fix: streamline code for unit tests and LLM client logic

05-assistive-chatbot/README.md

@@ -18,7 +18,7 @@ After running the chatbot and providing feedback in the UI, review the feedback
 ## Running an application
 
 There are several ways to run the chatbot application, offering different ways to interact with the chatbot.
-All apps use configurations set in `.env`, which is *not* checked into git. These configurations (like `CHAT_ENGINE` and `LLM_MODEL_NAME`) can be overridden by environment variables set on the commandline. See `_init_settings()` in `chatbot/__init__.py` for other variables.
+All apps use configurations set in `.env`, which is *not* checked into git. These configurations (like `CHAT_ENGINE` and `LLM_MODEL_NAME`) can be overridden by environment variables set on the commandline. See `chatbot/__init__.py` for other variables.
 
 ### Run commandline app
 
@@ -53,7 +53,7 @@ This application runs the chatbot API for other applications to make requests to
 
 - Application entrypoints are in the root folder of the repo. Other Python files are under the `chatbot` folder.
 - The chatbot package `chatbot/__init__.py` is run for all apps because they `import chatbot`.
-- It initializes settings (`_init_settings()`) and creates a specified chat engine (`create_chat_engine(settings)`).
+- It initializes settings and creates a specified chat engine (`create_chat_engine(settings)`).
 
 ### Adding a chat engine
 
@@ -79,7 +79,7 @@ To create a new LLM client, add a new Python file under `chatbot/llms` with:
 - an LLM client class that:
  - sets `self.client` based on the provided `settings`, and
  - implements a `generate_reponse(self, message)` function that uses `self.client` to generate a response, which may need to be parsed so that a string is returned to `chat_engine.gen_response(self, query)`.
-- (optional) a `requirements_satisfied(settings)` function that checks if necessary environment variable(s) and other LLM client preconditions are satisfied;
+- (optional) a `requirements_satisfied()` function that checks if necessary environment variable(s) and other LLM client preconditions are satisfied;
 The new Python file will be automatically discovered and registered for display in the Chainlit settings web UI.
 
 An LLM client can be used in any arbitrary program by:

05-assistive-chatbot/chatbot-chainlit.py

@@ -20,7 +20,7 @@
 
 logger = logging.getLogger(f"chatbot.{__name__}")
 
-if chatbot.initial_settings["enable_api"]:
+if utils.is_env_var_true("ENABLE_CHATBOT_API", False):
  import chatbot_api
 
  logger.info("Chatbot API loaded: %s", chatbot_api.__name__)
@@ -33,8 +33,9 @@ async def init_chat():
  logger.debug("init_chat")
  git_sha = os.environ.get("GIT_SHA", "")
  build_date = os.environ.get("BUILD_DATE", "unknown")
+ initial_settings = chatbot.create_init_settings()
  metadata = {
- **chatbot.initial_settings,
+ **initial_settings,
  "build_date": build_date,
  "git_sha": git_sha,
  "hostname": socket.gethostname(),
@@ -50,26 +51,26 @@ async def init_chat():
  id="chat_engine",
  label="Chat Mode",
  values=engines.available_engines(),
- initial_value=chatbot.initial_settings["chat_engine"],
+ initial_value=initial_settings["chat_engine"],
  ),
  Select(
  id="model",
  label="Primary LLM Model",
  values=available_llms,
- initial_value=chatbot.initial_settings["model"],
+ initial_value=initial_settings["model"],
  ),
  Slider(
  id="temperature",
  label="Temperature for primary LLM",
- initial=chatbot.initial_settings["temperature"],
+ initial=initial_settings["temperature"],
  min=0,
  max=2,
  step=0.1,
  ),
  Slider(
  id="retrieve_k",
  label="Guru cards to retrieve",
- initial=chatbot.initial_settings["retrieve_k"],
+ initial=initial_settings["retrieve_k"],
  min=1,
  max=10,
  step=1,
@@ -78,12 +79,12 @@ async def init_chat():
  id="model2",
  label="LLM Model for summarizer",
  values=available_llms,
- initial_value=chatbot.initial_settings["model2"],
+ initial_value=initial_settings["model2"],
  ),
  Slider(
  id="temperature2",
  label="Temperature for summarizer",
- initial=chatbot.initial_settings["temperature2"],
+ initial=initial_settings["temperature2"],
  min=0,
  max=2,
  step=0.1,
@@ -98,7 +99,8 @@ async def init_chat():
  if error:
  assert False, f"Validation error: {error}"
 
- if chatbot.initial_settings["preload_chat_engine"]:
+ preload_chat_engine_default = "ENGINE_MODULES" in os.environ and "LLM_MODULES" in os.environ
+ if utils.is_env_var_true("PRELOAD_CHAT_ENGINE", preload_chat_engine_default):
  logger.info("Preloading chat engine")
  await apply_settings()
 

05-assistive-chatbot/chatbot/__init__.py

@@ -14,6 +14,8 @@
 ## Set default environment variables
 
 
+os.environ.setdefault("ENV", "DEV")
+
 # Opt out of telemetry -- https://docs.trychroma.com/telemetry
 os.environ.setdefault("ANONYMIZED_TELEMETRY", "False")
 
@@ -44,7 +46,7 @@ def configure_logging():
  logging.info("Configured logging level for root logger: %s", root_log_level)
 
 
-env = os.environ.get("ENV", "DEV")
+env = os.environ.get("ENV")
 print(f"Loading .env-{env}")
 dotenv.load_dotenv(f".env-{env}")
 dotenv.load_dotenv()
@@ -63,14 +65,10 @@ def configure_logging():
 
 
 @utils.verbose_timer(logger)
-def _init_settings():
- # Remember to update ChatSettings in chatbot-chainlit.py when adding new settings
- # and update chatbot/engines/__init.py:CHATBOT_SETTING_KEYS
- preload_chat_engine_default = "ENGINE_MODULES" in os.environ and "LLM_MODULES" in os.environ
+def create_init_settings():
+ # REMINDER: when adding new settings, update ChatSettings in chatbot-chainlit.py
+ # and chatbot/engines/__init.py:LLM_SETTING_KEYS, if applicable
  return {
- "env": env,
- "enable_api": is_env_var_true("ENABLE_CHATBOT_API", False),
- "preload_chat_engine": is_env_var_true("PRELOAD_CHAT_ENGINE", preload_chat_engine_default),
  "chat_engine": os.environ.get("CHAT_ENGINE", "Direct"),
  "model": os.environ.get("LLM_MODEL_NAME", "mock :: llm"),
  "temperature": float(os.environ.get("LLM_TEMPERATURE", 0.1)),
@@ -81,21 +79,10 @@ def _init_settings():
  }
 
 
-def is_env_var_true(var_name, default=False):
- if value:= os.environ.get(var_name, None):
- return value.lower() not in ["false", "f", "no", "n"]
- return default
-
-
-initial_settings = _init_settings()
-
-
 def reset():
  configure_logging()
  engines._engines.clear()
  llms._llms.clear()
- global initial_settings
- initial_settings = _init_settings()
 
 
 @utils.verbose_timer(logger)

05-assistive-chatbot/chatbot/engines/__init__.py

@@ -4,7 +4,6 @@
 from types import ModuleType
 from typing import Dict
 
-import chatbot
 from chatbot import llms, utils
 
 logger = logging.getLogger(__name__)
@@ -26,12 +25,11 @@ def _discover_chat_engines(force=False):
  if not engine_modules:
  engine_modules = utils.scan_modules(__package__)
 
- settings = chatbot.initial_settings
  for module_name, module in engine_modules.items():
  if not hasattr(module, "ENGINE_NAME"):
  logger.debug("Skipping module without an ENGINE_NAME: %s", module_name)
  continue
- if hasattr(module, "requirements_satisfied") and not module.requirements_satisfied(settings):
+ if hasattr(module, "requirements_satisfied") and not module.requirements_satisfied():
  logger.debug("Engine requirements not satisfied; skipping: %s", module_name)
  continue
  engine_name = module.ENGINE_NAME
@@ -50,13 +48,13 @@ def create_engine(engine_name, settings=None):
 
