docs: fix links (#16284)

docs/docs/integrations/document_loaders/psychic.ipynb

@@ -8,7 +8,7 @@
     "This notebook covers how to load documents from `Psychic`. See [here](/docs/integrations/providers/psychic) for more details.\n",
     "\n",
     "## Prerequisites\n",
-    "1. Follow the Quick Start section in [this document](/docs/ecosystem/integrations/psychic)\n",
+    "1. Follow the Quick Start section in [this document](/docs/integrations/providers/psychic)\n",
     "2. Log into the [Psychic dashboard](https://dashboard.psychic.dev/) and get your secret key\n",
     "3. Install the frontend react library into your web app and have a user authenticate a connection. The connection will be created using the connection id that you specify."
    ]

docs/docs/integrations/llms/llm_caching.ipynb

@@ -318,7 +318,7 @@
    "metadata": {},
    "source": [
     "### Standard Cache\n",
-    "Use [Redis](/docs/integrations/partners/redis) to cache prompts and responses."
+    "Use [Redis](/docs/integrations/providers/redis) to cache prompts and responses."
    ]
   },
   {
@@ -404,7 +404,7 @@
    "metadata": {},
    "source": [
     "### Semantic Cache\n",
-    "Use [Redis](/docs/integrations/partners/redis) to cache prompts and responses and evaluate hits based on semantic similarity."
+    "Use [Redis](/docs/integrations/providers/redis) to cache prompts and responses and evaluate hits based on semantic similarity."
    ]
   },
   {
@@ -728,7 +728,7 @@
    },
    "source": [
     "## `Momento` Cache\n",
-    "Use [Momento](/docs/integrations/partners/momento) to cache prompts and responses.\n",
+    "Use [Momento](/docs/integrations/providers/momento) to cache prompts and responses.\n",
     "\n",
     "Requires momento to use, uncomment below to install:"
    ]

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/code/twitter-the-algorithm-analysis-deeplake)
+2. [Twitter the-algorithm codebase analysis with Deep Lake](https://github.com/langchain-ai/langchain/blob/master/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb)
 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/chroma.mdx

@@ -18,7 +18,7 @@ whether for semantic search or example selection.
 from langchain_community.vectorstores import Chroma
 ```
 
-For a more detailed walkthrough of the Chroma wrapper, see [this notebook](/docs/integrations/vectorstores/chroma_self_query)
+For a more detailed walkthrough of the Chroma wrapper, see [this notebook](/docs/integrations/vectorstores/chroma)
 
 ## Retriever
 

docs/docs/integrations/providers/ragatouille.ipynb

@@ -66,7 +66,7 @@
    "source": [
     "## Document Compressor\n",
     "\n",
-    "We can also use RAGatouille off-the-shelf as a reranker. This will allow us to use ColBERT to rerank retrieved results from any generic retriever. The benefits of this are that we can do this on top of any existing index, so that we don't need to create a new idex. We can do this by using the [document compressor](/docs/modules/data_connections/retrievers/contextual_compression) abstraction in LangChain."
+    "We can also use RAGatouille off-the-shelf as a reranker. This will allow us to use ColBERT to rerank retrieved results from any generic retriever. The benefits of this are that we can do this on top of any existing index, so that we don't need to create a new idex. We can do this by using the [document compressor](/docs/modules/data_connection/retrievers/contextual_compression) abstraction in LangChain."
    ]
   },
   {

docs/docs/integrations/retrievers/activeloop.ipynb

@@ -51,7 +51,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Also you'll need to create a [Activeloop]((https://activeloop.ai/)) account."
+    "Also you'll need to create a [Activeloop](https://activeloop.ai) account."
    ]
   },
   {

docs/docs/modules/agents/agent_types/xml_agent.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "* Use with regular LLMs, not with chat models.\n",
     "* Use only with unstructured tools; i.e., tools that accept a single string input.\n",
-    "* See [AgentTypes](../index) documentation for more agent types.\n",
+    "* See [AgentTypes](/docs/moduels/agents/agent_types/) documentation for more agent types.\n",
     ":::"
    ]
   },

docs/docs/modules/callbacks/custom_chain.mdx

@@ -1,6 +0,0 @@
-# Callbacks for custom chains
-
- When you create a custom chain you can easily set it up to use the same callback system as all the built-in chains.
-`_call`, `_generate`, `_run`, and equivalent async methods on Chains / LLMs / Chat Models / Agents / Tools now receive a 2nd argument called `run_manager` which is bound to that run, and contains the logging methods that can be used by that object (i.e. `on_llm_new_token`). This is useful when constructing a custom chain. See this guide for more information on how to [create custom chains and use callbacks inside them](/docs/modules/chains/how_to/custom_chain).
-
-

docs/vercel.json

@@ -380,10 +380,6 @@
       "source": "/docs/modules/agents/agents/examples/mrkl_chat(.html?)",
       "destination": "/docs/modules/agents/"
     },
-    {
-      "source": "/docs/use_cases(/?)",
-      "destination": "/docs/use_cases/question_answering/"
-    },
     {
       "source": "/docs/integrations(/?)",
       "destination": "/docs/integrations/providers/"