Consolidate calls to openai

Starting to abstract away the calls to openai

scripts/agent_manager.py

@@ -1,4 +1,5 @@
 import openai
+from llm_utils import create_chat_completion
 
 next_key = 0
 agents = {}  # key, (task, full_message_history, model)
@@ -13,13 +14,11 @@ def create_agent(task, prompt, model):
     messages = [{"role": "user", "content": prompt}, ]
 
     # Start GTP3 instance
-    response = openai.ChatCompletion.create(
+    agent_reply = create_chat_completion(
         model=model,
         messages=messages,
     )
 
-    agent_reply = response.choices[0].message["content"]
-
     # Update full message history
     messages.append({"role": "assistant", "content": agent_reply})
 
@@ -42,14 +41,11 @@ def message_agent(key, message):
     messages.append({"role": "user", "content": message})
 
     # Start GTP3 instance
-    response = openai.ChatCompletion.create(
+    agent_reply = create_chat_completion(
         model=model,
         messages=messages,
     )
 
-    # Get agent response
-    agent_reply = response.choices[0].message["content"]
-
     # Update full message history
     messages.append({"role": "assistant", "content": agent_reply})
 

scripts/browse.py

@@ -4,6 +4,7 @@
 from readability import Document
 import openai
 from config import Config
+from llm_utils import create_chat_completion
 
 cfg = Config()
 
@@ -101,13 +102,11 @@ def summarize_text(text, is_website=True):
                     chunk},
             ]
 
-        response = openai.ChatCompletion.create(
+        summary = create_chat_completion(
             model=cfg.fast_llm_model,
             messages=messages,
             max_tokens=300,
         )
-
-        summary = response.choices[0].message.content
         summaries.append(summary)
     print("Summarized " + str(len(chunks)) + " chunks.")
 
@@ -129,11 +128,10 @@ def summarize_text(text, is_website=True):
                 combined_summary},
         ]
 
-    response = openai.ChatCompletion.create(
+    final_summary = create_chat_completion(
         model=cfg.fast_llm_model,
         messages=messages,
         max_tokens=300,
     )
 
-    final_summary = response.choices[0].message.content
     return final_summary

scripts/call_ai_function.py

@@ -3,6 +3,8 @@
 from config import Config
 cfg = Config()
 
+from llm_utils import create_chat_completion
+
 # This is a magic function that can do anything with no-code. See
 # https://github.com/Torantulino/AI-Functions for more info.
 def call_ai_function(function, args, description, model=cfg.smart_llm_model):
@@ -18,8 +20,8 @@ def call_ai_function(function, args, description, model=cfg.smart_llm_model):
         {"role": "user", "content": args},
     ]
 
-    response = openai.ChatCompletion.create(
+    response = create_chat_completion(
         model=model, messages=messages, temperature=0
     )
 
-    return response.choices[0].message["content"]
+    return response

scripts/chat.py

@@ -5,6 +5,7 @@
 from config import Config
 cfg = Config()
 
+from llm_utils import create_chat_completion
 
 def create_chat_message(role, content):
     """
@@ -62,13 +63,11 @@ def chat_with_ai(
                 print("----------- END OF CONTEXT ----------------")
 
             # TODO: use a model defined elsewhere, so that model can contain temperature and other settings we care about
-            response = openai.ChatCompletion.create(
+            assistant_reply = create_chat_completion(
                 model=cfg.smart_llm_model,
                 messages=current_context,
             )
 
-            assistant_reply = response.choices[0].message["content"]
-
             # Update full message history
             full_message_history.append(
                 create_chat_message(
@@ -79,5 +78,6 @@ def chat_with_ai(
 
             return assistant_reply
         except openai.error.RateLimitError:
+            # TODO: WHen we switch to langchain, this is built in
             print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
             time.sleep(10)

scripts/llm_utils.py

@@ -0,0 +1,16 @@
+import openai
+from config import Config
+cfg = Config()
+
+openai.api_key = cfg.openai_api_key
+
+# Overly simple abstraction until we create something better
+def create_chat_completion(messages, model=None, temperature=None, max_tokens=None)->str:
+    response = openai.ChatCompletion.create(
+        model=model,
+        messages=messages,
+        temperature=temperature,
+        max_tokens=max_tokens
+    )
+
+    return response.choices[0].message["content"]