mirror of
https://github.com/open-webui/pipelines
synced 2025-05-11 16:10:45 +00:00
feat: blueprints
This commit is contained in:
Timothy J. Baek
parent
be5f596d2a
commit
313f1a7592
172
blueprints/function_calling_blueprint.py
Normal file
172
blueprints/function_calling_blueprint.py
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@ -0,0 +1,172 @@
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from typing import List, Optional
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from pydantic import BaseModel
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from schemas import OpenAIChatMessage
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import os
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import requests
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import json
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from utils.main import (
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get_last_user_message,
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add_or_update_system_message,
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get_tools_specs,
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)
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class Pipeline:
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class Valves(BaseModel):
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# List target pipeline ids (models) that this filter will be connected to.
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# If you want to connect this filter to all pipelines, you can set pipelines to ["*"]
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pipelines: List[str] = []
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# Assign a priority level to the filter pipeline.
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# The priority level determines the order in which the filter pipelines are executed.
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# The lower the number, the higher the priority.
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priority: int = 0
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# Valves for function calling
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OPENAI_API_BASE_URL: str
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OPENAI_API_KEY: str
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TASK_MODEL: str
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TEMPLATE: str
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def __init__(self):
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# Pipeline filters are only compatible with Open WebUI
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# You can think of filter pipeline as a middleware that can be used to edit the form data before it is sent to the OpenAI API.
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self.type = "filter"
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# Assign a unique identifier to the pipeline.
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# The identifier must be unique across all pipelines.
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# The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes.
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self.id = "function_calling_blueprint"
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self.name = "Function Calling Blueprint"
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# Initialize valves
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self.valves = self.Valves(
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**{
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"pipelines": ["*"], # Connect to all pipelines
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"OPENAI_API_BASE_URL": os.getenv(
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"OPENAI_API_BASE_URL", "https://api.openai.com/v1"
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),
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"OPENAI_API_KEY": os.getenv("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY"),
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"TASK_MODEL": os.getenv("TASK_MODEL", "gpt-3.5-turbo"),
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"TEMPLATE": """Use the following context as your learned knowledge, inside <context></context> XML tags.
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<context>
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{{CONTEXT}}
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</context>
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When answer to user:
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- If you don't know, just say that you don't know.
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- If you don't know when you are not sure, ask for clarification.
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Avoid mentioning that you obtained the information from the context.
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And answer according to the language of the user's question.""",
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}
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)
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async def on_startup(self):
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# This function is called when the server is started.
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print(f"on_startup:{__name__}")
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pass
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async def on_shutdown(self):
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# This function is called when the server is stopped.
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print(f"on_shutdown:{__name__}")
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pass
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async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
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# If title generation is requested, skip the function calling filter
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if body.get("title", False):
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return body
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print(f"pipe:{__name__}")
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print(user)
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# Get the last user message
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user_message = get_last_user_message(body["messages"])
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# Get the tools specs
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tools_specs = get_tools_specs(self.tools)
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# System prompt for function calling
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fc_system_prompt = (
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f"Tools: {json.dumps(tools_specs, indent=2)}"
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+ """
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If a function tool doesn't match the query, return an empty string. Else, pick a function tool, fill in the parameters from the function tool's schema, and return it in the format { "name": \"functionName\", "parameters": { "key": "value" } }. Only pick a function if the user asks. Only return the object. Do not return any other text."
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"""
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)
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r = None
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try:
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# Call the OpenAI API to get the function response
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r = requests.post(
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url=f"{self.valves.OPENAI_API_BASE_URL}/chat/completions",
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json={
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"model": self.valves.TASK_MODEL,
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"messages": [
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{
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"role": "system",
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"content": fc_system_prompt,
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},
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{
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"role": "user",
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"content": "History:\n"
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+ "\n".join(
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[
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f"{message['role']}: {message['content']}"
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for message in body["messages"][::-1][:4]
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]
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)
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+ f"Query: {user_message}",
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},
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],
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# TODO: dynamically add response_format?
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# "response_format": {"type": "json_object"},
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},
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headers={
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"Authorization": f"Bearer {self.valves.OPENAI_API_KEY}",
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"Content-Type": "application/json",
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},
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stream=False,
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)
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r.raise_for_status()
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response = r.json()
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content = response["choices"][0]["message"]["content"]
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# Parse the function response
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if content != "":
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result = json.loads(content)
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print(result)
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# Call the function
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if "name" in result:
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function = getattr(self.tools, result["name"])
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function_result = None
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try:
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function_result = function(**result["parameters"])
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except Exception as e:
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print(e)
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# Add the function result to the system prompt
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if function_result:
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system_prompt = self.valves.TEMPLATE.replace(
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"{{CONTEXT}}", function_result
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)
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print(system_prompt)
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messages = add_or_update_system_message(
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system_prompt, body["messages"]
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)
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# Return the updated messages
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return {**body, "messages": messages}
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except Exception as e:
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print(f"Error: {e}")
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if r:
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try:
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print(r.json())
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except:
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pass
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return body
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@ -1,231 +1,80 @@
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from typing import List, Optional
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from pydantic import BaseModel
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from schemas import OpenAIChatMessage
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import os
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import requests
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import json
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from utils.main import (
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get_last_user_message,
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add_or_update_system_message,
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get_function_specs,
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)
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from typing import Literal
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from typing import Literal, List, Optional
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from blueprints.function_calling_blueprint import Pipeline as FunctionCallingBlueprint
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class Pipeline:
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class Pipeline(FunctionCallingBlueprint):
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class Valves(FunctionCallingBlueprint.Valves):
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# Add your custom parameters here
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OPENWEATHERMAP_API_KEY: str = ""
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pass
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class Tools:
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def __init__(self, pipeline) -> None:
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self.pipeline = pipeline
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def get_current_weather(
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self,
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location: str,
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unit: Literal["metric", "fahrenheit"] = "fahrenheit",
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) -> str:
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"""
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Get the current weather for a location. If the location is not found, return an empty string.
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:param location: The location to get the weather for.
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:param unit: The unit to get the weather in. Default is fahrenheit.
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:return: The current weather for the location.
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"""
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# https://openweathermap.org/api
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if self.pipeline.valves.OPENWEATHERMAP_API_KEY == "":
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return "OpenWeatherMap API Key not set, ask the user to set it up."
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else:
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units = "imperial" if unit == "fahrenheit" else "metric"
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params = {
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"q": location,
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"appid": self.pipeline.valves.OPENWEATHERMAP_API_KEY,
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"units": units,
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}
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response = requests.get(
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"http://api.openweathermap.org/data/2.5/weather", params=params
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)
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response.raise_for_status() # Raises an HTTPError for bad responses
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data = response.json()
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weather_description = data["weather"][0]["description"]
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temperature = data["main"]["temp"]
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return f"{location}: {weather_description.capitalize()}, {temperature}°{unit.capitalize()[0]}"
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def calculator(self, equation: str) -> str:
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"""
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Calculate the result of an equation.
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:param equation: The equation to calculate.
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"""
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# Avoid using eval in production code
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# https://nedbatchelder.com/blog/201206/eval_really_is_dangerous.html
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try:
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result = eval(equation)
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return f"{equation} = {result}"
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except Exception as e:
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print(e)
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return "Invalid equation"
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def __init__(self):
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# Pipeline filters are only compatible with Open WebUI
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# You can think of filter pipeline as a middleware that can be used to edit the form data before it is sent to the OpenAI API.
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self.type = "filter"
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# Assign a unique identifier to the pipeline.
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# The identifier must be unique across all pipelines.
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# The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes.
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self.id = "function_calling_filter_pipeline"
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self.name = "Function Calling Filter"
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class Valves(BaseModel):
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# List target pipeline ids (models) that this filter will be connected to.
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# If you want to connect this filter to all pipelines, you can set pipelines to ["*"]
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pipelines: List[str] = []
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# Assign a priority level to the filter pipeline.
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# The priority level determines the order in which the filter pipelines are executed.
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# The lower the number, the higher the priority.
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priority: int = 0
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# Valves for function calling
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OPENAI_API_BASE_URL: str
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OPENAI_API_KEY: str
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TASK_MODEL: str
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TEMPLATE: str
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OPENWEATHERMAP_API_KEY: str = ""
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# Initialize valves
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self.valves = Valves(
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super().__init__()
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self.id = "my_tools_pipeline"
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self.name = "My Tools Pipeline"
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self.valves = self.Valves(
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**{
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**self.valves.model_dump(),
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"pipelines": ["*"], # Connect to all pipelines
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"OPENAI_API_BASE_URL": "https://api.openai.com/v1",
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"OPENAI_API_KEY": os.getenv("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY"),
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"TASK_MODEL": "gpt-3.5-turbo",
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"TEMPLATE": """Use the following context as your learned knowledge, inside <context></context> XML tags.
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<context>
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{{CONTEXT}}
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</context>
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When answer to user:
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- If you don't know, just say that you don't know.
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- If you don't know when you are not sure, ask for clarification.
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Avoid mentioning that you obtained the information from the context.
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And answer according to the language of the user's question.""",
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}
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"OPENWEATHERMAP_API_KEY": os.getenv("OPENWEATHERMAP_API_KEY", ""),
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},
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)
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class Functions:
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def __init__(self, pipeline) -> None:
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self.pipeline = pipeline
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def get_current_weather(
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self,
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location: str,
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unit: Literal["metric", "fahrenheit"] = "fahrenheit",
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) -> str:
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"""
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Get the current weather for a location. If the location is not found, return an empty string.
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:param location: The location to get the weather for.
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:param unit: The unit to get the weather in. Default is fahrenheit.
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:return: The current weather for the location.
