Storage backend setup

How to install, configure, and run each Astrocyte storage adapter. All adapters are optional PyPI packages that register via Python entry points — install the one you need, set the config key, and Astrocyte picks it up automatically.

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Which backend do I need?

Store type	Role	Adapters	When to use
Vector store	Semantic search (embeddings)	pgvector, qdrant	Always — required for Tier 1 recall
Graph store	Entity relationships and traversal	neo4j	When you need “who knows whom” or relationship-aware recall
Document store	BM25 full-text / keyword search	elasticsearch	When you need keyword recall alongside semantic

You can combine stores for hybrid recall — e.g. pgvector (semantic) + Neo4j (graph) + Elasticsearch (keyword). Results are fused with reciprocal rank fusion (RRF).

In-memory (development only)

For quick prototyping, use the built-in in-memory store (no install required):

provider_tier: storage
vector_store: in_memory
llm_provider: mock

No persistence — data is lost on restart.

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PostgreSQL + pgvector

Recommended default for production. Stores embeddings in PostgreSQL with the pgvector extension.

Install

pip install astrocyte-pgvector
# or from source:
cd adapters-storage-py/astrocyte-pgvector && pip install -e .

Run PostgreSQL

# Docker (quickest)
docker run -d --name astrocyte-pg \
  -e POSTGRES_USER=astrocyte \
  -e POSTGRES_PASSWORD=astrocyte \
  -e POSTGRES_DB=astrocyte \
  -p 5433:5432 \
  pgvector/pgvector:pg16

Or use the included Docker Compose stack:

cd astrocyte-services-py
cp .env.example .env
docker compose up -d postgres

Configure

provider_tier: storage
vector_store: pgvector
vector_store_config:
  dsn: ${DATABASE_URL}
  embedding_dimensions: 1536        # must match your embedding model
  bootstrap_schema: true            # auto-create tables (dev)

Key	Type	Default	Description
dsn	string	required	PostgreSQL connection URI
table_name	string	astrocyte_vectors	Table name
embedding_dimensions	int	128	Vector width — must match your embedding model output
bootstrap_schema	bool	true	Auto-create extension, table, and indexes on first use

Connection string format:

postgresql://user:password@host:port/database
postgresql://astrocyte:astrocyte@127.0.0.1:5433/astrocyte
postgresql://user:pass@host:5432/db?sslmode=require

Production: run migrations

For production, disable bootstrap_schema and run migrations explicitly:

export DATABASE_URL='postgresql://astrocyte:astrocyte@127.0.0.1:5433/astrocyte'
cd adapters-storage-py/astrocyte-pgvector
./scripts/migrate.sh

vector_store_config:
  dsn: ${DATABASE_URL}
  embedding_dimensions: 1536
  bootstrap_schema: false           # migrations already applied

Migrations are plain SQL in migrations/:

File	What it does
001_extension.sql	Install pgvector extension
002_astrocytes_vectors.sql	Create vectors table
003_indexes.sql	B-tree on bank_id, HNSW on embeddings
004_memory_layer.sql	Add memory layer column

Requires: psql client on PATH, PostgreSQL 15+.

Production checklist

• Set bootstrap_schema: false and run migrate.sh before deploying
• Match embedding_dimensions to your embedding model (OpenAI text-embedding-3-small = 1536)
• Use ?sslmode=require in DSN for remote databases
• Store DSN in env var or secrets manager, not in YAML

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Qdrant

Cloud-native vector database with built-in collection management.

Install

pip install astrocyte-qdrant
# or from source:
cd adapters-storage-py/astrocyte-qdrant && pip install -e .

Run Qdrant

docker run -d --name astrocyte-qdrant \
  -p 6333:6333 \
  qdrant/qdrant:v1.17.0

Configure

provider_tier: storage
vector_store: qdrant
vector_store_config:
  url: http://localhost:6333
  collection_name: astrocyte_mem
  vector_size: 1536                 # must match your embedding model

Key	Type	Default	Description
url	string	required	Qdrant HTTP API URL
collection_name	string	required	Collection name
vector_size	int	required	Embedding dimension
api_key	string	null	API key for authentication
timeout	float	30.0	Request timeout in seconds

Collections are created automatically on first use with cosine distance.

Qdrant Cloud

vector_store: qdrant
vector_store_config:
  url: https://your-cluster.cloud.qdrant.io
  collection_name: astrocyte_mem
  vector_size: 1536
  api_key: ${QDRANT_API_KEY}

Production checklist

• Match vector_size to your embedding model
• Use api_key if Qdrant is network-accessible
• Monitor collection size and memory usage

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Neo4j

Graph database for entity relationships and neighborhood-aware recall.

Install

pip install astrocyte-neo4j
# or from source:
cd adapters-storage-py/astrocyte-neo4j && pip install -e .

