diff --git a/docs/docs/integrations/providers/astradb.mdx b/docs/docs/integrations/providers/astradb.mdx
index 69347144cf..f1e9cf0705 100644
--- a/docs/docs/integrations/providers/astradb.mdx
+++ b/docs/docs/integrations/providers/astradb.mdx
@@ -1,49 +1,71 @@
 # Astra DB
 
-> DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Apache Cassandra® and made conveniently available
+> [DataStax Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless 
+> vector-capable database built on `Apache Cassandra®`and made conveniently available
 > through an easy-to-use JSON API.
 
-### Setup
+See a [tutorial provided by DataStax](https://docs.datastax.com/en/astra/astra-db-vector/tutorials/chatbot.html).
+
+## Installation and Setup
 
 Install the following Python package:
+```bash
+pip install "langchain-astradb>=0.0.1"
+```
 
+Some old integrations require the `astrapy` package:
 ```bash
 pip install "astrapy>=0.7.1"
 ```
 
+Get the [connection secrets](https://docs.datastax.com/en/astra/astra-db-vector/get-started/quickstart.html).
+Set up the following environment variables:
+
+```python
+ASTRA_DB_APPLICATION_TOKEN="TOKEN"
+ASTRA_DB_API_ENDPOINT="API_ENDPOINT"
+```
+
 ## Vector Store
 
 ```python
 from langchain_astradb import AstraDBVectorStore
+
 vector_store = AstraDBVectorStore(
     embedding=my_embedding,
     collection_name="my_store",
-    api_endpoint="...",
-    token="...",
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
 )
 ```
 
 Learn more in the [example notebook](/docs/integrations/vectorstores/astradb).
 
+See the [example provided by DataStax](https://docs.datastax.com/en/astra/astra-db-vector/integrations/langchain.html).
+
 ## Chat message history
 
 ```python
 from langchain_astradb import AstraDBChatMessageHistory
+
 message_history = AstraDBChatMessageHistory(
     session_id="test-session",
-    api_endpoint="...",
-    token="...",
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
 )
 ```
 
+See the [usage example](/docs/integrations/memory/astradb_chat_message_history#example).
+
 ## LLM Cache
 
 ```python
 from langchain.globals import set_llm_cache
 from langchain_community.cache import AstraDBCache
+
 set_llm_cache(AstraDBCache(
-    api_endpoint="...",
-    token="...",
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
 ))
 ```
 
@@ -54,11 +76,12 @@ Learn more in the [example notebook](/docs/integrations/llms/llm_caching#astra-d
 
 ```python
 from langchain.globals import set_llm_cache
-from langchain_community.cache import AstraDBSemanticCache
+from langchain_community.cache import 
+
 set_llm_cache(AstraDBSemanticCache(
     embedding=my_embedding,
-    api_endpoint="...",
-    token="...",
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
 ))
 ```
 
@@ -70,10 +93,11 @@ Learn more in the [example notebook](/docs/integrations/memory/astradb_chat_mess
 
 ```python
 from langchain_community.document_loaders import AstraDBLoader
+
 loader = AstraDBLoader(
     collection_name="my_collection",
-    api_endpoint="...",
-    token="..."
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
 )
 ```
 
@@ -88,8 +112,8 @@ from langchain.retrievers.self_query.base import SelfQueryRetriever
 vector_store = AstraDBVectorStore(
     embedding=my_embedding,
     collection_name="my_store",
-    api_endpoint="...",
-    token="...",
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
 )
 
 retriever = SelfQueryRetriever.from_llm(
@@ -105,11 +129,12 @@ Learn more in the [example notebook](/docs/integrations/retrievers/self_query/as
 ## Store
 
 ```python
-from langchain_astradb import AstraDBStore
+from langchain_community.storage import AstraDBStore
+
 store = AstraDBStore(
     collection_name="my_kv_store",
-    api_endpoint="...",
-    token="..."
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
 )
 ```
 
@@ -118,11 +143,12 @@ Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbst
 ## Byte Store
 
 ```python
-from langchain_astradb import AstraDBByteStore
+from langchain_community.storage import AstraDBByteStore
+
 store = AstraDBByteStore(
     collection_name="my_kv_store",
-    api_endpoint="...",
-    token="..."
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
 )
 ```
 
diff --git a/docs/docs/integrations/providers/awadb.md b/docs/docs/integrations/providers/awadb.md
index 4290444752..0bc4d374a8 100644
--- a/docs/docs/integrations/providers/awadb.md
+++ b/docs/docs/integrations/providers/awadb.md
@@ -9,8 +9,7 @@ pip install awadb
 ```
 
 
-## Vector Store
-
+## Vector store
 
 ```python
 from langchain_community.vectorstores import AwaDB
@@ -19,7 +18,7 @@ from langchain_community.vectorstores import AwaDB
 See a [usage example](/docs/integrations/vectorstores/awadb).
 
