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Adding weaviate, faiss, pgvector, chroma, redis, elasticsearch, milvus vector store to openmemory (#3366)

Browse Source 
Co-authored-by: Vikram Iyer <vikramiyer@mac.local.meter>
This commit is contained in:
VikramIyer125
2025-08-26 13:17:39 -07:00
committed by GitHub
parent 8c8368781d
commit 6237a6acb9
13 changed files with 562 additions and 21 deletions
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openmemory/api/app/routers/memories.py
+11 -1
View File
@@ -260,6 +260,8 @@ async def create_memory(
# Process Qdrant response
if isinstance(qdrant_response, dict) and 'results' in qdrant_response:
created_memories = []
for result in qdrant_response['results']:
if result['event'] == 'ADD':
# Get the Qdrant-generated ID
@@ -294,9 +296,17 @@ async def create_memory(
)
db.add(history)
created_memories.append(memory)
# Commit all changes at once
if created_memories:
db.commit()
for memory in created_memories:
db.refresh(memory)
return memory
# Return the first memory (for API compatibility)
# but all memories are now saved to the database
return created_memories[0]
except Exception as qdrant_error:
logging.warning(f"Qdrant operation failed: {qdrant_error}.")
# Return a json response with the error
openmemory/api/app/utils/memory.py
+105 -5
View File
@@ -135,14 +135,111 @@ def reset_memory_client():
def get_default_memory_config():
"""Get default memory client configuration with sensible defaults."""
return {
"vector_store": {
"provider": "qdrant",
"config": {
# Detect vector store based on environment variables
vector_store_config = {
"collection_name": "openmemory",
"host": "mem0_store",
"port": 6333,
}
# Check for different vector store configurations based on environment variables
if os.environ.get('CHROMA_HOST') and os.environ.get('CHROMA_PORT'):
vector_store_provider = "chroma"
vector_store_config.update({
"host": os.environ.get('CHROMA_HOST'),
"port": int(os.environ.get('CHROMA_PORT'))
})
elif os.environ.get('QDRANT_HOST') and os.environ.get('QDRANT_PORT'):
vector_store_provider = "qdrant"
vector_store_config.update({
"host": os.environ.get('QDRANT_HOST'),
"port": int(os.environ.get('QDRANT_PORT'))
})
elif os.environ.get('WEAVIATE_CLUSTER_URL') or (os.environ.get('WEAVIATE_HOST') and os.environ.get('WEAVIATE_PORT')):
vector_store_provider = "weaviate"
# Prefer an explicit cluster URL if provided; otherwise build from host/port
cluster_url = os.environ.get('WEAVIATE_CLUSTER_URL')
if not cluster_url:
weaviate_host = os.environ.get('WEAVIATE_HOST')
weaviate_port = int(os.environ.get('WEAVIATE_PORT'))
cluster_url = f"http://{weaviate_host}:{weaviate_port}"
vector_store_config = {
"collection_name": "openmemory",
"cluster_url": cluster_url
}
elif os.environ.get('REDIS_URL'):
vector_store_provider = "redis"
vector_store_config = {
"collection_name": "openmemory",
"redis_url": os.environ.get('REDIS_URL')
}
elif os.environ.get('PG_HOST') and os.environ.get('PG_PORT'):
vector_store_provider = "pgvector"
vector_store_config.update({
"host": os.environ.get('PG_HOST'),
"port": int(os.environ.get('PG_PORT')),
"dbname": os.environ.get('PG_DB', 'mem0'),
"user": os.environ.get('PG_USER', 'mem0'),
"password": os.environ.get('PG_PASSWORD', 'mem0')
})
elif os.environ.get('MILVUS_HOST') and os.environ.get('MILVUS_PORT'):
vector_store_provider = "milvus"
# Construct the full URL as expected by MilvusDBConfig
milvus_host = os.environ.get('MILVUS_HOST')
milvus_port = int(os.environ.get('MILVUS_PORT'))
milvus_url = f"http://{milvus_host}:{milvus_port}"
vector_store_config = {
"collection_name": "openmemory",
"url": milvus_url,
"token": os.environ.get('MILVUS_TOKEN', ''), # Always include, empty string for local setup
"db_name": os.environ.get('MILVUS_DB_NAME', ''),
"embedding_model_dims": 1536,
"metric_type": "COSINE" # Using COSINE for better semantic similarity
}
elif os.environ.get('ELASTICSEARCH_HOST') and os.environ.get('ELASTICSEARCH_PORT'):
vector_store_provider = "elasticsearch"
# Construct the full URL with scheme since Elasticsearch client expects it
elasticsearch_host = os.environ.get('ELASTICSEARCH_HOST')
elasticsearch_port = int(os.environ.get('ELASTICSEARCH_PORT'))
# Use http:// scheme since we're not using SSL
full_host = f"http://{elasticsearch_host}"
vector_store_config.update({
"host": full_host,
"port": elasticsearch_port,
"user": os.environ.get('ELASTICSEARCH_USER', 'elastic'),
"password": os.environ.get('ELASTICSEARCH_PASSWORD', 'changeme'),
"verify_certs": False,
"use_ssl": False,
"embedding_model_dims": 1536
})
elif os.environ.get('OPENSEARCH_HOST') and os.environ.get('OPENSEARCH_PORT'):
vector_store_provider = "opensearch"
vector_store_config.update({
"host": os.environ.get('OPENSEARCH_HOST'),
"port": int(os.environ.get('OPENSEARCH_PORT'))
})
elif os.environ.get('FAISS_PATH'):
vector_store_provider = "faiss"
vector_store_config = {
"collection_name": "openmemory",
"path": os.environ.get('FAISS_PATH'),
"embedding_model_dims": 1536,
"distance_strategy": "cosine"
}
else:
# Default fallback to Qdrant
vector_store_provider = "qdrant"
vector_store_config.update({
"port": 6333,
})
print(f"Auto-detected vector store: {vector_store_provider} with config: {vector_store_config}")
return {
"vector_store": {
"provider": vector_store_provider,
"config": vector_store_config
},
"llm": {
"provider": "openai",
@@ -242,6 +339,9 @@ def get_memory_client(custom_instructions: str = None):
# Fix Ollama URLs for Docker if needed
if config["embedder"].get("provider") == "ollama":
config["embedder"] = _fix_ollama_urls(config["embedder"])
if "vector_store" in mem0_config and mem0_config["vector_store"] is not None:
config["vector_store"] = mem0_config["vector_store"]
else:
print("No configuration found in database, using defaults")
openmemory/compose/chroma.yml
+11
View File
@@ -0,0 +1,11 @@
services:
mem0_store:
image: ghcr.io/chroma-core/chroma:latest
restart: unless-stopped
environment:
- CHROMA_SERVER_HOST=0.0.0.0
- CHROMA_SERVER_HTTP_PORT=8000
ports:
- "8000:8000"
volumes:
- mem0_storage:/data
openmemory/compose/elasticsearch.yml
+15
View File
@@ -0,0 +1,15 @@
services:
mem0_store:
image: docker.elastic.co/elasticsearch/elasticsearch:8.13.4
restart: unless-stopped
environment:
- discovery.type=single-node
- xpack.security.enabled=false
- ES_JAVA_OPTS=-Xms512m -Xmx512m
ulimits:
memlock: { soft: -1, hard: -1 }
nofile: { soft: 65536, hard: 65536 }
ports:
- "9200:9200"
volumes:
- mem0_storage:/usr/share/elasticsearch/data
openmemory/compose/faiss.yml
+3
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@@ -0,0 +1,3 @@
services:
# FAISS is a local file-based vector store, so no separate container is needed
# Data will be persisted through volume mounts in the main application
openmemory/compose/milvus.yml
+43
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@@ -0,0 +1,43 @@
services:
etcd:
image: quay.io/coreos/etcd:v3.5.5
restart: unless-stopped
environment:
- ETCD_AUTO_COMPACTION_MODE=revision
- ETCD_QUOTA_BACKEND_BYTES=4294967296
- ETCD_SNAPSHOT_COUNT=50000
- ETCD_LISTEN_CLIENT_URLS=http://0.0.0.0:2379
- ETCD_ADVERTISE_CLIENT_URLS=http://etcd:2379
- ETCD_LISTEN_PEER_URLS=http://0.0.0.0:2380
- ETCD_INITIAL_ADVERTISE_PEER_URLS=http://etcd:2380
- ETCD_INITIAL_CLUSTER=default=http://etcd:2380
- ETCD_NAME=default
- ETCD_DATA_DIR=/etcd
volumes:
- ./data/milvus/etcd:/etcd
minio:
image: minio/minio:RELEASE.2023-10-25T06-33-25Z
restart: unless-stopped
command: server /minio_data
environment:
- MINIO_ACCESS_KEY=minioadmin
- MINIO_SECRET_KEY=minioadmin
volumes:
- ./data/milvus/minio:/minio_data
mem0_store:
image: milvusdb/milvus:v2.4.7
restart: unless-stopped
command: ["milvus", "run", "standalone"]
depends_on:
- etcd
- minio
environment:
- ETCD_ENDPOINTS=etcd:2379
- MINIO_ADDRESS=minio:9000
ports:
- "19530:19530"
- "9091:9091"
volumes:
- ./data/milvus/milvus:/var/lib/milvus
openmemory/compose/opensearch.yml
+19
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@@ -0,0 +1,19 @@
services:
mem0_store:
image: opensearchproject/opensearch:2.13.0
restart: unless-stopped
user: "1000:1000"
environment:
- discovery.type=single-node
- plugins.security.disabled=true
- OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m
- OPENSEARCH_INITIAL_ADMIN_PASSWORD=Openmemory123!
