feat: ai features docs (#88)

## Summary by Sourcery

Documentation:
- Add a comprehensive guide on utilizing AI features in SettleMint,
focusing on OpenAI nodes and pgvector in Hasura for building AI-powered
workflows.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Introduced a comprehensive guide for utilizing AI features within the
SettleMint platform, focusing on OpenAI integration and similarity
searches.

- **Documentation**
- Added `ai-features.md` detailing workflows for data vectorization and
similarity searches using OpenAI and Hasura, including setup
instructions and advanced use cases.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Author: robbeverhelst

docs/developer-guides/ai-features.md

@@ -0,0 +1,167 @@
+---
+title: Utilizing AI Features in SettleMint - OpenAI Nodes and pgvector in Hasura
+description: A Guide to Building an AI-Powered Workflow with OpenAI Nodes and Vector Storage in Hasura
+sidebar_position: 2
+keywords: [integration studio, OpenAI, Hasura, pgvector, AI, SettleMint]
+---
+
+This guide will demonstrate how to use the **SettleMint Integration Studio** to create a flow that incorporates OpenAI nodes for vectorization and utilizes the `pgvector` plugin in Hasura for similarity searches. If you are new to SettleMint, check out the [Getting Started Guide](../about-settlemint/0_intro.mdx).
+
+In this guide, you will learn to create workflows that:
+- Use **OpenAI nodes** to vectorize data.
+- Store vectorized data in **Hasura** using `pgvector`.
+- Conduct similarity searches to find relevant matches for new queries.
+
+### Prerequisites
+- A SettleMint Platform account with **Integration Studio** and **Hasura** deployed
+- Access to the Integration Studio and Hasura consoles in your SettleMint environment
+- An OpenAI API key for using the OpenAI nodes
+- A data source to vectorize (e.g., Graph Node, Attestation Indexer, or external API endpoint)
+
+### Example Flow Available
+The Integration Studio includes a pre-built AI example flow that demonstrates these concepts. The flow uses the SettleMint Platform's attestation indexer as a data source, showing how to:
+- Fetch attestation data via HTTP endpoint
+- Process and vectorize the attestation content
+- Store vectors in Hasura
+- Perform similarity searches
+
+You can use this flow as a reference while building your own implementation. Each step described in this guide can be found in the example flow.
+
+---
+
+## Part 1: Creating a Workflow to Fetch, Vectorize, and Store Data
+
+### Step 1: Set Up Vector Storage in Hasura
+
+1. Access your SettleMint's Hasura instance through the admin console.
+
+2. Create a new table called `document_embeddings` with the following columns:
+   - `id` (type: UUID, primary key)
+   - `embedding` (type: vector(1536)) - For storing OpenAI embeddings
+
+### Step 2: Set Up the Integration Studio Flow
+
+1. **Open Integration Studio** in SettleMint and click on **Create Flow** to start a new workflow.
+
+### Step 3: Fetch Data from an External API
+
+1. **Add an HTTP Request Node** to retrieve data from an external API, such as a document or product listing service.
+2. Configure the **API endpoint** and any necessary authentication settings.
+3. **Add a JSON Node** to parse the response data, focusing on fields like `id` and `content` for further processing.
+
+### Step 4: Vectorize Data with OpenAI Node
+
+1. **Insert an OpenAI Node** in the workflow:
+   - Use this node to generate vector embeddings for the text data using OpenAI's Embedding API.
+   - Configure the OpenAI node to use the appropriate model and input data, such as `text-embedding-ada-002`.
+
+![Create an OpenAI node](../../static/img/developer-guides/openai-node.png)
+
+### Step 5: Store Vectors in Hasura with pgvector
+
+1. **Add a GraphQL Node** to save the vector embeddings and data `id` in Hasura.
+2. Set up a **GraphQL Mutation** to store the vectors and associated IDs in a table enabled with `pgvector`.
+
+Example Mutation:
+```graphql
+mutation insertVector($id: uuid!, $vector: [Float!]!) {
+  insert_vectors(objects: {id: $id, vector: $vector}) {
+    affected_rows
+  }
+}
+```
+
+3. Ensure correct data mapping from the fetched data and vectorized output.
+
+### Step 6: Deploy and Test the Workflow
+
+1. **Deploy the Flow** within Integration Studio and **run it** to confirm that data is fetched, vectorized, and stored in Hasura.
+2. **Verify Hasura Data** by checking the table to ensure vectorized entries and corresponding IDs are stored correctly.
+
+---
+
+## Part 2: Setting Up a Similarity Search Endpoint
+
+### Step 1: Create a POST Endpoint
+
+1. **Add an HTTP POST Node** to accept a JSON payload with a `query` string to be vectorized and compared to stored data.
+
+Payload Example:
+```json
+{
+  "query": "input string for similarity search"
+}
+```
+
+2. **Parse the Request** by adding a JSON node to extract the `query` field from the incoming POST request.
+
+### Step 2: Vectorize the Input Query
+
+1. **Add an OpenAI Node** to convert the incoming `query` string into a vector representation.
+
+Example Configuration:
+```text
+Model: text-embedding-ada-002
+Input: {{msg.payload.query}}
+```
+
+### Step 3: Perform a Similarity Search with Hasura
+
+1. **Add a GraphQL Node** to perform a vector similarity search within Hasura using the `pgvector` plugin.
+2. Use a **GraphQL Query** to order results by similarity, returning the top 5 most similar records.
+
+Example Query:
+```graphql
+query searchVectors($vector: [Float!]!) {
+  vectors(order_by: {vector: {_vector_distance: $vector}}, limit: 5) {
+    id
+    vector
+  }
+}
+```
+
+3. Map the vector from the OpenAI node output as the `vector` input for the Hasura query.
+
+### Step 4: Format and Return the Results
+
+1. **Add a Function Node** to format the response, listing the top 5 matches in a structured JSON format.
+
+### Step 5: Test the Flow
+
+1. **Deploy the Flow** and send a POST request to confirm the similarity search functionality.
+2. **Verify Response** to ensure that the flow accurately returns the top 5 matches from the vectorized data in Hasura.
+
+---
+
+## Next Steps
+
+Now that you have built an AI-powered workflow, here are some blockchain-specific applications you can explore:
+
+### Vectorize On-Chain Data
+- Index and vectorize smart contract events for similarity-based event monitoring
+- Create embeddings from transaction data to detect patterns or anomalies
+- Vectorize NFT metadata for content-based recommendations
+- Build semantic search for on-chain attestations
+
+### Advanced Use Cases
+- Combine transaction data with natural language descriptions for enhanced search
+- Create AI-powered analytics dashboards using vectorized blockchain metrics
+- Implement fraud detection by vectorizing transaction patterns
+- Build a semantic search engine for smart contract code and documentation
+
+### Integration Ideas
+- Connect to multiple blockchain indexers to vectorize data across networks
+- Combine off-chain and on-chain data vectors for comprehensive analysis
+- Set up automated alerts based on similarity to known patterns
+- Create a knowledge base from vectorized blockchain documentation
+
+For further resources, check out:
+
+- [SettleMint Integration Studio Documentation](https://console.settlemint.com/documentation/docs/using-platform/integration-studio/)
+- [Node-RED Documentation](https://nodered.org/docs/)
+- [OpenAI API Documentation](https://beta.openai.com/docs/)
+- [Hasura pgvector Documentation](https://hasura.io/docs/3.0/connectors/postgresql/native-operations/vector-search/)
+
+---
+
+This guide should enable you to build AI-powered workflows with SettleMint's new OpenAI nodes and `pgvector` support in Hasura for efficient similarity searches.