From b4494678e885954e8a4375608b8852a36a4e3c1c Mon Sep 17 00:00:00 2001 From: Adamantios Date: Wed, 13 Sep 2023 16:31:37 +0300 Subject: [PATCH 1/3] feat: install `anthropic`'s client sdk --- poetry.lock | 454 ++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + tox.ini | 1 + 3 files changed, 453 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index 3073977c..7720ec82 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. 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a/pyproject.toml b/pyproject.toml index 3b359114..da17c959 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,7 @@ google-api-python-client = "==2.95.0" eth-utils = "==2.2.0" eth-abi = "==4.0.0" pycryptodome = "==3.18.0" +anthropic = "^0.3.11" [tool.poetry.group.dev.dependencies.tomte] version = "==0.2.12" diff --git a/tox.ini b/tox.ini index 28d28e18..7dd9db95 100644 --- a/tox.ini +++ b/tox.ini @@ -53,6 +53,7 @@ deps = eth-utils==2.2.0 eth-abi==4.0.0 pycryptodome==3.18.0 + anthropic==0.3.11 [testenv] basepython = python3 From 7cbff7a9823fbe446be77a423db88e14440d6d58 Mon Sep 17 00:00:00 2001 From: Adamantios Date: Wed, 13 Sep 2023 16:32:43 +0300 Subject: [PATCH 2/3] feat: create a tool to predict using `claude` --- tools/prediction_request_claude.py | 259 +++++++++++++++++++++++++++++ 1 file changed, 259 insertions(+) create mode 100644 tools/prediction_request_claude.py diff --git a/tools/prediction_request_claude.py b/tools/prediction_request_claude.py new file mode 100644 index 00000000..3898727b --- /dev/null +++ b/tools/prediction_request_claude.py @@ -0,0 +1,259 @@ +# -*- coding: utf-8 -*- +# ------------------------------------------------------------------------------ +# +# Copyright 2023 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 implements a Mech tool for binary predictions.""" + +from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT, Stream +import json +from concurrent.futures import Future, ThreadPoolExecutor +from typing import Any, Dict, Generator, List, Optional, Tuple, Union + +import openai +import requests +from anthropic.types import Completion +from bs4 import BeautifulSoup +from googleapiclient.discovery import build + + +NUM_URLS_EXTRACT = 5 +DEFAULT_OPENAI_SETTINGS = { + "max_tokens": 500, + "temperature": 0.7, +} +ALLOWED_TOOLS = [ + "claude-prediction-offline", + "claude-prediction-online", +] +TOOL_TO_ENGINE = { + "claude-prediction-offline": "claude-2", + "claude-prediction-online": "claude-2", +} + +PREDICTION_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: +``` +{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. +""" + +URL_QUERY_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 your response in the format specified under "OUTPUT_FORMAT". +* Do not include any other contents in your response. + +USER_PROMPT: +``` +{user_prompt} +``` + +OUTPUT_FORMAT +* Your output response must be only a single JSON object to be parsed by Python's "json.loads()". +* The JSON must contain two fields: "queries", and "urls". + - "queries": An array of strings of size between 1 and 5. Each string must be a search engine query that can help obtain relevant information to estimate + the probability that the event in "USER_PROMPT" occurs. You must provide original information in each query, and they should not overlap + or lead to obtain the same set of results. +* Output only the JSON object. Do not include any other contents in your response. +""" + + +def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[str]: + service = build("customsearch", "v1", developerKey=api_key) + search = ( + service.cse() + .list( + q=query, + cx=engine, + num=num, + ) + .execute() + ) + return [result["link"] for result in search["items"]] + + +def get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List[str]: + """Get URLs from search engine queries""" + results = [] + for query in queries: + for url in search_google( + query=query, + api_key=api_key, + engine=engine, + num=3, # Number of returned results + ): + results.append(url) + unique_results = list(set(results)) + return unique_results + + +def extract_text( + html: str, + num_words: int = 300, # TODO: summarise using LLM instead of limit +) -> str: + """Extract text from a single HTML document""" + soup = BeautifulSoup(html, "html.parser") + for script in soup(["script", "style"]): + script.extract() + text = soup.get_text() + lines = (line.strip() for line in text.splitlines()) + chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) + text = "\n".join(chunk for chunk in chunks if chunk) + return text[:num_words] + + +def process_in_batches( + urls: List[str], window: int = 5, timeout: int = 10 +) -> Generator[None, None, List[Tuple[Future, str]]]: + """Iter URLs in batches.""" + with ThreadPoolExecutor() as executor: + for i in range(0, len(urls), window): + batch = urls[i : i + window] + futures = [ + (executor.submit(requests.get, url, timeout=timeout), url) + for url in batch + ] + yield futures + + +def extract_texts(urls: List[str], num_words: int = 300) -> List[str]: + """Extract texts from URLs""" + max_allowed = 5 + extracted_texts = [] + count = 0 + stop = False + for batch in process_in_batches(urls=urls): + for future, url in batch: + try: + result = future.result() + if result.status_code != 200: + continue + extracted_texts.append( + extract_text(html=result.text, num_words=num_words) + ) + count += 1 + if count >= max_allowed: + stop = True + break + except requests.exceptions.ReadTimeout: + print(f"Request timed out: {url}.") + except Exception as e: + print(f"An error occurred: {e}") + if stop: + break + return extracted_texts + + +def fetch_additional_information( + prompt: str, + engine: str, + google_api_key: str, + google_engine: str, + anthropic: Anthropic, +) -> str: + """Fetch additional information.""" + url_query_prompt = URL_QUERY_PROMPT.format(user_prompt=prompt) + url_query_prompt = f"{HUMAN_PROMPT}{url_query_prompt}{AI_PROMPT}" + completion = anthropic.completions.create( + model=engine, + max_tokens_to_sample=300, + prompt=url_query_prompt, + ) + json_data = json.loads(completion.completion) + urls = get_urls_from_queries( + json_data["queries"], + api_key=google_api_key, + engine=google_engine, + ) + texts = extract_texts(urls) + return "\n".join(["- " + text for text in texts]) + + +def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: + """Run the task""" + tool = kwargs["tool"] + prompt = kwargs["prompt"] + anthropic = Anthropic(api_key=kwargs["api_keys"]["anthropic"]) + + if tool not in ALLOWED_TOOLS: + raise ValueError(f"Tool {tool} is not supported.") + + engine = TOOL_TO_ENGINE[tool] + additional_information = ( + fetch_additional_information( + prompt=prompt, + engine=engine, + google_api_key=kwargs["api_keys"]["google_api_key"], + google_engine=kwargs["api_keys"]["google_engine_id"], + anthropic=anthropic, + ) + if tool == "claude-prediction-online" + else "" + ) + prediction_prompt = PREDICTION_PROMPT.format( + user_prompt=prompt, additional_information=additional_information + ) + prediction_prompt = f"{HUMAN_PROMPT}{prediction_prompt}{AI_PROMPT}" + + completion = anthropic.completions.create( + model=engine, + max_tokens_to_sample=300, + prompt=prediction_prompt, + ) + return completion.completion, None From bb05300f37fb1b450dd11c2ede1cfed7322ac774 Mon Sep 17 00:00:00 2001 From: Adamantios Date: Wed, 13 Sep 2023 16:53:27 +0300 Subject: [PATCH 3/3] chore: remove command for non-existing skills --- Makefile | 2 -- 1 file changed, 2 deletions(-) diff --git a/Makefile b/Makefile index 10ddb82d..5967a82b 100644 --- a/Makefile +++ b/Makefile @@ -95,9 +95,7 @@ all-checks: clean format code-checks security generators common-checks-1 common- .PHONY: fix-abci-app-specs fix-abci-app-specs: - autonomy analyse fsm-specs --update --app-class MultiplexerAbciApp --package packages/valory/skills/multiplexer_abci autonomy analyse fsm-specs --update --app-class MechAbciApp --package packages/valory/skills/mech_abci - autonomy analyse fsm-specs --update --app-class TaskExecutionAbciApp --package packages/valory/skills/task_execution_abci echo "Successfully validated abcis!" protolint_install: