feat: add refine

[Sang-it]

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[greptile-apps]

Greptile Summary

This PR introduces significant architectural improvements and new capabilities across three main areas:

Major Changes:

• Added umem_refine crate for query segmentation using tokenization, stemming, and stopword filtering to enhance search quality through multi-vector retrieval
• Added umem_rerank crate with Pinecone and Cohere provider implementations to improve search result ranking
• Added Amazon Bedrock AI provider support with text and object generation capabilities
• Introduced umem_memory_machine crate to centralize service initialization, replacing scattered initialization logic across binaries
• Restructured umem_core memory types into subdirectory organization for better maintainability
• Enhanced umem_controller with multi-search capabilities that leverage query segmentation and parallel vector search

Issues Found:

• Amazon Bedrock provider contains multiple .unwrap() calls that will panic on error instead of propagating errors properly (lines 50, 166, 184, 207)
• Both Cohere and Pinecone rerankers hardcode top_n: 2 instead of using the options.top_n parameter
• Cohere error message incorrectly references "pinecone" instead of "cohere"
• Previous thread identified issues in umem_refine tokenizer module that need resolution

Architecture Improvements:
The centralization through MemoryMachine is a significant improvement that eliminates duplicated initialization code and provides a clean API surface for service configuration.

Confidence Score: 2/5

• This PR has critical error handling issues in the Bedrock provider that will cause panics in production
• Score reflects multiple logic bugs that will cause runtime failures: unwrap calls in Bedrock provider will panic instead of returning errors, and hardcoded rerank parameters ignore user configuration. While the architectural changes are sound, the error handling issues in new code are critical.
• Pay close attention to crates/umem_ai/src/providers/amazon_bedrock.rs, crates/umem_rerank/src/cohere.rs, and crates/umem_rerank/src/pinecone.rs - these contain logic bugs that need immediate fixes

Important Files Changed

Filename	Overview
crates/umem_refine/src/lib.rs	new query segmentation module with tokenization and stopword filtering - previous issues flagged in thread
crates/umem_memory_machine/src/lib.rs	centralized initialization through MemoryMachine with clean builder pattern and proper error handling
crates/umem_rerank/src/cohere.rs	Cohere reranker implementation with hardcoded top_n and incorrect error message
crates/umem_rerank/src/pinecone.rs	Pinecone reranker implementation with hardcoded top_n parameter
crates/umem_ai/src/providers/amazon_bedrock.rs	Amazon Bedrock provider with text/object generation - multiple unwrap calls that can panic
crates/umem_controller/src/search_memory.rs	integrates refine segmentation into multi-search with parallel embedding and reranking

Sequence Diagram

sequenceDiagram
    participant Client
    participant MemoryMachine
    participant MemoryController
    participant Segmenter
    participant Embedder
    participant VectorStore
    participant Reranker
    participant LLM

    Note over MemoryMachine: Initialization
    Client->>MemoryMachine: new()
    MemoryMachine->>Embedder: get_embedder()
    MemoryMachine->>VectorStore: get_store()
    MemoryMachine->>Reranker: get_reranker()
    MemoryMachine->>LLM: get_model()
    MemoryMachine-->>Client: MemoryMachine instance

    Note over Client,LLM: Create Memory Flow
    Client->>MemoryController: create(request)
    MemoryController->>LLM: generate annotations
    LLM-->>MemoryController: LLMAnnotated
    MemoryController->>Embedder: generate_embedding(summary)
    Embedder-->>MemoryController: vector
    MemoryController->>VectorStore: insert(vector, memory)
    VectorStore-->>MemoryController: ok
    MemoryController-->>Client: Memory

    Note over Client,Reranker: Multi-Search Flow (with Refine)
    Client->>MemoryController: multi_search_with_context(query)
    MemoryController->>Segmenter: process(query)
    Segmenter-->>MemoryController: sub_queries
    MemoryController->>Embedder: generate_embeddings(sub_queries)
    Embedder-->>MemoryController: vectors
    
    par Parallel Vector Search
        MemoryController->>VectorStore: search(vector1)
        MemoryController->>VectorStore: search(vector2)
        MemoryController->>VectorStore: search(vectorN)
        VectorStore-->>MemoryController: memories1
        VectorStore-->>MemoryController: memories2
        VectorStore-->>MemoryController: memoriesN
    end
    
    MemoryController->>Reranker: rank(query, all_memories)
    Reranker-->>MemoryController: ranked_results
    MemoryController-->>Client: Vec<Memory>

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[greptile-apps]

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[vidurkhanal]

@greptile re-review.

[greptile-apps]

_{76 files reviewed, 7 comments}

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