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06ed757 4e1037f 06ed757 4b4ff9e 4e1037f 06ed757 4b4ff9e 06ed757 4e1037f 06ed757 8b8c11e 06ed757 4e1037f 06ed757 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | """Node: ask codestral (or any LLMProvider) for SQL given the schema context.
Builds the prompt from the active context bundle, dispatches to the provider,
parses the JSON response into a `GenerateSQLOutput`. The same node powers the
*initial* generation pass — the repair pass is a separate node that calls
this same provider with a different prompt.
"""
from __future__ import annotations
from collections.abc import Callable
from nl_sql.agent.nodes._support import (
parse_generate_sql_output,
render_fewshot_block,
render_m_schema,
render_schema_block,
)
from nl_sql.agent.prompts import load_prompt
from nl_sql.agent.state import PipelineState
from nl_sql.llm.providers.base import GenerateRequest, LLMProvider
from nl_sql.schema_index.value_retrieval import format_value_grounding
def make_generate_sql_node(
provider: LLMProvider,
*,
max_tokens: int = 1024,
temperature: float = 0.0,
sort_schema_block: bool = False,
use_m_schema: bool = False,
use_dac_prompt: bool = False,
use_compact_prompt: bool = False,
enable_bird_rescue_hints: bool = False,
) -> Callable[[PipelineState], PipelineState]:
def node(state: PipelineState) -> PipelineState:
question = state.get("question", "")
dialect = state.get("dialect", "sqlite")
context = state.get("context")
plan_raw = (state.get("plan") or "").strip()
plan_block = plan_raw if plan_raw else "(no plan — generate SQL directly from question)"
# Schema rendering: M-Schema (XiYan-SQL compact) vs verbose card layout.
# Driven by `PipelineConfig.use_m_schema`; api/main.py bootstraps the
# flag from `NLSQL_M_SCHEMA=1` env so existing eval scripts keep working.
if use_m_schema:
schema_text = render_m_schema(context)
else:
schema_text = render_schema_block(
context,
sort_alphabetically=sort_schema_block,
enable_bird_rescue_hints=enable_bird_rescue_hints,
)
# A8: targeted column descriptions ride as a schema appendix. Empty
# when the lever is off, keeping the prompt (and LLM cache) identical
# to the historical path.
column_notes = (state.get("column_notes") or "").strip()
if column_notes:
schema_text = f"{schema_text}\n\n{column_notes}"
# CHESS-style value grounding: append short "value X appears in T.C"
# lines to the question so they sit next to the NL (and any Hint).
# Empty when the flag is off or nothing matched — prompt text then
# matches the historical path, so the LLM cache stays warm.
value_block = ""
if context is not None and getattr(context, "value_matches", None):
value_block = format_value_grounding(list(context.value_matches))
question_for_prompt = f"{question}\n\n{value_block}" if value_block else question
# A4 (E-SQL): explicit restatement rides NEXT TO the question, never
# instead of it — an enrichment mistake must not override the original.
# Empty when the lever is off, so the prompt text (and the LLM cache)
# matches the historical path exactly.
enriched = (state.get("enriched_question") or "").strip()
if enriched:
question_for_prompt = (
f"{question_for_prompt}\n\n"
f"Explicit restatement (auxiliary; the original question above is "
f"authoritative):\n{enriched}"
)
# Prompt selection. The default `generate_sql` grew around codestral:
# heavy projection coaching, a DISTINCT rule taught on Chinook tables,
# per-database disambiguation. `generate_sql_compact` keeps only what is
# dataset- or dialect-true and drops the model-specific coaching, which
# matters for a frontier model on a metered browser path — the long
# prompt is both the coaching it does not need and the latency it pays
# for. DAC (CHASE-SQL divide-and-conquer) is the third option.
if use_compact_prompt:
prompt_name = "generate_sql_compact"
elif use_dac_prompt:
prompt_name = "generate_sql_dac"
else:
prompt_name = "generate_sql"
prompt = load_prompt(
prompt_name,
dialect=dialect,
schema_block=schema_text,
fewshot_block=render_fewshot_block(context),
plan_block=plan_block,
question=question_for_prompt,
)
response = provider.generate(
GenerateRequest(prompt=prompt, max_tokens=max_tokens, temperature=temperature)
)
parsed = parse_generate_sql_output(response.text)
trace = list(state.get("trace") or [])
trace.append(
{
"node": "generate_sql",
"model": response.model,
"confidence": parsed.confidence,
"tables_used": list(parsed.tables_used),
"input_tokens": response.input_tokens,
"output_tokens": response.output_tokens,
}
)
# Reset any stale outcome / error from a previous repair iteration.
return {
"generated": parsed,
"outcome": None,
"last_error": "",
"trace": trace,
}
return node
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