 
-## Text Embedding Model
+## Embedding models
 
 ```python
 from langchain_community.embeddings import AwaEmbeddings
diff --git a/docs/docs/integrations/providers/baichuan.mdx b/docs/docs/integrations/providers/baichuan.mdx
index ddac4cf65e..409a66d6f8 100644
--- a/docs/docs/integrations/providers/baichuan.mdx
+++ b/docs/docs/integrations/providers/baichuan.mdx
@@ -1,16 +1,33 @@
 # Baichuan
 
->[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.
+>[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.
 
-## Visit Us
-Visit us at https://www.baichuan-ai.com/.
-Register and get an API key if you are trying out our APIs.
 
-## Baichuan LLM Endpoint
-An example is available at [example](/docs/integrations/llms/baichuan)
+## Installation and Setup
 
-## Baichuan Chat Model
-An example is available at [example](/docs/integrations/chat/baichuan).
+Register and get an API key [here](https://platform.baichuan-ai.com/).
 
-## Baichuan Text Embedding Model
-An example is available at [example](/docs/integrations/text_embedding/baichuan)
+## LLMs
+
+See a [usage example](/docs/integrations/llms/baichuan).
+
+```python
+from langchain_community.llms import BaichuanLLM
+```
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/baichuan).
+
+```python
+from langchain_community.chat_models import ChatBaichuan
+```
+
+## Embedding models
+
+See a [usage example](/docs/integrations/text_embedding/baichuan).
+
+```python
+from langchain_community.embeddings import BaichuanTextEmbeddings
+```
diff --git a/docs/docs/integrations/providers/bananadev.mdx b/docs/docs/integrations/providers/bananadev.mdx
index ebc39bcf56..9972bc159a 100644
--- a/docs/docs/integrations/providers/bananadev.mdx
+++ b/docs/docs/integrations/providers/bananadev.mdx
@@ -1,18 +1,20 @@
 # Banana
 
-Banana provided serverless GPU inference for AI models, including a CI/CD build pipeline and a simple Python framework (Potassium) to server your models.
+>[Banana](https://www.banana.dev/) provided serverless GPU inference for AI models, 
+> a CI/CD build pipeline and a simple Python framework (`Potassium`) to server your models.
 
 This page covers how to use the [Banana](https://www.banana.dev) ecosystem within LangChain.
 
-It is broken into two parts: 
-* installation and setup, 
-* and then references to specific Banana wrappers.
-
 ## Installation and Setup
 
-- Install with `pip install banana-dev`
+- Install the python package `banana-dev`:
+
+```bash
+pip install banana-dev
+```
+
 - Get an Banana api key from the [Banana.dev dashboard](https://app.banana.dev) and set it as an environment variable (`BANANA_API_KEY`)
-- Get your model's key and url slug from the model's details page
+- Get your model's key and url slug from the model's details page.
 
 ## Define your Banana Template
 
@@ -24,7 +26,7 @@ Other starter repos are available [here](https://github.com/orgs/bananaml/reposi
 
 ## Build the Banana app
 
-To use Banana apps within Langchain, they must include the `outputs` key 
+To use Banana apps within Langchain, you must include the `outputs` key 
 in the returned json, and the value must be a string.
 
 ```python
@@ -55,18 +57,12 @@ def handler(context: dict, request: Request) -> Response:
 
 This example is from the `app.py` file in [CodeLlama-7B-Instruct-GPTQ](https://github.com/bananaml/demo-codellama-7b-instruct-gptq).
 
-## Wrappers
 
-### LLM
+## LLM
 
-Within Langchain, there exists a Banana LLM wrapper, which you can access with
 
 ```python
 from langchain_community.llms import Banana
 ```
 
-You need to provide a model key and model url slug, which you can get from the model's details page in the [Banana.dev dashboard](https://app.banana.dev).
-
-```python
-llm = Banana(model_key="YOUR_MODEL_KEY", model_url_slug="YOUR_MODEL_URL_SLUG")
-```
+See a [usage example](/docs/integrations/llms/banana).
diff --git a/docs/docs/integrations/providers/baseten.md b/docs/docs/integrations/providers/baseten.md
index 68c9e61ab9..d5ced9dc05 100644
--- a/docs/docs/integrations/providers/baseten.md
+++ b/docs/docs/integrations/providers/baseten.md
@@ -1,18 +1,23 @@
 # Baseten
 
-[Baseten](https://baseten.co) provides all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently.
+>[Baseten](https://baseten.co) is a provider of all the infrastructure you need to deploy and serve 
+> ML models performantly, scalably, and cost-efficiently.
 
