docs: Astra DB, replace leftover links to "community" legacy package + modernize doc loader signature (#30969)

This PR brings some much-needed updates to some of the Astra DB shorter
example notebooks,

- ensuring imports are from the partner package instead of the
(deprecated) community legacy package
- improving the wording in a few related places
- updating the constructor signature introduced with the latest partner
package's AstraDBLoader
- marking the community package counterpart of the LLM caches as
deprecated in the summary table at the end of the page.

docs/docs/integrations/document_loaders/astradb.ipynb

@@ -34,33 +34,46 @@
     "id": "juAmbgoWD17u"
    },
    "source": [
-    "The AstraDB Document Loader returns a list of Langchain Documents from an AstraDB database.\n",
+    "The Astra DB Document Loader returns a list of Langchain `Document` objects read from an Astra DB collection.\n",
     "\n",
-    "The Loader takes the following parameters:\n",
+    "The loader takes the following parameters:\n",
     "\n",
-    "* `api_endpoint`: AstraDB API endpoint. Looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
+    "* `api_endpoint`: Astra DB API endpoint. Looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
-    "* `token`: AstraDB token. Looks like `AstraCS:6gBhNmsk135....`\n",
+    "* `token`: Astra DB token. Looks like `AstraCS:aBcD0123...`\n",
     "* `collection_name` : AstraDB collection name\n",
-    "* `namespace`: (Optional) AstraDB namespace\n",
+    "* `namespace`: (Optional) AstraDB namespace (called _keyspace_ in Astra DB)\n",
     "* `filter_criteria`: (Optional) Filter used in the find query\n",
     "* `projection`: (Optional) Projection used in the find query\n",
-    "* `find_options`: (Optional) Options used in the find query\n",
+    "* `limit`: (Optional) Maximum number of documents to retrieve\n",
-    "* `nb_prefetched`: (Optional) Number of documents pre-fetched by the loader\n",
     "* `extraction_function`: (Optional) A function to convert the AstraDB document to the LangChain `page_content` string. Defaults to `json.dumps`\n",
     "\n",
-    "The following metadata is set to the LangChain Documents metadata output:\n",
+    "The loader sets the following metadata for the documents it reads:\n",
     "\n",
     "```python\n",
-    "{\n",
+    "metadata={\n",
-    "    metadata : {\n",
     "    \"namespace\": \"...\", \n",
     "    \"api_endpoint\": \"...\", \n",
     "    \"collection\": \"...\"\n",
-    "    }\n",
     "}\n",
     "```"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"langchain-astradb>=0.6,<0.7\""
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -71,24 +84,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import AstraDBLoader"
+    "from langchain_astradb import AstraDBLoader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[**API Reference:** `AstraDBLoader`](https://python.langchain.com/api_reference/astradb/document_loaders/langchain_astradb.document_loaders.AstraDBLoader.html#langchain_astradb.document_loaders.AstraDBLoader)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:41:22.643335Z",
      "start_time": "2024-01-08T12:40:57.759116Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com\n",
+      "ASTRA_DB_APPLICATION_TOKEN =  ········\n"
+     ]
+    }
+   ],
    "source": [
     "from getpass import getpass\n",
     "\n",
@@ -98,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:25.395162Z",
@@ -112,19 +144,22 @@
     "    token=ASTRA_DB_APPLICATION_TOKEN,\n",
     "    collection_name=\"movie_reviews\",\n",
     "    projection={\"title\": 1, \"reviewtext\": 1},\n",
-    "    find_options={\"limit\": 10},\n",
+    "    limit=10,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:30.236489Z",
      "start_time": "2024-01-08T12:42:29.612133Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -133,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:31.369394Z",
@@ -144,10 +179,10 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='{\"_id\": \"659bdffa16cbc4586b11a423\", \"title\": \"Dangerous Men\", \"reviewtext\": \"\\\\\"Dangerous Men,\\\\\" the picture\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\"}', metadata={'namespace': 'default_keyspace', 'api_endpoint': 'https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com', 'collection': 'movie_reviews'})"
+       "Document(metadata={'namespace': 'default_keyspace', 'api_endpoint': 'https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com', 'collection': 'movie_reviews'}, page_content='{\"_id\": \"659bdffa16cbc4586b11a423\", \"title\": \"Dangerous Men\", \"reviewtext\": \"\\\\\"Dangerous Men,\\\\\" the picture\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\"}')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -179,7 +214,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llm_caching.ipynb

