|
|
| import os |
| from langchain.text_splitter import RecursiveCharacterTextSplitter |
| from langchain.vectorstores import FAISS |
| from langchain.embeddings import OpenAIEmbeddings |
| from langchain.chains.question_answering import load_qa_chain |
| from langchain.llms import OpenAI |
|
|
| |
| os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY") |
|
|
| def split_text(text): |
| splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) |
| return splitter.split_text(text) |
|
|
| def create_vector_store(chunks): |
| embeddings = OpenAIEmbeddings() |
| vectorstore = FAISS.from_texts(chunks, embedding=embeddings) |
| return vectorstore |
|
|
| def answer_question(vectorstore, query): |
| docs = vectorstore.similarity_search(query) |
| llm = OpenAI(temperature=0) |
| chain = load_qa_chain(llm, chain_type="stuff") |
| return chain.run(input_documents=docs, question=query) |
|
|
