Document Analysis
Transform your documents into intelligent, searchable knowledge with SnapThinkโs powerful document analysis features.
Supported File Types
SnapThink can process and analyze various document formats:
๐ PDF Documents
- Research papers and reports
- Books and manuals
- Forms and contracts
- Presentations
๐ CSV Datasets
- Spreadsheet data
- Survey results
- Sales and financial data
- Scientific measurements
๐ Text Files
.txt
- Plain text documents.md
- Markdown files- Research notes and documentation
How Document Analysis Works
1. Upload Process
- Click โ๐ Upload Documentโ button
- Select your file
- SnapThink automatically:
- Extracts text content
- Breaks it into searchable chunks
- Creates AI embeddings for semantic search
- Stores everything locally
2. Intelligent Processing
- Text Extraction: Converts PDFs and other formats to searchable text
- Smart Chunking: Breaks documents into logical sections
- Embedding Creation: Generates semantic vectors for AI understanding
- Metadata Storage: Preserves file information and structure
3. Ready for Questions
Once processed, you can ask the AI anything about your documents!
Document Intelligence Features
๐ Semantic Search
Ask questions in natural language:
"What does this paper say about climate change?"
"Find mentions of machine learning algorithms"
"What are the conclusions of this study?"
๐ Automatic Summarization
Get instant summaries:
"Summarize this document in 3 bullet points"
"What are the key findings?"
"Give me the main topics covered"
๐ Cross-Document Analysis
Compare multiple documents:
"How do these two papers differ in their approach?"
"Find common themes across all my uploaded documents"
"Which document best explains topic X?"
๐ Data Insights (CSV files)
For datasets, get automatic analysis:
- Column descriptions and data types
- Basic statistics and distributions
- Suggested visualizations
- Data quality insights
Working with Different File Types
๐ PDF Documents
Best Practices
- Text-based PDFs work best (searchable text)
- Scanned PDFs may have limited text extraction
- Large files (>50MB) may take longer to process
Example Workflow
1. Upload: research-paper.pdf
2. Wait for processing (30-60 seconds)
3. Ask: "What methodology did the authors use?"
4. Follow up: "What were their main findings?"
5. Deep dive: "Explain the statistical analysis in detail"
๐ CSV Datasets
Automatic Processing
When you upload a CSV, SnapThink automatically:
- Detects column headers and data types
- Provides file path for Python analysis
- Suggests initial exploration questions
File Path Integration
SnapThink provides the exact file path for Python:
import pandas as pd
df = pd.read_csv("/path/to/your/notebook/docs/data.csv")
Example Analysis
User: Upload sales-data.csv
AI: I've processed your CSV file with 1,000 rows and 5 columns:
- Date, Product, Quantity, Price, Region
- File path: /Users/.../notebook-123/docs/sales-data.csv
User: Show me sales trends by month
AI: [Creates Python code to analyze and visualize monthly trends]
๐ Text Files
Ideal for
- Research notes and documentation
- Code documentation (README files)
- Interview transcripts
- Meeting notes
Processing Features
- Preserves formatting and structure
- Maintains line breaks and paragraphs
- Searchable by keywords and concepts
Advanced Analysis Techniques
๐ Data Visualization
For CSV files, request specific visualizations:
"Create a bar chart of sales by region"
"Show the distribution of customer ages"
"Make a time series plot of monthly revenue"
๐ Targeted Search
Find specific information:
"What does page 15 say about methodology?"
"Find all mentions of 'statistical significance'"
"Search for data about customer satisfaction"
๐ Content Extraction
Extract structured information:
"List all the research questions from this paper"
"Extract the key performance metrics"
"What are all the cited authors?"
๐งฎ Quantitative Analysis
For data-rich documents:
"Calculate the average response time"
"What's the correlation between variables X and Y?"
"Identify outliers in the dataset"
File Management
๐ Notebook Organization
- Each notebook has its own
docs/
folder - Files are automatically organized by type
- Full file paths provided for Python access
๐พ Storage and Privacy
- All files stored locally on your computer
- No uploading to external servers
- Files remain in your notebook even offline
๐ File Operations
- View: Preview file contents in the sidebar
- Remove: Delete files from the notebook
- Export: Include files when exporting notebooks
Best Practices
๐ Document Preparation
For PDFs
- โ Use text-based PDFs when possible
- โ Ensure good scan quality for scanned documents
- โ Break very large documents into sections
For CSV Files
- โ Include clear column headers
- โ Use consistent data formats
- โ Clean data before uploading when possible
For Text Files
- โ Use clear, descriptive filenames
- โ Structure content with headings when possible
- โ Include context and metadata
๐ฏ Effective Questioning
Start Broad, Get Specific
1. "What is this document about?" (overview)
2. "What are the main sections?" (structure)
3. "Tell me more about section X" (detail)
4. "How does this relate to concept Y?" (analysis)
Use Context Clues
- Reference specific sections:
"In the methodology section..."
- Mention page numbers:
"What does page 10 discuss?"
- Build on previous answers:
"Expand on that last point"
Request Different Formats
"Summarize in bullet points"
"Create a table of the key findings"
"Explain this in simple terms"
Troubleshooting
Common Issues
โDocument failed to processโ
- File too large: Try files under 50MB
- Unsupported format: Use PDF, CSV, TXT, or MD
- Corrupted file: Try re-saving or using a different version
โNo text found in PDFโ
- Scanned document: PDF might be image-only
- Encrypted PDF: Remove password protection first
- Complex layout: Simple text PDFs work best
โCanโt find informationโ
- Check file processing: Ensure upload completed successfully
- Rephrase question: Try different wording
- Be more specific: Add context or page references
CSV not loading in Python
- Check file path: SnapThink provides the exact path
- Encoding issues: Try saving CSV as UTF-8
- Large files: May need to process in chunks
Example Use Cases
๐ Academic Research
Upload: research-paper.pdf
Ask: "What's the research question and hypothesis?"
Follow: "How did they measure the variables?"
Analyze: "What were the limitations of this study?"
๐ Business Analysis
Upload: sales-report.csv
Ask: "What were our top-performing products?"
Visualize: "Show monthly revenue trends"
Insights: "Which regions need improvement?"
๐ Document Review
Upload: contract.pdf
Ask: "What are the key terms and conditions?"
Search: "Find all mentions of payment terms"
Summary: "What are the main obligations for each party?"
Next Steps
- Python Environment - Learn to combine document data with coding
- CSV Analysis Guide - Detailed data analysis workflows
- PDF Analysis Guide - Specific techniques for PDF documents
Ready to analyze your first document? Upload a file and start asking questions!