mirror of https://github.com/hwchase17/langchain
docs: `providers` update 2 (#18407)
Formatted pages into a consistent form. Added descriptions and links when needed.pull/18949/head
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# Baichuan
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>[Baichuan Inc.](https://www.baichuan-ai.com/) is a Chinese startup in the era of AGI, dedicated to addressing fundamental human needs: Efficiency, Health, and Happiness.
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>[Baichuan Inc.](https://www.baichuan-ai.com/) is a Chinese startup in the era of AGI,
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> dedicated to addressing fundamental human needs: Efficiency, Health, and Happiness.
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## Visit Us
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Visit us at https://www.baichuan-ai.com/.
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Register and get an API key if you are trying out our APIs.
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## Baichuan LLM Endpoint
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An example is available at [example](/docs/integrations/llms/baichuan)
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## Installation and Setup
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## Baichuan Chat Model
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An example is available at [example](/docs/integrations/chat/baichuan).
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Register and get an API key [here](https://platform.baichuan-ai.com/).
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## Baichuan Text Embedding Model
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An example is available at [example](/docs/integrations/text_embedding/baichuan)
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## LLMs
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See a [usage example](/docs/integrations/llms/baichuan).
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```python
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from langchain_community.llms import BaichuanLLM
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```
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## Chat models
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See a [usage example](/docs/integrations/chat/baichuan).
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```python
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from langchain_community.chat_models import ChatBaichuan
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```
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## Embedding models
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See a [usage example](/docs/integrations/text_embedding/baichuan).
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```python
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from langchain_community.embeddings import BaichuanTextEmbeddings
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```
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# BREEBS (Open Knowledge)
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# Breebs (Open Knowledge)
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[BREEBS](https://www.breebs.com/) is an open collaborative knowledge platform.
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Anybody can create a Breeb, a knowledge capsule based on PDFs stored on a Google Drive folder.
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A breeb can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources.
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Behind the scenes, Breebs implements several Retrieval Augmented Generation (RAG) models to seamlessly provide useful context at each iteration.
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>[Breebs](https://www.breebs.com/) is an open collaborative knowledge platform.
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>Anybody can create a `Breeb`, a knowledge capsule based on PDFs stored on a Google Drive folder.
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>A `Breeb` can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources.
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>Behind the scenes, `Breebs` implements several `Retrieval Augmented Generation (RAG)` models
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> to seamlessly provide useful context at each iteration.
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## List of available Breebs
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To get the full list of Breebs, including their key (breeb_key) and description :
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https://breebs.promptbreeders.com/web/listbreebs.
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Dozens of Breebs have already been created by the community and are freely available for use. They cover a wide range of expertise, from organic chemistry to mythology, as well as tips on seduction and decentralized finance.
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## Creating a new Breeb
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To generate a new Breeb, simply compile PDF files in a publicly shared Google Drive folder and initiate the creation process on the [BREEBS website](https://www.breebs.com/) by clicking the "Create Breeb" button. You can currently include up to 120 files, with a total character limit of 15 million.
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## Retriever
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```python
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from langchain.retrievers import BreebsRetriever
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```
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# Example
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[See usage example (Retrieval & ConversationalRetrievalChain)](https://python.langchain.com/docs/integrations/retrievers/breebs)
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[See a usage example (Retrieval & ConversationalRetrievalChain)](/docs/integrations/retrievers/breebs)
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# CerebriumAI
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This page covers how to use the CerebriumAI ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.
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>[Cerebrium](https://docs.cerebrium.ai/cerebrium/getting-started/introduction) is a serverless GPU infrastructure provider.
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> It provides API access to several LLM models.
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See the examples in the [CerebriumAI documentation](https://docs.cerebrium.ai/examples/langchain).
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## Installation and Setup
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- Install with `pip install cerebrium`
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- Get an CerebriumAI api key and set it as an environment variable (`CEREBRIUMAI_API_KEY`)
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## Wrappers
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- Install a python package:
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```bash
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pip install cerebrium
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```
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- [Get an CerebriumAI api key](https://docs.cerebrium.ai/cerebrium/getting-started/installation) and set
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it as an environment variable (`CEREBRIUMAI_API_KEY`)
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## LLMs
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See a [usage example](/docs/integrations/llms/cerebriumai).
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### LLM
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There exists an CerebriumAI LLM wrapper, which you can access with
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```python
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from langchain_community.llms import CerebriumAI
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```
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