diff --git a/docs/docs/integrations/providers/dappierai.mdx b/docs/docs/integrations/providers/dappierai.mdx new file mode 100644 index 00000000000..d142ae00af4 --- /dev/null +++ b/docs/docs/integrations/providers/dappierai.mdx @@ -0,0 +1,18 @@ +# Dappier AI + +> [Dappier](https://platform.dappier.com/) is a platform enabling access to diverse, +> real-time data models. Enhance your AI applications with `Dappier’s` pre-trained, +> LLM-ready data models and ensure accurate, current responses with reduced inaccuracies. + +## Installation and Setup + +To use one of the `Dappier AI` Data Models, you will need an API key. Visit +[Dappier Platform](https://platform.dappier.com/) to log in and create an API key in your profile. + +## Chat models + +See a [usage example](/docs/integrations/chat/dappier). + +```python +from langchain_community.chat_models import ChatDappierAI +``` diff --git a/docs/docs/integrations/providers/everlyai.mdx b/docs/docs/integrations/providers/everlyai.mdx new file mode 100644 index 00000000000..2ec50703019 --- /dev/null +++ b/docs/docs/integrations/providers/everlyai.mdx @@ -0,0 +1,17 @@ +# Everly AI + +> [Everly AI](https://everlyai.xyz/) allows you to run your ML models at scale in the cloud. +> It also provides API access to [several LLM models](https://everlyai.xyz/). + +## Installation and Setup + +To use `Everly AI`, you will need an API key. Visit +[Everly AI](https://everlyai.xyz/) to create an API key in your profile. + +## Chat models + +See a [usage example](/docs/integrations/chat/everlyai). + +```python +from langchain_community.chat_models import ChatEverlyAI +``` diff --git a/docs/docs/integrations/providers/fireworks.md b/docs/docs/integrations/providers/fireworks.md index d277ad4cf01..ea5c7de34f9 100644 --- a/docs/docs/integrations/providers/fireworks.md +++ b/docs/docs/integrations/providers/fireworks.md @@ -1,7 +1,9 @@ -# Fireworks +# Fireworks AI + +>[Fireworks AI](https://fireworks.ai) is a generative AI inference platform to run and +> customize models with industry-leading speed and production-readiness. + -This page covers how to use [Fireworks](https://fireworks.ai/) models within -Langchain. ## Installation and setup @@ -14,7 +16,7 @@ Langchain. - Get a Fireworks API key by signing up at [fireworks.ai](https://fireworks.ai). - Authenticate by setting the FIREWORKS_API_KEY environment variable. -## Authentication +### Authentication There are two ways to authenticate using your Fireworks API key: @@ -29,20 +31,26 @@ There are two ways to authenticate using your Fireworks API key: ```python llm = Fireworks(api_key="") ``` +## Chat models -## Using the Fireworks LLM module +See a [usage example](/docs/integrations/chat/fireworks). -Fireworks integrates with Langchain through the LLM module. In this example, we -will work the mixtral-8x7b-instruct model. +```python +from langchain_fireworks import ChatFireworks +``` + +## LLMs + +See a [usage example](/docs/integrations/llms/fireworks). ```python from langchain_fireworks import Fireworks - -llm = Fireworks( - api_key="", - model="accounts/fireworks/models/mixtral-8x7b-instruct", - max_tokens=256) -llm("Name 3 sports.") ``` -For a more detailed walkthrough, see [here](/docs/integrations/llms/Fireworks). +## Embedding models + +See a [usage example](/docs/integrations/text_embedding/fireworks). + +```python +from langchain_fireworks import FireworksEmbeddings +``` diff --git a/docs/docs/integrations/providers/forefrontai.mdx b/docs/docs/integrations/providers/forefrontai.mdx index a0045f75a41..4d447ee37a6 100644 --- a/docs/docs/integrations/providers/forefrontai.mdx +++ b/docs/docs/integrations/providers/forefrontai.mdx @@ -1,16 +1,19 @@ -# ForefrontAI +# Forefront AI + +> [Forefront AI](https://forefront.ai/) is a platform enabling you to +> fine-tune and inference open-source text generation models -This page covers how to use the ForefrontAI ecosystem within LangChain. -It is broken into two parts: installation and setup, and then references to specific ForefrontAI wrappers. ## Installation and Setup -- Get an ForefrontAI api key and set it as an environment variable (`FOREFRONTAI_API_KEY`) -## Wrappers +Get an `ForefrontAI` API key +visiting [this page](https://accounts.forefront.ai/sign-in?redirect_url=https%3A%2F%2Fforefront.ai%2Fapp%2Fapi-keys). + and set it as an environment variable (`FOREFRONTAI_API_KEY`). -### LLM +## LLM + +See a [usage example](/docs/integrations/llms/forefrontai). -There exists an ForefrontAI LLM wrapper, which you can access with ```python from langchain_community.llms import ForefrontAI ``` \ No newline at end of file diff --git a/docs/docs/integrations/providers/friendli.md b/docs/docs/integrations/providers/friendli.md new file mode 100644 index 00000000000..e0f3a49b68b --- /dev/null +++ b/docs/docs/integrations/providers/friendli.md @@ -0,0 +1,31 @@ +# Friendli AI + +>[FriendliAI](https://friendli.ai/) enhances AI application performance and optimizes +> cost savings with scalable, efficient deployment options, tailored for high-demand AI workloads. + +## Installation and setup + +Install the `friendli-client` python package. + +```bash +pip install friendli-client +``` +Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, +and set it as the `FRIENDLI_TOKEN` environment variable. + + +## Chat models + +See a [usage example](/docs/integrations/chat/friendli). + +```python +from langchain_community.chat_models.friendli import ChatFriendli +``` + +## LLMs + +See a [usage example](/docs/integrations/llms/friendli). + +```python +from langchain_community.llms.friendli import Friendli +``` diff --git a/docs/docs/integrations/providers/gooseai.mdx b/docs/docs/integrations/providers/gooseai.mdx index 49909481a00..f0bdf819d2e 100644 --- a/docs/docs/integrations/providers/gooseai.mdx +++ b/docs/docs/integrations/providers/gooseai.mdx @@ -1,9 +1,13 @@ # GooseAI -This page covers how to use the GooseAI ecosystem within LangChain. -It is broken into two parts: installation and setup, and then references to specific GooseAI wrappers. +>[GooseAI](https://goose.ai) makes deploying NLP services easier and more accessible. +> `GooseAI` is a fully managed inference service delivered via API. +> With feature parity to other well known APIs, `GooseAI` delivers a plug-and-play solution +> for serving open source language models at the industry's best economics by simply +> changing 2 lines in your code. ## Installation and Setup + - Install the Python SDK with `pip install openai` - Get your GooseAI api key from this link [here](https://goose.ai/). - Set the environment variable (`GOOSEAI_API_KEY`). @@ -13,11 +17,11 @@ import os os.environ["GOOSEAI_API_KEY"] = "YOUR_API_KEY" ``` -## Wrappers -### LLM +## LLMs + +See a [usage example](/docs/integrations/llms/gooseai). -There exists an GooseAI LLM wrapper, which you can access with: ```python from langchain_community.llms import GooseAI ``` \ No newline at end of file diff --git a/docs/docs/integrations/providers/groq.mdx b/docs/docs/integrations/providers/groq.mdx index a1e4b050ce0..826e7699ae4 100644 --- a/docs/docs/integrations/providers/groq.mdx +++ b/docs/docs/integrations/providers/groq.mdx @@ -1,17 +1,20 @@ # Groq -Welcome to Groq! 🚀 At Groq, we've developed the world's first Language Processing Unit™, or LPU. The Groq LPU has a deterministic, single core streaming architecture that sets the standard for GenAI inference speed with predictable and repeatable performance for any given workload. - -Beyond the architecture, our software is designed to empower developers like you with the tools you need to create innovative, powerful AI applications. With Groq as your engine, you can: - -* Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥 -* Know the exact performance and compute time for any given workload 🔮 -* Take advantage of our cutting-edge technology to stay ahead of the competition 💪 - -Want more Groq? Check out our [website](https://groq.com) for more resources and join our [Discord community](https://discord.gg/JvNsBDKeCG) to connect with our developers! +>[Groq](https://groq.com)developed the world's first Language Processing Unit™, or `LPU`. +> The `Groq LPU` has a deterministic, single core streaming architecture that sets the standard +> for GenAI inference speed with predictable and repeatable performance for any given workload. +> +>Beyond the architecture, `Groq` software is designed to empower developers like you with +> the tools you need to create innovative, powerful AI applications. +> +>With Groq as your engine, you can: +>* Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥 +>* Know the exact performance and compute time for any given workload 🔮 +>* Take advantage of our cutting-edge technology to stay ahead of the competition 💪 ## Installation and Setup + Install the integration package: ```bash @@ -24,5 +27,10 @@ Request an [API key](https://wow.groq.com) and set it as an environment variable export GROQ_API_KEY=gsk_... ``` -## Chat Model +## Chat models + See a [usage example](/docs/integrations/chat/groq). + +```python +from langchain_groq import ChatGroq +``` diff --git a/docs/docs/integrations/providers/littlellm.md b/docs/docs/integrations/providers/littlellm.md new file mode 100644 index 00000000000..9c15750a326 --- /dev/null +++ b/docs/docs/integrations/providers/littlellm.md @@ -0,0 +1,37 @@ +# LiteLLM + +>[LiteLLM](https://docs.litellm.ai/docs/) is a library that simplifies calling Anthropic, +> Azure, Huggingface, Replicate, etc. LLMs in a unified way. +> +>You can use `LiteLLM` through either: +> +>* [LiteLLM Proxy Server](https://docs.litellm.ai/docs/#openai-proxy) - Server to call 100+ LLMs, load balance, cost tracking across projects +>* [LiteLLM python SDK](https://docs.litellm.ai/docs/#basic-usage) - Python Client to call 100+ LLMs, load balance, cost tracking + +## Installation and setup + +Install the `litellm` python package. + +```bash +pip install litellm +``` + +## Chat models + +### ChatLiteLLM + +See a [usage example](/docs/integrations/chat/litellm). + +```python +from langchain_community.chat_models import ChatLiteLLM +``` + +### ChatLiteLLMRouter + +You also can use the `ChatLiteLLMRouter` to route requests to different LLMs or LLM providers. + +See a [usage example](/docs/integrations/chat/litellm_router). + +```python +from langchain_community.chat_models import ChatLiteLLMRouter +```