forked from Archives/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
from langchain.docstore.document import Document
|
|
from langchain.vectorstores.vectara import Vectara
|
|
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
|
|
|
|
|
def get_abbr(s: str) -> str:
|
|
words = s.split(" ") # Split the string into words
|
|
first_letters = [word[0] for word in words] # Extract the first letter of each word
|
|
return "".join(first_letters) # Join the first letters into a single string
|
|
|
|
|
|
def test_vectara_add_documents() -> None:
|
|
"""Test end to end construction and search."""
|
|
|
|
# start with some initial documents
|
|
texts = ["grounded generation", "retrieval augmented generation", "data privacy"]
|
|
docsearch: Vectara = Vectara.from_texts(
|
|
texts,
|
|
embedding=FakeEmbeddings(),
|
|
metadatas=[{"abbr": "gg"}, {"abbr": "rag"}, {"abbr": "dp"}],
|
|
)
|
|
|
|
# then add some additional documents
|
|
new_texts = ["large language model", "information retrieval", "question answering"]
|
|
docsearch.add_documents(
|
|
[Document(page_content=t, metadata={"abbr": get_abbr(t)}) for t in new_texts]
|
|
)
|
|
|
|
# finally do a similarity search to see if all works okay
|
|
output = docsearch.similarity_search("large language model", k=2)
|
|
assert output[0].page_content == "large language model"
|
|
assert output[0].metadata == {"abbr": "llm"}
|
|
assert output[1].page_content == "information retrieval"
|
|
assert output[1].metadata == {"abbr": "ir"}
|