-As a model inference platform, Baseten is a `Provider` in the LangChain ecosystem. The Baseten integration currently implements a single `Component`, LLMs, but more are planned!
+>As a model inference platform, `Baseten` is a `Provider` in the LangChain ecosystem. 
+The `Baseten` integration currently implements a single `Component`, LLMs, but more are planned!
 
-Baseten lets you run both open source models like Llama 2 or Mistral and run proprietary or fine-tuned models on dedicated GPUs. If you're used to a provider like OpenAI, using Baseten has a few differences:
+>`Baseten` lets you run both open source models like Llama 2 or Mistral and run proprietary or 
+fine-tuned models on dedicated GPUs. If you're used to a provider like OpenAI, using Baseten has a few differences:
 
-* Rather than paying per token, you pay per minute of GPU used.
-* Every model on Baseten uses [Truss](https://truss.baseten.co/welcome), our open-source model packaging framework, for maximum customizability.
-* While we have some [OpenAI ChatCompletions-compatible models](https://docs.baseten.co/api-reference/openai), you can define your own I/O spec with Truss.
+>* Rather than paying per token, you pay per minute of GPU used.
+>* Every model on Baseten uses [Truss](https://truss.baseten.co/welcome), our open-source model packaging framework, for maximum customizability.
+>* While we have some [OpenAI ChatCompletions-compatible models](https://docs.baseten.co/api-reference/openai), you can define your own I/O spec with `Truss`.
 
-You can learn more about Baseten in [our docs](https://docs.baseten.co/) or read on for LangChain-specific info.
+>[Learn more](https://docs.baseten.co/deploy/lifecycle) about model IDs and deployments.
 
-## Setup: LangChain + Baseten
+>Learn more about Baseten in [the Baseten docs](https://docs.baseten.co/).
+
+## Installation and Setup
 
 You'll need two things to use Baseten models with LangChain:
 
@@ -25,47 +30,10 @@ Export your API key to your as an environment variable called `BASETEN_API_KEY`.
 export BASETEN_API_KEY="paste_your_api_key_here"
 ```
 
-## Component guide: LLMs
+## LLMs
 
-Baseten integrates with LangChain through the [LLM component](https://python.langchain.com/docs/integrations/llms/baseten), which provides a standardized and interoperable interface for models that are deployed on your Baseten workspace.
-
-You can deploy foundation models like Mistral and Llama 2 with one click from the [Baseten model library](https://app.baseten.co/explore/) or if you have your own model, [deploy it with Truss](https://truss.baseten.co/welcome).
-
-In this example, we'll work with Mistral 7B. [Deploy Mistral 7B here](https://app.baseten.co/explore/mistral_7b_instruct) and follow along with the deployed model's ID, found in the model dashboard.
-
-To use this module, you must:
-
-* Export your Baseten API key as the environment variable BASETEN_API_KEY
-* Get the model ID for your model from your Baseten dashboard
-* Identify the model deployment ("production" for all model library models)
-
-[Learn more](https://docs.baseten.co/deploy/lifecycle) about model IDs and deployments.
-
-Production deployment (standard for model library models)
+See a [usage example](/docs/integrations/llms/baseten).
 
 ```python
 from langchain_community.llms import Baseten
-
-mistral = Baseten(model="MODEL_ID", deployment="production")
-mistral("What is the Mistral wind?")
 ```
-
-Development deployment
-
-```python
-from langchain_community.llms import Baseten
-
-mistral = Baseten(model="MODEL_ID", deployment="development")
-mistral("What is the Mistral wind?")
-```
-
-Other published deployment
-
-```python
-from langchain_community.llms import Baseten
-
-mistral = Baseten(model="MODEL_ID", deployment="DEPLOYMENT_ID")
-mistral("What is the Mistral wind?")
-```
-
-Streaming LLM output, chat completions, embeddings models, and more are all supported on the Baseten platform and coming soon to our LangChain integration. Contact us at [support@baseten.co](mailto:support@baseten.co) with any questions about using Baseten with LangChain.
diff --git a/docs/docs/integrations/providers/beam.mdx b/docs/docs/integrations/providers/beam.mdx
index 329e22276a..7f723eb0de 100644
--- a/docs/docs/integrations/providers/beam.mdx
+++ b/docs/docs/integrations/providers/beam.mdx
@@ -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://www.beam.cloud/) is a cloud computing platform that allows you to run your code 
+> on remote servers with GPUs.
+
 
 ## Installation and Setup
 
@@ -9,84 +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:
 
-## Wrappers
+```bash
+pip install beam-sdk
+```
 
-### LLM
 
-There exists a Beam LLM wrapper, which you can access with
+## LLMs
+
+See a [usage example](/docs/integrations/llms/beam).
+
+See another example in the [Beam documentation](https://docs.beam.cloud/examples/langchain).
 
