Start cookbook and move stuff from use cases (#11636)

docs/docs/integrations/chat/ollama.ipynb

@@ -124,7 +124,7 @@
    "source": [
     "## RAG\n",
     "\n",
-    "We can use Olama with RAG, [just as shown here](https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa).\n",
+    "We can use Olama with RAG, [just as shown here](https://python.langchain.com/docs/use_cases/question_answering/local_retrieval_qa).\n",
     "\n",
     "Let's use the 13b model:\n",
     "\n",

docs/docs/integrations/llms/ollama.ipynb

@@ -102,7 +102,7 @@
    "source": [
     "## RAG\n",
     "\n",
-    "We can use Olama with RAG, [just as shown here](https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa).\n",
+    "We can use Olama with RAG, [just as shown here](https://python.langchain.com/docs/use_cases/question_answering/local_retrieval_qa).\n",
     "\n",
     "Let's use the 13b model:\n",
     "\n",

docs/docs/integrations/providers/activeloop_deeplake.mdx

@@ -13,7 +13,7 @@ Activeloop Deep Lake supports SelfQuery Retrieval:
 
 ## More Resources
 1. [Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial Data](https://www.activeloop.ai/resources/ultimate-guide-to-lang-chain-deep-lake-build-chat-gpt-to-answer-questions-on-your-financial-data/)
-2. [Twitter the-algorithm codebase analysis with Deep Lake](/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake)
+2. [Twitter the-algorithm codebase analysis with Deep Lake](/docs/use_cases/question_answering/code/twitter-the-algorithm-analysis-deeplake)
 4. [Code Understanding](/docs/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query)
 3. Here is [whitepaper](https://www.deeplake.ai/whitepaper) and [academic paper](https://arxiv.org/pdf/2209.10785.pdf) for Deep Lake
 4. Here is a set of additional resources available for review: [Deep Lake](https://github.com/activeloopai/deeplake), [Get started](https://docs.activeloop.ai/getting-started) and [Tutorials](https://docs.activeloop.ai/hub-tutorials)

docs/docs/integrations/providers/arangodb.mdx

@@ -13,7 +13,7 @@ pip install python-arango
 
 Connect your ArangoDB Database with a chat model to get insights on your data. 
 
-See the notebook example [here](/docs/use_cases/more/graph/graph_arangodb_qa.html).
+See the notebook example [here](/docs/use_cases/graph/graph_arangodb_qa.html).
 
 ```python
 from arango import ArangoClient

docs/docs/integrations/providers/neo4j.mdx

@@ -41,4 +41,4 @@ from langchain.graphs import Neo4jGraph
 from langchain.chains import GraphCypherQAChain
 ```
 
-For a more detailed walkthrough of Cypher generating chain, see [this notebook](/docs/use_cases/more/graph/graph_cypher_qa.html)
+For a more detailed walkthrough of Cypher generating chain, see [this notebook](/docs/use_cases/graph/graph_cypher_qa.html)

docs/docs/integrations/vectorstores/timescalevector.ipynb

@@ -338,7 +338,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at an example of using Timescale Vector as a retriever with the [RetrievalQA chain](https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_qa) and the [stuff chain](https://python.langchain.com/docs/modules/chains/document/stuff).\n",
+    "Let's look at an example of using Timescale Vector as a retriever with the [RetrievalQA chain](https://python.langchain.com/docs/use_cases/question_answering/vector_db_qa) and the [stuff chain](https://python.langchain.com/docs/modules/chains/document/stuff).\n",
     "\n",
     "In this example, we'll ask the same query as above, but this time we'll pass the relevant documents returned from Timescale Vector to an LLM to use as context to answer our question.\n",
     "\n",