fix links

docs/docs/guides/debugging.md

@@ -656,6 +656,6 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
 
 ## Other callbacks
 
-`Callbacks` are what we use to execute any functionality within a component outside the primary component logic. All of the above solutions use `Callbacks` under the hood to log intermediate steps of components. There are a number of `Callbacks` relevant for debugging that come with LangChain out of the box, like the [FileCallbackHandler](/docs/modules/callbacks/how_to/filecallbackhandler). You can also implement your own callbacks to execute custom functionality.
+`Callbacks` are what we use to execute any functionality within a component outside the primary component logic. All of the above solutions use `Callbacks` under the hood to log intermediate steps of components. There are a number of `Callbacks` relevant for debugging that come with LangChain out of the box, like the [FileCallbackHandler](/docs/modules/callbacks/filecallbackhandler). You can also implement your own callbacks to execute custom functionality.
 
 See here for more info on [Callbacks](/docs/modules/callbacks/), how to use them, and customize them.

docs/docs/guides/deployments/index.mdx

@@ -20,11 +20,11 @@ This guide aims to provide a comprehensive overview of the requirements for depl
 
 Understanding these components is crucial when assessing serving systems. LangChain integrates with several open-source projects designed to tackle these issues, providing a robust framework for productionizing your LLM applications. Some notable frameworks include:
 
-- [Ray Serve](/docs/ecosystem/integrations/ray_serve)
+- [Ray Serve](/docs/integrations/providers/ray_serve)
 - [BentoML](https://github.com/bentoml/BentoML)
-- [OpenLLM](/docs/ecosystem/integrations/openllm)
-- [Modal](/docs/ecosystem/integrations/modal)
-- [Jina](/docs/ecosystem/integrations/jina#deployment)
+- [OpenLLM](/docs/integrations/providers/openllm)
+- [Modal](/docs/integrations/providers/modal)
+- [Jina](/docs/integrations/providers/jina)
 
 These links will provide further information on each ecosystem, assisting you in finding the best fit for your LLM deployment needs.
 

docs/docs/guides/safety/hugging_face_prompt_injection.ipynb

@@ -28,9 +28,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "9bdbfdc7c949a9c1",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "!pip install \"optimum[onnxruntime]\""
@@ -44,8 +42,7 @@
     "ExecuteTime": {
      "end_time": "2023-12-18T11:41:24.738278Z",
      "start_time": "2023-12-18T11:41:20.842567Z"
-    },
-    "collapsed": false
+    }
    },
    "outputs": [],
    "source": [
@@ -80,7 +77,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "'hugging_face_injection_identifier'"
+      "text/plain": [
+       "'hugging_face_injection_identifier'"
+      ]
      },
      "execution_count": 10,
      "metadata": {},
@@ -119,7 +118,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "'Name 5 cities with the biggest number of inhabitants'"
+      "text/plain": [
+       "'Name 5 cities with the biggest number of inhabitants'"
+      ]
      },
      "execution_count": 11,
      "metadata": {},
@@ -374,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/guides/safety/index.mdx

@@ -4,6 +4,6 @@ One of the key concerns with using LLMs is that they may generate harmful or une
 
 - [Amazon Comprehend moderation chain](/docs/guides/safety/amazon_comprehend_chain): Use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle Personally Identifiable Information (PII) and toxicity.
 - [Constitutional chain](/docs/guides/safety/constitutional_chain): Prompt the model with a set of principles which should guide the model behavior.
-- [Hugging Face prompt injection identification](/docs/guides/safety/huggingface_prompt_injection_identification): Detect and handle prompt injection attacks. 
+- [Hugging Face prompt injection identification](/docs/guides/safety/hugging_face_prompt_injection): Detect and handle prompt injection attacks. 
 - [Logical Fallacy chain](/docs/guides/safety/logical_fallacy_chain): Checks the model output against logical fallacies to correct any deviation.
 - [Moderation chain](/docs/guides/safety/moderation): Check if any output text is harmful and flag it.

docs/docs/integrations/callbacks/streamlit.md

@@ -7,7 +7,7 @@
 [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/streamlit-agent?quickstart=1)
 
 In this guide we will demonstrate how to use `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an
-interactive Streamlit app. Try it out with the running app below using the [MRKL agent](/docs/modules/agents/how_to/mrkl/):
+interactive Streamlit app. Try it out with the running app below using the MRKL agent:
 
 <iframe loading="lazy" src="https://langchain-mrkl.streamlit.app/?embed=true&embed_options=light_theme"
     style={{ width: 100 + '%', border: 'none', marginBottom: 1 + 'rem', height: 600 }}

docs/docs/integrations/document_loaders/docugami.ipynb

@@ -346,7 +346,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use a [self-querying retriever](/docs/modules/data_connection/retrievers/how_to/self_query/) to improve our query accuracy, using this additional metadata:"
+    "We can use a [self-querying retriever](/docs/modules/data_connection/retrievers/self_query/) to improve our query accuracy, using this additional metadata:"
    ]
   },
   {
@@ -656,7 +656,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/psychic.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Psychic\n",
-    "This notebook covers how to load documents from `Psychic`. See [here](/docs/ecosystem/integrations/psychic) for more details.\n",
+    "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",
@@ -118,7 +118,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.1"
   },
   "vscode": {
    "interpreter": {