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"""
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# https://openweathermap.org/api
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if self.pipeline.valves.OPENWEATHERMAP_API_KEY == "":
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return "OpenWeatherMap API Key not set, ask the user to set it up."
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else:
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units = "imperial" if unit == "fahrenheit" else "metric"
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params = {
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"q": location,
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"appid": self.pipeline.valves.OPENWEATHERMAP_API_KEY,
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"units": units,
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}
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response = requests.get(
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"http://api.openweathermap.org/data/2.5/weather", params=params
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)
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response.raise_for_status() # Raises an HTTPError for bad responses
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data = response.json()
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weather_description = data["weather"][0]["description"]
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temperature = data["main"]["temp"]
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return f"{location}: {weather_description.capitalize()}, {temperature}°{unit.capitalize()[0]}"
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def calculator(self, equation: str) -> str:
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"""
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Calculate the result of an equation.
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:param equation: The equation to calculate.
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"""
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# Avoid using eval in production code
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# https://nedbatchelder.com/blog/201206/eval_really_is_dangerous.html
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try:
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result = eval(equation)
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return f"{equation} = {result}"
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except Exception as e:
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print(e)
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return "Invalid equation"
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self.functions = Functions(self)
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async def on_startup(self):
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# This function is called when the server is started.
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print(f"on_startup:{__name__}")
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pass
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async def on_shutdown(self):
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# This function is called when the server is stopped.
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print(f"on_shutdown:{__name__}")
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pass
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async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
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# If title generation is requested, skip the function calling filter
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if body.get("title", False):
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return body
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print(f"pipe:{__name__}")
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print(user)
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# Get the last user message
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user_message = get_last_user_message(body["messages"])
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# Get the function specs
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function_specs = get_function_specs(self.functions)
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# System prompt for function calling
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fc_system_prompt = (
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f"Functions: {json.dumps(function_specs, indent=2)}"
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+ """
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If a function doesn't match the query, return an empty string. Else, pick a function, fill in the parameters from the function's schema, and return it in the format { "name": \"functionName\", "parameters": { "key": "value" } }. Only pick a function if the user asks. Only return the object. Do not return any other text."
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"""
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)
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r = None
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try:
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# Call the OpenAI API to get the function response
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r = requests.post(
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url=f"{self.valves.OPENAI_API_BASE_URL}/chat/completions",
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json={
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"model": self.valves.TASK_MODEL,
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"messages": [
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{
|
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"role": "system",
|
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"content": fc_system_prompt,
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},
|
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{
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"role": "user",
|
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"content": "History:\n"
|
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+ "\n".join(
|
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[
|
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f"{message['role']}: {message['content']}"
|
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for message in body["messages"][::-1][:4]
|
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]
|
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)
|
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+ f"Query: {user_message}",
|
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},
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],
|
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# TODO: dynamically add response_format?
|
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# "response_format": {"type": "json_object"},
|
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},
|
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headers={
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"Authorization": f"Bearer {self.valves.OPENAI_API_KEY}",
|
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"Content-Type": "application/json",
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},
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stream=False,
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)
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r.raise_for_status()
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response = r.json()
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content = response["choices"][0]["message"]["content"]
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# Parse the function response
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if content != "":
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result = json.loads(content)
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print(result)
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# Call the function
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if "name" in result:
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function = getattr(self.functions, result["name"])
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function_result = None
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try:
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function_result = function(**result["parameters"])
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except Exception as e:
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print(e)
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# Add the function result to the system prompt
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if function_result:
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system_prompt = self.valves.TEMPLATE.replace(
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"{{CONTEXT}}", function_result
|
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)
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print(system_prompt)
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messages = add_or_update_system_message(
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system_prompt, body["messages"]
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)
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# Return the updated messages
|
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return {**body, "messages": messages}
|
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|
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except Exception as e:
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print(f"Error: {e}")
|
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|
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if r:
|
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try:
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print(r.json())
|
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except:
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pass
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return body
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self.tools = self.Tools(self)
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1
main.py
1
main.py
@ -203,7 +203,6 @@ async def get_models():
|
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Returns the available pipelines
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"""
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app.state.PIPELINES = get_all_pipelines()
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|
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return {
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"data": [
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{
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@ -80,12 +80,11 @@ def doc_to_dict(docstring):
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return ret_dict
|
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|
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|
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def get_function_specs(functions) -> List[dict]:
|
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|
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def get_tools_specs(tools) -> List[dict]:
|
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function_list = [
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{"name": func, "function": getattr(functions, func)}
|
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for func in dir(functions)
|
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if callable(getattr(functions, func)) and not func.startswith("__")
|
||||
{"name": func, "function": getattr(tools, func)}
|
||||
for func in dir(tools)
|
||||
if callable(getattr(tools, func)) and not func.startswith("__")
|
||||
]
|
||||
|
||||
specs = []
|
||||
|
||||
Loading…
Reference in New Issue
Block a user