Search Futures - rollthecloudinc/hedge GitHub Wiki

type conversion issues

Since you've stabilized core relevance scoring and complex aggregation, the next powerful feature to add is Nested Aggregation Metrics (specifically, calculating metrics on the inner documents and rolling up to the parent bucket).

While you have the framework for Nested Aggregation (which flattens the array and runs sub-aggregations), you cannot currently perform simple metrics (like Average) on the inner documents and have that metric appear alongside the grouping bucket.

This feature is particularly useful for fields like "reviews," where you want to group parent items (e.g., Cars) but display a key metric calculated from the inner items (e.g., the Average Review Rating for all reviews aggregated).

"aggs": {
    "name": "reviews_agg",
    "path": "reviews",
    "metrics": [
        {"type": "avg", "field": "reviewRating"} // <--- NEW NESTED METRIC
    ],
    "subAggs": {
        "rating_buckets": {
            "groupBy": ["reviewRating"] // <--- SUB-AGGREGATION
        }
    }
}

"aggregations": {
    "reviews_agg": [
        {
            "key": "rating_buckets",
            "doc_count": 120, // Total number of unnested reviews
            "metrics": {
                "avg_reviewRating": 6.85 // <-- METRIC ROLLED UP HERE
            },
            "buckets": [
                {
                    "key": "7",
                    "doc_count": 50,
                    // ... (sub-aggregation metrics)
                },
                // ...
            ]
        }
    ]
}

To make your Vertigo Search & Analytics Engine truly industry-competitive and even more powerful, you should focus on features that enhance real-time performance, query understanding, and relevance management.

Here are five major categories of features you can add:

Enhancing Performance and Data Management

Your current system relies on fetching and processing data on-demand from GitHub. To scale and speed up queries, you need a faster intermediate data layer.

A. Caching and Materialized Views ⚡

Document Caching Layer: Implement a fast, in-memory cache (like Redis or Memcached) to store the results of recent, complex queries or frequently accessed index documents. This dramatically reduces latency by bypassing the full GitHub fetch and processing steps.

Materialized Aggregations: For common, time-intensive aggregations (e.g., "Monthly Sales by Region"), pre-calculate and store the results. When a user requests that specific aggregation, serve the materialized view instantly, only recalculating it if the underlying GitHub data changes.

B. Index Optimization (Full-Text)

N-gram Analysis: Augment your existing single-word stemming with n-gram tokenization (bi-grams, tri-grams) to improve matching on short phrases and foreign languages, offering better precision than just single stemmed words.

Synonym/Thesaurus Filter: Add a configuration file to the analysis pipeline that maps synonyms (e.g., car → auto, vehicle) to a common token, ensuring a query for "car" returns results containing "vehicle."

Advanced Relevance and Scoring

You have basic function scoring, but you can build a full, professional-grade relevance tuning system.

A. Learning to Rank (L2R) 🧠

Machine-Learned Scoring: Instead of manually creating score functions, integrate a component that collects user interaction data (clicks, purchases) and uses a machine learning model (like LambdaMART) to automatically learn the optimal weights for various features (e.g., matching title, matching tag, recency, price). This would replace your manual ScoreModifiers with an optimized, data-driven score.

B. Decay Functions

Dynamic Field Decay: Introduce distance decay functions (Gaussian, exponential, linear) to the relevance score. This allows the system to automatically penalize documents based on their distance from a target value.

    Example: Decrease the score exponentially the further a document's price is from the average price in the query results, or the older its created_at date is. This is a common pattern in commercial search platforms.

Query Understanding and User Experience

Improve the likelihood of users finding what they need even if they mistype or use uncommon terms.

A. Spell Check and Suggestions

"Did You Mean?" / Auto-Correction: Use the token counts collected during the analysis phase (or a dedicated fuzzy index) to suggest correct spellings for mistyped query terms.

B. Phrase and Proximity Matching

Slop Parameter: Enhance your boolean query with a proximity search capability. A user could query for "red car"~3, which would match documents where the words "red" and "car" appear within 3 words of each other, providing high-relevance results for multi-word queries.

Enhanced Aggregation and Analytics

Deepen the types of insights you can extract from the data.

