docs: integrations updates 17 (#27015)

Added missed provider pages. Added missed descriptions and links.
I fixed the Ipex-LLM titles, so the ToC is now sorted properly for these
titles.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

docs/docs/integrations/providers/baai.mdx

@@ -0,0 +1,44 @@
+# BAAI
+
+>[Beijing Academy of Artificial Intelligence (BAAI) (Wikipedia)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence), 
+> also known as `Zhiyuan Institute`, is a Chinese non-profit artificial 
+> intelligence (AI) research laboratory. `BAAI` conducts AI research 
+> and is dedicated to promoting collaboration among academia and industry, 
+> as well as fostering top talent and a focus on long-term research on 
+> the fundamentals of AI technology. As a collaborative hub, BAAI's founding 
+> members include leading AI companies, universities, and research institutes.
+
+
+## Embedding Models
+
+### HuggingFaceBgeEmbeddings
+
+>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en-v1.5) 
+> are one of [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
+
+See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
+
+```python
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+```
+
+### IpexLLMBgeEmbeddings
+
+>[IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch 
+> library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, 
+> discrete GPU such as Arc, Flex and Max) with very low latency.
+
+See a [usage example running model on Intel CPU](/docs/integrations/text_embedding/ipex_llm).
+See a [usage example running model on Intel GPU](/docs/integrations/text_embedding/ipex_llm_gpu).
+
+```python
+from langchain_community.embeddings import IpexLLMBgeEmbeddings
+```
+
+### QuantizedBgeEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/itrex).
+
+```python
+from langchain_community.embeddings import QuantizedBgeEmbeddings
+```

docs/docs/integrations/providers/jina.mdx

@@ -1,20 +1,35 @@
-# Jina
+# Jina AI
 
-This page covers how to use the Jina Embeddings within LangChain.
+>[Jina AI](https://jina.ai/about-us) is a search AI company. `Jina` helps businesses and developers unlock multimodal data with a better search.
-It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
 
 ## Installation and Setup
 - Get a Jina AI API token from [here](https://jina.ai/embeddings/) and set it as an environment variable (`JINA_API_TOKEN`)
 
-There exists a Jina Embeddings wrapper, which you can access with 
+## Chat Models
 
 ```python
-from langchain_community.embeddings import JinaEmbeddings
+from langchain_community.chat_models import JinaChat
-
-# you can pas jina_api_key, if none is passed it will be taken from `JINA_API_TOKEN` environment variable
-embeddings = JinaEmbeddings(jina_api_key='jina_**', model_name='jina-embeddings-v2-base-en')
 ```
 
+See a [usage examples](/docs/integrations/chat/jinachat).
+
+## Embedding Models
+
 You can check the list of available models from [here](https://jina.ai/embeddings/)
 
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/jina)
+```python
+from langchain_community.embeddings import JinaEmbeddings
+```
+
+See a [usage examples](/docs/integrations/text_embedding/jina).
+
+## Document Transformers
+
+### Jina Rerank
+
+```python
+from langchain_community.document_compressors import JinaRerank
+```
+
+See a [usage examples](/docs/integrations/document_transformers/jina_rerank).
+

docs/docs/integrations/text_embedding/ipex_llm.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Local BGE Embeddings with IPEX-LLM on Intel CPU\n",
+    "# IPEX-LLM: Local BGE Embeddings on Intel CPU\n",
     "\n",
     "> [IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low latency.\n",
     "\n",
@@ -92,10 +92,24 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/text_embedding/ipex_llm_gpu.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Local BGE Embeddings with IPEX-LLM on Intel GPU\n",
+    "# IPEX-LLM: Local BGE Embeddings on Intel GPU\n",
     "\n",
     "> [IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low latency.\n",
     "\n",
@@ -155,10 +155,24 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/text_embedding/jina.ipynb

@@ -5,7 +5,11 @@
    "id": "1c0cf975",
    "metadata": {},
    "source": [
-    "# Jina"
+    "# Jina\n",
+    "\n",
+    "You can check the list of available models from [here](https://jina.ai/embeddings/).\n",
+    "\n",
+    "## Installation and setup"
    ]
   },
   {
@@ -231,7 +235,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/sql_database.ipynb

@@ -209,15 +209,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f5751e3-2e98-485f-8164-db8094039c25",
+   "id": "4e3fd064-aa86-448d-8db3-3c55eaa5bc15",
    "metadata": {},
    "source": [
-    "API references:\n",
+    "You can use the individual tools directly:"
-    "\n",
+   ]
-    "- [QuerySQLDataBaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLDataBaseTool.html)\n",
+  },
-    "- [InfoSQLDatabaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.InfoSQLDatabaseTool.html)\n",
+  {
-    "- [ListSQLDatabaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.ListSQLDatabaseTool.html)\n",
+   "cell_type": "code",
-    "- [QuerySQLCheckerTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLCheckerTool.html)"
+   "execution_count": null,
+   "id": "7fa8d00c-750c-4803-9b66-057d12b26b06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.tools.sql_database.tool import (\n",
+    "    InfoSQLDatabaseTool,\n",
+    "    ListSQLDatabaseTool,\n",
+    "    QuerySQLCheckerTool,\n",
+    "    QuerySQLDataBaseTool,\n",
+    ")"
    ]
   },
   {
@@ -604,7 +614,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,