docs[patch]: link and description cleanup (#14471)

Fixed inconsistencies; added links and descriptions

---------

Co-authored-by: Erick Friis <erickfriis@gmail.com>

docs/docs/guides/evaluation/string/json.ipynb

@@ -5,13 +5,13 @@
    "id": "465cfbef-5bba-4b3b-b02d-fe2eba39db17",
    "metadata": {},
    "source": [
-    "# Evaluating Structured Output: JSON Evaluators\n",
+    "# JSON Evaluators\n",
     "\n",
-    "Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following JSON validators provide provide functionality to check your model's output in a consistent way.\n",
+    "Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following `JSON` validators provide functionality to check your model's output consistently.\n",
     "\n",
     "## JsonValidityEvaluator\n",
     "\n",
-    "The `JsonValidityEvaluator` is designed to check the validity of a JSON string prediction.\n",
+    "The `JsonValidityEvaluator` is designed to check the validity of a `JSON` string prediction.\n",
     "\n",
     "### Overview:\n",
     "- **Requires Input?**: No\n",
@@ -377,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/guides/evaluation/string/string_distance.ipynb

@@ -8,9 +8,12 @@
     "# String Distance\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
     "\n",
-    "One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
+    ">In information theory, linguistics, and computer science, the [Levenshtein distance (Wikipedia)](https://en.wikipedia.org/wiki/Levenshtein_distance) is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. It is named after the Soviet mathematician Vladimir Levenshtein, who considered this distance in 1965.\n",
     "\n",
-    "This can be accessed using the `string_distance` evaluator, which uses distance metric's from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
+    "\n",
+    "One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as `Levenshtein` or `postfix` distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
+    "\n",
+    "This can be accessed using the `string_distance` evaluator, which uses distance metrics from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
     "\n",
     "**Note:** The returned scores are _distances_, meaning lower is typically \"better\".\n",
     "\n",
@@ -213,9 +216,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/docs/integrations/platforms/huggingface.mdx

@@ -115,6 +115,17 @@ See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).
 from langchain.embeddings import HuggingFaceInstructEmbeddings
 ```
 
+#### HuggingFaceBgeEmbeddings
+
+>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
+>BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://www.baai.ac.cn/english.html). `BAAI` is a private non-profit organization engaged in AI research and development.
+
+See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
+
+```python
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+```
+
 
 ## Tools
 

docs/docs/integrations/providers/dataforseo.mdx

@@ -1,14 +1,23 @@
 # DataForSEO
 
+>[DataForSeo](https://dataforseo.com/) provides comprehensive SEO and digital marketing data solutions via API.
+
 This page provides instructions on how to use the DataForSEO search APIs within LangChain.
 
 ## Installation and Setup
 
-- Get a DataForSEO API Access login and password, and set them as environment variables (`DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` respectively). You can find it in your dashboard.
+Get a [DataForSEO API Access login and password](https://app.dataforseo.com/register), and set them as environment variables 
+(`DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` respectively).
+
+```python
+import os
+
+os.environ["DATAFORSEO_LOGIN"] = "your_login"
+os.environ["DATAFORSEO_PASSWORD"] = "your_password"
+```
 
-## Wrappers
 
-### Utility
+## Utility
 
 The DataForSEO utility wraps the API. To import this utility, use:
 
@@ -18,7 +27,7 @@ from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapper
 
 For a detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/dataforseo).
 
-### Tool
+## Tool
 
 You can also load this wrapper as a Tool to use with an Agent:
 
@@ -34,18 +43,3 @@ dataforseo = DataForSeoAPIWrapper(api_login="your_login", api_password="your_pas
 result = dataforseo.run("Bill Gates")
 print(result)
 ```
-
-## Environment Variables
-
-You can store your DataForSEO API Access login and password as environment variables. The wrapper will automatically check for these environment variables if no values are provided:
-
-```python
-import os
-
-os.environ["DATAFORSEO_LOGIN"] = "your_login"
-os.environ["DATAFORSEO_PASSWORD"] = "your_password"
-
-dataforseo = DataForSeoAPIWrapper()
-result = dataforseo.run("weather in Los Angeles")
-print(result)
-```

docs/docs/integrations/tools/dataforseo.ipynb

@@ -4,9 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# DataForSeo\n",
+    "# DataForSEO\n",
     "\n",
-    "This notebook demonstrates how to use the `DataForSeo API` to obtain search engine results. The `DataForSeo API` retrieves `SERP` from most popular search engines like `Google`, `Bing`, `Yahoo`. It also allows to get SERPs from different search engine types like `Maps`, `News`, `Events`, etc.\n"
+    ">[DataForSeo](https://dataforseo.com/) provides comprehensive SEO and digital marketing data solutions via API.\n",
+    ">\n",
+    ">The `DataForSeo API` retrieves `SERP` from the most popular search engines like `Google`, `Bing`, `Yahoo`. It also allows to >get SERPs from different search engine types like `Maps`, `News`, `Events`, etc.\n",
+    "\n",
+    "This notebook demonstrates how to use the [DataForSeo API](https://dataforseo.com/apis) to obtain search engine results. "
    ]
   },
   {