sofvBV_mcp重构v2

Embedding copy

mcp/SearchPaperByEmbedding/README.md

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+# Paper Semantic Search
+
+Find similar papers using semantic search. Supports both local models (free) and OpenAI API (better quality).
+
+## Features
+
+- Request for papers from OpenReview (e.g., ICLR2026 submissions)
+- Semantic search with example papers or text queries
+- Support embedding caching 
+- Embed model support: Open-source (e.g., all-MiniLM-L6-v2) or OpenAI
+
+## Quick Start
+
+```bash
+pip install -r requirements.txt
+```
+
+### 1. Prepare Papers
+
+```python
+from crawl import crawl_papers
+
+crawl_papers(
+    venue_id="ICLR.cc/2026/Conference/Submission",
+    output_file="iclr2026_papers.json"
+)
+```
+
+### 2. Search Papers
+
+```python
+from search import PaperSearcher
+
+# Local model (free)
+searcher = PaperSearcher('iclr2026_papers.json', model_type='local')
+
+# OpenAI model (better, requires API key)
+# export OPENAI_API_KEY='your-key'
+# searcher = PaperSearcher('iclr2026_papers.json', model_type='openai')
+
+searcher.compute_embeddings()
+
+# Search with example papers that you are interested in
+examples = [
+    {
+        "title": "Your paper title",
+        "abstract": "Your paper abstract..."
+    }
+]
+
+results = searcher.search(examples=examples, top_k=100)
+
+# Or search with text query
+results = searcher.search(query="interesting topics", top_k=100)
+
+searcher.display(results, n=10)
+searcher.save(results, 'results.json')
+```
+
+
+
+## How It Works
+
+1. Paper titles and abstracts are converted to embeddings
+2. Embeddings are cached automatically
+3. Your query is embedded using the same model
+4. Cosine similarity finds the most similar papers
+5. Results are ranked by similarity score
+
+## Cache
+
+Embeddings are cached as `cache_<filename>_<hash>_<model>.npy`. Delete to recompute.
+
+## Example Output
+
+```
+================================================================================
+Top 100 Results (showing 10)
+================================================================================
+
+1. [0.8456] Paper a
+   #12345 | foundation or frontier models, including LLMs
+   https://openreview.net/forum?id=xxx
+
+2. [0.8234] Paper b
+   #12346 | applications to robotics, autonomy, planning
+   https://openreview.net/forum?id=yyy
+```
+
+## Tips
+
+- Use 1-5 example papers for best results, or a paragraph of description of your interested topic
+- Local model is good enough for most cases
+- OpenAI model for critical search (~$1 for 18k queries)
+
+If it's useful, please consider giving a star~