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wac.py
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wac.py
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from fastapi import FastAPI, Request, HTTPException, Form
from fastapi.templating import Jinja2Templates
from fastapi.responses import JSONResponse, PlainTextResponse, HTMLResponse
from jsonget import json_get, json_get_default
from pydantic import BaseModel
from typing import Optional
import json
import logging
import requests
from datetime import datetime
from decouple import config
import typesense
# For typesense-server when not in dev mode
import subprocess
import threading
import time
HA_URL = config('HA_URL', default="http://homeassistant.local:8123", cast=str)
HA_TOKEN = config('HA_TOKEN', default=None, cast=str)
LOG_LEVEL = config('LOG_LEVEL', default="debug", cast=str).upper()
TGI_URL = config(f'TGI_URL', default=None, cast=str)
# Typesense config vars
TYPESENSE_API_KEY = config('TYPESENSE_API_KEY', default='testing', cast=str)
TYPESENSE_HOST = config('TYPESENSE_HOST', default='127.0.0.1', cast=str)
TYPESENSE_PORT = config('TYPESENSE_PORT', default=8108, cast=int)
TYPESENSE_PROTOCOL = config('TYPESENSE_PROTOCOL', default='http', cast=str)
TYPESENSE_SLOW_TIMEOUT = config(
'TYPESENSE_SLOW_TIMEOUT', default=120, cast=int)
TYPESENSE_THREADS = config('TYPESENSE_THREADS', default=8, cast=int)
TYPESENSE_TIMEOUT = config('TYPESENSE_TIMEOUT', default=1, cast=int)
# "Prod" vs "dev"
RUN_MODE = config(f'RUN_MODE', default="prod", cast=str)
if RUN_MODE == "prod":
TYPESENSE_HOST = "127.0.0.1"
TYPESENSE_PORT = 8108
TYPESENSE_PROTOCOL = "http"
# Provide user feedback for learned and corrected commands
FEEDBACK = config(f'FEEDBACK', default=True, cast=bool)
# HA
HA_TOKEN = f'Bearer {HA_TOKEN}'
# Default number of search results and attempts
CORRECT_ATTEMPTS = config(
'CORRECT_ATTEMPTS', default=1, cast=int)
# Search distance for text string distance
SEARCH_DISTANCE = config(
'SEARCH_DISTANCE', default=2, cast=int)
# The number of matching tokens to consider a successful WAC search
# More tokens = closer match
TOKEN_MATCH_THRESHOLD = config(
'TOKEN_MATCH_THRESHOLD', default=3, cast=int)
# The number of matching tokens to consider a successful WAC search
# larger float = further away (less close in meaning)
# NOTE: Different models have different score mechanisms
# This will likely need to get adjusted if you use models other than all-MiniLM-L12-v2
VECTOR_DISTANCE_THRESHOLD = config(
'VECTOR_DISTANCE_THRESHOLD', default=0.29, cast=float)
