feat: wire PDF and text file content into AI chat messages

PDF uploads were stored in S3 and had text extracted during upload, but
fetch_upload_images() filtered exclusively for image MIME types, so
document content never reached the AI.

- Add fetch_upload_documents() in storage_service.py to retrieve
  extracted_content for PDFs and text files
- Update ai_sessions.py chat endpoint to call both fetch_upload_images
  and fetch_upload_documents, injecting document text as context
- Add PDF text extraction in _generate_ai_description (pypdf)
- Add pypdf>=4.0.0 to requirements.txt
- Fix test_db teardown to avoid connection pool issues
- Add 5 tests for fetch_upload_documents

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/api/endpoints/uploads.py

@@ -61,6 +61,40 @@ async def _generate_ai_description(upload_id: UUID, file_data: bytes, content_ty
                     max_tokens=100,
                 )
                 upload.ai_description = description
+            elif content_type == "application/pdf":
+                try:
+                    from pypdf import PdfReader
+                    import io as _io
+
+                    reader = PdfReader(_io.BytesIO(file_data))
+                    pages_text = []
+                    for page in reader.pages:
+                        page_text = page.extract_text()
+                        if page_text:
+                            pages_text.append(page_text)
+                    text_content = "\n\n".join(pages_text)
+                except Exception:
+                    logger.warning("PDF text extraction failed for upload %s", upload_id)
+                    text_content = ""
+
+                if text_content:
+                    upload.extracted_content = text_content[:10000]
+
+                    if len(text_content) > 2000:
+                        summary, _, _ = await _call_ai(
+                            system_base="You are a technical document analyst for IT troubleshooting.",
+                            rag_context="",
+                            history=[],
+                            new_message=f"Summarize this PDF content in 2-3 sentences:\n\n{text_content[:5000]}",
+                            max_tokens=200,
+                        )
+                        upload.content_summary = summary
+                        upload.ai_description = summary
+                    else:
+                        upload.ai_description = f"PDF document: {upload.filename}"
+                else:
+                    upload.ai_description = f"PDF document (no extractable text): {upload.filename}"
+
             elif content_type.startswith("text/") or content_type in (
                 "application/json", "application/xml", "application/yaml",
             ):