update text_splitter

chains/local_doc_qa.py

@@ -1,16 +1,13 @@
-from langchain.chains import RetrievalQA
-from langchain.prompts import PromptTemplate
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.document_loaders import UnstructuredFileLoader
 from models.chatglm_llm import ChatGLM
-import sentence_transformers
-import os
 from configs.model_config import *
 import datetime
-from typing import List
 from textsplitter import ChineseTextSplitter
+from typing import List, Tuple
 from langchain.docstore.document import Document
+import numpy as np
 
 # return top-k text chunk from vector store
 VECTOR_SEARCH_TOP_K = 6
@@ -48,10 +45,70 @@ def get_docs_with_score(docs_with_score):
  docs.append(doc)
  return docs
 
+
+def seperate_list(ls: List[int]) -> List[List[int]]:
+ lists = []
+ ls1 = [ls[0]]
+ for i in range(1, len(ls)):
+ if ls[i-1] + 1 == ls[i]:
+ ls1.append(ls[i])
+ else:
+ lists.append(ls1)
+ ls1 = [ls[i]]
+ lists.append(ls1)
+ return lists
+
+
+
+def similarity_search_with_score_by_vector(
+ self,
+ embedding: List[float],
+ k: int = 4,
+ ) -> List[Tuple[Document, float]]:
+ scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
+ docs = []
+ id_set = set()
+ for j, i in enumerate(indices[0]):
+ if i == -1:
+ # This happens when not enough docs are returned.
+ continue
+ _id = self.index_to_docstore_id[i]
+ doc = self.docstore.search(_id)
+ id_set.add(i)
+ docs_len = len(doc.page_content)
+ for k in range(1, max(i, len(docs)-i)):
+ for l in [i+k, i-k]:
+ if 0 <= l < len(self.index_to_docstore_id):
+ _id0 = self.index_to_docstore_id[l]
+ doc0 = self.docstore.search(_id0)
+ if docs_len + len(doc0.page_content) > self.chunk_size:
+ break
+ elif doc0.metadata["source"] == doc.metadata["source"]:
+ docs_len += len(doc0.page_content)
+ id_set.add(l)
+ id_list = sorted(list(id_set))
+ id_lists = seperate_list(id_list)
+ for id_seq in id_lists:
+ for id in id_seq:
+ if id == id_seq[0]:
+ _id = self.index_to_docstore_id[id]
+ doc = self.docstore.search(_id)
+ else:
+ _id0 = self.index_to_docstore_id[id]
+ doc0 = self.docstore.search(_id0)
+ doc.page_content += doc0.page_content
+ if not isinstance(doc, Document):
+ raise ValueError(f"Could not find document for id {_id}, got {doc}")
+ docs.append((doc, scores[0][j]))
+ return docs
+
+
+
 class LocalDocQA:
  llm: object = None
  embeddings: object = None
  top_k: int = VECTOR_SEARCH_TOP_K
+ chunk_size: int = CHUNK_SIZE
 
  def init_cfg(self,
  embedding_model: str = EMBEDDING_MODEL,
@@ -133,6 +190,8 @@ def get_knowledge_based_answer(self,
  streaming=True):
  self.llm.streaming = streaming
  vector_store = FAISS.load_local(vs_path, self.embeddings)
+ FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector
+ vector_store.chunk_size=self.chunk_size
  related_docs_with_score = vector_store.similarity_search_with_score(query,
  k=self.top_k)
  related_docs = get_docs_with_score(related_docs_with_score)

configs/model_config.py

@@ -39,4 +39,7 @@
 
 # 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
 PROMPT_TEMPLATE = """基于以下已知信息，简洁和专业的来回答用户的问题，问题是"{question}"。如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。已知内容如下: 
-{context} """
+{context} """
+
+# 匹配后单段上下文长度
+CHUNK_SIZE = 500