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import os
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings import CacheBackedEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.storage import LocalFileStore
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
from langchain_core.callbacks import StdOutCallbackHandler
def create_index():
# Load the data from CSV file
data_loader = CSVLoader(file_path="data.csv")
data = data_loader.load()
# Create the embeddings model
embeddings_model = OpenAIEmbeddings()
# Create the cache backed embeddings in vector store
store = LocalFileStore("./cache")
cached_embedder = CacheBackedEmbeddings.from_bytes_store(
embeddings_model, store, namespace=embeddings_model.model
)
# Create FAISS vector store from documents
vector_store = FAISS.from_documents(data, embeddings_model)
return vector_store.as_retriever()
def setup_openai(openai_key):
# Set the API key for OpenAI
os.environ["OPENAI_API_KEY"] = openai_key
# Create index retriever
retriever = create_index()
# Initialize ChatOpenAI model
chat_openai_model = ChatOpenAI(temperature=0)
return retriever, chat_openai_model
def ai_doctor_chat(openai_key, query):
# Setup OpenAI environment
retriever, chat_model = setup_openai(openai_key)
# Create the QA chain
handler = StdOutCallbackHandler()
qa_with_sources_chain = RetrievalQA.from_chain_type(
llm=chat_model,
retriever=retriever,
callbacks=[handler],
return_source_documents=True
)
# Ask a question/query
res = qa_with_sources_chain({"query": query})
return res['result']
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