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feat(blocks): add dedicated Perplexity block (#11164)
Fixes #11162 ## Summary Implements a new Perplexity block that allows users to query Perplexity's sonar models via OpenRouter with support for extracting URL citations and annotations. ## Changes - Add new block for Perplexity sonar models (sonar, sonar-pro, sonar-deep-research) - Support model selection for all three Perplexity models - Implement annotations output pin for URL citations and source references - Integrate with OpenRouter API for accessing Perplexity models - Follow existing block patterns from AI text generator block ## Test Plan ✅ Block successfully instantiates ✅ Block is properly loaded by the dynamic loading system ✅ Output fields include response and annotations as required Generated with [Claude Code](https://claude.ai/code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - New Features - Added a Perplexity integration block to query Sonar models via OpenRouter. - Supports multiple model options, optional system prompt, and adjustable max tokens. - Returns concise responses with citation-style annotations extracted from the model output. - Provides clear error messages when requests fail. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Toran Bruce Richards <Torantulino@users.noreply.github.com> Co-authored-by: Bentlybro <Github@bentlybro.com>
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226
autogpt_platform/backend/backend/blocks/perplexity.py
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226
autogpt_platform/backend/backend/blocks/perplexity.py
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# flake8: noqa: E501
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import logging
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from enum import Enum
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from typing import Any, Literal
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import openai
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from pydantic import SecretStr
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from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
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from backend.data.model import (
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APIKeyCredentials,
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CredentialsField,
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CredentialsMetaInput,
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NodeExecutionStats,
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SchemaField,
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)
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from backend.integrations.providers import ProviderName
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from backend.util.logging import TruncatedLogger
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logger = TruncatedLogger(logging.getLogger(__name__), "[Perplexity-Block]")
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class PerplexityModel(str, Enum):
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"""Perplexity sonar models available via OpenRouter"""
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SONAR = "perplexity/sonar"
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SONAR_PRO = "perplexity/sonar-pro"
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SONAR_DEEP_RESEARCH = "perplexity/sonar-deep-research"
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PerplexityCredentials = CredentialsMetaInput[
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Literal[ProviderName.OPEN_ROUTER], Literal["api_key"]
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]
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TEST_CREDENTIALS = APIKeyCredentials(
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id="test-perplexity-creds",
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provider="open_router",
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api_key=SecretStr("mock-openrouter-api-key"),
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title="Mock OpenRouter API key",
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expires_at=None,
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)
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TEST_CREDENTIALS_INPUT = {
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"provider": TEST_CREDENTIALS.provider,
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"id": TEST_CREDENTIALS.id,
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"type": TEST_CREDENTIALS.type,
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"title": TEST_CREDENTIALS.title,
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}
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def PerplexityCredentialsField() -> PerplexityCredentials:
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return CredentialsField(
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description="OpenRouter API key for accessing Perplexity models.",
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)
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class PerplexityBlock(Block):
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class Input(BlockSchema):
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prompt: str = SchemaField(
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description="The query to send to the Perplexity model.",
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placeholder="Enter your query here...",
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)
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model: PerplexityModel = SchemaField(
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title="Perplexity Model",
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default=PerplexityModel.SONAR,
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description="The Perplexity sonar model to use.",
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advanced=False,
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)
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credentials: PerplexityCredentials = PerplexityCredentialsField()
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system_prompt: str = SchemaField(
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title="System Prompt",
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default="",
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description="Optional system prompt to provide context to the model.",
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advanced=True,
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)
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max_tokens: int | None = SchemaField(
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advanced=True,
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default=None,
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description="The maximum number of tokens to generate.",
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)
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class Output(BlockSchema):
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response: str = SchemaField(
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description="The response from the Perplexity model."
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)
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annotations: list[dict[str, Any]] = SchemaField(
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description="List of URL citations and annotations from the response."
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)
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error: str = SchemaField(description="Error message if the API call failed.")
