Python: Add Perplexity connector (chat completion)

[jliounis]

Summary

Adds a Perplexity chat completion connector to the Python SDK, mirroring the existing third-party connector patterns (NVIDIA NIM, DeepSeek, Mistral AI). The Perplexity Agent API is OpenAI-compatible, so this connector reuses the openai Python SDK with a custom base_url (https://api.perplexity.ai) and an attribution header sent on every request.

• Docs: https://docs.perplexity.ai/api-reference/chat-completions-post
• OpenAI-compatible: yes (same shape, same SDK, different host)
• Default model: sonar-pro

What's included

• semantic_kernel/connectors/ai/perplexity/
  • settings/perplexity_settings.py — PERPLEXITY_* env-var-driven Pydantic settings (mirrors NvidiaSettings / MistralAISettings)
  • prompt_execution_settings/perplexity_prompt_execution_settings.py — OpenAI-shaped chat parameters plus Perplexity-specific search filters (search_recency_filter, search_domain_filter, return_images, return_related_questions)
  • services/perplexity_chat_completion.py — PerplexityChatCompletion service supporting streaming and non-streaming, decorated @experimental
• tests/unit/connectors/ai/perplexity/ — 16 unit tests covering settings, prompt execution settings, and the chat completion service (mocked AsyncOpenAI)
• samples/concepts/setup/chat_completion_services.py — new Services.PERPLEXITY registry entry
• samples/concepts/setup/ALL_SETTINGS.md — documents the new PERPLEXITY_* env vars

Attribution header

Every outgoing request carries X-Pplx-Integration: semantic-kernel/<package-version>, set as a default header on the AsyncOpenAI client. A unit test asserts this is present.

Notes

• This is a chat completion connector only. A search-API connector can be added in a follow-up if useful.
• Mirrors the DeepSeek-style "OpenAI-compatible third-party provider" pattern, with the addition of the attribution header and Perplexity-specific search controls.
• Does not add any new third-party dependencies — uses openai (already a core dep) with base_url overridden.
• sonar-pro is the only user-facing model in docs/samples.

Test plan

• uv run ruff check — passes
• uv run ruff format --check — passes
• uv run mypy semantic_kernel/connectors/ai/perplexity/ — Success: no issues found in 7 source files
• uv run pytest tests/unit/connectors/ai/perplexity/ -v — 20 passed
• Live smoke test against https://api.perplexity.ai/chat/completions (not run here — requires a real PERPLEXITY_API_KEY)

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[Copilot]

Pull request overview

Adds a new Perplexity (OpenAI-compatible) chat completion connector to the Python SDK, including env-var settings, prompt execution settings, a chat completion service (with attribution header), unit tests, and sample/docs updates.

Changes:

• Introduces semantic_kernel.connectors.ai.perplexity (settings, prompt execution settings, chat completion service w/ streaming + attribution header).
• Adds unit tests for Perplexity settings, prompt execution settings, and chat completion behavior (mocked AsyncOpenAI).
• Updates samples and settings documentation to include the new PERPLEXITY_* environment variables and service registry entry.

Reviewed changes

Copilot reviewed 16 out of 16 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
python/semantic_kernel/connectors/ai/perplexity/init.py	Exposes Perplexity connector public API.
python/semantic_kernel/connectors/ai/perplexity/settings/perplexity_settings.py	Adds env-var driven settings (PERPLEXITY_*) with defaults.
python/semantic_kernel/connectors/ai/perplexity/settings/init.py	Package init.
python/semantic_kernel/connectors/ai/perplexity/prompt_execution_settings/perplexity_prompt_execution_settings.py	Adds OpenAI-shaped chat settings plus Perplexity search filters.
python/semantic_kernel/connectors/ai/perplexity/prompt_execution_settings/init.py	Package init.
python/semantic_kernel/connectors/ai/perplexity/services/perplexity_chat_completion.py	Implements Perplexity chat completion service using AsyncOpenAI + attribution header.
python/semantic_kernel/connectors/ai/perplexity/services/init.py	Package init.
python/tests/unit/connectors/ai/perplexity/conftest.py	Test fixture for Perplexity env vars.
python/tests/unit/connectors/ai/perplexity/init.py	Test package init.
python/tests/unit/connectors/ai/perplexity/settings/init.py	Test package init.
python/tests/unit/connectors/ai/perplexity/services/init.py	Test package init.
python/tests/unit/connectors/ai/perplexity/test_perplexity_prompt_execution_settings.py	Unit tests for settings defaults/validation/serialization aliasing.
python/tests/unit/connectors/ai/perplexity/settings/test_perplexity_settings.py	Unit tests for env loading and defaults.
python/tests/unit/connectors/ai/perplexity/services/test_perplexity_chat_completion.py	Unit tests for init, header merging, request pass-through, and error handling.
python/samples/concepts/setup/chat_completion_services.py	Adds Services.PERPLEXITY + factory method for sample setup.
python/samples/concepts/setup/ALL_SETTINGS.md	Documents PERPLEXITY_* env vars in the settings matrix.

