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refactor

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mudler
2023-07-30 17:30:12 +02:00
parent ac2cfc7fc4
commit 13fc22a373
3 changed files with 127 additions and 88 deletions
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2
.env
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@@ -1,7 +1,7 @@
DEBUG=true DEBUG=true
MODELS_PATH=/models MODELS_PATH=/models
GALLERIES=[{"name":"model-gallery", "url":"github:go-skynet/model-gallery/index.yaml"}, {"url": "github:go-skynet/model-gallery/huggingface.yaml","name":"huggingface"}] GALLERIES=[{"name":"model-gallery", "url":"github:go-skynet/model-gallery/index.yaml"}, {"url": "github:go-skynet/model-gallery/huggingface.yaml","name":"huggingface"}]
PRELOAD_MODELS=[{"id":"model-gallery@stablediffusion"},{"id":"model-gallery@voice-en-us-kathleen-low"},{"url": "github:go-skynet/model-gallery/base.yaml", "name": "all-MiniLM-L6-v2", "overrides": {"embeddings": true, "backend":"huggingface-embeddings", "parameters": {"model": "all-MiniLM-L6-v2"}}}, {"id": "huggingface@thebloke/wizardlm-13b-v1.0-uncensored-ggml/wizardlm-13b-v1.0-uncensored.ggmlv3.q4_0.bin", "name": "functions", "overrides": { "context_size": 2048, "template": {"chat": "", "completion": "" }, "roles": { "assistant": "ASSISTANT:", "system": "SYSTEM:", "assistant_function_call": "FUNCTION_CALL:", "function": "FUNCTION CALL RESULT:" }, "parameters": { "temperature": 0.1, "top_k": 40, "top_p": 0.95, "rope_freq_base": 10000.0, "rope_freq_scale": 1.0 }, "function": { "disable_no_action": true }, "mmap": true, "f16": true }},{"id": "huggingface@thebloke/wizardlm-13b-v1.0-uncensored-ggml/wizardlm-13b-v1.0-uncensored.ggmlv3.q4_k_m.bin", "name":"gpt-4", "overrides": { "context_size": 2048, "mmap": true, "f16": true, "parameters": { "temperature": 0.1, "top_k": 40, "top_p": 0.95, "rope_freq_base": 10000.0, "rope_freq_scale": 1.0 }}}] PRELOAD_MODELS=[{"id":"model-gallery@stablediffusion"},{"id":"model-gallery@voice-en-us-kathleen-low"},{"url": "github:go-skynet/model-gallery/base.yaml", "name": "all-MiniLM-L6-v2", "overrides": {"embeddings": true, "backend":"huggingface-embeddings", "parameters": {"model": "all-MiniLM-L6-v2"}}}, {"id": "huggingface@thebloke/wizardlm-13b-v1.0-uncensored-ggml/wizardlm-13b-v1.0-uncensored.ggmlv3.q4_0.bin", "name": "functions", "overrides": { "context_size": 2048, "template": {"chat": "", "completion": "" }, "roles": { "assistant": "ASSISTANT:", "system": "SYSTEM:", "assistant_function_call": "FUNCTION_CALL:", "function": "FUNCTION CALL RESULT:" }, "parameters": { "temperature": 0.1, "top_k": 40, "top_p": 0.95 }, "function": { "disable_no_action": true }, "mmap": true, "f16": true }},{"id": "huggingface@thebloke/wizardlm-13b-v1.0-uncensored-ggml/wizardlm-13b-v1.0-uncensored.ggmlv3.q4_k_m.bin", "name":"gpt-4", "overrides": { "context_size": 2048, "mmap": true, "f16": true, "parameters": { "temperature": 0.1, "top_k": 40, "top_p": 0.95 }}}]
OPENAI_API_KEY=sk--- OPENAI_API_KEY=sk---
OPENAI_API_BASE=http://api:8080 OPENAI_API_BASE=http://api:8080
IMAGE_PATH=/tmp IMAGE_PATH=/tmp

210
main.py
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@@ -3,6 +3,7 @@ import openai
from langchain.embeddings import LocalAIEmbeddings from langchain.embeddings import LocalAIEmbeddings
import uuid import uuid
import requests import requests
from loguru import logger
from ascii_magic import AsciiArt from ascii_magic import AsciiArt
# these three lines swap the stdlib sqlite3 lib with the pysqlite3 package for chroma # these three lines swap the stdlib sqlite3 lib with the pysqlite3 package for chroma
@@ -21,12 +22,15 @@ VOICE_MODEL= os.environ.get("TTS_MODEL","en-us-kathleen-low.onnx")
DEFAULT_SD_MODEL = os.environ.get("DEFAULT_SD_MODEL", "stablediffusion") DEFAULT_SD_MODEL = os.environ.get("DEFAULT_SD_MODEL", "stablediffusion")
DEFAULT_SD_PROMPT = os.environ.get("DEFAULT_SD_PROMPT", "floating hair, portrait, ((loli)), ((one girl)), cute face, hidden hands, asymmetrical bangs, beautiful detailed eyes, eye shadow, hair ornament, ribbons, bowties, buttons, pleated skirt, (((masterpiece))), ((best quality)), colorful|((part of the head)), ((((mutated hands and fingers)))), deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, Octane renderer, lowres, bad anatomy, bad hands, text") DEFAULT_SD_PROMPT = os.environ.get("DEFAULT_SD_PROMPT", "floating hair, portrait, ((loli)), ((one girl)), cute face, hidden hands, asymmetrical bangs, beautiful detailed eyes, eye shadow, hair ornament, ribbons, bowties, buttons, pleated skirt, (((masterpiece))), ((best quality)), colorful|((part of the head)), ((((mutated hands and fingers)))), deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, Octane renderer, lowres, bad anatomy, bad hands, text")
REPLY_ACTION = "reply"
#embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") #embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
embeddings = LocalAIEmbeddings(model="all-MiniLM-L6-v2") embeddings = LocalAIEmbeddings(model="all-MiniLM-L6-v2")
chroma_client = Chroma(collection_name="memories", persist_directory="db", embedding_function=embeddings) chroma_client = Chroma(collection_name="memories", persist_directory="db", embedding_function=embeddings)
# Function to create images with OpenAI # Function to create images with OpenAI
def create_image(input_text=DEFAULT_SD_PROMPT, model=DEFAULT_SD_MODEL): def create_image(input_text=DEFAULT_SD_PROMPT, model=DEFAULT_SD_MODEL):
response = openai.Image.create( response = openai.Image.create(
@@ -67,10 +71,8 @@ def tts(input_text, model=VOICE_MODEL):
# remove the audio file # remove the audio file
os.remove(output_file_path) os.remove(output_file_path)
def calculate_plan(user_input): def calculate_plan(user_input, agent_actions={}):
print("--> Calculating plan ") logger.info("--> Calculating plan: {description}", description=res["description"])
res = json.loads(user_input)
print(res["description"])
messages = [ messages = [
{"role": "user", {"role": "user",
"content": f"""Transcript of AI assistant responding to user requests. "content": f"""Transcript of AI assistant responding to user requests.
@@ -80,6 +82,13 @@ Request: {res["description"]}
Function call: """ Function call: """
} }
] ]
# get list of plannable actions
plannable_actions = []
for action in agent_actions:
if agent_actions[action]["plannable"]:
# append the key of the dict to plannable_actions
plannable_actions.append(action)
functions = [ functions = [
{ {
"name": "plan", "name": "plan",
@@ -98,7 +107,7 @@ Function call: """
}, },
"function": { "function": {
"type": "string", "type": "string",
"enum": ["save_memory", "search_memory"], "enum": plannable_actions,
}, },
}, },
}, },
@@ -130,14 +139,17 @@ Function call: """
return res return res
return {"action": "none"} return {"action": "none"}
def needs_to_do_action(user_input): def needs_to_do_action(user_input,agent_actions={}):
# Get the descriptions and the actions name (the keys)
descriptions=""
for action in agent_actions:
descriptions+=agent_actions[action]["description"]+"\n"
messages = [ messages = [
{"role": "user", {"role": "user",
"content": f"""Transcript of AI assistant responding to user requests. Replies with the action to perform, including reasoning, and the confidence interval from 0 to 100. "content": f"""Transcript of AI assistant responding to user requests. Replies with the action to perform, including reasoning, and the confidence interval from 0 to 100.
For saving a memory, the assistant replies with the action "save_memory" and the string to save. {descriptions}
For searching a memory, the assistant replies with the action "search_memory" and the query to search.
For generating a plan for complex tasks, the assistant replies with the action "generate_plan" and a detailed plan to execute the user goal.
For replying to the user, the assistant replies with the action "reply" and the reply to the user.
