Vector (lexigram-vector)

Vector storage infrastructure for the Lexigram Framework with Qdrant, ChromaDB, PGVector, Pinecone, and in-memory backends. Provides embedding clients, vector search, hybrid retrieval, reranking, and Named DI multi-store support.

Install

uv add lexigram lexigram-vector

# With Qdrant support
uv add qdrant-client

# With ChromaDB support
uv add chromadb

# With PGVector support
uv add pgvector  # Requires lexigram-sql for database access

# With Pinecone support
uv add pinecone-client

# With embedding support
uv add openai  # or anthropic, cohere, etc.

Quick Start

from __future__ import annotations

import asyncio

from lexigram.app import Application
from lexigram.contracts.data.vector.protocols import VectorStoreProtocol
from lexigram.di.module import Module, module
from lexigram.vector import VectorModule
from lexigram.vector.config import QdrantConfig, VectorConfig


@module(
    imports=[
        VectorModule.configure(
            VectorConfig(
                backend="qdrant",
                qdrant=QdrantConfig(
                    url="http://localhost:6333",
                ),
            )
        )
    ]
)
class AppModule(Module):
    pass


async def main() -> None:
    async with Application.boot(modules=[AppModule]) as app:
        store = await app.container.resolve(VectorStoreProtocol)

        # Create a collection
        await store.create_collection(
            name="documents",
            dimension=1536,
        )

        # Upsert vectors
        await store.upsert(
            collection_name="documents",
            ids=["doc1", "doc2"],
            vectors=[[0.1] * 1536, [0.2] * 1536],
            metadata=[
                {"title": "Document 1", "category": "tech"},
                {"title": "Document 2", "category": "science"},
            ],
        )

        # Search
        results = await store.search(
            collection_name="documents",
            query_vector=[0.15] * 1536,
            limit=5,
        )

        for result in results:
            print(f"{result.id}: {result.metadata['title']} (score: {result.score})")


if __name__ == "__main__":
    asyncio.run(main())

What It Provides

lexigram-vector ships with:

• Multi-backend vector storage — Qdrant, ChromaDB, PGVector, Pinecone, and in-memory backends
• Embedding client — OpenAI-compatible async client for generating embeddings (OpenAICompatibleEmbeddingClient)
• Embedding cache — In-memory and persistent caching to reduce embedding API calls (EmbeddingCache, InMemoryEmbeddingCache)
• Vector search — Similarity search with metadata filtering and distance metrics
• Hybrid retrieval — BM25 + vector search with reciprocal rank fusion (HybridRetriever, BM25Retriever, RRFReranker)
• Reranking — Cross-encoder reranking, diversity reranking, and similarity reranking for improved relevance (CrossEncoderReranker, DiversityReranker, RerankerPipeline)
• Metadata filtering — Structured filtering on metadata fields with backend-specific filter compilers
• Named DI multi-store — Multiple vector stores registered as Annotated[VectorStoreProtocol, Named("rag")]
• Collection management — Create, delete, list collections with automatic schema inference
• Batch operations — Efficient batch upsert, delete, and search with configurable batch sizes
• Distance metrics — Cosine, Euclidean, and dot product similarity metrics
• Index types — HNSW, IVFFlat, and backend-specific index configuration

Architecture note: This package provides infrastructure and data-layer functionality for vector storage and retrieval. While it is commonly used by AI and RAG features (lexigram-ai-rag), it is a general-purpose vector database abstraction suitable for any use case requiring semantic search, similarity matching, or high-dimensional data storage.

Configuration

Zero-config usage: Call VectorModule.configure() with no arguments to start with all built-in defaults — no config file or environment variables needed. See the Config reference below for all default values.

from lexigram.vector import VectorModule

app.add_module(VectorModule.configure())  # all defaults

Option 1 — YAML file (use when config lives in a single explicit file)

Declare config in a YAML file loaded at a fixed, explicit path. LEX_* environment variables override YAML values at startup. Use this for local development, simple self-hosted setups, or when you control exactly which file is loaded. For multi-environment deployments (staging, production) prefer Option 2, which automatically selects the right profile file.

config_section = "vector" is already set on this class — section= can be omitted in all calls. Pass an explicit section= only to override the default (e.g. when this config is nested under a non-standard key).

