[Feature Request] Improve Code Index search results by implementing Reranking

[sealad886]

Technical Proposal: Advanced Reranking Module

1. Introduction

Proposal to implement a robust, efficient reranking module. Building on the momentum from completing Code Index, the objective is to enhance search result precision, optimize token usage, and implement yet another common feature that is implemented in other mature tools.

Objectives and Success Criteria

• Objective: Enhance search result quality and efficiency by introducing a semantic reranking module that prioritizes contextually relevant results and optimizes downstream LLM usage.
• Success Criteria:
  • Achieve at least a 30% reduction in average LLM token usage per query.
  • Improve top-5 search precision by a measurable margin (to be determined via benchmark datasets or user feedback).
  • Maintain or improve system response latency.

2. Reranking Fundamentals: Semantic Match Enhancement

Reranking involves simple reordering by implementing a two-stage process:

1. Initial Retrieval: A broad, efficient search is performed against the code index to gather a candidate set of results.
2. Semantic Reranking: A more sophisticated, computationally intensive model analyzes the semantic relationship between the user's query and the initial results. This stage refines the list, prioritizing items with high contextual relevance and filtering out false positives that may have matched keywords but lack true semantic alignment.

This approach significantly improves the signal-to-noise ratio of the final results presented to the user.

Reranking Algorithm Overview

The reranking module will use a transformer-based cross-encoder to compute semantic similarity scores between the user query and candidate results. Multiple signals (e.g., embedding similarity, code context, metadata) will be combined using a weighted scoring function. The top N results will be selected for downstream processing. The design will allow for easy extension with new signals or alternative ranking strategies. Fallback logic will ensure that, in case of model errors, the system gracefully returns the initial retrieval results.

3. Technical Benefits: Efficiency, Precision, and Token Optimization

Implementing a reranking module delivers several key technical advantages that go beyond just reducing token usage:

• Improved Result Precision: Semantic reranking surfaces results that are contextually relevant to the user’s query, filtering out false positives and noisy matches. This leads to higher-quality, more actionable search outcomes.
• Token Usage Optimization: Only the top N reranked, highly relevant results are sent to the Large Language Model (LLM) for synthesis. This minimizes the number of tokens processed, directly reducing operational costs and resource consumption.
• Lower Latency and Faster Response: Smaller, more relevant payloads mean the LLM can process requests more quickly, resulting in a more responsive and interactive user experience.
• Scalability: The two-stage retrieval and reranking approach allows the system to efficiently handle large codebases. The initial retrieval can remain broad and fast, while the computationally intensive reranking is applied only to a manageable subset of candidates.
• Extensibility: The modular design of the reranking logic makes it straightforward to incorporate new signals, models, or ranking strategies in the future, supporting ongoing improvements without major architectural changes.
• Cost Efficiency: By reducing both the number of tokens and the computational workload, the reranking module helps control infrastructure costs as usage scales.

These benefits collectively ensure that the reranking module not only optimizes resource usage but also enhances the overall quality, speed, and maintainability of the search experience.

4. Proposed Integration Strategy

4.1. Workflow Diagram

The proposed data flow integrates the reranking logic as a distinct step post-retrieval.

flowchart TD
   subgraph "Logical Path"
      A[User Query] --> B@{ shape: rounded, label: "Initial Retrieval" };
      B --> C[Candidate Results];
      C --> D@{ shape: diamond, label: "Reranking Enabled?"};
      D yes@-->|yes| E[*NEW* Reranking];
      E --> F[*NEW* Top-P Reranked Results];
      F --> G[LLM Synthesis];

      D no@-->|no| G;
   end


   %% B -. "user query generates<br>search text" .-> rooDb@{ shape: database, label: "Code Index" };
   %% rooDb -. "returns unordered<br>*N* results" .-> C;
   %% E -. "*N* results from query<br>all sent for rerank".-> rooDb
   %% rooDb -. "*Top P* results returned" .-> F

   B ~~~ cautionTruncate@{ shape: odd, label: "If too few results returned<br>may not find actually<br>relevant code snippet#40;s#41;" };
   E ~~~ noteTopP@{ shape: odd, label: "Top-P is *much* smaller<br>than *N*" };
   allResultsUsed@{ shape: odd, label: "All *N* snippets used" } ~~~~ G

