Add retry logic to OpenAI API queries to increase Chat tenacity

- Move completion and chat_completion into helper methods under utils.py
- Add retry with exponential backoff on OpenAI exceptions using
  tenacity package. This is officially suggested and used by other
  popular GPT based libraries

pyproject.toml

@@ -42,6 +42,7 @@ dependencies = [
     "jinja2 == 3.1.2",
     "openai >= 0.27.0",
     "tiktoken >= 0.3.0",
+    "tenacity >= 8.2.2",
     "pillow == 9.3.0",
     "pydantic == 1.9.1",
     "pyqt6 == 6.3.1",

src/khoj/processor/conversation/gpt.py

@@ -1,15 +1,16 @@
 # Standard Packages
-import os
 import json
 import logging
 from datetime import datetime
 
-# External Packages
-import openai
-
 # Internal Packages
 from khoj.utils.constants import empty_escape_sequences
-from khoj.processor.conversation.utils import message_to_prompt, generate_chatml_messages_with_context
+from khoj.processor.conversation.utils import (
+    chat_completion_with_backoff,
+    completion_with_backoff,
+    message_to_prompt,
+    generate_chatml_messages_with_context,
+)
 
 
 logger = logging.getLogger(__name__)
@@ -19,9 +20,6 @@ def answer(text, user_query, model, api_key=None, temperature=0.5, max_tokens=50
     """
     Answer user query using provided text as reference with OpenAI's GPT
     """
-    # Initialize Variables
-    openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
-
     # Setup Prompt based on Summary Type
     prompt = f"""
 You are a friendly, helpful personal assistant.
@@ -35,8 +33,13 @@ Question: {user_query}
 Answer (in second person):"""
     # Get Response from GPT
     logger.debug(f"Prompt for GPT: {prompt}")
-    response = openai.Completion.create(
+    response = completion_with_backoff(
-        prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, stop='"""'
+        prompt=prompt,
+        model=model,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        stop='"""',
+        api_key=api_key,
     )
 
     # Extract, Clean Message from GPT's Response
@@ -48,9 +51,6 @@ def summarize(text, summary_type, model, user_query=None, api_key=None, temperat
     """
     Summarize user input using OpenAI's GPT
     """
-    # Initialize Variables
-    openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
-
     # Setup Prompt based on Summary Type
     if summary_type == "chat":
         prompt = f"""
@@ -69,8 +69,14 @@ Summarize the notes in second person perspective:"""
 
     # Get Response from GPT
     logger.debug(f"Prompt for GPT: {prompt}")
-    response = openai.Completion.create(
+    response = completion_with_backoff(
-        prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, frequency_penalty=0.2, stop='"""'
+        prompt=prompt,
+        model=model,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        frequency_penalty=0.2,
+        stop='"""',
+        api_key=api_key,
     )
 
     # Extract, Clean Message from GPT's Response
@@ -82,9 +88,6 @@ def extract_questions(text, model="text-davinci-003", conversation_log={}, api_k
     """
     Infer search queries to retrieve relevant notes to answer user query
     """
-    # Initialize Variables
-    openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
-
     # Extract Past User Message and Inferred Questions from Conversation Log
     chat_history = "".join(
         [
@@ -158,8 +161,13 @@ Q: {text}
 """
 
     # Get Response from GPT
-    response = openai.Completion.create(
+    response = completion_with_backoff(
-        prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, stop=["A: ", "\n"]
+        prompt=prompt,
+        model=model,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        stop=["A: ", "\n"],
+        api_key=api_key,
     )
 
     # Extract, Clean Message from GPT's Response
@@ -184,7 +192,6 @@ def extract_search_type(text, model, api_key=None, temperature=0.5, max_tokens=1
     Extract search type from user query using OpenAI's GPT
     """
     # Initialize Variables
-    openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
     understand_primer = """
 Objective: Extract search type from user query and return information as JSON
 
@@ -214,8 +221,14 @@ A:{ "search-type": "notes" }"""
 
     # Get Response from GPT
     logger.debug(f"Prompt for GPT: {prompt}")
-    response = openai.Completion.create(
+    response = completion_with_backoff(
-        prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, frequency_penalty=0.2, stop=["\n"]
+        prompt=prompt,
+        model=model,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        frequency_penalty=0.2,
+        stop=["\n"],
+        api_key=api_key,
     )
 
     # Extract, Clean Message from GPT's Response
@@ -229,7 +242,6 @@ def converse(references, user_query, conversation_log={}, api_key=None, temperat
     """
     # Initialize Variables
     model = "gpt-3.5-turbo"
-    openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
     compiled_references = "\n\n".join({f"# {item}" for item in references})
 
     personality_primer = "You are Khoj, a friendly, smart and helpful personal assistant."
@@ -252,10 +264,11 @@ Question: {user_query}"""
 
     # Get Response from GPT
     logger.debug(f"Conversation Context for GPT: {messages}")
-    response = openai.ChatCompletion.create(
+    response = chat_completion_with_backoff(
         messages=messages,
         model=model,
         temperature=temperature,
+        api_key=api_key,
     )
 
     # Extract, Clean Message from GPT's Response

src/khoj/processor/conversation/utils.py

@@ -1,16 +1,64 @@
 # Standard Packages
+import os
+import logging
 from datetime import datetime
 
 # External Packages
+import openai
 import tiktoken
+from tenacity import (
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+    wait_random_exponential,
+)
 
 # Internal Packages
 from khoj.utils.helpers import merge_dicts
 
 
+logger = logging.getLogger(__name__)
 max_prompt_size = {"gpt-3.5-turbo": 4096, "gpt-4": 8192}
 
 
+@retry(
+    retry=(
+        retry_if_exception_type(openai.error.Timeout)
+        | retry_if_exception_type(openai.error.APIError)
+        | retry_if_exception_type(openai.error.APIConnectionError)
+        | retry_if_exception_type(openai.error.RateLimitError)
+        | retry_if_exception_type(openai.error.ServiceUnavailableError)
+    ),
+    wait=wait_random_exponential(min=1, max=30),
+    stop=stop_after_attempt(6),
+    before_sleep=before_sleep_log(logger, logging.DEBUG),
+    reraise=True,
+)
+def completion_with_backoff(**kwargs):
+    openai.api_key = kwargs["api_key"] if kwargs.get("api_key") else os.getenv("OPENAI_API_KEY")
+    return openai.Completion.create(**kwargs, request_timeout=60)
+
+
+@retry(
+    retry=(
+        retry_if_exception_type(openai.error.Timeout)
+        | retry_if_exception_type(openai.error.APIError)
+        | retry_if_exception_type(openai.error.APIConnectionError)
+        | retry_if_exception_type(openai.error.RateLimitError)
+        | retry_if_exception_type(openai.error.ServiceUnavailableError)
+    ),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    stop=stop_after_attempt(6),
+    before_sleep=before_sleep_log(logger, logging.DEBUG),
+    reraise=True,
+)
+def chat_completion_with_backoff(**kwargs):
+    openai.api_key = kwargs["api_key"] if kwargs.get("api_key") else os.getenv("OPENAI_API_KEY")
+    return openai.ChatCompletion.create(**kwargs, request_timeout=60)
+
+
 def generate_chatml_messages_with_context(
     user_message, system_message, conversation_log={}, model_name="gpt-3.5-turbo", lookback_turns=2
 ):