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Merge branch 'open-webui:main' into routellm-pipeline

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Justin Hayes 2024-08-11 23:29:25 -04:00 committed by GitHub
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5 changed files with 110 additions and 56 deletions
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50
examples/filters/langfuse_filter_pipeline.py
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@ -11,6 +11,7 @@ requirements: langfuse
from typing import List, Optional
from schemas import OpenAIChatMessage
import os
import uuid
from utils.pipelines.main import get_last_user_message, get_last_assistant_message
from pydantic import BaseModel
@ -20,64 +21,36 @@ from langfuse.api.resources.commons.errors.unauthorized_error import Unauthorize
class Pipeline:
class Valves(BaseModel):
# List target pipeline ids (models) that this filter will be connected to.
# If you want to connect this filter to all pipelines, you can set pipelines to ["*"]
# e.g. ["llama3:latest", "gpt-3.5-turbo"]
pipelines: List[str] = []
# Assign a priority level to the filter pipeline.
# The priority level determines the order in which the filter pipelines are executed.
# The lower the number, the higher the priority.
priority: int = 0
# Valves
secret_key: str
public_key: str
host: str
def __init__(self):
# Pipeline filters are only compatible with Open WebUI
# 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.
self.type = "filter"
# Optionally, you can set the id and name of the pipeline.
# Best practice is to not specify the id so that it can be automatically inferred from the filename, so that users can install multiple versions of the same pipeline.
# The identifier must be unique across all pipelines.
# The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes.
# self.id = "langfuse_filter_pipeline"
self.name = "Langfuse Filter"
# Initialize
self.valves = self.Valves(
**{
"pipelines": ["*"], # Connect to all pipelines
"pipelines": ["*"],
"secret_key": os.getenv("LANGFUSE_SECRET_KEY", "your-secret-key-here"),
"public_key": os.getenv("LANGFUSE_PUBLIC_KEY", "your-public-key-here"),
"host": os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com"),
}
)
self.langfuse = None
self.chat_generations = {}
pass
async def on_startup(self):
# This function is called when the server is started.
print(f"on_startup:{__name__}")
self.set_langfuse()
pass
async def on_shutdown(self):
# This function is called when the server is stopped.
print(f"on_shutdown:{__name__}")
self.langfuse.flush()
pass
async def on_valves_updated(self):
# This function is called when the valves are updated.
self.set_langfuse()
pass
def set_langfuse(self):
try:
@ -97,6 +70,22 @@ class Pipeline:
async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
print(f"inlet:{__name__}")
print(f"Received body: {body}")
print(f"User: {user}")
# Check for presence of required keys and generate chat_id if missing
if "chat_id" not in body:
unique_id = f"SYSTEM MESSAGE {uuid.uuid4()}"
body["chat_id"] = unique_id
print(f"chat_id was missing, set to: {unique_id}")
required_keys = ["model", "messages"]
missing_keys = [key for key in required_keys if key not in body]
if missing_keys:
error_message = f"Error: Missing keys in the request body: {', '.join(missing_keys)}"
print(error_message)
raise ValueError(error_message)
trace = self.langfuse.trace(
name=f"filter:{__name__}",
@ -128,9 +117,6 @@ class Pipeline:
user_message = get_last_user_message(body["messages"])
generated_message = get_last_assistant_message(body["messages"])
# Update usage cost based on the length of the input and output messages
# Below does not reflect the actual cost of the API
# You can adjust the cost based on your requirements
generation.end(
output=generated_message,
usage={

15
examples/pipelines/providers/google_manifold_pipeline.py
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@ -2,7 +2,7 @@
title: Google GenAI Manifold Pipeline
author: Marc Lopez (refactor by justinh-rahb)
date: 2024-06-06
version: 1.2
version: 1.3
license: MIT
description: A pipeline for generating text using Google's GenAI models in Open-WebUI.
requirements: google-generativeai
@ -127,11 +127,14 @@ class Pipeline:
"role": "user" if message["role"] == "user" else "model",
"parts": [{"text": message["content"]}]
})
if system_message:
contents.insert(0, {"role": "user", "parts": [{"text": f"System: {system_message}"}]})
model = genai.GenerativeModel(model_name=model_id)
if "gemini-1.5" in model_id:
model = genai.GenerativeModel(model_name=model_id, system_instruction=system_message)
else:
if system_message:
contents.insert(0, {"role": "user", "parts": [{"text": f"System: {system_message}"}]})
model = genai.GenerativeModel(model_name=model_id)
generation_config = GenerationConfig(
temperature=body.get("temperature", 0.7),

10
examples/pipelines/providers/litellm_manifold_pipeline.py
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@ -2,7 +2,7 @@
title: LiteLLM Manifold Pipeline
author: open-webui
date: 2024-05-30
version: 1.0
version: 1.0.1
license: MIT
description: A manifold pipeline that uses LiteLLM.
"""
@ -46,12 +46,15 @@ class Pipeline:
"LITELLM_PIPELINE_DEBUG": os.getenv("LITELLM_PIPELINE_DEBUG", False),
}
)
self.pipelines = []
# Get models on initialization
self.pipelines = self.get_litellm_models()
pass
async def on_startup(self):
# This function is called when the server is started.
print(f"on_startup:{__name__}")
# Get models on startup
self.pipelines = self.get_litellm_models()
pass
async def on_shutdown(self):
@ -85,7 +88,7 @@ class Pipeline:
for model in models["data"]
]
except Exception as e:
print(f"Error: {e}")
print(f"Error fetching models from LiteLLM: {e}")
return [
{
"id": "error",
@ -93,6 +96,7 @@ class Pipeline:
},
]
else:
print("LITELLM_BASE_URL not set. Please configure it in the valves.")
return []
def pipe(

