feat: add Perplexity contextualized embeddings API as a new model provider (#13709)

### What problem does this PR solve?

Adds Perplexity contextualized embeddings API as a new model provider,
as requested in #13610.

- `PerplexityEmbed` provider in `rag/llm/embedding_model.py` supporting
both standard (`/v1/embeddings`) and contextualized
(`/v1/contextualizedembeddings`) endpoints
- All 4 Perplexity embedding models registered in
`conf/llm_factories.json`: `pplx-embed-v1-0.6b`, `pplx-embed-v1-4b`,
`pplx-embed-context-v1-0.6b`, `pplx-embed-context-v1-4b`
- Frontend entries (enum, icon mapping, API key URL) in
`web/src/constants/llm.ts`
- Updated `docs/guides/models/supported_models.mdx`
- 22 unit tests in `test/unit_test/rag/llm/test_perplexity_embed.py`

Perplexity's API returns `base64_int8` encoded embeddings (not
OpenAI-compatible), so this uses a custom `requests`-based
implementation. Contextualized vs standard model is auto-detected from
the model name.

Closes #13610

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update

test/unit_test/rag/llm/conftest.py

@@ -0,0 +1,61 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+"""
+Prevent rag.llm.__init__ from running its heavy auto-discovery loop.
+
+The __init__.py dynamically imports ALL model modules (chat_model,
+cv_model, ocr_model, etc.), which pull in deepdoc, xgboost, torch,
+and other heavy native deps. We pre-install a lightweight stub for
+the rag.llm package so that `from rag.llm.embedding_model import X`
+works without triggering the full init.
+"""
+
+import os
+import sys
+import types
+
+# Resolve the real path to rag/llm/ so sub-module imports can find files
+_RAGFLOW_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
+_RAG_LLM_DIR = os.path.join(_RAGFLOW_ROOT, "rag", "llm")
+
+
+def _install_rag_llm_stub():
+    """Replace rag.llm with a minimal package stub if not yet loaded.
+
+    The stub has __path__ pointing to the real rag/llm/ directory so that
+    `from rag.llm.embedding_model import X` resolves to the actual file,
+    but the __init__.py auto-discovery loop is skipped.
+    """
+    if "rag.llm" in sys.modules:
+        return
+
+    # Create a stub rag.llm package that does NOT run the real __init__
+    llm_pkg = types.ModuleType("rag.llm")
+    llm_pkg.__path__ = [_RAG_LLM_DIR]
+    llm_pkg.__package__ = "rag.llm"
+    # Provide empty dicts for the mappings the real __init__ would build
+    llm_pkg.EmbeddingModel = {}
+    llm_pkg.ChatModel = {}
+    llm_pkg.CvModel = {}
+    llm_pkg.RerankModel = {}
+    llm_pkg.Seq2txtModel = {}
+    llm_pkg.TTSModel = {}
+    llm_pkg.OcrModel = {}
+    sys.modules["rag.llm"] = llm_pkg
+
+
+_install_rag_llm_stub()