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def __init__(self):
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super().__init__(
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id="c8a5f2e9-8b3d-4a7e-9f6c-1d5e3c9b7a4f",
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description="Query Perplexity's sonar models with real-time web search capabilities and receive annotated responses with source citations.",
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categories={BlockCategory.AI, BlockCategory.SEARCH},
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input_schema=PerplexityBlock.Input,
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output_schema=PerplexityBlock.Output,
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test_input={
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"prompt": "What is the weather today?",
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"model": PerplexityModel.SONAR,
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"credentials": TEST_CREDENTIALS_INPUT,
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},
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test_credentials=TEST_CREDENTIALS,
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test_output=[
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("response", "The weather varies by location..."),
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("annotations", list),
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],
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test_mock={
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"call_perplexity": lambda *args, **kwargs: {
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"response": "The weather varies by location...",
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"annotations": [
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{
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"type": "url_citation",
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"url_citation": {
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"title": "weather.com",
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"url": "https://weather.com",
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},
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}
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],
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}
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},
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)
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self.execution_stats = NodeExecutionStats()
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async def call_perplexity(
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self,
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credentials: APIKeyCredentials,
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model: PerplexityModel,
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prompt: str,
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system_prompt: str = "",
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max_tokens: int | None = None,
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) -> dict[str, Any]:
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"""Call Perplexity via OpenRouter and extract annotations."""
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client = openai.AsyncOpenAI(
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base_url="https://openrouter.ai/api/v1",
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api_key=credentials.api_key.get_secret_value(),
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)
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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try:
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response = await client.chat.completions.create(
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extra_headers={
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"HTTP-Referer": "https://agpt.co",
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"X-Title": "AutoGPT",
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},
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model=model.value,
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messages=messages,
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max_tokens=max_tokens,
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)
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if not response.choices:
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raise ValueError("No response from Perplexity via OpenRouter.")
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# Extract the response content
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response_content = response.choices[0].message.content or ""
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# Extract annotations if present in the message
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annotations = []
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if hasattr(response.choices[0].message, "annotations"):
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# If annotations are directly available
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annotations = response.choices[0].message.annotations
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else:
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# Check if there's a raw response with annotations
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raw = getattr(response.choices[0].message, "_raw_response", None)
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if isinstance(raw, dict) and "annotations" in raw:
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annotations = raw["annotations"]
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if not annotations and hasattr(response, "model_extra"):
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# Check model_extra for annotations
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model_extra = response.model_extra
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if isinstance(model_extra, dict):
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# Check in choices
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if "choices" in model_extra and len(model_extra["choices"]) > 0:
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choice = model_extra["choices"][0]
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if "message" in choice and "annotations" in choice["message"]:
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annotations = choice["message"]["annotations"]
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# Also check the raw response object for annotations
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if not annotations:
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raw = getattr(response, "_raw_response", None)
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if isinstance(raw, dict):
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# Check various possible locations for annotations
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if "annotations" in raw:
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annotations = raw["annotations"]
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elif "choices" in raw and len(raw["choices"]) > 0:
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choice = raw["choices"][0]
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if "message" in choice and "annotations" in choice["message"]:
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annotations = choice["message"]["annotations"]
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# Update execution stats
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if response.usage:
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self.execution_stats.input_token_count = response.usage.prompt_tokens
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self.execution_stats.output_token_count = (
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response.usage.completion_tokens
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)
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return {"response": response_content, "annotations": annotations or []}
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except Exception as e:
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logger.error(f"Error calling Perplexity: {e}")
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raise
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async def run(
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self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
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) -> BlockOutput:
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logger.debug(f"Running Perplexity block with model: {input_data.model}")
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try:
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result = await self.call_perplexity(
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credentials=credentials,
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model=input_data.model,
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prompt=input_data.prompt,
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system_prompt=input_data.system_prompt,
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max_tokens=input_data.max_tokens,
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)
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yield "response", result["response"]
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yield "annotations", result["annotations"]
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except Exception as e:
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error_msg = f"Error calling Perplexity: {str(e)}"
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logger.error(error_msg)
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yield "error", error_msg
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