Anvia Docs

Use this path when an agent needs searchable knowledge that should not be placed directly in static context.

Goal

By the end, you should know how to:

choose an embedding model
embed documents
store vectors
filter results
attach retrieved context to an agent run

Path

Read Embeddings to choose an embedding model.
Read Embed Documents to convert text into vectors.
Read Vector Stores to store and search embeddings.
Read RAG Context to attach retrieved documents to an agent.
Read Metadata Filters when retrieval should respect tenant, user, or document filters.
Read LSH when local search needs candidate narrowing.

Static Context vs Retrieval

Use static context when	Use retrieval when
The text is short	The knowledge base is large
The same fact applies to every prompt	The relevant facts depend on the prompt
The content changes rarely	The content is searched, filtered, or refreshed

Retrieval Flow

Think about retrieval in two phases:

Phase	What happens
Preprocess	Load documents, normalize or chunk text, create embeddings, and build an index.
Retrieve	Search the index for the current prompt and pass the matches into the agent run.

1. Preprocess Documents

Run preprocessing before the user prompt. In a real app, this usually belongs in a build step, startup task, admin action, or background ingestion job.

import { InMemoryVectorStore, embedDocuments } from "@anvia/core";
import { OpenAIClient } from "@anvia/openai";

const client = new OpenAIClient({ apiKey });
const embeddings = client.embeddingModel("text-embedding-3-small");

const documents = [
  {
    id: "password-reset",
    title: "Password reset policy",
    body: "Password reset links expire after 30 minutes.",
  },
  {
    id: "priority-support",
    title: "Priority support",
    body: "Enterprise customers receive priority support.",
  },
];

const preprocessed = documents.map((doc) => ({
  ...doc,
  title: doc.title.trim(),
  body: doc.body.replace(/\s+/g, " ").trim(),
}));

const embedded = await embedDocuments(embeddings, preprocessed, {
  id: (doc) => doc.id,
  content: (doc) => `${doc.title}\n${doc.body}`,
  metadata: (doc) => ({ title: doc.title }),
});

export const supportDocs = InMemoryVectorStore.fromDocuments(embedded);
export const supportDocsIndex = supportDocs.index(embeddings);

For production, store the embedded documents in a durable vector store instead of rebuilding the index on every request.

2. Retrieve During a Prompt

Use the preprocessed index at runtime. The agent receives only the matches for the current prompt.

import { AgentBuilder } from "@anvia/core";
import { OpenAIClient } from "@anvia/openai";
import { supportDocsIndex } from "./support-docs";

const model = new OpenAIClient({ apiKey }).completionModel("gpt-5");

const agent = new AgentBuilder("support", model)
  .instructions("Use retrieved context when answering.")
  .dynamicContext(supportDocsIndex, {
    topK: 2,
    format: (result) => ({
      id: result.id,
      text: `${result.document.title}\n${result.document.body}`,
    }),
  })
  .build();

const response = await agent.prompt("How long does a reset link last?").send();

console.log(response.output);

Search Tool Shape

Use the same preprocessed index as a tool when the model should decide when to search.

const searchDocs = supportDocsIndex.asTool({
  name: "search_docs",
  description: "Search support documentation.",
  topK: 3,
});

const agent = new AgentBuilder("support", model)
  .instructions("Search docs before answering policy questions.")
  .tool(searchDocs)
  .defaultMaxTurns(3)
  .build();

Add Next

Need	Read
Local vector search	LSH
Provider embeddings	Clients and Models
Tracing retrieval workflows	Add Observability

Add Retrieval