Create ai_doctor

ai_doctor

@@ -0,0 +1,53 @@
+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
+import os
+
+def create_index():
+    # load the data
+    dir = os.path.dirname(__file__)    
+    df_path = dir + '/data/Mental_Health_FAQ.csv'
+    loader = CSVLoader(file_path = df_path)
+    data = loader.load()
+    
+    # create the embeddings model
+    embeddings_model = OpenAIEmbeddings()
+
+    # create the cache backed embeddings in vector store
+    store = LocalFileStore("./cache")
+    cached_embeder = CacheBackedEmbeddings.from_bytes_store(
+        embeddings_model, store, namespace=embeddings_model.model
+    )
+    vector_store = FAISS.from_documents(data, embeddings_model)
+
+    return vector_store.as_retriever()
+
+def setup(openai_key):
+     # Set the API key for OpenAI
+    os.environ["OPENAI_API_KEY"] = openai_key
+    retriver = create_index()
+    llm = ChatOpenAI(model="gpt-4")
+    return retriver, llm
+
+def mh_assistant(openai_key,query):
+
+    # Setup
+    retriever,llm = setup(openai_key)
+    # Create the QA chain
+    handler = StdOutCallbackHandler()
+
+    qa_with_sources_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        retriever=retriever,
+        callbacks=[handler],
+        return_source_documents=True
+    )
+
+    # Ask a question
+    res = qa_with_sources_chain({"query":query})
+    return (res['result'])
+