> ## Documentation Index > Fetch the complete documentation index at: https://rasa.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # LLM Configuration for Rasa Pro ≤ 3.10 > For Rasa Pro versions 3.11 and above, refer to the LLM Configuration for >=3.11 page. **LLM Configuration for Rasa Pro 3.11 and above** For Rasa Pro versions `3.11` and above, refer to the [LLM Configuration for `>=3.11`](/docs/reference/config/components/llm-configuration) page. ## Overview This page applies to the following components which use LLMs: * [SingleStepLLMCommandGenerator](/docs/reference/config/components/deprecated-components#singlestepllmcommandgenerator) * [MultiStepLLMCommandGenerator](/docs/reference/config/components/deprecated-components#multistepllmcommandgenerator) * [EnterpriseSearchPolicy](/docs/reference/config/policies/enterprise-search-policy) * [IntentlessPolicy](/docs/reference/config/policies/intentless-policy) * [ContextualResponseRephraser](/docs/reference/primitives/contextual-response-rephraser) * [LLMBasedRouter](/docs/reference/config/components/coexistence-routers#llmbasedrouter) All the above components can be configured to change: * the LLM provider * the model to be used Starting with version Rasa Pro `3.10`, CALM uses [LiteLLM](https://litellm.vercel.app/) under the hood to integrate with different LLM providers. Hence, all [LiteLLM's integrated providers](https://litellm.vercel.app/docs/providers) are supported with CALM as well. We explicitly mention the settings required for the most frequently used ones in the sections below. If you want to try a provider other than OpenAI / Azure OpenAI, it is recommended to install Rasa Pro versions `>= 3.10`. ## Recommended Models The table below documents the versions of each model we recommend for use with various Rasa components. As new models are published, Rasa will test these and where appropriate add them as a recommended model. | Component | Providing platform | Recommended models | | ----------------------------------------------------------------------------- | ------------------ | ----------------------------------------------------------------------------------------- | | `SingleStepLLMCommandGenerator`, `EnterpriseSearchPolicy`, `IntentlessPolicy` | OpenAI, Azure | `gpt-4-0613` | | `ContextualResponseRephraser` | OpenAI, Azure | `gpt-4-0613`, `gpt-3.5-turbo-0125` | | `MultiStepLLMCommandGenerator` | OpenAI, Azure | `gpt-4-turbo-2024-04-09`, `gpt-3.5-turbo-0125`, `gpt-3.5-turbo-1106`, `gpt-4o-2024-08-06` | ## Chat completion models **Default Provider** CALM is LLM agnostic and can be configured with different LLMs, but OpenAI is the default model provider. Majority of our experiments have been with models available on OpenAI or OpenAI Azure service. The performance of your assistant may vary when using other LLMs, but improvements can be made by tuning flow and collect step descriptions. To configure components that use a chat completion model as the LLM, declare the configuration under the `llm` key of that component's configuration. For example: ```yaml title="config.yml" {5} theme={null} recipe: default.v1 language: en pipeline: - name: SingleStepLLMCommandGenerator llm: ... ``` #### Required Parameters There are certain required parameters under the `llm` key: 1. `model` - Specifies the name of the model identifier available from the LLM provider's documentation, for e.g. `gpt-4-0613` 2. `provider` - Unique identifier of the provider to be used for invoking the specified model. ```yaml title="config.yaml" {6-7} theme={null} recipe: default.v1 language: en pipeline: - name: SingleStepLLMCommandGenerator llm: model: gpt-4-0613 provider: openai ``` #### Optional Parameters The `llm` key also accepts inference time parameters like `temperature`, etc which are optional but can be useful in extracting the best performance out of the model being used. Please refer to the [official LiteLLM documentation](https://litellm.vercel.app/docs/completion/input) for a list of such parameters supported. When configuring a particular provider, there are a few provider specific settings which are explained under each provider's individual sub-section below. If you switch to a different LLM provider, all default parameters for the old provider will be overriden with the default parameters of the new provider. E.g. If a provider sets `temperature=0.7` as the default value and you switch to a different LLM provider, this default will be ignored and it is up to you to set the temperature for the new provider. ### OpenAI #### API Token The API token authenticates your requests to the OpenAI API. To configure the API token, follow these steps: 1. If you haven't already, sign up for an account on the OpenAI platform. 2. Navigate to the [OpenAI Key Management page](https://platform.openai.com/account/api-keys), and click on the "Create New Secret Key" button to initiate the process of obtaining ``. 