7702a6dfcc
- Created a base mail template - Adapt and improve document API to usage of catalogs and processors - Adapt eveai_sync to new authentication mechanism and usage of catalogs and processors
360 lines
14 KiB
Python
360 lines
14 KiB
Python
from datetime import datetime as dt, timezone as tz
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from celery import states
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from flask import current_app
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# OpenAI imports
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from langchain.text_splitter import MarkdownHeaderTextSplitter
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from langchain_core.exceptions import LangChainException
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnablePassthrough
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from sqlalchemy import or_
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from sqlalchemy.exc import SQLAlchemyError
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from common.extensions import db, minio_client
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from common.models.document import DocumentVersion, Embedding, Document, Processor, Catalog
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from common.models.user import Tenant
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from common.utils.celery_utils import current_celery
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from common.utils.database import Database
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from common.utils.model_utils import create_language_template, get_model_variables
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from common.utils.business_event import BusinessEvent
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from common.utils.business_event_context import current_event
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from config.type_defs.processor_types import PROCESSOR_TYPES
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from eveai_workers.processors.processor_registry import ProcessorRegistry
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# Healthcheck task
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@current_celery.task(name='ping', queue='embeddings')
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def ping():
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return 'pong'
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@current_celery.task(name='create_embeddings', queue='embeddings')
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def create_embeddings(tenant_id, document_version_id):
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try:
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# Retrieve Tenant for which we are processing
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tenant = Tenant.query.get(tenant_id)
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if tenant is None:
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raise Exception(f'Tenant {tenant_id} not found')
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# Ensure we are working in the correct database schema
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Database(tenant_id).switch_schema()
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# Retrieve document version to process
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document_version = DocumentVersion.query.get(document_version_id)
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if document_version is None:
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raise Exception(f'Document version {document_version_id} not found')
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# Retrieve the Catalog ID
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doc = Document.query.get_or_404(document_version.doc_id)
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catalog_id = doc.catalog_id
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catalog = Catalog.query.get_or_404(catalog_id)
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# Select variables to work with depending on tenant and model
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model_variables = get_model_variables(tenant_id)
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# Define processor related information
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processor_type, processor_class = ProcessorRegistry.get_processor_for_file_type(document_version.file_type)
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processor = get_processor_for_document(catalog_id, document_version.file_type, document_version.sub_file_type)
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except Exception as e:
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current_app.logger.error(f'Create Embeddings request received '
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f'for badly configured document version {document_version_id} '
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f'for tenant {tenant_id}, '
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f'error: {e}')
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raise
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# BusinessEvent creates a context, which is why we need to use it with a with block
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with BusinessEvent('Create Embeddings', tenant_id,
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document_version_id=document_version_id,
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document_version_file_size=document_version.file_size):
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current_app.logger.info(f'Creating embeddings for tenant {tenant_id} on document version {document_version_id}')
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try:
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db.session.add(document_version)
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# start processing
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document_version.processing = True
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document_version.processing_started_at = dt.now(tz.utc)
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document_version.processing_finished_at = None
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document_version.processing_error = None
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db.session.commit()
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except SQLAlchemyError as e:
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current_app.logger.error(f'Unable to save Embedding status information '
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f'in document version {document_version_id} '
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f'for tenant {tenant_id}')
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raise
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delete_embeddings_for_document_version(document_version)
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try:
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with current_event.create_span(f"{processor_type} Processing"):
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document_processor = processor_class(
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tenant=tenant,
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model_variables=model_variables,
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document_version=document_version,
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catalog=catalog,
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processor=processor
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)
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markdown, title = document_processor.process()
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with current_event.create_span("Embedding"):
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embed_markdown(tenant, model_variables, document_version, catalog, markdown, title)
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current_event.log("Finished Embedding Creation Task")
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except Exception as e:
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current_app.logger.error(f'Error creating embeddings for tenant {tenant_id} '
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f'on document version {document_version_id} '
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f'error: {e}')
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document_version.processing = False
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document_version.processing_finished_at = dt.now(tz.utc)
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document_version.processing_error = str(e)[:255]
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db.session.commit()
