fix: merge embedding

.gitignore

@@ -136,4 +136,5 @@ dmypy.json
 .DS_Store
 logs
 nltk_data
-.vectordb
+.vectordb
+pilot/data/

pilot/configs/model_config.py

@@ -20,8 +20,10 @@ DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 LLM_MODEL_CONFIG = {
     "flan-t5-base": os.path.join(MODEL_PATH, "flan-t5-base"),
     "vicuna-13b": os.path.join(MODEL_PATH, "vicuna-13b"),
+    "text2vec": os.path.join(MODEL_PATH, "text2vec-large-chinese"),
     "sentence-transforms": os.path.join(MODEL_PATH, "all-MiniLM-L6-v2")
 }
+
 # Load model config
 ISLOAD_8BIT = True
 ISDEBUG = False
@@ -29,4 +31,4 @@ ISDEBUG = False
 
 VECTOR_SEARCH_TOP_K = 3
 VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vs_store")
-KNOWLEDGE_UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge")
+KNOWLEDGE_UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data")

pilot/conversation.py

@@ -247,6 +247,13 @@ conv_qa_prompt_template = """ 基于以下已知的信息, 专业、简要的回
             {question}
 """
 
+# conv_qa_prompt_template = """ Please provide the known information so that I can professionally and briefly answer the user's question. If the answer cannot be obtained from the provided content,
+#             please say: "The information provided in the knowledge base is insufficient to answer this question." Fabrication is prohibited.。
+#             known information:
+#             {context}
+#             question:
+#             {question}
+# """
 default_conversation = conv_one_shot
 
 conversation_sql_mode ={

pilot/server/webserver.py

@@ -19,7 +19,7 @@ from langchain import PromptTemplate
 ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.append(ROOT_PATH)
 
-from pilot.configs.model_config import KNOWLEDGE_UPLOAD_ROOT_PATH, LLM_MODEL_CONFIG
+from pilot.configs.model_config import DB_SETTINGS, KNOWLEDGE_UPLOAD_ROOT_PATH, LLM_MODEL_CONFIG, TOP_RETURN_SIZE
 from pilot.server.vectordb_qa import KnownLedgeBaseQA
 from pilot.connections.mysql import MySQLOperator
 from pilot.source_embedding.knowledge_embedding import KnowledgeEmbedding
@@ -256,11 +256,13 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
 
     if mode == conversation_types["custome"] and not db_selector:
         persist_dir = os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vector_store_name["vs_name"] + ".vectordb")
-        print("向量数据库持久化地址: ", persist_dir)
+        print("vector store path: ", persist_dir)
-        knowledge_embedding_client = KnowledgeEmbedding(file_path="", model_name=LLM_MODEL_CONFIG["sentence-transforms"], vector_store_config={"vector_store_name": vector_store_name["vs_name"],
+        knowledge_embedding_client = KnowledgeEmbedding(file_path="", model_name=LLM_MODEL_CONFIG["text2vec"],
+                                                        local_persist=False,
+                                                        vector_store_config={"vector_store_name": vector_store_name["vs_name"],
                                                       "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH})
         query = state.messages[-2][1]
-        docs = knowledge_embedding_client.similar_search(query, 1)
+        docs = knowledge_embedding_client.similar_search(query, TOP_RETURN_SIZE)
         context = [d.page_content for d in docs]
         prompt_template = PromptTemplate(
             template=conv_qa_prompt_template,
@@ -269,6 +271,20 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
         result = prompt_template.format(context="\n".join(context), question=query)
         state.messages[-2][1] = result
         prompt = state.get_prompt()
+        print("prompt length:" + str(len(prompt)))
+
+        if len(prompt) > 4000:
+            logger.info("prompt length greater than 4000, rebuild")
+            context = context[:2000]
+            prompt_template = PromptTemplate(
+                template=conv_qa_prompt_template,
+                input_variables=["context", "question"]
+            )
+            result = prompt_template.format(context="\n".join(context), question=query)
+            state.messages[-2][1] = result
+            prompt = state.get_prompt()
+            print("new prompt length:" + str(len(prompt)))
+
         state.messages[-2][1] = query
         skip_echo_len = len(prompt.replace("</s>", " ")) + 1
 
