Create user_utils.py

user_utils.py

@@ -0,0 +1,43 @@
+import pinecone
+from langchain.vectorstores import Pinecone
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain.llms import OpenAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.callbacks import get_openai_callback
+import joblib
+
+
+#Function to pull index data from Pinecone
+def pull_from_pinecone(pinecone_apikey,pinecone_environment,pinecone_index_name,embeddings):
+
+    pinecone.init(
+    api_key=pinecone_apikey,
+    environment=pinecone_environment
+    )
+
+    index_name = pinecone_index_name
+
+    index = Pinecone.from_existing_index(index_name, embeddings)
+    return index
+
+def create_embeddings():
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    return embeddings
+
+#This function will help us in fetching the top relevent documents from our vector store - Pinecone Index
+def get_similar_docs(index,query,k=2):
+
+    similar_docs = index.similarity_search(query, k=k)
+    return similar_docs
+
+def get_answer(docs,user_input):
+    chain = load_qa_chain(OpenAI(), chain_type="stuff")
+    with get_openai_callback() as cb:
+        response = chain.run(input_documents=docs, question=user_input)
+    return response
+
+
+def predict(query_result):
+    Fitmodel = joblib.load('modelsvm.pk1')
+    result=Fitmodel.predict([query_result])
+    return result[0]