Update llm_assist.py
This commit is contained in:
Roland Tannous
GitHub
parent
bdadc87857
commit
810820362d
1 changed files with 19 additions and 28 deletions
|
|
@ -103,7 +103,6 @@ def _run_with_helper(prompt: str, max_tokens: int = 256) -> Optional[str]:
|
|||
|
||||
backend = LlamaCppBackend()
|
||||
logger.info(f"Loading helper model: {repo} ({variant})")
|
||||
print(f"🤖 Loading helper model: {repo} ({variant})...")
|
||||
|
||||
ok = backend.load_model(
|
||||
hf_repo = repo,
|
||||
|
|
@ -141,7 +140,7 @@ def _run_with_helper(prompt: str, max_tokens: int = 256) -> Optional[str]:
|
|||
if backend is not None:
|
||||
try:
|
||||
backend.unload_model()
|
||||
print("🤖 Helper model unloaded")
|
||||
logger.info("Helper model unloaded")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
|
@ -201,7 +200,7 @@ def llm_generate_vlm_instruction(
|
|||
logger.warning(f"Helper model returned unusable instruction: {instruction!r}")
|
||||
return None
|
||||
|
||||
print(f"🤖 LLM-generated instruction: {instruction}")
|
||||
logger.info(f"LLM-generated instruction: {instruction}")
|
||||
return {
|
||||
"instruction": instruction,
|
||||
"confidence": 0.85,
|
||||
|
|
@ -297,7 +296,7 @@ def llm_classify_columns(
|
|||
logger.warning(f"Helper model mapping missing user/assistant: {cleaned}")
|
||||
return None
|
||||
|
||||
print(f"🤖 LLM-classified columns: {cleaned}")
|
||||
logger.info(f"LLM-classified columns: {cleaned}")
|
||||
return cleaned
|
||||
|
||||
|
||||
|
|
@ -347,7 +346,7 @@ def llm_generate_dataset_warning(
|
|||
if len(warning) < 10 or len(warning) > 500:
|
||||
return None
|
||||
|
||||
print(f"🤖 LLM-generated warning: {warning}")
|
||||
logger.info(f"LLM-generated warning: {warning}")
|
||||
return warning
|
||||
|
||||
|
||||
|
|
@ -480,7 +479,7 @@ def _run_multi_pass_advisor(
|
|||
from core.inference.llama_cpp import LlamaCppBackend
|
||||
|
||||
backend = LlamaCppBackend()
|
||||
print(f"🤖 Loading advisor model: {repo} ({variant})...")
|
||||
logger.info(f"Loading advisor model: {repo} ({variant})")
|
||||
t0 = time.monotonic()
|
||||
|
||||
ok = backend.load_model(
|
||||
|
|
@ -495,8 +494,7 @@ def _run_multi_pass_advisor(
|
|||
logger.warning("Advisor model failed to start")
|
||||
return None
|
||||
|
||||
print(f"🤖 Advisor model loaded in {time.monotonic() - t0:.1f}s")
|
||||
|
||||
logger.info(f"Advisor model loaded in {time.monotonic() - t0:.1f}s")
|
||||
# ── Format samples ──
|
||||
samples_text = ""
|
||||
for i, row in enumerate(samples[:5], 1):
|
||||
|
|
@ -542,7 +540,7 @@ def _run_multi_pass_advisor(
|
|||
)
|
||||
|
||||
# ── Pass 1: Classify ──
|
||||
print("🤖 Pass 1: Classifying dataset...", flush = True)
|
||||
logger.info("Pass 1: Classifying dataset...")
|
||||
t1 = time.monotonic()
|
||||
messages1 = [
|
||||
{
|
||||
|
|
@ -589,7 +587,7 @@ def _run_multi_pass_advisor(
|
|||
]
|
||||
raw1 = _generate_with_backend(backend, messages1, max_tokens = 256)
|
||||
pass1 = _parse_json_response(raw1)
|
||||
print(f"🤖 Pass 1 done ({time.monotonic() - t1:.1f}s): {pass1}", flush = True)
|
||||
logger.info(f"Pass 1 done ({time.monotonic() - t1:.1f}s): {pass1}")
|
||||
|
||||
if not pass1:
|
||||
logger.warning(f"Advisor Pass 1 failed to produce JSON: {raw1[:200]}")
|
||||
|
|
@ -608,7 +606,9 @@ def _run_multi_pass_advisor(
|
|||
}
|
||||
|
||||
# ── Pass 2: Map columns to roles ──
|
||||
print("🤖 Pass 2: Mapping columns to roles...", flush = True)
|
||||
logger.info("Pass 2: Mapping columns to roles...")
