Integrate data from third party API calls into your Lamatic flow.
Drop this widget into your app to give users the ability to get answers via an interactive chat.
Parse data into chunks (to prepare it for vectorization and subsequent semantic retrieval).
Add custom logic to your Lamatic flow with this JavaScript code node.
Add custom logic to your Lamatic flow with this easy-to-use condition builder.
Create Crawl or Scrape Pages instantly
Embed a full text search of a connected vector database within a Lamatic flow.
Monitors specific file types for changes in your repository and sends the modified file content to a webhook endpoint
Fetch files from Google Drive (to support chunk vectorization and RAG)
Fetch rows from a Google Sheet (to support row vectorization and RAG)
Trigger a custom Lamatic flow from another program and return a JSON payload with this self-documenting API node.
Embed a hybrid search (combining vector and text search) of a connected vector database within a Lamatic flow.
Generate image output from an LLM programmatically.
Insert records to a vector database (to support fast semantic retrieval)
Generate structured JSON output from an LLM programmatically.
Enhance observability, monitoring and insights by adding Langfuse to your Lamatic project.
Generate multimodal output from an LLM programmatically.
Fetch rows from a PostgreSQL database (to support row vectorization and RAG)
Build better apps with enhanced instrumentation by adding PostHog to your Lamatic project.
Efficiently generate a more relevant LLM response using Retrieval Augmented Generation (RAG).
Drop this widget into your app to give users the ability to query via text, vector or hybrid search.
Reply to questions posed by users in a Slack channel with the /ask command; or post messages to a Slack channel
Generate text output from an LLM programmatically.
Embed a vector search of a connected vector database within a Lamatic flow.
Apply an embedding model to convert data chunks into numeric vector representations.
Insert, update and retrieve records from this high-performance vector store.
Trigger a custom Lamatic flow from another program and deliver a JSON payload to the webhook endpoint.
A conversational AI chat widget that engages users with interactive discussions about content from a connected vector database. Easily deployable to applications and websites, ideal for user documentation, release notes, and more.
This flow integrates vector search into your website, allowing you to combine results from multiple vector databases and run parallel searches. It then consolidates the results and returns them to users.
This flow adds keyword search (BM25 Search) to your website. It combines results from different vector databases, runs parallel searches, and returns the combined results to users.