reorder files

.sesskey

@@ -0,0 +1 @@
+49ed5f3e-d51f-4fbe-abdb-c909287df6a0

benchmarks/locomo/locomo_benchmark.py

@@ -9,9 +9,6 @@ from pathlib import Path
 from benchmarks.common.benchmark_runner import BenchmarkRunner
 from memora import TemporalSemanticMemory
 
-# Add parent directory to path for imports
-sys.path.insert(0, str(Path(__file__).parent.parent.parent))
-
 import json
 from datetime import datetime, timezone
 from typing import List, Dict, Any, Tuple, Optional
@@ -20,9 +17,7 @@ import pydantic
 from openai import AsyncOpenAI
 import os
 
-# Import common framework
-sys.path.insert(0, str(Path(__file__).parent.parent))
-from common.benchmark_runner import BenchmarkDataset, LLMAnswerGenerator, LLMAnswerEvaluator
+from benchmarks.common.benchmark_runner import BenchmarkDataset, LLMAnswerGenerator, LLMAnswerEvaluator
 from memora.llm_wrapper import LLMConfig
 
 class LoComoDataset(BenchmarkDataset):

benchmarks/longmemeval/longmemeval_benchmark.py

@@ -9,9 +9,6 @@ from pathlib import Path
 from benchmarks.common.benchmark_runner import BenchmarkRunner
 from memora import TemporalSemanticMemory
 
-# Add parent directory to path for imports
-sys.path.insert(0, str(Path(__file__).parent.parent.parent))
-
 import json
 from datetime import datetime, timezone
 from typing import List, Dict, Any, Tuple, Optional
@@ -20,9 +17,7 @@ import pydantic
 from openai import AsyncOpenAI
 import os
 
-# Import common framework
-sys.path.insert(0, str(Path(__file__).parent.parent))
-from common.benchmark_runner import BenchmarkDataset, LLMAnswerGenerator, LLMAnswerEvaluator
+from benchmarks.common.benchmark_runner import BenchmarkDataset, LLMAnswerGenerator, LLMAnswerEvaluator
 from memora.llm_wrapper import LLMConfig
 
 

benchmarks/pyproject.toml

@@ -0,0 +1,23 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "benchmarks"
+version = "0.1.0"
+description = "Benchmarks for Memora memory system"
+requires-python = ">=3.11"
+dependencies = [
+    "memora",
+    "python-fasthtml>=0.12.33",
+    "streamlit>=1.51.0",
+    "openai>=1.0.0",
+    "rich>=13.0.0",
+    "pydantic>=2.0.0",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["benchmarks"]
+
+[tool.uv.sources]
+memora = { workspace = true }

control-plane/.dockerignore

@@ -0,0 +1,12 @@
+.next
+node_modules
+npm-debug.log
+.git
+.gitignore
+.env.local
+.env*.local
+README.md
+Dockerfile
+.dockerignore
+build-docker.sh
+docker-compose.yml

control-plane/.env.local.example

@@ -1,3 +1,3 @@
 # Dataplane API Configuration
-# URL of the Python FastAPI dataplane server
-NEXT_PUBLIC_DATAPLANE_API_URL=http://localhost:8080
+# URL of the Python FastAPI dataplane server (server-side only, not exposed to browser)
+DATAPLANE_API_URL=http://localhost:8080

control-plane/Dockerfile

@@ -0,0 +1,39 @@
+FROM node:20-alpine AS base
+
+# Install dependencies only when needed
+FROM base AS deps
+RUN apk add --no-cache libc6-compat
+WORKDIR /app
+
+COPY package.json package-lock.json ./
+RUN npm ci
+
+# Rebuild the source code only when needed
+FROM base AS builder
+WORKDIR /app
+COPY --from=deps /app/node_modules ./node_modules
+COPY . .
+
+RUN npm run build
+
+# Production image, copy all the files and run next
+FROM base AS runner
+WORKDIR /app
+
+ENV NODE_ENV=production
+
+RUN addgroup --system --gid 1001 nodejs
+RUN adduser --system --uid 1001 nextjs
+
+# Automatically leverage output traces to reduce image size
+COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
+COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
+
+USER nextjs
+
+EXPOSE 3000
+
+ENV PORT=3000
+ENV HOSTNAME="0.0.0.0"
+
+CMD ["node", "server.js"]

control-plane/README.md

@@ -84,7 +84,7 @@ cp .env.local.example .env.local
 Edit `.env.local`:
 
 ```env
-NEXT_PUBLIC_DATAPLANE_API_URL=http://localhost:8080
+DATAPLANE_API_URL=http://localhost:8080
 ```
 
 ### Development

control-plane/build-docker.sh

@@ -0,0 +1,74 @@
+#!/bin/bash
+set -e
+
+# Default values
+IMAGE_NAME="memora-control-plane"
+IMAGE_TAG="latest"
+REGISTRY=""
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --name)
+            IMAGE_NAME="$2"
+            shift 2
+            ;;
+        --tag)
+            IMAGE_TAG="$2"
+            shift 2
+            ;;
+        --registry)
+            REGISTRY="$2"
+            shift 2
+            ;;
+        --help)
+            echo "Usage: $0 [OPTIONS]"
+            echo ""
+            echo "Options:"
+            echo "  --name NAME        Docker image name (default: control-plane)"
+            echo "  --tag TAG          Docker image tag (default: latest)"
+            echo "  --registry REG     Docker registry URL (optional)"
+            echo "  --help             Show this help message"
+            echo ""
+            echo "Example:"
+            echo "  $0 --name myapp --tag v1.0.0"
+            echo "  $0 --registry docker.io/myuser --name control-plane --tag v1.0.0"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# Construct full image name
+if [ -n "$REGISTRY" ]; then
+    FULL_IMAGE_NAME="${REGISTRY}/${IMAGE_NAME}:${IMAGE_TAG}"
+else
+    FULL_IMAGE_NAME="${IMAGE_NAME}:${IMAGE_TAG}"
+fi
+
+echo "Building Docker image: ${FULL_IMAGE_NAME}"
+echo "========================================"
+
+# Build the Docker image
+docker build -t "${FULL_IMAGE_NAME}"  .
+
+echo ""
+echo "Build completed successfully!"
+echo "Image: ${FULL_IMAGE_NAME}"
+echo ""
+echo "To run the container:"
+echo "  docker run -p 3000:3000 \\"
+echo "    -e DATAPLANE_API_URL=http://your-api-url:8080 \\"
+echo "    ${FULL_IMAGE_NAME}"
+echo ""
+echo "Or with an env file:"
+echo "  docker run -p 3000:3000 --env-file .env.local ${FULL_IMAGE_NAME}"
+echo ""
+echo "To push to registry (if registry specified):"
+if [ -n "$REGISTRY" ]; then
+    echo "  docker push ${FULL_IMAGE_NAME}"
+fi

control-plane/next.config.ts

@@ -1,7 +1,7 @@
 import type { NextConfig } from "next";
 
 const nextConfig: NextConfig = {
-  /* config options here */
+  output: 'standalone',
 };
 
 export default nextConfig;

control-plane/src/app/api/agents/route.ts

@@ -1,6 +1,6 @@
 import { NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function GET() {
   try {

control-plane/src/app/api/documents/[documentId]/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function GET(
   request: NextRequest,

control-plane/src/app/api/documents/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function GET(request: NextRequest) {
   try {

control-plane/src/app/api/graph/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function GET(request: NextRequest) {
   try {

control-plane/src/app/api/list/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function GET(request: NextRequest) {
   try {

control-plane/src/app/api/memories/batch/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function POST(request: NextRequest) {
   try {

control-plane/src/app/api/memories/batch_async/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function POST(request: NextRequest) {
   try {

control-plane/src/app/api/operations/[agentId]/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function GET(
   request: NextRequest,

control-plane/src/app/api/search/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function POST(request: NextRequest) {
   try {

control-plane/src/app/api/stats/[agentId]/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function GET(
   request: NextRequest,

control-plane/src/app/api/think/route.ts

@@ -1,6 +1,6 @@
 import { NextRequest, NextResponse } from 'next/server';
 
-const DATAPLANE_URL = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+const DATAPLANE_URL = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
 
 export async function POST(request: NextRequest) {
   try {

control-plane/src/lib/api.ts

@@ -213,7 +213,7 @@ export class ServerDataplaneClient {
   private baseUrl: string;
 
   constructor() {
-    this.baseUrl = process.env.NEXT_PUBLIC_DATAPLANE_API_URL || 'http://localhost:8080';
+    this.baseUrl = process.env.DATAPLANE_API_URL || 'http://localhost:8080';
   }
 
   async fetchDataplane<T>(

memora-dev/memora_dev/__init__.py

@@ -0,0 +1 @@
+"""Utility scripts for Memora development."""

memora-dev/memora_dev/generate_openapi.py

@@ -9,9 +9,6 @@ import sys
 import os
 from pathlib import Path
 
-# Add parent directory to path to import memory module
-sys.path.insert(0, str(Path(__file__).parent))
-
 from memora.api import create_app
 from memora import TemporalSemanticMemory
 

memora-dev/pyproject.toml

@@ -0,0 +1,18 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "memora-dev"
+version = "0.1.0"
+description = "Development utilities for Memora"
+requires-python = ">=3.11"
+dependencies = [
+    "memora",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["memora_dev"]
+
+[tool.uv.sources]
+memora = { workspace = true }

memora/alembic/env.py

@@ -23,14 +23,17 @@ def load_env():
     if os.getenv("DATABASE_URL"):
         return
 
+    # Look for .env files in the parent directory (root of the workspace)
+    root_dir = Path(__file__).parent.parent.parent
+
     # Default to local environment
-    env_file = ".env.local"
-    if Path(env_file).exists():
+    env_file = root_dir / ".env.local"
+    if env_file.exists():
         load_dotenv(env_file)
     else:
         # Fallback to dev
-        env_file = ".env.dev"
-        if Path(env_file).exists():
+        env_file = root_dir / ".env.dev"
+        if env_file.exists():
             load_dotenv(env_file)
 
 load_env()

memora/pyproject.toml

@@ -0,0 +1,43 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "memora"
+version = "0.1.0"
+description = "Temporal + Semantic + Entity Memory System for AI agents using PostgreSQL"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "asyncpg>=0.29.0",
+    "python-dotenv>=1.0.0",
+    "openai>=1.0.0",
+    "pydantic>=2.0.0",
+    "rich>=13.0.0",
+    "sentence-transformers>=2.2.0",
+    "pytest>=7.0.0",
+    "pytest-asyncio>=0.21.0",
+    "langchain-text-splitters>=0.3.0",
+    "fastapi[standard]>=0.120.3",
+    "uvicorn>=0.38.0",
+    "sqlalchemy>=2.0.44",
+    "alembic>=1.17.1",
+    "pgvector>=0.4.1",
+    "greenlet>=3.2.4",
+    "psycopg2-binary>=2.9.11",
+    "pytest-timeout>=2.4.0",
+    "dateparser>=1.2.0",
+    "tiktoken>=0.12.0",
+    "httpx>=0.27.0",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["memora"]
+
+[tool.pytest.ini_options]
+log_cli = true
+log_cli_level = "INFO"
+log_cli_format = "%(asctime)s %(levelname)s %(message)s"
+log_cli_date_format = "%Y-%m-%d %H:%M:%S"
+addopts = "--timeout 60 -p no:warnings"
+asyncio_default_fixture_loop_scope = "function"

memora/tests/conftest.py

@@ -19,7 +19,8 @@ LOCAL_DB_URL = "postgresql://memora:memora_dev@localhost:5432/memora"
 # Load environment variables from .env.local at the start of test session
 def pytest_configure(config):
     """Load environment variables before running tests."""
-    env_file = Path(__file__).parent.parent / ".env.local"
+    # Look for .env.local in the workspace root (two levels up from tests dir)
+    env_file = Path(__file__).parent.parent.parent / ".env.local"
     if env_file.exists():
         load_dotenv(env_file)
     else:

openapi.json

@@ -472,7 +472,7 @@
           "Memory Storage"
         ],
         "summary": "Store multiple memories",
-        "description": "Store multiple memory items in batch with automatic fact extraction.\n\n    Features:\n    - Efficient batch processing\n    - Automatic fact extraction from natural language\n    - Entity recognition and linking\n    - Document tracking with optional upsert\n    - Temporal and semantic linking\n\n    The system automatically:\n    1. Extracts semantic facts from the content\n    2. Generates embeddings\n    3. Deduplicates similar facts\n    4. Creates temporal, semantic, and entity links\n    5. Tracks document metadata",
+        "description": "Store multiple memory items in batch with automatic fact extraction.\n\n    Features:\n    - Efficient batch processing\n    - Automatic fact extraction from natural language\n    - Entity recognition and linking\n    - Document tracking with automatic upsert (when document_id is provided)\n    - Temporal and semantic linking\n\n    The system automatically:\n    1. Extracts semantic facts from the content\n    2. Generates embeddings\n    3. Deduplicates similar facts\n    4. Creates temporal, semantic, and entity links\n    5. Tracks document metadata\n\n    Note: If document_id is provided and already exists, the old document and its memory units will be deleted before creating new ones (upsert behavior).",
         "operationId": "api_batch_put_api_memories_batch_post",
         "requestBody": {
           "content": {
@@ -514,7 +514,7 @@
           "Memory Storage"
         ],
         "summary": "Store multiple memories asynchronously",
-        "description": "Store multiple memory items in batch asynchronously using the task backend.\n\n    This endpoint returns immediately after queuing the task, without waiting for completion.\n    The actual processing happens in the background.\n\n    Features:\n    - Immediate response (non-blocking)\n    - Background processing via task queue\n    - Efficient batch processing\n    - Automatic fact extraction from natural language\n    - Entity recognition and linking\n    - Document tracking with optional upsert\n    - Temporal and semantic linking\n\n    The system automatically:\n    1. Queues the batch put task\n    2. Returns immediately with success=True, queued=True\n    3. Processes in background: extracts facts, generates embeddings, creates links",
+        "description": "Store multiple memory items in batch asynchronously using the task backend.\n\n    This endpoint returns immediately after queuing the task, without waiting for completion.\n    The actual processing happens in the background.\n\n    Features:\n    - Immediate response (non-blocking)\n    - Background processing via task queue\n    - Efficient batch processing\n    - Automatic fact extraction from natural language\n    - Entity recognition and linking\n    - Document tracking with automatic upsert (when document_id is provided)\n    - Temporal and semantic linking\n\n    The system automatically:\n    1. Queues the batch put task\n    2. Returns immediately with success=True, queued=True\n    3. Processes in background: extracts facts, generates embeddings, creates links\n\n    Note: If document_id is provided and already exists, the old document and its memory units will be deleted before creating new ones (upsert behavior).",
         "operationId": "api_batch_put_async_api_memories_batch_async_post",
         "requestBody": {
           "content": {
@@ -776,23 +776,6 @@
               }
             ],
             "title": "Document Id"
-          },
-          "document_metadata": {
-            "anyOf": [
-              {
-                "additionalProperties": true,
-                "type": "object"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "title": "Document Metadata"
-          },
-          "upsert": {
-            "type": "boolean",
-            "title": "Upsert",
-            "default": false
           }
         },
         "type": "object",
@@ -814,8 +797,7 @@
               "content": "Bob went hiking yesterday",
               "event_date": "2024-01-15T10:00:00Z"
             }
-          ],
-          "upsert": false
+          ]
         }
       },
       "BatchPutResponse": {
@@ -1188,7 +1170,10 @@
             "title": "Query"
           },
           "fact_type": {
-            "type": "string",
+            "items": {
+              "type": "string"
+            },
+            "type": "array",
             "title": "Fact Type"
           },
           "agent_id": {
@@ -1215,6 +1200,17 @@
             "type": "boolean",
             "title": "Trace",
             "default": false
+          },
+          "question_date": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Question Date"
           }
         },
         "type": "object",
@@ -1226,9 +1222,13 @@
         "description": "Request model for search endpoint.",
         "example": {
           "agent_id": "user123",
-          "fact_type": "world",
+          "fact_type": [
+            "world",
+            "agent"
+          ],
           "max_tokens": 4096,
           "query": "What did Alice say about machine learning?",
+          "question_date": "2023-05-30T23:40:00",
           "reranker": "heuristic",
           "thinking_budget": 100,
           "trace": true

pyproject.toml

@@ -1,45 +1,5 @@
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
+[tool.uv.workspace]
+members = ["memora", "benchmarks", "memora-dev"]
 
-[project]
-name = "memora"
-version = "0.1.0"
-description = "Temporal + Semantic + Entity Memory System for AI agents using PostgreSQL"
-readme = "README.md"
-requires-python = ">=3.11"
-dependencies = [
-    "asyncpg>=0.29.0",
-    "python-dotenv>=1.0.0",
-    "openai>=1.0.0",
-    "pydantic>=2.0.0",
-    "rich>=13.0.0",
-    "sentence-transformers>=2.2.0",
-    "pytest>=7.0.0",
-    "pytest-asyncio>=0.21.0",
-    "langchain-text-splitters>=0.3.0",
-    "fastapi[standard]>=0.120.3",
-    "uvicorn>=0.38.0",
-    "sqlalchemy>=2.0.44",
-    "alembic>=1.17.1",
-    "pgvector>=0.4.1",
-    "greenlet>=3.2.4",
-    "psycopg2-binary>=2.9.11",
-    "pytest-timeout>=2.4.0",
-    "dateparser>=1.2.0",
-    "tiktoken>=0.12.0",
-    "httpx>=0.27.0",
-    "streamlit>=1.51.0",
-    "python-fasthtml>=0.12.33",
-]
-
-[tool.hatch.build.targets.wheel]
-packages = ["memora"]
-
-[tool.pytest.ini_options]
-log_cli = true
-log_cli_level = "INFO"
-log_cli_format = "%(asctime)s %(levelname)s %(message)s"
-log_cli_date_format = "%Y-%m-%d %H:%M:%S"
-addopts = "--timeout 60 -p no:warnings"
-asyncio_default_fixture_loop_scope = "function"
+[tool.uv]
+dev-dependencies = []

scripts/erase-local-db.sh

@@ -7,7 +7,7 @@ echo "🛑 Stopping and erasing local PostgreSQL..."
 echo ""
 
 # Stop and remove containers, networks, volumes
-cd docker
+cd local-db
 docker-compose down -v
 
 echo ""

scripts/migrate-db.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 set -e
 
-cd "$(dirname "$0")/.."
+cd "$(dirname "$0")/../memora"
 
 echo "🔄 Database Migration Script"
 echo "============================"

scripts/start-local-db.sh

@@ -7,7 +7,7 @@ echo "🚀 Starting local PostgreSQL..."
 echo ""
 
 # Start docker compose
-cd docker
+cd local-db
 docker-compose up -d
 
 echo ""
@@ -25,7 +25,9 @@ cd ..
 export DATABASE_URL="postgresql://memora:memora_dev@localhost:5432/memora"
 
 echo "📊 Running database migrations..."
+cd memora
 uv run alembic upgrade head
+cd ..
 
 echo ""
 echo "✅ Database initialized successfully!"

uv.lock

@@ -6,6 +6,13 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
+[manifest]
+members = [
+    "benchmarks",
+    "memora",
+    "memora-dev",
+]
+
 [[package]]
 name = "alembic"
 version = "1.17.1"
@@ -208,6 +215,29 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/94/fe/3aed5d0be4d404d12d36ab97e2f1791424d9ca39c2f754a6285d59a3b01d/beautifulsoup4-4.14.2-py3-none-any.whl", hash = "sha256:5ef6fa3a8cbece8488d66985560f97ed091e22bbc4e9c2338508a9d5de6d4515", size = 106392 },
 ]
 
+[[package]]
+name = "benchmarks"
+version = "0.1.0"
+source = { editable = "benchmarks" }
+dependencies = [
+    { name = "memora" },
+    { name = "openai" },
+    { name = "pydantic" },
+    { name = "python-fasthtml" },
+    { name = "rich" },
+    { name = "streamlit" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "memora", editable = "memora" },
+    { name = "openai", specifier = ">=1.0.0" },
+    { name = "pydantic", specifier = ">=2.0.0" },
+    { name = "python-fasthtml", specifier = ">=0.12.33" },
+    { name = "rich", specifier = ">=13.0.0" },
+    { name = "streamlit", specifier = ">=1.51.0" },
+]
+
 [[package]]
 name = "blinker"
 version = "1.9.0"
@@ -1015,7 +1045,7 @@ wheels = [
 [[package]]
 name = "memora"
 version = "0.1.0"
-source = { editable = "." }
+source = { editable = "memora" }
 dependencies = [
     { name = "alembic" },
     { name = "asyncpg" },
@@ -1032,11 +1062,9 @@ dependencies = [
     { name = "pytest-asyncio" },
     { name = "pytest-timeout" },
     { name = "python-dotenv" },
-    { name = "python-fasthtml" },
     { name = "rich" },
     { name = "sentence-transformers" },
     { name = "sqlalchemy" },
-    { name = "streamlit" },
     { name = "tiktoken" },
     { name = "uvicorn" },
 ]
@@ -1058,15 +1086,24 @@ requires-dist = [
     { name = "pytest-asyncio", specifier = ">=0.21.0" },
     { name = "pytest-timeout", specifier = ">=2.4.0" },
     { name = "python-dotenv", specifier = ">=1.0.0" },
-    { name = "python-fasthtml", specifier = ">=0.12.33" },
     { name = "rich", specifier = ">=13.0.0" },
     { name = "sentence-transformers", specifier = ">=2.2.0" },
     { name = "sqlalchemy", specifier = ">=2.0.44" },
-    { name = "streamlit", specifier = ">=1.51.0" },
     { name = "tiktoken", specifier = ">=0.12.0" },
     { name = "uvicorn", specifier = ">=0.38.0" },
 ]
 
+[[package]]
+name = "memora-dev"
+version = "0.1.0"
+source = { editable = "memora-dev" }
+dependencies = [
+    { name = "memora" },
+]
+
+[package.metadata]
+requires-dist = [{ name = "memora", editable = "memora" }]
+
 [[package]]
 name = "mpmath"
 version = "1.3.0"