diff --git a/404.html b/404.html index bd9b1a96..a9bb480e 100644 --- a/404.html +++ b/404.html @@ -4,7 +4,7 @@
OpenAI: Create an OpenAI API key.
+Anthropic: Create an Anthropic API key. +Anthropic provides language models only; you must select an additional provider for embeddings.
+IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
+Ollama: Deploy an Ollama instance and models locally, in the cloud, or on a remote server. Then, get your Ollama server's base URL and the names of the models that you want to use.
+OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama. +For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
+The OpenRAG team recommends the following models when using Ollama as your model provider:
+Language models: gpt-oss:20b or mistral-nemo:12b.
If you choose gpt-oss:20b, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
Embedding models: nomic-embed-text:latest, mxbai-embed-large:latest, or embeddinggemma:latest.
You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models. +You can submit an OpenRAG GitHub issue to request support for specific models.
Continue through the overview slides for a brief introduction to OpenRAG, or click Skip overview. The overview demonstrates some basic functionality that is covered in the quickstart and in other parts of the OpenRAG documentation.
Ollama isn't installed with OpenRAG. You must install it separately if you want to use Ollama as a model provider.
Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models, but it can be advanced for new users. -The recommendations given here are a reasonable starting point for users with at least one GPU and experience running LLMs locally.
The OpenRAG team recommends the OpenAI gpt-oss:20b lanuage model and the nomic-embed-text embedding model.
-However, gpt-oss:20b uses 16GB of RAM, so consider using Ollama Cloud or running Ollama on a remote machine.
Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models. +However, it requires additional setup because Ollama isn't included with OpenRAG. +You must deploy Ollama separately if you want to use Ollama as a model provider.
OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama. +For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
+The OpenRAG team recommends the following models when using Ollama as your model provider:
+Language models: gpt-oss:20b or mistral-nemo:12b.
If you choose gpt-oss:20b, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
Embedding models: nomic-embed-text:latest, mxbai-embed-large:latest, or embeddinggemma:latest.
You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models. +You can submit an OpenRAG GitHub issue to request support for specific models.
Install Ollama locally or on a remote server, or run models in Ollama Cloud.
If you are running a remote server, it must be accessible from your OpenRAG deployment.
diff --git a/index.html b/index.html index 4bbbc1f4..a390764f 100644 --- a/index.html +++ b/index.html @@ -4,7 +4,7 @@OpenAI: Create an OpenAI API key.
+Anthropic: Create an Anthropic API key. +Anthropic provides language models only; you must select an additional provider for embeddings.
+IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
+Ollama: Deploy an Ollama instance and models locally, in the cloud, or on a remote server. Then, get your Ollama server's base URL and the names of the models that you want to use.
+OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama. +For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
+The OpenRAG team recommends the following models when using Ollama as your model provider:
+Language models: gpt-oss:20b or mistral-nemo:12b.
If you choose gpt-oss:20b, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
Embedding models: nomic-embed-text:latest, mxbai-embed-large:latest, or embeddinggemma:latest.
You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models. +You can submit an OpenRAG GitHub issue to request support for specific models.
Continue through the overview slides for a brief introduction to OpenRAG, or click Skip overview. The overview demonstrates some basic functionality that is covered in the quickstart and in other parts of the OpenRAG documentation.
Ollama isn't installed with OpenRAG. You must install it separately if you want to use Ollama as a model provider.
Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models, but it can be advanced for new users. -The recommendations given here are a reasonable starting point for users with at least one GPU and experience running LLMs locally.
The OpenRAG team recommends the OpenAI gpt-oss:20b lanuage model and the nomic-embed-text embedding model.
-However, gpt-oss:20b uses 16GB of RAM, so consider using Ollama Cloud or running Ollama on a remote machine.
Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models. +However, it requires additional setup because Ollama isn't included with OpenRAG. +You must deploy Ollama separately if you want to use Ollama as a model provider.
OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama. +For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
+The OpenRAG team recommends the following models when using Ollama as your model provider:
+Language models: gpt-oss:20b or mistral-nemo:12b.
If you choose gpt-oss:20b, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
Embedding models: nomic-embed-text:latest, mxbai-embed-large:latest, or embeddinggemma:latest.
You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models. +You can submit an OpenRAG GitHub issue to request support for specific models.
Install Ollama locally or on a remote server, or run models in Ollama Cloud.
If you are running a remote server, it must be accessible from your OpenRAG deployment.
diff --git a/install-uvx/index.html b/install-uvx/index.html index ac43ac51..3aaa9743 100644 --- a/install-uvx/index.html +++ b/install-uvx/index.html @@ -4,7 +4,7 @@OpenAI: Create an OpenAI API key.