A. Nested Document Aggregation

Inner Hits/Nested Fields: Allow aggregations to run against fields within nested arrays inside a document (e.g., analyzing the sizes and colors of all inventory items within a product document). This is crucial for rich document models.

B. Pivot/Matrix Aggregation

Two-Dimensional Bucketing: Implement an aggregation that groups data simultaneously by two different fields (a matrix or pivot table), enabling complex cross-tabulation (e.g., Group documents by region AND category in a single request).

Security and Access Control

Given the GitHub-backed nature of your system, explicit access controls are essential.

A. Document-Level Security (DLS) 🔒

Per-Document Filtering: Integrate a policy layer that uses the authenticated user's GitHub permissions or assigned roles to automatically filter the allDocuments array before scoring and sorting. This ensures users only ever see content they are authorized to access, even if the query logic matches the document.

That's a great direction! To make your Vertigo Search & Analytics Engine a powerhouse for data discovery, you should focus on two key areas: Nested Aggregation (for complex documents) and Metric Depth (for sophisticated statistical analysis).

Here's how you can enhance your aggregation system:

Nested Aggregation 📦

Currently, your aggregation likely works well on top-level fields. Nested Aggregation allows you to perform Bucketing and Metric calculations on fields within nested arrays (arrays of objects) inside your documents. This is essential for documents that adhere to a rich, complex structure, like a product catalog with multiple inventory items.

A. New Aggregation Type: Nested

Introduce a new Type to your Aggregation structure, perhaps called "nested", which requires a Path to the array field and an inner Aggregation definition.

Conceptual Request Body Snippet: JSON

{ "aggs": { "colors_and_sizes": { "type": "nested", "path": "variants", // The array of objects (e.g., product inventory) "aggs": { "color_buckets": { "type": "groupby", "field": "variants.color" // Aggregate on a field inside the array } } } } }

B. Implementation Logic in ExecuteAggregation

The core function ExecuteAggregation would need to be modified:

Detection: If the aggregation Type is "nested", iterate through every document.

Unnesting: For each document, iterate through the array specified by Path (e.g., the variants array).

Virtual Documents: Treat each object within the array (variants[0], variants[1], etc.) as a virtual document.

Recursive Call: Pass the collection of these virtual documents to a recursive call of ExecuteAggregation using the inner aggregation definition (color_buckets).

Result Mapping: The final buckets produced by the inner aggregation are returned as the result of the outer colors_and_sizes aggregation.

Advanced Metric Depth (Pipeline Aggregations) 📊

Beyond simple counts and sums, professional analytics platforms provide pipeline metrics—calculations based on the results of other aggregations.

A. Implement New Metric Types

You should expand your ExecuteMetric function to include:

Percentiles: Calculate the Pth percentile for a numeric field (e.g., "What is the 95th percentile price for blue shirts?"). This provides insight into the distribution of data beyond the median.

    Metric: Percentile (requires a Field and a PercentileValue float).

Cardinality: Calculate the number of unique values for a non-numeric field (e.g., "How many unique color names exist in this dataset?"). This is a high-performance substitute for a full GroupBy count when only uniqueness is required.

    Metric: Cardinality (requires a Field).

B. Introduce Bucket Script (Pipeline Metric) 🧪

This is the most powerful addition: a new metric that calculates a value based on the results of previously calculated metrics within the same bucket.

Goal: Allow users to define a calculation using a small expression (like a Go template function or simple arithmetic string) over metric values.

Example: Calculate the "conversion rate" in a bucket using the metric results: (metric["sales_sum"] / metric["views_count"]).

Metric: BucketScript

    Definition: Requires a BucketsPath (mapping metric names to variables) and an Script string (the formula).

Conceptual Aggregation Request (Combining Metrics): JSON

{ "aggs": { "daily_stats": { "type": "date_histogram", "field": "date", "interval": "day", "metrics": [ {"name": "sales_sum", "type": "sum", "field": "revenue"}, {"name": "views_count", "type": "sum", "field": "page_views"}, // Pipeline Metric using the two metrics above {"name": "conversion_rate", "type": "bucket_script", "script": "params.sales / params.views", "buckets_path": {"sales": "sales_sum", "views": "views_count"} } ] } } }

This requires that the ExecuteAggregation function first computes all simple metrics (sum, avg) and then iterates a second time over the results to compute any bucket_script metrics.