- Allowing for multiple types of Catalogs
- Introduction of retrievers - Ensuring processing information is collected from Catalog iso Tenant - Introduction of a generic Form class to enable dynamic fields based on a configuration - Realisation of Retriever functionality to support dynamic fields
This commit is contained in:
Josako
145
common/langchain/eveai_default_rag_retriever.py
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145
common/langchain/eveai_default_rag_retriever.py
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from langchain_core.retrievers import BaseRetriever
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from sqlalchemy import func, and_, or_, desc
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from sqlalchemy.exc import SQLAlchemyError
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from pydantic import BaseModel, Field, PrivateAttr
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from typing import Any, Dict
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from flask import current_app
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from common.extensions import db
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from common.models.document import Document, DocumentVersion
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from common.utils.datetime_utils import get_date_in_timezone
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from common.utils.model_utils import ModelVariables
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class EveAIDefaultRagRetriever(BaseRetriever, BaseModel):
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_catalog_id: int = PrivateAttr()
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_model_variables: ModelVariables = PrivateAttr()
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_tenant_info: Dict[str, Any] = PrivateAttr()
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def __init__(self, catalog_id: int, model_variables: ModelVariables, tenant_info: Dict[str, Any]):
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super().__init__()
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current_app.logger.debug(f'Model variables type: {type(model_variables)}')
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self._catalog_id = catalog_id
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self._model_variables = model_variables
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self._tenant_info = tenant_info
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@property
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def catalog_id(self) -> int:
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return self._catalog_id
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@property
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def model_variables(self) -> ModelVariables:
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return self._model_variables
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@property
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def tenant_info(self) -> Dict[str, Any]:
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return self._tenant_info
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def _get_relevant_documents(self, query: str):
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current_app.logger.debug(f'Retrieving relevant documents for query: {query}')
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query_embedding = self._get_query_embedding(query)
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current_app.logger.debug(f'Model Variables Private: {type(self._model_variables)}')
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current_app.logger.debug(f'Model Variables Property: {type(self.model_variables)}')
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db_class = self.model_variables['embedding_db_model']
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similarity_threshold = self.model_variables['similarity_threshold']
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k = self.model_variables['k']
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if self.model_variables['rag_tuning']:
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try:
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current_date = get_date_in_timezone(self.tenant_info['timezone'])
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current_app.rag_tuning_logger.debug(f'Current date: {current_date}\n')
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# Debug query to show similarity for all valid documents (without chunk text)
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debug_query = (
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db.session.query(
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Document.id.label('document_id'),
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DocumentVersion.id.label('version_id'),
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db_class.id.label('embedding_id'),
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(1 - db_class.embedding.cosine_distance(query_embedding)).label('similarity')
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)
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.join(DocumentVersion, db_class.doc_vers_id == DocumentVersion.id)
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.join(Document, DocumentVersion.doc_id == Document.id)
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.filter(
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or_(Document.valid_from.is_(None), func.date(Document.valid_from) <= current_date),
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or_(Document.valid_to.is_(None), func.date(Document.valid_to) >= current_date)
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)
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.order_by(desc('similarity'))
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)
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debug_results = debug_query.all()
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current_app.logger.debug("Debug: Similarity for all valid documents:")
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for row in debug_results:
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current_app.rag_tuning_logger.debug(f"Doc ID: {row.document_id}, "
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f"Version ID: {row.version_id}, "
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f"Embedding ID: {row.embedding_id}, "
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f"Similarity: {row.similarity}")
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current_app.rag_tuning_logger.debug(f'---------------------------------------\n')
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except SQLAlchemyError as e:
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current_app.logger.error(f'Error generating overview: {e}')
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db.session.rollback()
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if self.model_variables['rag_tuning']:
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current_app.rag_tuning_logger.debug(f'Parameters for Retrieval of documents: \n')
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current_app.rag_tuning_logger.debug(f'Similarity Threshold: {similarity_threshold}\n')
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current_app.rag_tuning_logger.debug(f'K: {k}\n')
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current_app.rag_tuning_logger.debug(f'---------------------------------------\n')
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try:
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current_date = get_date_in_timezone(self.tenant_info['timezone'])
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# Subquery to find the latest version of each document
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subquery = (
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db.session.query(
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DocumentVersion.doc_id,
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func.max(DocumentVersion.id).label('latest_version_id')
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)
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.group_by(DocumentVersion.doc_id)
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.subquery()
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)
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# Main query to filter embeddings
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query_obj = (
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db.session.query(db_class,
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(1 - db_class.embedding.cosine_distance(query_embedding)).label('similarity'))
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.join(DocumentVersion, db_class.doc_vers_id == DocumentVersion.id)
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.join(Document, DocumentVersion.doc_id == Document.id)
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.join(subquery, DocumentVersion.id == subquery.c.latest_version_id)
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.filter(
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or_(Document.valid_from.is_(None), func.date(Document.valid_from) <= current_date),
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or_(Document.valid_to.is_(None), func.date(Document.valid_to) >= current_date),
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(1 - db_class.embedding.cosine_distance(query_embedding)) > similarity_threshold,
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Document.catalog_id == self._catalog_id
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)
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.order_by(desc('similarity'))
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.limit(k)
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)
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if self.model_variables['rag_tuning']:
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current_app.rag_tuning_logger.debug(f'Query executed for Retrieval of documents: \n')
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current_app.rag_tuning_logger.debug(f'{query_obj.statement}\n')
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current_app.rag_tuning_logger.debug(f'---------------------------------------\n')
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res = query_obj.all()
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if self.model_variables['rag_tuning']:
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current_app.rag_tuning_logger.debug(f'Retrieved {len(res)} relevant documents \n')
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current_app.rag_tuning_logger.debug(f'Data retrieved: \n')
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current_app.rag_tuning_logger.debug(f'{res}\n')
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current_app.rag_tuning_logger.debug(f'---------------------------------------\n')
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result = []
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for doc in res:
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if self.model_variables['rag_tuning']:
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current_app.rag_tuning_logger.debug(f'Document ID: {doc[0].id} - Distance: {doc[1]}\n')
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current_app.rag_tuning_logger.debug(f'Chunk: \n {doc[0].chunk}\n\n')
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result.append(f'SOURCE: {doc[0].id}\n\n{doc[0].chunk}\n\n')
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except SQLAlchemyError as e:
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current_app.logger.error(f'Error retrieving relevant documents: {e}')
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db.session.rollback()
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return []
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return result
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def _get_query_embedding(self, query: str):
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embedding_model = self.model_variables['embedding_model']
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query_embedding = embedding_model.embed_query(query)
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return query_embedding
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