使用paddleocr实现 (#342)

* jpg and png ocr

* fix

* write docs to tmp file

* fix

* [BUGFIX] local_doc_qa.py line 172: logging have no end args. (#323)

* image loader

* fix

* fix

* update api.py

* update api.py

* update api.py

* update README.md

* update api.py

* add pdf_loader

* fix

---------

Co-authored-by: RainGather <3255329+RainGather@users.noreply.github.com>
Co-authored-by: imClumsyPanda <littlepanda0716@gmail.com>

README.md

@@ -207,6 +207,6 @@ Web UI 可以实现如下功能：
   - [ ] 实现调用 API 的 Web UI Demo
 
 ## 项目交流群
-![二维码](img/qr_code_16.jpg)
+![二维码](img/qr_code_17.jpg)
 
 🎉 langchain-ChatGLM 项目交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。

api.py

@@ -22,6 +22,7 @@ from configs.model_config import (VS_ROOT_PATH, UPLOAD_ROOT_PATH, EMBEDDING_DEVI
 
 nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
 
+
 class BaseResponse(BaseModel):
     code: int = pydantic.Field(200, description="HTTP status code")
     msg: str = pydantic.Field("success", description="HTTP status message")
@@ -87,7 +88,7 @@ def get_vs_path(local_doc_id: str):
 def get_file_path(local_doc_id: str, doc_name: str):
     return os.path.join(UPLOAD_ROOT_PATH, local_doc_id, doc_name)
 
-async def single_upload_file(
+async def upload_file(
         file: UploadFile = File(description="A single binary file"),
         knowledge_base_id: str = Form(..., description="Knowledge Base Name", example="kb1"),
 ):
@@ -106,21 +107,15 @@ async def single_upload_file(
         f.write(file_content)
 
     vs_path = get_vs_path(knowledge_base_id)
-    if os.path.exists(vs_path):
-        added_files = await local_doc_qa.add_files_to_knowledge_vector_store(vs_path, [file_path])
-        if len(added_files) > 0:
-            file_status = f"文件 {file.filename} 已上传并已加载知识库，请开始提问。"
-            return BaseResponse(code=200, msg=file_status)
-    else:
-        vs_path, loaded_files = await local_doc_qa.init_knowledge_vector_store([file_path], vs_path)
+    vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store([file_path], vs_path)
     if len(loaded_files) > 0:
         file_status = f"文件 {file.filename} 已上传至新的知识库，并已加载知识库，请开始提问。"
         return BaseResponse(code=200, msg=file_status)
-
+    else:
         file_status = "文件上传失败，请重新上传"
         return BaseResponse(code=500, msg=file_status)
 
-async def upload_file(
+async def upload_files(
         files: Annotated[
             List[UploadFile], File(description="Multiple files as UploadFile")
         ],
@@ -203,7 +198,7 @@ async def delete_docs(
     return BaseResponse()
 
 
-async def chat(
+async def local_doc_chat(
         knowledge_base_id: str = Body(..., description="Knowledge Base Name", example="kb1"),
         question: str = Body(..., description="Question", example="工伤保险是什么？"),
         history: List[List[str]] = Body(
@@ -238,7 +233,8 @@ async def chat(
         source_documents=source_documents,
     )
 
-async def no_knowledge_chat(
+
+async def chat(
         question: str = Body(..., description="Question", example="工伤保险是什么？"),
         history: List[List[str]] = Body(
             [],
@@ -251,12 +247,19 @@ async def no_knowledge_chat(
             ],
         ),
 ):
-
-    for resp, history in local_doc_qa._call(
-            query=question, chat_history=history, streaming=True
+    for resp, history in local_doc_qa.llm._call(
+            prompt=question, history=history, streaming=True
     ):
         pass
 
