Day 19 challenge
Goal: build document-based retrieval with MongoDB for unstructured data retrievalTheme: context engineering week - NoSQL and document retrievalTime investment: ~25 minutes
What you’ll accomplish today
- Set up MongoDB Atlas with vector search capabilities for document retrieval
- Load Amazon product reviews as example unstructured data
- Connect your agent to MongoDB for intelligent document search
- Build retrieval patterns for sentiment analysis and contextual understanding
This requires setting up MongoDB Atlas (free tier available). You’ll be
working with unstructured documents that require different indexing and search
strategies than structured data.
Step 1: Understanding document-based retrieval
Document databases handle unstructured data differently than relational databases:Document and relational data
Relational data (Day 18):- Fixed schema with defined columns
- Structured relationships between tables
- Efficient for transactional operations
- Great for precise, attribute-based queries
- Flexible schema with nested objects and arrays
- Self-contained documents with varying structures
- Efficient for content storage and retrieval
- Great for text analysis and semantic search
When to use document-based retrieval
Ideal for:- Product reviews and customer feedback
- Content analysis (blogs, articles, documentation)
- Social media posts and comments
- Support tickets and conversation logs
- Research papers and knowledge articles
- Highly structured transactional data
- Complex multi-table relationships
- Frequent schema changes requiring migrations
Document thinking Think of document retrieval as searching through a
library of books rather than looking up entries in a catalog. Context and
content matter more than exact structure.
Step 2: Set up MongoDB Atlas with vector search
MongoDB Atlas provides managed MongoDB with built-in vector search:Create your MongoDB Atlas cluster
- Visit mongodb.com/atlas and create a free account
- Create a new cluster (M0 free tier is sufficient for learning)
- Set up database access with username/password authentication
- Configure network access to allow connections from your IP
- Get your connection string for later use
Create the reviews collection
Load Amazon product reviews data
Create a realistic dataset of product reviews with varying structures:Sample review documents
Connect MongoDB to your agent
Integrate MongoDB with your Hypermode agent for document retrieval:Add MongoDB connection
- Navigate to your agent’s connections in the About section
- Add MongoDB connection with your Atlas cluster credentials
- Test the connection by querying the reviews collection
Create a review analysis agent
If needed, create a specialized agent with Concierge:Configure document retrieval patterns
Add these instructions to your agent:Implement document search and analysis
Test your document-based retrieval system:Semantic review search
- Search reviews for camera-related content
- Extract specific mentions of camera features and performance
- Analyze sentiment around camera quality
- Provide quotes from actual reviews
- Summarize trends across multiple customer experiences
Sentiment analysis queries
- Aggregate sentiment across multiple reviews
- Identify common themes in feedback
- Distinguish between different types of issues or praise
- Provide actionable insights from unstructured feedback
Comparative analysis
- Cross-product analysis using semantic search
- Pattern recognition across different product categories
- Balanced reporting of pros and cons
- Context-aware insights about user preferences
Document retrieval insight The power of document-based RAG is in
understanding context and nuance that structured data misses. Focus on
semantic meaning over exact keyword matches.
What you’ve accomplished
In 25 minutes, you’ve mastered document-based RAG systems: MongoDB foundation: set up Atlas with vector search for document retrieval Unstructured data handling: loaded and indexed product reviews with flexible schemas Document search: implemented search across varying content structures Sentiment analysis: built patterns for understanding customer feedback and satisfaction trends Advanced aggregation: explored complex queries for multi-dimensional document analysisThe power of document-based retrieval
Document RAG unlocks insights from unstructured content: Before document RAG Limited to structured product information and marketing claims After document RAG Access to real customer experiences, sentiment trends, and nuanced feedback Combined with yesterday’s structured RAG, you now have comprehensive information retrieval capabilities.Tomorrow - Day 20
Explore GraphRAG with Neo4j for complex relationship reasoning and knowledge
discovery through connected data.
Pro tip for today
Test document retrieval with nuanced queries:Time to complete: ~25 minutes Skills learned MongoDB Atlas setup, document-based retrieval, semantic search across unstructured data, sentiment analysis, advanced aggregation patterns Next: day 20 - GraphRAG with Neo4j for relationship-based knowledge discovery
Remember: document-based RAG excels at capturing human context, emotion,
and nuanced experiences that structured data often misses. Use it for
understanding “why” not just “what.”