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[Copilot]

response_format allows passing Python types (e.g., type[BaseModel]/type), but this connector doesn’t implement the OpenAI handler’s structured-output conversion (turning those into a JSON-schema response_format dict) before calling AsyncOpenAI.chat.completions.create. As a result, providing a type here will likely raise a serialization/validation error at request time. Consider either (a) restricting response_format to only the dict shapes Perplexity/OpenAI expects, or (b) adding conversion logic similar to OpenAIHandler._handle_structured_output and a validator that sets structured_json_response.

[Copilot]

from_dict accepts env_file_encoding via the constructor, but it isn’t passed through here. This makes from_dict unable to fully reconstruct an instance when a non-default encoding is used.

Suggested change

	env_file_path=settings.get("env_file_path"),
	env_file_path=settings.get("env_file_path"),
	env_file_encoding=settings.get("env_file_encoding"),

[Copilot]

to_dict() doesn’t include base_url even though the connector supports overriding it and from_dict() expects it. This breaks round-tripping for custom endpoints; consider adding base_url: str(self.client.base_url) to the serialized settings.

Suggested change

	"api_key": self.client.api_key,
	"api_key": self.client.api_key,
	"base_url": str(self.client.base_url),

[github-actions]

Automated Code Review

Reviewers: 4 | Confidence: 92%

✓ Correctness

The Perplexity connector adds several Perplexity-specific fields (search_domain_filter, search_recency_filter, return_images, return_related_questions) and top_k to PerplexityChatPromptExecutionSettings. These fields are not valid parameters of the openai SDK's AsyncCompletions.create() method (verified against openai v2.33.0 which has no **kwargs). When any of these fields are set, prepare_settings_dict() includes them in the output dict and client.chat.completions.create(**settings.prepare_settings_dict()) will raise TypeError: create() got an unexpected keyword argument. The test test_search_filters_passed_through passes only because create() is mocked. The fix is to override prepare_settings_dict() to move these non-OpenAI fields into the extra_body dict, which the SDK forwards to the HTTP request body — the same pattern the NVIDIA connector uses for its nvext field.

✓ Security Reliability

This PR adds a Perplexity AI chat completion connector following established patterns from the NVIDIA, DepSeek, and OpenAI connectors. The implementation is clean from a security and reliability standpoint: API keys use Pydantic SecretStr, settings load from environment variables with proper prefix isolation, the AsyncOpenAI client is configured with validated credentials, and error handling wraps API failures in ServiceResponseException. The to_dict() method exposes the raw API key (via self.client.api_key), but this is the same pattern used by every other connector in the codebase (NVIDIA, Azure, OpenAI). No injection risks, unsafe deserialization, secrets in code, resource leaks, or unhandled failure modes were found.

✓ Test Coverage

The PR adds a Perplexity AI connector with reasonable unit test coverage for initialization, settings, attribution headers, non-streaming chat completion, Perplexity-specific search filters, and error handling. However, several significant code paths lack test coverage: (1) streaming chat completion (_inner_get_streaming_chat_message_contents) is a non-trivial async generator with its own content-creation logic but has no tests — other connectors like Bedrock and Anthropic test streaming; (2) the Perplexity-specific citations metadata extraction in _get_metadata_from_chat_response is untested; (3) from_dict/to_dict round-tripping is untested despite to_dict containing custom merge logic. The test patterns (conftest fixtures, parametrize indirect) are consistent with existing connectors.

✓ Design Approach

The Perplexity connector is aimed at an OpenAI-compatible chat endpoint, but the current implementation narows Semantic Kernel’s existing chat contract in two important ways: it hand-rolls request serialization in a way that only preserves plain-text transcripts, and it introduces a provider-specific n field instead of the SDK’s canonical number_of_responses surface. Both are design-level compatibility regressions rather than isolated bugs.

Suggestions

• Add a test for streaming chat completion (get_streaming_chat_message_contents). The _inner_get_streaming_chat_message_contents method is a separate async-generator code path constructing StreamingChatMessageContent with StreamingTextContent items—none of this logic is exercised by current tests. A mock AsyncStream[ChatCompletionChunk] yielding one or two chunks would cover this path, consistent with Bedrock and Anthropic connector tests.

---

Automated review by james-pplx's agents

[github-actions]

Using the provider-wire field name n here leaks transport shape into the SDK surface. The shared conversion path only copies fields that exist on the target settings type, so callers setting the canonical number_of_responses will silently lose it for Perplexity. The existing OpenAI-compatible settings keep the SK field name and alias it to n for serialization.

Suggested change

	n: Annotated[int | None, Field(ge=1)] = None
	number_of_responses: Annotated[int | None, Field(ge=1, serialization_alias="n")] = None