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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>
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docs/docs/integrations/providers/baai.mdx
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docs/docs/integrations/providers/baai.mdx
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# BAAI
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>[Beijing Academy of Artificial Intelligence (BAAI) (Wikipedia)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence),
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> also known as `Zhiyuan Institute`, is a Chinese non-profit artificial
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> intelligence (AI) research laboratory. `BAAI` conducts AI research
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> and is dedicated to promoting collaboration among academia and industry,
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> as well as fostering top talent and a focus on long-term research on
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> the fundamentals of AI technology. As a collaborative hub, BAAI's founding
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> members include leading AI companies, universities, and research institutes.
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## Embedding Models
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### HuggingFaceBgeEmbeddings
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>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en-v1.5)
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> are one of [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
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See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
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```python
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from langchain_community.embeddings import HuggingFaceBgeEmbeddings
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```
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### IpexLLMBgeEmbeddings
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>[IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch
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> library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU,
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> discrete GPU such as Arc, Flex and Max) with very low latency.
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See a [usage example running model on Intel CPU](/docs/integrations/text_embedding/ipex_llm).
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See a [usage example running model on Intel GPU](/docs/integrations/text_embedding/ipex_llm_gpu).
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```python
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from langchain_community.embeddings import IpexLLMBgeEmbeddings
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```
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### QuantizedBgeEmbeddings
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See a [usage example](/docs/integrations/text_embedding/itrex).
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```python
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from langchain_community.embeddings import QuantizedBgeEmbeddings
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```
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@ -1,20 +1,35 @@
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# Jina
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# Jina AI
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This page covers how to use the Jina Embeddings within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
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>[Jina AI](https://jina.ai/about-us) is a search AI company. `Jina` helps businesses and developers unlock multimodal data with a better search.
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## Installation and Setup
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- Get a Jina AI API token from [here](https://jina.ai/embeddings/) and set it as an environment variable (`JINA_API_TOKEN`)
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There exists a Jina Embeddings wrapper, which you can access with
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## Chat Models
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```python
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from langchain_community.embeddings import JinaEmbeddings
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# you can pas jina_api_key, if none is passed it will be taken from `JINA_API_TOKEN` environment variable
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embeddings = JinaEmbeddings(jina_api_key='jina_**', model_name='jina-embeddings-v2-base-en')
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from langchain_community.chat_models import JinaChat
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```
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See a [usage examples](/docs/integrations/chat/jinachat).
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## Embedding Models
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You can check the list of available models from [here](https://jina.ai/embeddings/)
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For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/jina)
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```python
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from langchain_community.embeddings import JinaEmbeddings
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```
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See a [usage examples](/docs/integrations/text_embedding/jina).
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## Document Transformers
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### Jina Rerank
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```python
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from langchain_community.document_compressors import JinaRerank
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```
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See a [usage examples](/docs/integrations/document_transformers/jina_rerank).
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@ -4,7 +4,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Local BGE Embeddings with IPEX-LLM on Intel CPU\n",
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"# IPEX-LLM: Local BGE Embeddings on Intel CPU\n",
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"\n",
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"> [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",
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"\n",
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@ -92,10 +92,24 @@
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Local BGE Embeddings with IPEX-LLM on Intel GPU\n",
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"# IPEX-LLM: Local BGE Embeddings on Intel GPU\n",
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"\n",
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"> [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",
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"\n",
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@ -155,10 +155,24 @@
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}
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"id": "1c0cf975",
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"metadata": {},
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"source": [
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"# Jina"
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"# Jina\n",
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"\n",
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"You can check the list of available models from [here](https://jina.ai/embeddings/).\n",
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"\n",
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"## Installation and setup"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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@ -209,15 +209,25 @@
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},
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{
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"cell_type": "markdown",
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"id": "5f5751e3-2e98-485f-8164-db8094039c25",
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"id": "4e3fd064-aa86-448d-8db3-3c55eaa5bc15",
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"metadata": {},
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"source": [
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"API references:\n",
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"\n",
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"- [QuerySQLDataBaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLDataBaseTool.html)\n",
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"- [InfoSQLDatabaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.InfoSQLDatabaseTool.html)\n",
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"- [ListSQLDatabaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.ListSQLDatabaseTool.html)\n",
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"- [QuerySQLCheckerTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLCheckerTool.html)"
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"You can use the individual tools directly:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7fa8d00c-750c-4803-9b66-057d12b26b06",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.tools.sql_database.tool import (\n",
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" InfoSQLDatabaseTool,\n",
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" ListSQLDatabaseTool,\n",
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" QuerySQLCheckerTool,\n",
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" QuerySQLDataBaseTool,\n",
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")"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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