feat(library): add business + finance types, workspace_id, IngestJob

Adds two new content-type-aware library types — `business` for
proposals/marketing/strategy (used by the work-team agents) and `finance`
for statements/tax/market commentary (used by Garth). Each ships with
chunking config, embedding/reranker instructions, an LLM-context prompt
that forbids fabricating financial figures, and a vision prompt.

Adds a unique-indexed `workspace_id` property to `Library` so a node
can be scoped to a Daedalus workspace. Null means a global library;
non-null means workspace-scoped. Search Cypher (added in a later
commit) enforces the boundary.

Adds an `IngestJob` Django ORM model — separate from neomodel — that
tracks asynchronous ingestion lifecycle (Daedalus → S3 → Celery →
embedding pipeline) with idempotency on (library, source_ref, hash).
Migration 0001_initial creates the table.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

mnemosyne/library/content_types.py

@@ -210,6 +210,69 @@ LIBRARY_TYPE_DEFAULTS = {
             "4) Context clues about when and where this was taken or created."
         ),
     },
+    "business": {
+        "chunking_config": {
+            "strategy": "section_aware",
+            "chunk_size": 640,
+            "chunk_overlap": 96,
+            "respect_boundaries": ["section", "subsection", "list", "table"],
+        },
+        "embedding_instruction": (
+            "Represent this passage from a business document for retrieval. "
+            "Focus on value propositions, positioning, pricing, scope of work, "
+            "client outcomes, and commercial commitments."
+        ),
+        "reranker_instruction": (
+            "Re-rank passages from business documents based on commercial relevance. "
+            "Prioritize value framing, deliverables, client outcomes, and specific "
+            "pricing or scope language."
+        ),
+        "llm_context_prompt": (
+            "The following excerpts are from business documents (proposals, marketing, "
+            "sales, strategy). Interpret in commercial context. Distinguish positioning "
+            "claims from committed deliverables. Preserve numbers, scope language, and "
+            "client names exactly as written."
+        ),
+        "vision_prompt": (
+            "Analyze this image from a business document. Identify:\n"
+            "1) Image type (logo, chart, diagram, screenshot, photograph, table).\n"
+            "2) What it depicts — brand marks, data, organizational structure, products.\n"
+            "3) Any visible text — company names, figures, captions, headings.\n"
+            "4) The commercial purpose — positioning, pricing, capability demonstration."
+        ),
+    },
+    "finance": {
+        "chunking_config": {
+            "strategy": "section_aware",
+            "chunk_size": 512,
+            "chunk_overlap": 64,
+            "respect_boundaries": ["section", "table", "row", "paragraph"],
+        },
+        "embedding_instruction": (
+            "Represent this passage from a financial document for retrieval. "
+            "Focus on accounts, instruments, dates, amounts, balances, and "
+            "analytical commentary."
+        ),
+        "reranker_instruction": (
+            "Re-rank passages from financial documents based on relevance to the query. "
+            "Prioritize the matching account, instrument, time period, and figures."
+        ),
+        "llm_context_prompt": (
+            "The following excerpts are from financial documents (statements, tax "
+            "documents, market commentary, planning). Distinguish factual figures "
+            "(statements, transactions, balances) from opinion (forecasts, commentary). "
+            "Quote numbers, dates, and account identifiers exactly as they appear. "
+            "Do not infer, round, or fabricate financial figures. If a figure is not "
+            "present in the excerpts, say so explicitly."
+        ),
+        "vision_prompt": (
+            "Analyze this image from a financial document. Identify:\n"
+            "1) Image type (chart, table, statement scan, dashboard screenshot, receipt).\n"
+            "2) What it depicts — account, instrument, time period, data series.\n"
+            "3) Any visible text — figures, dates, account identifiers, labels.\n"
+            "4) Whether the data is factual (statement) or analytical (forecast/commentary)."
+        ),
+    },
 }
 
 
@@ -219,7 +282,7 @@ def get_library_type_config(library_type):
 
     Args:
         library_type: One of 'fiction', 'nonfiction', 'technical', 'music',
-                      'film', 'art', 'journal'
+                      'film', 'art', 'journal', 'business', 'finance'
 
     Returns:
         dict with keys: chunking_config, embedding_instruction,

mnemosyne/library/migrations/0001_initial.py

@@ -0,0 +1,45 @@
+# Generated by Django 5.2.13 on 2026-04-28 12:36
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = [
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='IngestJob',
+            fields=[
+                ('id', models.CharField(max_length=64, primary_key=True, serialize=False)),
+                ('item_uid', models.CharField(blank=True, db_index=True, max_length=64)),
+                ('library_uid', models.CharField(db_index=True, max_length=64)),
+                ('celery_task_id', models.CharField(blank=True, max_length=255)),
+                ('status', models.CharField(choices=[('pending', 'Pending'), ('processing', 'Processing'), ('completed', 'Completed'), ('failed', 'Failed')], db_index=True, default='pending', max_length=20)),
+                ('progress', models.CharField(default='queued', max_length=50)),
+                ('error', models.TextField(blank=True, null=True)),
+                ('retry_count', models.PositiveIntegerField(default=0)),
+                ('chunks_created', models.PositiveIntegerField(default=0)),
+                ('concepts_extracted', models.PositiveIntegerField(default=0)),
+                ('embedding_model', models.CharField(blank=True, max_length=100)),
+                ('content_hash', models.CharField(blank=True, db_index=True, max_length=64)),
+                ('source', models.CharField(default='', max_length=50)),
+                ('source_ref', models.CharField(blank=True, db_index=True, max_length=200)),
+                ('s3_key', models.CharField(max_length=500)),
+                ('title', models.CharField(blank=True, max_length=500)),
+                ('file_type', models.CharField(blank=True, max_length=50)),
+                ('file_size', models.PositiveBigIntegerField(default=0)),
+                ('collection_uid', models.CharField(blank=True, max_length=64)),
+                ('created_at', models.DateTimeField(auto_now_add=True)),
+                ('started_at', models.DateTimeField(blank=True, null=True)),
+                ('completed_at', models.DateTimeField(blank=True, null=True)),
+            ],
+            options={
+                'ordering': ['-created_at'],
+                'indexes': [models.Index(fields=['status', '-created_at'], name='library_ing_status_9c95b2_idx'), models.Index(fields=['source', 'source_ref'], name='library_ing_source_a48684_idx')],
+            },
+        ),
+    ]

mnemosyne/library/models.py

@@ -1,11 +1,16 @@
 """
-Neo4j graph models for the Mnemosyne content library.
+Models for the Mnemosyne content library.
 
-All content data (libraries, collections, items, chunks, concepts, images)
+Most content (libraries, collections, items, chunks, concepts, images)
-lives in Neo4j as a knowledge graph. These models use neomodel's StructuredNode
+lives in Neo4j as a knowledge graph via neomodel StructuredNode. These do
-OGM — they do NOT participate in Django's ORM or migrations.
+NOT participate in Django's ORM or migrations.
+
+The IngestJob model at the bottom of this file is the exception: it tracks
+the lifecycle of asynchronous ingestion requests (file → embedding pipeline)
+in PostgreSQL via Django's ORM.
 """
 
+from django.db import models
 from neomodel import (
     ArrayProperty,
     DateTimeProperty,
@@ -50,8 +55,14 @@ class Library(StructuredNode):
     """
     Top-level container representing a content library.
 
-    Each library has a type (fiction, technical, music, film, art, journal)
+    Each library has a type (fiction, nonfiction, technical, music, film,
-    that drives chunking strategy, embedding instructions, and LLM prompts.
+    art, journal, business, finance) that drives chunking strategy,
+    embedding instructions, and LLM prompts.
+
+    A library may be either *global* (workspace_id is null — searchable
+    across the whole instance) or *workspace-scoped* (workspace_id set —
+    visible only to agents inside that Daedalus workspace). Scoping is
+    enforced structurally by every search query.
     """
 
     uid = UniqueIdProperty()
@@ -66,10 +77,16 @@ class Library(StructuredNode):
             "film": "Film",
             "art": "Art",
             "journal": "Journal",
+            "business": "Business",
+            "finance": "Finance",
         },
     )
     description = StringProperty(default="")
 
