Added the creation of an agent with tools to LangChain lab

Author: codingbandit

Labs/lab_4_langchain_vector_search.ipynb Labs/lab_4_langchain.ipynbLabs/lab_4_langchain_vector_search.ipynb renamed to Labs/lab_4_langchain.ipynb

@@ -15,6 +15,7 @@
    "source": [
     "import os\n",
     "import json\n",
+    "import pymongo\n",
     "from typing import List\n",
     "from dotenv import load_dotenv\n",
     "from pymongo import MongoClient\n",
@@ -74,7 +75,7 @@
     "\n",
     "In the previous lab, the `pymongo` library was used to perform a vector search through a db command to find product documents that were most similar to the user's input. In this lab, you will use the `langchain` library to perform the same search. LangChain has a vector store class named **AzureCosmosDBVectorSearch**, a community contribution, that supports vector search in Azure CosmosDB for MongoDB API vCore.\n",
     "\n",
-    "When establishing the connection to the vector store (MongoDB vCore), remember that in previous labs the products collection was populated and a contentVector field added that contains the vectorized embeddings of the document itself. Finally, a vector index was also created on the contentVector field to enable vector search.\n",
+    "When establishing the connection to the vector store (MongoDB vCore), recall that in previous labs the products collection was populated and a contentVector field added that contains the vectorized embeddings of the document itself. Finally, a vector index was also created on the contentVector field to enable vector search. The vector index in each collection is named `VectorSearchIndex`.\n",
     "\n",
     "The return value of a vector search in LangChain is a list of `Document` objects. The LangChain `Document` class contains two properties: `page_content`, that represents the textual content that is typically used to augment the prompt, and `metadata` that contains all other attributes of the document. In the cell below, we'll use the `_id` field as the page_content, and the rest of the fields are returned as metadata.\n",
     "\n",
@@ -206,6 +207,33 @@
     "response = rag_chain.invoke(question)\n",
     "print(response)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LangChain Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = pymongo.MongoClient(CONNECTION_STRING)\n",
+    "db = client.cosmic_works"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create custom code lookup tools that retrieves documents from the customers, products, and salesorders collections by ID.\n",
+    "def"
+   ]
   }
  ],
  "metadata": {