chore: initial snapshot for gitea/github upload
This commit is contained in:
Your Name
@@ -0,0 +1,251 @@
|
||||
"""
|
||||
Translate from OpenAI's `/v1/chat/completions` to Perplexity's `/v1/chat/completions`
|
||||
"""
|
||||
|
||||
from typing import Any, List, Optional, Tuple
|
||||
|
||||
import httpx
|
||||
import litellm
|
||||
from litellm._logging import verbose_logger
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.llms.openai import AllMessageValues
|
||||
from litellm.types.utils import Usage, PromptTokensDetailsWrapper
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
|
||||
from litellm.types.utils import ModelResponse
|
||||
from litellm.types.llms.openai import ChatCompletionAnnotation
|
||||
from litellm.types.llms.openai import ChatCompletionAnnotationURLCitation
|
||||
|
||||
|
||||
class PerplexityChatConfig(OpenAIGPTConfig):
|
||||
@property
|
||||
def custom_llm_provider(self) -> Optional[str]:
|
||||
return "perplexity"
|
||||
|
||||
def _get_openai_compatible_provider_info(
|
||||
self, api_base: Optional[str], api_key: Optional[str]
|
||||
) -> Tuple[Optional[str], Optional[str]]:
|
||||
api_base = api_base or get_secret_str("PERPLEXITY_API_BASE") or "https://api.perplexity.ai" # type: ignore
|
||||
dynamic_api_key = (
|
||||
api_key
|
||||
or get_secret_str("PERPLEXITYAI_API_KEY")
|
||||
or get_secret_str("PERPLEXITY_API_KEY")
|
||||
)
|
||||
return api_base, dynamic_api_key
|
||||
|
||||
def get_supported_openai_params(self, model: str) -> list:
|
||||
"""
|
||||
Perplexity supports a subset of OpenAI params
|
||||
|
||||
Ref: https://docs.perplexity.ai/api-reference/chat-completions
|
||||
|
||||
Eg. Perplexity does not support tools, tool_choice, function_call, functions, etc.
|
||||
"""
|
||||
base_openai_params = [
|
||||
"frequency_penalty",
|
||||
"max_tokens",
|
||||
"max_completion_tokens",
|
||||
"presence_penalty",
|
||||
"response_format",
|
||||
"stream",
|
||||
"temperature",
|
||||
"top_p",
|
||||
"max_retries",
|
||||
"extra_headers",
|
||||
]
|
||||
|
||||
try:
|
||||
if litellm.supports_reasoning(
|
||||
model=model, custom_llm_provider=self.custom_llm_provider
|
||||
):
|
||||
base_openai_params.append("reasoning_effort")
|
||||
except Exception as e:
|
||||
verbose_logger.debug(f"Error checking if model supports reasoning: {e}")
|
||||
|
||||
try:
|
||||
if litellm.supports_web_search(
|
||||
model=model, custom_llm_provider=self.custom_llm_provider
|
||||
):
|
||||
base_openai_params.append("web_search_options")
|
||||
except Exception as e:
|
||||
verbose_logger.debug(f"Error checking if model supports web search: {e}")
|
||||
|
||||
return base_openai_params
|
||||
|
||||
def transform_response(
|
||||
self,
|
||||
model: str,
|
||||
raw_response: httpx.Response,
|
||||
model_response: ModelResponse,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
request_data: dict,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
encoding: Any,
|
||||
api_key: Optional[str] = None,
|
||||
json_mode: Optional[bool] = None,
|
||||
) -> ModelResponse:
|
||||
# Call the parent transform_response first to handle the standard transformation
|
||||
model_response = super().transform_response(
|
||||
model=model,
|
||||
raw_response=raw_response,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
request_data=request_data,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params,
|
||||
encoding=encoding,
|
||||
api_key=api_key,
|
||||
json_mode=json_mode,
|
||||
)
|
||||
|
||||
# Extract and enhance usage with Perplexity-specific fields
|
||||
try:
|
||||
raw_response_json = raw_response.json()
|
||||
self._enhance_usage_with_perplexity_fields(
|
||||
model_response, raw_response_json
|
||||
)
|
||||
self._add_citations_as_annotations(model_response, raw_response_json)
|
||||
except Exception as e:
|
||||
verbose_logger.debug(
|
||||
f"Error extracting Perplexity-specific usage fields: {e}"
|
||||
)
|
||||
|
||||
return model_response
|
||||
|
||||
def _enhance_usage_with_perplexity_fields(
|
||||
self, model_response: ModelResponse, raw_response_json: dict
|
||||
) -> None:
|
||||
"""
|
||||
Extract citation tokens and search queries from Perplexity API response
|
||||
and add them to the usage object using standard LiteLLM fields.
