Summary Points
- Retrieval in enterprise document systems should be viewed as filtering structured tables (line_df and toc_df), not as traditional search, enabling precise, column-based, and join-based filtering methods.
- The process involves two separate granularities: anchors (small, precise units like lines or titles) for scoring, and contexts (larger chunks like sections or paragraphs) for passing relevant information to the generator.
- Effective retrieval uses a two-phase approach: first, identify where the answer exists (anchors), then size the surrounding context based on question intent, avoiding collapsing these scopes for better precision.
- The best method balances cost, simplicity, and accuracy—often favoring LLM-driven boundary detection over complex custom segmentation—embracing a pragmatic, enterprise-friendly retrieval pipeline built on existing model inference.
Retrieval as Filtering, Not Search
Retrieval isn’t just about finding keywords. Think of it more like filtering data. When a document is parsed, it turns into structured tables. These tables include line_df, with every line of text, and toc_df, with sections and titles. Instead of a free-text search, retrieval becomes a matter of selecting rows that match specific criteria. This approach is similar to querying a database rather than using a simple search engine. By filtering on columns and joining tables, we can target relevant parts of the document more precisely. This method enables better accuracy and efficiency, especially for enterprise documents.
Separate Granularities: Anchor and Context
Filtering involves two important steps: locating the anchor and sizing the context. The anchor is a small, precise part of the document—like a specific line or title—that signals where to look. The context is larger—like a paragraph or entire section—that provides enough information to answer the question. These two levels are independent; for example, you might anchor on a keyword in a section title but pass the entire section to a language model. Maintaining this separation improves precision. Small anchors help find exact information, while larger contexts ensure the answer is well-founded and comprehensive.
Choosing the Right Approach for Enterprise Documents
Initially, many systems rely on simple methods, like cosine similarity, to find related text. However, these often fall short for complex questions. In enterprise settings, it’s better to combine filtering with intelligent expansion strategies. For example, after pinpointing a section, expand to the full paragraph or section instead of relying solely on keyword matches. Cost and latency are important considerations. Today’s large language models make it feasible to add a single call that improves accuracy without significant expense. The key is to choose methods that fit the specific question and document structure, rather than defaulting to more expensive or complicated techniques. This balanced approach leads to more reliable document intelligence in real-world use cases.
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