updated notebook titles and text.

docs/extras/modules/memory/types/kg.ipynb

@@ -5,11 +5,17 @@
    "id": "44c9933a",
    "metadata": {},
    "source": [
-    "# Conversation Knowledge Graph Memory\n",
+    "# Conversation Knowledge Graph\n",
     "\n",
-    "This type of memory uses a knowledge graph to recreate memory.\n",
-    "\n",
-    "Let's first walk through how to use the utilities"
+    "This type of memory uses a knowledge graph to recreate memory.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c798006-ca04-4de3-83eb-cf167fb2bd01",
+   "metadata": {},
+   "source": [
+    "## Using memory with LLM"
    ]
   },
   {
@@ -162,6 +168,7 @@
    "metadata": {},
    "source": [
     "## Using in a chain\n",
+    "\n",
     "Let's now use this in a chain!"
    ]
   },
@@ -348,7 +355,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/types/summary_buffer.ipynb

@@ -5,13 +5,22 @@
    "id": "ff4be5f3",
    "metadata": {},
    "source": [
-    "# ConversationSummaryBufferMemory\n",
+    "# Conversation Summary Buffer\n",
     "\n",
-    "`ConversationSummaryBufferMemory` combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. Unlike the previous implementation though, it uses token length rather than number of interactions to determine when to flush interactions.\n",
+    "`ConversationSummaryBufferMemory` combines the two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. \n",
+    "It uses token length rather than number of interactions to determine when to flush interactions.\n",
     "\n",
     "Let's first walk through how to use the utilities"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0309636e-a530-4d2a-ba07-0916ea18bb20",
+   "metadata": {},
+   "source": [
+    "## Using memory with LLM"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -320,7 +329,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/types/token_buffer.ipynb

@@ -5,13 +5,21 @@
    "id": "ff4be5f3",
    "metadata": {},
    "source": [
-    "# ConversationTokenBufferMemory\n",
+    "# Conversation Token Buffer\n",
     "\n",
     "`ConversationTokenBufferMemory` keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.\n",
     "\n",
     "Let's first walk through how to use the utilities"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0e528ef0-7b04-4a4a-8ff2-493c02027e83",
+   "metadata": {},
+   "source": [
+    "## Using memory with LLM"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -286,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,