
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"你是一个乐于助人的助手!你的任务是对用户可能会问的下一个问题给出建议。 "
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"\nHere is the conversation history"
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"\n这是对话历史记录"
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"\n---------------------\n{conversation}\n---------------------"
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"\n---------------------\n{conversation}\n---------------------"
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"Given the conversation history, please give me $number_of_questions questions that you might ask next!"
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"考虑到对话历史记录,仅限于现在知识库已有内容, 请给我 $number_of_questions 个你接下来可能会问题的问题!"
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)
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)
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N_QUESTION_TO_GENERATE = 3
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N_QUESTION_TO_GENERATE = 3
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Generated
+3979
File diff suppressed because it is too large
Load Diff
@@ -1,4 +1,4 @@
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rmdir /S /Q storage_vector
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rmdir /S /Q storage_vector
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rmdir /S /Q storage
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rmdir /S /Q storage
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C:\Users\liuyue\AppData\Local\pypoetry\Cache\virtualenvs\app-laEO4lY0-py3.11\Scripts\python app/engine/generate.py
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python app/engine/generate.py
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@@ -1,4 +1,4 @@
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rmdir /S /Q storage_vector
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rmdir /S /Q storage_vector
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rmdir /S /Q storage
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rmdir /S /Q storage
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C:\Users\liuyue\AppData\Local\pypoetry\Cache\virtualenvs\app-laEO4lY0-py3.11\Scripts\python tests/query.py
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python tests/query.py
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+1
-1
@@ -1 +1 @@
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C:\Users\liuyue\AppData\Local\pypoetry\Cache\virtualenvs\app-laEO4lY0-py3.11\Scripts\python main.py
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python main.py
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@@ -0,0 +1,10 @@
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# The backend API for chat endpoint.
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#NEXT_PUBLIC_CHAT_API=http://localhost:8000/api/chat
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NEXT_PUBLIC_CHAT_API=http://10.1.6.41:8000/api/chat
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#PHOENIX_SERVER_URL=http://localhost:6006/
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PHOENIX_SERVER_URL=http://10.1.6.41:6006/
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# Let's the user change indexes in LlamaCloud projects
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NEXT_PUBLIC_USE_LLAMACLOUD=false
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@@ -4,7 +4,7 @@ const phoenixUrl = process.env.PHOENIX_SERVER_URL;
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export default function Header() {
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export default function Header() {
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return (
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return (
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<div className="z-10 max-w-5xl w-full items-center justify-between font-mono text-sm lg:flex">
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<div className="z-10 w-full items-center justify-between font-mono text-sm lg:flex">
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<p className="fixed left-0 top-0 flex w-full justify-center border-b border-gray-300 bg-gradient-to-b from-zinc-200 pb-6 pt-8 backdrop-blur-2xl dark:border-neutral-800 dark:bg-zinc-800/30 dark:from-inherit lg:static lg:w-auto lg:rounded-xl lg:border lg:bg-gray-200 lg:p-4 lg:dark:bg-zinc-800/30">
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<p className="fixed left-0 top-0 flex w-full justify-center border-b border-gray-300 bg-gradient-to-b from-zinc-200 pb-6 pt-8 backdrop-blur-2xl dark:border-neutral-800 dark:bg-zinc-800/30 dark:from-inherit lg:static lg:w-auto lg:rounded-xl lg:border lg:bg-gray-200 lg:p-4 lg:dark:bg-zinc-800/30">
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<code className="font-mono font-bold"><a href="javascript:location.reload();">清空当前会话</a></code>
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<code className="font-mono font-bold"><a href="javascript:location.reload();">清空当前会话</a></code>
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</p>
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</p>
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@@ -99,9 +99,8 @@ export default function ChatInput(
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</div>
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</div>
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)}
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)}
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<div className="flex w-full items-start justify-between gap-4 ">
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<div className="flex w-full items-start justify-between gap-4 ">
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<textarea
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<Input
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autoFocus
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autoFocus
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rows={2}
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name="message"
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name="message"
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placeholder="请输入消息"
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placeholder="请输入消息"
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className="flex-1"
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className="flex-1"
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@@ -127,9 +127,26 @@ function NodeInfo({ nodeInfo }: { nodeInfo: NodeInfo }) {
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}
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}
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// node generated by unknown loader, implement renderer by analyzing logged out metadata
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// node generated by unknown loader, implement renderer by analyzing logged out metadata
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// return (
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// <p>
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// 对不起, 未知文件类型. 无法打开当前的来源文件。
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// </p>
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// );
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return (
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return (
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<p>
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<div className="flex items-center my-2">
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对不起, 未知文件类型. 无法打开当前的来源文件。
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<span>{nodeInfo.text}</span>
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</p>
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<Button
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onClick={() => copyToClipboard(nodeInfo.url!)}
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size="icon"
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variant="ghost"
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className="h-12 w-12 shrink-0"
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>
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{isCopied ? (
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<Check className="h-4 w-4" />
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) : (
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<Copy className="h-4 w-4" />
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)}
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</Button>
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</div>
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);
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);
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}
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}
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@@ -10,7 +10,7 @@ export interface ChatHandler {
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data?: any;
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data?: any;
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},
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},
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) => void;
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) => void;
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handleInputChange: (e: React.ChangeEvent<HTMLTextAreaElement>) => void;
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handleInputChange: (e: React.ChangeEvent<HTMLInputElement>) => void;
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reload?: () => void;
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reload?: () => void;
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stop?: () => void;
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stop?: () => void;
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onFileUpload?: (file: File) => Promise<void>;
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onFileUpload?: (file: File) => Promise<void>;
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Generated
+16885
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,3 @@
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ENV_PHOENIX_HOST=0.0.0.0
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ENV_PHOENIX_PORT=6006
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PHOENIX_HOST_ROOT_PATH=./.phoenix/
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@@ -2,4 +2,4 @@ SET ENV_PHOENIX_HOST=0.0.0.0
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SET ENV_PHOENIX_PORT=6006
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SET ENV_PHOENIX_PORT=6006
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SET PHOENIX_HOST_ROOT_PATH=./.phoenix/
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SET PHOENIX_HOST_ROOT_PATH=./.phoenix/
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C:\Users\liuyue\AppData\Local\pypoetry\Cache\virtualenvs\app-pCyqx0Uo-py3.11\Scripts\python phoenixserver.py
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python phoenixserver.py
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Reference in New Issue
Block a user