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Proposed Directory Structure

The following directory structure shows how the reranking logic integrates with existing components:

src/services/code-index/
├── cmd/
│   ├── manager.ts   # Manages index data
│   └── main.ts      # Main configuration entry point
│
├── internal/
│   ├── search/
│   │   ├── handlers.ts   # Processes API commands
│   │   ├── rerank/
│   │   │   ├── index.ts  # Main reranker logic
│   │   │   ├── signals.ts  # Reranking factors
│   │   │   ├── config/
│   │   │   │   ├── index.ts    # Defines configuration types used across the reranking module
 This validation logic is implemented in the existing `config-manager.ts` under `src/services/code-index/`.
│   │   │   │   └── utils.ts    # Validation/utility functions
│   │
│
└── pkg/
    └── config/
        └── config.ts      # Loads application configuration

The core idea is to separate:

• Index management
• The reranking process
• Configuration handling

4.2. Token Reduction Summary

By passing only the top 15% of reranked results to downstream consumers, token consumption reduces by:

• Average 30% token savings per query
• Significant computational efficiency for language model processing

4.3. Configuration Relationships

Shared Configuration Validation
To avoid code duplication, the new directory structure creates a shared configuration hierarchy:

1. BaseModelConfig.ts: Defines common parameters like embedding length and model endpoints
2. IndexerConfig.ts: Extends base for indexing parameters
3. RerankerConfig.ts: Extends base for reranking signals

Validation Strategy: Config validation occurs once at the BaseModelConfig level through config-manager.ts, then propagates through interface hierarchy.

classDiagram
class AppConfig
class BaseModelConfig
class IndexerConfig
class RerankerConfig
class ConfigManager

AppConfig o-- ConfigManager : loads
ConfigManager "1" o-- "*" BaseModelConfig : manages
BaseModelConfig --|> IndexerConfig
BaseModelConfig --|> RerankerConfig

ConfigManager : +validateAll()
ConfigManager : +validate(BaseModelConfig)
ConfigManager : +validate(IndexerConfig)
ConfigManager : +validate(RerankerConfig)

AppConfig --> ConfigManager : requires validation

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4.4. Core Integration Points

1. Code Index Manager:

   • A post-processing hook will be introduced in searchIndex to intercept the initial search results and pass them to the reranking module.

   Toggles will use Tailwind styles from index.css per project conventions.

2. Experimental Settings Component:

   • Configuration toggles will be added to the experimental features block in ExperimentalSettings.tsx, allowing users to enable/disable or fine-tune the reranking behavior.

5. Conclusion

Reranking is the logical next step to undertake after completion of the Code Index project.

[nnWhisperer]

I remember seeing reranking at the earlier days of LLM rag popularity, then graphrags became popular and I couldn't catch what's the solution to go nowadays.
I can imagine that the solution you are talking about can be embedded inside the rag module, by keeping the module interface away from the implementation, the roo-code team could similarly integrate both a re-ranking solution and a graphrag solution in addition to the vanilla rag, using already published libraries easily.

[nikhil-swamix]

SO true, this apis and similar become useless (unsuable) ,
https://docs.voyageai.com/docs/reranker
however spawning a seperate "context gatherer" agent with lite system prompt, and a slave model like "qwen coder 32b" woule fare much better, and merge the relevant files to master thread. like an errand boy. what do you think?

[sealad886]

I mean, yeah if you wanna use a massive model to do the work of what a smaller model can do more effectively--the brute force method always suits.

But embed+rerank model pairs are still being released (see Qwen3-Embedding and Qwen3-Reranker as just one SOTA example, and currently top of MTEB leaderboard).

I mean so many other parts of this project are about really finessing the actual instruction and context given to each task, so there really isn't a reason not to do rerank next.

[nikhil-swamix]

agreed. We must pitch this to Kilo code also.
because they even have autocomplete, and commit message.
what roo is to cline, is kilo code to roo, much fast moving people there...
and for some reason, roo buries most issues/proposals. something really off these days.