Create ask.py

ask.py

@@ -0,0 +1,45 @@
+from llama_index import GPTPineconeIndex, LLMPredictor, ServiceContext
+import pinecone
+from langchain import OpenAI
+import os
+
+
+# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+
+def askQuestion(brain, question, temperature, maxTokens):
+    print(brain, question, temperature)
+    Brain_Name = brain
+    pinecone.init(api_key=os.environ['PINECONE_KEY'],
+                  environment="us-west4-gcp")
+    pineconeindex = pinecone.Index(Brain_Name)
+    index = GPTPineconeIndex([], pinecone_index=pineconeindex)
+    # index = GPTSimpleVectorIndex.load_from_disk('index.json')
+
+    # For Q-A set this value to 4, For Content-Genration set this value b/w 7-10.
+    data_chunks = 4
+
+    # prompt query goes here
+    # query="summarize in full detail the solution that dimetyd is providing, and previous email sequences which can be used as a context knowledge"
+    query = question
+    # relevant info from brain goes here
+    info = ["pdf"]
+
+    llm_predictor = LLMPredictor(llm=OpenAI(
+        temperature=temperature, model_name="text-davinci-003", max_tokens=maxTokens))
+    service_context_gpt4 = ServiceContext.from_defaults(
+        llm_predictor=llm_predictor)
+
+    response = index.query(query, service_context=service_context_gpt4,
+                           similarity_top_k=data_chunks, response_mode="compact")
+
+    return response
+
+
+def getBrains():
+    pinecone.init(api_key=os.environ['PINECONE_KEY'],
+                  environment="us-west4-gcp")
+    active_indexes = pinecone.list_indexes()
+    print(active_indexes)
+    return active_indexes