fix: complete notebook formatting updates for consolidation

- Clear execution counts in notebooks to match clean state
- Update source formatting to single-line format
- Remove execution outputs from langgraph_self_rag.ipynb
- Ensures notebooks are in clean, consistent state for release

Author: abrookins

libs/redis/docs/cache.ipynb

@@ -79,19 +79,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import time\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_core.globals import set_llm_cache\n",
-    "from langchain_core.outputs import Generation\n",
-    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
-    "\n",
-    "from langchain_redis import RedisCache, RedisSemanticCache"
-   ]
+   "source": "import time\nfrom getpass import getpass\n\nfrom langchain_core.globals import set_llm_cache\nfrom langchain_core.outputs import Generation\nfrom langchain_openai import OpenAI, OpenAIEmbeddings\n\nfrom langchain_redis import RedisCache, RedisSemanticCache"
   },
   {
    "cell_type": "markdown",

libs/redis/docs/kitchensink.ipynb

@@ -96,17 +96,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_redis import RedisVectorStore, RedisCache, RedisChatMessageHistory\n",
-    "from langchain_openai import OpenAIEmbeddings, OpenAI\n",
-    "from langchain_core.globals import set_llm_cache\n",
-    "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "import wikipedia"
-   ]
+   "source": "from langchain_redis import RedisVectorStore, RedisCache, RedisChatMessageHistory\nfrom langchain_openai import OpenAIEmbeddings, OpenAI\nfrom langchain_core.globals import set_llm_cache\nfrom langchain_core.prompts import PromptTemplate\nfrom langchain_core.output_parsers import StrOutputParser\nimport wikipedia"
   },
   {
    "cell_type": "markdown",

libs/redis/docs/langgraph_self_rag.ipynb

@@ -160,46 +160,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "565a6d44-2c9f-4fff-b1ec-eea05df9350d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "00:26:48 redisvl.index.index INFO   Index already exists, not overwriting.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.document_loaders import WebBaseLoader\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
-    "\n",
-    "from langchain_redis import RedisVectorStore\n",
-    "\n",
-    "urls = [\n",
-    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
-    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
-    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
-    "]\n",
-    "\n",
-    "docs = [WebBaseLoader(url).load() for url in urls]\n",
-    "docs_list = [item for sublist in docs for item in sublist]\n",
-    "\n",
-    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
-    "    chunk_size=250, chunk_overlap=0\n",
-    ")\n",
-    "doc_splits = text_splitter.split_documents(docs_list)\n",
-    "\n",
-    "# Add to vectorDB\n",
-    "vectorstore = RedisVectorStore.from_documents(\n",
-    "    doc_splits, OpenAIEmbeddings(), redis_url=REDIS_URL, index_name=\"rag-redis\"\n",
-    ")\n",
-    "\n",
-    "retriever = vectorstore.as_retriever()"
-   ]
+   "outputs": [],
+   "source": "from langchain_text_splitters import RecursiveCharacterTextSplitter\nfrom langchain_community.document_loaders import WebBaseLoader\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom langchain_redis import RedisVectorStore\n\nurls = [\n    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n]\n\ndocs = [WebBaseLoader(url).load() for url in urls]\ndocs_list = [item for sublist in docs for item in sublist]\n\ntext_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n    chunk_size=250, chunk_overlap=0\n)\ndoc_splits = text_splitter.split_documents(docs_list)\n\n# Add to vectorDB\nvectorstore = RedisVectorStore.from_documents(\n    doc_splits, OpenAIEmbeddings(), redis_url=REDIS_URL, index_name=\"rag-redis\"\n)\n\nretriever = vectorstore.as_retriever()"
   },
   {
    "cell_type": "markdown",

libs/redis/docs/vectorstores.ipynb

@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [