feat: Add end-to-end tests for document upload pipeline and shared test utilities

- Introduced new test files for end-to-end testing of document uploads, including support for .txt, .md, and .pdf formats.
- Created shared fixtures and helper functions for authentication, document management, and cleanup.
- Added sample documents for testing purposes.
- Established a conftest.py file to provide reusable fixtures across test modules.

surfsense_backend/tests/fixtures/sample.md

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+# SurfSense Test Document
+
+## Overview
+
+This is a **sample markdown document** used for end-to-end testing of the manual
+document upload pipeline. It includes various markdown formatting elements.
+
+## Key Features
+
+- Document upload and processing
+- Automatic chunking of content
+- Embedding generation for semantic search
+- Real-time status tracking via ElectricSQL
+
+## Technical Architecture
+
+### Backend Stack
+
+The SurfSense backend is built with:
+
+1. **FastAPI** for the REST API
+2. **PostgreSQL** with pgvector for vector storage
+3. **Celery** with Redis for background task processing
+4. **Docling/Unstructured** for document parsing (ETL)
+
+### Processing Pipeline
+
+Documents go through a multi-stage pipeline:
+
+| Stage | Description |
+|-------|-------------|
+| Upload | File received via API endpoint |
+| Parsing | Content extracted using ETL service |
+| Chunking | Text split into semantic chunks |
+| Embedding | Vector representations generated |
+| Storage | Chunks stored with embeddings in pgvector |
+
+## Code Example
+
+```python
+async def process_document(file_path: str) -> Document:
+    content = extract_content(file_path)
+    chunks = create_chunks(content)
+    embeddings = generate_embeddings(chunks)
+    return store_document(chunks, embeddings)
+```
+
+## Conclusion
+
+This document serves as a test fixture to validate the complete document processing
+pipeline from upload through to chunk creation and embedding storage.

surfsense_backend/tests/fixtures/sample.txt

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+SurfSense Document Upload Test
+
+This is a sample text document used for end-to-end testing of the manual document
+upload pipeline in SurfSense. The document contains multiple paragraphs to ensure
+that the chunking system has enough content to work with.
+
+Artificial Intelligence and Machine Learning
+
+Artificial intelligence (AI) is a broad field of computer science concerned with
+building smart machines capable of performing tasks that typically require human
+intelligence. Machine learning is a subset of AI that enables systems to learn and
+improve from experience without being explicitly programmed.
+
+Natural Language Processing
+
+Natural language processing (NLP) is a subfield of linguistics, computer science,
+and artificial intelligence concerned with the interactions between computers and
+human language. Key applications include machine translation, sentiment analysis,
+text summarization, and question answering systems.
+
+Vector Databases and Semantic Search
+
+Vector databases store data as high-dimensional vectors, enabling efficient
+similarity search operations. When combined with embedding models, they power
+semantic search systems that understand the meaning behind queries rather than
+relying on exact keyword matches. This technology is fundamental to modern
+retrieval-augmented generation (RAG) systems.
+
+Document Processing Pipelines
+
+Modern document processing pipelines involve several stages: extraction, transformation,
+chunking, embedding generation, and storage. Each stage plays a critical role in
+converting raw documents into searchable, structured knowledge that can be retrieved
+and used by AI systems for accurate information retrieval and generation.