Small fixups - write file works. WIP search internet
This commit is contained in:
mudler
242
main.py
242
main.py
@@ -3,9 +3,11 @@ import openai
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from langchain.embeddings import LocalAIEmbeddings
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import uuid
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import requests
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import sys
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from loguru import logger
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from ascii_magic import AsciiArt
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logger.add(sys.stderr, format="{time} {level} {message}", filter="miniAGI", level="INFO")
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# these three lines swap the stdlib sqlite3 lib with the pysqlite3 package for chroma
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__import__('pysqlite3')
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import sys
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@@ -32,7 +34,7 @@ chroma_client = Chroma(collection_name="memories", persist_directory="db", embed
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# Function to create images with OpenAI
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def create_image(input_text=DEFAULT_SD_PROMPT, model=DEFAULT_SD_MODEL):
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def display_avatar(input_text=DEFAULT_SD_PROMPT, model=DEFAULT_SD_MODEL):
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response = openai.Image.create(
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prompt=input_text,
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n=1,
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@@ -71,74 +73,6 @@ def tts(input_text, model=VOICE_MODEL):
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# remove the audio file
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os.remove(output_file_path)
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def calculate_plan(user_input, agent_actions={}):
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logger.info("--> Calculating plan: {description}", description=res["description"])
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messages = [
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{"role": "user",
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"content": f"""Transcript of AI assistant responding to user requests.
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Replies with a plan to achieve the user's goal with a list of subtasks with logical steps.
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Request: {res["description"]}
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Function call: """
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}
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]
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# get list of plannable actions
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plannable_actions = []
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for action in agent_actions:
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if agent_actions[action]["plannable"]:
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# append the key of the dict to plannable_actions
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plannable_actions.append(action)
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functions = [
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{
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"name": "plan",
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"description": """Decide to do an action.""",
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"parameters": {
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"type": "object",
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"properties": {
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"subtasks": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"reasoning": {
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"type": "string",
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"description": "subtask list",
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},
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"function": {
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"type": "string",
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"enum": plannable_actions,
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},
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},
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},
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},
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},
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"required": ["subtasks"]
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}
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},
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]
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response = openai.ChatCompletion.create(
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#model="gpt-3.5-turbo",
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model=FUNCTIONS_MODEL,
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messages=messages,
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functions=functions,
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max_tokens=200,
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stop=None,
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temperature=0.5,
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#function_call="auto"
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function_call={"name": "plan"},
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)
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response_message = response["choices"][0]["message"]
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if response_message.get("function_call"):
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function_name = response.choices[0].message["function_call"].name
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function_parameters = response.choices[0].message["function_call"].arguments
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# read the json from the string
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res = json.loads(function_parameters)
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print(">>> function name: "+function_name)
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print(">>> function parameters: "+function_parameters)
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return res
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return {"action": "none"}
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def needs_to_do_action(user_input,agent_actions={}):
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# Get the descriptions and the actions name (the keys)
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@@ -202,22 +136,6 @@ Function call: """
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return res
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return {"action": REPLY_ACTION}
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### Agent capabilities
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def save(memory, agent_actions={}):
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print(">>> saving to memories: ")
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print(memory)
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chroma_client.add_texts([memory],[{"id": str(uuid.uuid4())}])
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chroma_client.persist()
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return f"The object was saved permanently to memory."
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def search(query, agent_actions={}):
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res = chroma_client.similarity_search(query)
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print(">>> query: ")
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print(query)
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print(">>> retrieved memories: ")
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print(res)
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return res
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def process_functions(user_input, action="", agent_actions={}):
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descriptions=""
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@@ -330,7 +248,7 @@ def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_acti
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# if there are no subtasks, we can just reply,
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# otherwise we execute the subtasks
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# First we check if it's an object
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if isinstance(function_results, dict) and len(function_results["subtasks"]) != 0:
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if isinstance(function_results, dict) and function_results.get("subtasks") and len(function_results["subtasks"]) > 0:
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# cycle subtasks and execute functions
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for subtask in function_results["subtasks"]:
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print("==> subtask: ")
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@@ -384,9 +302,156 @@ def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_acti
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tts(conversation_history[-1]["content"])
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return conversation_history
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#create_image()
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### Agent capabilities
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def save(memory, agent_actions={}):
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print(">>> saving to memories: ")
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print(memory)
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chroma_client.add_texts([memory],[{"id": str(uuid.uuid4())}])
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chroma_client.persist()
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return f"The object was saved permanently to memory."