Run Neo4j

docker run -d --name astrocyte-neo4j \
  -p 7687:7687 -p 7474:7474 \
  -e NEO4J_AUTH=neo4j/your-password \
  neo4j:5

Web browser: http://localhost:7474

Configure

graph_store: neo4j
graph_store_config:
  uri: bolt://localhost:7687
  user: neo4j
  password: ${NEO4J_PASSWORD}
  database: neo4j

Key	Type	Default	Description
uri	string	required	Bolt URI (bolt://host:port)
user	string	required	Neo4j username
password	string	required	Neo4j password
database	string	neo4j	Database name

Graph model

Astrocyte stores entities and relationships isolated by bank:

• Nodes: AstrocyteEntity with properties entity_id, bank, name, entity_type, aliases
• Relationships: ENTITY_LINK with link_type and metadata

Production checklist

• Use Neo4j 5+ for best compatibility
• Store credentials in env vars or secrets manager
• Monitor transaction throughput and heap usage
• Consider Neo4j Aura (managed) for production

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Elasticsearch

BM25 full-text search for keyword-based recall. Complements vector search for hybrid retrieval.

Install

pip install astrocyte-elasticsearch
# or from source:
cd adapters-storage-py/astrocyte-elasticsearch && pip install -e .

Run Elasticsearch

docker run -d --name astrocyte-es \
  -p 9200:9200 \
  -e discovery.type=single-node \
  -e xpack.security.enabled=false \
  -e ES_JAVA_OPTS="-Xms512m -Xmx512m" \
  docker.elastic.co/elasticsearch/elasticsearch:8.15.3

Configure

document_store: elasticsearch
document_store_config:
  url: http://localhost:9200
  index_prefix: astrocyte_docs

Key	Type	Default	Description
url	string	required	Elasticsearch HTTP URL
index_prefix	string	astrocyte_docs	Index name prefix — one index per bank ({prefix}_{bank_id})

Indexes are created automatically on first use.

Elastic Cloud

document_store: elasticsearch
document_store_config:
  url: https://user:password@your-cluster.es.cloud.elastic.co:9243
  index_prefix: astrocyte_docs

Production checklist

• Use Elasticsearch 8.12+ with security enabled (xpack.security)
• Configure index lifecycle management (ILM) for old indices
• Size heap appropriately (50% of available RAM, max 32 GB)
• Monitor disk usage — especially for high-volume ingestion

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Hybrid recall (multiple backends)

Combine vector, graph, and document stores for richer retrieval. Results are fused with reciprocal rank fusion (RRF).

provider_tier: storage

# Semantic search
vector_store: pgvector
vector_store_config:
  dsn: ${DATABASE_URL}
  embedding_dimensions: 1536
  bootstrap_schema: false

# Entity relationships
graph_store: neo4j
graph_store_config:
  uri: bolt://localhost:7687
  user: neo4j
  password: ${NEO4J_PASSWORD}

# Keyword / full-text search
document_store: elasticsearch
document_store_config:
  url: http://localhost:9200

# LLM for embedding + reflect
llm_provider: openai
llm_provider_config:
  api_key: ${OPENAI_API_KEY}
  model: gpt-4o-mini

Recall automatically queries all configured stores and fuses results. No additional configuration needed — just install the adapter packages and add the config sections.

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Comparison

	pgvector	Qdrant	Neo4j	Elasticsearch
Store type	Vector	Vector	Graph	Document
Search	Semantic (HNSW)	Semantic (HNSW)	Neighborhood traversal	BM25 keyword
Managed options	Any managed Postgres	Qdrant Cloud	Neo4j Aura	Elastic Cloud
Best for	Default choice; existing Postgres	Dedicated vector DB; large scale	Relationship-heavy domains	Keyword recall alongside semantic
Persistence	SQL (full ACID)	On-disk snapshots	On-disk	Lucene segments
Python package	astrocyte-pgvector	astrocyte-qdrant	astrocyte-neo4j	astrocyte-elasticsearch
Config key	vector_store: pgvector	vector_store: qdrant	graph_store: neo4j	document_store: elasticsearch

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Docker quick reference

Start all backends for local development:

# pgvector
docker run -d --name pg -p 5433:5432 \
  -e POSTGRES_USER=astrocyte -e POSTGRES_PASSWORD=astrocyte -e POSTGRES_DB=astrocyte \
  pgvector/pgvector:pg16

# Qdrant
docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:v1.17.0

# Neo4j
docker run -d --name neo4j -p 7687:7687 -p 7474:7474 \
  -e NEO4J_AUTH=neo4j/testpass neo4j:5

# Elasticsearch
docker run -d --name es -p 9200:9200 \
  -e discovery.type=single-node -e xpack.security.enabled=false \
  -e ES_JAVA_OPTS="-Xms512m -Xmx512m" \
  docker.elastic.co/elasticsearch/elasticsearch:8.15.3

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Further reading

• Configuration reference — full astrocyte.yaml schema
• Memory API reference — retain/recall/reflect/forget signatures
• Bank management — bank creation, multi-bank queries, hybrid recall patterns
• Provider SPI — build your own storage adapter
• Storage adapter packages — architecture and entry point conventions
• Storage and data planes — retrieval vs export architecture