3. To set the API key as an environment variable, you can use the following command in a terminal or command prompt: ```shell theme={null} export OPENAI_API_KEY= ``` ```shell theme={null} setx OPENAI_API_KEY ``` This will apply to future cmd prompt window, so you will need to open a new one to use that variable. Replace `` with the actual API key you obtained from the OpenAI platform. #### Configuration There are no additional OpenAI specific parameters to be configured. However, there could be model specific parameters like `temperature` that you might want to modify. Names for such parameters can found in [OpenAI's API documentation](https://platform.openai.com/docs/api-reference/chat) and defined under `llm` key of the component's configuration. Please refer to [LiteLLM's documentation](https://litellm.vercel.app/docs/providers/openai#openai-chat-completion-models) to know the list of models supported from the OpenAI platform. #### Model deprecations OpenAI regularly publishes a deprecation schedule for its models. This schedule can be accessed in the [documentation published by OpenAI](https://platform.openai.com/docs/deprecations). ### Azure OpenAI Service #### API Token The API token authenticates your requests to the Azure OpenAI Service. Set the API token as an environment variable. You can use the following command in a terminal or command prompt: ```shell theme={null} export AZURE_API_KEY= ``` ```shell theme={null} setx AZURE_API_KEY ``` This will apply to future cmd prompt window, so you will need to open a new one to use that variable. Replace `` with the actual API key you obtained from the Azure OpenAI Service platform. #### Configuration To access models provided by [Azure OpenAI Service](https://azure.microsoft.com/en-in/products/ai-services/openai-service), there are a few additional parameters that need to be configured: * `provider` - Set to `azure`. * `api_type` - The type of API to use. This should be set to "azure" to indicate the use of Azure OpenAI Service. * `api_base` - The URL for your Azure OpenAI instance. An example might look like this: `https://my-azure.openai.azure.com/`. * `api_version` - The API version to use for this operation. This follows the YYYY-MM-DD format and the value should be enclosed in single or double quotes. * `engine`/`deployment_name` - Alias for `deployment` parameter. Name of the deployment on Azure. Model specific parameters like `temperature` can be defined as well. Refer to [OpenAI Azure service's API documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#request-body) for information on available parameter names. A complete example configuration of the `SingleStepLLMCommandGenerator` using Azure OpenAI Service would look like this: ```yaml title="config.yml" theme={null} - name: LLMCommandGenerator llm: deployment: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 ``` ```yaml title="config.yml" theme={null} - name: SingleStepLLMCommandGenerator llm: engine: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 ``` ```yaml title="config.yml" theme={null} - name: SingleStepLLMCommandGenerator llm: provider: azure deployment: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" timeout: 7 ``` A more comprehensive example using the Azure OpenAI service in more CALM components is available [here](/docs/reference/config/components/llm-configuration-before-3-10#azure). #### Model deprecations Azure regularly publishes a deprecation schedule for its models that come under the OpenAI Azure Service. This schedule can be accessed in the [documentation published by Azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/model-retirements). #### Debugging If you encounter timeout errors, configure `request_timeout` parameter to a larger value. The exact value depends on how your azure instance is configured. ### Amazon Bedrock #### Requirements: 1. Make sure you have `rasa-pro>=3.10.x` installed. 2. Install `boto3>=1.28.57`. 3. Set the following environment variables - `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_REGION_NAME`. 4. (Optional) Might have to set `AWS_SESSION_TOKEN` if your organisation mandates the usage of [temporary credentials](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_use-resources.html) for security. Once the above steps are complete, edit `config.yaml` to use an appropriate model and set `provider` to `bedrock`: ```yaml title="config.yml" {3-4} theme={null} - name: SingleStepLLMCommandGenerator llm: provider: bedrock model: anthropic.claude-instant-v1 ``` Model specific parameters like `temperature` can be defined as well. Refer to LiteLLM's documentation for information on [available parameter names](https://litellm.vercel.app/docs/completion/input#translated-openai-params) and [supported models](https://litellm.vercel.app/docs/providers/bedrock#supported-aws-bedrock-models). ### Gemini - Google AI Studio #### Requirements: 1. Make sure you have `rasa-pro>=3.10.x` installed. 2. Install python package `google-generativeai`. 3. Get API Key at [https://aistudio.google.com/](https://aistudio.google.com/) . 4. Set the API key to an environment variable `GEMINI_API_KEY`. Once the above steps are complete, edit `config.yaml` to use an appropriate model and set `provider` to `gemini`: ```yaml title="config.yml" {3-4} theme={null} - name: SingleStepLLMCommandGenerator llm: provider: gemini model: gemini-pro ``` Refer to LiteLLM's documentation to know which [additional parameters](https://litellm.vercel.app/docs/providers/gemini#supported-openai-params) and [models](https://litellm.vercel.app/docs/providers/gemini#chat-models) are supported. ### HuggingFace Inference Endpoints #### Requirements: 1. Make sure you have `rasa-pro>=3.10.x` installed. 2. Set an API Key to the environment variable `HUGGINGFACE_API_KEY`. 3. Edit `config.yaml` to use an appropriate model, set `provider` to `huggingface` and `api_base` to the base URL of the deployed endpoint: ```yaml title="config.yml" {3-5} theme={null} - name: SingleStepLLMCommandGenerator llm: provider: huggingface model: meta-llama/CodeLlama-7b-Instruct-hf api_base: "https://my-endpoint.huggingface.cloud" ``` ### Self Hosted Model Server CALM's components can also be configured to work with an open source LLM that is hosted on an open source model server like [vLLM](https://github.com/vllm-project/vllm)(recommended), [Ollama](https://ollama.com/) or [Llama.cpp web server](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#web-server). The only requirement is that the model server should adhere to the [OpenAI API format](https://platform.openai.com/docs/api-reference/introduction). Once you have your model server running, configure the CALM assistant's config.yaml: **vLLM** ```yaml title="config.yml" {3-5} theme={null} - name: SingleStepLLMCommandGenerator llm: provider: self-hosted model: meta-llama/CodeLlama-7b-Instruct-hf api_base: "https://my-endpoint/v1" ``` Important to note: 1. Recommended version of `vllm` to use is `0.6.0`. 2. CALM exclusively utilizes the chat completions endpoint of the model server, so it's essential that the model's tokenizer includes a chat template. Models lacking a chat template will not be compatible with CALM. 3. `model` should contain the name of the model supplied to the vllm startup command, for example if your model server is started with: ```bash theme={null} vllm serve meta-llama/CodeLlama-7b-Instruct-hf ``` `model` should be set to `meta-llama/CodeLlama-7b-Instruct-hf`. 4\. `api_base` should contain the full exposed URL of the model server with `v1` attached as suffix to the URL. **Ollama** Once the ollama model server is running, edit the `config.yaml` file: ```yaml title="config.yml" {3-5} theme={null} - name: SingleStepLLMCommandGenerator llm: provider: ollama model: llama3.1 api_base: "https://my-endpoint" ``` ### Other Providers If you want to try one of these providers, it is recommended to install Rasa Pro versions `>= 3.10`. Other than the above mentioned providers, we have also tested support for the following providers: | Platform | `provider` | API-KEY variable | | ----------- | ------------- | -------------------- | | Anthropic | `anthropic` | `ANTHROPIC_API_KEY` | | Cohere | `cohere` | `COHERE_API_KEY` | | Mistral | `mistral` | `MISTRAL_API_KEY` | | Together AI | `together_ai` | `TOGETHERAI_API_KEY` | | Groq | `groq` | `GROQ_API_KEY` | For each of the above ones, ensure you have set an environment variable named by the value in `API-KEY variable` column to the API key of that platform and set the `provider` parameter under `llm` key of the component's config to the value in `provider` column. ## Embedding models To configure components that use an embedding model, declare the configuration under the `embeddings` key of that component's configuration. For example: ```yaml title="config.yml" {6} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: model: gpt-4-0613 flow_retrieval: embeddings: ... ``` The `embeddings` property needs two mandatory parameters: 1. `model` - Specifies the name of the model identifier available from the LLM provider's documentation, for e.g. `text-embedding-3-large`. 