Delete research_1/test.py

research_1/test.py

@@ -1,38 +0,0 @@
-from langchain_community.vectorstores import FAISS
-from langchain_openai import OpenAIEmbeddings
-from langchain_text_splitters import CharacterTextSplitter
-from langchain_community.document_loaders import TextLoader
-
-
-
-loader = TextLoader("../../state_of_the_union.txt")
-documents = loader.load()
-text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-texts = text_splitter.split_documents(documents)
-embeddings = OpenAIEmbeddings()
-db = FAISS.from_documents(texts, embeddings)
-
-retriever = db.as_retriever()
-docs = retriever.invoke("what did he say about ketanji brown jackson")
-
-
-# Maximum marginal relevance retrieval
-#By default, the vector store retriever uses similarity search. If the underlying vector store supports maximum marginal relevance search, you can specify that as the search type.
-retriever = db.as_retriever(search_type="mmr")
-docs = retriever.invoke("what did he say about ketanji brown jackson")
-
-
-
-#Similarity score threshold retrieval
-retriever = db.as_retriever(
-    search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.5}
-)
-docs = retriever.invoke("what did he say about ketanji brown jackson")
-
-
-
-#Specifying top k
-#You can also specify search kwargs like k to use when doing retrieval.
-retriever = db.as_retriever(search_kwargs={"k": 1})
-docs = retriever.invoke("what did he say about ketanji brown jackson")
-len(docs)