fix: video example, remove finetuning\knowledge\document module (#17)
Co-authored-by: zhengweijun <weijun.zheng@aminer.cn>
This commit is contained in:
wellenzheng
GitHub
parent
d344be2ca2
commit
508eaf4f4c
44 changed files with 173 additions and 1644 deletions
2
.env
2
.env
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@ -1,2 +1,2 @@
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PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring
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ZAI_API_KEY={your apikey}
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ZAI_API_KEY={your apikey}
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@ -180,11 +180,12 @@ def ofZhipu():
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if __name__ == '__main__':
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# completion()
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# completion_with_websearch()
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# multi_modal_chat()
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# role_play()
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# assistant_conversation()
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# video_generation()
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ofZai()
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ofZhipu()
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# completion_with_stream()
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# completion_with_websearch()
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multi_modal_chat()
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# role_play()
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# assistant_conversation()
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# video_generation()
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# ofZai()
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# ofZhipu()
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120
examples/function_call_example.py
Normal file
120
examples/function_call_example.py
Normal file
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@ -0,0 +1,120 @@
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from zai import ZhipuAiClient
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import json
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client = ZhipuAiClient()
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def get_flight_number(date: str, departure: str, destination: str):
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flight_number = {
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"Beijing": {
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"Shanghai": "1234",
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"Guangzhou": "5678",
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},
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"Shanghai": {
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"Beijing": "4321",
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"Guangzhou": "8765",
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}
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}
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return {"flight_number": flight_number[departure][destination]}
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def get_ticket_price(date: str, flight_number: str):
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return {"ticket_price": "1000"}
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def parse_function_call(model_response, messages):
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# Handle function call results. According to the model's returned parameters, call the corresponding function.
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# After getting the function result, construct a tool message and call the model again, passing the function result as input.
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# The model will return the function result to the user in natural language.
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if model_response.choices[0].message.tool_calls:
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tool_call = model_response.choices[0].message.tool_calls[0]
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args = tool_call.function.arguments
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function_result = {}
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if tool_call.function.name == "get_flight_number":
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function_result = get_flight_number(**json.loads(args))
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if tool_call.function.name == "get_ticket_price":
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function_result = get_ticket_price(**json.loads(args))
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messages.append({
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"role": "tool",
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"content": f"{json.dumps(function_result)}",
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"tool_call_id": tool_call.id
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})
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response = client.chat.completions.create(
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model="glm-4", # Specify the model name to use
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messages=messages,
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tools=tools,
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)
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print(response.choices[0].message)
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messages.append(response.choices[0].message.model_dump())
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messages = []
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_flight_number",
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"description": "Query the flight number for a given date, departure, and destination",
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"parameters": {
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"type": "object",
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"properties": {
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"departure": {
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"description": "Departure city",
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"type": "string"
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},
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"destination": {
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"description": "Destination city",
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"type": "string"
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},
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"date": {
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"description": "Date",
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"type": "string",
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}
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},
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"required": ["departure", "destination", "date"]
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},
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}
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},
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{
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"type": "function",
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"function": {
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"name": "get_ticket_price",
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"description": "Query the ticket price for a specific flight on a specific date",
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"parameters": {
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"type": "object",
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"properties": {
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"flight_number": {
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"description": "Flight number",
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"type": "string"
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},
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"date": {
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"description": "Date",
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"type": "string",
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}
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},
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"required": ["flight_number", "date"]
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},
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}
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},
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]
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# Clear conversation
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messages = []
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messages.append({"role": "system", "content": "Do not assume or guess the values of function parameters. If the user's description is unclear, ask the user to provide the necessary information."})
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messages.append({"role": "user", "content": "Help me check the flights from Beijing to Guangzhou on January 23."})
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response = client.chat.completions.create(
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model="glm-4", # Specify the model name to use
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messages=messages,
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tools=tools,
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)
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print(response.choices[0].message)
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messages.append(response.choices[0].message.model_dump())
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parse_function_call(response, messages)
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messages.append({"role": "user", "content": "What is the price of flight 8321?"})
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response = client.chat.completions.create(
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model="glm-4", # Specify the model name to use
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messages=messages,
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tools=tools,
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)
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print(response.choices[0].message)
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messages.append(response.choices[0].message.model_dump())
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parse_function_call(response, messages)
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@ -29,7 +29,7 @@ def stream_web_search_example():
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stream=True
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)
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for chunk in response:
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print(chunk.choices[0].delta)
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print(chunk.choices[0].delta.content, end="", flush=True)
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def sync_example():
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print("=== GLM-4 Synchronous Example ===")
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "zai-sdk"
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version = "0.0.1b3"
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version = "0.0.1b4"
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description = "A SDK library for accessing big model apis from Z.ai"
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authors = ["Z.ai"]
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readme = "README.md"
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@ -16,9 +16,7 @@ if TYPE_CHECKING:
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from zai.api_resource.chat import Chat
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from zai.api_resource.embeddings import Embeddings
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from zai.api_resource.files import Files
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from zai.api_resource.fine_tuning import FineTuning
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from zai.api_resource.images import Images
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from zai.api_resource.knowledge import Knowledge
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from zai.api_resource.moderations import Moderations
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from zai.api_resource.tools import Tools
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from zai.api_resource.videos import Videos
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@ -132,24 +130,12 @@ class BaseClient(HttpClient):
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return Embeddings(self)
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@cached_property
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def fine_tuning(self) -> FineTuning:
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from zai.api_resource.fine_tuning import FineTuning
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return FineTuning(self)
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@cached_property
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def batches(self) -> Batches:
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from zai.api_resource.batch import Batches
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return Batches(self)
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@cached_property
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def knowledge(self) -> Knowledge:
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from zai.api_resource.knowledge import Knowledge
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return Knowledge(self)
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@cached_property
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def tools(self) -> Tools:
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from zai.api_resource.tools import Tools
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@ -1,2 +1,2 @@
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__title__ = 'Z.ai'
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__version__ = '0.0.1b2'
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__version__ = '0.0.1b4'
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@ -11,9 +11,7 @@ from .chat import (
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)
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from .embeddings import Embeddings
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from .files import Files, FilesWithRawResponse
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from .fine_tuning import FineTuning
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from .images import Images
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from .knowledge import Knowledge
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from .moderations import Moderations
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from .tools import Tools
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from .videos import (
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@ -30,9 +28,7 @@ __all__ = [
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'Embeddings',
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'Files',
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'FilesWithRawResponse',
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'FineTuning',
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'Batches',
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'Knowledge',
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'Tools',
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'Assistant',
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'Audio',
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@ -1,5 +0,0 @@
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from .fine_tuning import FineTuning
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from .jobs import Jobs
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from .models import FineTunedModels
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__all__ = ['Jobs', 'FineTunedModels', 'FineTuning']
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@ -1,19 +0,0 @@
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from typing import TYPE_CHECKING
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from zai.core import BaseAPI, cached_property
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from .jobs import Jobs
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from .models import FineTunedModels
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if TYPE_CHECKING:
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pass
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class FineTuning(BaseAPI):
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@cached_property
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def jobs(self) -> Jobs:
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return Jobs(self._client)
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@cached_property
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def models(self) -> FineTunedModels:
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return FineTunedModels(self._client)
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@ -1,3 +0,0 @@
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from .jobs import Jobs
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__all__ = ['Jobs']
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@ -1,152 +0,0 @@
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from __future__ import annotations
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from typing import TYPE_CHECKING, Optional
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import httpx
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from zai.core import (
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NOT_GIVEN,
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BaseAPI,
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Body,
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Headers,
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NotGiven,
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make_request_options,
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)
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from zai.types.fine_tuning import (
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FineTuningJob,
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FineTuningJobEvent,
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ListOfFineTuningJob,
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job_create_params,
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)
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if TYPE_CHECKING:
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from zai._client import ZaiClient
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class Jobs(BaseAPI):
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def __init__(self, client: 'ZaiClient') -> None:
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super().__init__(client)
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def create(
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self,
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*,
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model: str,
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training_file: str,
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hyperparameters: job_create_params.Hyperparameters | NotGiven = NOT_GIVEN,
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suffix: Optional[str] | NotGiven = NOT_GIVEN,
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request_id: Optional[str] | NotGiven = NOT_GIVEN,
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validation_file: Optional[str] | NotGiven = NOT_GIVEN,
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extra_headers: Headers | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> FineTuningJob:
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return self._post(
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'/fine_tuning/jobs',
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body={
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'model': model,
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'training_file': training_file,
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'hyperparameters': hyperparameters,
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'suffix': suffix,
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'validation_file': validation_file,
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'request_id': request_id,
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},
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options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
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cast_type=FineTuningJob,
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)
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def retrieve(
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self,
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fine_tuning_job_id: str,
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*,
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extra_headers: Headers | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> FineTuningJob:
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return self._get(
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f'/fine_tuning/jobs/{fine_tuning_job_id}',
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options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
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cast_type=FineTuningJob,
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)
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def list(
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self,
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*,
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after: str | NotGiven = NOT_GIVEN,
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limit: int | NotGiven = NOT_GIVEN,
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extra_headers: Headers | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> ListOfFineTuningJob:
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return self._get(
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'/fine_tuning/jobs',
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cast_type=ListOfFineTuningJob,
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options=make_request_options(
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extra_headers=extra_headers,
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extra_body=extra_body,
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timeout=timeout,
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query={
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'after': after,
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'limit': limit,
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},
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),
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)
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def cancel(
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self,
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fine_tuning_job_id: str,
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*,
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# Use the following arguments if you need to pass additional parameters to the API
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# that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client
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# or passed to this method.
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extra_headers: Headers | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> FineTuningJob:
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if not fine_tuning_job_id:
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raise ValueError(f'Expected a non-empty value for `fine_tuning_job_id` but received {fine_tuning_job_id!r}')
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return self._post(
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f'/fine_tuning/jobs/{fine_tuning_job_id}/cancel',
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options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
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cast_type=FineTuningJob,
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)
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def list_events(
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self,
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fine_tuning_job_id: str,
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*,
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after: str | NotGiven = NOT_GIVEN,
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limit: int | NotGiven = NOT_GIVEN,
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extra_headers: Headers | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> FineTuningJobEvent:
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return self._get(
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f'/fine_tuning/jobs/{fine_tuning_job_id}/events',
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cast_type=FineTuningJobEvent,
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options=make_request_options(
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extra_headers=extra_headers,
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extra_body=extra_body,
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timeout=timeout,
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query={
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'after': after,
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'limit': limit,
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},
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),
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)
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def delete(
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self,
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fine_tuning_job_id: str,
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*,
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extra_headers: Headers | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> FineTuningJob:
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if not fine_tuning_job_id:
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raise ValueError(f'Expected a non-empty value for `fine_tuning_job_id` but received {fine_tuning_job_id!r}')
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return self._delete(
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f'/fine_tuning/jobs/{fine_tuning_job_id}',
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options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
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cast_type=FineTuningJob,
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)
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@ -1,3 +0,0 @@
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from .fine_tuned_models import FineTunedModels
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__all__ = ['FineTunedModels']
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@ -1,39 +0,0 @@
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from __future__ import annotations
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from typing import TYPE_CHECKING
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import httpx
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from zai.core import (
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NOT_GIVEN,
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BaseAPI,
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Body,
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Headers,
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NotGiven,
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make_request_options,
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)
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from zai.types.fine_tuning.models import FineTunedModelsStatus
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if TYPE_CHECKING:
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from zai._client import ZaiClient
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class FineTunedModels(BaseAPI):
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def __init__(self, client: 'ZaiClient') -> None:
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super().__init__(client)
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def delete(
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self,
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fine_tuned_model: str,
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*,
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extra_headers: Headers | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> FineTunedModelsStatus:
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if not fine_tuned_model:
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raise ValueError(f'Expected a non-empty value for `fine_tuned_model` but received {fine_tuned_model!r}')
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return self._delete(
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f'fine_tuning/fine_tuned_models/{fine_tuned_model}',
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options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
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cast_type=FineTunedModelsStatus,
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)
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@ -1,3 +0,0 @@
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from .knowledge import Knowledge
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__all__ = ['Knowledge']
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@ -1,3 +0,0 @@
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from .document import Document
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__all__ = ['Document']
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@ -1,254 +0,0 @@
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from __future__ import annotations
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from typing import TYPE_CHECKING, Dict, List, Mapping, Optional, cast
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import httpx
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from typing_extensions import Literal
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from zai.core import (
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NOT_GIVEN,
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BaseAPI,
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Body,
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FileTypes,
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Headers,
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NotGiven,
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deepcopy_minimal,
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extract_files,
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make_request_options,
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maybe_transform,
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)
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from zai.types.files import UploadDetail, file_create_params
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from zai.types.knowledge.document import (
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DocumentData,
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DocumentObject,
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document_edit_params,
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document_list_params,
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)
|
||||
from zai.types.knowledge.document.document_list_resp import DocumentPage
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from zai._client import ZaiClient
|
||||
|
||||
|
||||
class Document(BaseAPI):
|
||||
"""
|
||||
API resource for document operations in knowledge bases
|
||||
"""
|
||||
def __init__(self, client: 'ZaiClient') -> None:
|
||||
super().__init__(client)
|
||||
|
||||
def create(
|
||||
self,
|
||||
*,
|
||||
file: FileTypes = None,
|
||||
custom_separator: Optional[List[str]] = None,
|
||||
upload_detail: List[UploadDetail] = None,
|
||||
purpose: Literal['retrieval'],
|
||||
knowledge_id: str = None,
|
||||
sentence_size: int = None,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> DocumentObject:
|
||||
"""
|
||||
Create a new document in a knowledge base
|
||||
|
||||
Arguments:
|
||||
file (FileTypes): File to upload
|
||||
custom_separator (Optional[List[str]]): Custom separators for text splitting
|
||||
upload_detail (List[UploadDetail]): Upload details for the document
|
||||
purpose (Literal['retrieval']): Purpose of the document
|
||||
knowledge_id (str): ID of the knowledge base
|
||||
sentence_size (int): Size of sentences for splitting
|
||||
extra_headers (Headers): Additional headers to send
|
||||
extra_body (Body): Additional body parameters
|
||||
timeout (float | httpx.Timeout): Request timeout
|
||||
"""
|
||||
if not file and not upload_detail:
|
||||
raise ValueError('At least one of `file` and `upload_detail` must be provided.')
