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e4d1897
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1 Parent(s): 74e51b1

renamed faiss to faisslang

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Files changed (1) hide show
  1. app/vectorstore.py +4 -4
app/vectorstore.py CHANGED
@@ -12,7 +12,7 @@ from bs4 import BeautifulSoup
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  # Updated LangChain imports
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  from langchain_community.document_loaders import PyPDFLoader
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  from langchain_community.document_loaders import WebBaseLoader
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- from langchain_community.vectorstores import FAISS
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  from langchain_huggingface import HuggingFaceEmbeddings
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@@ -72,7 +72,7 @@ class vectorstore:
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  # Create embeddings and FAISS vectorstore with LangChain
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  embedder = HuggingFaceEmbeddings(model_name=embedder_model)
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- vectorstore = FAISS.from_texts(chunks, embedder)
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  # Save the vectorstore
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  index_path = self.save_vectorstore_with_timestamp_and_without(vectorstore, embedder_model)
@@ -103,7 +103,7 @@ class vectorstore:
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  # Create embeddings and FAISS vectorstore
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  embedder = HuggingFaceEmbeddings(model_name=embedder_model)
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- vectorstore = FAISS.from_texts(chunks, embedder)
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  # Save the vectorstore
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  index_path = self.save_vectorstore_with_timestamp_and_without(vectorstore, embedder_model)
@@ -202,7 +202,7 @@ class vectorstore:
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  # Create embedder and load vectorstore
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  embedder = HuggingFaceEmbeddings(model_name=embedder_model)
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- vectorstore = FAISS.load_local(index_path, embedder,allow_dangerous_deserialization=True)
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  return vectorstore
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  # Updated LangChain imports
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  from langchain_community.document_loaders import PyPDFLoader
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  from langchain_community.document_loaders import WebBaseLoader
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+ from langchain_community.vectorstores import FAISSLang
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  from langchain_huggingface import HuggingFaceEmbeddings
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  # Create embeddings and FAISS vectorstore with LangChain
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  embedder = HuggingFaceEmbeddings(model_name=embedder_model)
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+ vectorstore = FAISSLang.from_texts(chunks, embedder)
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  # Save the vectorstore
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  index_path = self.save_vectorstore_with_timestamp_and_without(vectorstore, embedder_model)
 
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  # Create embeddings and FAISS vectorstore
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  embedder = HuggingFaceEmbeddings(model_name=embedder_model)
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+ vectorstore = FAISSLang.from_texts(chunks, embedder)
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  # Save the vectorstore
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  index_path = self.save_vectorstore_with_timestamp_and_without(vectorstore, embedder_model)
 
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  # Create embedder and load vectorstore
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  embedder = HuggingFaceEmbeddings(model_name=embedder_model)
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+ vectorstore = FAISSLang.load_local(index_path, embedder,allow_dangerous_deserialization=True)
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  return vectorstore
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