@@ -3112,8 +3112,8 @@
     "|------------|---------|\n",
     "| langchain_astradb.cache | [AstraDBCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBCache.html) |\n",
     "| langchain_astradb.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBSemanticCache.html) |\n",
-    "| langchain_community.cache | [AstraDBCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBCache.html) |\n",
+    "| langchain_community.cache | [AstraDBCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBCache.html) (deprecated since `langchain-community==0.0.28`) |\n",
-    "| langchain_community.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBSemanticCache.html) |\n",
+    "| langchain_community.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBSemanticCache.html) (deprecated since `langchain-community==0.0.28`) |\n",
     "| langchain_community.cache | [AzureCosmosDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AzureCosmosDBSemanticCache.html) |\n",
     "| langchain_community.cache | [CassandraCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraCache.html) |\n",
     "| langchain_community.cache | [CassandraSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraSemanticCache.html) |\n",

docs/docs/integrations/memory/astradb_chat_message_history.ipynb

@@ -17,22 +17,22 @@
    "id": "f507f58b-bf22-4a48-8daf-68d869bcd1ba",
    "metadata": {},
    "source": [
-    "## Setting up\n",
+    "## Setup\n",
     "\n",
     "To run this notebook you need a running Astra DB. Get the connection secrets on your Astra dashboard:\n",
     "\n",
     "- the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`;\n",
-    "- the Token looks like `AstraCS:6gBhNmsk135...`."
+    "- the Database Token looks like `AstraCS:aBcD0123...`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d7092199",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  \"astrapy>=0.7.1 langchain-community\" "
+    "!pip install \"langchain-astradb>=0.6,<0.7\""
    ]
   },
   {
@@ -45,12 +45,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "163d97f0",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stdin",
      "output_type": "stream",
      "text": [
       "ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com\n",
@@ -65,14 +65,6 @@
     "ASTRA_DB_APPLICATION_TOKEN = getpass.getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "55860b2d",
-   "metadata": {},
-   "source": [
-    "Depending on whether local or cloud-based Astra DB, create the corresponding database connection \"Session\" object."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "36c163e8",
@@ -83,12 +75,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "d15e3302",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_message_histories import AstraDBChatMessageHistory\n",
+    "from langchain_astradb import AstraDBChatMessageHistory\n",
     "\n",
     "message_history = AstraDBChatMessageHistory(\n",
     "    session_id=\"test-session\",\n",
@@ -98,22 +90,31 @@
     "\n",
     "message_history.add_user_message(\"hi!\")\n",
     "\n",
-    "message_history.add_ai_message(\"whats up?\")"
+    "message_history.add_ai_message(\"hello, how are you?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53acb4a8-d536-4a58-9fee-7d70033d9c81",
+   "metadata": {},
+   "source": [
+    "[**API Reference:** `AstraDBChatMessageHistory`](https://python.langchain.com/api_reference/astradb/chat_message_histories/langchain_astradb.chat_message_histories.AstraDBChatMessageHistory.html#langchain_astradb.chat_message_histories.AstraDBChatMessageHistory)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "64fc465e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[HumanMessage(content='hi!'), AIMessage(content='whats up?')]"
+       "[HumanMessage(content='hi!', additional_kwargs={}, response_metadata={}),\n",
+       " AIMessage(content='hello, how are you?', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -139,7 +140,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

