Start working on chunking en embedding task. Continu with embeddings.
This commit is contained in:
Josako
@@ -1,8 +1,19 @@
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from datetime import datetime as dt, timezone as tz
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from langchain_community.document_loaders.unstructured import UnstructuredAPIFileLoader
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from flask import current_app
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import os
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# Unstructured commercial client imports
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from unstructured_client import UnstructuredClient
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from unstructured_client.models import shared
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from unstructured_client.models.errors import SDKError
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# OpenAI imports
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from langchain_openai import OpenAIEmbeddings, ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate
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from langchain.chains.summarize import load_summarize_chain
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from langchain.text_splitter import CharacterTextSplitter
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from langchain_core.exceptions import LangChainException
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from common.utils.database import Database
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from common.models.document import DocumentVersion, EmbeddingMistral, EmbeddingSmallOpenAI
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from common.extensions import db
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@@ -11,8 +22,11 @@ from common.utils.celery_utils import current_celery
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@current_celery.task(name='create_embeddings', queue='embeddings')
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def create_embeddings(tenant_id, document_version_id, default_embedding_model):
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import pydevd_pycharm
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pydevd_pycharm.settrace('localhost', port=50170, stdoutToServer=True, stderrToServer=True)
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# Setup Remote Debugging only if PYCHARM_DEBUG=True
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if current_app.config['PYCHARM_DEBUG']:
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import pydevd_pycharm
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pydevd_pycharm.settrace('localhost', port=50170, stdoutToServer=True, stderrToServer=True)
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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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f'with model {default_embedding_model}')
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@@ -32,38 +46,130 @@ def create_embeddings(tenant_id, document_version_id, default_embedding_model):
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document_version.processing_started_at = dt.now(tz.utc)
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db.session.commit()
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embedding_provider = default_embedding_model.rsplit('.', 1)[0]
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embedding_model = default_embedding_model.rsplit('.', 1)[1]
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embed_provider = default_embedding_model.rsplit('.', 1)[0]
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embed_model = default_embedding_model.rsplit('.', 1)[1]
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# define embedding variables
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match (embedding_provider, embedding_model):
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match (embed_provider, embed_model):
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case ('openai', 'text-embedding-3-small'):
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embedding_model = EmbeddingSmallOpenAI()
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case ('mistral', 'text-embedding-3-small'):
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embedding_model = EmbeddingMistral()
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embedding_function = embed_chunks_for_text_embedding_3_small
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case ('mistral', 'mistral.mistral-embed'):
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embedding_function = embed_chunks_for_mistral_embed
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match document_version.file_type:
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case 'pdf':
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url = current_app.config.get('UNSTRUCTURED_FULL_URL')
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api_key = current_app.config.get('UNSTRUCTURED_API_KEY')
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file_path = os.path.join(current_app.config['UPLOAD_FOLDER'],
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document_version.file_location,
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document_version.file_name)
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with open(file_path, 'rb') as f:
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loader = UnstructuredAPIFileLoader(f,
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url=url,
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api_key=api_key,
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mode='elements',
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strategy='hi-res',
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include_page_breaks=True,
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unique_element_ids=True,
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chunking_strategy='by_title',
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max_characters=3000,
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)
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documents = loader.load()
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print(documents)
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if os.path.exists(file_path):
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with open(file_path, 'rb') as f:
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files = shared.Files(content=f.read(), file_name=document_version.file_name)
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req = shared.PartitionParameters(
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files=files,
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strategy='hi_res',
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hi_res_model_name='yolox',
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coordinates=True,
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extract_image_block_types=['Image', 'Table'],
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chunking_strategy='by_title',
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combine_under_n_chars=2000,
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max_characters=3000,
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)
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try:
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chunks = partition_doc_unstructured(tenant_id, document_version, req)
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enriched_chunk_docs = enrich_chunks(tenant_id, document_version, chunks)
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embedding_function(tenant_id, document_version, enriched_chunk_docs)
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except Exception as e:
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current_app.logger.error(f'Unable to create Embeddings for tenant {tenant_id} '
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f'on document version {document_version.id} '
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f'with model {default_embedding_model} '
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f'error: {e}')
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return
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else: # file exists
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current_app.logger.error(f'The physical file for document version {document_version_id} '
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f'at {file_path} does not exist')
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return
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@current_celery.task(name='ask_eve_ai', queue='llm_interactions')
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def ask_eve_ai(query):
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# Interaction logic with LLMs like GPT (Langchain API calls, etc.)
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pass
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def enrich_chunks(tenant_id, document_version, chunks):
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# We're adding filename and a summary of the first chunk to all the chunks to create global context
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# using openAI to summarise
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api_key = current_app.config.get('OPENAI_API_KEY')
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# TODO: model selection to be adapted to model approach
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llm = ChatOpenAI(api_key=api_key, temperature=0, model='gpt-4-turbo')
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summary_template = current_app.config.get('GPT4_SUMMARY_TEMPLATE')
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prompt = ChatPromptTemplate.from_template(summary_template)
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chain = load_summarize_chain(llm, chain_type='stuff', prompt=prompt)
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doc_creator = CharacterTextSplitter(chunk_size=9000, chunk_overlap=0)
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text_to_summarize = doc_creator.create_documents(chunks[0]['text'])
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try:
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summary = chain.run(text_to_summarize)
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chunk_global_context = f'Filename: {document_version.file_name}\nSummary:\n {summary}'
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enriched_chunks = []
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for chunk in chunks[1:]:
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enriched_chunk_raw = f'{chunk_global_context}\n{chunk}'
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enriched_chunk_doc = doc_creator.create_documents([enriched_chunk_raw])
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enriched_chunks.append(enriched_chunk_doc)
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return enriched_chunks
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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 partition_doc_unstructured(tenant_id, document_version, unstructured_request):
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# Initiate the connection to unstructured.io
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url = current_app.config.get('UNSTRUCTURED_FULL_URL')
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api_key = current_app.config.get('UNSTRUCTURED_API_KEY')
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unstructured_client = UnstructuredClient(server_url=url, api_key_auth=api_key)
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try:
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res = unstructured_client.general.partition(unstructured_request)
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chunks = []
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for el in res.elements:
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match el['type']:
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case 'Composite_element':
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chunks.append(el['text'])
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case 'Image':
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pass
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case 'Table':
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chunks.append(el['metadata']['text_as_html'])
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return chunks
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except SDKError 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 chuncking'
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f'error: {e}')
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raise
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def embed_chunks_for_text_embedding_3_small(tenant_id, document_version, chunks):
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# Create embedding vectors using OpenAI
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api_key = current_app.config.get('OPENAI_API_KEY')
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embeddings_model = OpenAIEmbeddings(api_key=api_key, model='text-embedding-3-small')
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try:
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embeddings = embeddings_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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for chunk, embedding in zip(chunks, embeddings):
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new_embedding = EmbeddingSmallOpenAI()
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# TODO: continue here
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return embeddings
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def embed_chunks_for_mistral_embed(tenant_id, document_version, chunks):
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pass
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