feat: add Prompt module for grounded RAG query construction - #52
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ParamThakkar123 wants to merge 2 commits into
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feat: add Prompt module for grounded RAG query construction#52ParamThakkar123 wants to merge 2 commits into
ParamThakkar123 wants to merge 2 commits into
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Introduces a Prompt module with: - FUNSQL system prompt and customizable PromptTemplate - Context formatting from retrieved chunks - Build prompts for LLM query construction Includes full test coverage and documentation page.
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Additional details and impacted files@@ Coverage Diff @@
## retrieval #52 +/- ##
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+ Coverage 65.96% 69.05% +3.09%
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Files 12 13 +1
Lines 526 585 +59
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+ Hits 347 404 +57
- Misses 179 181 +2 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
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The Documentation job failed with six unresolvable `@ref` targets, from two separate causes: - `Chunk`, `HeaderChunk` and the rest of the chunking API are defined and exported by the `Ingestion` submodule but were never re-exported from `HealthLLM`, so `[`Chunk`](@ref)` in querying.md (which runs under `CurrentModule = HealthLLM`) had no binding to resolve against. Add them to the `import .Ingestion:` and `export` lists alongside the other ingestion names. - Documenter resolves `@ref`s inside a docstring in that docstring's own module. The `Prompt` docstrings reference `retrieve`, `search` and `Chunk`, none of which `Prompt` imports, so they failed as `HealthLLM.Prompt.retrieve` and friends. Qualify them with the `[`name`](@ref Module.name)` form, which keeps the rendered link text unchanged. `julia --project=docs docs/make.jl` now completes CrossReferences and RenderDocument with no errors. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01RYi1NAjjGQ848cUMLEbh1x
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Summary
src/prompt.jl): IntroducesFUNSQL_SYSTEM_PROMPT,PromptTemplate,DEFAULT_FUNSQL_TEMPLATE,format_context, andbuild_promptfor constructing grounded LLM prompts from retrieved chunksHealthLLM.jlpublic API and docstest/PromptTest.jlquerying.mdpage with usage examplesThis completes the RAG pipeline by adding the final step: formatting retrieved context into a structured prompt for LLM-based FUNSQL query generation.