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llama_index.core.indices import VectorStoreIndex, load_index_from_storage, SimpleKeywordTableIndex -from private_gpt.utils.vector_store import VectorStoreIndex1 +from llama_index.core.indices import VectorStoreIndex, load_index_from_storage from llama_index.core.indices.base import BaseIndex from llama_index.core.ingestion import run_transformations from llama_index.core.schema import BaseNode, Document, TransformComponent @@ -84,7 +83,7 @@ class BaseIngestComponentWithIndex(BaseIngestComponent, abc.ABC): except ValueError: # There are no index in the storage context, creating a new one logger.info("Creating a new vector store index") - index = VectorStoreIndex1.from_documents( + index = VectorStoreIndex.from_documents( [], storage_context=self.storage_context, store_nodes_override=True, # Force store nodes in index and document stores @@ -93,17 +92,6 @@ class BaseIngestComponentWithIndex(BaseIngestComponent, abc.ABC): transformations=self.transformations, ) index.storage_context.persist(persist_dir=local_data_path) - - keyword_index = SimpleKeywordTableIndex.from_documents( - [], - storage_context=self.storage_context, - store_nodes_override=True, # Force store nodes in index and document stores - show_progress=self.show_progress, - transformations=self.transformations, - llm= - ) - # Store the keyword index in the vector store - index.keyword_index = keyword_index return index def _save_index(self) -> None: diff --git a/private_gpt/components/ocr_components/table_ocr.py b/private_gpt/components/ocr_components/table_ocr.py index 523735eb..147d3a8d 100644 --- a/private_gpt/components/ocr_components/table_ocr.py +++ b/private_gpt/components/ocr_components/table_ocr.py @@ -1,57 +1,58 @@ -# from paddleocr import PaddleOCR +import io +from typing import Union + import cv2 import torch -from doctr.models import ocr_predictor from doctr.io import DocumentFile -from injector import singleton, inject -device = "cuda" if torch.cuda.is_available() else "cpu" +from doctr.models import ocr_predictor +from injector import inject, singleton +from pdf2image import convert_from_bytes + +# device = "cuda" if torch.cuda.is_available() else "cpu" +device = "cpu" @singleton class GetOCRText: @inject def __init__(self) -> None: self._image = None - # self.ocr = PaddleOCR(use_angle_cls=True, lang='en') self.doctr = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True).to(device) def _preprocess_image(self, img): - resized_image = cv2.resize(img, None, fx=1.6, fy=1.6, interpolation=cv2.INTER_CUBIC) + resized_image = cv2.resize(img, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC) gray_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY) - _, binary = cv2.threshold(gray_image, 128, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) + _, binary = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) return binary - - ## paddleOCR - # def extract_text(self, cell_image): - # text = "" - # self._image = cell_image - # preprocessd_image = self._preprocess_image(self._image) - # results = self.ocr.ocr(preprocessd_image, cls=True) - # print(results) - # if len(results) > 0: - # for result in results[0]: - # text += f"{result[-1][0]} " - # else: - # text = "" - # return text - - ## docTR OCR - def extract_text(self, cell_image=None, image_file=False, file_path=None): + + def extract_text(self, cell_image: Union[None, bytes] = None, image_file: bool = False, file_path: Union[None, str] = None): text = "" + if image_file: + if file_path is None: + raise ValueError("file_path must be provided when image_file is True.") pdf_file = DocumentFile.from_images(file_path) result = self.doctr(pdf_file) output = result.export() else: - self._image = cell_image - preprocessd_image = self._preprocess_image(self._image) - result = self.doctr([self._image]) - output = result.export() + if cell_image is None: + raise ValueError("cell_image must be provided when image_file is False.") + + if isinstance(cell_image, bytes): + images = convert_from_bytes(cell_image) + pdf_file = DocumentFile.from_images(images) + result = self.doctr(pdf_file) + else: + self._image = cell_image + preprocessed_image = self._preprocess_image(self._image) + result = self.doctr([preprocessed_image]) + output = result.export() + for obj1 in output['pages'][0]["blocks"]: for obj2 in obj1["lines"]: for obj3 in obj2["words"]: text += (f"{obj3['value']} ").replace("\n", "") - - text = text + "\n" + text += "\n" + text += "\n" if text: - return text - return " " \ No newline at end of file + return text.strip() + return " " diff --git a/private_gpt/components/ocr_components/table_ocr_api.py b/private_gpt/components/ocr_components/table_ocr_api.py index 854681e5..169b5e62 100644 --- a/private_gpt/components/ocr_components/table_ocr_api.py +++ b/private_gpt/components/ocr_components/table_ocr_api.py @@ -34,10 +34,9 @@ async def save_uploaded_file(file: UploadFile, upload_dir: str): async def process_images_and_generate_doc(request: Request, pdf_path: str, upload_dir: str): - doc = Document() - ocr = request.state.injector.get(GetOCRText) - img_tab = request.state.injector.get(ImageToTable) + # img_tab = request.state.injector.get(ImageToTable) + pdf_writer = fitz.open() pdf_doc = fitz.open(pdf_path) for page_index in range(len(pdf_doc)): @@ -55,18 +54,19 @@ async def process_images_and_generate_doc(request: Request, pdf_path: str, uploa pix = fitz.Pixmap(fitz.csRGB, pix)( "RGB", [pix.width, pix.height], pix.samples) - image_path = f"page_{page_index}-image_{image_index}.png" + image_path = f"temp_page_{page_index}_image_{image_index}.png" pix.save(image_path) + extracted_text = ocr.extract_text( image_file=True, file_path=image_path) - doc.add_paragraph(extracted_text) - table_data = img_tab.table_to_csv(image_path) - doc.add_paragraph(table_data) + # Create a new page with the same dimensions as the original page + pdf_page = pdf_writer.new_page(width=page.rect.width, height=page.rect.height) + pdf_page.insert_text((10, 10), extracted_text, fontsize=9) os.remove(image_path) - save_path = os.path.join( - upload_dir, f"{os.path.splitext(os.path.basename(pdf_path))[0]}_ocr.docx") - doc.save(save_path) + save_path = os.path.join(upload_dir, f"{os.path.splitext(os.path.basename(pdf_path))[0]}.pdf") + pdf_writer.save(save_path) + pdf_writer.close() return save_path @@ -82,6 +82,7 @@ async def process_pdf_ocr( try: pdf_path = await save_uploaded_file(file, UPLOAD_DIR) ocr_doc_path = await process_images_and_generate_doc(request, pdf_path, UPLOAD_DIR) + print("FILE PATH:", ocr_doc_path) ingested_documents = await common_ingest_logic( request=request, db=db, ocr_file=ocr_doc_path, current_user=current_user, original_file=None, log_audit=log_audit, departments=departments ) @@ -111,26 +112,6 @@ async def process_ocr( detail=f"There was an error processing OCR: {e}" ) -async def process_both_ocr( - request: Request, - pdf_path: str -): - UPLOAD_DIR = OCR_UPLOAD - try: - ocr_doc_path = await process_images_and_generate_doc(request, pdf_path, UPLOAD_DIR) - ingested_ocr_documents = await ingest(request=request, file_path=ocr_doc_path) # ingest ocr - ingested_documents = await ingest(request=request, file_path=pdf_path) # ingest pdf - return ingested_documents - - except Exception as e: - print(traceback.print_exc()) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"There was an error processing OCR: {e}" - ) - - - async def process_both( request: Request, db: Session, @@ -170,18 +151,3 @@ async def get_pdf_ocr_wrapper( ) ): return await process_pdf_ocr(request, db, file, current_user, log_audit, departments) - - -@pdf_router.post("/both") -async def get_both_wrapper( - request: Request, - departments: schemas.DocumentDepartmentList = Depends(), - db: Session = Depends(deps.get_db), - log_audit: models.Audit = Depends(deps.get_audit_logger), - file: UploadFile = File(...), - current_user: models.User = Security( - deps.get_current_user, - scopes=[Role.ADMIN["name"], Role.SUPER_ADMIN["name"]], - ) -): - return await process_both(request, db, file, current_user, log_audit, departments) diff --git a/private_gpt/components/vector_store/retriever.py b/private_gpt/components/vector_store/retriever.py deleted file mode 100644 index 90cf18f8..00000000 --- a/private_gpt/components/vector_store/retriever.py +++ /dev/null @@ -1,55 +0,0 @@ - - -# import QueryBundle -from llama_index.core import QueryBundle - -# import NodeWithScore -from llama_index.core.schema import NodeWithScore - -# Retrievers -from llama_index.core.retrievers import ( - BaseRetriever, - VectorIndexRetriever, - KeywordTableSimpleRetriever, -) - -from typing import List - - -class CustomRetriever(BaseRetriever): - """Custom retriever that performs both semantic search and hybrid search.""" - - def __init__( - self, - vector_retriever: VectorIndexRetriever, - keyword_retriever: KeywordTableSimpleRetriever, - mode: str = "AND", - ) -> None: - """Init params.""" - - self._vector_retriever = vector_retriever - self._keyword_retriever = keyword_retriever - if mode not in ("AND", "OR"): - raise ValueError("Invalid mode.") - self._mode = mode - super().