 ## Utility functions
 
-# Settings that are specific to our chatbot and shouldn't be passed onto the LLM client
-CHATBOT_SETTING_KEYS = ["env", "enable_api", "chat_engine", "model", "model2", "temperature2", "retrieve_k"]
+# Settings that are specific to LLMs and should be passed onto the LLM client
+LLM_SETTING_KEYS = ["temperature"]
 
 
 @utils.timer
 def create_llm_client(settings):
  llm_name = settings["model"]
- remaining_settings = {k: settings[k] for k in settings if k not in CHATBOT_SETTING_KEYS}
+ remaining_settings = {k: settings[k] for k in settings if k in LLM_SETTING_KEYS}
  client = llms.init_client(llm_name, remaining_settings)
  return client

05-assistive-chatbot/chatbot/llms/__init__.py

@@ -4,7 +4,6 @@
 from types import ModuleType
 from typing import Dict, Tuple
 
-import chatbot
 from chatbot import utils
 
 logger = logging.getLogger(__name__)
@@ -26,17 +25,16 @@ def _discover_llms(force=False):
  if not llm_modules:
  llm_modules = utils.scan_modules(__package__)
 
- settings = chatbot.initial_settings
  for module_name, module in llm_modules.items():
  if not module or ignore(module_name):
  logger.debug("Skipping module: %s", module_name)
  continue
- if hasattr(module, "requirements_satisfied") and not module.requirements_satisfied(settings):
+ if hasattr(module, "requirements_satisfied") and not module.requirements_satisfied():
  logger.debug("Module requirements not satisfied; skipping: %s", module_name)
  continue
  client_name = module.CLIENT_NAME or module_name
  if hasattr(module, "model_names"):
- model_names = module.model_names(settings)
+ model_names = module.model_names()
  else:
  model_names = module.MODEL_NAMES
 

05-assistive-chatbot/chatbot/llms/dspy_client.py

@@ -12,9 +12,9 @@
 MODEL_NAMES = _OLLAMA_LLMS + _OPENAI_LLMS + _GOOGLE_LLMS + _GROQ_LLMS
 
 
-def model_names(settings):
+def model_names():
  available_models = []
- if settings["env"] != "PROD":
+ if os.environ.get("ENV") != "PROD":
  # Include Ollama models if not in production b/c it requires a local Ollama installation
  available_models += _OLLAMA_LLMS
 

05-assistive-chatbot/chatbot/llms/google_gemini_client.py

@@ -9,7 +9,7 @@
 MODEL_NAMES = ["gemini-pro", "gemini-1.5-pro", "gemini-1.5-flash"]
 
 
-def requirements_satisfied(_settings):
+def requirements_satisfied():
  if not os.environ.get("GOOGLE_API_KEY"):
  return False
  return True

05-assistive-chatbot/chatbot/llms/groq_client.py

@@ -9,7 +9,7 @@
 MODEL_NAMES = ["llama3-70b-8192", "mixtral-8x7b-32768"]
 
 
-def requirements_satisfied(_settings):
+def requirements_satisfied():
  if not os.environ.get("GROQ_API_KEY"):
  return False
  return True
@@ -20,12 +20,15 @@ def init_client(model_name, settings):
 
 
 class GroqClient:
+ INIT_ARGS = ["timeout", "max_retries", "default_headers", "default_query", "base_url", "http_client"]
+
  def __init__(self, model_name, settings):
  self.model_name = model_name
  self.settings = settings
- logger.info("Creating LLM client '%s' with %s", model_name, self.settings)
- # TODO: remove temperature from settings
- self.client = Groq(**self.settings)
+
+ init_settings = {k: settings[k] for k in settings if k in self.INIT_ARGS}
+ logger.info("Creating LLM client '%s' with %s", model_name, init_settings)
+ self.client = Groq(**init_settings)
 
  def generate_reponse(self, message):
  chat_completion = self.client.chat.completions.create(