Request: {user_input} Request: {user_input}
Function call: """ Function call: """
@@ -156,7 +168,7 @@ Function call: """
}, },
"action": { "action": {
"type": "string", "type": "string",
"enum": ["save_memory", "search_memory", "reply", "generate_plan"], "enum": list(agent_actions.keys()),
"description": "user intent" "description": "user intent"
}, },
"reasoning": { "reasoning": {
@@ -188,17 +200,17 @@ Function call: """
print(">>> function name: "+function_name) print(">>> function name: "+function_name)
print(">>> function parameters: "+function_parameters) print(">>> function parameters: "+function_parameters)
return res return res
return {"action": "reply"} return {"action": REPLY_ACTION}
### Agent capabilities ### Agent capabilities
def save(memory): def save(memory, agent_actions={}):
print(">>> saving to memories: ") print(">>> saving to memories: ")
print(memory) print(memory)
chroma_client.add_texts([memory],[{"id": str(uuid.uuid4())}]) chroma_client.add_texts([memory],[{"id": str(uuid.uuid4())}])
chroma_client.persist() chroma_client.persist()
return f"The object was saved permanently to memory." return f"The object was saved permanently to memory."
def search(query): def search(query, agent_actions={}):
res = chroma_client.similarity_search(query) res = chroma_client.similarity_search(query)
print(">>> query: ") print(">>> query: ")
print(query) print(query)
@@ -206,21 +218,23 @@ def search(query):
print(res) print(res)
return res return res
def process_functions(user_input, action=""): def process_functions(user_input, action="", agent_actions={}):
descriptions=""
for a in agent_actions:
descriptions+=agent_actions[a]["description"]+"\n"
messages = [ messages = [
# {"role": "system", "content": "You are a helpful assistant."}, # {"role": "system", "content": "You are a helpful assistant."},
{"role": "user", {"role": "user",
"content": f"""Transcript of AI assistant responding to user requests. "content": f"""Transcript of AI assistant responding to user requests.
For saving a memory, the assistant replies with the action "save_memory" and the string to save. {descriptions}
For searching a memory, the assistant replies with the action "search_memory" and the query to search to find information stored previously.
For replying to the user, the assistant replies with the action "reply" and the reply to the user directly when there is nothing to do.
For generating a plan for complex tasks, the assistant replies with the action "generate_plan" and a detailed list of all the subtasks needed to execute the user goal using the available actions.
Request: {user_input} Request: {user_input}
Function call: """ Function call: """
} }
] ]
response = function_completion(messages, action=action) response = function_completion(messages, action=action,agent_actions=agent_actions)
response_message = response["choices"][0]["message"] response_message = response["choices"][0]["message"]
response_result = "" response_result = ""
function_result = {} function_result = {}
@@ -228,15 +242,8 @@ Function call: """
function_name = response.choices[0].message["function_call"].name function_name = response.choices[0].message["function_call"].name
function_parameters = response.choices[0].message["function_call"].arguments function_parameters = response.choices[0].message["function_call"].arguments
function_to_call = agent_actions[function_name]["function"]
available_functions = { function_result = function_to_call(function_parameters, agent_actions=agent_actions)
"save_memory": save,
"generate_plan": calculate_plan,
"search_memory": search,
}
function_to_call = available_functions[function_name]
function_result = function_to_call(function_parameters)
print("==> function result: ") print("==> function result: ")
print(function_result) print(function_result)
messages = [ messages = [
@@ -262,60 +269,22 @@ Function call: """
) )
return messages, function_result return messages, function_result
def function_completion(messages, action=""): def function_completion(messages, action="", agent_actions={}):
function_call = "auto" function_call = "auto"
if action != "": if action != "":
function_call={"name": action} function_call={"name": action}
print("==> function_call: ") print("==> function_call: ")
print(function_call) print(function_call)
functions = [
{ # get the functions from the signatures of the agent actions, if exists
"name": "save_memory", functions = []
"description": """Save or store informations into memory.""", for action in agent_actions:
"parameters": { if agent_actions[action].get("signature"):
"type": "object", functions.append(agent_actions[action]["signature"])
"properties": { print("==> available functions for the LLM: ")
"thought": { print(functions)
"type": "string", print("==> messages LLM: ")
"description": "information to save" print(messages)
},
},
"required": ["thought"]
}
},
{
"name": "generate_plan",
"description": """Plan complex tasks.""",
"parameters": {
"type": "object",
"properties": {
"description": {
"type": "string",
"description": "reasoning behind the planning"
},
},
"required": ["description"]
}
},
{
"name": "search_memory",
"description": """Search in memory""",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The query to be used to search informations"
},
"reasoning": {
"type": "string",
"description": "reasoning behind the intent"
},
},