__init__() - - def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: - """Retrieve nodes given query.""" - - vector_nodes = self._vector_retriever.retrieve(query_bundle) - keyword_nodes = self._keyword_retriever.retrieve(query_bundle) - - vector_ids = {n.node.node_id for n in vector_nodes} - keyword_ids = {n.node.node_id for n in keyword_nodes} - - combined_dict = {n.node.node_id: n for n in vector_nodes} - combined_dict.update({n.node.node_id: n for n in keyword_nodes}) - - if self._mode == "AND": - retrieve_ids = vector_ids.intersection(keyword_ids) - else: - retrieve_ids = vector_ids.union(keyword_ids) - - retrieve_nodes = [combined_dict[rid] for rid in retrieve_ids] - return retrieve_nodes \ No newline at end of file diff --git a/private_gpt/components/vector_store/vector_store_component.py b/private_gpt/components/vector_store/vector_store_component.py index 6e2aa491..316e4c63 100644 --- a/private_gpt/components/vector_store/vector_store_component.py +++ b/private_gpt/components/vector_store/vector_store_component.py @@ -3,14 +3,12 @@ import typing from injector import inject, singleton from llama_index.core.indices.vector_store import VectorIndexRetriever, VectorStoreIndex -from llama_index.core.indices import SimpleKeywordTableIndex from llama_index.core.vector_stores.types import ( FilterCondition, MetadataFilter, MetadataFilters, VectorStore, ) -from private_gpt.utils.vector_store import VectorStoreIndex1 from private_gpt.open_ai.extensions.context_filter import ContextFilter from private_gpt.paths import local_data_path @@ -132,43 +130,21 @@ class VectorStoreComponent: def get_retriever( self, - index: VectorStoreIndex1, + index: VectorStoreIndex, context_filter: ContextFilter | None = None, similarity_top_k: int = 2, ) -> VectorIndexRetriever: # This way we support qdrant (using doc_ids) and the rest (using filters) - - # from llama_index.core import get_response_synthesizer - # from llama_index.core.query_engine import RetrieverQueryEngine - from .retriever import CustomRetriever - from llama_index.core.retrievers import ( - VectorIndexRetriever, - KeywordTableSimpleRetriever, - ) - - vector_retriever = VectorIndexRetriever( + return VectorIndexRetriever( index=index, similarity_top_k=similarity_top_k, doc_ids=context_filter.docs_ids if context_filter else None, filters=( - _doc_id_metadata_filter(context_filter) + _doc_id_metadata_filter(context_filter) if self.settings.vectorstore.database != "qdrant" else None ), ) - keyword_retriever = KeywordTableSimpleRetriever(index=index.keyword_index) - custom_retriever = CustomRetriever(vector_retriever, keyword_retriever) - - # define response synthesizer - # response_synthesizer = get_response_synthesizer() - - # # assemble query engine - # custom_query_engine = RetrieverQueryEngine( - # retriever=custom_retriever, - # response_synthesizer=response_synthesizer, - # ) - - return custom_retriever def close(self) -> None: if hasattr(self.vector_store.client, "close"): diff --git a/private_gpt/server/chat/chat_service.py b/private_gpt/server/chat/chat_service.py index 3f00e518..c031996d 100644 --- a/private_gpt/server/chat/chat_service.py +++ b/private_gpt/server/chat/chat_service.py @@ -5,7 +5,7 @@ from llama_index.core.chat_engine import ContextChatEngine, SimpleChatEngine from llama_index.core.chat_engine.types import ( BaseChatEngine, ) -from llama_index.core.indices import VectorStoreIndex, SimpleKeywordTableIndex +from llama_index.core.indices import VectorStoreIndex from llama_index.core.indices.postprocessor import MetadataReplacementPostProcessor from llama_index.core.llms import ChatMessage, MessageRole from llama_index.core.postprocessor import ( @@ -99,12 +99,6 @@ class ChatService: embed_model=embedding_component.embedding_model, show_progress=True, ) - self.keyword_index = SimpleKeywordTableIndex.from_documents( - vector_store_component.vector_store, - storage_context=self.storage_context, - embed_model=embedding_component.embedding_model, - show_progress=True, - ) def _chat_engine( self, @@ -116,7 +110,6 @@ class ChatService: if use_context: vector_index_retriever = self.vector_store_component.get_retriever( index=self.index, - keyword_index=self.keyword_index, context_filter=context_filter, similarity_top_k=self.settings.rag.similarity_top_k, ) @@ -195,17 +188,17 @@ class ChatService: ) system_prompt = ( """ - You are QuickGPT, a helpful assistant by Quickfox Consulting. + You are a helpful assistant named QuickGPT by Quickfox Consulting. + Your responses must be strictly and exclusively based on the context documents provided. - Responses should be based on the context documents provided - and should be relevant, informative, and easy to understand. - You should aim to deliver high-quality responses that are - respectful and helpful, using clear and concise language. - Avoid providing information outside of the context documents unless - it is necessary for clarity or completeness. Focus on providing - accurate and reliable answers based on the given context. - If answer is not in the context documents, just say I don't have answer - in respectful way. + You