Move GenAI docs to top-level folder#11302
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| The GenAI Showcase App uses this module to take care of creating the required tables in the remote vector database, including the open-source extension called pgvector. If you want to know more about the extension and how the tables look like in combination with this extension, see the [pgvector source code and documentation on GitHub](https://github.com/pgvector/pgvector). | ||
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| If you want to know more about RAG, embeddings, or [vector databases](/appstore/modules/genai/pgvector-setup/), make sure to review the links on the **Resources** page of the GenAI Showcase App or in the [Read More](#read-more) section. | ||
| If you want to know more about RAG, embeddings, or [vector databases](/agents/pgvector-setup/), make sure to review the links on the **Resources** page of the GenAI Showcase App or in the [Read More](#read-more) section. |
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[Mendix.LinkText] Use descriptive link text instead of '[Read More]('.
| ### Context Prompt | ||
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| Depending on the project or use case, adding contextual information to the model may be necessary. Normally, this information, called context prompt or conversation history, is sent in the same interaction as the system and user prompt. It captures the historical information of the conversation to maintain coherence with the end user and be context aware. In the Mendix app chatbot setup, developers configure this within their application, and it is included in the request sent to the LLM using the [Chat Completions (with history)](/appstore/modules/genai/genai-for-mx/commons/#chat-completions-with-history) operation. | ||
| Depending on the project or use case, adding contextual information to the model may be necessary. Normally, this information, called context prompt or conversation history, is sent in the same interaction as the system and user prompt. It captures the historical information of the conversation to maintain coherence with the end user and be context aware. In the Mendix app chatbot setup, developers configure this within their application, and it is included in the request sent to the LLM using the [Chat Completions (with history)](/agents/genai-for-mx/commons/#chat-completions-with-history) operation. |
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[Mendix.CompoundWords] Use 'end-user' instead of 'end user'
| This changes how action microflows are used, because they are called each time a tool is called and the UI changes for the user, for example, displaying a tool call or waiting for a user decision if a tool can be executed. Logic that only needs to happen right after the user sends their message (preprocessing) or after the final assistant's message was returned (postprocessing), should perhaps only be executed for those cases. | ||
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| If no [user-visibility](/appstore/modules/genai/genai-for-mx/commons/#enum-useraccessapproval) is configured for tools and you would like not to store tool messages (and therefore retain the behavior from versions before 6.0.0), you can change the boolean `SaveToolCallHistory` to *false* on the [Request](/appstore/modules/genai/genai-for-mx/commons/#request). Note that [knowledge base retrievals](/appstore/modules/genai/genai-for-mx/commons/#add-knowledge-base-to-request) are set to `HiddenForUser` by default. | ||
| If no [user-visibility](/agents/genai-for-mx/commons/#enum-useraccessapproval) is configured for tools and you would like not to store tool messages (and therefore retain the behavior from versions before 6.0.0), you can change the boolean `SaveToolCallHistory` to *false* on the [Request](/agents/genai-for-mx/commons/#request). Note that [knowledge base retrievals](/agents/genai-for-mx/commons/#add-knowledge-base-to-request) are set to `HiddenForUser` by default. |
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[Mendix.Capitalization] Use ''Boolean'' (capitalized)
| #### Chat Completions | ||
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| Operations for chat completions focus on the generation of text based on a certain input. In this context, system prompts and user prompts are two key components that help guide the language model in generating relevant and contextually appropriate responses. For more information on the type of prompts and message roles, see the [ENUM_MessageRole](/appstore/modules/genai/genai-for-mx/commons/#enum-messagerole) enumeration. To learn more about how to create the right prompts for your use case, see the [Read More](#read-more) section below | ||
| Operations for chat completions focus on the generation of text based on a certain input. In this context, system prompts and user prompts are two key components that help guide the language model in generating relevant and contextually appropriate responses. For more information on the type of prompts and message roles, see the [ENUM_MessageRole](/agents/genai-for-mx/commons/#enum-messagerole) enumeration. To learn more about how to create the right prompts for your use case, see the [Read More](#read-more) section below |
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[Mendix.LinkText] Use descriptive link text instead of '[Read More]('.