+    return ChatMessage(
+        question=question,
+        response=resp,
+        history=history,
+        source_documents=[],
+    )
+
+
 async def stream_chat(websocket: WebSocket, knowledge_base_id: str):
     await websocket.accept()
     vs_path = os.path.join(VS_ROOT_PATH, knowledge_base_id)
@@ -323,15 +326,19 @@ def main():
             allow_methods=["*"],
             allow_headers=["*"],
         )
-    app.websocket("/chat-docs/stream-chat/{knowledge_base_id}")(stream_chat)
-    app.post("/chat-docs/chat", response_model=ChatMessage)(chat)
-    app.post("/chat-docs/chatno", response_model=ChatMessage)(no_knowledge_chat)
-    app.post("/chat-docs/upload", response_model=BaseResponse)(upload_file)
-    app.post("/chat-docs/uploadone", response_model=BaseResponse)(single_upload_file)
-    app.get("/chat-docs/list", response_model=ListDocsResponse)(list_docs)
-    app.delete("/chat-docs/delete", response_model=BaseResponse)(delete_docs)
+    app.websocket("/local_doc_qa/stream-chat/{knowledge_base_id}")(stream_chat)
+
     app.get("/", response_model=BaseResponse)(document)
 
+    app.post("/chat", response_model=ChatMessage)(chat)
+
+    app.post("/local_doc_qa/upload_file", response_model=BaseResponse)(upload_file)
+    app.post("/local_doc_qa/upload_files", response_model=BaseResponse)(upload_files)
+    app.post("/local_doc_qa/local_doc_chat", response_model=ChatMessage)(local_doc_chat)
+    app.get("/local_doc_qa/list_files", response_model=ListDocsResponse)(list_docs)
+    app.delete("/local_doc_qa/delete_file", response_model=BaseResponse)(delete_docs)
+
+
     local_doc_qa = LocalDocQA()
     local_doc_qa.init_cfg(
         llm_model=LLM_MODEL,

chains/local_doc_qa.py

@@ -10,6 +10,8 @@ import numpy as np
 from utils import torch_gc
 from tqdm import tqdm
 from pypinyin import lazy_pinyin
+from loader import UnstructuredPaddleImageLoader
+from loader import UnstructuredPaddlePDFLoader
 
 DEVICE_ = EMBEDDING_DEVICE
 DEVICE_ID = "0" if torch.cuda.is_available() else None
@@ -21,16 +23,31 @@ def load_file(filepath, sentence_size=SENTENCE_SIZE):
         loader = UnstructuredFileLoader(filepath, mode="elements")
         docs = loader.load()
     elif filepath.lower().endswith(".pdf"):
-        loader = UnstructuredFileLoader(filepath, strategy="fast")
+        loader = UnstructuredPaddlePDFLoader(filepath)
         textsplitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size)
         docs = loader.load_and_split(textsplitter)
+    elif filepath.lower().endswith(".jpg") or filepath.lower().endswith(".png"):
+        loader = UnstructuredPaddleImageLoader(filepath, mode="elements")
+        textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
+        docs = loader.load_and_split(text_splitter=textsplitter)
     else:
         loader = UnstructuredFileLoader(filepath, mode="elements")
         textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
         docs = loader.load_and_split(text_splitter=textsplitter)
+    write_check_file(filepath, docs)
     return docs
 
 
+def write_check_file(filepath, docs):
+    fout = open('load_file.txt', 'a')
+    fout.write("filepath=%s,len=%s" % (filepath, len(docs)))
+    fout.write('\n')
+    for i in docs:
+        fout.write(str(i))
+        fout.write('\n')
+    fout.close()
+
+
 def generate_prompt(related_docs: List[str], query: str,
                     prompt_template=PROMPT_TEMPLATE) -> str:
     context = "\n".join([doc.page_content for doc in related_docs])
@@ -176,7 +193,7 @@ class LocalDocQA:
                 if len(failed_files) > 0:
                     logger.info("以下文件未能成功加载：")
                     for file in failed_files:
-                        logger.info(file, end="\n")
+                        logger.info(f"{file}\n")
 
         else:
             docs = []
@@ -212,7 +229,7 @@ class LocalDocQA:
             if not vs_path or not one_title or not one_conent:
                 logger.info("知识库添加错误，请确认知识库名字、标题、内容是否正确！")
                 return None, [one_title]
-            docs = [Document(page_content=one_conent+"\n", metadata={"source": one_title})]
+            docs = [Document(page_content=one_conent + "\n", metadata={"source": one_title})]
             if not one_content_segmentation:
                 text_splitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
                 docs = text_splitter.split_documents(docs)

