From b72ea0ea1b08e13a0181537340a0b7f47d8b64e2 Mon Sep 17 00:00:00 2001
From: Lance Martin <122662504+rlancemartin@users.noreply.github.com>
Date: Tue, 23 Jul 2024 17:13:20 -0700
Subject: [PATCH] Update MR docs (#1098)

---
 examples/map-reduce.ipynb | 20 ++++----
 examples/subgraph.ipynb   | 99 ++++++++++++++++++++++++++++++++++-----
 2 files changed, 98 insertions(+), 21 deletions(-)

diff --git a/examples/map-reduce.ipynb b/examples/map-reduce.ipynb
index cc1b50d96..6fee482a4 100644
--- a/examples/map-reduce.ipynb
+++ b/examples/map-reduce.ipynb
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "id": "0f0f78e4-423d-4e2d-aa1a-01efaec4715f",
    "metadata": {},
    "outputs": [],
@@ -141,7 +141,7 @@
     "\n",
     "# Here we will judge the best joke\n",
     "def best_joke(state: OverallState):\n",
-    "    jokes = \"\\n\\n\".format()\n",
+    "    jokes = \"\\n\\n\".join(state[\"jokes\"])\n",
     "    prompt = best_joke_prompt.format(topic=state[\"topic\"], jokes=jokes)\n",
     "    response = model.with_structured_output(BestJoke).invoke(prompt)\n",
     "    return {\"best_selected_joke\": state[\"jokes\"][response.id]}\n",
@@ -161,18 +161,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "id": "37ed1f71-63db-416f-b715-4617b33d4b7f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "fd90cace",
    "metadata": {},
    "outputs": [
@@ -193,12 +193,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'generate_topics': {'subjects': ['lion', 'elephant', 'penguin', 'giraffe']}}\n",
-      "{'generate_joke': {'jokes': [\"Why don't you see penguins in Britain? Because they're afraid of Wales!\"]}}\n",
+      "{'generate_topics': {'subjects': ['lion', 'elephant', 'penguin', 'dolphin']}}\n",
       "{'generate_joke': {'jokes': [\"Why don't elephants use computers? They're afraid of the mouse!\"]}}\n",
+      "{'generate_joke': {'jokes': [\"Why don't dolphins use smartphones? They're afraid of phishing!\"]}}\n",
       "{'generate_joke': {'jokes': [\"Why don't lions like fast food? Because they can't catch it!\"]}}\n",
-      "{'generate_joke': {'jokes': [\"Why don't giraffes ever get caught in traffic jams? Because they can always stick their necks out and see what's ahead!\"]}}\n",
-      "{'best_joke': {'best_selected_joke': \"Why don't elephants use computers? They're afraid of the mouse!\"}}\n"
+      "{'generate_joke': {'jokes': [\"Why don't you see penguins in Britain? Because they're afraid of Wales!\"]}}\n",
+      "{'best_joke': {'best_selected_joke': \"Why don't you see penguins in Britain? Because they're afraid of Wales!\"}}\n"
      ]
     }
    ],
diff --git a/examples/subgraph.ipynb b/examples/subgraph.ipynb
index 9e98ba844..cbc582165 100644
--- a/examples/subgraph.ipynb
+++ b/examples/subgraph.ipynb
@@ -18,11 +18,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%capture --no-stderr\n%pip install -U langgraph"
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph"
    ]
   },
   {
@@ -45,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,12 +144,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -211,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -235,12 +236,21 @@
        "   'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).',\n",
        "   'grade': 0,\n",
        "   'grader': 'Document Relevance Recall',\n",
+       "   'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'},\n",
+       "  {'id': '1',\n",
+       "   'question': 'How can I import ChatOllama?',\n",
+       "   'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"},\n",
+       "  {'id': '2',\n",
+       "   'question': 'How can I use Chroma vector store?',\n",
+       "   'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).',\n",
+       "   'grade': 0,\n",
+       "   'grader': 'Document Relevance Recall',\n",
        "   'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}],\n",
        " 'fa_summary': 'Poor quality retrieval of Chroma documentation.',\n",
        " 'report': 'foo bar'}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -338,7 +348,10 @@
     }
    ],
    "source": [
-    "from IPython.display import Image, display\n\n# Setting xray to 1 will show the internal structure of the nested graph\ndisplay(Image(graph.get_graph(xray=1).draw_mermaid_png()))"
+    "from IPython.display import Image, display\n",
+    "\n",
+    "# Setting xray to 1 will show the internal structure of the nested graph\n",
+    "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))"
    ]
   },
   {
@@ -456,7 +469,42 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import uuid\n\n\ndef reduce_list(left: list | None, right: list | None) -> list:\n    \"\"\"Append the right-hand list, replacing any elements with the same id in the left-hand list.\"\"\"\n    if not left:\n        left = []\n    if not right:\n        right = []\n    left_, right_ = [], []\n    for orig, new in [(left, left_), (right, right_)]:\n        for val in orig:\n            if not isinstance(val, dict):\n                val = {\"val\": val}\n            if \"id\" not in val:\n                val[\"id\"] = str(uuid.uuid4())\n            new.append(val)\n    # Merge the two lists\n    left_idx_by_id = {val[\"id\"]: i for i, val in enumerate(left_)}\n    merged = left_.copy()\n    for val in right_:\n        if (existing_idx := left_idx_by_id.get(val[\"id\"])) is not None:\n            merged[existing_idx] = val\n        else:\n            merged.append(val)\n    return merged\n\n\nclass ChildState(TypedDict):\n    name: str\n    path: Annotated[list[str], reduce_list]\n\n\nclass ParentState(TypedDict):\n    name: str\n    path: Annotated[list[str], reduce_list]"