| {{% /alert %}} | ||
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| The microflow action `Generate Embeddings (String)` supports scenarios where the vector embedding of a single string must be generated, e.g. to use for a nearest neighbor search across an existing knowledge base. This input string can be passed directly as the `InputText` parameter of this microflow. Additionally, [EmbeddingsOptions](/appstore/modules/genai/genai-for-mx/commons/#embeddingsoptions-entity) is optional and can be instantiated using [Embeddings: Create EmbeddingsOptions](/appstore/modules/genai/genai-for-mx/commons/#embeddingsoptions-create) from GenAI Commons. Use the GenAI Commons toolbox action [Embeddings: Get First Vector from Response](/appstore/modules/genai/genai-for-mx/commons/#embeddings-get-first-vector) to retrieve the generated embeddings vector. Both mentioned operations can be found under **GenAI Knowledge Base (Content)** in the **Toolbox** in Mendix Studio Pro. | ||
| The microflow action `Generate Embeddings (String)` supports scenarios where the vector embedding of a single string must be generated, e.g. to use for a nearest neighbor search across an existing knowledge base. This input string can be passed directly as the `InputText` parameter of this microflow. Additionally, [EmbeddingsOptions](/agents/genai-for-mx/commons/#embeddingsoptions-entity) is optional and can be instantiated using [Embeddings: Create EmbeddingsOptions](/agents/genai-for-mx/commons/#embeddingsoptions-create) from GenAI Commons. Use the GenAI Commons toolbox action [Embeddings: Get First Vector from Response](/agents/genai-for-mx/commons/#embeddings-get-first-vector) to retrieve the generated embeddings vector. Both mentioned operations can be found under **GenAI Knowledge Base (Content)** in the **Toolbox** in Mendix Studio Pro. |
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[Mendix.Foreign] Use 'for example, ' instead of 'e.g. '.
| * The `HumanReadableId` of a chunk is used for the reference title in the response, which is shown to the end user in the [ConversationalUI](/appstore/modules/genai/genai-for-mx/conversational-ui/). | ||
| * To utilize the `Source` attribute of the references, include `MetaData` with the key `sourceUrl`. In [ConversationalUI](/appstore/modules/genai/genai-for-mx/conversational-ui/), this will appear as a clickable link for the end user. | ||
| * In some cases, a knowledge chunk consists of two texts: one for the semantic search (retrieval) step, and another for the generation step. For example, when solving a problem based on historical solutions, semantic search identifies similar problems using their descriptions, while the generation step produces a solution based on the corresponding historical solutions. In such cases, you can add [MetaData](/appstore/modules/genai/genai-for-mx/commons/#chunkcollection-add-knowledgebasechunk) with the key `knowledge` to each chunk during insertion. This allows the model to generate its response using the specified metadata instead of the input text (only the value of `knowledge` is passed to the model). | ||
| * The `HumanReadableId` of a chunk is used for the reference title in the response, which is shown to the end user in the [ConversationalUI](/agents/genai-for-mx/conversational-ui/). |
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[Mendix.CompoundWords] Use 'end-user' instead of 'end user'
| * To utilize the `Source` attribute of the references, include `MetaData` with the key `sourceUrl`. In [ConversationalUI](/appstore/modules/genai/genai-for-mx/conversational-ui/), this will appear as a clickable link for the end user. | ||
| * In some cases, a knowledge chunk consists of two texts: one for the semantic search (retrieval) step, and another for the generation step. For example, when solving a problem based on historical solutions, semantic search identifies similar problems using their descriptions, while the generation step produces a solution based on the corresponding historical solutions. In such cases, you can add [MetaData](/appstore/modules/genai/genai-for-mx/commons/#chunkcollection-add-knowledgebasechunk) with the key `knowledge` to each chunk during insertion. This allows the model to generate its response using the specified metadata instead of the input text (only the value of `knowledge` is passed to the model). | ||
