[BUG] Rename ChucksHybridSearchRetriever and add error handling for embedding generation

[Dayang26]

Bug Description

Currently, there are two issues in
chunks_hybrid_search.py :

1. Typo: The class is named
   ChucksHybridSearchRetriever instead of ChunksHybridSearchRetriever.

SurfSense/surfsense_backend/app/retriever/chunks_hybrid_search.py

Lines 3 to 5 in 8c60910

	class ChucksHybridSearchRetriever:
	def __init__(self, db_session):

2. Lack of Error Handling:
   The embedding_model.embed(query_text) call is not wrapped in a try-except block. If the embedding service (e.g. OpenAI or Azure) fails or times out, the entire request crashes with a 500 error instead of gracefully falling back to keyword search.

SurfSense/surfsense_backend/app/retriever/chunks_hybrid_search.py

Lines 35 to 43 in 8c60910

	from sqlalchemy import select
	from sqlalchemy.orm import joinedload
	from app.config import config
	from app.db import Chunk, Document
	# Get embedding for the query
	embedding_model = config.embedding_model_instance
	query_embedding = embedding_model.embed(query_text)

Deployment Type

• SurfSense Cloud (hosted version)
• Self-hosted version

Steps to Reproduce

Expected Behavior

1. Rename class to ChunksHybridSearchRetriever.
2. Catch exceptions during embedding generation. If it fails， log the error and retry or skip vector search , so that keyword search can still work.

I am happy to contribute a pull request to fix these issues once they are confirmed by the maintainers.

Actual Behavior

Screenshots/Videos

Environment Information

• Browser: Chrome 144
• Operating System: macOS 15
• SurfSense Version: self-hosted(latest version）

Additional Environment Details (for Self-hosted only)

• Python Version: [3.12.11]
• Node.js Version: [25.5.0]
• Database: [PostgreSQL 16]
• Deployment Method: [Docker]

Additional Context

Logs/Error Messages

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Checklist

• I have searched existing issues to ensure this is not a duplicate
• I have provided all the required information above
• I have added appropriate labels (bug, deployment type)