docs: ecosystem/integrations update 5 (#5752)

- added missed integration to `docs/ecosystem/integrations/`
- updated notebooks to consistent format: changed titles, file names;
added descriptions

#### Who can review?
 @hwchase17 
 @dev2049

docs/integrations/amazon_bedrock.md

@@ -1,4 +1,4 @@
-# Bedrock
+# Amazon Bedrock
 
 >[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.
 
@@ -18,7 +18,7 @@ from langchain import Bedrock
 
 ## Text Embedding Models
 
-See a [usage example](../modules/models/text_embedding/examples/bedrock.ipynb).
+See a [usage example](../modules/models/text_embedding/examples/amazon_bedrock.ipynb).
 ```python
 from langchain.embeddings import BedrockEmbeddings
 ```

docs/integrations/anthropic.md

@@ -0,0 +1,26 @@
+# Anthropic
+
+>[Anthropic](https://en.wikipedia.org/wiki/Anthropic) is an American artificial intelligence (AI) startup and 
+> public-benefit corporation, founded by former members of OpenAI. `Anthropic` specializes in developing general AI 
+> systems and language models, with a company ethos of responsible AI usage.
+> `Anthropic` develops a chatbot, named `Claude`. Similar to `ChatGPT`, `Claude` uses a messaging 
+> interface where users can submit questions or requests and receive highly detailed and relevant responses.
+ 
+## Installation and Setup
+
+
+```bash
+pip install anthropic
+```
+
+See the [setup documentation](https://console.anthropic.com/docs/access).
+
+
+
+## Chat Models
+
+See a [usage example](../modules/models/chat/integrations/anthropic.ipynb)
+
+```python
+from langchain.chat_models import ChatAnthropic
+```

docs/integrations/beam.md

@@ -1,7 +1,8 @@
 # Beam
 
-This page covers how to use Beam within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Beam wrappers.
+>[Beam](https://docs.beam.cloud/introduction) makes it easy to run code on GPUs, deploy scalable web APIs, 
+> schedule cron jobs, and run massively parallel workloads — without managing any infrastructure.
+ 
 
 ## Installation and Setup
 
@@ -9,19 +10,19 @@ It is broken into two parts: installation and setup, and then references to spec
 - Install the Beam CLI with `curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | sh`
 - Register API keys with `beam configure`
 - Set environment variables (`BEAM_CLIENT_ID`) and (`BEAM_CLIENT_SECRET`)
-- Install the Beam SDK `pip install beam-sdk`
+- Install the Beam SDK:
+```bash
+pip install beam-sdk
+```
 
-## Wrappers
+## LLM
 
-### LLM
-
-There exists a Beam LLM wrapper, which you can access with
 
 ```python
 from langchain.llms.beam import Beam
 ```
 
-## Define your Beam app.
+### Example of the Beam app
 
 This is the environment you’ll be developing against once you start the app.
 It's also used to define the maximum response length from the model.
@@ -44,7 +45,7 @@ llm = Beam(model_name="gpt2",
            verbose=False)
 ```
 
-## Deploy your Beam app
+### Deploy the Beam app
 
 Once defined, you can deploy your Beam app by calling your model's `_deploy()` method.
 
@@ -52,9 +53,9 @@ Once defined, you can deploy your Beam app by calling your model's `_deploy()` m
 llm._deploy()
 ```
 
-## Call your Beam app
+### Call the Beam app
 
-Once a beam model is deployed, it can be called by callying your model's `_call()` method.
+Once a beam model is deployed, it can be called by calling your model's `_call()` method.
 This returns the GPT2 text response to your prompt.
 
 ```python

docs/integrations/google_vertex_ai.md

@@ -0,0 +1,24 @@
+# Google Vertex AI
+
+>[Vertex AI](https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform) is a machine learning (ML) 
+> platform that lets you train and deploy ML models and AI applications. 
+> `Vertex AI` combines data engineering, data science, and ML engineering workflows, enabling your teams to 
+> collaborate using a common toolset.
+ 
+## Installation and Setup
+
+
+```bash
+pip install google-cloud-aiplatform
+```
+
+See the [setup instructions](../modules/models/chat/integrations/google_vertex_ai_palm.ipynb)
+
+
+## Chat Models
+
+See a [usage example](../modules/models/chat/integrations/google_vertex_ai_palm.ipynb)
+
+```python
+from langchain.chat_models import ChatVertexAI
+```

docs/integrations/huggingface.md

@@ -47,7 +47,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
 ```python
 from langchain.embeddings import HuggingFaceHubEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/huggingfacehub.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/huggingface_hub.ipynb)
 
 ### Tokenizer
 

docs/integrations/openai.md

@@ -35,7 +35,6 @@ from langchain.llms import AzureOpenAI
 For a more detailed walkthrough of the `Azure` wrapper, see [this notebook](../modules/models/llms/integrations/azure_openai_example.ipynb)
 
