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import requests
from langchain.llms import BaseLLM # type: ignore
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.outputs.llm_result import LLMResult
from typing import List, Optional, Any
from openai import OpenAI
import os
api_key_from_env = os.getenv("API_KEY_PERPLEXITY")
class PerplexityCrewLLM(BaseLLM):
api_key: str
model_name: str
def call_perplexity_ai(self, prompt: str) -> LLMResult:
url = "https://api.perplexity.ai/chat/completions"
payload = {
"model": self.model_name,
"messages": [
{
"role": "system",
"content": "Be precise and concise."
},
{
"role": "user",
"content": prompt
}
]
}
headers = {
"Authorization": f"Bearer {self.api_key}",
"accept": "application/json",
"content-type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
# Convert the response JSON to dictionary
json_response = response.json()
return json_response
def _generate(self, prompts: List[str], stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) -> LLMResult:
generations = []
for prompt in prompts:
generations.append([self._call(prompt, stop=stop, **kwargs)])
return LLMResult.construct(generations=generations)
def _call(self, prompt: str, stop: Optional[List[str]] = None, max_tokens: Optional[int] = None) -> LLMResult:
response_data = self.call_perplexity_ai(prompt)
model = LLMResult.construct(text=response_data['choices'][0]["message"]["content"]) # type: ignore
return model
@property
def _llm_type(self) -> str:
return "PerplexityAI"
def PerplexitySearchLLM(api_key,model_name="sonar-medium-online",base_url="https://api.perplexity.ai"):
client = OpenAI(api_key=api_key_from_env, base_url=base_url)
def call_model(query:str):
messages = [
#It is recommended to use only single-turn conversations and avoid system prompts for the online LLMs (sonar-small-online and sonar-medium-online).
# {
# "role": "system",
# "content": (
# "You are an artificial intelligence assistant and you need to "
# "engage in a helpful, detailed, polite conversation with a user."
# ),
# },
{
"role": "user",
"content": (
query
),
},
]
response = client.chat.completions.create(
model=model_name,
messages=messages, # type: ignore
)
result = response.choices[0].message.content #only the text is returned
return result
return call_model
call_llm = PerplexitySearchLLM(api_key=api_key_from_env,model_name="sonar-medium-online")
def perplexity_search(search_query: str):
return call_llm(search_query) |