Quickstart

uv add lexigram-vector

Install the backend driver for your vector store:

uv add "lexigram-vector[pgvector]"    # PostgreSQL + pgvector
uv add "lexigram-vector[qdrant]"      # Qdrant
uv add "lexigram-vector[pinecone]"    # Pinecone
uv add "lexigram-vector[chroma]"      # ChromaDB
uv add "lexigram-vector[weaviate]"    # Weaviate
uv add "lexigram-vector[all]"         # Every backend

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Minimal Example

import asyncio
from lexigram import Application
from lexigram.vector import VectorModule


async def main() -> None:
    async with Application.boot(
        name="vector-demo",
        modules=[VectorModule.configure()],
    ) as app:
        store = await app.container.resolve(VectorStoreProtocol)
        await store.create_collection(
            CollectionConfig(name="my_docs", dimension=1536)
        )
        print("Collection ready!")


asyncio.run(main())

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What Just Happened

1. VectorModule.configure() created a VectorProvider with default config (in-memory backend).
2. The provider registered VectorStoreProtocol and VectorCollectionProtocol in the container.
3. An in-memory MemoryVectorStore was created and connected during boot().
4. store.create_collection() created a vector collection capable of storing 1536-dimensional embeddings.

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Next Steps

• Guide — mental model, core concepts, common patterns
• Configuration — all config keys and env vars
• How-Tos — copy-pasteable recipes