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Rm retriever kwargs (#7013)

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Doesn't actually limit the Retriever interface but hopefully in practice
it does
This commit is contained in:
Bagatur
2023-07-02 08:22:24 -06:00
committed by GitHub
parent 9dc77614e3
commit 7acd524210
32 changed files with 131 additions and 319 deletions
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7
tests/integration_tests/chains/test_retrieval_qa.py
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@@ -7,18 +7,19 @@ from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.vectorstores import FAISS
def test_retrieval_qa_saving_loading(tmp_path: Path) -> None:
"""Test saving and loading."""
loader = TextLoader("docs/modules/state_of_the_union.txt")
loader = TextLoader("docs/extras/modules/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()
docsearch = Chroma.from_documents(texts, embeddings)
docsearch = FAISS.from_documents(texts, embeddings)
qa = RetrievalQA.from_llm(llm=OpenAI(), retriever=docsearch.as_retriever())
qa.run("What did the president say about Ketanji Brown Jackson?")
file_path = tmp_path / "RetrievalQA_chain.yaml"
qa.save(file_path=file_path)

4
tests/integration_tests/retrievers/test_contextual_compression.py
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@@ -1,7 +1,7 @@
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import EmbeddingsFilter
from langchain.vectorstores import Chroma
from langchain.vectorstores import FAISS
def test_contextual_compression_retriever_get_relevant_docs() -> None:
@@ -13,7 +13,7 @@ def test_contextual_compression_retriever_get_relevant_docs() -> None:
]
embeddings = OpenAIEmbeddings()
base_compressor = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.75)
base_retriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(
base_retriever = FAISS.from_texts(texts, embedding=embeddings).as_retriever(
search_kwargs={"k": len(texts)}
)
retriever = ContextualCompressionRetriever(