- bootstrap.memory_lock=true
ulimits:
memlock: { soft: -1, hard: -1 }
nofile: { soft: 65536, hard: 65536 }
ports:
- "9200:9200"
- "9600:9600"
volumes:
- mem0_storage:/usr/share/opensearch/data
openmemory/compose/pgvector.yml
+12
View File
@@ -0,0 +1,12 @@
services:
mem0_store:
image: pgvector/pgvector:pg16
restart: unless-stopped
environment:
- POSTGRES_DB=mem0
- POSTGRES_USER=mem0
- POSTGRES_PASSWORD=mem0
ports:
- "5432:5432"
volumes:
- mem0_storage:/var/lib/postgresql/data
openmemory/compose/qdrant.yml
+8
View File
@@ -0,0 +1,8 @@
services:
mem0_store:
image: qdrant/qdrant:latest
restart: unless-stopped
ports:
- "6333:6333"
volumes:
- mem0_storage:/mem0/storage
openmemory/compose/redis.yml
+13
View File
@@ -0,0 +1,13 @@
services:
mem0_store:
image: redis/redis-stack-server:latest
restart: unless-stopped
ports:
- "6379:6379"
volumes:
- mem0_storage:/var/lib/redis-stack
command: >
redis-stack-server
--appendonly yes
--appendfsync everysec
--save 900 1 300 10 60 10000
openmemory/compose/weaviate.yml
+14
View File
@@ -0,0 +1,14 @@
services:
mem0_store:
image: semitechnologies/weaviate:latest
restart: unless-stopped
environment:
- QUERY_DEFAULTS_LIMIT=25
- AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true
- PERSISTENCE_DATA_PATH=/var/lib/weaviate
- CLUSTER_HOSTNAME=node1
- WEAVIATE_CLUSTER_URL=http://mem0_store:8080
ports:
- "8080:8080"
volumes:
- mem0_storage:/var/lib/weaviate
openmemory/run.sh
+300 -11
View File
@@ -54,36 +54,325 @@ export NEXT_PUBLIC_API_URL
export NEXT_PUBLIC_USER_ID="$USER"
export FRONTEND_PORT
# Create docker-compose.yml file
echo "📝 Creating docker-compose.yml..."
cat > docker-compose.yml <<EOF
services:
mem0_store:
image: qdrant/qdrant
ports:
- "6333:6333"
volumes:
- mem0_storage:/mem0/storage
# Parse vector store selection (env var or flag). Default: qdrant
VECTOR_STORE="${VECTOR_STORE:-qdrant}"
EMBEDDING_DIMS="${EMBEDDING_DIMS:-1536}"
for arg in "$@"; do
case $arg in
--vector-store=*)
VECTOR_STORE="${arg#*=}"
shift
;;
--vector-store)
VECTOR_STORE="$2"
shift 2
;;
*)
;;
esac
done
export VECTOR_STORE
echo "🧰 Using vector store: $VECTOR_STORE"
# Function to create compose file by merging vector store config with openmemory-mcp service
create_compose_file() {
local vector_store=$1
local compose_file="compose/${vector_store}.yml"
local volume_name="${vector_store}_data" # Vector-store-specific volume name
# Check if the compose file exists
if [ ! -f "$compose_file" ]; then
echo "❌ Compose file not found: $compose_file"
echo "Available vector stores: $(ls compose/*.yml | sed 's/compose\///g' | sed 's/\.yml//g' | tr '\n' ' ')"
exit 1
fi
echo "📝 Creating docker-compose.yml using $compose_file..."
