421 lines
19 KiB
Python
421 lines
19 KiB
Python
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.document_loaders import UnstructuredFileLoader, TextLoader
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from configs.model_config import *
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import datetime
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from textsplitter import ChineseTextSplitter
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from typing import List, Tuple, Dict
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from langchain.docstore.document import Document
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import numpy as np
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from utils import torch_gc
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from tqdm import tqdm
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from pypinyin import lazy_pinyin
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from loader import UnstructuredPaddleImageLoader, UnstructuredPaddlePDFLoader
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from models.base import (BaseAnswer,
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AnswerResult,
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AnswerResultStream,
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AnswerResultQueueSentinelTokenListenerQueue)
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from models.loader.args import parser
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from models.loader import LoaderCheckPoint
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import models.shared as shared
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from agent import bing_search
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from langchain.docstore.document import Document
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from sentence_transformers import SentenceTransformer, CrossEncoder, util
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from sklearn.neighbors import NearestNeighbors
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class SemanticSearch:
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def __init__(self):
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self.use= SentenceTransformer('GanymedeNil_text2vec-large-chinese')
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self.fitted = False
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def fit(self, data, batch=100, n_neighbors=10):
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self.data = data
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self.embeddings = self.get_text_embedding(data, batch=batch)
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n_neighbors = min(n_neighbors, len(self.embeddings))
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self.nn = NearestNeighbors(n_neighbors=n_neighbors)
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self.nn.fit(self.embeddings)
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self.fitted = True
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def __call__(self, text, return_data=True):
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inp_emb = self.use.encode([text])
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neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
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if return_data:
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return [self.data[i] for i in neighbors]
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else:
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return neighbors
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def get_text_embedding(self, texts, batch=100):
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embeddings = []
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for i in range(0, len(texts), batch):
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text_batch = texts[i : (i + batch)]
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emb_batch = self.use.encode(text_batch)
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embeddings.append(emb_batch)
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embeddings = np.vstack(embeddings)
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return embeddings
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def get_docs_with_score(docs_with_score):
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docs = []
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for doc, score in docs_with_score:
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doc.metadata["score"] = score
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docs.append(doc)
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return docs
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def load_file(filepath, sentence_size=SENTENCE_SIZE):
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if filepath.lower().endswith(".md"):
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loader = UnstructuredFileLoader(filepath, mode="elements")
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docs = loader.load()
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elif filepath.lower().endswith(".txt"):
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loader = TextLoader(filepath, autodetect_encoding=True)
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textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
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docs = loader.load_and_split(textsplitter)
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elif filepath.lower().endswith(".pdf"):
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loader = UnstructuredPaddlePDFLoader(filepath)
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textsplitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size)
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docs = loader.load_and_split(textsplitter)
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elif filepath.lower().endswith(".jpg") or filepath.lower().endswith(".png"):
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loader = UnstructuredPaddleImageLoader(filepath, mode="elements")
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textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
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docs = loader.load_and_split(text_splitter=textsplitter)
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else:
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loader = UnstructuredFileLoader(filepath, mode="elements")
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textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
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docs = loader.load_and_split(text_splitter=textsplitter)
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write_check_file(filepath, docs)
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return docs
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def write_check_file(filepath, docs):
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folder_path = os.path.join(os.path.dirname(filepath), "tmp_files")
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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fp = os.path.join(folder_path, 'load_file.txt')
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with open(fp, 'a+', encoding='utf-8') as fout:
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fout.write("filepath=%s,len=%s" % (filepath, len(docs)))
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fout.write('\n')
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for i in docs:
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fout.write(str(i))
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fout.write('\n')
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fout.close()
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def generate_prompt(related_docs: List[str],
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query: str,
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prompt_template: str = PROMPT_TEMPLATE, ) -> str:
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context = "\n".join([doc.page_content for doc in related_docs])
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prompt = prompt_template.replace("{question}", query).replace("{context}", context)
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return prompt
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def seperate_list(ls: List[int]) -> List[List[int]]:
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lists = []
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ls1 = [ls[0]]
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for i in range(1, len(ls)):
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if ls[i - 1] + 1 == ls[i]:
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ls1.append(ls[i])
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else:
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lists.append(ls1)
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ls1 = [ls[i]]
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lists.append(ls1)
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return lists
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def similarity_search_with_score_by_vector(
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self, embedding: List[float], k: int = 4
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) -> List[Tuple[Document, float]]:
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scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
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docs = []
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id_set = set()
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store_len = len(self.index_to_docstore_id)
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for j, i in enumerate(indices[0]):
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if i == -1 or 0 < self.score_threshold < scores[0][j]:
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# This happens when not enough docs are returned.
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continue
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_id = self.index_to_docstore_id[i]
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doc = self.docstore.search(_id)
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if not self.chunk_conent:
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if not isinstance(doc, Document):
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raise ValueError(f"Could not find document for id {_id}, got {doc}")
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doc.metadata["score"] = int(scores[0][j])
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docs.append(doc)
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continue
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id_set.add(i)
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docs_len = len(doc.page_content)
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for k in range(1, max(i, store_len - i)):
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break_flag = False
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for l in [i + k, i - k]:
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if 0 <= l < len(self.index_to_docstore_id):
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_id0 = self.index_to_docstore_id[l]
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doc0 = self.docstore.search(_id0)
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if docs_len + len(doc0.page_content) > self.chunk_size:
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break_flag = True
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break
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elif doc0.metadata["source"] == doc.metadata["source"]:
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docs_len += len(doc0.page_content)
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id_set.add(l)
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if break_flag:
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break
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if not self.chunk_conent:
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return docs
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if len(id_set) == 0 and self.score_threshold > 0:
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return []
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id_list = sorted(list(id_set))
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id_lists = seperate_list(id_list)
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for id_seq in id_lists:
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for id in id_seq:
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if id == id_seq[0]:
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_id = self.index_to_docstore_id[id]
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doc = self.docstore.search(_id)
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else:
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_id0 = self.index_to_docstore_id[id]
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doc0 = self.docstore.search(_id0)
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doc.page_content += " " + doc0.page_content
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if not isinstance(doc, Document):
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raise ValueError(f"Could not find document for id {_id}, got {doc}")
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doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
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doc.metadata["score"] = int(doc_score)
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docs.append(doc)
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torch_gc()
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return docs
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def search_result2docs(search_results):
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docs = []
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for result in search_results:
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doc = Document(page_content=result["snippet"] if "snippet" in result.keys() else "",
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metadata={"source": result["link"] if "link" in result.keys() else "",
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"filename": result["title"] if "title" in result.keys() else ""})
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docs.append(doc)
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return docs
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class LocalDocQA:
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llm: BaseAnswer = None
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embeddings: object = None
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top_k: int = VECTOR_SEARCH_TOP_K
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chunk_size: int = CHUNK_SIZE
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chunk_conent: bool = True
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score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
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def init_cfg(self,
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embedding_model: str = EMBEDDING_MODEL,
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embedding_device=EMBEDDING_DEVICE,
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llm_model: BaseAnswer = None,
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top_k=VECTOR_SEARCH_TOP_K,
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):
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self.llm = llm_model
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self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[embedding_model],
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model_kwargs={'device': embedding_device})
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self.top_k = top_k
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def init_knowledge_vector_store(self,
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filepath: str or List[str],
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vs_path: str or os.PathLike = None,
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sentence_size=SENTENCE_SIZE):
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loaded_files = []
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failed_files = []
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if isinstance(filepath, str):
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if not os.path.exists(filepath):
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print("路径不存在")
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return None
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elif os.path.isfile(filepath):
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file = os.path.split(filepath)[-1]
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try:
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docs = load_file(filepath, sentence_size)
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logger.info(f"{file} 已成功加载")
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loaded_files.append(filepath)
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except Exception as e:
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logger.error(e)
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logger.info(f"{file} 未能成功加载")
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return None
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elif os.path.isdir(filepath):
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docs = []
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for file in tqdm(os.listdir(filepath), desc="加载文件"):
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fullfilepath = os.path.join(filepath, file)
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try:
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docs += load_file(fullfilepath, sentence_size)
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loaded_files.append(fullfilepath)
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except Exception as e:
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logger.error(e)
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failed_files.append(file)
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if len(failed_files) > 0:
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logger.info("以下文件未能成功加载：")
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for file in failed_files:
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logger.info(f"{file}\n")
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else:
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docs = []
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for file in filepath:
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try:
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docs += load_file(file)
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logger.info(f"{file} 已成功加载")
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loaded_files.append(file)
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except Exception as e:
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logger.error(e)
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logger.info(f"{file} 未能成功加载")
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if len(docs) > 0:
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logger.info("文件加载完毕，正在生成向量库")
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if vs_path and os.path.isdir(vs_path):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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vector_store.add_documents(docs)
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torch_gc()
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else:
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if not vs_path:
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vs_path = os.path.join(VS_ROOT_PATH,
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f"""{"".join(lazy_pinyin(os.path.splitext(file)[0]))}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}""")
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vector_store = FAISS.from_documents(docs, self.embeddings) # docs 为Document列表
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torch_gc()
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vector_store.save_local(vs_path)
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return vs_path, loaded_files
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else:
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logger.info("文件均未成功加载，请检查依赖包或替换为其他文件再次上传。")
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return None, loaded_files
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def one_knowledge_add(self, vs_path, one_title, one_conent, one_content_segmentation, sentence_size):
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try:
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if not vs_path or not one_title or not one_conent:
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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！")
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return None, [one_title]
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docs = [Document(page_content=one_conent + "\n", metadata={"source": one_title})]
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if not one_content_segmentation:
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text_splitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
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docs = text_splitter.split_documents(docs)
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if os.path.isdir(vs_path):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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vector_store.add_documents(docs)
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else:
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vector_store = FAISS.from_documents(docs, self.embeddings) ##docs 为Document列表
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torch_gc()
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vector_store.save_local(vs_path)
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return vs_path, [one_title]
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except Exception as e:
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logger.error(e)
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return None, [one_title]
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def get_knowledge_based_answer(self, query, vs_path, chat_history=[], streaming: bool = STREAMING):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector
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vector_store.chunk_size = self.chunk_size
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vector_store.chunk_conent = self.chunk_conent
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vector_store.score_threshold = self.score_threshold
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related_docs_with_score = vector_store.similarity_search_with_score(query, k=self.top_k)
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###########################################精排 之前faiss检索作为粗排 需要设置model_config参数VECTOR_SEARCH_TOP_K =300
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###########################################原理：粗排：faiss+semantic search 检索得到大量相关文档，需要设置ECTOR_SEARCH_TOP为300，然后合并文档，重新切分，
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#############################################利用knn+ semantic search 进行二次检索，输入到prompt
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####提取文档
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related_docs = get_docs_with_score(related_docs_with_score)
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text_batch0=[]
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for i in range(len(related_docs)):
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cut_txt = " ".join([w for w in list(related_docs[i].page_content)])
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cut_txt =cut_txt.replace(" ", "")
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text_batch0.append(cut_txt)
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######文档去重
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text_batch_new=[]
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for i in range(len(text_batch0)):
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if text_batch0[i] in text_batch_new:
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continue
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else:
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while text_batch_new and text_batch_new[-1] > text_batch0[i] and text_batch_new[-1] in text_batch0[i + 1:]:
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text_batch_new.pop() # 弹出栈顶元素
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text_batch_new.append(text_batch0[i])
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text_batch_new0 = "\n".join([doc for doc in text_batch_new])
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###精排 采用knn和semantic search
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recommender = SemanticSearch()
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chunks = text_to_chunks(text_batch_new0, start_page=1)
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recommender.fit(chunks)
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topn_chunks = recommender(query)
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torch_gc()
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#去掉文字中的空格
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topn_chunks0=[]
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for i in range(len(topn_chunks)):
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cut_txt =topn_chunks[i].replace(" ", "")
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topn_chunks0.append(cut_txt)
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############生成prompt
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prompt = generate_prompt(topn_chunks0, query)
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########################
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for answer_result in self.llm.generatorAnswer(prompt=prompt, history=chat_history,
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streaming=streaming):
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resp = answer_result.llm_output["answer"]
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history = answer_result.history
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history[-1][0] = query
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response = {"query": query,
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"result": resp,
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"source_documents": related_docs_with_score}
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yield response, history
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# query 查询内容
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# vs_path 知识库路径
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# chunk_conent 是否启用上下文关联
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# score_threshold 搜索匹配score阈值
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# vector_search_top_k 搜索知识库内容条数，默认搜索5条结果
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# chunk_sizes 匹配单段内容的连接上下文长度
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def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent,
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score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
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vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector
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vector_store.chunk_conent = chunk_conent
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vector_store.score_threshold = score_threshold
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vector_store.chunk_size = chunk_size
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related_docs_with_score = vector_store.similarity_search_with_score(query, k=vector_search_top_k)
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if not related_docs_with_score:
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response = {"query": query,
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"source_documents": []}
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return response, ""
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torch_gc()
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prompt = "\n".join([doc.page_content for doc in related_docs_with_score])
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response = {"query": query,
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"source_documents": related_docs_with_score}
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return response, prompt
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def get_search_result_based_answer(self, query, chat_history=[], streaming: bool = STREAMING):
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results = bing_search(query)
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result_docs = search_result2docs(results)
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prompt = generate_prompt(result_docs, query)
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for answer_result in self.llm.generatorAnswer(prompt=prompt, history=chat_history,
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streaming=streaming):
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resp = answer_result.llm_output["answer"]
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history = answer_result.history
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history[-1][0] = query
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response = {"query": query,
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"result": resp,
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"source_documents": result_docs}
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yield response, history
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if __name__ == "__main__":
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# 初始化消息
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args = None
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args = parser.parse_args(args=['--model-dir', '/media/checkpoint/', '--model', 'chatglm-6b', '--no-remote-model'])
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args_dict = vars(args)
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shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
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llm_model_ins = shared.loaderLLM()
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llm_model_ins.set_history_len(LLM_HISTORY_LEN)
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local_doc_qa = LocalDocQA()
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local_doc_qa.init_cfg(llm_model=llm_model_ins)
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query = "本项目使用的embedding模型是什么，消耗多少显存"
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vs_path = "/media/gpt4-pdf-chatbot-langchain/dev-langchain-ChatGLM/vector_store/test"
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last_print_len = 0
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# for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
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# vs_path=vs_path,
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# chat_history=[],
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# streaming=True):
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for resp, history in local_doc_qa.get_search_result_based_answer(query=query,
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chat_history=[],
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streaming=True):
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print(resp["result"][last_print_len:], end="", flush=True)
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last_print_len = len(resp["result"])
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source_text = [f"""出处 [{inum + 1}] {doc.metadata['source'] if doc.metadata['source'].startswith("http")
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else os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
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# f"""相关度：{doc.metadata['score']}\n\n"""
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for inum, doc in
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enumerate(resp["source_documents"])]
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logger.info("\n\n" + "\n\n".join(source_text))
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pass
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