|
||||
body = deepcopy_minimal(
|
||||
{
|
||||
'file': file,
|
||||
'upload_detail': upload_detail,
|
||||
'purpose': purpose,
|
||||
'custom_separator': custom_separator,
|
||||
'knowledge_id': knowledge_id,
|
||||
'sentence_size': sentence_size,
|
||||
}
|
||||
)
|
||||
files = extract_files(cast(Mapping[str, object], body), paths=[['file']])
|
||||
if files:
|
||||
# It should be noted that the actual Content-Type header that will be
|
||||
# sent to the server will contain a `boundary` parameter, e.g.
|
||||
# multipart/form-data; boundary=---abc--
|
||||
extra_headers = {
|
||||
'Content-Type': 'multipart/form-data',
|
||||
**(extra_headers or {}),
|
||||
}
|
||||
return self._post(
|
||||
'/files',
|
||||
body=maybe_transform(body, file_create_params.FileCreateParams),
|
||||
files=files,
|
||||
options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
|
||||
cast_type=DocumentObject,
|
||||
)
|
||||
|
||||
def edit(
|
||||
self,
|
||||
document_id: str,
|
||||
knowledge_type: str,
|
||||
*,
|
||||
custom_separator: Optional[List[str]] = None,
|
||||
sentence_size: Optional[int] = None,
|
||||
callback_url: Optional[str] = None,
|
||||
callback_header: Optional[Dict[str, str]] = None,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> httpx.Response:
|
||||
"""
|
||||
|
||||
Args:
|
||||
document_id: Knowledge ID
|
||||
knowledge_type: Knowledge type:
|
||||
1: Article knowledge: supports pdf, url, docx
|
||||
2. Q&A knowledge-document: supports pdf, url, docx
|
||||
3. Q&A knowledge-table: supports xlsx
|
||||
4. Product library-table: supports xlsx
|
||||
5. Custom: supports pdf, url, docx
|
||||
extra_headers: Send extra headers
|
||||
|
||||
extra_body: Add additional JSON properties to the request
|
||||
|
||||
timeout: Override the client-level default timeout for this request, in seconds
|
||||
:param knowledge_type:
|
||||
:param document_id:
|
||||
:param timeout:
|
||||
:param extra_body:
|
||||
:param callback_header:
|
||||
:param sentence_size:
|
||||
:param extra_headers:
|
||||
:param callback_url:
|
||||
:param custom_separator:
|
||||
"""
|
||||
if not document_id:
|
||||
raise ValueError(f'Expected a non-empty value for `document_id` but received {document_id!r}')
|
||||
|
||||
body = deepcopy_minimal(
|
||||
{
|
||||
'id': document_id,
|
||||
'knowledge_type': knowledge_type,
|
||||
'custom_separator': custom_separator,
|
||||
'sentence_size': sentence_size,
|
||||
'callback_url': callback_url,
|
||||
'callback_header': callback_header,
|
||||
}
|
||||
)
|
||||
|
||||
return self._put(
|
||||
f'/document/{document_id}',
|
||||
body=maybe_transform(body, document_edit_params.DocumentEditParams),
|
||||
options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
|
||||
cast_type=httpx.Response,
|
||||
)
|
||||
|
||||
def list(
|
||||
self,
|
||||
knowledge_id: str,
|
||||
*,
|
||||
purpose: str | NotGiven = NOT_GIVEN,
|
||||
page: str | NotGiven = NOT_GIVEN,
|
||||
limit: str | NotGiven = NOT_GIVEN,
|
||||
order: Literal['desc', 'asc'] | NotGiven = NOT_GIVEN,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> DocumentPage:
|
||||
"""
|
||||
List documents in a knowledge base
|
||||
|
||||
Arguments:
|
||||
knowledge_id (str): ID of the knowledge base
|
||||
purpose (str): Purpose filter for documents
|
||||
page (str): Page number for pagination
|
||||
limit (str): Number of documents per page
|
||||
order (Literal['desc', 'asc']): Sort order for results
|
||||
extra_headers (Headers): Additional headers to send
|
||||
extra_body (Body): Additional body parameters
|
||||
timeout (float | httpx.Timeout): Request timeout
|
||||
"""
|
||||
return self._get(
|
||||
'/files',
|
||||
options=make_request_options(
|
||||
extra_headers=extra_headers,
|
||||
extra_body=extra_body,
|
||||
timeout=timeout,
|
||||
query=maybe_transform(
|
||||
{
|
||||
'knowledge_id': knowledge_id,
|
||||
'purpose': purpose,
|
||||
'page': page,
|
||||
'limit': limit,
|
||||
'order': order,
|
||||
},
|
||||
document_list_params.DocumentListParams,
|
||||
),
|
||||
),
|
||||
cast_type=DocumentPage,
|
||||
)
|
||||
|
||||
def delete(
|
||||
self,
|
||||
document_id: str,
|
||||
*,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> httpx.Response:
|
||||
"""
|
||||
Delete a file.
|
||||
|
||||
Args:
|
||||
|
||||
document_id: Knowledge ID
|
||||
extra_headers: Send extra headers
|
||||
|
||||
extra_body: Add additional JSON properties to the request
|
||||
|
||||
timeout: Override the client-level default timeout for this request, in seconds
|
||||
"""
|
||||
if not document_id:
|
||||
raise ValueError(f'Expected a non-empty value for `document_id` but received {document_id!r}')
|
||||
|
||||
return self._delete(
|
||||
f'/document/{document_id}',
|
||||
options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
|
||||
cast_type=httpx.Response,
|
||||
)
|
||||
|
||||
def retrieve(
|
||||
self,
|
||||
document_id: str,
|
||||
*,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> DocumentData:
|
||||
"""
|
||||
|
||||
Args:
|
||||
extra_headers: Send extra headers
|
||||
|
||||
extra_body: Add additional JSON properties to the request
|
||||
|
||||
timeout: Override the client-level default timeout for this request, in seconds
|
||||
"""
|
||||
|
||||
if not document_id:
|
||||
raise ValueError(f'Expected a non-empty value for `document_id` but received {document_id!r}')
|
||||
|
||||
return self._get(
|
||||
f'/document/{document_id}',
|
||||
options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
|
||||
cast_type=DocumentData,
|
||||
)
|
||||
|
|
@ -1,178 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Optional
|
||||
|
||||
import httpx
|
||||
from typing_extensions import Literal
|
||||
|
||||
from zai.core import (
|
||||
NOT_GIVEN,
|
||||
BaseAPI,
|
||||
Body,
|
||||
Headers,
|
||||
NotGiven,
|
||||
cached_property,
|
||||
deepcopy_minimal,
|
||||
make_request_options,
|
||||
maybe_transform,
|
||||
)
|
||||
from zai.types.knowledge import (
|
||||
KnowledgeInfo,
|
||||
KnowledgeUsed,
|
||||
knowledge_create_params,
|
||||
knowledge_list_params,
|
||||
)
|
||||
from zai.types.knowledge.knowledge_list_resp import KnowledgePage
|
||||
|
||||
from .document import Document
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from zai._client import ZaiClient
|
||||
|
||||
|
||||
class Knowledge(BaseAPI):
|
||||
def __init__(self, client: 'ZaiClient') -> None:
|
||||
super().__init__(client)
|
||||
|
||||
@cached_property
|
||||
def document(self) -> Document:
|
||||
return Document(self._client)
|
||||
|
||||
def create(
|
||||
self,
|
||||
embedding_id: int,
|
||||
name: str,
|
||||
*,
|
||||
customer_identifier: Optional[str] = None,
|
||||
description: Optional[str] = None,
|
||||
background: Optional[Literal['blue', 'red', 'orange', 'purple', 'sky']] = None,
|
||||
icon: Optional[Literal['question', 'book', 'seal', 'wrench', 'tag', 'horn', 'house']] = None,
|
||||
bucket_id: Optional[str] = None,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> KnowledgeInfo:
|
||||
body = deepcopy_minimal(
|
||||
{
|
||||
'embedding_id': embedding_id,
|
||||
'name': name,
|
||||
'customer_identifier': customer_identifier,
|
||||
'description': description,
|
||||
'background': background,
|
||||
'icon': icon,
|
||||
'bucket_id': bucket_id,
|
||||
}
|
||||
)
|
||||
return self._post(
|
||||
'/knowledge',
|
||||
body=maybe_transform(body, knowledge_create_params.KnowledgeBaseParams),
|
||||
options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
|
||||
cast_type=KnowledgeInfo,
|
||||
)
|
||||
|
||||
def modify(
|
||||
self,
|
||||
knowledge_id: str,
|
||||
embedding_id: int,
|
||||
*,
|
||||
name: str,
|
||||
description: Optional[str] = None,
|
||||
background: Optional[Literal['blue', 'red', 'orange', 'purple', 'sky']] = None,
|
||||
icon: Optional[Literal['question', 'book', 'seal', 'wrench', 'tag', 'horn', 'house']] = None,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> httpx.Response:
|
||||
body = deepcopy_minimal(
|
||||
{
|
||||
'id': knowledge_id,
|
||||
'embedding_id': embedding_id,
|
||||
'name': name,
|
||||
'description': description,
|
||||
'background': background,
|
||||
'icon': icon,
|
||||
}
|
||||
)
|
||||
return self._put(
|
||||
f'/knowledge/{knowledge_id}',
|
||||
body=maybe_transform(body, knowledge_create_params.KnowledgeBaseParams),
|
||||
options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
|
||||
cast_type=httpx.Response,
|
||||
)
|
||||
|
||||
def query(
|
||||
self,
|
||||
*,
|
||||
page: int | NotGiven = 1,
|
||||
size: int | NotGiven = 10,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> KnowledgePage:
|
||||
return self._get(
|
||||
'/knowledge',
|
||||
options=make_request_options(
|
||||
extra_headers=extra_headers,
|
||||
extra_body=extra_body,
|
||||
timeout=timeout,
|
||||
query=maybe_transform(
|
||||
{
|
||||
'page': page,
|
||||
'size': size,
|
||||
},
|
||||
knowledge_list_params.KnowledgeListParams,
|
||||
),
|
||||
),
|
||||
cast_type=KnowledgePage,
|
||||
)
|
||||
|
||||
def delete(
|
||||
self,
|
||||
knowledge_id: str,
|
||||
*,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> httpx.Response:
|
||||
"""
|
||||
Delete a knowledge.
|
||||
|
||||
Args:
|
||||
knowledge_id: Knowledge ID
|
||||
extra_headers: Send extra headers
|
||||
|
||||
extra_body: Add additional JSON properties to the request
|
||||
|
||||
timeout: Override the client-level default timeout for this request, in seconds
|
||||
"""
|
||||
if not knowledge_id:
|
||||
raise ValueError('Expected a non-empty value for `knowledge_id`')
|
||||
|
||||
return self._delete(
|
||||
f'/knowledge/{knowledge_id}',
|
||||
options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
|
||||
cast_type=httpx.Response,
|
||||
)
|
||||
|
||||
def used(
|
||||
self,
|
||||
*,
|
||||
extra_headers: Headers | None = None,
|
||||
extra_body: Body | None = None,
|
||||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||||
) -> KnowledgeUsed:
|
||||
"""
|
||||
Returns the contents of the specified file.
|
||||
|
||||
Args:
|
||||
extra_headers: Send extra headers
|
||||
|
||||
extra_body: Add additional JSON properties to the request
|
||||
|
||||
timeout: Override the client-level default timeout for this request, in seconds
|
||||
"""
|
||||
return self._get(
|
||||
'/knowledge/capacity',
|
||||
options=make_request_options(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
|
||||
cast_type=KnowledgeUsed,
|
||||
)
|
||||
|
|
@ -46,7 +46,7 @@ class AgentsCompletionChoice(BaseModel):
|
|||
message: AgentsCompletionMessage
|
||||
|
||||
|
||||
class AgentsError:
|
||||
class AgentsError(BaseModel):
|
||||
"""
|
||||
Represents an error in agents completion.
|
||||
|
||||
|
|
|
|||
|
|
@ -43,7 +43,7 @@ class AgentsCompletionUsage(BaseModel):
|
|||
total_tokens: int
|
||||
|
||||
|
||||
class AgentsError:
|
||||
class AgentsError(BaseModel):
|
||||
"""
|
||||
Represents an error in agents completion chunk.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,16 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from .fine_tuning_job import Error, FineTuningJob, Hyperparameters, ListOfFineTuningJob
|
||||
from .fine_tuning_job_event import FineTuningJobEvent, JobEvent, Metric
|
||||
from .job_create_params import Hyperparameters as JobHyperparameters
|
||||
|
||||
__all__ = [
|
||||
'FineTuningJob',
|
||||
'Error',
|
||||
'Hyperparameters',
|
||||
'ListOfFineTuningJob',
|
||||
'FineTuningJobEvent',
|
||||
'Metric',
|
||||
'JobEvent',
|
||||
'JobHyperparameters',
|
||||
]
|
||||
|
|
@ -1,76 +0,0 @@
|
|||
from typing import List, Optional, Union
|
||||
|
||||
from zai.core import BaseModel
|
||||
|
||||
|
||||
class Error(BaseModel):
|
||||
"""
|
||||
Error information for fine-tuning job
|
||||
|
||||
Attributes:
|
||||
code: Error code
|
||||
message: Error message description
|
||||
param: Optional parameter that caused the error
|
||||
"""
|
||||
code: str
|
||||
message: str
|
||||
param: Optional[str] = None
|
||||
|
||||
|
||||
class Hyperparameters(BaseModel):
|
||||
"""
|
||||
Hyperparameters for fine-tuning job
|
||||
|
||||
Attributes:
|
||||
n_epochs: Number of training epochs
|
||||
"""
|
||||
n_epochs: Union[str, int, None] = None
|
||||
|
||||
|
||||
class FineTuningJob(BaseModel):
|
||||
"""
|
||||
Fine-tuning job information
|
||||
|
||||
Attributes:
|
||||
id: Unique identifier for the fine-tuning job
|
||||
request_id: Request identifier
|
||||
created_at: Timestamp when the job was created
|
||||
error: Error information if the job failed
|
||||
fine_tuned_model: Name of the fine-tuned model
|
||||
finished_at: Timestamp when the job finished
|
||||
hyperparameters: Hyperparameters used for training
|
||||
model: Base model used for fine-tuning
|
||||
object: Object type identifier
|
||||
result_files: List of result file paths
|
||||
status: Current status of the fine-tuning job
|
||||
trained_tokens: Number of tokens used for training
|
||||
training_file: Path to the training file
|
||||
validation_file: Path to the validation file
|
||||
"""
|
||||
id: Optional[str] = None
|
||||
request_id: Optional[str] = None
|
||||
created_at: Optional[int] = None
|
||||
error: Optional[Error] = None
|
||||
fine_tuned_model: Optional[str] = None
|
||||
finished_at: Optional[int] = None
|
||||
hyperparameters: Optional[Hyperparameters] = None
|
||||
model: Optional[str] = None
|
||||
object: Optional[str] = None
|
||||
result_files: List[str]
|
||||
status: str
|
||||
trained_tokens: Optional[int] = None
|
||||
training_file: str
|
||||
validation_file: Optional[str] = None
|
||||
|
||||
|
||||
class ListOfFineTuningJob(BaseModel):
|
||||
"""
|
||||
List of fine-tuning jobs response
|
||||
|
||||
Attributes:
|
||||
object: Object type identifier
|
||||
data: List of fine-tuning job objects
|
||||
"""
|
||||
object: Optional[str] = None
|
||||
data: List[FineTuningJob]
|
||||
has_more: Optional[bool] = None
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
from typing import List, Optional, Union
|
||||
|
||||
from zai.core import BaseModel
|
||||
|
||||
|
||||
class Metric(BaseModel):
|
||||
epoch: Optional[Union[str, int, float]] = None
|
||||
current_steps: Optional[int] = None
|
||||
total_steps: Optional[int] = None
|
||||
elapsed_time: Optional[str] = None
|
||||
remaining_time: Optional[str] = None
|
||||
trained_tokens: Optional[int] = None
|
||||
loss: Optional[Union[str, int, float]] = None
|
||||
eval_loss: Optional[Union[str, int, float]] = None
|
||||
acc: Optional[Union[str, int, float]] = None
|
||||
eval_acc: Optional[Union[str, int, float]] = None
|
||||
learning_rate: Optional[Union[str, int, float]] = None
|
||||
|
||||
|
||||
class JobEvent(BaseModel):
|
||||
object: Optional[str] = None
|
||||
id: Optional[str] = None
|
||||
type: Optional[str] = None
|
||||
created_at: Optional[int] = None
|
||||
level: Optional[str] = None
|
||||
message: Optional[str] = None
|
||||
data: Optional[Metric] = None
|
||||
|
||||
|
||||
class FineTuningJobEvent(BaseModel):