2. `provider` - Unique identifier of the provider to be used for invoking the specified model, for e.g. `openai` ```yaml title="config.yml" {7-8} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: model: gpt-4-0613 flow_retrieval: embeddings: type: openai model: text-embedding-3-large ``` ```yaml title="config.yml" {7-8} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: provider: openai model: gpt-4-0613 flow_retrieval: embeddings: provider: openai model: text-embedding-3-large ``` When configuring a particular provider, there are a few provider specific settings which are explained under each provider's individual sub-section below. ### OpenAI OpenAI is used as the default embedding model provider. To start using, ensure you have configured an API token as you would do for a [chat completion model from OpenAI platform](/docs/reference/config/components/llm-configuration-before-3-10#api-token) #### Configuration ```yaml title="config.yml" {7-8} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: model: gpt-4-0613 flow_retrieval: embeddings: type: openai model: text-embedding-3-large ``` ```yaml title="config.yml" {7-8} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: provider: openai model: gpt-4-0613 flow_retrieval: embeddings: provider: openai model: text-embedding-3-large ``` ### Azure OpenAI Service Ensure you have configured an API token as you would do for a [chat completion model for Azure OpenAI Service](/docs/reference/config/components/llm-configuration-before-3-10#api-token-1) #### Configuration Configuring an embedding model from Azure OpenAI Service needs values for the same set of parameters that are required for configuring a [chat completion model from Azure OpenAI Service](/docs/reference/config/components/llm-configuration-before-3-10#configuration-1) ```yaml title="config.yml" {7-12} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: model: gpt-4-0613 flow_retrieval: embeddings: type: azure engine: engine-embed api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 ``` ```yaml title="config.yml" {7-12} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: model: gpt-4-0613 flow_retrieval: embeddings: provider: azure deployment: engine-embed api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" timeout: 7 ``` ### Amazon Bedrock Configuring an embedding model from amazon bedrock needs the same pre-requisites as a [chat completion model](/docs/reference/config/components/llm-configuration-before-3-10#requirements). Please ensure you have addressed these before proceeding further. #### Configuration ```yaml title="config.yml" {7-8} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: provider: openai model: gpt-4-0613 flow_retrieval: embeddings: provider: bedrock model: amazon.titan-embed-text-v1 ``` Please refer to LiteLLM's documentation on [list of supported embedding models](https://litellm.vercel.app/docs/providers/bedrock#supported-aws-bedrock-embedding-models) from Amazon Bedrock ### In-Memory CALM also provides an option to load lightweight embedding models in-memory without needing them to be exposed over an API. It uses the sentence transformers library under the hood to load and run inference on them. #### Configuration ```yaml title="config.yml" {7-12} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: model: gpt-4-0613 flow_retrieval: embeddings: type: "huggingface" model: "BAAI/bge-small-en-v1.5" model_kwargs: # used during instantiation device: 'cpu' encode_kwargs: # used during inference normalize_embeddings: True ``` ```yaml title="config.yml" {7-12} theme={null} pipeline: - name: "SingleStepLLMCommandGenerator" llm: provider: "openai" model: "gpt-4-0613" flow_retrieval: embeddings: provider: "huggingface_local" model: "BAAI/bge-small-en-v1.5" model_kwargs: # used during instantiation device: "cpu" encode_kwargs: # used during inference normalize_embeddings: true ``` * `model` parameter can take as value either any embedding model repository available on the HuggingFace hub or a path to a local model. * `model_kwargs` parameter is used to provide [load time arguments](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#id1) to the sentence transformer library. * `encode_kwargs` parameter is used to provide [inference time arguments](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode) to the sentence transformer library. ### Other Providers Other than the above mentioned providers, we have also tested support for the following providers - | Platform | `provider` | API-KEY variable | | --------- | ---------- | ----------------- | | Cohere | `cohere` | `COHERE_API_KEY` | | Mistral | `mistral` | `MISTRAL_API_KEY` | | Voyage AI | `voyage` | `VOYAGE_API_KEY` | For each of the above ones, ensure you have set an environment variable named by the value in `API-KEY variable` column to the API key of that platform and set the `provider` parameter under `llm` key of the component's config to the value in `provider` column. ## Configuring self-signed SSL certificates In environments where a proxy performs TLS interception, Rasa may need to be configured to trust the certificates used by your proxy. By default, certificates are loaded from the OS certificate store. However, if your setup involves custom self-signed certificates, you can specify these by setting the `RASA_CA_BUNDLE` environment variable. This variable points to the path of the certificate file that Rasa should use to validate SSL connections: ```bash theme={null} export RASA_CA_BUNDLE="path/to/your/certificate.pem" ``` The `REQUESTS_CA_BUNDLE` environment variable is deprecated