@@ -435,7 +451,7 @@ def build_single_model_ui():
         max_output_tokens = gr.Slider(
             minimum=0,
             maximum=1024,
-            value=1024,
+            value=512,
             step=64,
             interactive=True,
             label="最大输出Token数",
@@ -585,7 +601,8 @@ def knowledge_embedding_store(vs_id, files):
         shutil.move(file.name, os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename))
         knowledge_embedding_client = KnowledgeEmbedding(
             file_path=os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename),
-            model_name=LLM_MODEL_CONFIG["sentence-transforms"],
+            model_name=LLM_MODEL_CONFIG["text2vec"],
+            local_persist=False,
             vector_store_config={
                 "vector_store_name": vector_store_name["vs_name"],
                 "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH})
@@ -610,8 +627,7 @@ if __name__ == "__main__":
     # 配置初始化
     cfg = Config()
 
-    dbs = get_database_list()
+    # dbs = get_database_list()
-
     cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
 
     # 加载插件可执行命令

pilot/source_embedding/chn_document_splitter.py

@@ -0,0 +1,59 @@
+import re
+from typing import List
+from langchain.text_splitter import CharacterTextSplitter
+
+
+class CHNDocumentSplitter(CharacterTextSplitter):
+    def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
+        super().__init__(**kwargs)
+        self.pdf = pdf
+        self.sentence_size = sentence_size
+
+    # def split_text_version2(self, text: str) -> List[str]:
+    #     if self.pdf:
+    #         text = re.sub(r"\n{3,}", "\n", text)
+    #         text = re.sub('\s', ' ', text)
+    #         text = text.replace("\n\n", "")
+    #     sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))')  # del ：；
+    #     sent_list = []
+    #     for ele in sent_sep_pattern.split(text):
+    #         if sent_sep_pattern.match(ele) and sent_list:
+    #             sent_list[-1] += ele
+    #         elif ele:
+    #             sent_list.append(ele)
+    #     return sent_list
+
+    def split_text(self, text: str) -> List[str]:
+        if self.pdf:
+            text = re.sub(r"\n{3,}", r"\n", text)
+            text = re.sub('\s', " ", text)
+            text = re.sub("\n\n", "", text)
+
+        text = re.sub(r'([;；.!?。！？\?])([^”’])', r"\1\n\2", text)  # 单字符断句符
+        text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text)  # 英文省略号
+        text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text)  # 中文省略号
+        text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r'\1\n\2', text)
+        # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
+        text = text.rstrip()  # 段尾如果有多余的\n就去掉它
+        # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。
+        ls = [i for i in text.split("\n") if i]
+        for ele in ls:
+            if len(ele) > self.sentence_size:
+                ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele)
+                ele1_ls = ele1.split("\n")
+                for ele_ele1 in ele1_ls:
+                    if len(ele_ele1) > self.sentence_size:
+                        ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1)
+                        ele2_ls = ele_ele2.split("\n")
+                        for ele_ele2 in ele2_ls:
+                            if len(ele_ele2) > self.sentence_size:
+                                ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2)
+                                ele2_id = ele2_ls.index(ele_ele2)
+                                ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[
+                                                                                                       ele2_id + 1:]
+                        ele_id = ele1_ls.index(ele_ele1)
+                        ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:]
+
+                id = ls.index(ele)
+                ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:]
+        return ls

pilot/source_embedding/knowledge_embedding.py

@@ -1,20 +1,35 @@
+import os
+
+from bs4 import BeautifulSoup
+from langchain.document_loaders import PyPDFLoader, TextLoader, markdown
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from pilot.configs.model_config import DATASETS_DIR
+from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
 from pilot.source_embedding.csv_embedding import CSVEmbedding
 from pilot.source_embedding.markdown_embedding import MarkdownEmbedding
 from pilot.source_embedding.pdf_embedding import PDFEmbedding
+import markdown
 
 
 class KnowledgeEmbedding:
-    def __init__(self, file_path, model_name, vector_store_config):
+    def __init__(self, file_path, model_name, vector_store_config, local_persist=True):
         """Initialize with Loader url, model_name, vector_store_config"""
         self.file_path = file_path
         self.model_name = model_name
         self.vector_store_config = vector_store_config
         self.vector_store_type = "default"
-        self.knowledge_embedding_client = self.init_knowledge_embedding()
+        self.embeddings = HuggingFaceEmbeddings(model_name=self.model_name)
+        self.local_persist = local_persist
+        if not self.local_persist:
+            self.knowledge_embedding_client = self.init_knowledge_embedding()
 
     def knowledge_embedding(self):
         self.knowledge_embedding_client.source_embedding()
 
