Create agents/scout.py

agents/scout.py

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+import requests
+from bs4 import BeautifulSoup
+from sentence_transformers import SentenceTransformer, util
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+device = torch.device("cpu")
+model_id = "TheBloke/Mistral-7B-Instruct-v0.1"  # replace if needed
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+llm = AutoModelForCausalLM.from_pretrained(model_id).to(device)
+
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+
+def search_and_summarize(query, max_papers=5):
+    url = "https://arxiv.org/rss/cs.AI"
+    res = requests.get(url)
+    soup = BeautifulSoup(res.text, 'xml')
+    items = soup.find_all('item')
+
+    papers = []
+    for item in items:
+        title = item.title.text
+        abstract = item.description.text
+        link = item.link.text
+        papers.append({'title': title, 'abstract': abstract, 'link': link})
+
+    # embed & find top matches
+    query_emb = embedder.encode(query)
+    paper_embs = embedder.encode([p['abstract'] for p in papers])
+    sims = util.cos_sim(query_emb, paper_embs)[0]
+    top_idx = sims.argsort(descending=True)[:max_papers]
+
+    results = []
+    for idx in top_idx:
+        paper = papers[idx]
+        context = f"Title: {paper['title']}\nAbstract: {paper['abstract']}"
+        prompt = f"{context}\n\nExplain this paper in simple terms for an AI researcher:"
+        inputs = tokenizer(prompt, return_tensors="pt").to(device)
+        outputs = llm.generate(**inputs, max_new_tokens=200)
+        explanation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        explanation = explanation[len(prompt):].strip()
+        results.append({
+            'title': paper['title'],
+            'summary': explanation,
+            'link': paper['link']
+        })
+    return results