link added

rag-homework.ipynb

@@ -142,7 +142,7 @@
    "source": [
     "### Step 3: Add a reranker\n",
     "\n",
-    "A reranker is a second-level model which produces similarity scores for pairs of (input query + retrieved document). Cross-encoders are conventionally used for reranking, their architecture is slightly different from retrieval models (more on it [here] and [here](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)). Cross-encoders are much more costly to run, therefore a retrieval model is used to get a few (dozens) highest-scoring items, and a reranker picks the best among these. The overall pipeline is similar to the recommender system indudustry standard: a light model retrieves top-n, a precise and heavy model reranks n to get top k, n is orders of magnitude larger than k.\n",
+    "A reranker is a second-level model which produces similarity scores for pairs of (input query + retrieved document). Cross-encoders are conventionally used for reranking, their architecture is slightly different from retrieval models (more on it [here](https://www.pinecone.io/learn/series/rag/rerankers/) and [here](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)). Cross-encoders are much more costly to run, therefore a retrieval model is used to get a few (dozens) highest-scoring items, and a reranker picks the best among these. The overall pipeline is similar to the recommender system indudustry standard: a light model retrieves top-n, a precise and heavy model reranks n to get top k, n is orders of magnitude larger than k.\n",
     "\n",
     "Cross-encoders are optional because of the overhead their usage implies. Your task is to implement a reranker using a cross-encoder and assess pros and cons of having it. Do not forget that the process of pulling the most relevant documents becomes two-staged: retrieve a larger number of items first, than rerank and keep the best top-k for context.\n",
     "\n",