Integration
Automate with LangChain
LangChain is the orchestration layer for complex AI workflows. NVS uses it to build multi-step agents, retrieval-augmented generation pipelines, and tool-calling systems that go beyond what simple prompts can do.
Use Cases
Your internal docs, SOPs, and wikis are indexed into a vector store. Incoming support questions are answered by an agent that retrieves the most relevant context before responding — accurate, grounded answers every time.
Trigger
Question Received
slack.trigger
Team member asks a question in the #ask-ai Slack channel via @bot mention.
Embed Question
langchain.embeddings
Question text converted to a vector embedding using OpenAI text-embedding-3-small.
Retrieve Context
langchain.retriever
Top-5 most relevant document chunks retrieved from the Supabase vector store.
Generate Answer
langchain.chain
LangChain QA chain generates a grounded answer with source citations from retrieved docs.
Output
Reply in Slack
slack.message
Answer posted as a thread reply with source document links for verification.
A LangChain agent is given a research topic, autonomously searches the web, fetches and reads relevant pages, synthesizes the findings, and delivers a structured report to your team.
Trigger
Research Request
slack.trigger
User submits a research request in Slack with a topic and scope description.
Agent: Plan Steps
langchain.agent
LangChain agent with ReAct reasoning plans the search queries needed to answer the brief.
Agent: Search + Read
langchain.tools
Agent uses web search and browse tools to fetch, read, and extract from relevant sources.
Synthesize Report
langchain.chain
Findings synthesized into a structured report with sections, bullet points, and sources.
Output
Deliver Report
google-docs.create
Report created as a Google Doc and link shared in the original Slack thread.
Every time a new document is added to your shared Drive folder, it's automatically chunked, embedded, and upserted into your vector store — keeping your AI knowledge base current without manual re-indexing.
Trigger
File Added to Drive
google-drive.trigger
New file uploaded to the /knowledge-base folder in Google Drive.
Load + Split
langchain.loader
Document loaded and split into 512-token chunks with 50-token overlap for context.
Embed Chunks
langchain.embeddings
Each chunk embedded with OpenAI embeddings and metadata tags added for filtering.
Output
Upsert to Vector Store
supabase.vector
Embeddings upserted into Supabase pgvector — existing chunks replaced, new ones added.
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