docs retrievers fixes (#6299)

Fixed several inconsistencies:
- file names and notebook titles should be similar otherwise ToC on the
[retrievers
page](https://python.langchain.com/en/latest/modules/indexes/retrievers.html)
and on the left ToC tab are different. For example, now, `Self-querying
with Chroma` is not correctly alphabetically sorted because its file
named `chroma_self_query.ipynb`
- `Stringing compressors and document transformers...` demoted from `#`
to `##`. Otherwise, it appears in Toc.
- several formatting problems

#### Who can review?

@hwchase17 
@dev2049

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

docs/extras/modules/data_connection/retrievers/how_to/self_query/chroma_self_query.ipynb

@@ -5,7 +5,7 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Self-querying with Chroma\n",
+    "# Chroma self-querying \n",
     "\n",
     ">[Chroma](https://docs.trychroma.com/getting-started) is a database for building AI applications with embeddings.\n",
     "\n",

docs/extras/modules/data_connection/retrievers/how_to/self_query/qdrant_self_query.ipynb

@@ -6,7 +6,7 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Self-querying with Qdrant\n",
+    "# Qdrant self-querying \n",
     "\n",
     ">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support. It makes it useful \n",
     "\n",
@@ -222,9 +222,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "fc3f1e6e",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -422,7 +420,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/how_to/self_query/weaviate_self_query.ipynb

@@ -5,7 +5,7 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# Self-querying with Weaviate"
+    "# Weaviate self-querying "
    ]
   },
   {
@@ -293,7 +293,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/retrievers/integrations/merger_retriever.ipynb

@@ -1,16 +1,15 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "fc0db1bc",
    "metadata": {},
    "source": [
     "# LOTR (Merger Retriever)\n",
     "\n",
-    "Lord of the Retrievers, also known as MergerRetriever, takes a list of retrievers as input and merges the results of their get_relevant_documents() methods into a single list. The merged results will be a list of documents that are relevant to the query and that have been ranked by the different retrievers.\n",
+    "`Lord of the Retrievers`, also known as `MergerRetriever`, takes a list of retrievers as input and merges the results of their get_relevant_documents() methods into a single list. The merged results will be a list of documents that are relevant to the query and that have been ranked by the different retrievers.\n",
     "\n",
-    "The MergerRetriever class can be used to improve the accuracy of document retrieval in a number of ways. First, it can combine the results of multiple retrievers, which can help to reduce the risk of bias in the results. Second, it can rank the results of the different retrievers, which can help to ensure that the most relevant documents are returned first."
+    "The `MergerRetriever` class can be used to improve the accuracy of document retrieval in a number of ways. First, it can combine the results of multiple retrievers, which can help to reduce the risk of bias in the results. Second, it can rank the results of the different retrievers, which can help to ensure that the most relevant documents are returned first."
    ]
   },
   {
@@ -71,7 +70,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c152339d",
    "metadata": {},

docs/extras/modules/data_connection/retrievers/integrations/pubmed.ipynb

@@ -5,11 +5,11 @@
    "id": "3df0dcf8",
    "metadata": {},
    "source": [
-    "# PubMed Retriever\n",
+    "# PubMed\n",
     "\n",
-    "This notebook goes over how to use PubMed as a retriever\n",
+    "This notebook goes over how to use `PubMed` as a retriever\n",
     "\n",
-    "PubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites."
+    "`PubMed®` comprises more than 35 million citations for biomedical literature from `MEDLINE`, life science journals, and online books. Citations may include links to full text content from `PubMed Central` and publisher web sites."
    ]
   },
   {
@@ -72,7 +72,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,