Add Perplexity copilot to Terminal Pro

perplexity-copilot/README.md

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+# Perplexity AI Copilot
+
+This example provides a basic copilot that utilizes the Perplexity AI API for natural language processing and generation.
+
+## Overview
+
+This implementation uses a FastAPI application as the backend for the copilot. The core functionality is powered by the Perplexity AI API, which offers advanced language models and various capabilities.
+
+You can adapt this implementation to suit your needs or preferences. The key is to adhere to the schema defined by the `/query` endpoint and the specifications in `copilot.json`.
+
+This repository serves as a starting point for you to experiment, modify, and extend. You can build copilots with various capabilities, leveraging Perplexity AI's features such as different model options, chat completions, and more.
+
+## Getting Started
+
+Follow these steps to set up and run your Perplexity AI-powered copilot:
+
+### Prerequisites
+
+- Python 3.7 or higher
+- Poetry (for dependency management)
+- A Perplexity AI API key (sign up at https://www.perplexity.ai/ if you don't have one)
+
+
+### Installation and Running
+
+1. Clone this repository to your local machine.
+2. Set the Perplexity API key as an environment variable in your .bashrc or .zshrc file:
+
+``` sh
+# in .zshrc or .bashrc
+export PERPLEXITY_API_KEY=pplx=<your-api-key>
+```
+
+3. Install the necessary dependencies:
+
+``` sh
+poetry install --no-root
+```
+
+4.Start the API server:
+
+``` sh
+poetry run uvicorn perplexity_copilot.main:app --port 7777 --reload
+```
+
+This command runs the FastAPI application, making it accessible on your network.
+
+### Testing the Copilot
+
+The example copilot has a small, basic test suite to ensure it's
+working correctly. As you develop your copilot, you are highly encouraged to
+expand these tests.
+
+You can run the tests with:
+
+``` sh
+pytest tests
+```
+
+### Accessing the Documentation
+
+Once the API server is running, you can view the documentation and interact with the API by visiting: http://localhost:7777/docs

perplexity-copilot/perplexity_copilot/copilots.json

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+{
+	"llama_copilot": {
+		"name": "Perplexity Copilot",
+		"description": "Enables users to search for information on the web using Perplexity.",
+		"image": "https://github.com/user-attachments/assets/f079bda8-aff1-4da7-8385-4657824020e8",
+		"hasStreaming": true,
+		"hasDocuments": false,
+		"hasFunctionCalling": false,
+		"endpoints": {
+			"query": "http://localhost:7777/v1/query"
+		}
+	}
+}

perplexity-copilot/perplexity_copilot/main.py

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+import re
+import json
+import os
+from pathlib import Path
+from typing import AsyncGenerator
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from openai import OpenAI
+from sse_starlette.sse import EventSourceResponse
+
+from dotenv import load_dotenv
+from .models import AgentQueryRequest
+from .prompts import SYSTEM_PROMPT
+
+
+load_dotenv(".env")
+app = FastAPI()
+
+origins = [
+    "http://localhost",
+    "http://localhost:1420",
+    "http://localhost:5050",
+    "https://pro.openbb.dev",
+    "https://pro.openbb.co",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+client = OpenAI(
+    api_key=os.getenv("PERPLEXITY_API_KEY"),
+    base_url="https://api.perplexity.ai"
+)
+
+
+def sanitize_message(message: str) -> str:
+    """Sanitize a message by escaping forbidden characters."""
+    cleaned_message = re.sub(r"(?<!\{)\{(?!{)", "{{", message)
+    cleaned_message = re.sub(r"(?<!\})\}(?!})", "}}", cleaned_message)
+    return cleaned_message
+
+
+async def create_message_stream(
+    content: AsyncGenerator[str, None],
+) -> AsyncGenerator[dict, None]:
+    async for chunk in content:
+        yield {"event": "copilotMessageChunk", "data": {"delta": chunk}}
+
+
+@app.get("/copilots.json")
+def get_copilot_description():
+    """Widgets configuration file for the OpenBB Terminal Pro"""
+    return JSONResponse(
+        content=json.load(open((Path(__file__).parent.resolve() / "copilots.json")))
+    )
+
+
+@app.post("/v1/query")
+async def query(request: AgentQueryRequest) -> EventSourceResponse:
+    """Query the Copilot."""
+
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+    for message in request.messages:
+        role = message.role.lower()
+        if role not in ['system', 'user', 'assistant']:
+            role = 'user'  # Default to 'user' if an invalid role is provided
+        messages.append({
+            "role": role,
+            "content": sanitize_message(message.content)
+        })
+
+    async def generate():
+        response_stream = client.chat.completions.create(
+            model="llama-3-sonar-large-32k-online",
+            messages=messages,
+            stream=True,
+        )
+        for response in response_stream:
+            if response.choices[0].delta.content is not None:
+                yield response.choices[0].delta.content
+
+    return EventSourceResponse(
+        content=create_message_stream(generate()),
+        media_type="text/event-stream",
+    )

perplexity-copilot/perplexity_copilot/models.py

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+from typing import Any
+from uuid import UUID
+from pydantic import BaseModel, Field, field_validator
+from enum import Enum
+
+
+class RoleEnum(str, Enum):
+    ai = "ai"
+    human = "human"
+
+
+class LlmMessage(BaseModel):
+    role: RoleEnum = Field(
+        description="The role of the entity that is creating the message"
+    )
+    content: str = Field(description="The content of the message")
+
+
+class BaseContext(BaseModel):
+    uuid: UUID = Field(description="The UUID of the widget.")
+    name: str = Field(description="The name of the widget.")
+    description: str = Field(
+        description="A description of the data contained in the widget"
+    )
+    content: Any = Field(description="The data content of the widget")
+    metadata: dict[str, Any] | None = Field(
+        default=None,
+        description="Additional widget metadata (eg. the selected ticker, etc)",
+    )
+
+
+class AgentQueryRequest(BaseModel):
+    messages: list[LlmMessage] = Field(
+        description="A list of messages to submit to the copilot."
+    )
+    context: list[BaseContext] | None = Field(
+        default=None,
+        description="Additional context.",
+    )
+    use_docs: bool = Field(
+        default=None, description="Set True to use uploaded docs when answering query."
+    )
+
+    @field_validator("messages")
+    def check_messages_not_empty(cls, value):
+        if not value:
+            raise ValueError("messages list cannot be empty.")
+        return value

perplexity-copilot/perplexity_copilot/prompts.py

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+SYSTEM_PROMPT = """\n
+You are a helpful financial assistant working for Example Co.
+Your name is "Llama 3.1 Copilot", and you were created by Example Co.
+You will do your best to answer the user's query.
+
+Use the following guidelines:
+- Formal and Professional Tone: Maintain a business-like, sophisticated tone, suitable for a professional audience.
+- Clarity and Conciseness: Keep explanations clear and to the point, avoiding unnecessary complexity.
+- Focus on Expertise and Experience: Emphasize expertise and real-world experiences, using direct quotes to add a personal touch.
+- Subject-Specific Jargon: Use industry-specific terms, ensuring they are accessible to a general audience through explanations.
+- Narrative Flow: Ensure a logical flow, connecting ideas and points effectively.
+- Incorporate Statistics and Examples: Support points with relevant statistics, examples, or case studies for real-world context.
+
+## Context
+Use the following context to help formulate your answer:
+
+{context}
+
+"""