| * The `HumanReadableId` of a chunk is used for the reference title in the response, which is shown to the end user in the [ConversationalUI](/agents/genai-for-mx/conversational-ui/). | ||
| * To utilize the `Source` attribute of the references, include `MetaData` with the key `sourceUrl`. In [ConversationalUI](/agents/genai-for-mx/conversational-ui/), this will appear as a clickable link for the end user. |
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🚫 [vale] reported by reviewdog 🐶
[Mendix.CompoundWords] Use 'end-user' instead of 'end user'
| #### Chat Completions | ||
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| Operations for chat completions focus on the generation of text based on a certain input. In this context, system prompts and user prompts are two key components that help guide the language model in generating relevant and contextually appropriate responses. For more information on the type of prompts and message roles, see the [ENUM_MessageRole](/appstore/modules/genai/genai-for-mx/commons/#enum-messagerole) enumeration. To learn more about how to create the right prompts for your use case, see the prompt engineering links in the [Read More](#read-more) section. | ||
| Operations for chat completions focus on the generation of text based on a certain input. In this context, system prompts and user prompts are two key components that help guide the language model in generating relevant and contextually appropriate responses. For more information on the type of prompts and message roles, see the [ENUM_MessageRole](/agents/genai-for-mx/commons/#enum-messagerole) enumeration. To learn more about how to create the right prompts for your use case, see the prompt engineering links in the [Read More](#read-more) section. |
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[Mendix.LinkText] Use descriptive link text instead of '[Read More]('.
| Depending on the operation you use in the microflow, an `InputText` String or a [ChunkCollection](/agents/genai-for-mx/commons/#chunkcollection) needs to be provided. The current version of this operation only supports the float representation of the resulting vector. | ||
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| The microflow action `Generate Embeddings (String)` supports scenarios where the vector embedding of a single string must be generated, e.g. to use for a nearest neighbor search across an existing knowledge base. This input string can be passed directly as the `InputText` parameter of this microflow. Additionally, [EmbeddingsOptions](/appstore/modules/genai/genai-for-mx/commons/#embeddingsoptions-entity) is optional and can be instantiated using [Embeddings: Create EmbeddingsOptions](/appstore/modules/genai/genai-for-mx/commons/#embeddingsoptions-create) from GenAI Commons. Use the GenAI Commons toolbox action [Embeddings: Get First Vector from Response](/appstore/modules/genai/genai-for-mx/commons/#embeddings-get-first-vector) to retrieve the generated embeddings vector. Both mentioned operations can be found under **GenAI Knowledge Base (Content)** in the **Toolbox** in Mendix Studio Pro. | ||
| The microflow action `Generate Embeddings (String)` supports scenarios where the vector embedding of a single string must be generated, e.g. to use for a nearest neighbor search across an existing knowledge base. This input string can be passed directly as the `InputText` parameter of this microflow. Additionally, [EmbeddingsOptions](/agents/genai-for-mx/commons/#embeddingsoptions-entity) is optional and can be instantiated using [Embeddings: Create EmbeddingsOptions](/agents/genai-for-mx/commons/#embeddingsoptions-create) from GenAI Commons. Use the GenAI Commons toolbox action [Embeddings: Get First Vector from Response](/agents/genai-for-mx/commons/#embeddings-get-first-vector) to retrieve the generated embeddings vector. Both mentioned operations can be found under **GenAI Knowledge Base (Content)** in the **Toolbox** in Mendix Studio Pro. |
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🚫 [vale] reported by reviewdog 🐶
[Mendix.Foreign] Use 'for example, ' instead of 'e.g. '.