echo "💾 Using volume: $volume_name"
# Start the compose file with services section
echo "services:" > docker-compose.yml
# Extract services from the compose file and replace volume name
# First get everything except the last volumes section
tail -n +2 "$compose_file" | sed '/^volumes:/,$d' | sed "s/mem0_storage/${volume_name}/g" >> docker-compose.yml
# Add a newline to ensure proper YAML formatting
echo "" >> docker-compose.yml
# Add the openmemory-mcp service
cat >> docker-compose.yml <<EOF
openmemory-mcp:
image: mem0/openmemory-mcp:latest
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- USER=${USER}
EOF
# Add vector store specific environment variables
case "$vector_store" in
weaviate)
cat >> docker-compose.yml <<EOF
- WEAVIATE_HOST=mem0_store
- WEAVIATE_PORT=8080
EOF
;;
redis)
cat >> docker-compose.yml <<EOF
- REDIS_URL=redis://mem0_store:6379
EOF
;;
pgvector)
cat >> docker-compose.yml <<EOF
- PG_HOST=mem0_store
- PG_PORT=5432
- PG_DB=mem0
- PG_USER=mem0
- PG_PASSWORD=mem0
EOF
;;
qdrant)
cat >> docker-compose.yml <<EOF
- QDRANT_HOST=mem0_store
- QDRANT_PORT=6333
EOF
;;
chroma)
cat >> docker-compose.yml <<EOF
- CHROMA_HOST=mem0_store
- CHROMA_PORT=8000
EOF
;;
milvus)
cat >> docker-compose.yml <<EOF
- MILVUS_HOST=mem0_store
- MILVUS_PORT=19530
EOF
;;
elasticsearch)
cat >> docker-compose.yml <<EOF
- ELASTICSEARCH_HOST=mem0_store
- ELASTICSEARCH_PORT=9200
- ELASTICSEARCH_USER=elastic
- ELASTICSEARCH_PASSWORD=changeme
EOF
;;
faiss)
cat >> docker-compose.yml <<EOF
- FAISS_PATH=/tmp/faiss
EOF
;;
*)
echo "⚠️ Unknown vector store: $vector_store. Using default Qdrant configuration."
cat >> docker-compose.yml <<EOF
- QDRANT_HOST=mem0_store
- QDRANT_PORT=6333
EOF
;;
esac
# Add common openmemory-mcp service configuration
if [ "$vector_store" = "faiss" ]; then
# FAISS doesn't need a separate service, just volume mounts
cat >> docker-compose.yml <<EOF
ports:
- "8765:8765"
volumes:
- openmemory_db:/usr/src/openmemory
- ${volume_name}:/tmp/faiss
volumes:
${volume_name}:
openmemory_db:
EOF
else
cat >> docker-compose.yml <<EOF
depends_on:
- mem0_store
ports:
- "8765:8765"
volumes:
- openmemory_db:/usr/src/openmemory
volumes:
mem0_storage:
${volume_name}:
openmemory_db:
EOF
fi
}
# Create docker-compose.yml file based on selected vector store
echo "📝 Creating docker-compose.yml..."
create_compose_file "$VECTOR_STORE"
# Ensure local data directories exist for bind-mounted vector stores
if [ "$VECTOR_STORE" = "milvus" ]; then
echo "🗂️ Ensuring local data directories for Milvus exist..."
mkdir -p ./data/milvus/etcd ./data/milvus/minio ./data/milvus/milvus
fi
# Function to install vector store specific packages
install_vector_store_packages() {
local vector_store=$1
echo "📦 Installing packages for vector store: $vector_store..."
