💻AI Search using UI
Last updated
Last updated
Several new fields have been incorporated into policy formulation to enhance AI search capabilities. These fields include:
Sectoral scope
Project Scale
Conditions for applicability
URL on policy details page
Typical projects, Description
Important parameters (at validation / monitored)
Applied Technology by Type
Mitigation Activity Type
Sub Type
The .env file contains the following parameters:
OPENAI_API_KEY
OpenAI API Key
GPT_VERSION
GPT version; by default, it is set to 'gpt-3.5-turbo'
VECTOR_STORAGE_PATH
The path where vector will be stored
DOCS_STORAGE_PATH
The path where generated methodology files will be stored.
These parameters are essential for configuring the AI Search tool.
Vector construction is a pivotal process that involves compiling policy data and extracting descriptions from policy schemas. This process ensures the AI Search tool accurately interprets and utilizes policy-related information.
Every time a user publishes a policy, the vector is rebuilt through the following step-by-step process:
Retrieving the required data from the newly added fields in the "create policy" modal window for each published policy from the database
Retrieving the descriptions from the policy schemas and adding them to the resultant policy files. Descriptions containing fewer than 5 words are avoided to exclude unnecessary data for the language model.
Creating separate files based on the fetched data, with each file containing the information describing one policy. Additionally, a file named metadata.txt is created, which contains shared data about all policies.
Generating a new vector to replace the previous one.
Once the vector is ready, standard registry users can utilize the AI search feature to find the most suitable methodology:
Every response contains text and may include tiles with methodology data if the language model identifies relevant methodologies to suggest. Each tile comprises the policy name, a short description, and two links: as shown below:
