docs: integrations reference updates 6 (#25188)

Added missed provider pages. Added missed references to the integration
components.

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
+```

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
+```

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="<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="<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 
+```

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
 ```

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
+```

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
 ```

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
+```

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
+```