From 8a5e1e8d98dfed9f1dc4cef4ea0d93cd89d8835f Mon Sep 17 00:00:00 2001 From: Alex Date: Wed, 15 Feb 2023 20:29:09 +0000 Subject: [PATCH] cleanups --- application/app.py | 4 +--- scripts/ingest.py | 1 - 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/application/app.py b/application/app.py index 8a66ff9d..b4a02370 100644 --- a/application/app.py +++ b/application/app.py @@ -78,8 +78,6 @@ def api_answer(): else: embeddings_key = os.getenv("EMBEDDINGS_KEY") - print(embeddings_key) - print(api_key) # check if the vectorstore is set if "active_docs" in data: @@ -115,7 +113,7 @@ def api_answer(): qa_chain = load_qa_chain(llm=llm, chain_type="map_reduce", combine_prompt=c_prompt) - chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=2) + chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=4) # fetch the answer result = chain({"query": question}) diff --git a/scripts/ingest.py b/scripts/ingest.py index ca4b8b6b..2194a7cf 100644 --- a/scripts/ingest.py +++ b/scripts/ingest.py @@ -50,7 +50,6 @@ def ingest(yes: bool = typer.Option(False, "-y", "--yes", prompt=False, required_exts=formats, num_files_limit=limit, exclude_hidden=exclude).load_data() raw_docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs] - print(raw_docs) # Here we split the documents, as needed, into smaller chunks. # We do this due to the context limits of the LLMs. text_splitter = RecursiveCharacterTextSplitter()