| Purely to generate embeddings, it does not matter whether the ChunkCollection contains Chunks or its specialization KnowledgeBaseChunks. However, if the end goal is to store the generated embedding vectors in a knowledge base (e.g. using the [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) module), then Mendix recommends adding `KnowledgeBaseChunks` to the `ChunkCollection` and using these as an input for the embeddings operations, so they can afterward directly be used to populate the knowledge base with. | ||
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| Note that currently, the OpenAI connector does not support knowledge base interaction (e.g. inserting or retrieving chunks). For more information on possible ways to work with knowledge bases when using the OpenAI Connector for embedding generation, read more about [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) and [setting up a Vector Database](/appstore/modules/genai/pgvector-setup/). | ||
| Note that currently, the OpenAI connector does not support knowledge base interaction (e.g. inserting or retrieving chunks). For more information on possible ways to work with knowledge bases when using the OpenAI Connector for embedding generation, read more about [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) and [setting up a Vector Database](/agents/pgvector-setup/). |
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🚫 [vale] reported by reviewdog 🐶
[Mendix.Foreign] Use 'for example, ' instead of 'e.g. '.
| The GenAI Showcase App uses this module to take care of creating the required tables in the remote vector database, including the open-source extension called pgvector. If you want to know more about the extension and how the tables look like in combination with this extension, see the [pgvector source code and documentation on GitHub](https://github.com/pgvector/pgvector). | ||
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| If you want to know more about RAG, embeddings, or [vector databases](/appstore/modules/genai/pgvector-setup/), make sure to review the links on the **Resources** page of the GenAI Showcase App or in the [Read More](#read-more) section. | ||
| If you want to know more about RAG, embeddings, or [vector databases](/agents/reference-guide/external-connectors/pgvector-setup/), make sure to review the links on the **Resources** page of the GenAI Showcase App or in the [Read More](#read-more) section. |
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🚫 [vale] reported by reviewdog 🐶
[Mendix.LinkText] Use descriptive link text instead of '[Read More]('.
| Purely to generate embeddings, it does not matter whether the ChunkCollection contains Chunks or its specialization KnowledgeBaseChunks. However, if the end goal is to store the generated embedding vectors in a knowledge base (e.g. using the [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) module), then Mendix recommends adding `KnowledgeBaseChunks` to the `ChunkCollection` and using these as an input for the embeddings operations, so they can later be used directly to populate the knowledge base. | ||
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| Note that, currently, the knowledge base interaction (e.g. inserting or retrieving chunks) is not supported for OpenAI compatible APIs. For more information on possible ways to work with knowledge bases for embedding generation, see [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) and [setting up a Vector Database](/appstore/modules/genai/pgvector-setup/). | ||
| Note that, currently, the knowledge base interaction (e.g. inserting or retrieving chunks) is not supported for OpenAI compatible APIs. For more information on possible ways to work with knowledge bases for embedding generation, see [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) and [setting up a Vector Database](/agents/reference-guide/external-connectors/pgvector-setup/). |
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There was a problem hiding this comment.
🚫 [vale] reported by reviewdog 🐶
[Mendix.Foreign] Use 'for example, ' instead of 'e.g. '.
| Purely to generate embeddings, it does not matter whether the ChunkCollection contains Chunks or its specialization KnowledgeBaseChunks. However, if the end goal is to store the generated embedding vectors in a knowledge base (e.g. using the [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) module), then Mendix recommends adding `KnowledgeBaseChunks` to the `ChunkCollection` and using these as an input for the embeddings operations, so they can afterward directly be used to populate the knowledge base with. | ||
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| Note that currently, the OpenAI connector does not support knowledge base interaction (e.g. inserting or retrieving chunks). For more information on possible ways to work with knowledge bases when using the OpenAI Connector for embedding generation, read more about [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) and [setting up a Vector Database](/appstore/modules/genai/pgvector-setup/). | ||
| Note that currently, the OpenAI connector does not support knowledge base interaction (e.g. inserting or retrieving chunks). For more information on possible ways to work with knowledge bases when using the OpenAI Connector for embedding generation, read more about [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/) and [setting up a Vector Database](/agents/reference-guide/external-connectors/pgvector-setup/). |
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[Mendix.Foreign] Use 'for example, ' instead of 'e.g. '.
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