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Legal Assistant

This solution addresses "high-evidence legal-assistant RAG." The goal is not to make answers sound more human, but to make the chain of evidence more traceable.

1. What scenarios it fits​

  • Q&A over regulatory, policy, and case-law knowledge bases
  • Professional assistants that require evidence citations
  • Industries with strict requirements on versioning, provenance, and audit

It is essentially a high-constraint variant of RAG, not something you can force with "generic chat + a long prompt."

2. Core module composition​

This solution typically combines:

  • Document parsing and chunking
  • IngestionPipeline
  • VectorStore
  • RagService
  • metadata governance
  • citations / trace / evidence output

Compared with ordinary RAG, it places more emphasis on:

  • Metadata completeness
  • Version governance
  • Evidence first

3. Why this is a high-constraint scenario​

In legal scenarios, what really matters is usually not "natural phrasing," but:

  • What this statement is based on
  • Which document and which version a citation comes from
  • Whether results allow human review and replay

So retrieval quality and evidence citations are often more important than the wording of the answer itself.

4. What to watch out for​

Legal scenarios are high-risk
  • Legal scenarios are high-risk
  • Outputs should ideally carry explicit evidence sources
  • Key results should go through a human-review process
  • Don't disguise "retrieval returned nothing" as "there is no legal basis"

5. Which main pages to read first​

  1. Core SDK / Search & RAG
  2. Core SDK / Citations and Trace
  3. RAG Ingestion Vector Store

6. Implementation details​

If you want to see:

  • Data flow
  • Metadata design recommendations
  • How the evidence chain is organized
  • Example pseudocode

Continue to the deep page:

7. Key objects​

The objects most worth examining next in this solution are usually:

  • IngestionPipeline
  • VectorStore
  • RagService
  • citations / trace result objects

Together they determine whether the evidence chain can be preserved from document ingestion all the way to the final answer.

8. Why this kind of solution can't rely on prompts alone​

For high-constraint scenarios such as law, regulation, and policy, simply writing a stricter prompt does not replace:

  • Stable document identity
  • Complete metadata design
  • Replayable retrieval results
  • A clear human-review process

So what this page really emphasizes is evidence engineering, not answer style.

9. What to confirm first during implementation​

  • Whether document version and effective time enter metadata
  • Whether citation output can locate the original clause, section, or page
  • Whether the system explicitly surfaces uncertainty when no supporting evidence is retrieved