diff --git a/packages/dvilela/customs/__init__.py b/packages/dvilela/customs/__init__.py new file mode 100644 index 00000000..ba697745 --- /dev/null +++ b/packages/dvilela/customs/__init__.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# ------------------------------------------------------------------------------ +# +# Copyright 2024 Valory AG +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ------------------------------------------------------------------------------ diff --git a/packages/dvilela/customs/corcel_request/__init__.py b/packages/dvilela/customs/corcel_request/__init__.py new file mode 100644 index 00000000..014e7773 --- /dev/null +++ b/packages/dvilela/customs/corcel_request/__init__.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +# ------------------------------------------------------------------------------ +# +# Copyright 2023-2024 Valory AG +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ------------------------------------------------------------------------------ + +"""This module contains the corcel request tool.""" diff --git a/packages/dvilela/customs/corcel_request/component.yaml b/packages/dvilela/customs/corcel_request/component.yaml new file mode 100644 index 00000000..1b7b4821 --- /dev/null +++ b/packages/dvilela/customs/corcel_request/component.yaml @@ -0,0 +1,16 @@ +name: corcel_request +author: dvilela +version: 0.1.0 +type: custom +description: A tool to make LLM requests to Corcel. +license: Apache-2.0 +aea_version: '>=1.0.0, <2.0.0' +fingerprint: + __init__.py: bafybeicuohbwuyxqyv5ovgimdz326ivvvzatnw7mczil5n3tktur56cw6u + corcel_request.py: bafybeic73hwu7xmh5gsslvsvwiobtzjkydjoksmfs5wiklafg54lu4hep4 +fingerprint_ignore_patterns: [] +entry_point: corcel_request.py +callable: run +dependencies: + requests: + version: ==2.28.1 diff --git a/packages/dvilela/customs/corcel_request/corcel_request.py b/packages/dvilela/customs/corcel_request/corcel_request.py new file mode 100644 index 00000000..6ac922dd --- /dev/null +++ b/packages/dvilela/customs/corcel_request/corcel_request.py @@ -0,0 +1,180 @@ +import requests +import re +import json +from typing import Optional, Dict, Any, Tuple, Callable +import functools + +class CorcelAPIException(Exception): + pass + +MechResponse = Tuple[str, Optional[str], Optional[Dict[str, Any]], Any, Any] + + +def with_key_rotation(func: Callable): + @functools.wraps(func) + def wrapper(*args, **kwargs) -> MechResponse: + # this is expected to be a KeyChain object, + # although it is not explicitly typed as such + api_keys = kwargs["api_keys"] + retries_left: Dict[str, int] = api_keys.max_retries() + + def execute() -> MechResponse: + """Retry the function with a new key.""" + try: + result = func(*args, **kwargs) + return result + (api_keys,) + except CorcelAPIException: + # try with a new key again + service = "corcel" + if retries_left[service] <= 0: + raise Exception("Error: API retries exhausted") + retries_left[service] -= 1 + api_keys.rotate(service) + return execute() + except Exception as e: + return str(e), "", None, None, api_keys + + mech_response = execute() + return mech_response + + return wrapper + + +PREDICTION_OFFLINE_PROMPT = """ +You are an LLM inside a multi-agent system that takes in a prompt of a user requesting a probability estimation +for a given event. You are provided with an input under the label "USER_PROMPT". You must follow the instructions +under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". + +INSTRUCTIONS +* Read the input under the label "USER_PROMPT" delimited by three backticks. +* The "USER_PROMPT" specifies an event. +* The event will only have two possible outcomes: either the event will happen or the event will not happen. +* If the event has more than two possible outcomes, you must ignore the rest of the instructions and output the response "Error". +* You must provide a probability estimation of the event happening, based on your training data. +* You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION" delimited by three