case "$vector_store" in
qdrant)
docker exec openmemory-openmemory-mcp-1 pip install "qdrant-client>=1.9.1" || echo "⚠️ Failed to install qdrant packages"
;;
chroma)
docker exec openmemory-openmemory-mcp-1 pip install "chromadb>=0.4.24" || echo "⚠️ Failed to install chroma packages"
;;
weaviate)
docker exec openmemory-openmemory-mcp-1 pip install "weaviate-client>=4.4.0,<4.15.0" || echo "⚠️ Failed to install weaviate packages"
;;
faiss)
docker exec openmemory-openmemory-mcp-1 pip install "faiss-cpu>=1.7.4" || echo "⚠️ Failed to install faiss packages"
;;
pgvector)
docker exec openmemory-openmemory-mcp-1 pip install "vecs>=0.4.0" "psycopg>=3.2.8" || echo "⚠️ Failed to install pgvector packages"
;;
redis)
docker exec openmemory-openmemory-mcp-1 pip install "redis>=5.0.0,<6.0.0" "redisvl>=0.1.0,<1.0.0" || echo "⚠️ Failed to install redis packages"
;;
elasticsearch)
docker exec openmemory-openmemory-mcp-1 pip install "elasticsearch>=8.0.0,<9.0.0" || echo "⚠️ Failed to install elasticsearch packages"
;;
milvus)
docker exec openmemory-openmemory-mcp-1 pip install "pymilvus>=2.4.0,<2.6.0" || echo "⚠️ Failed to install milvus packages"
;;
*)
echo "⚠️ Unknown vector store: $vector_store. Installing default qdrant packages."
docker exec openmemory-openmemory-mcp-1 pip install "qdrant-client>=1.9.1" || echo "⚠️ Failed to install qdrant packages"
;;
esac
}
# Start services
echo "🚀 Starting backend services..."
docker compose up -d
# Wait for container to be ready before installing packages
echo "⏳ Waiting for container to be ready..."
for i in {1..30}; do
if docker exec openmemory-openmemory-mcp-1 python -c "import sys; print('ready')" >/dev/null 2>&1; then
break
fi
sleep 1
done
# Install vector store specific packages
install_vector_store_packages "$VECTOR_STORE"
# If a specific vector store is selected, seed the backend config accordingly
if [ "$VECTOR_STORE" = "milvus" ]; then
echo "⏳ Waiting for API to be ready at ${NEXT_PUBLIC_API_URL}..."
for i in {1..60}; do
if curl -fsS "${NEXT_PUBLIC_API_URL}/api/v1/config" >/dev/null 2>&1; then
break
fi
sleep 1
done
echo "🧩 Configuring vector store (milvus) in backend..."
curl -fsS -X PUT "${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store" \
-H 'Content-Type: application/json' \
-d "{\"provider\":\"milvus\",\"config\":{\"collection_name\":\"openmemory\",\"embedding_model_dims\":${EMBEDDING_DIMS},\"url\":\"http://mem0_store:19530\",\"token\":\"\",\"db_name\":\"\",\"metric_type\":\"COSINE\"}}" >/dev/null || true
elif [ "$VECTOR_STORE" = "weaviate" ]; then
echo "⏳ Waiting for API to be ready at ${NEXT_PUBLIC_API_URL}..."
for i in {1..60}; do
if curl -fsS "${NEXT_PUBLIC_API_URL}/api/v1/config" >/dev/null 2>&1; then
break
fi
sleep 1
done
echo "🧩 Configuring vector store (weaviate) in backend..."
curl -fsS -X PUT "${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store" \
-H 'Content-Type: application/json' \
-d "{\"provider\":\"weaviate\",\"config\":{\"collection_name\":\"openmemory\",\"embedding_model_dims\":${EMBEDDING_DIMS},\"cluster_url\":\"http://mem0_store:8080\"}}" >/dev/null || true
elif [ "$VECTOR_STORE" = "redis" ]; then
echo "⏳ Waiting for API to be ready at ${NEXT_PUBLIC_API_URL}..."
for i in {1..60}; do
if curl -fsS "${NEXT_PUBLIC_API_URL}/api/v1/config" >/dev/null 2>&1; then
break
fi
sleep 1
done
echo "🧩 Configuring vector store (redis) in backend..."
curl -fsS -X PUT "${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store" \
-H 'Content-Type: application/json' \
-d "{\"provider\":\"redis\",\"config\":{\"collection_name\":\"openmemory\",\"embedding_model_dims\":${EMBEDDING_DIMS},\"redis_url\":\"redis://mem0_store:6379\"}}" >/dev/null || true
elif [ "$VECTOR_STORE" = "pgvector" ]; then
echo "⏳ Waiting for API to be ready at ${NEXT_PUBLIC_API_URL}..."
for i in {1..60}; do
if curl -fsS "${NEXT_PUBLIC_API_URL}/api/v1/config" >/dev/null 2>&1; then
break
fi
sleep 1
done
echo "🧩 Configuring vector store (pgvector) in backend..."
