- Refining & Enhancing dynamic fields

- Creating a specialized Form class for handling dynamic fields
- Refinement of HTML-macros to handle dynamic fields
- Introduction of dynamic fields for Catalogs

common/langchain/eveai_dossier_retriever.py

@@ -0,0 +1,154 @@
+from langchain_core.retrievers import BaseRetriever
+from sqlalchemy import func, and_, or_, desc, cast, JSON
+from sqlalchemy.exc import SQLAlchemyError
+from pydantic import BaseModel, Field, PrivateAttr
+from typing import Any, Dict, List, Optional
+from flask import current_app
+from contextlib import contextmanager
+
+from common.extensions import db
+from common.models.document import Document, DocumentVersion, Catalog
+from common.utils.datetime_utils import get_date_in_timezone
+from common.utils.model_utils import ModelVariables
+
+
+class EveAIDossierRetriever(BaseRetriever, BaseModel):
+    _catalog_id: int = PrivateAttr()
+    _model_variables: ModelVariables = PrivateAttr()
+    _tenant_info: Dict[str, Any] = PrivateAttr()
+    _active_filters: Optional[Dict[str, Any]] = PrivateAttr()
+
+    def __init__(self, catalog_id: int, model_variables: ModelVariables, tenant_info: Dict[str, Any]):
+        super().__init__()
+        self._catalog_id = catalog_id
+        self._model_variables = model_variables
+        self._tenant_info = tenant_info
+        self._active_filters = None
+
+    @contextmanager
+    def filtering(self, metadata_filters: Dict[str, Any]):
+        """Context manager for temporarily setting metadata filters"""
+        previous_filters = self._active_filters
+        self._active_filters = metadata_filters
+        try:
+            yield self
+        finally:
+            self._active_filters = previous_filters
+
+    def _build_metadata_filter_conditions(self, query):
+        """Build SQL conditions for metadata filtering"""
+        if not self._active_filters:
+            return query
+
+        conditions = []
+        for field, value in self._active_filters.items():
+            if value is None:
+                continue
+
+            # Handle both single values and lists of values
+            if isinstance(value, (list, tuple)):
+                # Multiple values - create OR condition
+                or_conditions = []
+                for val in value:
+                    or_conditions.append(
+                        cast(DocumentVersion.user_metadata[field].astext, JSON) == str(val)
+                    )
+                if or_conditions:
+                    conditions.append(or_(*or_conditions))
+            else:
+                # Single value - direct comparison
+                conditions.append(
+                    cast(DocumentVersion.user_metadata[field].astext, JSON) == str(value)
+                )
+
+        if conditions:
+            query = query.filter(and_(*conditions))
+
+        return query
+
+    def _get_relevant_documents(self, query: str):
+        current_app.logger.debug(f'Retrieving relevant documents for dossier query: {query}')
+        if self._active_filters:
+            current_app.logger.debug(f'Using metadata filters: {self._active_filters}')
+
+        query_embedding = self._get_query_embedding(query)
+        db_class = self.model_variables['embedding_db_model']
+        similarity_threshold = self.model_variables['similarity_threshold']
+        k = self.model_variables['k']
+
+        try:
+            current_date = get_date_in_timezone(self.tenant_info['timezone'])
+
+            # Subquery to find the latest version of each document
+            subquery = (
+                db.session.query(
+                    DocumentVersion.doc_id,
+                    func.max(DocumentVersion.id).label('latest_version_id')
+                )
+                .group_by(DocumentVersion.doc_id)
+                .subquery()
+            )
+
+            # Build base query
+            # Build base query
+            query_obj = (
+                db.session.query(db_class,
+                                 (1 - db_class.embedding.cosine_distance(query_embedding)).label('similarity'))
+                .join(DocumentVersion, db_class.doc_vers_id == DocumentVersion.id)
+                .join(Document, DocumentVersion.doc_id == Document.id)
+                .join(subquery, DocumentVersion.id == subquery.c.latest_version_id)
+                .filter(
+                    or_(Document.valid_from.is_(None), func.date(Document.valid_from) <= current_date),
+                    or_(Document.valid_to.is_(None), func.date(Document.valid_to) >= current_date),
+                    (1 - db_class.embedding.cosine_distance(query_embedding)) > similarity_threshold,
+                    Document.catalog_id == self._catalog_id
+                )
+            )
+
+            # Apply metadata filters
+            query_obj = self._build_metadata_filter_conditions(query_obj)
+
+            # Order and limit results
+            query_obj = query_obj.order_by(desc('similarity')).limit(k)
+
+            # Debug logging for RAG tuning if enabled
+            if self.model_variables['rag_tuning']:
+                self._log_rag_tuning(query_obj, query_embedding)
+
+            res = query_obj.all()
+
+            result = []
+            for doc in res:
+                if self.model_variables['rag_tuning']:
+                    current_app.logger.debug(f'Document ID: {doc[0].id} - Distance: {doc[1]}\n')
+                    current_app.logger.debug(f'Chunk: \n {doc[0].chunk}\n\n')
+                result.append(f'SOURCE: {doc[0].id}\n\n{doc[0].chunk}\n\n')
+
+        except SQLAlchemyError as e:
+            current_app.logger.error(f'Error retrieving relevant documents: {e}')
+            db.session.rollback()
+            return []
+
+        return result
+
+    def _log_rag_tuning(self, query_obj, query_embedding):
+        """Log debug information for RAG tuning"""
+        current_app.rag_tuning_logger.debug("Debug: Query execution plan:")
+        current_app.rag_tuning_logger.debug(f"{query_obj.statement}")
+        if self._active_filters:
+            current_app.rag_tuning_logger.debug("Debug: Active metadata filters:")
+            current_app.rag_tuning_logger.debug(f"{self._active_filters}")
+
+    def _get_query_embedding(self, query: str):
+        """Get embedding for the query text"""
+        embedding_model = self.model_variables['embedding_model']
+        query_embedding = embedding_model.embed_query(query)
+        return query_embedding
+
+    @property
+    def model_variables(self) -> ModelVariables:
+        return self._model_variables
+
+    @property
+    def tenant_info(self) -> Dict[str, Any]:
+        return self._tenant_info

common/models/document.py

@@ -8,7 +8,6 @@ import sqlalchemy as sa
 
 class Catalog(db.Model):
     id = db.Column(db.Integer, primary_key=True)
-    parent_id = db.Column(db.Integer, db.ForeignKey('catalog.id'), nullable=True)
     name = db.Column(db.String(50), nullable=False)
     description = db.Column(db.Text, nullable=True)
     type = db.Column(db.String(50), nullable=False, default="DEFAULT_CATALOG")
@@ -95,6 +94,7 @@ class DocumentVersion(db.Model):
     system_context = db.Column(db.Text, nullable=True)
     user_metadata = db.Column(JSONB, nullable=True)
     system_metadata = db.Column(JSONB, nullable=True)
+    catalog_configuration = db.Column(JSONB, nullable=True)
 
     # Versioning Information
     created_at = db.Column(db.DateTime, nullable=False, server_default=db.func.now())