 
-
 ## Text Embedding Model
 
 ```python
@@ -44,6 +43,14 @@ from langchain.embeddings import OpenAIEmbeddings
 For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/openai.ipynb)
 
 
+## Chat Model
+
+```python
+from langchain.chat_models import ChatOpenAI
+```
+For a more detailed walkthrough of this, see [this notebook](../modules/models/chat/integrations/openai.ipynb)
+
+
 ## Tokenizer
 
 There are several places you can use the `tiktoken` tokenizer. By default, it is used to count tokens

docs/integrations/predictionguard.md

@@ -1,19 +1,23 @@
 # Prediction Guard
 
-This page covers how to use the Prediction Guard ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.
+>[Prediction Guard](https://docs.predictionguard.com/) gives a quick and easy access to state-of-the-art open and closed access LLMs, without  needing to spend days and weeks figuring out all of the implementation details, managing a bunch of different API specs, and setting up the infrastructure for model deployments.
+
 
 ## Installation and Setup
-- Install the Python SDK with `pip install predictionguard`
+- Install the Python SDK:
+```bash
+pip install predictionguard
+```
+
 - Get an Prediction Guard access token (as described [here](https://docs.predictionguard.com/)) and set it as an environment variable (`PREDICTIONGUARD_TOKEN`)
 
-## LLM Wrapper
+## LLM 
 
-There exists a Prediction Guard LLM wrapper, which you can access with 
 ```python
 from langchain.llms import PredictionGuard
 ```
 
+### Example
 You can provide the name of the Prediction Guard model as an argument when initializing the LLM:
 ```python
 pgllm = PredictionGuard(model="MPT-7B-Instruct")
@@ -24,14 +28,12 @@ You can also provide your access token directly as an argument:
 pgllm = PredictionGuard(model="MPT-7B-Instruct", token="<your access token>")
 ```
 
-Finally, you can provide an "output" argument that is used to structure/ control the output of the LLM:
+Also, you can provide an "output" argument that is used to structure/ control the output of the LLM:
 ```python
 pgllm = PredictionGuard(model="MPT-7B-Instruct", output={"type": "boolean"})
 ```
 
-## Example usage
-
-Basic usage of the controlled or guarded LLM wrapper:
+#### Basic usage of the controlled or guarded LLM:
 ```python
 import os
 
@@ -72,7 +74,7 @@ pgllm = PredictionGuard(model="MPT-7B-Instruct",
 pgllm(prompt.format(query="What kind of post is this?"))
 ```
 
-Basic LLM Chaining with the Prediction Guard wrapper:
+#### Basic LLM Chaining with the Prediction Guard:
 ```python
 import os
 

docs/integrations/promptlayer.md

@@ -1,31 +1,35 @@
 # PromptLayer
 
-This page covers how to use [PromptLayer](https://www.promptlayer.com) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
+>[PromptLayer](https://docs.promptlayer.com/what-is-promptlayer/wxpF9EZkUwvdkwvVE9XEvC/how-promptlayer-works/dvgGSxNe6nB1jj8mUVbG8r) 
+> is a devtool that allows you to track, manage, and share your GPT prompt engineering. 
+> It acts as a middleware between your code and OpenAI's python library, recording all your API requests 
+> and saving relevant metadata for easy exploration and search in the [PromptLayer](https://www.promptlayer.com) dashboard.
 
 ## Installation and Setup
 
-If you want to work with PromptLayer:
-- Install the promptlayer python library `pip install promptlayer`
+- Install the `promptlayer` python library 
+```bash
+pip install promptlayer
+```
 - Create a PromptLayer account
 - Create an api token and set it as an environment variable (`PROMPTLAYER_API_KEY`)
 
-## Wrappers
 
-### LLM
+## LLM
 
-There exists an PromptLayer OpenAI LLM wrapper, which you can access with
 ```python
 from langchain.llms import PromptLayerOpenAI
 ```
 
-To tag your requests, use the argument `pl_tags` when instanializing the LLM
+### Example
+
+To tag your requests, use the argument `pl_tags` when instantiating the LLM
 ```python
 from langchain.llms import PromptLayerOpenAI
 llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
 ```
 
-To get the PromptLayer request id, use the argument `return_pl_id` when instanializing the LLM
+To get the PromptLayer request id, use the argument `return_pl_id` when instantiating the LLM
 ```python
 from langchain.llms import PromptLayerOpenAI
 llm = PromptLayerOpenAI(return_pl_id=True)
@@ -42,8 +46,14 @@ You can use the PromptLayer request ID to add a prompt, score, or other metadata
 
 This LLM is identical to the [OpenAI LLM](./openai.md), except that
 - all your requests will be logged to your PromptLayer account
-- you can add `pl_tags` when instantializing to tag your requests on PromptLayer
-- you can add `return_pl_id` when instantializing to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
+- you can add `pl_tags` when instantiating to tag your requests on PromptLayer
+- you can add `return_pl_id` when instantiating to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
 
+## Chat Model
+
+```python
+from langchain.chat_models import PromptLayerChatOpenAI
+```
+
+See a [usage example](../modules/models/chat/integrations/promptlayer_chatopenai.ipynb).
 
-PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](../modules/models/chat/integrations/promptlayer_chatopenai.ipynb) and `PromptLayerOpenAIChat`

docs/integrations/tensorflow_hub.md

@@ -0,0 +1,22 @@
+# Tensorflow Hub
+
+>[TensorFlow Hub](https://www.tensorflow.org/hub) is a repository of trained machine learning models ready for fine-tuning and deployable anywhere.
+
+>[TensorFlow Hub](https://tfhub.dev/) lets you search and discover hundreds of trained, ready-to-deploy machine learning models in one place.
+ 
+## Installation and Setup
+
+
+```bash
+pip install tensorflow-hub
+pip install tensorflow_text
+```
+
+
+## Text Embedding Models
+
+See a [usage example](../modules/models/text_embedding/examples/tensorflowhub.ipynb)
+
+```python
+from langchain.embeddings import TensorflowHubEmbeddings
+```