curl -fsS -X PUT "${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store" \
-H 'Content-Type: application/json' \
-d "{\"provider\":\"pgvector\",\"config\":{\"collection_name\":\"openmemory\",\"embedding_model_dims\":${EMBEDDING_DIMS},\"dbname\":\"mem0\",\"user\":\"mem0\",\"password\":\"mem0\",\"host\":\"mem0_store\",\"port\":5432,\"diskann\":false,\"hnsw\":true}}" >/dev/null || true
elif [ "$VECTOR_STORE" = "qdrant" ]; then
echo "⏳ Waiting for API to be ready at ${NEXT_PUBLIC_API_URL}..."
for i in {1..60}; do
if curl -fsS "${NEXT_PUBLIC_API_URL}/api/v1/config" >/dev/null 2>&1; then
break
fi
sleep 1
done
echo "🧩 Configuring vector store (qdrant) in backend..."
curl -fsS -X PUT "${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store" \
-H 'Content-Type: application/json' \
-d "{\"provider\":\"qdrant\",\"config\":{\"collection_name\":\"openmemory\",\"embedding_model_dims\":${EMBEDDING_DIMS},\"host\":\"mem0_store\",\"port\":6333}}" >/dev/null || true
elif [ "$VECTOR_STORE" = "chroma" ]; then
echo "⏳ Waiting for API to be ready at ${NEXT_PUBLIC_API_URL}..."
for i in {1..60}; do
if curl -fsS "${NEXT_PUBLIC_API_URL}/api/v1/config" >/dev/null 2>&1; then
break
fi
sleep 1
done
echo "🧩 Configuring vector store (chroma) in backend..."
curl -fsS -X PUT "${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store" \
-H 'Content-Type: application/json' \
-d "{\"provider\":\"chroma\",\"config\":{\"collection_name\":\"openmemory\",\"host\":\"mem0_store\",\"port\":8000}}" >/dev/null || true
elif [ "$VECTOR_STORE" = "elasticsearch" ]; then
echo "⏳ Waiting for API to be ready at ${NEXT_PUBLIC_API_URL}..."
for i in {1..60}; do
if curl -fsS "${NEXT_PUBLIC_API_URL}/api/v1/config" >/dev/null 2>&1; then
break
fi
sleep 1
done
echo "🧩 Configuring vector store (elasticsearch) in backend..."
curl -fsS -X PUT "${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store" \
-H 'Content-Type: application/json' \
-d "{\"provider\":\"elasticsearch\",\"config\":{\"collection_name\":\"openmemory\",\"embedding_model_dims\":${EMBEDDING_DIMS},\"host\":\"http://mem0_store\",\"port\":9200,\"user\":\"elastic\",\"password\":\"changeme\",\"verify_certs\":false,\"use_ssl\":false}}" >/dev/null || true
elif [ "$VECTOR_STORE" = "faiss" ]; then
echo "⏳ Waiting for API to be ready at ${NEXT_PUBLIC_API_URL}..."
for i in {1..60}; do
if curl -fsS "${NEXT_PUBLIC_API_URL}/api/v1/config" >/dev/null 2>&1; then
break
fi
sleep 1
done
echo "🧩 Configuring vector store (faiss) in backend..."
curl -fsS -X PUT "${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store" \
-H 'Content-Type: application/json' \
-d "{\"provider\":\"faiss\",\"config\":{\"collection_name\":\"openmemory\",\"embedding_model_dims\":${EMBEDDING_DIMS},\"path\":\"/tmp/faiss\",\"distance_strategy\":\"cosine\"}}" >/dev/null || true
fi
# Start the frontend
echo "🚀 Starting frontend on port $FRONTEND_PORT..."
docker run -d \
pyproject.toml
+5 -1
View File
@@ -43,6 +43,10 @@ vector_stores = [
"pymochow>=2.2.9",
"databricks-sdk>=0.63.0",
"azure-identity>=1.24.0",
"redis>=5.0.0,<6.0.0",
"redisvl>=0.1.0,<1.0.0",
"elasticsearch>=8.0.0,<9.0.0",
"pymilvus>=2.4.0,<2.6.0",
]
llms = [
"groq>=0.3.0",
@@ -58,7 +62,7 @@ extras = [
"boto3>=1.34.0",
"langchain-community>=0.0.0",
"sentence-transformers>=5.0.0",
"elasticsearch>=8.0.0",
"elasticsearch>=8.0.0,<9.0.0",
"opensearch-py>=2.0.0",
"langchain-memgraph>=0.1.0",
]