 ```python
 from langchain_community.llms.beam import Beam
 ```
-
-## Define your 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.
-```python
-llm = Beam(model_name="gpt2",
-           name="langchain-gpt2-test",
-           cpu=8,
-           memory="32Gi",
-           gpu="A10G",
-           python_version="python3.8",
-           python_packages=[
-               "diffusers[torch]>=0.10",
-               "transformers",
-               "torch",
-               "pillow",
-               "accelerate",
-               "safetensors",
-               "xformers",],
-           max_length="50",
-           verbose=False)
-```
-
-## Deploy your Beam app
-
-Once defined, you can deploy your Beam app by calling your model's `_deploy()` method.
-
-```python
-llm._deploy()
-```
-
-## Call your Beam app
-
-Once a beam model is deployed, it can be called by callying your model's `_call()` method.
-This returns the GPT2 text response to your prompt.
-
-```python
-response = llm._call("Running machine learning on a remote GPU")
-```
-
-An example script which deploys the model and calls it would be:
-
-```python
-from langchain_community.llms.beam import Beam
-import time
-
-llm = Beam(model_name="gpt2",
-           name="langchain-gpt2-test",
-           cpu=8,
-           memory="32Gi",
-           gpu="A10G",
-           python_version="python3.8",
-           python_packages=[
-               "diffusers[torch]>=0.10",
-               "transformers",
-               "torch",
-               "pillow",
-               "accelerate",
-               "safetensors",
-               "xformers",],
-           max_length="50",
-           verbose=False)
-
-llm._deploy()
-
-response = llm._call("Running machine learning on a remote GPU")
-
-print(response)
-```
\ No newline at end of file
diff --git a/docs/docs/integrations/providers/bittensor.mdx b/docs/docs/integrations/providers/bittensor.mdx
index b818ae885e..7bc6719678 100644
--- a/docs/docs/integrations/providers/bittensor.mdx
+++ b/docs/docs/integrations/providers/bittensor.mdx
@@ -1,37 +1,20 @@
-# NIBittensor
+# Bittensor
 
-This page covers how to use the BittensorLLM inference runtime within LangChain.
-It is broken into two parts: installation and setup, and then examples of NIBittensorLLM usage.
+>[Neural Internet Bittensor](https://neuralinternet.ai/) network, an open source protocol 
+> that powers a decentralized, blockchain-based, machine learning network.
 
 ## Installation and Setup
 
-- Install the Python package with `pip install langchain`
+Get your API_KEY from [Neural Internet](https://api.neuralinternet.ai).
 
-## Wrappers
+You can [analyze API_KEYS](https://api.neuralinternet.ai/api-keys) 
+and [logs of your usage](https://api.neuralinternet.ai/logs).
 
-### LLM
 
-There exists a NIBittensor LLM wrapper, which you can access with:
+## LLMs
+
+See a [usage example](/docs/integrations/llms/bittensor).
 
 ```python
 from langchain_community.llms import NIBittensorLLM
 ```
-
-It provides a unified interface for all models:
-
-```python
-llm = NIBittensorLLM(system_prompt="Your task is to provide concise and accurate response based on user prompt")
-
-print(llm('Write a fibonacci function in python with golder ratio'))
-```
-
-Multiple responses from top miners can be accessible using the `top_responses` parameter:
-
-```python
-multi_response_llm = NIBittensorLLM(top_responses=10)
-multi_resp = multi_response_llm("What is Neural Network Feeding Mechanism?")
-json_multi_resp = json.loads(multi_resp)
-
-print(json_multi_resp)
-```
-
diff --git a/docs/docs/integrations/providers/breebs.md b/docs/docs/integrations/providers/breebs.md
index 6d4262e163..fc6826874b 100644
--- a/docs/docs/integrations/providers/breebs.md
+++ b/docs/docs/integrations/providers/breebs.md
@@ -1,24 +1,16 @@
-# BREEBS (Open Knowledge)
+# Breebs (Open Knowledge)
 
-[BREEBS](https://www.breebs.com/) is an open collaborative knowledge platform. 
-Anybody can create a Breeb, a knowledge capsule based on PDFs stored on a Google Drive folder.
-A breeb can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources.
-Behind the scenes, Breebs implements several Retrieval Augmented Generation (RAG) models to seamlessly provide useful context at each iteration.  
+>[Breebs](https://www.breebs.com/) is an open collaborative knowledge platform. 
+>Anybody can create a `Breeb`, a knowledge capsule based on PDFs stored on a Google Drive folder.
+>A `Breeb` can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources.
+>Behind the scenes, `Breebs` implements several `Retrieval Augmented Generation (RAG)` models 
+> to seamlessly provide useful context at each iteration.  
 
-## List of available Breebs
-
-To get the full list of Breebs, including their key (breeb_key) and description : 
-https://breebs.promptbreeders.com/web/listbreebs.  
-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.
-
-## Creating a new Breeb
-
-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.  
 
 ## Retriever
+
 ```python
 from langchain.retrievers import BreebsRetriever
 ```
 
-# Example
-[See usage example (Retrieval & ConversationalRetrievalChain)](https://python.langchain.com/docs/integrations/retrievers/breebs)
\ No newline at end of file
+[See a usage example (Retrieval & ConversationalRetrievalChain)](/docs/integrations/retrievers/breebs)
\ No newline at end of file
diff --git a/docs/docs/integrations/providers/cassandra.mdx b/docs/docs/integrations/providers/cassandra.mdx
index ab33297ff8..f75176c4d0 100644
--- a/docs/docs/integrations/providers/cassandra.mdx
+++ b/docs/docs/integrations/providers/cassandra.mdx
@@ -7,7 +7,7 @@ The integrations outlined in this page can be used with `Cassandra` as well as o
 i.e. those using the `Cassandra Query Language` protocol.
 
 
-### Setup
+## Installation and Setup
 
 Install the following Python package:
 
@@ -15,15 +15,10 @@ Install the following Python package:
 pip install "cassio>=0.1.4"
 ```
 
-
 ## Vector Store
 
 ```python
 from langchain_community.vectorstores import Cassandra
-vector_store = Cassandra(
-    embedding=my_embedding,
-    table_name="my_store",
-)
 ```
 
 Learn more in the [example notebook](/docs/integrations/vectorstores/cassandra).
@@ -32,7 +27,6 @@ Learn more in the [example notebook](/docs/integrations/vectorstores/cassandra).
 
 ```python
 from langchain_community.chat_message_histories import CassandraChatMessageHistory
-message_history = CassandraChatMessageHistory(session_id="my-session")
 ```
 
 Learn more in the [example notebook](/docs/integrations/memory/cassandra_chat_message_history).
@@ -66,12 +60,11 @@ Learn more in the [example notebook](/docs/integrations/llms/llm_caching#cassand
 
 ```python
 from langchain_community.document_loaders import CassandraLoader
-loader = CassandraLoader(table="my_table")
-docs = loader.load()
 ```
 
 Learn more in the [example notebook](/docs/integrations/document_loaders/cassandra).
 
 #### Attribution statement
 
-> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries.
+> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of 
+> the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries.
diff --git a/docs/docs/integrations/providers/cerebriumai.mdx b/docs/docs/integrations/providers/cerebriumai.mdx
index 733b5f9a0a..912dbd90f6 100644
--- a/docs/docs/integrations/providers/cerebriumai.mdx
+++ b/docs/docs/integrations/providers/cerebriumai.mdx
@@ -1,17 +1,26 @@
 # CerebriumAI
 
-This page covers how to use the CerebriumAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.
+>[Cerebrium](https://docs.cerebrium.ai/cerebrium/getting-started/introduction)  is a serverless GPU infrastructure provider.
+> It provides API access to several LLM models.
+
+See the examples in the [CerebriumAI documentation](https://docs.cerebrium.ai/examples/langchain).
 
 ## Installation and Setup
-- Install with `pip install cerebrium`
-- Get an CerebriumAI api key and set it as an environment variable (`CEREBRIUMAI_API_KEY`)
 
-## Wrappers
+- Install a python package:
+```bash
+pip install cerebrium
+```
+
+- [Get an CerebriumAI api key](https://docs.cerebrium.ai/cerebrium/getting-started/installation) and set 
+  it as an environment variable (`CEREBRIUMAI_API_KEY`)
+
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/cerebriumai).
 
-### LLM
 
-There exists an CerebriumAI LLM wrapper, which you can access with 
 ```python
 from langchain_community.llms import CerebriumAI
 ```
\ No newline at end of file