+    "import uuid\n",
+    "\n",
+    "\n",
+    "def reduce_list(left: list | None, right: list | None) -> list:\n",
+    "    \"\"\"Append the right-hand list, replacing any elements with the same id in the left-hand list.\"\"\"\n",
+    "    if not left:\n",
+    "        left = []\n",
+    "    if not right:\n",
+    "        right = []\n",
+    "    left_, right_ = [], []\n",
+    "    for orig, new in [(left, left_), (right, right_)]:\n",
+    "        for val in orig:\n",
+    "            if not isinstance(val, dict):\n",
+    "                val = {\"val\": val}\n",
+    "            if \"id\" not in val:\n",
+    "                val[\"id\"] = str(uuid.uuid4())\n",
+    "            new.append(val)\n",
+    "    # Merge the two lists\n",
+    "    left_idx_by_id = {val[\"id\"]: i for i, val in enumerate(left_)}\n",
+    "    merged = left_.copy()\n",
+    "    for val in right_:\n",
+    "        if (existing_idx := left_idx_by_id.get(val[\"id\"])) is not None:\n",
+    "            merged[existing_idx] = val\n",
+    "        else:\n",
+    "            merged.append(val)\n",
+    "    return merged\n",
+    "\n",
+    "\n",
+    "class ChildState(TypedDict):\n",
+    "    name: str\n",
+    "    path: Annotated[list[str], reduce_list]\n",
+    "\n",
+    "\n",
+    "class ParentState(TypedDict):\n",
+    "    name: str\n",
+    "    path: Annotated[list[str], reduce_list]"
    ]
   },
   {
@@ -465,7 +513,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "child_builder = StateGraph(ChildState)\n\nchild_builder.add_node(\"child_start\", lambda state: {\"path\": [\"child_start\"]})\nchild_builder.add_edge(START, \"child_start\")\nchild_builder.add_node(\"child_middle\", lambda state: {\"path\": [\"child_middle\"]})\nchild_builder.add_node(\"child_end\", lambda state: {\"path\": [\"child_end\"]})\nchild_builder.add_edge(\"child_start\", \"child_middle\")\nchild_builder.add_edge(\"child_middle\", \"child_end\")\nchild_builder.add_edge(\"child_end\", END)\n\nbuilder = StateGraph(ParentState)\n\nbuilder.add_node(\"grandparent\", lambda state: {\"path\": [\"grandparent\"]})\nbuilder.add_edge(START, \"grandparent\")\nbuilder.add_node(\"parent\", lambda state: {\"path\": [\"parent\"]})\nbuilder.add_node(\"child\", child_builder.compile())\nbuilder.add_node(\"sibling\", lambda state: {\"path\": [\"sibling\"]})\nbuilder.add_node(\"fin\", lambda state: {\"path\": [\"fin\"]})\n\n# Add connections\nbuilder.add_edge(\"grandparent\", \"parent\")\nbuilder.add_edge(\"parent\", \"child\")\nbuilder.add_edge(\"parent\", \"sibling\")\nbuilder.add_edge(\"child\", \"fin\")\nbuilder.add_edge(\"sibling\", \"fin\")\nbuilder.add_edge(\"fin\", END)\ngraph = builder.compile()"
+    "child_builder = StateGraph(ChildState)\n",
+    "\n",
+    "child_builder.add_node(\"child_start\", lambda state: {\"path\": [\"child_start\"]})\n",
+    "child_builder.add_edge(START, \"child_start\")\n",
+    "child_builder.add_node(\"child_middle\", lambda state: {\"path\": [\"child_middle\"]})\n",
+    "child_builder.add_node(\"child_end\", lambda state: {\"path\": [\"child_end\"]})\n",
+    "child_builder.add_edge(\"child_start\", \"child_middle\")\n",
+    "child_builder.add_edge(\"child_middle\", \"child_end\")\n",
+    "child_builder.add_edge(\"child_end\", END)\n",
+    "\n",
+    "builder = StateGraph(ParentState)\n",
+    "\n",
+    "builder.add_node(\"grandparent\", lambda state: {\"path\": [\"grandparent\"]})\n",
+    "builder.add_edge(START, \"grandparent\")\n",
+    "builder.add_node(\"parent\", lambda state: {\"path\": [\"parent\"]})\n",
+    "builder.add_node(\"child\", child_builder.compile())\n",
+    "builder.add_node(\"sibling\", lambda state: {\"path\": [\"sibling\"]})\n",
+    "builder.add_node(\"fin\", lambda state: {\"path\": [\"fin\"]})\n",
+    "\n",
+    "# Add connections\n",
+    "builder.add_edge(\"grandparent\", \"parent\")\n",
+    "builder.add_edge(\"parent\", \"child\")\n",
+    "builder.add_edge(\"parent\", \"sibling\")\n",
+    "builder.add_edge(\"child\", \"fin\")\n",
+    "builder.add_edge(\"sibling\", \"fin\")\n",
+    "builder.add_edge(\"fin\", END)\n",
+    "graph = builder.compile()"
    ]
   },
   {
@@ -485,7 +559,10 @@
     }
    ],
    "source": [
-    "from IPython.display import Image, display\n\n# Setting xray to 1 will show the internal structure of the nested graph\ndisplay(Image(graph.get_graph(xray=1).draw_mermaid_png()))"
+    "from IPython.display import Image, display\n",
+    "\n",
+    "# Setting xray to 1 will show the internal structure of the nested graph\n",
+    "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))"
    ]
   },
   {