File size: 5,964 Bytes
f3ab765 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | import os
import sys
import torch
import torch.nn.functional as F
# Add project root to path
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(project_root)
try:
from sentence_transformers import SentenceTransformer
except ImportError:
print("CRITICAL: 'sentence-transformers' library is missing.")
print("Please install it by running: pip3 install sentence-transformers")
sys.exit(1)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
def load_chunks(file_path, max_chars=1500, overlap=200):
"""Reads the file and creates overlapping chunks of a strict maximum size."""
with open(file_path, "r", encoding="utf-8") as f:
text = f.read()
# Sliding window chunking to guarantee no chunk is too large
chunks = []
start = 0
while start < len(text):
end = start + max_chars
# Try to break at a natural boundary (newline or space) if possible
if end < len(text):
next_newline = text.rfind('\n', start, end)
next_space = text.rfind(' ', start, end)
# Break at the last newline if it's in the second half of the chunk, else space
if next_newline > start + (max_chars // 2):
end = next_newline
elif next_space > start + (max_chars // 2):
end = next_space
chunk = text[start:end].strip()
if len(chunk) > 20:
chunks.append(chunk)
start = end - overlap
return chunks
def find_latest_adapter(search_roots):
"""Return the newest PEFT adapter directory across the given roots, or None.
Matches any dir containing adapter_config.json so adapters from any stage
(SFT final_adapter, SEAL seal_adapter, HITL/DPO lora_adapter_v*) are found.
"""
found = []
for root in search_roots:
if not os.path.isdir(root):
continue
for dirpath, _dirs, files in os.walk(root):
if "adapter_config.json" in files:
found.append(dirpath)
if not found:
return None
found.sort(key=os.path.getmtime, reverse=True)
return found[0]
def main():
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
print("\n[1/4] Loading RAG Document Chunks...")
data_path = os.path.join(project_root, "data", "duk_data.txt")
chunks = load_chunks(data_path)
print(f"Loaded {len(chunks)} paragraphs from the document.")
print("\n[2/4] Loading Embedding Model (all-MiniLM-L6-v2)...")
embedder = SentenceTransformer("all-MiniLM-L6-v2", device=device)
print("\n[3/4] Embedding Document Chunks...")
chunk_embeddings = embedder.encode(chunks, convert_to_tensor=True, device=device)
print("\n[4/4] Loading HuggingFace Model & LoRA Adapters...")
# Load Base Qwen Model
base_model_path = "Qwen/Qwen2.5-1.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(base_model_path)
print(f"Loading Base Model: {base_model_path}")
model = AutoModelForCausalLM.from_pretrained(
base_model_path,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None
)
# Discover the newest LoRA adapter from any stage (SFT/SEAL/HITL/DPO).
adapter = find_latest_adapter([
os.path.join(project_root, "ckpts", "adapters"),
os.path.join(project_root, "model_weights"),
])
if adapter:
print(f"Loading latest LoRA adapter: {adapter}")
model = PeftModel.from_pretrained(model, adapter)
else:
print("WARNING: No trained LoRA adapters found. Using base model only.")
model.eval()
print("Model loaded successfully!")
print("\n" + "="*50)
print(" RAG Chat Terminal - Type 'quit' to exit")
print("="*50)
while True:
try:
query = input("\n[You] Ask a question: ").strip()
except EOFError:
break
if query.lower() in ['quit', 'exit', 'q']:
break
if not query:
continue
# 1. Embed the user's question
query_embedding = embedder.encode(query, convert_to_tensor=True, device=device)
# 2. Calculate Cosine Similarity
similarities = F.cosine_similarity(query_embedding.unsqueeze(0), chunk_embeddings)
# 3. Find the best chunk
best_idx = torch.argmax(similarities).item()
retrieved_context = chunks[best_idx]
score = similarities[best_idx].item()
print(f"\n[Retrieval] Found context (Score: {score:.2f}):")
print(f"--- {retrieved_context[:100]}... ---")
# 4. Format for Hugging Face Chat Template
messages = [
{"role": "system", "content": "You are a helpful assistant. Answer the user's question accurately using ONLY the provided context."},
{"role": "user", "content": f"Context: {retrieved_context}\n\nQuestion: {query}"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(device)
# 5. Generate Answer
with torch.no_grad():
output_ids = model.generate(
**inputs,
max_new_tokens=150,
temperature=0.1,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
# Decode only the newly generated tokens
generated_tokens = output_ids[0][inputs.input_ids.shape[1]:]
answer = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(f"\n[AI Answer]: {answer.strip()}")
if __name__ == "__main__":
main()
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