backticks. +* You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data. +* If an item in "ADDITIONAL_INFORMATION" is not relevant, you must ignore that item for the estimation. +* You must provide your response in the format specified under "OUTPUT_FORMAT". +* Do not include any other contents in your response. + +USER_PROMPT: +``` +{user_prompt} +``` + +ADDITIONAL_INFORMATION: +``` +``` + +OUTPUT_FORMAT +* Your output response must be only a single JSON object to be parsed by Python's "json.loads()". +* The JSON must contain four fields: "p_yes", "p_no", "confidence", and "info_utility". +* Each item in the JSON must have a value between 0 and 1. + - "p_yes": Estimated probability that the event in the "USER_PROMPT" occurs. + - "p_no": Estimated probability that the event in the "USER_PROMPT" does not occur. + - "confidence": A value between 0 and 1 indicating the confidence in the prediction. 0 indicates lowest + confidence value; 1 maximum confidence value. + - "info_utility": Utility of the information provided in "ADDITIONAL_INFORMATION" to help you make the prediction. + 0 indicates lowest utility; 1 maximum utility. +* The sum of "p_yes" and "p_no" must equal 1. +* Output only the JSON object. Do not include any other contents in your response. +* This is incorrect:"```json{{\n \"p_yes\": 0.2,\n \"p_no\": 0.8,\n \"confidence\": 0.7,\n \"info_utility\": 0.5\n}}```" +* This is incorrect:```json"{{\n \"p_yes\": 0.2,\n \"p_no\": 0.8,\n \"confidence\": 0.7,\n \"info_utility\": 0.5\n}}"``` +* This is correct:"{{\n \"p_yes\": 0.2,\n \"p_no\": 0.8,\n \"confidence\": 0.7,\n \"info_utility\": 0.5\n}}" +""" + +CORCEL_URL = "https://api.corcel.io/v1/chat/completions" + +AVAILABLE_TOOLS = ["corcel-prediction", "corcel-completion"] + +DEFAULT_VALUES = { + "model": "llama-3", + "temperature": 0.1, + "max_tokens": 500 +} + + +def send_corcel_request(api_key: str, prompt: str, **kwargs) -> str: + """Makes a request to Corcel API""" + + payload = {key: kwargs.get(key, default_value) for key, default_value in DEFAULT_VALUES.items()} + + payload["messages"] = [ + { + "role": "system", + "content": prompt + } + ] + + headers = { + "accept": "application/json", + "content-type": "application/json", + "Authorization": api_key + } + + response = requests.post(CORCEL_URL, json=payload, headers=headers, timeout=60) + + if response.status_code != 200: + raise CorcelAPIException(f"Corcel API error: {response.json()}") + + return response.text + + +def error_response(msg: str) -> Tuple[str, None, None, None]: + """Return an error mech response.""" + return msg, None, None, None + + +def response_post_process(response: str, tool_name: str) -> str: + """Loads the prediction into a json object""" + + # Join all the response pieces of content + content_regex = r'\{"content":\s"(.*?)(?:",\s|"})' + matches = re.findall(content_regex, response, re.DOTALL) + joined = ("").join(matches) + + # Clean and parse the response + clean_response = joined.replace('\\n', '').replace('"', '').replace('\\', '') + + if tool_name != "corcel-prediction": + return clean_response + + # Add missing quotes to the json keys + clean_response = re.sub(r'(\w+):', r'"\1":', clean_response) + + # Load the response + try: + json_response = json.loads(clean_response) + return json.dumps(json_response) + except json.JSONDecodeError: + return f"Error: response could not be properly postprocessed: {clean_response}" + + +@with_key_rotation +def run(**kwargs) -> Tuple[Optional[str], Optional[Dict[str, Any]], Any, Any]: + """Run the task""" + + api_key = kwargs["api_keys"]["corcel"] + tool_name = kwargs.get("tool", None) + prompt = kwargs.get("prompt", None) + + if api_key is None: + return error_response("Corcel API key is not available.") + + if tool_name is None: + return error_response("No tool name has been specified.") + + if tool_name not in AVAILABLE_TOOLS: + return error_response(f"Tool {tool_name} is not an available tool [{AVAILABLE_TOOLS}].") + + if prompt is None: + return error_response("No prompt has been given.") + + if tool_name == "corcel-prediction": + kwargs["prompt"] = PREDICTION_OFFLINE_PROMPT.format(user_prompt=prompt) + + response = send_corcel_request(api_key=api_key, **kwargs) + + response = response_post_process(response, tool_name) + + return response, prompt, None, None diff --git a/packages/dvilela/customs/gemini_prediction/__init__.py b/packages/dvilela/customs/gemini_prediction/__init__.py new file mode 100644 index 00000000..23f2ff64 --- /dev/null +++ b/packages/dvilela/customs/gemini_prediction/__init__.