Update

cookbook/langsmith_intro.ipynb

@@ -558,7 +558,7 @@
    "id": "50b294c2-5048-4561-b610-2253c24fce62",
    "metadata": {},
    "source": [
-    "## 8. Summary\n",
+    "## 8. Summary evaluators\n",
     "\n",
     "We can use summary evaluators to compute summary metrics over a dataset.\n",
     "\n",
@@ -604,7 +604,13 @@
     "    ]\n",
     ")\n",
     "\n",
-    "retrieval_grader_oai = grade_prompt | structured_llm_grader"
+    "retrieval_grader_oai = grade_prompt | structured_llm_grader\n",
+    "\n",
+    "def predict_oai(inputs: dict) -> dict:\n",
+    "\n",
+    "    # Returns pydantic object\n",
+    "    grade = retrieval_grader_oai.invoke({\"question\": inputs[\"question\"], \"document\": inputs[\"doc_txt\"]})\n",
+    "    return {\"grade\":grade.score}"
    ]
   },
   {
@@ -634,7 +640,13 @@
     "    input_variables=[\"question\", \"document\"],\n",
     ")\n",
     "\n",
-    "retrieval_grader_mistral = prompt | llm | JsonOutputParser()"
+    "retrieval_grader_mistral = prompt | llm | JsonOutputParser()\n",
+    "\n",
+    "def predict_mistral(inputs: dict) -> dict:\n",
+    "\n",
+    "    # Returns JSON\n",
+    "    grade = retrieval_grader_mistral.invoke({\"question\": inputs[\"question\"], \"document\": inputs[\"doc_txt\"]})\n",
+    "    return {\"grade\":grade['score']}"
    ]
   },
   {
@@ -684,26 +696,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "becddb03-a738-41bd-a0e5-cd46c996893c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def predict_mistral(inputs: dict) -> dict:\n",
-    "\n",
-    "    # Returns JSON\n",
-    "    grade = retrieval_grader_mistral.invoke({\"question\": inputs[\"question\"], \"document\": inputs[\"doc_txt\"]})\n",
-    "    return {\"grade\":grade['score']}\n",
-    "\n",
-    "def predict_oai(inputs: dict) -> dict:\n",
-    "\n",
-    "    # Returns pydantic object\n",
-    "    grade = retrieval_grader_oai.invoke({\"question\": inputs[\"question\"], \"document\": inputs[\"doc_txt\"]})\n",
-    "    return {\"grade\":grade.score}"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 27,
@@ -875,44 +867,12 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "91604245-abc1-4177-b517-fb5cc01ba017",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf249e42-ce05-4502-992a-df0c57dc1573",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7d30d196-0c09-482b-921b-4c247913f46b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ac68a2f2-7c20-4d9a-8dbe-964185713e4f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "486cd37c-a783-479e-b8b7-91aaec0660a6",
+   "cell_type": "markdown",
+   "id": "4189003c-0293-4edf-ba85-5ea438ce17b9",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "# TODO: Add "
+   ]
   },
   {
    "attachments": {
@@ -924,7 +884,7 @@
    "id": "48b6ce44-4d65-4ad3-b37f-dbbf8e4543c4",
    "metadata": {},
    "source": [
-    "# 8. Trace tagging and feedback\n",
+    "# Trace tagging and feedback\n",
     "\n",
     "Nice blog post on some best practices:\n",
     "\n",
@@ -1125,7 +1085,7 @@
    "id": "8590debc-93a5-4d1f-9bde-53e91834aea9",
    "metadata": {},
    "source": [
-    "# 9. Evaluating RAG\n",
+    "# Evaluating RAG\n",
     "\n",
     "Nice blog post on some best practices:\n",
     "\n",
@@ -1346,7 +1306,7 @@
    "id": "fde3aa77-6959-465b-bbc1-c9d1ddc6c9a5",
    "metadata": {},
    "source": [
-    "# 10. Online unit tests \n",
+    "# Online unit tests \n",
     "\n",
     "Nice blog post on some best practices:\n",
     "\n",