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def search(query, agent_actions={}):
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res = chroma_client.similarity_search(query)
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print(">>> query: ")
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print(query)
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print(">>> retrieved memories: ")
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print(res)
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return res
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def calculate_plan(user_input, agent_actions={}):
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res = json.loads(user_input)
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logger.info("--> Calculating plan: {description}", description=res["description"])
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messages = [
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{"role": "user",
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"content": f"""Transcript of AI assistant responding to user requests.
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Replies with a plan to achieve the user's goal with a list of subtasks with logical steps.
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Request: {res["description"]}
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Function call: """
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}
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]
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# get list of plannable actions
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plannable_actions = []
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for action in agent_actions:
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if agent_actions[action]["plannable"]:
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# append the key of the dict to plannable_actions
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plannable_actions.append(action)
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functions = [
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{
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"name": "plan",
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"description": """Decide to do an action.""",
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"parameters": {
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"type": "object",
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"properties": {
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"subtasks": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"reasoning": {
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"type": "string",
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"description": "subtask list",
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},
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"function": {
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"type": "string",
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"enum": plannable_actions,
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},
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},
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},
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},
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},
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"required": ["subtasks"]
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}
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},
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]
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response = openai.ChatCompletion.create(
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#model="gpt-3.5-turbo",
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model=FUNCTIONS_MODEL,
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messages=messages,
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functions=functions,
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max_tokens=200,
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stop=None,
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temperature=0.5,
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#function_call="auto"
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function_call={"name": "plan"},
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)
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response_message = response["choices"][0]["message"]
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if response_message.get("function_call"):
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function_name = response.choices[0].message["function_call"].name
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function_parameters = response.choices[0].message["function_call"].arguments
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# read the json from the string
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res = json.loads(function_parameters)
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logger.info("<<< function name: {function_name} >>>> parameters: {parameters}", function_name=function_name,parameters=function_parameters)
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return res
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return {"action": REPLY_ACTION}
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# write file to disk with content
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def write_file(arg, agent_actions={}):
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arg = json.loads(arg)
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filename = arg["filename"]
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content = arg["content"]
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with open(filename, 'w') as f:
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f.write(content)
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return f"File {filename} saved successfully."
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## Search on duckduckgo
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def search_duckduckgo(args, agent_actions={}):
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args = json.loads(args)
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url = "https://api.duckduckgo.com/?q="+args["query"]+"&format=json&pretty=1"
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response = requests.get(url)
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if response.status_code == 200:
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return response.json()
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else:
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return {"error": "No results found."}
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### End Agent capabilities
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agent_actions = {
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"search_internet": {
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"function": search_duckduckgo,
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"plannable": True,
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"description": 'For searching the internet with a query, the assistant replies with the action "search_internet" and the query to search.',
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"signature": {
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"name": "search_internet",
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"description": """For searching internet.""",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "information to save"
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},
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},
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}
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},
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},
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"write_file": {
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"function": write_file,
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"plannable": True,
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"description": 'For writing a file to disk with content, the assistant replies with the action "write_file" and the filename and content to save.',
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"signature": {
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"name": "write_file",
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"description": """For saving a file to disk with content.""",
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"parameters": {
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"type": "object",
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"properties": {
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"filename": {
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"type": "string",
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"description": "information to save"
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},
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"content": {
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"type": "string",
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"description": "information to save"
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},
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},
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}
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},
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},
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"save_memory": {
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"function": save,
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"plannable": True,
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@@ -456,6 +521,9 @@ agent_actions = {
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}
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conversation_history = []
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display_avatar()
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# TODO: process functions also considering the conversation history? conversation history + input
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while True:
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user_input = input("> ")
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