are not allowed to use any information, knowledge, or external sources outside of the given context documents. + If the answer to a query is not present in the context documents, + you should respond with "I do not have enough information in the provided context to answer this question." + + Your responses should be relevant, informative, and easy to understand. + Aim to deliver high-quality answers that are respectful and helpful, using clear and concise language. + Focus on providing accurate and reliable answers based solely on the given context. + Do not make assumptions, inferences, or draw upon any prior knowledge beyond what is explicitly stated in the context documents. """ ) chat_history = ( diff --git a/private_gpt/server/ingest/ingest_router.py b/private_gpt/server/ingest/ingest_router.py index 2fa92092..a6623c6c 100644 --- a/private_gpt/server/ingest/ingest_router.py +++ b/private_gpt/server/ingest/ingest_router.py @@ -253,34 +253,6 @@ async def common_ingest_logic( f.write(file.read()) file.seek(0) ingested_documents = service.ingest_bin_data(file_name, file) - # Handling Original File - if original_file: - try: - print("ORIGINAL PDF FILE PATH IS :: ", original_file) - file_name = Path(original_file).name - upload_path = Path(f"{UPLOAD_DIR}/{file_name}") - - if file_name is None: - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="No file name provided", - ) - - await create_documents(db, file_name, current_user, departments, log_audit) - - with open(upload_path, "wb") as f: - with open(original_file, "rb") as original_file_reader: - f.write(original_file_reader.read()) - - with open(upload_path, "rb") as f: - ingested_documents = service.ingest_bin_data(file_name, f) - except Exception as e: - print(traceback.print_exc()) - raise HTTPException( - status_code=500, - detail="Internal Server Error: Unable to ingest file.", - ) - logger.info( f"{file_name} is uploaded by the {current_user.username}.") diff --git a/private_gpt/users/api/v1/routers/documents.py b/private_gpt/users/api/v1/routers/documents.py index c025c030..ffe19640 100644 --- a/private_gpt/users/api/v1/routers/documents.py +++ b/private_gpt/users/api/v1/routers/documents.py @@ -17,7 +17,7 @@ from private_gpt.users.core.config import settings from private_gpt.users import crud, models, schemas from private_gpt.server.ingest.ingest_router import create_documents, ingest from private_gpt.users.models.document import MakerCheckerActionType, MakerCheckerStatus -from private_gpt.components.ocr_components.table_ocr_api import process_both_ocr, process_ocr +from private_gpt.components.ocr_components.table_ocr_api import process_ocr logger = logging.getLogger(__name__) router = APIRouter(prefix='/documents', tags=['Documents']) @@ -385,8 +385,6 @@ async def verify_documents( if document.doc_type_id == 2: # For OCR return await process_ocr(request, unchecked_path) - elif document.doc_type_id == 3: # For BOTH - return await process_both_ocr(request, unchecked_path) else: return await ingest(request, unchecked_path) # For pdf diff --git a/private_gpt/utils/vector_store.py b/private_gpt/utils/vector_store.py deleted file mode 100644 index 9ab78ab2..00000000 --- a/private_gpt/utils/vector_store.py +++ /dev/null @@ -1,13 +0,0 @@ -from llama_index.core.indices.vector_store import VectorStoreIndex -from llama_index.core.indices import SimpleKeywordTableIndex - -class VectorStoreIndex1(VectorStoreIndex): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.keyword_index = None - - def set_keyword_index(self, keyword_index: SimpleKeywordTableIndex): - self.keyword_index = keyword_index - - def get_keyword_index(self) -> SimpleKeywordTableIndex: - return self.keyword_index \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 74828150..8f1e367e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -68,6 +68,8 @@ openpyxl = "^3.1.2" pandas = "^2.2.2" fastapi-pagination = "^0.12.23" xlsxwriter = "^3.2.0" +pdf2image = "^1.17.0" +pymupdf = "^1.24.4" [tool.poetry.extras] ui = ["gradio"] diff --git a/settings.yaml b/settings.yaml index 8e9f30d1..d3780fc3 100644 --- a/settings.yaml +++ b/settings.yaml @@ -57,8 +57,8 @@ rag: llamacpp: # llm_hf_repo_id: bartowski/Meta-Llama-3-8B-Instruct-GGUF # llm_hf_model_file: Meta-Llama-3-8B-Instruct-Q6_K.gguf - llm_hf_repo_id: NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF - llm_hf_model_file: Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q6_K.gguf + llm_hf_repo_id: qwp4w3hyb/Hermes-2-Pro-Llama-3-8B-iMat-GGUF + llm_hf_model_file: hermes-2-pro-llama-3-8b-imat-Q6_K.gguf tfs_z: 1.0 # Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting top_k: 40 # Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40) top_p: 0.9 # Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)