From d93ef4c01d17ada520586510d5e486d2c62a5fe3 Mon Sep 17 00:00:00 2001 From: Cyrille Bordier Date: Fri, 31 May 2024 10:35:24 -0700 Subject: [PATCH 1/5] first draft --- .../manage_marketplace/ai-assisted-search.md | 60 +++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 content/en/docs/manage_marketplace/ai-assisted-search.md diff --git a/content/en/docs/manage_marketplace/ai-assisted-search.md b/content/en/docs/manage_marketplace/ai-assisted-search.md new file mode 100644 index 000000000..ccaac6e1e --- /dev/null +++ b/content/en/docs/manage_marketplace/ai-assisted-search.md @@ -0,0 +1,60 @@ +--- +title: AI search assistant +linkTitle: AI search assistant +draft: no +weight: 50 +--- + +Leverage Artificial Intelligence models to get more outcomes of your Marketplace search. + +## Before you start + +You need to have some basis knowledge on the Retrieval Augmented Generation as well as a good understanding of your product quality in terms of specification and documentation. + +## Objectives + +Learn how provider can enable their consumers to use the Artificial Intelligence search assistant capabilities to provide accurate and relevant advanced information such as code snippet in various languages. + +## What is an AI search assistant and how does it work? + +First let's define the Retrieval Augmented Generation (RAG) concept. + +RAG is a natural language processing (NLP) technique that combines the strengths of both retrieval and generative-based artificial intelligence (AI) models. + +* **Retrieval**: when you enter a query, the assistant first uses a retriever model to search a vast database of documents and identify the most relevant pieces of information. This step ensures that the assistant has access to up-to-date and specific data related to your query based on what is already available in your Marketplace. + +* **Generation**: a generator model, usually a transformer-based language model, takes the retrieved documents and synthesizes a coherent and contextually relevant response. This combination allows the assistant to produce answers that are both accurate and well-informed. + +The RAG model blends the strengths of both retrieval-based and generation-based methods, enhancing the assistant's ability to provide high-quality information. + +But to provide high quality information, the models needs to contains data. To add those data, another phase is required: + +* **Training**: this phase helps to push data into the various model used by the RAG. Each time a product is published to a Marketplace, an ingestion process happens to chunk the product resources specification and the product documentation. Theses chunks are summarized by an Large Language Model (LLM) and then vectorized by another LLM to be finally stored in a vector/embeddings database. + +## Where are located the models? + +Axway does not provide yet all infrastructure to leverage the RAG models. Each customer must have their own implementation. Like this their data stays under their control. They can also add more relevant data into their models (for instance extra documentation pages, samples...) that could benefits their consumers. + +## How do I integrate my own model? + +TBD +Settings... + +## Activating the AI Search assistant + +For activating this feature, an Axway Platform entitlement is required. For that you have to ask your Axway Account Manager to make the change. + +Once the entitlement is activated, a new Marketplace setting is available to configure the model connectivity and a new button in the product search bar is available to launch the AI search assistant. + +## Using the AI Search assistant + +On the Browse product page, a new icon is available on the search bar to launch the AI Search Assistant. + +1. Login to the Marketplace. +2. Navigate to the PRoducts list page +3. Click the Search Assistant button. +4. it opens the AI Search assistant popup windows. The windows is divide into 2 sections: + 1. the prompt section at the bottom + 2. the answer section at the top + +From the prompt you can ask questions and the AI Search Assistant will run the query against the collected data and answer accordingly. On the answer, there will be some references. Those references are clickable and will redirect you to the appropriate object in the Marketplace on a new browser tab. From 08a5f1f0582f8b1b310b8d7d014cc04818e7410b Mon Sep 17 00:00:00 2001 From: Cyrille Bordier Date: Fri, 31 May 2024 10:36:16 -0700 Subject: [PATCH 2/5] adding limitation --- content/en/docs/manage_marketplace/ai-assisted-search.