From 3277f16ae1ec26c63033202c1fe78317792b3ead Mon Sep 17 00:00:00 2001
From: Ray <mailtangyu@gmail.com>
Date: Tue, 26 Aug 2025 19:10:07 +0800
Subject: [PATCH] fix output

---
 cookbook/pageindex_RAG_simple.ipynb | 45 +++++++++++++++--------------
 1 file changed, 23 insertions(+), 22 deletions(-)

diff --git a/cookbook/pageindex_RAG_simple.ipynb b/cookbook/pageindex_RAG_simple.ipynb
index 663638e..36dd687 100644
--- a/cookbook/pageindex_RAG_simple.ipynb
+++ b/cookbook/pageindex_RAG_simple.ipynb
@@ -211,7 +211,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -257,7 +257,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 41,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -369,7 +369,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 21,
       "metadata": {
         "id": "LLHNJAtTcG1O"
       },
@@ -409,7 +409,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 57,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -424,12 +424,13 @@
           "output_type": "stream",
           "text": [
             "Reasoning Process:\n",
-            "The question asks for the conclusions in the document. The most direct and relevant node is '5.\n",
-            "Conclusion, Limitations, and Future Work' (node_id: 0019), as it is specifically dedicated to the\n",
-            "conclusion and related topics. Other nodes, such as the Abstract (0001), Introduction (0003), and\n",
-            "Discussion (0018), may contain summary statements or high-level findings, but the explicit\n",
-            "conclusions are most likely found in node 0019. Therefore, node 0019 is the primary node likely to\n",
-            "contain the answer.\n",
+            "The question asks for the conclusions in the document. Typically, conclusions are found in sections\n",
+            "explicitly titled 'Conclusion' or in sections summarizing the findings and implications of the work.\n",
+            "In this document tree, node 0019 ('5. Conclusion, Limitations, and Future Work') is the most\n",
+            "directly relevant, as it is dedicated to the conclusion and related topics. Additionally, the\n",
+            "'Abstract' (node 0001) may contain a high-level summary that sometimes includes concluding remarks,\n",
+            "but it is less likely to contain the full conclusions. Other sections like 'Discussion' (node 0018)\n",
+            "may discuss implications but are not explicitly conclusions. Therefore, the primary node is 0019.\n",
             "\n",
             "Retrieved Nodes:\n",
             "Node ID: 0019\t Page: 16\t Title: 5. Conclusion, Limitations, and Future Work\n"
@@ -467,7 +468,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 58,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -519,7 +520,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 59,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -535,18 +536,18 @@
           "text": [
             "Generated Answer:\n",
             "\n",
-            "**Conclusions in this document:**\n",
+            "The conclusions in this document are:\n",
             "\n",
-            "- DeepSeek-R1-Zero, a pure reinforcement learning (RL) model without cold-start data, achieves\n",
+            "- DeepSeek-R1-Zero, a pure reinforcement learning (RL) approach without cold-start data, achieves\n",
             "strong performance across various tasks.\n",
-            "- DeepSeek-R1, which combines cold-start data with iterative RL fine-tuning, is even more powerful\n",
-            "and achieves performance comparable to OpenAI-o1-1217 on a range of tasks.\n",
-            "- The reasoning capabilities of DeepSeek-R1 can be successfully distilled into smaller dense models,\n",
-            "with DeepSeek-R1-Distill-Qwen-1.5B outperforming GPT-4o and Claude-3.5-Sonnet on math benchmarks.\n",
-            "- Other small dense models fine-tuned with DeepSeek-R1 data also significantly outperform other\n",
-            "instruction-tuned models based on the same checkpoints.\n",
-            "- Overall, the approaches described demonstrate promising results in enhancing model reasoning\n",
-            "abilities through RL and distillation.\n"
+            "- DeepSeek-R1, which combines cold-start data with iterative RL fine-tuning, is more powerful and\n",
+            "achieves performance comparable to OpenAI-o1-1217 on a range of tasks.\n",
+            "- Distilling DeepSeek-R1’s reasoning capabilities into smaller dense models is promising; for\n",
+            "example, DeepSeek-R1-Distill-Qwen-1.5B outperforms GPT-4o and Claude-3.5-Sonnet on math benchmarks,\n",
+            "and other dense models also show significant improvements over similar instruction-tuned models.\n",
+            "\n",
+            "These results demonstrate the effectiveness of the RL-based approach and the potential for\n",
+            "distilling reasoning abilities into smaller models.\n"
           ]
         }
       ],