+    # Daedalus workspace UUID this library is scoped to. Null for global
+    # libraries. Unique-indexed so a workspace cannot have two libraries.
+    workspace_id = StringProperty(unique_index=True, required=False)
+
     # Content-type configuration
     chunking_config = JSONProperty(default={})
     embedding_instruction = StringProperty(default="")
@@ -270,3 +287,78 @@ class ImageEmbedding(StructuredNode):
 
     def __str__(self):
         return f"ImageEmbedding ({self.uid})"
+
+
+# --- Django ORM models (PostgreSQL) ---
+
+
+class IngestJob(models.Model):
+    """
+    Tracks the lifecycle of an asynchronous ingestion + embedding job.
+
+    Created when an external client (e.g. Daedalus) posts a file via the
+    REST ingest API. The Celery worker reads and updates this row as the
+    job moves through fetch / chunk / embed / graph stages.
+
+    Idempotency: a (library, source_ref, content_hash) triple uniquely
+    identifies a piece of content. A second POST with the same triple
+    returns the existing job; a POST with the same source_ref but a new
+    content_hash supersedes the prior Item.
+    """
+
+    STATUS_CHOICES = [
+        ("pending", "Pending"),
+        ("processing", "Processing"),
+        ("completed", "Completed"),
+        ("failed", "Failed"),
+    ]
+
+    id = models.CharField(max_length=64, primary_key=True)
+    item_uid = models.CharField(max_length=64, db_index=True, blank=True)
+    library_uid = models.CharField(max_length=64, db_index=True)
+    celery_task_id = models.CharField(max_length=255, blank=True)
+
+    status = models.CharField(
+        max_length=20,
+        choices=STATUS_CHOICES,
+        default="pending",
+        db_index=True,
+    )
+    progress = models.CharField(max_length=50, default="queued")
+    error = models.TextField(blank=True, null=True)
+    retry_count = models.PositiveIntegerField(default=0)
+
+    chunks_created = models.PositiveIntegerField(default=0)
+    concepts_extracted = models.PositiveIntegerField(default=0)
+    embedding_model = models.CharField(max_length=100, blank=True)
+
+    # The file's content hash (sha256). Used for idempotency: a second
+    # ingest with the same source_ref + same hash is a no-op; a second
+    # ingest with the same source_ref + different hash supersedes.
+    content_hash = models.CharField(max_length=64, db_index=True, blank=True)
+
+    # Where the file came from. For Daedalus: source="daedalus",
+    # source_ref="<workspace_id>/<file_id>".
+    source = models.CharField(max_length=50, default="")
+    source_ref = models.CharField(max_length=200, blank=True, db_index=True)
+    s3_key = models.CharField(max_length=500)
+
+    # Optional metadata carried forward to the Item node.
+    title = models.CharField(max_length=500, blank=True)
+    file_type = models.CharField(max_length=50, blank=True)
+    file_size = models.PositiveBigIntegerField(default=0)
+    collection_uid = models.CharField(max_length=64, blank=True)
+
+    created_at = models.DateTimeField(auto_now_add=True)
+    started_at = models.DateTimeField(null=True, blank=True)
+    completed_at = models.DateTimeField(null=True, blank=True)
+
+    class Meta:
+        ordering = ["-created_at"]
+        indexes = [
+            models.Index(fields=["status", "-created_at"]),
+            models.Index(fields=["source", "source_ref"]),
+        ]
+
+    def __str__(self):
+        return f"IngestJob {self.id} [{self.status}]"

mnemosyne/library/tests/test_content_types.py

@@ -13,7 +13,17 @@ from library.content_types import LIBRARY_TYPE_DEFAULTS, get_library_type_config
 class LibraryTypeDefaultsTests(TestCase):
     """Tests for the LIBRARY_TYPE_DEFAULTS registry."""
 
-    EXPECTED_TYPES = {"fiction", "nonfiction", "technical", "music", "film", "art", "journal"}
+    EXPECTED_TYPES = {
+        "fiction",
+        "nonfiction",
+        "technical",
+        "music",
+        "film",
+        "art",
+        "journal",
+        "business",
+        "finance",
+    }
 
     def test_all_expected_types_present(self):
         for lib_type in self.EXPECTED_TYPES:
@@ -105,6 +115,16 @@ class VisionPromptTests(TestCase):
         prompt = config["vision_prompt"].lower()
         self.assertIn("historical", prompt)
 
+    def test_business_vision_prompt_mentions_logo_or_chart(self):
+        config = get_library_type_config("business")
+        prompt = config["vision_prompt"].lower()
+        self.assertTrue("logo" in prompt or "chart" in prompt)
+
+    def test_finance_llm_context_forbids_fabrication(self):
+        config = get_library_type_config("finance")
+        prompt = config["llm_context_prompt"].lower()
+        self.assertIn("fabricate", prompt)
+
 
 class GetLibraryTypeConfigTests(TestCase):
     """Tests for the get_library_type_config helper."""