# application.yaml — copy example.yaml for a fully-annotated starting point
vector:
  backend: "qdrant"             # memory, pgvector, pinecone, qdrant, chroma
  default_dimension: 1536       # 1536 = OpenAI text-embedding-3-small
  upsert_batch_size: 100
  qdrant:
    url: "http://localhost:6333"
    api_key: null               # LEX_VECTOR__QDRANT__API_KEY

Then load and wire it in your composition root:

from lexigram.vector.config import VectorConfig
from lexigram.vector import VectorModule

config = VectorConfig.from_yaml("application.yaml")
app.add_module(VectorModule.configure(config))

Environment variables override YAML values and use the LEX_VECTOR__ prefix:

LEX_VECTOR__BACKEND=qdrant

---

Option 2 — Profiles + Environment Variables (recommended for production, staging, Docker, CI/CD)

Loads a base application.yaml, then overlays an environment-specific file (application.production.yaml, application.staging.yaml, etc.) based on the LEX_PROFILE environment variable. LEX_* env vars are applied last as the final override layer. Use this in production, staging, Docker, Kubernetes, and CI/CD pipelines — set LEX_PROFILE=production and the right profile file loads automatically.

section is optional: specify it (e.g. section="vector") when this package’s config is nested inside a shared application.yaml; omit it when the file is dedicated to this package alone.

# Set LEX_VECTOR__* env vars before starting the process
export LEX_VECTOR__ENABLED=true

from lexigram.vector.config import VectorConfig
from lexigram.vector import VectorModule

config = VectorConfig.from_env_profile()
app.add_module(VectorModule.configure(config))

Loading order: application.yaml (base) → application.{profile}.yaml (overlay, if LEX_PROFILE is set) → LEX_* environment variables (final override). Missing files are silently skipped so this is safe to call in all environments.

---

Option 3 — Python (use when config is dynamic or computed at boot)

Build config in code at boot time. Use this when settings are derived at runtime — e.g. secrets fetched from a vault, per-tenant configurations, or when you need multiple module instances with different settings.

from lexigram.vector import VectorModule
from lexigram.vector.config import QdrantConfig, VectorConfig

app.add_module(VectorModule.configure(
    VectorConfig(
        backend="qdrant",
        qdrant=QdrantConfig(
            url="http://localhost:6333",
        ),
    )
))

---

Config reference

VectorConfig

Top-level configuration loaded from application.yaml’s vector: key or from LEX_VECTOR__* environment variables.

Field	Default	Env var	Description
enabled	True	LEX_VECTOR__ENABLED	Enable the vector store subsystem
backend	"memory"	LEX_VECTOR__BACKEND	Vector store backend ("memory", "qdrant", "chroma", "pgvector", "pinecone")
default_distance_metric	DistanceMetric.COSINE	LEX_VECTOR__DEFAULT_DISTANCE_METRIC	Default distance metric for new collections
default_index_type	IndexType.HNSW	LEX_VECTOR__DEFAULT_INDEX_TYPE	Default index type for new collections
default_dimension	1536	LEX_VECTOR__DEFAULT_DIMENSION	Default vector dimension (matches OpenAI text-embedding-ada-002)
upsert_batch_size	100	LEX_VECTOR__UPSERT_BATCH_SIZE	Number of vectors per upsert batch
max_retries	3	LEX_VECTOR__MAX_RETRIES	Maximum number of retries for operations
retry_delay	1.0	LEX_VECTOR__RETRY_DELAY	Delay between retries in seconds
pgvector	PgVectorConfig()	—	PGVector-specific settings
pinecone	PineconeConfig()	—	Pinecone-specific settings
qdrant	QdrantConfig()	—	Qdrant-specific settings
memory	MemoryConfig()	—	In-memory-specific settings
backends	[]	—	List of NamedVectorConfig entries for multi-store support

When backends is non-empty, each entry is registered under Annotated[VectorStoreProtocol, Named(entry.name)]. The first entry (or the one with primary=True) also receives the unnamed VectorStoreProtocol binding for backward compatibility.

Backend-Specific Configuration

QdrantConfig

Field	Default	Env var	Description
url	"http://localhost:6333"	LEX_VECTOR__QDRANT__URL	Qdrant server URL
api_key	None	LEX_VECTOR__QDRANT__API_KEY	Qdrant API key (optional)
grpc_port	6334	LEX_VECTOR__QDRANT__GRPC_PORT	gRPC port for Qdrant
prefer_grpc	True	LEX_VECTOR__QDRANT__PREFER_GRPC	Whether to prefer gRPC over HTTP
timeout	30.0	LEX_VECTOR__QDRANT__TIMEOUT	Request timeout in seconds

PgVectorConfig

Field	Default	Env var	Description
database	"primary"	LEX_VECTOR__PGVECTOR__DATABASE	Name of the database backend from db.backends to use
schema	"public"	LEX_VECTOR__PGVECTOR__SCHEMA	Database schema for vector tables
default_lists	100	LEX_VECTOR__PGVECTOR__DEFAULT_LISTS	Default number of lists for IVFFlat index
default_probes	10	LEX_VECTOR__PGVECTOR__DEFAULT_PROBES	Default number of probes for IVFFlat index
default_ef_search	40	LEX_VECTOR__PGVECTOR__DEFAULT_EF_SEARCH	Default ef_search for HNSW index
table_prefix	"vec_"	LEX_VECTOR__PGVECTOR__TABLE_PREFIX	Prefix for vector storage tables
create_extension	True	LEX_VECTOR__PGVECTOR__CREATE_EXTENSION	Whether to create pgvector extension if missing

Note: PGVector requires lexigram-sql and a configured DatabaseProviderProtocol. The database field refers to a named database backend from db.backends.

PineconeConfig

Field	Default	Env var	Description
api_key	""	LEX_VECTOR__PINECONE__API_KEY	Pinecone API key (required)
environment	""	LEX_VECTOR__PINECONE__ENVIRONMENT	Pinecone environment (e.g., "us-west1-gcp")
index_name	""	LEX_VECTOR__PINECONE__INDEX_NAME	Name of the Pinecone index
namespace	""	LEX_VECTOR__PINECONE__NAMESPACE	Default namespace for the index
timeout	30.0	LEX_VECTOR__PINECONE__TIMEOUT	Request timeout in seconds
pool_threads	4	LEX_VECTOR__PINECONE__POOL_THREADS	Number of threads for the connection pool

MemoryConfig

Field	Default	Env var	Description
max_collections	100	LEX_VECTOR__MEMORY__MAX_COLLECTIONS	Maximum number of collections in memory
max_vectors_per_collection	100,000	LEX_VECTOR__MEMORY__MAX_VECTORS_PER_COLLECTION	Maximum number of vectors per collection

NamedVectorConfig

Configuration for a single named vector store backend (used in multi-store setups):

Field	Description
name	Unique backend identifier (used as the Named() DI key)
primary	Whether this backend also receives the unnamed VectorStoreProtocol binding
backend	Vector store driver for this named backend
pgvector	PgVectorConfig for this backend
pinecone	PineconeConfig for this backend
qdrant	QdrantConfig for this backend
memory	MemoryConfig for this backend

Example multi-store setup:

from lexigram.vector.config import (
    NamedVectorConfig,
    PgVectorConfig,
    QdrantConfig,
    VectorConfig,
)

VectorModule.configure(
    VectorConfig(
        backends=[
            NamedVectorConfig(
                name="primary",
                primary=True,
                backend="qdrant",
                qdrant=QdrantConfig(
                    url="http://qdrant-primary:6333",
                ),
            ),
            NamedVectorConfig(
                name="rag",
                backend="pgvector",
                pgvector=PgVectorConfig(
                    database="rag",
                    schema="vectors",
                ),
            ),
        ]
    )
)