|
||||
object: Optional[str] = None
|
||||
data: List[JobEvent]
|
||||
has_more: Optional[bool] = None
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import Union
|
||||
|
||||
from typing_extensions import Literal, TypedDict
|
||||
|
||||
|
||||
class Hyperparameters(TypedDict, total=False):
|
||||
"""
|
||||
Hyperparameters for fine-tuning job configuration
|
||||
|
||||
Attributes:
|
||||
batch_size: The batch size to use for training (can be 'auto' or an integer)
|
||||
learning_rate_multiplier: The learning rate multiplier for training (can be 'auto' or a float)
|
||||
n_epochs: The number of epochs to train for (can be 'auto' or an integer)
|
||||
"""
|
||||
batch_size: Union[Literal['auto'], int]
|
||||
learning_rate_multiplier: Union[Literal['auto'], float]
|
||||
n_epochs: Union[Literal['auto'], int]
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
from .fine_tuned_models import FineTunedModelsStatus
|
||||
|
||||
__all__ = ['FineTunedModelsStatus']
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
from typing import ClassVar
|
||||
|
||||
from zai.core import PYDANTIC_V2, BaseModel, ConfigDict
|
||||
|
||||
|
||||
class FineTunedModelsStatus(BaseModel):
|
||||
"""
|
||||
Fine-tuned model status
|
||||
|
||||
Attributes:
|
||||
request_id (str): Request id
|
||||
model_name (str): Model name
|
||||
delete_status (str): Delete status: deleting (deleting), deleted (deleted)
|
||||
"""
|
||||
|
||||
if PYDANTIC_V2:
|
||||
model_config: ClassVar[ConfigDict] = ConfigDict(extra='allow', protected_namespaces=())
|
||||
request_id: str
|
||||
model_name: str
|
||||
delete_status: str
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
from .knowledge import KnowledgeInfo
|
||||
from .knowledge_create_params import KnowledgeBaseParams
|
||||
from .knowledge_list_params import KnowledgeListParams
|
||||
from .knowledge_list_resp import KnowledgePage
|
||||
from .knowledge_used import KnowledgeStatistics, KnowledgeUsed
|
||||
|
||||
__all__ = [
|
||||
'KnowledgeInfo',
|
||||
'KnowledgeStatistics',
|
||||
'KnowledgeUsed',
|
||||
'KnowledgeBaseParams',
|
||||
'KnowledgeListParams',
|
||||
'KnowledgePage',
|
||||
]
|
||||
|
|
@ -1,21 +0,0 @@
|
|||
from .document import (
|
||||
DocumentData,
|
||||
DocumentDataFailInfo,
|
||||
DocumentFailedInfo,
|
||||
DocumentObject,
|
||||
DocumentSuccessinfo,
|
||||
)
|
||||
from .document_edit_params import DocumentEditParams
|
||||
from .document_list_params import DocumentListParams
|
||||
from .document_list_resp import DocumentPage
|
||||
|
||||
__all__ = [
|
||||
'DocumentData',
|
||||
'DocumentObject',
|
||||
'DocumentSuccessinfo',
|
||||
'DocumentFailedInfo',
|
||||
'DocumentDataFailInfo',
|
||||
'DocumentEditParams',
|
||||
'DocumentListParams',
|
||||
'DocumentPage',
|
||||
]
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from zai.core import BaseModel
|
||||
|
||||
|
||||
class DocumentSuccessinfo(BaseModel):
|
||||
"""
|
||||
Represents successful document upload information.
|
||||
|
||||
Attributes:
|
||||
documentId (Optional[str]): Document ID
|
||||
filename (Optional[str]): Document filename
|
||||
"""
|
||||
|
||||
documentId: Optional[str] = None
|
||||
filename: Optional[str] = None
|
||||
|
||||
|
||||
class DocumentFailedInfo(BaseModel):
|
||||
"""
|
||||
Represents failed document upload information.
|
||||
|
||||
Attributes:
|
||||
failReason (Optional[str]): Reason for upload failure, including: unsupported file format,
|
||||
file size exceeds limit, knowledge base capacity is full,
|
||||
capacity limit is 500,000 words
|
||||
filename (Optional[str]): Document filename
|
||||
documentId (Optional[str]): Knowledge base ID
|
||||
"""
|
||||
|
||||
failReason: Optional[str] = None
|
||||
filename: Optional[str] = None
|
||||
documentId: Optional[str] = None
|
||||
|
||||
|
||||
class DocumentObject(BaseModel):
|
||||
"""
|
||||
Document information
|
||||
|
||||
Attributes:
|
||||
successInfos (Optional[List[DocumentSuccessinfo]]): Information about successfully uploaded files
|
||||
failedInfos (Optional[List[DocumentFailedInfo]]): Information about failed file uploads
|
||||
"""
|
||||
|
||||
successInfos: Optional[List[DocumentSuccessinfo]] = None
|
||||
failedInfos: Optional[List[DocumentFailedInfo]] = None
|
||||
|
||||
|
||||
class DocumentDataFailInfo(BaseModel):
|
||||
"""
|
||||
Document vectorization failure information
|
||||
|
||||
Attributes:
|
||||
embedding_code (Optional[int]): Error code (10001: Knowledge unavailable, knowledge base space limit reached;
|
||||
10002: Knowledge unavailable, word count exceeded)
|
||||
embedding_msg (Optional[str]): Failure reason description
|
||||
"""
|
||||
|
||||
embedding_code: Optional[int] = None
|
||||
embedding_msg: Optional[str] = None
|
||||
|
||||
|
||||
class DocumentData(BaseModel):
|
||||
"""
|
||||
Document data information
|
||||
|
||||
Attributes:
|
||||
id (str): Unique knowledge document identifier
|
||||
custom_separator (List[str]): Custom text slicing rules
|
||||
sentence_size (str): Text slice size configuration
|
||||
length (int): File size in bytes
|
||||
word_num (int): Total word count in the file
|
||||
name (str): Document file name
|
||||
url (str): File download URL
|
||||
embedding_stat (int): Vectorization status (0: vectorizing, 1: completed, 2: failed)
|
||||
failInfo (Optional[DocumentDataFailInfo]): Failure information when vectorization fails (embedding_stat=2)
|
||||
"""
|
||||
|
||||
id: str = None
|
||||
custom_separator: List[str] = None
|
||||
sentence_size: str = None
|
||||
length: int = None
|
||||
word_num: int = None
|
||||
name: str = None
|
||||
url: str = None
|
||||
embedding_stat: int = None
|
||||
failInfo: Optional[DocumentDataFailInfo] = None
|
||||
|
|
@ -1,29 +0,0 @@
|
|||
from typing import Dict, List, Optional, TypedDict
