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Document Analysis

Transform your documents into intelligent, searchable knowledge with SnapThinkโ€™s powerful document analysis features.

Supported File Types

SnapThink can process and analyze various document formats:

๐Ÿ“„ PDF Documents

๐Ÿ“Š CSV Datasets

๐Ÿ“ Text Files

How Document Analysis Works

1. Upload Process

  1. Click โ€œ๐Ÿ“„ Upload Documentโ€ button
  2. Select your file
  3. SnapThink automatically:
    • Extracts text content
    • Breaks it into searchable chunks
    • Creates AI embeddings for semantic search
    • Stores everything locally

2. Intelligent Processing

3. Ready for Questions

Once processed, you can ask the AI anything about your documents!

Document Intelligence Features

Ask questions in natural language:

๐Ÿ“‹ Automatic Summarization

Get instant summaries:

๐Ÿ”„ Cross-Document Analysis

Compare multiple documents:

๐Ÿ“Š Data Insights (CSV files)

For datasets, get automatic analysis:

Working with Different File Types

๐Ÿ“„ PDF Documents

Best Practices

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"

๐Ÿ“Š CSV Datasets

Automatic Processing

When you upload a CSV, SnapThink automatically:

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]

๐Ÿ“ Text Files

Ideal for

Processing Features

Advanced Analysis Techniques

๐Ÿ“ˆ Data Visualization

For CSV files, request specific visualizations:

Find specific information:

๐Ÿ“ Content Extraction

Extract structured information:

๐Ÿงฎ Quantitative Analysis

For data-rich documents:

File Management

๐Ÿ“‚ Notebook Organization

๐Ÿ’พ Storage and Privacy

๐Ÿ”„ File Operations

Best Practices

๐Ÿ“‹ Document Preparation

For PDFs

For CSV Files

For Text Files

๐ŸŽฏ 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

Request Different Formats

Troubleshooting

Common Issues

โ€œDocument failed to processโ€

โ€œNo text found in PDFโ€

โ€œCanโ€™t find informationโ€

CSV not loading in Python

Example Use Cases

๐Ÿ“š 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?"

๐Ÿ“Š Business Analysis

Upload: sales-report.csv
Ask: "What were our top-performing products?"
Visualize: "Show monthly revenue trends"
Insights: "Which regions need improvement?"

๐Ÿ“‹ 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

Ready to analyze your first document? Upload a file and start asking questions!