Quickstart

npm install @veclabs/solvec

pip install solvec --pre

import { SolVec } from '@veclabs/solvec';

const sv = new SolVec({ apiKey: process.env.RECALL_API_KEY });
const collection = sv.collection('agent-memory', { dimensions: 1536 });

from solvec import SolVec

sv = SolVec(api_key=os.environ["RECALL_API_KEY"])
collection = sv.collection("agent-memory", dimensions=1536)

await collection.upsert([{
  id: 'mem_001',
  values: embedding,         // float[] of length 1536
  metadata: { text: 'User prefers dark mode' }
}]);

collection.upsert([{
    "id": "mem_001",
    "values": embedding,     # list of 1536 floats
    "metadata": {"text": "User prefers dark mode"}
}])

const results = await collection.query({
  vector: queryEmbedding,
  topK: 5
});

// results.matches: [{ id, score, metadata }]

results = collection.query(vector=query_embedding, top_k=5)

# results.matches: [{"id": ..., "score": ..., "metadata": ...}]

npm install @veclabs/solvec @langchain/core

pip install solvec --pre langchain-core

import { RecallVectorStore } from '@veclabs/solvec/langchain';
import { OpenAIEmbeddings } from '@langchain/openai';

const store = await RecallVectorStore.fromTexts(
  ["User prefers dark mode", "Meeting at 3pm"],
  [{ source: "chat" }, { source: "calendar" }],
  new OpenAIEmbeddings(),
  { apiKey: process.env.RECALL_API_KEY, collection: "langchain-memory" }
);

const docs = await store.similaritySearch("user preferences", 3);

from solvec.langchain import RecallVectorStore
from langchain_openai import OpenAIEmbeddings

store = RecallVectorStore(
    api_key=os.environ["RECALL_API_KEY"],
    collection="langchain-memory",
    embeddings=OpenAIEmbeddings(),
)

store.add_texts(["User prefers dark mode", "Meeting at 3pm"])
docs = store.similarity_search("user preferences", k=3)