|
||||
|
||||
|
||||
class DocumentEditParams(TypedDict):
|
||||
"""
|
||||
Knowledge parameter type definition
|
||||
|
||||
Attributes:
|
||||
id (str): Knowledge ID
|
||||
knowledge_type (int): Knowledge type:
|
||||
1: Article knowledge: supports pdf,url,docx
|
||||
2: Q&A knowledge-document: supports pdf,url,docx
|
||||
3: Q&A knowledge-table: supports xlsx
|
||||
4: Product library-table: supports xlsx
|
||||
5: Custom: supports pdf,url,docx
|
||||
custom_separator (Optional[List[str]]): Slice rules when current knowledge type is custom
|
||||
(knowledge_type=5), default \n
|
||||
sentence_size (Optional[int]): Slice word count when current knowledge type is custom
|
||||
(knowledge_type=5), value range: 20-2000, default 300
|
||||
callback_url (Optional[str]): Callback address
|
||||
callback_header (Optional[dict]): Header carried during callback
|
||||
"""
|
||||
|
||||
id: str
|
||||
knowledge_type: int
|
||||
custom_separator: Optional[List[str]]
|
||||
sentence_size: Optional[int]
|
||||
callback_url: Optional[str]
|
||||
callback_header: Optional[Dict[str, str]]
|
||||
|
|
@ -1,27 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
|
||||
class DocumentListParams(TypedDict, total=False):
|
||||
"""
|
||||
File query parameter type definition
|
||||
|
||||
Attributes:
|
||||
purpose (Optional[str]): File purpose
|
||||
knowledge_id (Optional[str]): When file purpose is retrieval, need to provide the knowledge base ID for query
|
||||
page (Optional[int]): Page number, default 1
|
||||
limit (Optional[int]): Number of files to query in the list, default 10
|
||||
after (Optional[str]): Query file list after specified fileID (required when file purpose is fine-tune)
|
||||
order (Optional[str]): Sort rule, optional values ['desc', 'asc'], default desc
|
||||
(required when file purpose is fine-tune)
|
||||
"""
|
||||
|
||||
purpose: Optional[str]
|
||||
knowledge_id: Optional[str]
|
||||
page: Optional[int]
|
||||
limit: Optional[int]
|
||||
after: Optional[str]
|
||||
order: Optional[str]
|
||||
|
|
@ -1,11 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
from zai.core import BaseModel
|
||||
from zai.types.knowledge.document.document import DocumentData
|
||||
|
||||
|
||||
class DocumentPage(BaseModel):
|
||||
list: List[DocumentData]
|
||||
object: str
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from zai.core import BaseModel
|
||||
|
||||
|
||||
class KnowledgeInfo(BaseModel):
|
||||
# Knowledge base unique ID
|
||||
id: Optional[str] = None
|
||||
|
||||
# Vectorization model bound to knowledge base
|
||||
# See model list [Internal Service Open Interface Documentation](https://lslfd0slxc.feishu.cn/docx/YauWdbBiMopV0FxB7KncPWCEn8f#H15NduiQZo3ugmxnWQFcfAHpnQ4)
|
||||
embedding_id: Optional[str] = None
|
||||
|
||||
# Knowledge base name, 100 character limit
|
||||
name: Optional[str] = None
|
||||
|
||||
# User identifier, within 32 characters
|
||||
customer_identifier: Optional[str] = None
|
||||
|
||||
# Knowledge base description, 500 character limit
|
||||
description: Optional[str] = None
|
||||
|
||||
# Background color (enumeration) 'blue', 'red', 'orange', 'purple', 'sky'
|
||||
background: Optional[str] = None
|
||||
|
||||
# Knowledge base icon (enumeration) question: question mark, book: book, seal: seal, wrench: wrench,
|
||||
# tag: tag, horn: horn, house: house
|
||||
icon: Optional[str] = None
|
||||
|
||||
# Bucket ID, 32 character limit
|
||||
bucket_id: Optional[str] = None
|
||||
|
|
@ -1,28 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from typing_extensions import Literal, TypedDict
|
||||
|
||||
|
||||
class KnowledgeBaseParams(TypedDict):
|
||||
"""
|
||||
Knowledge base parameters.
|
||||
|
||||
Attributes:
|
||||
embedding_id (int): Embedding ID
|
||||
name (str): Knowledge base name, limited to 100 characters
|
||||
customer_identifier (Optional[str]): Customer identifier, limited to 32 characters
|
||||
description (Optional[str]): Knowledge base description, limited to 500 characters
|
||||
background (Optional[Literal['blue', 'red', 'orange', 'purple', 'sky']]): Background color
|
||||
icon (Optional[Literal['question', 'book', 'seal', 'wrench', 'tag', 'horn', 'house']]): Knowledge base icon
|
||||
bucket_id (Optional[str]): Bucket ID, limited to 32 characters
|
||||
"""
|
||||
|
||||
embedding_id: int
|
||||
name: str
|
||||
customer_identifier: Optional[str]
|
||||
description: Optional[str]
|
||||
background: Optional[Literal['blue', 'red', 'orange', 'purple', 'sky']] = None
|
||||
icon: Optional[Literal['question', 'book', 'seal', 'wrench', 'tag', 'horn', 'house']] = None
|
||||
bucket_id: Optional[str]
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
|
||||
class KnowledgeListParams(TypedDict, total=False):
|
||||
"""
|
||||
Parameters for listing knowledge resources.
|
||||
|
||||
Attributes:
|
||||
page (int): Page number, default 1, first page
|
||||
size (int): Number per page, default 10
|
||||
"""
|
||||
|
||||
page: int = 1
|
||||
size: int = 10
|
||||
|
|
@ -1,11 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
from zai.core import BaseModel
|
||||
from zai.types.knowledge.knowledge import KnowledgeInfo
|
||||
|
||||
|
||||
class KnowledgePage(BaseModel):
|
||||
list: List[KnowledgeInfo]
|
||||
object: str
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from zai.core import BaseModel
|
||||
|
||||
|
||||
class KnowledgeStatistics(BaseModel):
|
||||
"""
|
||||
Usage statistics
|
||||
"""
|
||||
|
||||
word_num: Optional[int] = None
|
||||
length: Optional[int] = None
|
||||
|
||||
|
||||
class KnowledgeUsed(BaseModel):
|
||||
used: Optional[KnowledgeStatistics] = None
|
||||
"""Used amount"""
|
||||
total: Optional[KnowledgeStatistics] = None
|
||||
"""Total knowledge base"""
|
||||
Binary file not shown.