+Anthropic: Create an Anthropic API key. +Anthropic provides language models only; you must select an additional provider for embeddings.
+IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
+Ollama: Deploy an Ollama instance and models locally, in the cloud, or on a remote server. Then, get your Ollama server's base URL and the names of the models that you want to use.
+OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama. +For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
+The OpenRAG team recommends the following models when using Ollama as your model provider:
+Language models: gpt-oss:20b or mistral-nemo:12b.
If you choose gpt-oss:20b, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
Embedding models: nomic-embed-text:latest, mxbai-embed-large:latest, or embeddinggemma:latest.
You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models. +You can submit an OpenRAG GitHub issue to request support for specific models.
Continue through the overview slides for a brief introduction to OpenRAG, or click Skip overview. The overview demonstrates some basic functionality that is covered in the quickstart and in other parts of the OpenRAG documentation.
Ollama isn't installed with OpenRAG. You must install it separately if you want to use Ollama as a model provider.
Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models, but it can be advanced for new users. -The recommendations given here are a reasonable starting point for users with at least one GPU and experience running LLMs locally.
The OpenRAG team recommends the OpenAI gpt-oss:20b lanuage model and the nomic-embed-text embedding model.
-However, gpt-oss:20b uses 16GB of RAM, so consider using Ollama Cloud or running Ollama on a remote machine.
Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models. +However, it requires additional setup because Ollama isn't included with OpenRAG. +You must deploy Ollama separately if you want to use Ollama as a model provider.
OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama. +For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
+The OpenRAG team recommends the following models when using Ollama as your model provider:
+Language models: gpt-oss:20b or mistral-nemo:12b.
If you choose gpt-oss:20b, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
Embedding models: nomic-embed-text:latest, mxbai-embed-large:latest, or embeddinggemma:latest.
You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models. +You can submit an OpenRAG GitHub issue to request support for specific models.
Install Ollama locally or on a remote server, or run models in Ollama Cloud.
If you are running a remote server, it must be accessible from your OpenRAG deployment.
diff --git a/install-windows/index.html b/install-windows/index.html index 5f97fd33..4bb7dfb5 100644 --- a/install-windows/index.html +++ b/install-windows/index.html @@ -4,7 +4,7 @@OpenAI: Create an OpenAI API key.
+Anthropic: Create an Anthropic API key. +Anthropic provides language models only; you must select an additional provider for embeddings.
+IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
+Ollama: Deploy an Ollama instance and models locally, in the cloud, or on a remote server. Then, get your Ollama server's base URL and the names of the models that you want to use.
+OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama. +For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
+The OpenRAG team recommends the following models when using Ollama as your model provider:
+Language models: gpt-oss:20b or mistral-nemo:12b.
If you choose gpt-oss:20b, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
Embedding models: nomic-embed-text:latest, mxbai-embed-large:latest, or embeddinggemma:latest.
You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models. +You can submit an OpenRAG GitHub issue to request support for specific models.
Continue through the overview slides for a brief introduction to OpenRAG, or click Skip overview. The overview demonstrates some basic functionality that is covered in the quickstart and in other parts of the OpenRAG documentation.
Ollama isn't installed with OpenRAG. You must install it separately if you want to use Ollama as a model provider.
Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models, but it can be advanced for new users. -The recommendations given here are a reasonable starting point for users with at least one GPU and experience running LLMs locally.
The OpenRAG team recommends the OpenAI gpt-oss:20b lanuage model and the nomic-embed-text embedding model.
-However, gpt-oss:20b uses 16GB of RAM, so consider using Ollama Cloud or running Ollama on a remote machine.
Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models. +However, it requires additional setup because Ollama isn't included with OpenRAG. +You must deploy Ollama separately if you want to use Ollama as a model provider.
OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama. +For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
+The OpenRAG team recommends the following models when using Ollama as your model provider:
+Language models: gpt-oss:20b or mistral-nemo:12b.
If you choose gpt-oss:20b, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
Embedding models: nomic-embed-text:latest, mxbai-embed-large:latest, or embeddinggemma:latest.
You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models. +You can submit an OpenRAG GitHub issue to request support for specific models.
Install Ollama locally or on a remote server, or run models in Ollama Cloud.
If you are running a remote server, it must be accessible from your OpenRAG deployment.
diff --git a/knowledge-filters/index.html b/knowledge-filters/index.html index 68b83285..73c3ea32 100644 --- a/knowledge-filters/index.html +++ b/knowledge-filters/index.html @@ -4,7 +4,7 @@easyocr because easyocr