"required": ["query"]
}
},
]
response = openai.ChatCompletion.create( response = openai.ChatCompletion.create(
#model="gpt-3.5-turbo", #model="gpt-3.5-turbo",
model=FUNCTIONS_MODEL, model=FUNCTIONS_MODEL,
@@ -343,21 +312,21 @@ def process_history(conversation_history):
return messages return messages
def evaluate(user_input, conversation_history = [],re_evaluate=False): def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_actions={}):
try: try:
action = needs_to_do_action(user_input) action = needs_to_do_action(user_input,agent_actions=agent_actions)
except Exception as e: except Exception as e:
print("==> error: ") print("==> error: ")
print(e) print(e)
action = {"action": "reply"} action = {"action": REPLY_ACTION}
if action["action"] != "reply": if action["action"] != REPLY_ACTION:
print("==> needs to do action: ") print("==> needs to do action: ")
print(action) print(action)
if action["action"] == "generate_plan": if action["action"] == "generate_plan":
print("==> It's a plan <==: ") print("==> It's a plan <==: ")
responses, function_results = process_functions(user_input+"\nReasoning: "+action["reasoning"], action=action["action"]) responses, function_results = process_functions(user_input+"\nReasoning: "+action["reasoning"], action=action["action"], agent_actions=agent_actions)
# if there are no subtasks, we can just reply, # if there are no subtasks, we can just reply,
# otherwise we execute the subtasks # otherwise we execute the subtasks
# First we check if it's an object # First we check if it's an object
@@ -366,7 +335,7 @@ def evaluate(user_input, conversation_history = [],re_evaluate=False):
for subtask in function_results["subtasks"]: for subtask in function_results["subtasks"]:
print("==> subtask: ") print("==> subtask: ")
print(subtask) print(subtask)
subtask_response, function_results = process_functions(subtask["reasoning"], subtask["function"]) subtask_response, function_results = process_functions(subtask["reasoning"], subtask["function"],agent_actions=agent_actions)
responses.extend(subtask_response) responses.extend(subtask_response)
if re_evaluate: if re_evaluate:
all = process_history(responses) all = process_history(responses)
@@ -374,7 +343,7 @@ def evaluate(user_input, conversation_history = [],re_evaluate=False):
print(all) print(all)
## Better output or this infinite loops.. ## Better output or this infinite loops..
print("-> Re-evaluate if another action is needed") print("-> Re-evaluate if another action is needed")
responses = evaluate(user_input+process_history(responses), responses, re_evaluate) responses = evaluate(user_input+process_history(responses), responses, re_evaluate,agent_actions=agent_actions)
response = openai.ChatCompletion.create( response = openai.ChatCompletion.create(
model=LLM_MODEL, model=LLM_MODEL,
messages=responses, messages=responses,
@@ -417,8 +386,77 @@ def evaluate(user_input, conversation_history = [],re_evaluate=False):
#create_image() #create_image()
agent_actions = {
"save_memory": {
"function": save,
"plannable": True,
"description": 'For saving a memory, the assistant replies with the action "save_memory" and the string to save.',
"signature": {
"name": "save_memory",
"description": """Save or store informations into memory.""",
"parameters": {
"type": "object",
"properties": {
"thought": {
"type": "string",
"description": "information to save"
},
},
"required": ["thought"]
}
},
},
"search_memory": {
"function": search,
"plannable": True,
"description": 'For searching a memory, the assistant replies with the action "search_memory" and the query to search to find information stored previously.',
"signature": {
"name": "search_memory",
"description": """Search in memory""",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The query to be used to search informations"
},
"reasoning": {
"type": "string",
"description": "reasoning behind the intent"
},
},
"required": ["query"]
}
},
},
"generate_plan": {
"function": calculate_plan,
"plannable": False,
"description": 'For generating a plan for complex tasks, the assistant replies with the action "generate_plan" and a detailed list of all the subtasks needed to execute the user goal using the available actions.',
"signature": {
"name": "generate_plan",
"description": """Plan complex tasks.""",
"parameters": {
"type": "object",
"properties": {
"description": {
"type": "string",
"description": "reasoning behind the planning"
},
},
"required": ["description"]
}
},
},
REPLY_ACTION: {
"function": None,
"plannable": False,
"description": 'For replying to the user, the assistant replies with the action "'+REPLY_ACTION+'" and the reply to the user directly when there is nothing to do.',
},
}
conversation_history = [] conversation_history = []
# TODO: process functions also considering the conversation history? conversation history + input # TODO: process functions also considering the conversation history? conversation history + input
while True: while True:
user_input = input("> ") user_input = input("> ")
conversation_history=evaluate(user_input, conversation_history, re_evaluate=True) conversation_history=evaluate(user_input, conversation_history, re_evaluate=True, agent_actions=agent_actions)

1
requirements.txt
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@@ -4,3 +4,4 @@ chromadb
pysqlite3-binary pysqlite3-binary
requests requests
ascii-magic ascii-magic
loguru