and will no longer be supported in future versions. Please use RASA\_CA\_BUNDLE instead to ensure compatibility. ## Configuring Proxy URLs In environments where LLM requests need to be routed through a proxy, Rasa relies on LiteLLM to handle proxy configurations. LiteLLM supports configuring proxy URLs through the `HTTP_PROXY` and `HTTPS_PROXY` environment variables. To ensure that all LLM requests are routed through the proxy, you can set the environment variables as follows: ```bash theme={null} export HTTP_PROXY="http://your-proxy-url:port" export HTTPS_PROXY="https://your-proxy-url:port" ``` ## FAQ ### Does OpenAI use my data to train their models? No. OpenAI does not use your data to train their models. From their [website](https://openai.com/enterprise-privacy/): > We do not train our models on your business data by default. ## Example Configurations ### Azure A comprehensive example which includes: * `llm` and `embeddings` configuration for components in `config.yml`: * `IntentlessPolicy` * `EnterpriseSearchPolicy` * `SingleStepLLMCommandGenerator` * `flow_retrieval` in 3.8.x * `llm` configuration for rephrase in `endpoints.yml` (`ContextualResponseRephraser`) ```yaml title="endpoints.yml" theme={null} nlg: type: rasa_plus.ml.ContextualResponseRephraser llm: engine: rasa-gpt-4 api_type: azure api_version: "2024-02-15-preview" api_base: https://my-azure.openai.azure.com request_timeout: 7 ``` ```yaml title="config.yml" theme={null} recipe: default.v1 language: en pipeline: - name: LLMCommandGenerator llm: engine: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 policies: - name: FlowPolicy - name: rasa_plus.ml.IntentlessPolicy llm: engine: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 embeddings: model: text-embedding-3-small engine: rasa-embedding-small api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 - name: rasa_plus.ml.EnterpriseSearchPolicy vector_store: type: "faiss" threshold: 0.0 llm: model: gpt-4-0613 engine: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 embeddings: model: text-embedding-3-small engine: rasa-embedding-small api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 ``` ```yaml title="endpoints.yml" theme={null} nlg: type: rephrase llm: engine: rasa-gpt-4 api_type: azure api_version: "2024-02-15-preview" api_base: https://my-azure.openai.azure.com request_timeout: 7 ``` ```yaml title="config.yml" theme={null} recipe: default.v1 language: en pipeline: - name: SingleStepLLMCommandGenerator llm: engine: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 flow_retrieval: embeddings: model: text-embedding-3-small engine: rasa-embedding-small api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 policies: - name: FlowPolicy - name: IntentlessPolicy llm: engine: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 embeddings: model: text-embedding-3-small engine: rasa-embedding-small api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 - name: EnterpriseSearchPolicy vector_store: type: "faiss" threshold: 0.0 llm: engine: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 embeddings: model: text-embedding-3-small engine: rasa-embedding-small api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" request_timeout: 7 ``` ```yaml title="endpoints.yml" theme={null} nlg: type: rephrase llm: provider: azure deployment: rasa-gpt-4 api_type: azure api_version: "2024-02-15-preview" api_base: https://my-azure.openai.azure.com timeout: 7 ``` ```yaml title="config.yml" theme={null} recipe: default.v1 language: en pipeline: - name: SingleStepLLMCommandGenerator llm: provider: azure deployment: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" timeout: 7 flow_retrieval: embeddings: provider: openai deployment: rasa-embedding-small api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" timeout: 7 policies: - name: FlowPolicy - name: IntentlessPolicy llm: provider: azure deployment: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" timeout: 7 embeddings: provider: azure deployment: rasa-embedding-small api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" timeout: 7 - name: EnterpriseSearchPolicy vector_store: type: "faiss" threshold: 0.0 llm: provider: azure deployment: rasa-gpt-4 api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" timeout: 7 embeddings: provider: azure deployment: rasa-embedding-small api_type: azure api_base: https://my-azure.openai.azure.com/ api_version: "2024-02-15-preview" timeout: 7 ``` ## Related topics - [LLM Configuration for Rasa Pro ≥ 3.11](/docs/reference/config/components/llm-configuration.md) - [Rasa Pro Version Migration Guide](/docs/reference/changelogs/rasa-pro-migration-guide.md) - [Fine-Tuning an LLM for Command Generation](/docs/pro/customize/fine-tuning-llm.md) - [Rasa Pro Change Log](/docs/reference/changelogs/rasa-pro-changelog.md) - [Rasa Pro Services Change Log](/docs/reference/changelogs/rasa-pro-services-changelog.md)