docs/docs/integrations/retrievers/self_query/astradb.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Astra DB (Cassandra)\n",
+    "# Astra DB\n",
     "\n",
     ">[DataStax Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on `Cassandra` and made conveniently available through an easy-to-use JSON API.\n",
     "\n",
@@ -16,32 +16,46 @@
    "metadata": {},
    "source": [
     "## Creating an Astra DB vector store\n",
-    "First we'll want to create an Astra DB VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
+    "First, create an Astra DB vector store and seed it with some data.\n",
     "\n",
-    "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `astrapy` package."
+    "We've created a small demo set of documents containing movie summaries.\n",
+    "\n",
+    "NOTE: The self-query retriever requires the `lark` package installed (`pip install lark`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet lark astrapy langchain-openai"
+    "!pip install \"langchain-astradb>=0.6,<0.7\" \\\n",
+    "  \"langchain_openai>=0.3,<0.4\" \\\n",
+    "  \"lark>=1.2,<2.0\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+    "In this example, you'll use the `OpenAIEmbeddings`. Please enter an OpenAI API Key."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "from getpass import getpass\n",
@@ -69,14 +83,23 @@
     "Create the Astra DB VectorStore:\n",
     "\n",
     "- the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
-    "- the Token looks like `AstraCS:6gBhNmsk135....`"
+    "- the Token looks like `AstraCS:aBcD0123...`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com\n",
+      "ASTRA_DB_APPLICATION_TOKEN =  ········\n"
+     ]
+    }
+   ],
    "source": [
     "ASTRA_DB_API_ENDPOINT = input(\"ASTRA_DB_API_ENDPOINT = \")\n",
     "ASTRA_DB_APPLICATION_TOKEN = getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")"
@@ -84,11 +107,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.vectorstores import AstraDB\n",
+    "from langchain_astradb import AstraDBVectorStore\n",
     "from langchain_core.documents import Document\n",
     "\n",
     "docs = [\n",
@@ -101,11 +124,13 @@
     "        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
     "    ),\n",
     "    Document(\n",
-    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
+    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams \"\n",
+    "        \"within dreams and Inception reused the idea\",\n",
     "        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
     "    ),\n",
     "    Document(\n",
-    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
+    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men \"\n",
+    "        \"pine after them\",\n",
     "        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
     "    ),\n",
     "    Document(\n",
@@ -123,7 +148,7 @@
     "    ),\n",
     "]\n",
     "\n",
-    "vectorstore = AstraDB.from_documents(\n",
+    "vectorstore = AstraDBVectorStore.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
     "    collection_name=\"astra_self_query_demo\",\n",
@@ -136,13 +161,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Creating our self-querying retriever\n",
+    "## Creating a self-querying retriever\n",
-    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
+    "\n",
+    "Now you can instantiate the retriever.\n",
+    "\n",
+    "To do this, you need to provide some information upfront about the metadata fields that the documents support, along with a short description of the documents' contents."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +202,11 @@
     "llm = OpenAI(temperature=0)\n",
     "\n",
     "retriever = SelfQueryRetriever.from_llm(\n",
-    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
+    "    llm,\n",
+    "    vectorstore,\n",
+    "    document_content_description,\n",
+    "    metadata_field_info,\n",
+    "    verbose=True,\n",
     ")"
    ]
   },
@@ -183,14 +215,29 @@
    "metadata": {},
    "source": [
     "## Testing it out\n",
-    "And now we can try actually using our retriever!"
+    "\n",
+    "Now you can try actually using our retriever:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='d7b9ec1edafa467caab524455e8c1f5d', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),\n",
+       " Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so'),\n",
+       " Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
+       " Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea')]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example only specifies a relevant query\n",
     "retriever.invoke(\"What are some movies about dinosaurs?\")"
@@ -198,9 +245,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
+       " Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea')]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example specifies a filter\n",
     "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
@@ -208,9 +267,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='0539843fd203484c9be486c2a0e2454c', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3}, page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them')]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example only specifies a query and a filter\n",
     "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
@@ -218,9 +288,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea'),\n",
+       " Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone')]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example specifies a composite filter\n",
     "retriever.invoke(\"What's a highly rated (above 8.5), science fiction movie ?\")"
@@ -228,9 +310,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so')]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example specifies a query and composite filter\n",
     "retriever.invoke(\n",
@@ -242,20 +335,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Filter k\n",
+    "## Set a limit ('k')\n",
     "\n",
-    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
+    "you can also use the self-query retriever to specify `k`, the number of documents to fetch.\n",
     "\n",
-    "We can do this by passing `enable_limit=True` to the constructor."
+    "You achieve this by passing `enable_limit=True` to the constructor."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "retriever = SelfQueryRetriever.from_llm(\n",
+    "retriever_k = SelfQueryRetriever.from_llm(\n",
     "    llm,\n",
     "    vectorstore,\n",
     "    document_content_description,\n",
@@ -267,12 +360,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='d7b9ec1edafa467caab524455e8c1f5d', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),\n",
+       " Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so')]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.invoke(\"What are two movies about dinosaurs?\")"
+    "retriever_k.invoke(\"What are two movies about dinosaurs?\")"
    ]
   },
   {
@@ -293,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -322,7 +427,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,