Update Utils (#3)

- Update Utils (06348be5a486c70b10e201f0f9cc7261b2d5d7e9)

utils/prompts.py

@@ -1,18 +1,19 @@
 def generate_multi_doc_context(context_group):
-    multi_doc_context = ""
-    for context_text_list, year, quarter in context_group:
-        print((context_text_list, year, quarter))
+    # Extract ticker
+    multi_doc_text = ""
+    for context_text_list, year, quarter, ticker in context_group:
+        print((context_text_list, year, quarter, ticker))
         if context_text_list == []:
             break
         else:
-            multi_doc_context = (
-                multi_doc_context
+            multi_doc_text = (
+                multi_doc_text
                 + "\n"
-                + f"Document: {quarter} {year}"
+                + f"Source: {quarter} {ticker} Earnings Call {year}"
                 + "\n"
                 + " ".join(context_text_list)
             )
-    return multi_doc_context
+    return multi_doc_text
 
 
 def generate_gpt_prompt_alpaca(query_text, context_list):
@@ -44,6 +45,21 @@ Context: {multi_doc_context}
 ### Response:"""
     return prompt
 
+def generate_gpt_prompt_alpaca_multi_doc_multi_company(query_text, context_group_first, context_group_second):
+    multi_doc_context_first = generate_multi_doc_context(context_group_first)
+    multi_doc_context_second = generate_multi_doc_context(context_group_second)
+    prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Use the following guidelines to write a response that that appropriately completes the request:
+### Instruction:
+- Write a detailed paragraph consisting of exactly five complete sentences that answer the question based on the provided context.
+- Focus on addressing the specific question posed, providing as much relevant information and detail as possible.
+- Only use details from the provided context that directly address the question; do not include any additional information that is not explicitly stated.
+- Aim to provide a clear and concise summary that fully addresses the question.
+
+Question: {query_text}
+Context: {multi_doc_context_first} {multi_doc_context_second}
+### Response:"""
+    return prompt
+
 
 def generate_gpt_prompt_original(query_text, context_list):
     context = " ".join(context_list)