fromlangchain.embeddingsimportOpenAIEmbeddingsfromlangchain.vectorstoresimportDocArrayInMemorySearchfromlangchain.schema.runnableimportRunnableMap#创建向量数据库vectorstore=DocArrayInMemorySearch.from_texts(["人是由恐龙进化而来","熊猫喜欢吃天鹅肉"],embedding=OpenAIEmbeddings())retriever=vectorstore.as_retriever(...
smalldb = Chroma.from_texts(texts, embedding=embedding) 步骤3:进行相似性搜索 # 告诉我有关带有大子实体的全白蘑菇的信息 question = "Tell me about all-white mushrooms with large fruiting bodies" smalldb.similarity_search(question, k=2) # [Document(page_content='A mushroom with a large fruitin...
我们将利用C transformer和LangChain进行集成。也就是说将在LangChain中使用CTransformers LLM包装器，它为GGML模型提供了一个统一的接口。from langchain.llms import CTransformers# Local CTransformers wrapper for Llama-2-7B-Chatllm = CTransformers(model='models/llama-2-7b-chat.ggmlv3.q8_0.bin', # ...
from langchain.retrievers import KNNRetrieverfrom langchain.embeddings import OpenAIEmbeddingswords = ["cat", "dog", "computer", "animal"]retriever = KNNRetriever.from_texts(words, OpenAIEmbeddings()) 创建了检索器后,您可以通过调用get_relevant_documents()方法并传递查询字符串来使用它来检索相关文档。...
from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import Chroma # Customize the layout st.set_page_config(page_title="DOCAI", page_icon="", layout="wide", ) st.markdown(f""" .stApp {} """, unsafe_allow_html=True) ...
检索器是一种便于模型查询的存储数据的方式,LangChain 约定检索器组件至少有一个方法 get_relevant_texts,这个方法接收查询字符串,返回一组文档。下面是一个简单的列子: from langchain.chains import RetrievalQA from langchain.llms import OpenAI from langchain.document_loaders import TextLoader from langchain....
()print(f'Loaded {len(pages)} pages from the PDF')text_splitter=RecursiveCharacterTextSplitter(chunk_size=200,chunk_overlap=10,length_function=len,add_start_index=True,)texts=text_splitter.split_documents(pages)print(f'Split the pages in {len(texts)} chunks')print(texts[0])print(texts[1]...
texts[0].page_content 在我们有了这些块之后，我们需要把它们变成嵌入。这样向量存储就能在查询时找到并返回每个块。我们将使用OpenAI的嵌入模型来做这个。# 导入并实例化 OpenAI embeddingsfrom langchain.embeddings import OpenAIEmbeddingsembeddings = OpenAIEmbeddings(model_name="ada") # 用嵌入把第一个文本...
add_texts add_documents similarity_search similarity_search_by_vector max_marginal_relevance_search max_marginal_relevance_search_by_vector from_documents from_texts举例: OpenSearchVectorStore调用 大模型的embedding 能力, 将其向量化, 并且写入索引. (需预先创建好索引, 自动创建的索引数据类型不对)...
""" list_text = text.split('\n') from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import FAISS db = FAISS.from_texts(list_text, OpenAIEmbeddings()) query = "信用卡的额度可以提高吗" docs = db.similarity_search(query) print(docs[0].page_content) ...