|
||||
|
||||
|
||||
t2 = time.monotonic()
|
||||
messages2 = [
|
||||
{
|
||||
|
|
@ -689,7 +689,7 @@ def _run_multi_pass_advisor(
|
|||
]
|
||||
raw2 = _generate_with_backend(backend, messages2, max_tokens = 512)
|
||||
pass2 = _parse_json_response(raw2)
|
||||
print(f"🤖 Pass 2 done ({time.monotonic() - t2:.1f}s): {pass2}", flush = True)
|
||||
logger.info(f"Pass 2 done ({time.monotonic() - t2:.1f}s): {pass2}")
|
||||
|
||||
if not pass2:
|
||||
logger.warning(f"Advisor Pass 2 failed to produce JSON: {raw2[:200]}")
|
||||
|
|
@ -702,10 +702,7 @@ def _run_multi_pass_advisor(
|
|||
# Validate: must have at least one user AND one assistant
|
||||
roles_present = set(column_roles.values())
|
||||
if "user" not in roles_present or "assistant" not in roles_present:
|
||||
print(
|
||||
f"🤖 Pass 2 sanity fail: missing user or assistant role: {column_roles}",
|
||||
flush = True,
|
||||
)
|
||||
logger.warning(f"Pass 2 sanity fail: missing user or assistant role: {column_roles}")
|
||||
return None # triggers fallback to simple classification
|
||||
|
||||
# ── Pass 3: System prompt (non-conversational datasets only) ──
|
||||
|
|
@ -714,7 +711,7 @@ def _run_multi_pass_advisor(
|
|||
is_conv = pass1.get("is_conversational", False)
|
||||
|
||||
if not is_conv:
|
||||
print("🤖 Pass 3: Generating system prompt...", flush = True)
|
||||
logger.info("Pass 3: Generating system prompt...")
|
||||
t3 = time.monotonic()
|
||||
|
||||
# Format label mapping info for the prompt
|
||||
|
|
@ -755,10 +752,7 @@ def _run_multi_pass_advisor(
|
|||
},
|
||||
]
|
||||
raw3 = _generate_with_backend(backend, messages3, max_tokens = 256)
|
||||
print(
|
||||
f"🤖 Pass 3 done ({time.monotonic() - t3:.1f}s): {raw3[:200] if raw3 else None}",
|
||||
flush = True,
|
||||
)
|
||||
logger.info(f"Pass 3 done ({time.monotonic() - t3:.1f}s): {raw3[:200] if raw3 else None}")
|
||||
|
||||
if raw3:
|
||||
# Pass 3 returns raw text, not JSON — clean it up
|
||||
|
|
@ -783,11 +777,8 @@ def _run_multi_pass_advisor(
|
|||
user_notification = " ".join(note_parts)
|
||||
|
||||
total_time = time.monotonic() - t0
|
||||
print(
|
||||
f"🤖 Advisor complete ({total_time:.1f}s): type={dtype}, "
|
||||
f"mapping={suggested_mapping}, sys_prompt={bool(sys_prompt)}, label_map={bool(label_map)}",
|
||||
flush = True,
|
||||
)
|
||||
logger.info(f"Advisor complete ({total_time:.1f}s): type={dtype}, mapping={suggested_mapping}, sys_prompt={bool(sys_prompt)}, label_map={bool(label_map)}")
|
||||
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
|
|
@ -807,7 +798,7 @@ def _run_multi_pass_advisor(
|
|||
if backend is not None:
|
||||
try:
|
||||
backend.unload_model()
|
||||
print("🤖 Advisor model unloaded")
|
||||
logger.info("Advisor model unloaded")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
|
@ -849,7 +840,7 @@ def llm_conversion_advisor(
|
|||
)
|
||||
|
||||
if result and result.get("success"):
|
||||
print(f"🤖 Conversion advisor succeeded: type={result.get('dataset_type')}")
|
||||
logger.info(f"Conversion advisor succeeded: type={result.get('dataset_type')}")
|
||||
return result
|
||||
|
||||
# Fallback: simple column classification
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue