59 lines
3.2 KiB
Python
59 lines
3.2 KiB
Python
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import json, torch, numpy as np
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from sentence_transformers import SentenceTransformer, CrossEncoder
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import faiss
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from transformers import AutoTokenizer, AutoModelForCausalLM
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class PapersRAG:
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def __init__(self, model_dir="."):
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with open(f"{model_dir}/rag_config.json") as f:
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config = json.load(f)
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self.embedder = SentenceTransformer(config["embedder_model"])
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self.index = faiss.read_index(f"{model_dir}/papersrag_index.faiss")
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with open(f"{model_dir}/chunks.txt", "r", encoding="utf-8") as f:
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raw = f.read().split("<|CHUNK_END|>")
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self.chunks = [c.strip() for c in raw if c.strip()]
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self.reranker = CrossEncoder(f"{model_dir}/cross_encoder_model")
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self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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self.model = AutoModelForCausalLM.from_pretrained(
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model_dir,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True
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)
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def ask(self, question, max_tokens=400):
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q = question.strip().lower().rstrip('?!.')
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greetings = ["hi", "hello", "hey", "yo", "sup", "good morning", "how are you"]
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if any(q == g or q.startswith(g) for g in greetings):
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return "Hello! I'm PapersRAG, your AI research assistant. I have 50 recent arXiv papers on computational linguistics and NLP. Ask me anything about them!"
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identity_qs = ["who are you", "what is your name", "what are you", "what do you do", "tell me about yourself"]
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if any(idq in q for idq in identity_qs):
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return "I'm PapersRAG 🧪, a research assistant that can answer questions about the latest 50 arXiv papers in cs.CL. I'll cite the paper titles in my answers. Ask me anything about the papers!"
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q_emb = self.embedder.encode([question]).astype("float32")
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_, indices = self.index.search(q_emb, 10)
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candidates = [self.chunks[i] for i in indices[0]]
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pairs = [(question, c) for c in candidates]
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scores = self.reranker.predict(pairs)
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if max(scores) < -4.5:
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return "I don't have enough information from my arXiv papers to answer that accurately. Try asking about specific NLP or computational linguistics papers."
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best = sorted(zip(scores, candidates), reverse=True)[:4]
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context = "\\n\\n".join([c for _, c in best])
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messages = [
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{"role": "system", "content": "You are PapersRAG, a scientific research assistant. Use ONLY the provided paper abstracts to answer. Always mention the paper title when you use information from it. If unsure, say you don't have that information."},
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{"role": "user", "content": f"Context:\\n{context}\\n\\nQuestion: {question}"}
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]
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prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
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outputs = self.model.generate(
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**inputs,
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max_new_tokens=max_tokens,
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temperature=0.7,
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do_sample=True,
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pad_token_id=self.tokenizer.eos_token_id
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)
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answer = self.tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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return answer.strip()
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