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create_embeddings.update_state(state=states.FAILURE)
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raise
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def delete_embeddings_for_document_version(document_version):
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embeddings_to_delete = db.session.query(Embedding).filter_by(doc_vers_id=document_version.id).all()
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for embedding in embeddings_to_delete:
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db.session.delete(embedding)
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try:
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db.session.commit()
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current_app.logger.info(f'Deleted embeddings for document version {document_version.id}')
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except SQLAlchemyError as e:
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current_app.logger.error(f'Unable to delete embeddings for document version {document_version.id}')
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raise
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def embed_markdown(tenant, model_variables, document_version, catalog, markdown, title):
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# Create potential chunks
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potential_chunks = create_potential_chunks_for_markdown(tenant.id, document_version, f"{document_version.id}.md")
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# Combine chunks for embedding
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chunks = combine_chunks_for_markdown(potential_chunks, catalog.min_chunk_size, catalog.max_chunk_size)
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# Enrich chunks
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with current_event.create_span("Enrich Chunks"):
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enriched_chunks = enrich_chunks(tenant, model_variables, document_version, title, chunks)
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# Create embeddings
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with current_event.create_span("Create Embeddings"):
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embeddings = embed_chunks(tenant, model_variables, document_version, enriched_chunks)
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# Update document version and save embeddings
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try:
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db.session.add(document_version)
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document_version.processing_finished_at = dt.now(tz.utc)
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document_version.processing = False
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db.session.add_all(embeddings)
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db.session.commit()
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except SQLAlchemyError as e:
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current_app.logger.error(f'Error saving embedding information for tenant {tenant.id} '
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f'on HTML, document version {document_version.id}'
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f'error: {e}')
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raise
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current_app.logger.info(f'Embeddings created successfully for tenant {tenant.id} '
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f'on document version {document_version.id} :-)')
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def enrich_chunks(tenant, model_variables, document_version, title, chunks):
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summary = ''
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if len(chunks) > 1:
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summary = summarize_chunk(tenant, model_variables, document_version, chunks[0])
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chunk_total_context = (f'Filename: {document_version.object_name}\n'
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f'User Context:\n{document_version.user_context}\n\n'
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f'User Metadata:\n{document_version.user_metadata}\n\n'
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f'Title: {title}\n'
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f'Summary:\n{summary}\n'
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f'System Context:\n{document_version.system_context}\n\n'
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f'System Metadata:\n{document_version.system_metadata}\n\n'
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)
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enriched_chunks = []
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initial_chunk = (f'Filename: {document_version.object_name}\n'
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f'User Context:\n{document_version.user_context}\n\n'
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f'User Metadata:\n{document_version.user_metadata}\n\n'
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f'Title: {title}\n'
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f'System Context:\n{document_version.system_context}\n\n'
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f'System Metadata:\n{document_version.system_metadata}\n\n'
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f'{chunks[0]}'
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)
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enriched_chunks.append(initial_chunk)
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for chunk in chunks[1:]:
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enriched_chunk = f'{chunk_total_context}\n{chunk}'
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enriched_chunks.append(enriched_chunk)
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return enriched_chunks
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def summarize_chunk(tenant, model_variables, document_version, chunk):
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current_event.log("Starting Summarizing Chunk")
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llm = model_variables.get_llm()
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template = model_variables.get_template("summary")
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language_template = create_language_template(template, document_version.language)
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summary_prompt = ChatPromptTemplate.from_template(language_template)
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setup = RunnablePassthrough()
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output_parser = StrOutputParser()
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chain = setup | summary_prompt | llm | output_parser
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try:
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summary = chain.invoke({"text": chunk})
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current_event.log("Finished Summarizing Chunk")
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return summary
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except LangChainException as e:
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current_app.logger.error(f'Error creating summary for chunk enrichment for tenant {tenant.id} '
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f'on document version {document_version.id} '
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f'error: {e}')
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raise
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def embed_chunks(tenant, model_variables, document_version, chunks):
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embedding_model = model_variables.embedding_model
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try:
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embeddings = embedding_model.embed_documents(chunks)
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except LangChainException as e:
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current_app.logger.error(f'Error creating embeddings for tenant {tenant.id} '
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f'on document version {document_version.id} while calling OpenAI API'
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f'error: {e}')
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raise
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# Add embeddings to the database
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new_embeddings = []
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for chunk, embedding in zip(chunks, embeddings):
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new_embedding = model_variables.embedding_model_class()
