Update utils.py
Browse files
utils.py
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@@ -4,14 +4,17 @@ from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTex
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from langchain_community.vectorstores import Chroma
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.retrievers import BM25Retriever
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import os
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def split_with_source(text, source):
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splitter = CharacterTextSplitter(
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separator = "\n",
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chunk_size =
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chunk_overlap =
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add_start_index = True,
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)
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documents = splitter.create_documents([text])
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@@ -83,5 +86,23 @@ def load_the_bm25_retrieve(k = 3):
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return bm25_retriever
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from langchain_community.vectorstores import Chroma
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.retrievers import BM25Retriever
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from langchain.llms import OpenAI
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from langchain_openai import ChatOpenAI
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from langchain.chains import RetrievalQA
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import os
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def split_with_source(text, source):
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splitter = CharacterTextSplitter(
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separator = "\n",
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chunk_size = 256,
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chunk_overlap = 0,
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length_function = len,
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add_start_index = True,
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)
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documents = splitter.create_documents([text])
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return bm25_retriever
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def get_qachain(llm_name = "gpt-3.5-turbo-0125", chain_type = "stuff", retriever = None, return_source_documents = True):
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llm = ChatOpenAI(temperature=0,
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model_name=llm_name)
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return RetrievalQA.from_chain_type(llm=llm,
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chain_type=chain_type,
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retriever=retriever,
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return_source_documents=return_source_documents)
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def process_llm_response(llm_response):
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print(llm_response['result'])
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print('\n\nSources:')
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for source in llm_response["source_documents"]:
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print(source.metadata['source'])
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