TLDR Language models can be used to extract entities and relationships from unstructured data to generate a Knowledge Graph graph database. The conversation focused on implementing functions to process project briefs and create database entries, as well as creating cipher statements to establish relationships within a Neo4j database. The video also covered cost tracking for using GPT models on Azure and highlighted significant price decreases in the OpenAI API service.
Setting Up Environment and Obtaining Credentials
To begin working with the language model and graph database, it's crucial to set up the environment and obtain the necessary credentials. Detailed steps were given for setting up the environment, including data types, Azure for connecting to the OpenAI API, and Neo4j for connecting to the database. This involved obtaining credentials and setting up the required parameters for the OpenAI API and Neo4j, ensuring a smooth workflow for the subsequent tasks.
Extracting Entities and Relationships using Language Model
The 'extract entities relationships' function is designed to analyze a folder of files and prompt templates to extract entities and relationships using a language model. By gathering all files in the given folder and utilizing specific prompt templates for different file types, the language model can extract relevant information. These prompts guide the language model in extracting entities and relationships, providing context and instructions for efficient data extraction.
Generating Cipher Statements for Database Entries
After encountering rate limits and discussing potential solutions, the conversation focused on a complex function that would take the extracted JSON and generate Cipher statements for creating entities and relationships in a Neo4j database. The function involved looping through the JSON objects, cleaning IDs, defining properties dynamically, and creating Cipher statements for entities and relationships, ensuring the accurate representation of data within the database.
Establishing Relationships in Neo4j Database
The conversation details the creation of cipher statements to establish relationships between nodes within a database, particularly in Neo4j. It also includes a discussion about running the defined functions and examining the outcomes in the Neo4j workspace, ensuring the effective establishment and visualization of relationships within the database.
Cost Tracking and Pricing Comparison for GPT Models
The conversation covers the cost tracking for using GPT models for data processing on Azure, highlighting the processed inference tokens and the associated pricing. Additionally, it compares the pricing between the OpenAI API and Azure OpenAI, noting significant price decreases for using OpenAI services. The current price mentioned for this use case is 25,000 tokens for less than 10 cents, making it an affordable option for data processing.
Upcoming Demo on Building Chat Interface to Graph Database
The speaker expressed plans to cover the second part of the demo, which involves building the chat interface to the graph database, in a separate video. Although not discussed in the current video, this upcoming installment is anticipated to provide valuable insights into integrating the graph database with a chat interface for enhanced functionality and user interaction.
How can a language model be used to identify entities and relationships in unstructured data to generate a Knowledge Graph graph database?
A language model can be used to identify entities and relationships in unstructured data by analyzing a folder of files and utilizing prompt templates to extract entities and relationships using a language model API.
What were the potential solutions discussed for encountering rate limits while running a function on project brief files?
The potential solutions discussed for encountering rate limits included requesting a limit increase or adding a delay between requests.
What did the conversation detail about the creation of cipher statements to establish relationships between nodes within a database, particularly in Neo4j?
The conversation detailed the creation of cipher statements to establish relationships between nodes within a database, particularly in Neo4j, involving looping through JSON objects, cleaning IDs, defining properties dynamically, and handling special characters.
What were the pricing details mentioned for using GPT models for data processing on Azure during the conversation?
The conversation covered the cost tracking for using GPT models for data processing on Azure, highlighting the processed inference tokens and the associated pricing.
What were the announced changes in pricing during the OpenAI Dev Day about 2 weeks ago?
During the OpenAI Dev Day about 2 weeks ago, significant price decreases were announced for the service and models, although it had not yet reflected in the pricing in Azure OpenAI. The prices for using the OpenAI API or Azure OpenAI have been well aligned, with Azure even being a bit cheaper.
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