# Hybrid/fusion search threshold.
# larger float = closer (reverse of vector distance)
HYBRID_SCORE_THRESHOLD = config(
'HYBRID_SCORE_THRESHOLD', default=0.85, cast=float)
# Typesense embedding model to use
TYPESENSE_SEMANTIC_MODEL = config(
'TYPESENSE_SEMANTIC_MODEL', default='all-MiniLM-L12-v2', cast=str)
# Default semantic mode
TYPESENSE_SEMANTIC_MODE = config(
'TYPESENSE_SEMANTIC_MODE', default='hybrid', cast=str)
# The typesense collection to use
COLLECTION = config(
'COLLECTION', default='commands', cast=str)
# "OpenAI" Configuration
OPENAI_BASE_URL = config(
'OPENAI_BASE_URL', default="https://api.endpoints.anyscale.com/v1", cast=str)
OPENAI_API_KEY = config(
'OPENAI_API_KEY', default="undefined", cast=str)
OPENAI_MODEL = config(
'OPENAI_MODEL', default="meta-llama/Llama-2-70b-chat-hf", cast=str)
OPENAI_SYSTEM_PROMPT = config(
'OPENAI_SYSTEM_PROMPT', default="Keep your answers as short as possible.", cast=str)
OPENAI_TEMPERATURE = config(
'OPENAI_TEMPERATURE', default=0.1, cast=float)
COMMAND_NOT_FOUND = config(
'COMMAND_NOT_FOUND', default="Sorry, I can't find that command", cast=str)
# Allow user to adjust feedback text for learned and corrected commands
COMMAND_LEARNED = config(
'COMMAND_LEARNED', default="and learned command", cast=str)
COMMAND_CORRECTED = config(
'COMMAND_CORRECTED', default="with corrected command", cast=str)
# Allow user to skip commands from autolearning
COMMANDS_TO_SKIP = config(
'COMMANDS_TO_SKIP', default='[]')
FORWARD_TO_CHAT = config(f'FORWARD_TO_CHAT', default=False, cast=bool)
COMMAND_FINAL_HA_FORWARD = config(
'COMMAND_FINAL_HA_FORWARD', default="", cast=str)
#HA include and exclude for "area awareness" hack :)
AREA_AWARENESS = config(f'AREA_AWARENESS', default=False, cast=bool)
WILLOW_LOCATIONS = config(
'WILLOW_LOCATIONS', default='{}')
AREA_AWARE_COMMANDS = config(
'AREA_AWARE_COMMANDS', default='["turn", "switch"]')
HA_AREAS = config(
'HA_AREAS', default='["bedroom", "breakfast room", "dining room", "garage", "living room", "kitchen", "office", "all"]')
# Getting list of commands to skip
# Convert the string to a Python list
try:
commands_to_skip_list = json.loads(COMMANDS_TO_SKIP)
except json.JSONDecodeError:
# Handle the case where the string is not a valid JSON list
log.info(f"Error: COMMANDS_TO_SKIP is not a valid JSON list.")
commands_to_skip_list = []
# Convert the WORDS_TO_INCLUDE string to a Python list
try:
words_to_include_list = json.loads(AREA_AWARE_COMMANDS)
except json.JSONDecodeError:
# Handle the case where the string is not a valid JSON list
log.info(f"Error: AREA_AWARE_COMMANDS is not a valid JSON list.")
words_to_include_list = []
# Convert the WORDS_TO_EXCLUDE string to a Python list
try:
words_to_exclude_list = json.loads(HA_AREAS)
except json.JSONDecodeError:
# Handle the case where the string is not a valid JSON list
log.info(f"Error: HA_AREAS is not a valid JSON list.")
words_to_exclude_list = []
# Getting dict of willow locations
# Convert to a dict
try:
willow_locations_dict = json.loads(WILLOW_LOCATIONS)
except json.JSONDecodeError:
# Handle the case where the string is not a valid JSON dict
log.info(f"Error: WILLOW_LOCATIONS is not a valid JSON dict.")
willow_locations_dict = {}
# Convert COMMANDS_TO_SKIP into a tuple for startswith
skip_tuple = tuple(commands_to_skip_list)
# Convert the lists to sets
include_set = set(words_to_include_list)
exclude_set = set(words_to_exclude_list)
def check_command(command):
lower_command = command.lower()
return any(phrase in lower_command for phrase in include_set) and not any(phrase in lower_command for phrase in exclude_set)
FORCE_OPENAI_MODEL = None
logging.basicConfig(
format='%(asctime)s %(levelname)-8s %(message)s',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S')
log = logging.getLogger("WAC")
try:
log.setLevel(LOG_LEVEL)
log.info(f"Set log level {LOG_LEVEL}")
except Exception as e:
log.exception(f"Set log level {LOG_LEVEL} failed with {e}")
pass
# OpenAI
if OPENAI_API_KEY != "undefined":
log.info(f"Initializing OpenAI Client")
import openai
openai_client = openai.OpenAI(
api_key=OPENAI_API_KEY, base_url=OPENAI_BASE_URL)
models = openai_client.models.list()
if len(models.data) == 1:
FORCE_OPENAI_MODEL = models.data[0].id
log.info(
f"Only one model on OpenAI endpoint - forcing model '{FORCE_OPENAI_MODEL}'")
else:
openai_client = None
# OpenAI Chat
def openai_chat(text, model=OPENAI_MODEL):
log.info(f"OpenAI Chat request for text '{text}'")
response = COMMAND_NOT_FOUND
if FORCE_OPENAI_MODEL is not None:
log.info(f"Forcing model '{FORCE_OPENAI_MODEL}'")
model = FORCE_OPENAI_MODEL
else:
log.info(f"Using model '{model}'")
if openai_client is not None:
try:
chat_completion = openai_client.chat.completions.create(
messages=[
{
"role": "system",
"content": OPENAI_SYSTEM_PROMPT,
},
{
"role": "user",
"content": text,
}
],
model=model,
temperature=OPENAI_TEMPERATURE,
)
response = chat_completion.choices[0].message.content
# Make it friendly for TTS and display output
response = response.replace('\n', ' ').replace('\r', '').lstrip()
log.info(f"Got OpenAI response '{response}'")
except Exception as e:
log.info(f"OpenAI failed with '{e}")
return response
# Typesense
def start_typesense():
def run(job):
proc = subprocess.Popen(job)
proc.wait()
return proc
# Fix this in prod to use some kind of unique/user provided/etc key. Not that big of a deal but...
job = ['/usr/local/sbin/typesense-server', '--data-dir=/app/data/ts',
f'--api-key={TYPESENSE_API_KEY}', '--log-dir=/dev/shm', f'--thread-pool-size={TYPESENSE_THREADS}']
# server thread will remain active as long as FastAPI thread is running
thread = threading.Thread(name='typesense-server',
target=run, args=(job,), daemon=True)
thread.start()
app = FastAPI(title="WAC Proxy",
description="Willow Auto Correct REST Proxy",
version="0.1",
openapi_url="/openapi.json",
docs_url="/docs",
redoc_url="/redoc")
templates = Jinja2Templates(directory="/app/templates")
###Simple temp UI
# Route to display the search page
@app.get("/", summary="Serve Simple UI page")
async def serve_page(request: Request):
# Initially, no search has been performed, so no results
return templates.TemplateResponse("search.html", {"request": request, "search_result": None})
# Routes to handle the simple ui search, delete, add
@app.get("/search", summary="Handle Search for Simple UI page")
async def handle_search(request: Request,
query: str,
distance: Optional[int] = 2,
num_results: Optional[int] = 1,
exact_match: Optional[bool] = False,
semantic: Optional[str] = "hybrid",
semantic_model: Optional[str] = "all-MiniLM-L12-v2"):
try:
time_start = datetime.now()
# Little fix for compatibility
if semantic == "true":
semantic = "on"
elif semantic == "false":
semantic = "off"
results = wac_search(query, exact_match=exact_match,
distance=distance, num_results=num_results, raw=True, semantic=semantic, semantic_model=semantic_model)
time_end = datetime.now()
search_time = time_end - time_start
search_time_milliseconds = search_time.total_seconds() * 1000
log.info('WAC search took ' + str(search_time_milliseconds) + ' ms')
except Exception as e:
log.exception(f"Search failed with {e}")
results = f"Search failed with {e}"
# Render the same page with the search result
return templates.TemplateResponse("search.html", {"request": request, "search_result": results})
@app.post("/add_command", summary="Handle Add Command for Simple UI page")
async def add_command(request: Request, new_command: str = Form(...),
is_alias: bool = Form(False),
alias: str = Form(None)):
try:
if is_alias and alias:
added_result=wac_add_alias(new_command, alias, rank=1.0, source='manual_entry')
else:
added_result=wac_add(new_command, rank=1.0, source='manual_entry')
response = "Command Added" if added_result else "Command Not Added. Refusing to add duplicate command"
except Exception as e:
log.exception(f"Add new command failed with {e}")
response = f"Add new command failed with {e}"
return templates.TemplateResponse("search.html", {"request": request, "add_message": response})
@app.post("/delete_command", summary="Handle Delete Command for Simple UI page")
async def delete_command(request: Request, command_id: int = Form(...)):
try:
log.info(f"Attempting to delete command ID {command_id}")
delete = typesense_client.collections[COLLECTION].documents[command_id].delete(
)
command = json_get(delete, "/command")
log.info(f"Successfully deleted command '{command}' with id {command_id}")
response = f"Successfully deleted command '{command}' with id {command_id}"
except:
log.info(f"Failed to deleted command with id {command_id}")
response = f"Failed to deleted command with id {command_id}"
return templates.TemplateResponse("search.html", {"request": request, "delete_message": response})
###End of Simple temp UI
# Basic stuff we need
ha_headers = {
"Authorization": HA_TOKEN,
}
# The real WAC MVP
typesense_client = typesense.Client({
'nodes': [{
'host': TYPESENSE_HOST,
'port': TYPESENSE_PORT,
'protocol': TYPESENSE_PROTOCOL,
}],
'api_key': TYPESENSE_API_KEY,
'connection_timeout_seconds': TYPESENSE_TIMEOUT
})
# For operations that take a while like initial vector schema and model download
slow_typesense_client = typesense.Client({
'nodes': [{
'host': TYPESENSE_HOST,
'port': TYPESENSE_PORT,
'protocol': TYPESENSE_PROTOCOL,
}],
'api_key': TYPESENSE_API_KEY,
'connection_timeout_seconds': TYPESENSE_SLOW_TIMEOUT
})
# The schema for WAC commands - you really do not want to mess with this
wac_commands_schema = {
'name': COLLECTION,
'fields': [
{'name': 'command', 'type': 'string', "sort": True},
{'name': 'rank', 'type': 'float'},
{'name': 'is_alias', 'type': 'bool', 'optional': True},
{'name': 'alias', 'type': 'string', 'optional': True, "sort": True},
{'name': 'accuracy', 'type': 'float', 'optional': True},
{'name': 'source', 'type': 'string', 'optional': True, "sort": True},
{'name': 'timestamp', 'type': 'int64', 'optional': True},
{
"name": "all-MiniLM-L12-v2",
"type": "float[]",
"embed": {
"from": [
"command"
],
"model_config": {
"model_name": "ts/all-MiniLM-L12-v2"
}
}
},
{
"name": "multilingual-e5-small",
"type": "float[]",
"embed": {
"from": [
"command"
],
"model_config": {
"model_name": "ts/multilingual-e5-small"
}
}
},
{
"name": "gte-small",
"type": "float[]",
"embed": {
"from": [
"command"
],
"model_config": {
"model_name": "ts/gte-small"
}
}
},
],
'default_sorting_field': 'rank',
"token_separators": [",", ".", "-"]
}
def init_typesense():
try:
typesense_client.collections[COLLECTION].retrieve()
except:
log.info(
f"WAC collection '{COLLECTION}' not found. Initializing with timeout {TYPESENSE_SLOW_TIMEOUT} - please wait.")
# Hack around slow initial schema generation because of model download
slow_typesense_client.collections.create(wac_commands_schema)
log.info(f"WAC collection '{COLLECTION}' initialized")
log.info(f"Connected to WAC Typesense host '{TYPESENSE_HOST}'")
@app.on_event("startup")
async def startup_event():
if RUN_MODE == "prod":
log.info('Starting Typesense')
start_typesense()
log.info('Typesense started. Waiting for ready...')
time.sleep(10)
init_typesense()
# Add HA entities
def add_ha_entities():
log.info('Adding entities from HA')
entity_types = ['cover.', 'fan.', 'light.', 'switch.']
url = f"{HA_URL}/api/states"
response = requests.get(url, headers=ha_headers)
entities = response.json()
devices = []
for type in entity_types:
for entity in entities:
entity_id = entity['entity_id']
if entity_id.startswith(type):
attr = entity.get('attributes')
friendly_name = attr.get('friendly_name')
if friendly_name is None:
# in case of blank or misconfigured HA entities
continue
# Add device
if friendly_name not in devices:
devices.append(friendly_name.lower())
# Make the devices unique
devices = [*set(devices)]
for device in devices:
on = (f'turn on {device}')
off = (f'turn off {device}')
wac_add(on, rank=0.5, source='ha_entities')
wac_add(off, rank=0.5, source='ha_entities')
# WAC Search
def wac_search(command, exact_match=False, distance=SEARCH_DISTANCE, num_results=CORRECT_ATTEMPTS, raw=False, token_match_threshold=TOKEN_MATCH_THRESHOLD, semantic="off", semantic_model=TYPESENSE_SEMANTIC_MODEL, vector_distance_threshold=VECTOR_DISTANCE_THRESHOLD, hybrid_score_threshold=HYBRID_SCORE_THRESHOLD):
# Set fail by default
success = False
wac_command = command
# Absurd values to always lose if something goes wrong
tokens_matched = 0
vector_distance = 10.0
hybrid_score = 0.0
# Do not change these unless you know what you are doing
wac_search_parameters = {
'q': command,
'query_by': 'command',
'sort_by': '_text_match:desc,rank:desc,accuracy:desc',
'text_match_type': 'max_score',
'prioritize_token_position': False,
'drop_tokens_threshold': 1,
'typo_tokens_threshold': 1,
'split_join_tokens': 'fallback',
'num_typos': distance,
'min_len_1typo': 3,
'min_len_2typo': 6,
'per_page': num_results,
'limit_hits': num_results,
'prefix': False,
'use_cache': False,
'exclude_fields': 'all-MiniLM-L12-v2,gte-small,multilingual-e5-small',
'search_cutoff_ms': 100,
'max_candidates': 4,
}
if exact_match is True:
log.info(f"Doing exact match WAC Search")
wac_search_parameters.update({'filter_by': f'command:={command}'})
# Support per request semantic or hybrid semantic search
if semantic == "hybrid":
log.info(
f"Doing hybrid semantic WAC Search with model {semantic_model}")
wac_search_parameters.update(
{'query_by': f'command,{semantic_model}'})
elif semantic == "on":
log.info(
f"Doing semantic WAC Search with model {semantic_model}")
wac_search_parameters.update(
{'query_by': f'{semantic_model}'})
# Try WAC search
try:
log.info(
f"Doing WAC Search for command '{command}' with distance {distance}")
wac_search_result = typesense_client.collections[COLLECTION].documents.search(
wac_search_parameters)
# For management API
if raw:
log.info(f"Returning raw results")
return wac_search_result
try:
id = json_get(wac_search_result, "/hits[0]/document/id")
text_score = json_get(wac_search_result, "/hits[0]/text_match")
tokens_matched = json_get(
wac_search_result, "/hits[0]/text_match_info/tokens_matched")
wac_command = json_get(
wac_search_result, "/hits[0]/document/command")
source = json_get(wac_search_result, "/hits[0]/document/source")
is_alias = json_get_default(wac_search_result, "/hits[0]/document/is_alias", False)
alias = json_get_default(wac_search_result, "/hits[0]/document/alias", "")
except:
log.info(f"Command '{command}' not found")
return success, command
if exact_match and wac_command:
log.info(
f"Returning exact command '{wac_command}' match with id {id}")
success = True
return success, wac_command if not is_alias else alias
log.info(
f"Trying scoring evaluation with top match '{wac_command}' with id {id} from source {source}")
# Semantic handling
if semantic == "on":
vector_distance = json_get(
wac_search_result, "/hits[0]/vector_distance")
if vector_distance <= vector_distance_threshold:
log.info(
f"WAC Semantic Search passed vector distance threshold {vector_distance_threshold} with result {vector_distance}")
success = True
else:
log.info(
f"WAC Semantic Search didn't meet vector distance threshold {vector_distance_threshold} with result {vector_distance}")
elif semantic == "hybrid":
hybrid_score = json_get(
wac_search_result, "/hits[0]/hybrid_search_info/rank_fusion_score")
if hybrid_score >= hybrid_score_threshold:
log.info(
f"WAC Semantic Hybrid Search passed hybrid score threshold {hybrid_score_threshold} with result {hybrid_score}")
success = True
else:
log.info(
f"WAC Semantic Hybrid Search didn't meet hybrid score threshold {hybrid_score_threshold} with result {hybrid_score}")
# Regular old token match
else:
if tokens_matched >= token_match_threshold:
log.info(
f"WAC Search passed token threshold {token_match_threshold} with result {tokens_matched}")
success = True
else:
log.info(
f"WAC Search didn't meet threshold {token_match_threshold} with result {tokens_matched}")
except Exception as e:
log.exception(f"WAC search for command '{command}' failed with {e}")
return success, wac_command if not is_alias else alias
# WAC Add
def wac_add(command, rank=0.9, source='autolearn'):
log.info(f"Doing WAC add for command '{command}'")
learned = False
try:
log.info(f"Searching WAC before adding command '{command}'")
wac_exact_search_status, wac_command = wac_search(
command, exact_match=True)
if wac_exact_search_status is True:
log.info('Refusing to add duplicate command')
return learned
# Get current time as int
curr_dt = datetime.now()
timestamp = int(round(curr_dt.timestamp()))
log.debug(f"Current timestamp: {timestamp}")
command_json = {
'command': command,
'rank': rank,
'accuracy': 1.0,
'source': source,
'timestamp': timestamp,
}
# Use create to update in real time
typesense_client.collections[COLLECTION].documents.create(command_json)
log.info(f"Added WAC command '{command}'")
learned = True
except Exception as e:
log.exception(f"WAC add for command '{command}' failed with {e}")
return learned
# WAC Add alias
def wac_add_alias(command, alias, rank=1.0, source='manual_entry'):
log.info(f"Doing WAC add for command '{command}' and alias '{alias}'")
learned = False
try:
log.info(f"Searching WAC before adding command '{command}'")
wac_exact_search_status, wac_command = wac_search(
command, exact_match=True)
if wac_exact_search_status is True:
log.info('Refusing to add duplicate command')
return learned
# Get current time as int
curr_dt = datetime.now()
timestamp = int(round(curr_dt.timestamp()))
log.debug(f"Current timestamp: {timestamp}")
command_json = {
'command': command,
'is_alias': True,
'alias': alias,
'rank': rank,
'accuracy': 1.0,
'source': source,
'timestamp': timestamp,
}
# Use create to update in real time
typesense_client.collections[COLLECTION].documents.create(command_json)
log.info(f"Added WAC command '{command}' and alias '{alias}")
learned = True
except Exception as e:
log.exception(f"WAC add for command '{command}' failed with {e}")
return learned
# Request coming from proxy
def api_post_proxy_handler(command, language, hostname, distance=SEARCH_DISTANCE, token_match_threshold=TOKEN_MATCH_THRESHOLD, exact_match=False, semantic="off", semantic_model=TYPESENSE_SEMANTIC_MODEL, vector_distance_threshold=VECTOR_DISTANCE_THRESHOLD, hybrid_score_threshold=HYBRID_SCORE_THRESHOLD, llm_model=OPENAI_MODEL):
log.info(
f"Processing proxy request from '{hostname}' for command '{command}' with distance {distance} token match threshold {token_match_threshold} exact match {exact_match} semantic {semantic} with vector distance threshold {vector_distance_threshold} and hybrid threshold {hybrid_score_threshold}")
# Init speech for when all else goes wrong
speech = COMMAND_NOT_FOUND
# Default to command isn't learned
learned = False
# For logging
second_ha_time_milliseconds = None
third_ha_time_milliseconds = None
url = f'{HA_URL}/api/conversation/process'
try:
if AREA_AWARENESS and check_command(command):
location = willow_locations_dict.get(hostname)
log.info(f"I probably should try '{command} in the {location}'")
ha_data = {"text": f"{command} in the {location}", "language": language}
else:
log.info(f"Trying initial HA intent match '{command}'")
ha_data = {"text": command, "language": language}
time_start = datetime.now()
ha_response = requests.post(
url, headers=ha_headers, json=ha_data, timeout=(1, 10))
time_end = datetime.now()
ha_time = time_end - time_start
first_ha_time_milliseconds = ha_time.total_seconds() * 1000
ha_response = ha_response.json()
code = json_get_default(
ha_response, "/response/data/code", "intent_match")
if code in ("no_intent_match", "no_valid_targets"):
log.info(f"No Initial HA Intent Match for command '{command}'")
else:
log.info(f"Initial HA Intent Match for command '{command}'")
if not command.startswith(skip_tuple):
learned = wac_add(command, rank=0.9, source='autolearn')
else:
log.info(f"Skipping command '{command}' as it's in the skip list")
speech = json_get_default(
ha_response, "/response/speech/plain/speech", "Success")
# Set speech to HA response and return
log.info(f"Setting speech to HA response '{speech}'")
if learned is True and FEEDBACK is True:
speech = f"{speech}. {COMMAND_LEARNED}"
log.info('HA took ' + str(first_ha_time_milliseconds) + ' ms')
return speech
except Exception as e:
log.exception(f"WAC FAILED with {e}")
return "Willow auto correct encountered an error!"
# Do WAC Search
wac_success, wac_command = wac_search(command, exact_match=exact_match, distance=distance, num_results=CORRECT_ATTEMPTS, raw=False,
token_match_threshold=token_match_threshold, semantic=semantic, semantic_model=semantic_model, vector_distance_threshold=vector_distance_threshold, hybrid_score_threshold=hybrid_score_threshold)
if wac_success:
# Re-run HA with WAC Command
try:
log.info(
f"Attempting WAC HA Intent Match with command '{wac_command}' from provided command '{command}'")
ha_data = {"text": wac_command, "language": language}
time_start = datetime.now()
ha_response = requests.post(
url, headers=ha_headers, json=ha_data, timeout=(1, 10))
time_end = datetime.now()
ha_time = time_end - time_start
second_ha_time_milliseconds = ha_time.total_seconds() * 1000
log.info('HA took ' + str(second_ha_time_milliseconds) + ' ms')
ha_response = ha_response.json()
code = json_get_default(
ha_response, "/response/data/code", "intent_match")
if code in ("no_intent_match", "no_valid_targets"):
log.info(f"No WAC Command HA Intent Match: '{wac_command}'")
else:
log.info(f"WAC Command HA Intent Match: '{wac_command}'")
# Set speech to HA response - whatever it is at this point
speech = json_get_default(
ha_response, "/response/speech/plain/speech", "Success")
log.info(f"HA speech: '{speech}'")
if FEEDBACK is True:
speech = f"{speech}. {COMMAND_CORRECTED}: {wac_command}"
log.info(f"Setting final speech to '{speech}'")
except Exception as e:
log.exception(f"WAC FAILED with {e}")
return "Willow auto correct encountered an error!"
elif not wac_success and OPENAI_API_KEY != "undefined":
# Attempt LLM/OpenAI
speech = openai_chat(command, model=llm_model)
else:
# Final forwarding to HA catch-all "chat"
if FORWARD_TO_CHAT:
try:
log.info(
f"Forwarding provided command '{command}' to final catch-all chat")
ha_data = {"text": f"{COMMAND_FINAL_HA_FORWARD}-{hostname} {command}", "language": language}
time_start = datetime.now()
ha_response = requests.post(
url, headers=ha_headers, json=ha_data, timeout=(1, 10))
time_end = datetime.now()
ha_time = time_end - time_start
third_ha_time_milliseconds = ha_time.total_seconds() * 1000
log.info('HA took ' + str(third_ha_time_milliseconds) + ' ms')
ha_response = ha_response.json()
code = json_get_default(
ha_response, "/response/data/code", "intent_match")
if code in ("no_intent_match", "no_valid_targets"):
log.info(f"No HA Command found for catch-all Intent: '{COMMAND_FINAL_HA_FORWARD}'")
else:
log.info(f"HA Command found for catch-all Intent: '{COMMAND_FINAL_HA_FORWARD}'")
# Set speech to HA response
speech = json_get_default(
ha_response, "/response/speech/plain/speech", "Success")
log.info(f"HA speech: '{speech}'")
if FEEDBACK is True:
speech = f"{speech}"
log.info(f"Setting final speech to '{speech}'")
except Exception as e:
log.exception(f"WAC FAILED with {e}")
return "Willow auto correct encountered an error!"
#Done with forwarding
if second_ha_time_milliseconds is not None and third_ha_time_milliseconds is not None:
total_ha_time = first_ha_time_milliseconds + second_ha_time_milliseconds + third_ha_time_milliseconds
elif second_ha_time_milliseconds is not None:
total_ha_time = first_ha_time_milliseconds + second_ha_time_milliseconds
else:
total_ha_time = first_ha_time_milliseconds
log.info(f"Final speech response '{speech}'")
log.info(f"Total HA time is {total_ha_time} ms")
return speech
@app.get("/api/add_ha_entities", summary="Add Entities from HA", response_description="Status")
async def api_add_ha_entities():
try:
add_ha_entities()
return JSONResponse(content={'success': True})
except Exception as e:
log.exception(f"Add HA Entities failed with {e}")
raise HTTPException(
status_code=500, detail="WAC Add HA Entities Failed")
@app.get("/api/re_init", summary="Wipe DB and Start Over", response_description="Status")
async def api_reinitialize():
try:
log.info('Re-initializing...')
typesense_client.collections[COLLECTION].delete()
init_typesense()
return JSONResponse(content={'success': True})
except Exception as e:
log.exception(f"Re-init failed with {e}")
raise HTTPException(status_code=500, detail="WAC Re-init Failed")
@app.get("/api/delete", summary="Delete command")
async def api_delete(id: int):
try:
log.info(f"Attempting to delete command ID {id}")
delete = typesense_client.collections[COLLECTION].documents[id].delete(
)
command = json_get(delete, "/command")
log.info(f"Successfully deleted command '{command}' with id {id}")
response = {'success': True, 'deleted': command}
except:
log.info(f"Failed to deleted command with id {id}")
response = {'success': False}
return JSONResponse(content=response)
@app.get("/api/search", summary="WAC Search", response_description="WAC Search")
async def api_get_wac(command, distance: Optional[str] = SEARCH_DISTANCE, num_results: Optional[str] = CORRECT_ATTEMPTS, exact_match: Optional[bool] = False, semantic: Optional[str] = TYPESENSE_SEMANTIC_MODE, semantic_model: Optional[str] = TYPESENSE_SEMANTIC_MODEL):
try:
time_start = datetime.now()
# Little fix for compatibility
if semantic == "true":
semantic = "on"
elif semantic == "false":
semantic = "off"
results = wac_search(command, exact_match=exact_match,
distance=distance, num_results=num_results, raw=True, semantic=semantic, semantic_model=semantic_model)
time_end = datetime.now()
search_time = time_end - time_start
search_time_milliseconds = search_time.total_seconds() * 1000
log.info('WAC search took ' + str(search_time_milliseconds) + ' ms')
return JSONResponse(content=results)
except Exception as e:
log.exception(f"Search failed with {e}")
raise HTTPException(status_code=500, detail="WAC Search Failed")
class PostProxyBody(BaseModel):
hostname: Optional[str] = "None"
text: Optional[str] = "How many lights are on?"
language: Optional[str] = "en"
@app.post("/api/proxy", summary="Proxy Willow Requests", response_description="WAC Response")
async def api_post_proxy(body: PostProxyBody, distance: Optional[int] = SEARCH_DISTANCE, token_match_threshold: Optional[int] = TOKEN_MATCH_THRESHOLD, exact_match: Optional[bool] = False, semantic: Optional[str] = TYPESENSE_SEMANTIC_MODE, vector_distance_threshold: Optional[float] = VECTOR_DISTANCE_THRESHOLD, hybrid_score_threshold: Optional[float] = HYBRID_SCORE_THRESHOLD, semantic_model: Optional[str] = TYPESENSE_SEMANTIC_MODEL, llm_model: Optional[str] = OPENAI_MODEL):
try:
time_start = datetime.now()
# Little fix for compatibility
if semantic == "true":
semantic = "on"
elif semantic == "false":
semantic = "off"
response = api_post_proxy_handler(body.text, body.language, body.hostname, distance=distance, token_match_threshold=token_match_threshold,
exact_match=exact_match, semantic=semantic, semantic_model=semantic_model, vector_distance_threshold=vector_distance_threshold, hybrid_score_threshold=hybrid_score_threshold, llm_model=llm_model)
time_end = datetime.now()
search_time = time_end - time_start
search_time_milliseconds = search_time.total_seconds() * 1000
log.info('WAC proxy total time ' +
str(search_time_milliseconds) + ' ms')
return PlainTextResponse(content=response)
except Exception as e:
log.exception(f"Proxy failed with {e}")
raise HTTPException(status_code=500, detail="WAC Proxy Failed")