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +# ------------------------------------------------------------------------------ +# +# Copyright 2023-2024 Valory AG +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ------------------------------------------------------------------------------ + +"""This module contains the Gemini prediction tool.""" diff --git a/packages/dvilela/customs/gemini_prediction/component.yaml b/packages/dvilela/customs/gemini_prediction/component.yaml new file mode 100644 index 00000000..0f01de61 --- /dev/null +++ b/packages/dvilela/customs/gemini_prediction/component.yaml @@ -0,0 +1,18 @@ +name: gemini_prediction +author: dvilela +version: 0.1.0 +type: custom +description: A tool to make LLM prediction requests to Gemini. +license: Apache-2.0 +aea_version: '>=1.0.0, <2.0.0' +fingerprint: + __init__.py: bafybeictllsxej6cl3hhrsfyroyiqcp5ian2wayajwpesbyjwnf5gwinxq + gemini_prediction.py: bafybeicx7fuf5hdbqc4i2uwup33quhoqzy5g4i6vyqcmcdl7rkmnsbvg3a +fingerprint_ignore_patterns: [] +entry_point: gemini_prediction.py +callable: run +dependencies: + google-generativeai: + version: ==0.5.3 + google-api-core: + version: '>=2.20.0' diff --git a/packages/dvilela/customs/gemini_prediction/gemini_prediction.py b/packages/dvilela/customs/gemini_prediction/gemini_prediction.py new file mode 100644 index 00000000..c5793a0a --- /dev/null +++ b/packages/dvilela/customs/gemini_prediction/gemini_prediction.py @@ -0,0 +1,136 @@ +import json +from typing import Optional, Dict, Any, Tuple, Callable +import google.generativeai as genai +import functools +from google.api_core.exceptions import GoogleAPIError + +MechResponse = Tuple[str, Optional[str], Optional[Dict[str, Any]], Any, Any] + +def with_key_rotation(func: Callable): + @functools.wraps(func) + def wrapper(*args, **kwargs) -> MechResponse: + # this is expected to be a KeyChain object, + # although it is not explicitly typed as such + api_keys = kwargs["api_keys"] + retries_left: Dict[str, int] = api_keys.max_retries() + + def execute() -> MechResponse: + """Retry the function with a new key.""" + try: + result = func(*args, **kwargs) + return result + (api_keys,) + except GoogleAPIError: + # try with a new key again + service = "gemini" + if retries_left[service] <= 0: + raise Exception("Error: API retries exhausted") + retries_left[service] -= 1 + api_keys.rotate(service) + return execute() + except Exception as e: + return str(e), "", None, None, api_keys + + mech_response = execute() + return mech_response + + return wrapper + + +PREDICTION_OFFLINE_PROMPT = """ +You are an LLM inside a multi-agent system that takes in a prompt of a user requesting a probability estimation +for a given event. You are provided with an input under the label "USER_PROMPT". You must follow the instructions +under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". + +INSTRUCTIONS +* Read the input under the label "USER_PROMPT" delimited by three backticks. +* The "USER_PROMPT" specifies an event. +* The event will only have two possible outcomes: either the event will happen or the event will not happen. +* If the event has more than two possible outcomes, you must ignore the rest of the instructions and output the response "Error". +* You must provide a probability estimation of the event happening, based on your training data. +* You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION" delimited by three backticks. +* You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data. +* If an item in "ADDITIONAL_INFORMATION" is not relevant, you must ignore that item for