Merge pull request #259 from langchain-ai/rlm/clean-rag-ntbks

Clean and normalize all langgraph RAG ntbks

examples/rag/langgraph_adaptive_rag.ipynb

@@ -22,7 +22,9 @@
     "* Single-shot RAG\n",
     "* Iterative RAG\n",
     "\n",
-    "Let's build on this to perform query analysis to route across some more interesting cases:\n",
+    "Let's build on this using LangGraph. \n",
+    "\n",
+    "In our implementation, we will route between:\n",
     "\n",
     "* Web search: for questions related to recent events\n",
     "* Self-corrective RAG: for questions related to our index\n",
@@ -45,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph"
+    "! pip install langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph  tavily-python"
    ]
   },
   {
@@ -58,7 +60,18 @@
     "### LLMs\n",
     "import os\n",
     "os.environ['OPENAI_API_KEY'] = <your-api-key>\n",
-    "os.environ['COHERE_API_KEY'] = <your-api-key>"
+    "os.environ['COHERE_API_KEY'] = <your-api-key>\n",
+    "os.environ['TAVILY_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "018c9e9f-8199-4f33-b5b4-7adfb66d6219",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
+    "\n",
+    "* Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom) by setting: "
    ]
   },
   {
@@ -157,7 +170,6 @@
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
-    "### from langchain_cohere import ChatCohere\n",
     "\n",
     "# Data model\n",
     "class RouteQuery(BaseModel):\n",
@@ -218,6 +230,7 @@
     "# Prompt \n",
     "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
     "    If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
     "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
     "grade_prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -440,7 +453,7 @@
     "from typing import List\n",
     "\n",
     "class GraphState(TypedDict):\n",
-    "    \"\"\"|\n",
+    "    \"\"\"\n",
     "    Represents the state of our graph.\n",
     "\n",
     "    Attributes:\n",
@@ -673,8 +686,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pprint import pprint\n",
-    "\n",
     "from langgraph.graph import END, StateGraph\n",
     "\n",
     "workflow = StateGraph(GraphState)\n",
@@ -751,6 +762,8 @@
     }
    ],
    "source": [
+    "from pprint import pprint\n",
+    "\n",
     "# Run \n",
     "inputs = {\"question\": \"What player at the Bears expected to draft first in the 2024 NFL draft?\"}\n",
     "for output in app.stream(inputs):\n",