|
||||
"""
|
||||
if not hasattr(model_response, "usage") or model_response.usage is None:
|
||||
# Create a usage object if it doesn't exist (when usage was None)
|
||||
model_response.usage = Usage( # type: ignore[attr-defined]
|
||||
prompt_tokens=0, completion_tokens=0, total_tokens=0
|
||||
)
|
||||
|
||||
usage = model_response.usage # type: ignore[attr-defined]
|
||||
|
||||
# Extract citation tokens count
|
||||
citations = raw_response_json.get("citations", [])
|
||||
citation_tokens = 0
|
||||
if citations:
|
||||
# Count total characters in citations as a proxy for citation tokens
|
||||
# This is an estimation - in practice, you might want to use proper tokenization
|
||||
total_citation_chars = sum(
|
||||
len(str(citation)) for citation in citations if citation
|
||||
)
|
||||
# Rough estimation: ~4 characters per token (OpenAI's general rule)
|
||||
if total_citation_chars > 0:
|
||||
citation_tokens = max(1, total_citation_chars // 4)
|
||||
|
||||
# Extract search queries count from usage or response metadata
|
||||
# Perplexity might include this in the usage object or as separate metadata
|
||||
perplexity_usage = raw_response_json.get("usage", {})
|
||||
|
||||
# Try to extract search queries from usage field first, then root level
|
||||
num_search_queries = perplexity_usage.get("num_search_queries")
|
||||
if num_search_queries is None:
|
||||
num_search_queries = raw_response_json.get("num_search_queries")
|
||||
if num_search_queries is None:
|
||||
num_search_queries = perplexity_usage.get("search_queries")
|
||||
if num_search_queries is None:
|
||||
num_search_queries = raw_response_json.get("search_queries")
|
||||
|
||||
# Create or update prompt_tokens_details to include web search requests and citation tokens
|
||||
if citation_tokens > 0 or (
|
||||
num_search_queries is not None and num_search_queries > 0
|
||||
):
|
||||
if usage.prompt_tokens_details is None:
|
||||
usage.prompt_tokens_details = PromptTokensDetailsWrapper()
|
||||
|
||||
# Store citation tokens count for cost calculation
|
||||
if citation_tokens > 0:
|
||||
setattr(usage, "citation_tokens", citation_tokens)
|
||||
|
||||
# Store search queries count in the standard web_search_requests field
|
||||
if num_search_queries is not None and num_search_queries > 0:
|
||||
usage.prompt_tokens_details.web_search_requests = num_search_queries
|
||||
|
||||
def _add_citations_as_annotations(
|
||||
self, model_response: ModelResponse, raw_response_json: dict
|
||||
) -> None:
|
||||
"""
|
||||
Extract citations and search_results from Perplexity API response
|
||||
and add them as ChatCompletionAnnotation objects to the message.
|
||||
"""
|
||||
if not model_response.choices:
|
||||
return
|
||||
|
||||
# Get the first choice (assuming single response)
|
||||
choice = model_response.choices[0]
|
||||
if not hasattr(choice, "message") or choice.message is None:
|
||||
return
|
||||
|
||||
message = choice.message
|
||||
annotations = []
|
||||
|
||||
# Extract citations from the response
|
||||
citations = raw_response_json.get("citations", [])
|
||||
search_results = raw_response_json.get("search_results", [])
|
||||
|
||||
# Create a mapping of URLs to search result titles
|
||||
url_to_title = {}
|
||||
for result in search_results:
|
||||
if isinstance(result, dict) and "url" in result and "title" in result:
|
||||
url_to_title[result["url"]] = result["title"]
|
||||
|
||||
# Get the message content to find citation positions
|
||||
content = getattr(message, "content", "")
|
||||
if not content:
|
||||
return
|
||||
|
||||
# Find all citation markers like [1], [2], [3], [4] in the text
|
||||
import re
|
||||
|
||||
citation_pattern = r"\[(\d+)\]"
|
||||
citation_matches = list(re.finditer(citation_pattern, content))
|
||||
|
||||
# Create a mapping of citation numbers to URLs
|
||||
citation_number_to_url = {}
|
||||
for i, citation in enumerate(citations):
|
||||
if isinstance(citation, str):
|
||||
citation_number_to_url[i + 1] = citation # 1-indexed
|
||||
|
||||
# Create annotations for each citation match found in the text
|
||||
for match in citation_matches:
|
||||
citation_number = int(match.group(1))
|
||||
if citation_number in citation_number_to_url:
|
||||
url = citation_number_to_url[citation_number]
|
||||
title = url_to_title.get(url, "")
|
||||
|
||||
# Create the URL citation annotation with actual text positions
|
||||
url_citation: ChatCompletionAnnotationURLCitation = {
|
||||
"url": url,
|
||||
"title": title,
|
||||
"start_index": match.start(),
|
||||
"end_index": match.end(),
|
||||
}
|
||||
|
||||
annotation: ChatCompletionAnnotation = {
|
||||
"type": "url_citation",
|
||||
"url_citation": url_citation,
|
||||
}
|
||||
|
||||
annotations.append(annotation)
|
||||
|
||||
# Add annotations to the message if we have any
|
||||
if annotations:
|
||||
if not hasattr(message, "annotations") or message.annotations is None:
|
||||
message.annotations = []
|
||||
message.annotations.extend(annotations)
|
||||
|
||||
# Also add the raw citations and search_results as attributes for backward compatibility
|
||||
if citations:
|
||||
setattr(model_response, "citations", citations)
|
||||
if search_results:
|
||||
setattr(model_response, "search_results", search_results)
|
||||
Reference in New Issue
Block a user