Inject named stores:

from typing import Annotated
from lexigram.contracts.data.vector.protocols import VectorStoreProtocol
from lexigram.di.named import Named


class MyService:
    def __init__(
        self,
        store: VectorStoreProtocol,  # primary
        rag: Annotated[VectorStoreProtocol, Named("rag")],
    ) -> None:
        self.store = store
        self.rag = rag

Module Factory Methods

VectorModule.configure(config=None, enable_reranking=False)

Create a VectorModule with explicit configuration.

Args:

• config: VectorConfig instance, dict of config values, or None to use environment variable defaults
• enable_reranking: Enable cross-encoder reranking of retrieval results (default: False)

Returns: DynamicModule that registers VectorStoreProtocol and VectorCollectionProtocol

Exports: VectorStoreProtocol, VectorCollectionProtocol

Example:

from lexigram.vector import VectorModule
from lexigram.vector.config import QdrantConfig, VectorConfig

@module(
    imports=[
        VectorModule.configure(
            VectorConfig(
                backend="qdrant",
                qdrant=QdrantConfig(url="http://localhost:6333"),
            ),
            enable_reranking=True,
        )
    ]
)
class AppModule(Module):
    pass

VectorModule.stub(config=None)

Create a VectorModule suitable for unit and integration testing. Uses an in-memory backend with no external service dependencies.

Args:

• config: Optional VectorConfig override (uses safe in-memory defaults when None)

Returns: DynamicModule that registers VectorStoreProtocol and VectorCollectionProtocol

Example:

from lexigram.vector import VectorModule

async def test_vector_search():
    async with Application.boot(
        modules=[VectorModule.stub()]
    ) as app:
        store = await app.container.resolve(VectorStoreProtocol)
        # Test with in-memory backend

Vector Store Operations

Create Collection

from lexigram.contracts.data.vector.enums import DistanceMetric, IndexType

await store.create_collection(
    name="documents",
    dimension=1536,
    distance_metric=DistanceMetric.COSINE,
    index_type=IndexType.HNSW,
)

Upsert Vectors

await store.upsert(
    collection_name="documents",
    ids=["doc1", "doc2", "doc3"],
    vectors=[
        [0.1] * 1536,
        [0.2] * 1536,
        [0.3] * 1536,
    ],
    metadata=[
        {"title": "Doc 1", "category": "tech"},
        {"title": "Doc 2", "category": "science"},
        {"title": "Doc 3", "category": "tech"},
    ],
)

Search with Filters

results = await store.search(
    collection_name="documents",
    query_vector=[0.15] * 1536,
    limit=10,
    filter={"category": "tech"},  # Metadata filter
)

for result in results:
    print(f"{result.id}: score={result.score}, metadata={result.metadata}")

Delete Vectors

await store.delete(
    collection_name="documents",
    ids=["doc1", "doc2"],
)

Embedding Client

Generate embeddings with an OpenAI-compatible client:

from lexigram.vector.embedding.client import OpenAICompatibleEmbeddingClient
from lexigram.vector.embedding.config import EmbeddingClientConfig

client = OpenAICompatibleEmbeddingClient(
    config=EmbeddingClientConfig(
        api_key="your-api-key",
        model="text-embedding-ada-002",
    )
)

# Single text
embedding = await client.embed("Hello, world!")

# Batch
embeddings = await client.embed_batch(["Text 1", "Text 2", "Text 3"])

Embedding Cache

Cache embeddings to reduce API calls:

from lexigram.vector.embedding.cache import InMemoryEmbeddingCache

cache = InMemoryEmbeddingCache(max_size=10000)

# Cache hit on second call
embedding1 = await client.embed("Hello, world!")
await cache.set("Hello, world!", embedding1)

embedding2 = await cache.get("Hello, world!")  # From cache

Hybrid Retrieval

Combine BM25 and vector search with reciprocal rank fusion:

from lexigram.vector.search.hybrid import HybridRetriever, HybridSearchConfig

retriever = HybridRetriever(
    vector_store=store,
    config=HybridSearchConfig(
        collection_name="documents",
        alpha=0.5,  # Weight between BM25 (0.0) and vector (1.0)
        k=60,       # RRF parameter
    ),
)

results = await retriever.search(
    query="machine learning",
    query_vector=[0.1] * 1536,
    limit=10,
)

Reranking

Rerank search results for improved relevance:

from lexigram.vector.search.reranking import (
    CrossEncoderReranker,
    RerankerPipeline,
    RerankingConfig,
)

reranker = CrossEncoderReranker(
    config=RerankingConfig(
        model_name="cross-encoder/ms-marco-MiniLM-L-6-v2",
    )
)

# Rerank results
reranked = await reranker.rerank(
    query="machine learning",
    results=search_results,
    limit=5,
)

Compose multiple rerankers in a pipeline:

from lexigram.vector.search.reranking import (
    CrossEncoderReranker,
    DiversityReranker,
    RerankerPipeline,
)

pipeline = RerankerPipeline(
    rerankers=[
        CrossEncoderReranker(config=cross_encoder_config),
        DiversityReranker(lambda_param=0.5),
    ]
)

reranked = await pipeline.rerank(query="machine learning", results=results)

Adapters

Use adapters to bridge vector stores with other abstractions:

VectorStoreAdapter

from lexigram.vector.adapters.vector_store import VectorStoreAdapter

adapter = VectorStoreAdapter(store=store)

# Higher-level operations
await adapter.index_documents(
    collection_name="documents",
    documents=[
        {"id": "doc1", "text": "Document 1", "metadata": {...}},
        {"id": "doc2", "text": "Document 2", "metadata": {...}},
    ],
    embedding_fn=client.embed_batch,
)

DocumentVectorStoreAdapter

from lexigram.vector.adapters.document_store import DocumentVectorStoreAdapter

adapter = DocumentVectorStoreAdapter(
    vector_store=store,
    document_store=nosql_store,
)

# Store documents in NoSQL and vectors in vector store
await adapter.index_with_metadata(
    collection_name="documents",
    documents=[...],
    embedding_fn=client.embed_batch,
)

Integration with AI and RAG

While lexigram-vector is a general-purpose vector storage layer, it integrates seamlessly with AI and RAG features:

• lexigram-ai-rag — Uses VectorStoreProtocol for retrieval-augmented generation
• lexigram-ai — Uses embedding clients and vector stores for semantic search and memory
• lexigram-cache — Can use SemanticCacheProtocol (backed by vector stores) for semantic caching

The Named DI system allows you to use different vector stores for different purposes (e.g., primary for semantic search, rag for retrieval, cache for semantic caching).

Key Source Files

• src/lexigram/vector/module.py — VectorModule.configure(), .stub()
• src/lexigram/vector/config.py — VectorConfig, NamedVectorConfig, backend configs
• src/lexigram/vector/di/provider.py — VectorProvider boot and registration
• src/lexigram/vector/di/factories.py — Factory functions for creating vector stores
• src/lexigram/vector/backends/qdrant/ — Qdrant backend implementation
• src/lexigram/vector/backends/pgvector/ — PGVector backend implementation
• src/lexigram/vector/backends/pinecone/ — Pinecone backend implementation
• src/lexigram/vector/backends/chroma.py — ChromaDB backend implementation
• src/lexigram/vector/backends/memory.py — In-memory backend implementation
• src/lexigram/vector/embedding/client.py — OpenAICompatibleEmbeddingClient
• src/lexigram/vector/embedding/cache.py — Embedding cache implementations
• src/lexigram/vector/search/hybrid.py — Hybrid retrieval and BM25
• src/lexigram/vector/search/reranking.py — Reranking strategies
• src/lexigram/vector/adapters/vector_store.py — VectorStoreAdapter
• src/lexigram/vector/adapters/document_store.py — DocumentVectorStoreAdapter