+    def knowledge_embedding_batch(self):
+        self.knowledge_embedding_client.batch_embedding()
+
     def init_knowledge_embedding(self):
         if self.file_path.endswith(".pdf"):
             embedding = PDFEmbedding(file_path=self.file_path, model_name=self.model_name,
@@ -31,4 +46,65 @@ class KnowledgeEmbedding:
         return embedding
 
     def similar_search(self, text, topk):
-        return self.knowledge_embedding_client.similar_search(text, topk)
+        return self.knowledge_embedding_client.similar_search(text, topk)
+
+    def knowledge_persist_initialization(self, append_mode):
+        vector_name = self.vector_store_config["vector_store_name"]
+        persist_dir = os.path.join(self.vector_store_config["vector_store_path"], vector_name + ".vectordb")
+        print("vector db path: ", persist_dir)
+        if os.path.exists(persist_dir):
+            if append_mode:
+                print("append knowledge return vector store")
+                new_documents = self._load_knownlege(self.file_path)
+                vector_store = Chroma.from_documents(documents=new_documents,
+                                                     embedding=self.embeddings,
+                                                     persist_directory=persist_dir)
+            else:
+                print("directly return vector store")
+                vector_store = Chroma(persist_directory=persist_dir, embedding_function=self.embeddings)
+        else:
+            print(vector_name + "is new vector store, knowledge begin load...")
+            documents = self._load_knownlege(self.file_path)
+            vector_store = Chroma.from_documents(documents=documents,
+                                                 embedding=self.embeddings,
+                                                 persist_directory=persist_dir)
+            vector_store.persist()
+        return vector_store
+
+    def _load_knownlege(self, path):
+        docments = []
+        for root, _, files in os.walk(path, topdown=False):
+            for file in files:
+                filename = os.path.join(root, file)
+                docs = self._load_file(filename)
+                new_docs = []
+                for doc in docs:
+                    doc.metadata = {"source": doc.metadata["source"].replace(DATASETS_DIR, "")}
+                    print("doc is embedding...", doc.metadata)
+                    new_docs.append(doc)
+                docments += new_docs
+        return docments
+
+    def _load_file(self, filename):
+        if filename.lower().endswith(".md"):
+            loader = TextLoader(filename)
+            text_splitter = CHNDocumentSplitter(pdf=True, sentence_size=100)
+            docs = loader.load_and_split(text_splitter)
+            i = 0
+            for d in docs:
+                content = markdown.markdown(d.page_content)
+                soup = BeautifulSoup(content, 'html.parser')
+                for tag in soup(['!doctype', 'meta', 'i.fa']):
+                    tag.extract()
+                docs[i].page_content = soup.get_text()
+                docs[i].page_content = docs[i].page_content.replace("\n", " ")
+                i += 1
+        elif filename.lower().endswith(".pdf"):
+            loader = PyPDFLoader(filename)
+            textsplitter = CHNDocumentSplitter(pdf=True, sentence_size=100)
+            docs = loader.load_and_split(textsplitter)
+        else:
+            loader = TextLoader(filename)
+            text_splitor = CHNDocumentSplitter(sentence_size=100)
+            docs = loader.load_and_split(text_splitor)
+        return docs

pilot/source_embedding/markdown_embedding.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
+import os
 from typing import List
 
 from bs4 import BeautifulSoup
@@ -8,6 +9,7 @@ from langchain.schema import Document
 import markdown
 
 from pilot.source_embedding import SourceEmbedding, register
+from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
 
 
 class MarkdownEmbedding(SourceEmbedding):
@@ -24,7 +26,28 @@ class MarkdownEmbedding(SourceEmbedding):
     def read(self):
         """Load from markdown path."""
         loader = TextLoader(self.file_path)
-        return loader.load()
+        text_splitter = CHNDocumentSplitter(pdf=True, sentence_size=100)
+        return loader.load_and_split(text_splitter)
+
+    @register
+    def read_batch(self):
+        """Load from markdown path."""
+        docments = []
+        for root, _, files in os.walk(self.file_path, topdown=False):
+            for file in files:
+                filename = os.path.join(root, file)
+                loader = TextLoader(filename)
+                # text_splitor = CHNDocumentSplitter(chunk_size=1000, chunk_overlap=20, length_function=len)
+                # docs = loader.load_and_split()
+                docs = loader.load()
+                # 更新metadata数据
+                new_docs = []
+                for doc in docs:
+                    doc.metadata = {"source": doc.metadata["source"].replace(self.file_path, "")}
+                    print("doc is embedding ... ", doc.metadata)
+                    new_docs.append(doc)
+                docments += new_docs
+        return docments
 
     @register
     def data_process(self, documents: List[Document]):
@@ -35,7 +58,7 @@ class MarkdownEmbedding(SourceEmbedding):
             for tag in soup(['!doctype', 'meta', 'i.fa']):
                 tag.extract()
             documents[i].page_content = soup.get_text()
-            documents[i].page_content = documents[i].page_content.replace(" ", "").replace("\n", " ")
+            documents[i].page_content = documents[i].page_content.replace("\n", " ")
             i += 1
         return documents
 

pilot/source_embedding/pdf_embedding.py

@@ -6,6 +6,7 @@ from langchain.document_loaders import PyPDFLoader
 from langchain.schema import Document
 
 from pilot.source_embedding import SourceEmbedding, register
+from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
 
 
 class PDFEmbedding(SourceEmbedding):
@@ -17,20 +18,19 @@ class PDFEmbedding(SourceEmbedding):
         self.file_path = file_path
         self.model_name = model_name
         self.vector_store_config = vector_store_config
-        # SourceEmbedding(file_path =file_path, );
-        SourceEmbedding(file_path, model_name, vector_store_config)
 
     @register
     def read(self):
         """Load from pdf path."""
         loader = PyPDFLoader(self.file_path)
-        return loader.load()
+        textsplitter = CHNDocumentSplitter(pdf=True, sentence_size=100)
+        return loader.load_and_split(textsplitter)
 
     @register
     def data_process(self, documents: List[Document]):
         i = 0
         for d in documents:
-            documents[i].page_content = d.page_content.replace(" ", "").replace("\n", "")
+            documents[i].page_content = d.page_content.replace("\n", "")
             i += 1
         return documents
 

pilot/source_embedding/pdf_loader.py

@@ -0,0 +1,53 @@
+"""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"):
+            full_dir_path = os.path.join(os.path.dirname(filepath), dir_path)
+            if not os.path.exists(full_dir_path):
+                os.makedirs(full_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(full_dir_path, "%s.txt" % (filename))
+            img_name = os.path.join(full_dir_path, '.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)
+
+
+if __name__ == "__main__":
+    filepath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "content", "samples", "test.pdf")
+    loader = UnstructuredPaddlePDFLoader(filepath, mode="elements")
+    docs = loader.load()
+    for doc in docs:
+        print(doc)

pilot/source_embedding/source_embedding.py

@@ -76,3 +76,15 @@ class SourceEmbedding(ABC):
             self.text_to_vector(text)
         if 'index_to_store' in registered_methods:
             self.index_to_store(text)
+
+    def batch_embedding(self):
+        if 'read_batch' in registered_methods:
+            text = self.read_batch()
+        if 'data_process' in registered_methods:
+            text = self.data_process(text)
+        if 'text_split' in registered_methods:
+            self.text_split(text)
+        if 'text_to_vector' in registered_methods:
+            self.text_to_vector(text)
+        if 'index_to_store' in registered_methods:
+            self.index_to_store(text)

pilot/source_embedding/url_embedding.py

@@ -1,4 +1,7 @@
 from typing import List
+
+from langchain.text_splitter import CharacterTextSplitter
+
 from pilot.source_embedding import SourceEmbedding, register
 
 from bs4 import BeautifulSoup
@@ -20,7 +23,8 @@ class URLEmbedding(SourceEmbedding):
     def read(self):
         """Load from url path."""
         loader = WebBaseLoader(web_path=self.file_path)
-        return loader.load()
+        text_splitor = CharacterTextSplitter(chunk_size=1000, chunk_overlap=20, length_function=len)
+        return loader.load_and_split(text_splitor)
 
     @register
     def data_process(self, documents: List[Document]):

pilot/vector_store/file_loader.py

@@ -48,12 +48,16 @@ class KnownLedge2Vector:
             # vector_store.add_documents(documents=documents)
         else:
             documents = self.load_knownlege()
+<<<<<<< HEAD
             # reinit 
+=======
+            # reinit
+>>>>>>> 31797ecdb53eff76cceb52454888c91c97572851
             vector_store = Chroma.from_documents(documents=documents, 
                                                  embedding=self.embeddings,
                                                  persist_directory=persist_dir)
             vector_store.persist()
-        return vector_store 
+        return vector_store
 
     def load_knownlege(self):
         docments = []
@@ -61,7 +65,11 @@ class KnownLedge2Vector:
             for file in files:
                 filename = os.path.join(root, file)
                 docs = self._load_file(filename)
+<<<<<<< HEAD
                 # update metadata. 
+=======
+                # update metadata.
+>>>>>>> 31797ecdb53eff76cceb52454888c91c97572851
                 new_docs = [] 
                 for doc in docs:
                     doc.metadata = {"source": doc.metadata["source"].replace(DATASETS_DIR, "")} 

requirements.txt

@@ -75,4 +75,5 @@ chromadb
 markdown2
 colorama
 playsound
-distro
+distro
+pypdf