Documentation fixes (linting and broken links) (#5563)

# Lint sphinx documentation and fix broken links

This PR lints multiple warnings shown in generation of the project
documentation (using "make docs_linkcheck" and "make docs_build").
Additionally documentation internal links to (now?) non-existent files
are modified to point to existing documents as it seemed the new correct
target.

The documentation is not updated content wise.
There are no source code changes.

Fixes # (issue)

- broken documentation links to other files within the project
- sphinx formatting (linting)

## Before submitting

No source code changes, so no new tests added.

---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>

docs/modules/agents/agent_executors/examples/sharedmemory_for_tools.ipynb

@@ -9,8 +9,8 @@
     "\n",
     "This notebook goes over adding memory to **both** of an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:\n",
     "\n",
-    "- [Adding memory to an LLM Chain](../../memory/examples/adding_memory.ipynb)\n",
-    "- [Custom Agents](custom_agent.ipynb)\n",
+    "- [Adding memory to an LLM Chain](../../../memory/examples/adding_memory.ipynb)\n",
+    "- [Custom Agents](../../agents/custom_agent.ipynb)\n",
     "\n",
     "We are going to create a custom Agent. The agent has access to a conversation memory, search tool, and a summarization tool. And, the summarization tool also needs access to the conversation memory."
    ]

docs/modules/agents/how_to_guides.rst

@@ -36,7 +36,7 @@ The first category of how-to guides here cover specific parts of working with ag
    :glob:
    :hidden:
 
-   ./examples/*
+   ./agents/examples/*
 
 
 Agent Toolkits
@@ -46,26 +46,26 @@ The next set of examples covers agents with toolkits.
 As opposed to the examples above, these examples are not intended to show off an agent `type`,
 but rather to show off an agent applied to particular use case.
 
-`SQLDatabase Agent <./agent_toolkits/sql_database.html>`_: This notebook covers how to interact with an arbitrary SQL database using an agent.
+`SQLDatabase Agent <./toolkits/sql_database.html>`_: This notebook covers how to interact with an arbitrary SQL database using an agent.
 
-`JSON Agent <./agent_toolkits/json.html>`_: This notebook covers how to interact with a JSON dictionary using an agent.
+`JSON Agent <./toolkits/json.html>`_: This notebook covers how to interact with a JSON dictionary using an agent.
 
-`OpenAPI Agent <./agent_toolkits/openapi.html>`_: This notebook covers how to interact with an arbitrary OpenAPI endpoint using an agent.
+`OpenAPI Agent <./toolkits/openapi.html>`_: This notebook covers how to interact with an arbitrary OpenAPI endpoint using an agent.
 
-`VectorStore Agent <./agent_toolkits/vectorstore.html>`_: This notebook covers how to interact with VectorStores using an agent.
+`VectorStore Agent <./toolkits/vectorstore.html>`_: This notebook covers how to interact with VectorStores using an agent.
 
-`Python Agent <./agent_toolkits/python.html>`_: This notebook covers how to produce and execute python code using an agent.
+`Python Agent <./toolkits/python.html>`_: This notebook covers how to produce and execute python code using an agent.
 
-`Pandas DataFrame Agent <./agent_toolkits/pandas.html>`_: This notebook covers how to do question answering over a pandas dataframe using an agent. Under the hood this calls the Python agent..
+`Pandas DataFrame Agent <./toolkits/pandas.html>`_: This notebook covers how to do question answering over a pandas dataframe using an agent. Under the hood this calls the Python agent..
 
-`CSV Agent <./agent_toolkits/csv.html>`_: This notebook covers how to do question answering over a csv file. Under the hood this calls the Pandas DataFrame agent.
+`CSV Agent <./toolkits/csv.html>`_: This notebook covers how to do question answering over a csv file. Under the hood this calls the Pandas DataFrame agent.
 
 .. toctree::
    :maxdepth: 1
    :glob:
    :hidden:
 
-   ./agent_toolkits/*
+   ./toolkits/*
 
 
 Agent Types

docs/modules/chains/generic/async_chain.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "LangChain provides async support for Chains by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
     "\n",
-    "Async methods are currently supported in `LLMChain` (through `arun`, `apredict`, `acall`) and `LLMMathChain` (through `arun` and `acall`), `ChatVectorDBChain`, and [QA chains](../indexes/chain_examples/question_answering.html). Async support for other chains is on the roadmap."
+    "Async methods are currently supported in `LLMChain` (through `arun`, `apredict`, `acall`) and `LLMMathChain` (through `arun` and `acall`), `ChatVectorDBChain`, and [QA chains](../index_examples/question_answering.ipynb). Async support for other chains is on the roadmap."
    ]
   },
   {
@@ -104,7 +104,7 @@
     "s = time.perf_counter()\n",
     "generate_serially()\n",
     "elapsed = time.perf_counter() - s\n",
-    "print('\\033[1m' + f\"Serial executed in {elapsed:0.2f} seconds.\" + '\\033[0m')"
+    "print('\\033[1m' + f\"Serial executed in {elapsed:0.2f} seconds.\" + '\\033[0m')\n"
    ]
   }
  ],

docs/modules/indexes/document_loaders.rst

@@ -88,7 +88,7 @@ We don't need any access permissions to these datasets and services.
 
 
 Proprietary dataset or service loaders
-------------------------------
+--------------------------------------
 These datasets and services are not from the public domain.
 These loaders mostly transform data from specific formats of applications or cloud services,
 for example **Google Drive**.

docs/modules/indexes/document_loaders/examples/docugami.ipynb

@@ -137,7 +137,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!poetry run pip -q install openai tiktoken chromadb "
+    "!poetry run pip -q install openai tiktoken chromadb"
    ]
   },
   {
@@ -317,7 +317,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use a [self-querying retriever](../../retrievers/examples/self_query_retriever.ipynb) to improve our query accuracy, using this additional metadata:"
+    "We can use a [self-querying retriever](../../retrievers/examples/self_query.ipynb) to improve our query accuracy, using this additional metadata:"
    ]
   },
   {

docs/modules/memory/examples/agent_with_memory.ipynb

@@ -10,7 +10,7 @@
     "This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n",
     "\n",
     "- [Adding memory to an LLM Chain](adding_memory.ipynb)\n",
-    "- [Custom Agents](../../agents/examples/custom_agent.ipynb)\n",
+    "- [Custom Agents](../../agents/agents/custom_agent.ipynb)\n",
     "\n",
     "In order to add a memory to an agent we are going to the the following steps:\n",
     "\n",

docs/modules/memory/examples/agent_with_memory_in_db.ipynb

@@ -10,8 +10,8 @@
     "This notebook goes over adding memory to an Agent where the memory uses an external message store. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n",
     "\n",
     "- [Adding memory to an LLM Chain](adding_memory.ipynb)\n",
-    "- [Custom Agents](../../agents/examples/custom_agent.ipynb)\n",
-    "- [Agent with Memory](agetn_with_memory.ipynb)\n",
+    "- [Custom Agents](../../agents/agents/custom_agent.ipynb)\n",
+    "- [Agent with Memory](agent_with_memory.ipynb)\n",
     "\n",
     "In order to add a memory with an external message store to an agent we are going to do the following steps:\n",
     "\n",

docs/reference/modules/experimental.rst

@@ -1,6 +1,6 @@
-==========
+====================
 Experimental Modules
-==========
+====================
 
 This module contains experimental modules and reproductions of existing work using LangChain primitives.