|
|
@ -3,7 +3,7 @@ import logging.config
|
|||
import time
|
||||
|
||||
import zai
|
||||
from zai import ZaiClient
|
||||
from zai import ZaiClient, ZhipuAiClient
|
||||
|
||||
|
||||
def test_completions_sync(logging_conf):
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ def test_audio_speech(logging_conf):
|
|||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
client = ZaiClient() # Fill in your own API Key
|
||||
try:
|
||||
speech_file_path = Path(__file__).parent / 'speech.wav'
|
||||
speech_file_path = Path(__file__).parent / 'asr1.wav'
|
||||
response = client.audio.speech(
|
||||
model='cogtts',
|
||||
input='Hello, welcome to Z.ai Open Platform',
|
||||
|
|
@ -30,9 +30,9 @@ def test_audio_speech(logging_conf):
|
|||
def test_audio_customization(logging_conf):
|
||||
logging.config.dictConfig(logging_conf)
|
||||
client = ZaiClient() # Fill in your own API Key
|
||||
with open(Path(__file__).parent / 'speech.wav', 'rb') as file:
|
||||
with open(Path(__file__).parent / 'asr1.wav', 'rb') as file:
|
||||
try:
|
||||
speech_file_path = Path(__file__).parent / 'speech.wav'
|
||||
speech_file_path = Path(__file__).parent / 'asr1.wav'
|
||||
response = client.audio.customization(
|
||||
model='cogtts',
|
||||
input='Hello, welcome to Z.ai Open Platform',
|
||||
|
|
|
|||
|
|
@ -1,134 +0,0 @@
|
|||
import logging
|
||||
import logging.config
|
||||
|
||||
import zai
|
||||
from zai import ZaiClient
|
||||
|
||||
|
||||
def test_finetuning_create(logging_conf):
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
client = ZaiClient() # Please fill in your own API Key
|
||||
try:
|
||||
job = client.fine_tuning.jobs.create(
|
||||
model='chatglm3-6b',
|
||||
training_file='file-20240428021923715-xjng4', # Please fill in the successfully uploaded file id
|
||||
validation_file='file-20240428021923715-xjng4', # Please fill in the successfully uploaded file id
|
||||
suffix='demo_test',
|
||||
)
|
||||
job_id = job.id
|
||||
print(job_id)
|
||||
fine_tuning_job = client.fine_tuning.jobs.retrieve(fine_tuning_job_id=job_id)
|
||||
print(fine_tuning_job)
|
||||
# ftjob-20240418110039323-j8lh2
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
|
||||
def test_finetuning_retrieve(logging_conf):
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
client = ZaiClient() # Please fill in your own API Key
|
||||
try:
|
||||
fine_tuning_job = client.fine_tuning.jobs.retrieve(fine_tuning_job_id='ftjob-20240429112551154-48vq7')
|
||||
print(fine_tuning_job)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
|
||||
def test_finetuning_job_list(logging_conf):
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
client = ZaiClient() # Please fill in your own API Key
|
||||
try:
|
||||
job_list = client.fine_tuning.jobs.list()
|
||||
|
||||
print(job_list)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
|
||||
def test_finetuning_job_cancel(logging_conf):
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
client = ZaiClient() # Please fill in your own API Key
|
||||
try:
|
||||
cancel = client.fine_tuning.jobs.cancel(fine_tuning_job_id='ftjob-20240429112551154-48vq7')
|
||||
|
||||
print(cancel)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
|
||||
def test_finetuning_job_delete(logging_conf):
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
client = ZaiClient() # Please fill in your own API Key
|
||||
try:
|
||||
delete = client.fine_tuning.jobs.delete(fine_tuning_job_id='ftjob-20240126113041678-cs6s9')
|
||||
|
||||
print(delete)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
|
||||
def test_model_check(logging_conf):
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
client = ZaiClient() # Fill in your own API Key
|
||||
try:
|
||||
response = client.chat.completions.create(
|
||||
model='chatglm3-6b-8572905046912426020-demo_test', # Fill in the model name to call
|
||||
messages=[
|
||||
{'role': 'user', 'content': 'You are a helpful, knowledgeable, and versatile AI assistant.'},
|
||||
{'role': 'user', 'content': 'Create a more precise and engaging slogan'},
|
||||
],
|
||||
extra_body={'temperature': 0.5, 'max_tokens': 50},
|
||||
)
|
||||
print(response.choices[0].message)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
|
||||
def test_model_delete(logging_conf):
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
client = ZaiClient() # Fill in your own API Key
|
||||
try:
|
||||
delete = client.fine_tuning.models.delete(fine_tuned_model='chatglm3-6b-8572905046912426020-demo_test')
|
||||
|
||||
print(delete)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_finetuning_create()
|
||||
|
|
@ -1,168 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import logging.config
|
||||
import os
|
||||
|
||||
import pytest
|
||||
|
||||
import zai
|
||||
from zai import ZaiClient
|
||||
|
||||
|
||||
@pytest.fixture(scope='class')
|
||||
def test_server():
|
||||
class SharedData:
|
||||
client = ZaiClient()
|
||||
test_knowledge_document_id = None
|
||||
test_knowledge_id = None
|
||||
|
||||
return SharedData()
|
||||
|
||||
|
||||
class TestZaiClientKnowledgeServer:
|
||||
def test_logs(self, logging_conf):
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
|
||||
def test_knowledge_create(self, test_server):
|
||||
try:
|
||||
result = test_server.client.knowledge.create(
|
||||
embedding_id=1,
|
||||
name='test',
|
||||
description='test',
|
||||
background='blue',
|
||||
icon='question',
|
||||
)
|
||||
print(result)
|
||||
test_server.test_knowledge_id = result.id
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_document_create(self, test_server, test_file_path):
|
||||
try:
|
||||
result = test_server.client.knowledge.document.create(
|
||||
file=open(os.path.join(test_file_path, 'file.xlsx'), 'rb'),
|
||||
purpose='retrieval',
|
||||
knowledge_id=test_server.test_knowledge_id,
|
||||
sentence_size=202,
|
||||
)
|
||||
print(result)
|
||||
test_server.test_knowledge_document_id = result.successInfos[0].documentId
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_modify(self, test_server):
|
||||
try:
|
||||
result = test_server.client.knowledge.modify(
|
||||
knowledge_id=test_server.test_knowledge_id,
|
||||
embedding_id=1,
|
||||
name='test1',
|
||||
background='red',
|
||||
icon='book',
|
||||
)
|
||||
print(result)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_query(self, test_server):
|
||||
try:
|
||||
result = test_server.client.knowledge.query()
|
||||
print(result)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_used(self, test_server):
|
||||
try:
|
||||
result = test_server.client.knowledge.used()
|
||||
print(result)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_document_retrieve(self, test_server, test_file_path):
|
||||
try:
|
||||
result = test_server.client.knowledge.document.retrieve(test_server.test_knowledge_document_id)
|
||||
print(result)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_document_edit(self, test_server):
|
||||
try:
|
||||
result = test_server.client.knowledge.document.edit(
|
||||
document_id=test_server.test_knowledge_document_id,
|
||||
knowledge_type='1',
|
||||
sentence_size=204,
|
||||
)
|
||||
print(result)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_document_list(self, test_server):
|
||||
try:
|
||||
result = test_server.client.knowledge.document.list(test_server.test_knowledge_id, purpose='retrieval')
|
||||
print(result)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_document_delete(self, test_server):
|
||||
try:
|
||||
file1 = test_server.client.knowledge.document.delete(test_server.test_knowledge_document_id)
|
||||
print(file1)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
||||
def test_knowledge_delete(self, test_server):
|
||||
try:
|
||||
result = test_server.client.knowledge.delete(knowledge_id=test_server.test_knowledge_id)
|
||||
print(result)
|
||||
|
||||
except zai.core._errors.APIRequestFailedError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIInternalError as err:
|
||||
print(err)
|
||||
except zai.core._errors.APIStatusError as err:
|
||||
print(err)
|
||||
|
|
@ -26,13 +26,6 @@ from zai.types.chat.chat_completion import (
|
|||
)
|
||||
from zai.types.embeddings import Embedding, EmbeddingsResponded
|
||||
from zai.types.files.file_object import FileObject, ListOfFileObject
|
||||
from zai.types.fine_tuning import FineTuningJobEvent
|
||||
from zai.types.fine_tuning.fine_tuning_job import Error, FineTuningJob
|
||||
from zai.types.fine_tuning.fine_tuning_job import (
|
||||
Hyperparameters as FineTuningHyperparameters,
|
||||
)
|
||||
from zai.types.fine_tuning.fine_tuning_job_event import JobEvent, Metric
|
||||
from zai.types.fine_tuning.models import FineTunedModelsStatus
|
||||
from zai.types.image import GeneratedImage, ImagesResponded
|
||||
|
||||
|
||||
|
|
@ -164,186 +157,6 @@ def test_response_chat_model_cast(R: Type[BaseModel]) -> None:
|
|||
assert False, f'Unexpected model type: {R}'
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
'R',
|
||||
[
|
||||
FineTunedModelsStatus,
|
||||
],
|
||||
)
|
||||
def test_response_finetuned_model_model_cast(R: Type[BaseModel]) -> None:
|
||||
MockClient._process_response_data = HttpClient._process_response_data
|
||||
response = httpx.Response(
|
||||
status_code=200,
|
||||
content="""{
|
||||
"request_id": "12345",
|
||||
"model_name": "my-fine-tuned-model",
|
||||
"delete_status": "deleted"
|
||||
}""",
|
||||
)
|
||||
|
||||
opts = FinalRequestOptions.construct(method='get', url='path')
|
||||
http_response = APIResponse(
|
||||
raw=response,
|
||||
cast_type=R,
|
||||
client=MockClient(),
|
||||
stream=False,
|
||||
stream_cls=None,
|
||||
options=opts,
|
||||
)
|
||||
model = http_response.parse()
|
||||
|
||||
assert R == model.__class__
|
||||
assert isinstance(model, FineTunedModelsStatus)
|
||||
assert model.request_id == '12345'
|
||||
assert model.model_name == 'my-fine-tuned-model'
|
||||
assert model.delete_status == 'deleted'
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
'R',
|
||||
[
|
||||
FineTuningJob,
|
||||
],
|
||||
)
|
||||
def test_response_job_model_cast(R: Type[BaseModel]) -> None:
|
||||
MockClient._process_response_data = HttpClient._process_response_data
|
||||
response = httpx.Response(
|
||||
status_code=200,
|
||||
content=""" {
|
||||
"id": "job123",
|
||||
"request_id": "req456",
|
||||
"created_at": 1617181723,
|
||||
"error": {
|
||||
"code": "404",
|
||||
"message": "Not Found",
|
||||
"param": "model_id"
|
||||
},
|
||||
"fine_tuned_model": "ft_model_1",
|
||||
"finished_at": 1617182000,
|
||||
"hyperparameters": {
|
||||
"n_epochs": 10
|
||||
},
|
||||
"model": "base_model",
|
||||
"object": "fine_tuning_job",
|
||||
"result_files": [
|
||||
"result1.txt",
|
||||
"result2.json"
|
||||
],
|
||||
"status": "completed",
|
||||
"trained_tokens": 1000000,
|
||||
"training_file": "training_data.csv",
|
||||
"validation_file": "validation_data.csv"
|
||||
}""",
|
||||
)
|
||||
|
||||
opts = FinalRequestOptions.construct(method='get', url='path')
|
||||
http_response = APIResponse(
|
||||
raw=response,
|
||||
cast_type=R,
|
||||
client=MockClient(),
|
||||
stream=False,
|
||||
stream_cls=None,
|
||||
options=opts,
|
||||
)
|
||||
model = http_response.parse()
|
||||
|
||||
assert R == model.__class__
|
||||
assert isinstance(model, FineTuningJob)
|
||||
assert model.id == 'job123'
|
||||
assert model.request_id == 'req456'
|
||||
assert model.created_at == 1617181723
|
||||
assert isinstance(model.error, Error)
|
||||
assert model.error.code == '404'
|
||||
assert model.error.message == 'Not Found'
|
||||
assert model.error.param == 'model_id'
|
||||
assert model.fine_tuned_model == 'ft_model_1'
|
||||
assert model.finished_at == 1617182000
|
||||
assert isinstance(model.hyperparameters, FineTuningHyperparameters)
|
||||
assert model.hyperparameters.n_epochs == 10
|
||||
assert model.model == 'base_model'
|
||||
assert model.object == 'fine_tuning_job'
|
||||
assert model.result_files == ['result1.txt', 'result2.json']
|
||||
assert model.status == 'completed'
|
||||
assert model.trained_tokens == 1000000
|
||||
assert model.training_file == 'training_data.csv'
|
||||
assert model.validation_file == 'validation_data.csv'
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
'R',
|
||||
[
|
||||
FineTuningJobEvent,
|
||||
],
|
||||
)
|
||||
def test_response_joblist_model_cast(R: Type[BaseModel]) -> None:
|
||||
MockClient._process_response_data = HttpClient._process_response_data
|
||||
response = httpx.Response(
|
||||
status_code=200,
|
||||
content="""{
|
||||
"object": "fine_tuning_job",
|
||||
"data": [
|
||||
{
|
||||
"object": "job_event",
|
||||
"id": "event123",
|
||||
"type": "training",
|
||||
"created_at": 1617181723,
|
||||
"level": "info",
|
||||
"message": "Training has started.",
|
||||
"data": {
|
||||
"epoch": 1,
|
||||
"current_steps": 100,
|
||||
"total_steps": 1000,
|
||||
"elapsed_time": "00:10:00",
|
||||
"remaining_time": "05:20:00",
|
||||
"trained_tokens": 500000,
|
||||
"loss": 0.05,
|
||||
"eval_loss": 0.03,
|
||||
"acc": 0.9,
|
||||
"eval_acc": 0.95,
|
||||
"learning_rate": 0.001
|
||||
}
|
||||
}
|
||||
],
|
||||
"has_more": false
|
||||
}""",
|
||||
)
|
||||
|
||||
opts = FinalRequestOptions.construct(method='get', url='path')
|
||||
http_response = APIResponse(
|
||||
raw=response,
|
||||
cast_type=R,
|
||||
client=MockClient(),
|
||||
stream=False,
|
||||
stream_cls=None,
|
||||
options=opts,
|
||||
)
|
||||
model = http_response.parse()
|
||||
|
||||
assert R == model.__class__
|
||||
assert isinstance(model, FineTuningJobEvent)
|
||||
assert isinstance(model.data, list)
|
||||
assert isinstance(model.data[0], JobEvent)
|
||||
assert model.data[0].object == 'job_event'
|
||||
assert model.data[0].id == 'event123'
|
||||
assert model.data[0].type == 'training'
|
||||
assert model.data[0].created_at == 1617181723
|
||||
assert model.data[0].level == 'info'
|
||||
assert model.data[0].message == 'Training has started.'
|
||||
assert isinstance(model.data[0].data, Metric)
|
||||
assert model.data[0].data.epoch == 1
|
||||
assert model.data[0].data.current_steps == 100
|
||||
assert model.data[0].data.total_steps == 1000
|
||||
assert model.data[0].data.elapsed_time == '00:10:00'
|
||||
assert model.data[0].data.remaining_time == '05:20:00'
|
||||
assert model.data[0].data.trained_tokens == 500000
|
||||
assert model.data[0].data.loss == 0.05
|
||||
assert model.data[0].data.eval_loss == 0.03
|
||||
assert model.data[0].data.acc == 0.9
|
||||
assert model.data[0].data.eval_acc == 0.95
|
||||
assert model.data[0].data.learning_rate == 0.001
|
||||
assert model.has_more == False
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
'R',
|
||||
[EmbeddingsResponded],
|
||||
|
|
|
|||
35
tests/unit_tests/test_agent.py
Normal file
35
tests/unit_tests/test_agent.py
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
from zai.types.agents.agents_completion import AgentsCompletion, AgentsError, AgentsCompletionChoice, AgentsCompletionMessage, AgentsCompletionUsage
|
||||
|
||||
def test_agents_completion_error_field():
|
||||
|
||||
# 构造一个 AgentsError
|
||||
error = AgentsError(code="404", message="Not Found")
|
||||
|
||||
# 构造一个完整的 AgentsCompletion
|
||||
completion = AgentsCompletion(
|
||||
agent_id="test_agent",
|
||||
conversation_id="conv_1",
|
||||
status="failed",
|
||||
choices=[
|
||||
AgentsCompletionChoice(
|
||||
index=0,
|
||||
finish_reason="error",
|
||||
message=AgentsCompletionMessage(content="error", role="system")
|
||||
)
|
||||
],
|
||||
request_id="req_1",
|
||||
id="id_1",
|
||||
usage=AgentsCompletionUsage(prompt_tokens=1, completion_tokens=1, total_tokens=2),
|
||||
error=error
|
||||
)
|
||||
|
||||
# 检查 error 字段是否为 AgentsError 实例
|
||||
assert isinstance(completion.error, AgentsError)
|
||||
assert completion.error.code == "404"
|
||||
assert completion.error.message == "Not Found"
|
||||
|
||||
# 检查序列化
|
||||
as_dict = completion.model_dump()
|
||||
assert as_dict["error"]["code"] == "404"
|
||||
assert as_dict["error"]["message"] == "Not Found"
|
||||
print("test_agents_completion_error_field passed.")
|
||||
Loading…
Reference in a new issue