the estimation. +* You must provide your response in the format specified under "OUTPUT_FORMAT". +* Do not include any other contents in your response. + +USER_PROMPT: +``` +{user_prompt} +``` + +ADDITIONAL_INFORMATION: +``` +``` + +OUTPUT_FORMAT +* Your output response must be only a single JSON object to be parsed by Python's "json.loads()". +* The JSON must contain four fields: "p_yes", "p_no", "confidence", and "info_utility". +* Each item in the JSON must have a value between 0 and 1. + - "p_yes": Estimated probability that the event in the "USER_PROMPT" occurs. + - "p_no": Estimated probability that the event in the "USER_PROMPT" does not occur. + - "confidence": A value between 0 and 1 indicating the confidence in the prediction. 0 indicates lowest + confidence value; 1 maximum confidence value. + - "info_utility": Utility of the information provided in "ADDITIONAL_INFORMATION" to help you make the prediction. + 0 indicates lowest utility; 1 maximum utility. +* The sum of "p_yes" and "p_no" must equal 1. +* Output only the JSON object. Do not include any other contents in your response. +* This is incorrect:"```json{{\n \"p_yes\": 0.2,\n \"p_no\": 0.8,\n \"confidence\": 0.7,\n \"info_utility\": 0.5\n}}```" +* This is incorrect:```json"{{\n \"p_yes\": 0.2,\n \"p_no\": 0.8,\n \"confidence\": 0.7,\n \"info_utility\": 0.5\n}}"``` +* This is correct:"{{\n \"p_yes\": 0.2,\n \"p_no\": 0.8,\n \"confidence\": 0.7,\n \"info_utility\": 0.5\n}}" +""" + +AVAILABLE_TOOLS = ["gemini-prediction", "gemini-completion"] +AVAILABLE_MODELS = ["gemini-1.5-flash", "gemini-1.5-pro"] + + +def error_response(msg: str) -> Tuple[str, None, None, None]: + """Return an error mech response.""" + return msg, None, None, None + + +def response_post_process(response: str) -> str: + """Loads the prediction into a json object""" + + try: + json_response = json.loads(response) + return json.dumps(json_response) + except json.JSONDecodeError: + return f"Error: response could not be properly postprocessed: {response}" + + +@with_key_rotation +def run(**kwargs) -> Tuple[Optional[str], Optional[Dict[str, Any]], Any, Any]: + """Run the task""" + + api_key = kwargs["api_keys"]["gemini"] + tool_name = kwargs.get("tool", None) + prompt = kwargs.get("prompt", None) + model = kwargs.get("model", "gemini-1.5-flash") + + if api_key is None: + return error_response("Gemini API key is not available.") + + if tool_name is None: + return error_response("No tool name has been specified.") + + if tool_name not in AVAILABLE_TOOLS: + return error_response(f"Tool {tool_name} is not an available tool [{AVAILABLE_TOOLS}].") + + if prompt is None: + return error_response("No prompt has been given.") + + if model not in AVAILABLE_MODELS: + return error_response(f"Model {model} is not an avaliable model: {AVAILABLE_MODELS}") + + if tool_name == "gemini-prediction": + prompt = PREDICTION_OFFLINE_PROMPT.format(user_prompt=prompt) + + genai.configure(api_key=api_key) + model = genai.GenerativeModel(model) + response = model.generate_content(prompt) + response = response.text + + if tool_name == "gemini-prediction": + response = response_post_process(response) + + return response, prompt, None, None diff --git a/packages/packages.json b/packages/packages.json index 4e2e0efb..c4c8345e 100644 --- a/packages/packages.json +++ b/packages/packages.json @@ -32,6 +32,8 @@ "custom/jhehemann/prediction_sentence_embeddings/0.1.0": "bafybeifyyb2wpa77tl7a7fs3fabns45llivhgccbnrpupubojmq2fwe4si", "custom/gnosis/ofv_market_resolver/0.1.0": "bafybeigapoti2ysukapphspjawktkb4qkeltlollt4d2z4u7mrddk3u3rq", "custom/valory/tee_openai_request/0.1.0": "bafybeictmezaorzxelsy4dztbxh5n2343zio3rk6vo7wc5lptxlobhdnku", + "custom/dvilela/corcel_request/0.1.0": "bafybeicjrix2wg23cm2j437isokkzd26uloqezlx3cg2pu7rf7mfwg3p6e", + "custom/dvilela/gemini_prediction/0.1.0": "bafybeigvwflupxzbjgmaxcxml5vkez3obl4fjo6bxzhquq56urnviq32u4", "protocol/valory/acn_data_share/0.1.0": "bafybeih5ydonnvrwvy2ygfqgfabkr47s4yw3uqxztmwyfprulwfsoe7ipq", 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