loader/__init__.py

+from .image_loader import UnstructuredPaddleImageLoader
+from .pdf_loader import UnstructuredPaddlePDFLoader

loader/image_loader.py

+"""Loader that loads image files."""
+from typing import List
+
+from langchain.document_loaders.unstructured import UnstructuredFileLoader
+from paddleocr import PaddleOCR
+import os
+
+
+class UnstructuredPaddleImageLoader(UnstructuredFileLoader):
+    """Loader that uses unstructured to load image files, such as PNGs and JPGs."""
+
+    def _get_elements(self) -> List:
+        def image_ocr_txt(filepath, dir_path="tmp_files"):
+            if not os.path.exists(dir_path):
+                os.makedirs(dir_path)
+            filename = os.path.split(filepath)[-1]
+            ocr = PaddleOCR(lang="ch", use_gpu=False, show_log=False)
+            result = ocr.ocr(img=filepath)
+
+            ocr_result = [i[1][0] for line in result for i in line]
+            txt_file_path = os.path.join(dir_path, "%s.txt" % (filename))
+            with open(txt_file_path, 'w', encoding='utf-8') as fout:
+                fout.write("\n".join(ocr_result))
+            return txt_file_path
+
+        txt_file_path = image_ocr_txt(self.file_path)
+        from unstructured.partition.text import partition_text
+        return partition_text(filename=txt_file_path, **self.unstructured_kwargs)

loader/pdf_loader.py

+"""Loader that loads image files."""
+from typing import List
+
+from langchain.document_loaders.unstructured import UnstructuredFileLoader
+from paddleocr import PaddleOCR
+import os
+import fitz
+
+
+class UnstructuredPaddlePDFLoader(UnstructuredFileLoader):
+    """Loader that uses unstructured to load image files, such as PNGs and JPGs."""
+
+    def _get_elements(self) -> List:
+        def pdf_ocr_txt(filepath, dir_path="tmp_files"):
+            if not os.path.exists(dir_path):
+                os.makedirs(dir_path)
+            filename = os.path.split(filepath)[-1]
+            ocr = PaddleOCR(lang="ch", use_gpu=False, show_log=False)
+            doc = fitz.open(filepath)
+            txt_file_path = os.path.join(dir_path, "%s.txt" % (filename))
+            img_name = './img/.tmp.png'
+            with open(txt_file_path, 'w', encoding='utf-8') as fout:
+
+                for i in range(doc.page_count):
+                    page = doc[i]
+                    text = page.get_text("")
+                    fout.write(text)
+                    fout.write("\n")
+
+                    img_list = page.get_images()
+                    for img in img_list:
+                        pix = fitz.Pixmap(doc, img[0])
+
+                        pix.save(img_name)
+
+                        result = ocr.ocr(img_name)
+                        ocr_result = [i[1][0] for line in result for i in line]
+                        fout.write("\n".join(ocr_result))
+            os.remove(img_name)
+            return txt_file_path
+
+        txt_file_path = pdf_ocr_txt(self.file_path)
+        from unstructured.partition.text import partition_text
+        return partition_text(filename=txt_file_path, **self.unstructured_kwargs)

requirements.txt

+pymupdf
+paddlepaddle==2.4.2
+paddleocr
 langchain==0.0.146
 transformers==4.27.1
 unstructured[local-inference]

test_image.py

+from configs.model_config import *
+import nltk
+
+nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
+
+filepath = "./img/test.jpg"
+from loader import UnstructuredPaddleImageLoader
+
+loader = UnstructuredPaddleImageLoader(filepath, mode="elements")
+docs = loader.load()
+for doc in docs:
+    print(doc)

test_pdf.py

+from configs.model_config import *
+import nltk
+
+nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
+
+filepath = "docs/test.pdf"
+from loader import UnstructuredPaddlePDFLoader
+
+loader = UnstructuredPaddlePDFLoader(filepath, mode="elements")
+docs = loader.load()
+for doc in docs:
+    print(doc)