初始化项目，由ModelHub XC社区提供模型

Model: metaresearch/PapersRAG-1.5B
Source: Original Platform

pipeline.py

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+
+import json, torch, numpy as np
+from sentence_transformers import SentenceTransformer, CrossEncoder
+import faiss
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+class PapersRAG:
+    def __init__(self, model_dir="."):
+        with open(f"{model_dir}/rag_config.json") as f:
+            config = json.load(f)
+        self.embedder = SentenceTransformer(config["embedder_model"])
+        self.index = faiss.read_index(f"{model_dir}/papersrag_index.faiss")
+        with open(f"{model_dir}/chunks.txt", "r", encoding="utf-8") as f:
+            raw = f.read().split("<|CHUNK_END|>")
+        self.chunks = [c.strip() for c in raw if c.strip()]
+        self.reranker = CrossEncoder(f"{model_dir}/cross_encoder_model")
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_dir,
+            torch_dtype=torch.float16,
+            device_map="auto",
+            trust_remote_code=True
+        )
+
+    def ask(self, question, max_tokens=400):
+        q = question.strip().lower().rstrip('?!.')
+        greetings = ["hi", "hello", "hey", "yo", "sup", "good morning", "how are you"]
+        if any(q == g or q.startswith(g) for g in greetings):
+            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!"
+        identity_qs = ["who are you", "what is your name", "what are you", "what do you do", "tell me about yourself"]
+        if any(idq in q for idq in identity_qs):
+            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!"
+
+        q_emb = self.embedder.encode([question]).astype("float32")
+        _, indices = self.index.search(q_emb, 10)
+        candidates = [self.chunks[i] for i in indices[0]]
+        pairs = [(question, c) for c in candidates]
+        scores = self.reranker.predict(pairs)
+        if max(scores) < -4.5:
+            return "I don't have enough information from my arXiv papers to answer that accurately. Try asking about specific NLP or computational linguistics papers."
+        best = sorted(zip(scores, candidates), reverse=True)[:4]
+        context = "\\n\\n".join([c for _, c in best])
+
+        messages = [
+            {"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."},
+            {"role": "user", "content": f"Context:\\n{context}\\n\\nQuestion: {question}"}
+        ]
+        prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+        outputs = self.model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=self.tokenizer.eos_token_id
+        )
+        answer = self.tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+        return answer.strip()