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perplexity_manifold_pipeline
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examples/pipelines/providers/perplexity_manifold_pipeline.py
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from typing import List, Union, Generator, Iterator | ||
from pydantic import BaseModel | ||
import os | ||
import requests | ||
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class Pipeline: | ||
class Valves(BaseModel): | ||
PERPLEXITY_API_BASE_URL: str = "https://api.perplexity.ai" | ||
PERPLEXITY_API_KEY: str = "" | ||
pass | ||
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def __init__(self): | ||
self.type = "manifold" | ||
self.name = "Perplexity: " | ||
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self.valves = self.Valves( | ||
**{ | ||
"PERPLEXITY_API_KEY": os.getenv( | ||
"PERPLEXITY_API_KEY", "your-perplexity-api-key-here" | ||
) | ||
} | ||
) | ||
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# Debugging: print the API key to ensure it's loaded | ||
print(f"Loaded API Key: {self.valves.PERPLEXITY_API_KEY}") | ||
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# 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"} | ||
] | ||
pass | ||
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async def on_startup(self): | ||
# This function is called when the server is started. | ||
print(f"on_startup:{__name__}") | ||
pass | ||
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async def on_shutdown(self): | ||
# This function is called when the server is stopped. | ||
print(f"on_shutdown:{__name__}") | ||
pass | ||
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async def on_valves_updated(self): | ||
# This function is called when the valves are updated. | ||
print(f"on_valves_updated:{__name__}") | ||
# No models to fetch, static setup | ||
pass | ||
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def pipe( | ||
self, user_message: str, model_id: str, messages: List[dict], body: dict | ||
) -> Union[str, Generator, Iterator]: | ||
# This is where you can add your custom pipelines like RAG. | ||
print(f"pipe:{__name__}") | ||
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print(messages) | ||
print(user_message) | ||
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headers = { | ||
"Authorization": f"Bearer {self.valves.PERPLEXITY_API_KEY}", | ||
"Content-Type": "application/json", | ||
"accept": "application/json" | ||
} | ||
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payload = { | ||
"model": model_id, | ||
"messages": [ | ||
{"role": "system", "content": "Be precise and concise."}, | ||
{"role": "user", "content": user_message} | ||
], | ||
"stream": body.get("stream", True), | ||
"return_citations": True, | ||
"return_images": True | ||
} | ||
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if "user" in payload: | ||
del payload["user"] | ||
if "chat_id" in payload: | ||
del payload["chat_id"] | ||
if "title" in payload: | ||
del payload["title"] | ||
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print(payload) | ||
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try: | ||
r = requests.post( | ||
url=f"{self.valves.PERPLEXITY_API_BASE_URL}/chat/completions", | ||
json=payload, | ||
headers=headers, | ||
stream=True, | ||
) | ||
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r.raise_for_status() | ||
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if body.get("stream", False): | ||
return r.iter_lines() | ||
else: | ||
response = r.json() | ||
formatted_response = { | ||
"id": response["id"], | ||
"model": response["model"], | ||
"created": response["created"], | ||
"usage": response["usage"], | ||
"object": response["object"], | ||
"choices": [ | ||
{ | ||
"index": choice["index"], | ||
"finish_reason": choice["finish_reason"], | ||
"message": { | ||
"role": choice["message"]["role"], | ||
"content": choice["message"]["content"] | ||
}, | ||
"delta": {"role": "assistant", "content": ""} | ||
} for choice in response["choices"] | ||
] | ||
} | ||
return formatted_response | ||
except Exception as e: | ||
return f"Error: {e}" | ||
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if __name__ == "__main__": | ||
import argparse | ||
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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") | ||
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args = parser.parse_args() | ||
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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}) | ||
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print("Response:", response) |