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new_embedding.document_version = document_version
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new_embedding.active = True
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new_embedding.chunk = chunk
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new_embedding.embedding = embedding
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new_embeddings.append(new_embedding)
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return new_embeddings
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def create_potential_chunks_for_markdown(tenant_id, document_version, input_file):
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try:
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current_app.logger.info(f'Creating potential chunks for tenant {tenant_id}')
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markdown_on = document_version.object_name.rsplit('.', 1)[0] + '.md'
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# Download the markdown file from MinIO
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markdown_data = minio_client.download_document_file(tenant_id,
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document_version.bucket_name,
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markdown_on,
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)
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markdown = markdown_data.decode('utf-8')
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headers_to_split_on = [
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("#", "Header 1"),
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("##", "Header 2"),
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]
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markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on, strip_headers=False)
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md_header_splits = markdown_splitter.split_text(markdown)
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potential_chunks = [doc.page_content for doc in md_header_splits]
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return potential_chunks
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except Exception as e:
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current_app.logger.error(f'Error creating potential chunks for tenant {tenant_id}, with error: {e}')
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raise
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def combine_chunks_for_markdown(potential_chunks, min_chars, max_chars):
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actual_chunks = []
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current_chunk = ""
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current_length = 0
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for chunk in potential_chunks:
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chunk_length = len(chunk)
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if current_length + chunk_length > max_chars:
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if current_length >= min_chars:
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actual_chunks.append(current_chunk)
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current_chunk = chunk
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current_length = chunk_length
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else:
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# If the combined chunk is still less than max_chars, keep adding
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current_chunk += f'\n{chunk}'
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current_length += chunk_length
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else:
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current_chunk += f'\n{chunk}'
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current_length += chunk_length
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# Handle the last chunk
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if current_chunk and current_length >= 0:
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actual_chunks.append(current_chunk)
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return actual_chunks
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def get_processor_for_document(catalog_id: int, file_type: str, sub_file_type: str = None) -> Processor:
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"""
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Get the appropriate processor for a document based on catalog_id, file_type and optional sub_file_type.
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Args:
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catalog_id: ID of the catalog
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file_type: Type of file (e.g., 'pdf', 'html')
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sub_file_type: Optional sub-type for specialized processing
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Returns:
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Processor instance
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Raises:
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ValueError: If no matching processor is found
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"""
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try:
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# Start with base query for catalog
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query = Processor.query.filter_by(catalog_id=catalog_id)
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# Find processor type that handles this file type
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matching_processor_type = None
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for proc_type, config in PROCESSOR_TYPES.items():
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supported_types = config['file_types']
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if isinstance(supported_types, str):
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supported_types = [t.strip() for t in supported_types.split(',')]
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if file_type in supported_types:
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matching_processor_type = proc_type
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break
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if not matching_processor_type:
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raise ValueError(f"No processor type found for file type: {file_type}")
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# Add processor type condition
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query = query.filter_by(type=matching_processor_type)
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# If sub_file_type is provided, add that condition
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if sub_file_type:
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query = query.filter_by(sub_file_type=sub_file_type)
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else:
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# If no sub_file_type, prefer processors without sub_file_type specification
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query = query.filter(or_(Processor.sub_file_type.is_(None),
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Processor.sub_file_type == ''))
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# Get the first matching processor
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processor = query.first()
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if not processor:
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if sub_file_type:
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raise ValueError(
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f"No processor found for catalog {catalog_id} of type {matching_processor_type}, "
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f"file type {file_type}, sub-type {sub_file_type}"
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)
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else:
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raise ValueError(
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f"No processor found for catalog {catalog_id}, "
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f"file type {file_type}"
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)
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return processor
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except Exception as e:
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current_app.logger.error(f"Error finding processor: {str(e)}")
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raise
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