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/en/docs/manage_marketplace/ai-assisted-search.md b/content/en/docs/manage_marketplace/ai-assisted-search.md index ccaac6e1e..a8971f85c 100644 --- a/content/en/docs/manage_marketplace/ai-assisted-search.md +++ b/content/en/docs/manage_marketplace/ai-assisted-search.md @@ -58,3 +58,7 @@ On the Browse product page, a new icon is available on the search bar to launch 2. the answer section at the top From the prompt you can ask questions and the AI Search Assistant will run the query against the collected data and answer accordingly. On the answer, there will be some references. Those references are clickable and will redirect you to the appropriate object in the Marketplace on a new browser tab. + +## Limitations + +TBD \ No newline at end of file From 9b4f21e8b4b46779d273fb3a0e38121dc04cffcc Mon Sep 17 00:00:00 2001 From: Cyrille Bordier Date: Fri, 31 May 2024 11:09:49 -0700 Subject: [PATCH 3/5] usage improvement --- content/en/docs/manage_marketplace/ai-assisted-search.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/en/docs/manage_marketplace/ai-assisted-search.md b/content/en/docs/manage_marketplace/ai-assisted-search.md index a8971f85c..6addcd7e6 100644 --- a/content/en/docs/manage_marketplace/ai-assisted-search.md +++ b/content/en/docs/manage_marketplace/ai-assisted-search.md @@ -59,6 +59,10 @@ On the Browse product page, a new icon is available on the search bar to launch From the prompt you can ask questions and the AI Search Assistant will run the query against the collected data and answer accordingly. On the answer, there will be some references. Those references are clickable and will redirect you to the appropriate object in the Marketplace on a new browser tab. +Since it is an assistant, the context between questions is kept. This mean you can ask a first question. When asking the second question, it automatically takes into the context the first question. Like this the model combine the first answer and what is found on the second question together to get a more relevant information. + +For instance, you may ask "Is there a product to do X and Y". The model will answer you what has been found and then you can ask "Write an http client in Java using that API". The model will automatically understand that you want Java code using one of the product from the first answer. + ## Limitations TBD \ No newline at end of file From 98a3b0253578e05f4e6a172c174064836d071d0e Mon Sep 17 00:00:00 2001 From: Cyrille Bordier Date: Mon, 3 Jun 2024 07:22:03 -0700 Subject: [PATCH 4/5] TBD --- content/en/docs/manage_marketplace/ai-assisted-search.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/content/en/docs/manage_marketplace/ai-assisted-search.md b/content/en/docs/manage_marketplace/ai-assisted-search.md index 6addcd7e6..58c3476f5 100644 --- a/content/en/docs/manage_marketplace/ai-assisted-search.md +++ b/content/en/docs/manage_marketplace/ai-assisted-search.md @@ -31,14 +31,19 @@ But to provide high quality information, the models needs to contains data. To a * **Training**: this phase helps to push data into the various model used by the RAG. Each time a product is published to a Marketplace, an ingestion process happens to chunk the product resources specification and the product documentation. Theses chunks are summarized by an Large Language Model (LLM) and then vectorized by another LLM to be finally stored in a vector/embeddings database. +**TBD** - schema of the architecture.. + ## Where are located the models? Axway does not provide yet all infrastructure to leverage the RAG models. Each customer must have their own implementation. Like this their data stays under their control. They can also add more relevant data into their models (for instance extra documentation pages, samples...) that could benefits their consumers. ## How do I integrate my own model? -TBD +**TBD** Settings... +Contract description + +### Ingestion process ## Activating the AI Search assistant @@ -65,4 +70,4 @@ For instance, you may ask "Is there a product to do X and Y". The model will ans ## Limitations -TBD \ No newline at end of file +**TBD** \ No newline at end of file From c692014fce4c0e5a7faa46c7a85b403024577b18 Mon Sep 17 00:00:00 2001 From: Cyrille Bordier Date: Mon, 3 Jun 2024 09:17:29 -0700 Subject: [PATCH 5/5] markdown --- content/en/docs/manage_marketplace/ai-assisted-search.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/en/docs/manage_marketplace/ai-assisted-search.md b/content/en/docs/manage_marketplace/ai-assisted-search.md index 58c3476f5..3c3d4d40f 100644 --- a/content/en/docs/manage_marketplace/ai-assisted-search.md +++ b/content/en/docs/manage_marketplace/ai-assisted-search.md @@ -70,4 +70,5 @@ For instance, you may ask "Is there a product to do X and Y". The model will ans ## Limitations -**TBD** \ No newline at end of file +**TBD** +if any \ No newline at end of file