Contextual Chunk Enrichment

[shreyaspimpalgaonkar]

Important

Adds contextual chunk enrichment feature using semantic and neighborhood strategies, updating configurations and database handling.

• Behavior:
  • Adds chunk_enrichment_settings to IngestionConfig in ingestion.py.
  • Implements chunk_enrichment() in ingestion_service.py to enrich document chunks using LLM and store enriched chunks.
  • Introduces get_semantic_neighbors() in vector.py to retrieve similar chunks based on vector similarity.
• Configuration:
  • Updates full.toml and r2r.toml to include chunk_enrichment_settings with strategies and model configuration.
• Models:
  • Adds ChunkEnrichmentStrategy and ChunkEnrichmentSettings in ingestion.py for defining enrichment strategies and settings.
• Prompts:
  • Adds chunk_enrichment.yaml to define the prompt template for chunk enrichment tasks.

^{This description was created by for 5cb14ab. It will automatically update as commits are pushed.}

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[ellipsis-dev]

👍 Looks good to me! Reviewed everything up to 5cb14ab in 24 seconds

More details

• Looked at 508 lines of code in 9 files
• Skipped 0 files when reviewing.
• Skipped posting 3 drafted comments based on config settings.

1. py/core/main/orchestration/hatchet/ingestion_workflow.py:144

• Draft comment:
  Remove commented-out code to keep the codebase clean and maintainable.
• Reason this comment was not posted:
  Confidence changes required: 50%
  The code in ingestion_workflow.py has commented-out sections that are not used. These should be removed to keep the code clean and maintainable.

2. py/core/main/services/ingestion_service.py:490

• Draft comment:
  Consider making the batch size configurable instead of hardcoding it to 128 for better flexibility and optimization.
• Reason this comment was not posted:
  Confidence changes required: 50%
  The chunk_enrichment method in ingestion_service.py uses a hardcoded batch size of 128 for processing tasks. This should be configurable to allow flexibility and optimization based on different environments.

3. py/core/providers/database/vector.py:569

• Draft comment:
  The similarity threshold comparison seems incorrect. It should likely be <= :similarity_threshold to find neighbors within the threshold.
• Reason this comment was not posted:
  Decided after close inspection that this draft comment was likely wrong and/or not actionable:
  The comment is about a change made in the diff, specifically the SQL query in the get_semantic_neighbors method. The comment suggests a change in logic, which is a valid concern if the intention is to find neighbors within a similarity threshold. The current logic selects vectors with similarity greater than or equal to the threshold, which might be incorrect if the goal is to find vectors with similarity less than or equal to the threshold.
  I might be missing the intended logic of the get_semantic_neighbors method. The current logic might be correct if the goal is to find vectors with a minimum similarity threshold. Without additional context on the intended behavior, it's hard to be certain.
  The comment raises a valid point about the logic of the similarity threshold comparison. If the goal is to find neighbors within a certain similarity range, the comment's suggestion might be correct.
  Keep the comment as it raises a valid concern about the logic of the similarity threshold comparison in the new method get_semantic_neighbors.

Workflow ID: wflow_dj4sx85UZRyvIFP3

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