05-assistive-chatbot/chatbot/llms/langchain_google_client.py

@@ -9,7 +9,7 @@
 MODEL_NAMES = ["gemini-pro", "gemini-1.5-pro", "gemini-1.5-flash"]
 
 
-def requirements_satisfied(_settings):
+def requirements_satisfied():
  if not os.environ.get("GOOGLE_API_KEY"):
  return False
  return True

05-assistive-chatbot/chatbot/llms/langchain_ollama_client.py

@@ -1,15 +1,16 @@
 import logging
+import os
 
 from langchain_community.llms.ollama import Ollama
 
 logger = logging.getLogger(__name__)
 
 CLIENT_NAME = "langchain.ollama"
-MODEL_NAMES = ["openhermes", "llama2", "mistral"]
+MODEL_NAMES = ["openhermes", "llama2", "llama2:chat", "llama3", "mistral", "mistral:instruct"]
 
 
-def requirements_satisfied(settings):
- if settings["env"] == "PROD":
+def requirements_satisfied():
+ if os.environ.get("ENV") == "PROD":
  # Exclude Ollama models in production b/c it requires a local Ollama installation
  return False
  return True

05-assistive-chatbot/chatbot/llms/mock_llm_client.py

@@ -13,9 +13,24 @@ def init_client(model_name, settings):
 class MockLlmClient:
  "Mock client that returns the mock_responses or the message itself."
 
+ sample_qa_pairs = {
+ "test Q1": {
+ "errorMsg": "Some error message",
+ "systemMsg": "Some system message",
+ "content": "Some content",
+ },
+ "test Q2": {
+ "errorMsg": "Some error message",
+ "systemMsg": {"content": "Some system message", "metadata": {"errorObj": 123}},
+ "content": "Some content",
+ "metadata": {"key1": "value1", "key2": "value2"},
+ },
+ }
+
  def __init__(self, model_name, settings):
  logger.info("Creating Mock LLM client '%s' with %s", model_name, settings)
- self.mock_responses = settings
+
+ self.mock_responses = self.sample_qa_pairs | settings
 
  def generate_reponse(self, message):
  return self.mock_responses.get(message, f"Mock LLM> Your query was: {message}")

05-assistive-chatbot/chatbot/llms/openai_client.py

@@ -13,7 +13,7 @@
 MODEL_NAMES = _CHAT_MODELS + _LEGACY_MODELS
 
 
-def requirements_satisfied(_settings):
+def requirements_satisfied():
  if not os.environ.get("OPENAI_API_KEY"):
  return False
  return True

05-assistive-chatbot/chatbot/utils.py

@@ -2,12 +2,19 @@
 import importlib
 import inspect
 import logging
+import os
 import pkgutil
 import pprint
 import textwrap
 import time
 
 
+def is_env_var_true(var_name, default=False):
+ if value := os.environ.get(var_name, None):
+ return value.lower() not in ["false", "f", "no", "n"]
+ return default
+
+
 def timer(func):
  "A decorator that logs the time it takes for the decorated function to run"
  module = inspect.getmodule(func)

05-assistive-chatbot/chatbot_api.py

@@ -35,7 +35,7 @@ class ApiState:
  @cached_property
  def chat_engine(self):
  # Load the initial settings
- settings = chatbot.initial_settings
+ settings = chatbot.create_init_settings()
  chatbot.validate_settings(settings)
 
  # Create the chat engine

05-assistive-chatbot/cmdline.py

@@ -15,7 +15,7 @@
 logger = logging.getLogger(f"chatbot.{__name__}")
 
 # Load the initial settings
-settings = chatbot.initial_settings
+settings = chatbot.create_init_settings()
 chatbot.validate_settings(settings)
 
 # List LLMs, when CHATBOT_LOG_LEVEL=DEBUG