53
examples/pipelines/providers/perplexity_manifold_pipeline.py
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@ -3,6 +3,9 @@ from pydantic import BaseModel
import os
import requests
from utils.pipelines.main import pop_system_message
class Pipeline:
class Valves(BaseModel):
PERPLEXITY_API_BASE_URL: str = "https://api.perplexity.ai"
@ -26,14 +29,28 @@ class Pipeline:
# List of models
self.pipelines = [
{"id": "llama-3-sonar-large-32k-online", "name": "Llama 3 Sonar Large 32K Online"},
{"id": "llama-3-sonar-small-32k-online", "name": "Llama 3 Sonar Small 32K Online"},
{"id": "llama-3-sonar-large-32k-chat", "name": "Llama 3 Sonar Large 32K Chat"},
{"id": "llama-3-sonar-small-32k-chat", "name": "Llama 3 Sonar Small 32K Chat"},
{"id": "llama-3-8b-instruct", "name": "Llama 3 8B Instruct"},
{"id": "llama-3-70b-instruct", "name": "Llama 3 70B Instruct"},
{"id": "mixtral-8x7b-instruct", "name": "Mixtral 8x7B Instruct"},
{"id": "related", "name": "Related"}
{
"id": "llama-3.1-sonar-large-128k-online",
"name": "Llama 3.1 Sonar Large 128k Online"
},
{
"id": "llama-3.1-sonar-small-128k-online",
"name": "Llama 3.1 Sonar Small 128k Online"
},
{
"id": "llama-3.1-sonar-large-128k-chat",
"name": "Llama 3.1 Sonar Large 128k Chat"
},
{
"id": "llama-3.1-sonar-small-128k-chat",
"name": "Llama 3.1 Sonar Small 128k Chat"
},
{
"id": "llama-3.1-8b-instruct", "name": "Llama 3.1 8B Instruct"
},
{
"id": "llama-3.1-70b-instruct", "name": "Llama 3.1 70B Instruct"
}
]
pass
@ -59,6 +76,12 @@ class Pipeline:
# This is where you can add your custom pipelines like RAG.
print(f"pipe:{__name__}")
system_message, messages = pop_system_message(messages)
system_prompt = "You are a helpful assistant."
if system_message is not None:
system_prompt = system_message["content"]
print(system_prompt)
print(messages)
print(user_message)
@ -71,8 +94,8 @@ class Pipeline:
payload = {
"model": model_id,
"messages": [
{"role": "system", "content": "Be precise and concise."},
{"role": "user", "content": user_message}
{"role": "system", "content": system_prompt},
*messages
],
"stream": body.get("stream", True),
"return_citations": True,
@ -124,17 +147,21 @@ class Pipeline:
except Exception as e:
return f"Error: {e}"
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Perplexity API Client")
parser.add_argument("--api-key", type=str, required=True, help="API key for Perplexity")
parser.add_argument("--prompt", type=str, required=True, help="Prompt to send to the Perplexity API")
parser.add_argument("--api-key", type=str, required=True,
help="API key for Perplexity")
parser.add_argument("--prompt", type=str, required=True,
help="Prompt to send to the Perplexity API")
args = parser.parse_args()
pipeline = Pipeline()
pipeline.valves.PERPLEXITY_API_KEY = args.api_key
response = pipeline.pipe(user_message=args.prompt, model_id="llama-3-sonar-large-32k-online", messages=[], body={"stream": False})
response = pipeline.pipe(
user_message=args.prompt, model_id="llama-3-sonar-large-32k-online", messages=[], body={"stream": False})
print("Response:", response)

38
main.py
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@ -26,6 +26,7 @@ import time
import json
import uuid
import sys
import subprocess
from config import API_KEY, PIPELINES_DIR
@ -105,12 +106,45 @@ def get_all_pipelines():
return pipelines
def parse_frontmatter(content):
frontmatter = {}
for line in content.split('\n'):
if ':' in line:
key, value = line.split(':', 1)
frontmatter[key.strip().lower()] = value.strip()
return frontmatter
def install_frontmatter_requirements(requirements):
if requirements:
req_list = [req.strip() for req in requirements.split(',')]
for req in req_list:
print(f"Installing requirement: {req}")
subprocess.check_call([sys.executable, "-m", "pip", "install", req])
else:
print("No requirements found in frontmatter.")
async def load_module_from_path(module_name, module_path):
spec = importlib.util.spec_from_file_location(module_name, module_path)
module = importlib.util.module_from_spec(spec)
try:
# Read the module content
with open(module_path, 'r') as file:
content = file.read()
# Parse frontmatter
frontmatter = {}
if content.startswith('"""'):
end = content.find('"""', 3)
if end != -1:
frontmatter_content = content[3:end]
frontmatter = parse_frontmatter(frontmatter_content)
# Install requirements if specified
if 'requirements' in frontmatter:
install_frontmatter_requirements(frontmatter['requirements'])
# Load the module
spec = importlib.util.spec_from_file_location(module_name, module_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
print(f"Loaded module: {module.__name__}")
if hasattr(module, "Pipeline"):