diff --git a/.github/workflows/common_checks.yaml b/.github/workflows/common_checks.yaml index a4715949..94ce8ec7 100644 --- a/.github/workflows/common_checks.yaml +++ b/.github/workflows/common_checks.yaml @@ -17,9 +17,10 @@ jobs: os: [ ubuntu-latest, macos-latest, windows-latest ] python-version: [ "3.10" ] poetry-version: ["1.3.2"] - timeout-minutes: 10 - + defaults: + run: + shell: bash steps: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 @@ -33,6 +34,10 @@ jobs: virtualenvs-in-project: false virtualenvs-path: ~/my-custom-path installer-parallel: true + - name: Check if dependencies can be locked + run: poetry lock + - name: Poetry install requirements and check they can be locked + run: poetry install --all-extras --no-interaction -vvv copyright_and_dependencies_check: continue-on-error: False diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 00000000..1cd3079c --- /dev/null +++ b/.pylintrc @@ -0,0 +1,58 @@ +[MASTER] +ignore-patterns=.*_pb2.py,contract_dispatcher.py,test_abci_messages.py,test_tendermint_messages.py +ignore=packages/valory/protocols/abci,packages/valory/connections/abci/gogoproto + +[MESSAGES CONTROL] +disable=C0103,R0801,C0301,C0201,C0204,C0209,W1203,C0302,R1735,R1729,W0511,E0611 + +# See here for more options: https://www.codeac.io/documentation/pylint-configuration.html +R1735: use-dict-literal +R1729: use-a-generator +C0103: invalid-name +C0201: consider-iterating-dictionary +W1203: logging-fstring-interpolation +C0204: bad-mcs-classmethod-argument +C0209: consider-using-f-string +C0301: http://pylint-messages.wikidot.com/messages:c0301 > Line too long +C0302: http://pylint-messages.wikidot.com/messages:c0302 > Too many lines in module +R0801: similar lines +E0611: no-name-in-module + +[IMPORTS] +ignored-modules=pandas,numpy,aea_cli_ipfs,compose,multidict,gql,anthropic,tiktoken + +[DESIGN] +# min-public-methods=1 +max-public-methods=58 +# max-returns=10 +# max-bool-expr=7 +max-args=6 +# max-locals=31 +# max-statements=80 +max-parents=10 +max-branches=36 +max-attributes=8 + +[REFACTORING] +# max-nested-blocks=6 + +[SPELLING] +# uncomment to enable +# spelling-dict=en_US + +# List of comma separated words that should not be checked. +spelling-ignore-words=nocover,pragma,params,noqa,kwargs,str,async,json,boolean,config,pytest,args,url,tx,jsonschema,traceback,api,nosec + +[SIMILARITIES] + +# Minimum lines number of a similarity. +min-similarity-lines=10 + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no \ No newline at end of file diff --git a/packages/packages.json b/packages/packages.json index 4f23d744..a95ae6bf 100644 --- a/packages/packages.json +++ b/packages/packages.json @@ -2,18 +2,18 @@ "dev": { "connection/valory/websocket_client/0.1.0": "bafybeiflmystocxaqblhpzqlcop2vkhsknpzjx2jomohomaxamwskeokzm", "skill/valory/contract_subscription/0.1.0": "bafybeicyugrkx5glat4p4ezwf6i7oduh26eycfie6ftd4uxrknztzl3ik4", - "agent/valory/mech/0.1.0": "bafybeigpvlyzu2d6xjf2ij4gvmtefrcn5rmmzp237mpsb7ejdpuzskvspu", - "skill/valory/mech_abci/0.1.0": "bafybeicekwscm72ujrwc5vzbgnshoxjna7a4vluugiylh4gq7r4yaskuly", + "agent/valory/mech/0.1.0": "bafybeiepdstu23qid2pcrugkpwtibh7jhlshmnmclwwbgjmefio27cp4im", + "skill/valory/mech_abci/0.1.0": "bafybeieimp7xzxcnbzsuunf2xkcy5juulhmzsmkq2v3sw3o3lgssb53cnu", "contract/valory/agent_mech/0.1.0": "bafybeiepxumywg6z2zapqzc3bg3iey23cmlgjzxisqox5j74o5i2texr5e", - "service/valory/mech/0.1.0": "bafybeiag3d54b7spafsicjx64ymh5hpmtwoggecnshrdndf67zjtlcksum", + "service/valory/mech/0.1.0": "bafybeifzkrmgejdce5nvp7s63dpvthde6wn6etolesh4dmf5pno7jplzcy", "protocol/valory/acn_data_share/0.1.0": "bafybeih5ydonnvrwvy2ygfqgfabkr47s4yw3uqxztmwyfprulwfsoe7ipq", - "skill/valory/task_submission_abci/0.1.0": "bafybeidned5sysejuakraqa4ssmc76chqnvyu7qqg4aywzddsayyooqflm", - "skill/valory/task_execution/0.1.0": "bafybeid7kneedwb3hu2nlsjusxgkfq4mvnsobxsbuqpfwkxnaeor6ffbpi", + "skill/valory/task_submission_abci/0.1.0": "bafybeib4m2bwgchloqss3wotsx4rz7qqkwydaesiqkls2zq7zbtp6jtpsi", + "skill/valory/task_execution/0.1.0": "bafybeicthrgfdv6q56htrsradow445smojjk2zqqizm4cdxyfxfor22vyy", "contract/valory/agent_registry/0.1.0": "bafybeiargayav6yiztdnwzejoejstcx4idssch2h4f5arlgtzj3tgsgfmu", "protocol/valory/websocket_client/0.1.0": "bafybeih43mnztdv3v2hetr2k3gezg7d3yj4ur7cxdvcyaqhg65e52s5sf4", "skill/valory/websocket_client/0.1.0": "bafybeidwntmkk4b2ixq5454ycbkknclqx7a6vpn7aqpm2nw3duszqrxvta", "contract/valory/hash_checkpoint/0.1.0": "bafybeieys27hidb5m267jwqsxdgorf5v37o6lvrfy2oga557stjn4xgoaa", - "skill/valory/subscription_abci/0.1.0": "bafybeick4zicydp42ieum74wekcz25uim3uyt7vugzjgfzsriqhswlxfti" + "skill/valory/subscription_abci/0.1.0": "bafybeigaxq7m2dqv2huhg5jvb4jx3rysqwvvjj2xhojow3t3zzuwq2k4ie" }, "third_party": { "skill/valory/transaction_settlement_abci/0.1.0": "bafybeiaefgqbs7zsn5xe5kdwrujj7ivygkn3ujpw6crnvi3knvxw75qmja", diff --git a/packages/valory/agents/mech/aea-config.yaml b/packages/valory/agents/mech/aea-config.yaml index c9d75b68..4d83fc06 100644 --- a/packages/valory/agents/mech/aea-config.yaml +++ b/packages/valory/agents/mech/aea-config.yaml @@ -38,12 +38,12 @@ skills: - valory/abstract_abci:0.1.0:bafybeiflcfufixmsrhobf56bn5745m2iipcfqyulwk2qegtnagb3kvaaxi - valory/abstract_round_abci:0.1.0:bafybeiaqcl7h2famylusiffigwem7tevkcsyocdu5xd42jkmgq6kvowzgq - valory/contract_subscription:0.1.0:bafybeicyugrkx5glat4p4ezwf6i7oduh26eycfie6ftd4uxrknztzl3ik4 -- valory/mech_abci:0.1.0:bafybeicekwscm72ujrwc5vzbgnshoxjna7a4vluugiylh4gq7r4yaskuly +- valory/mech_abci:0.1.0:bafybeieimp7xzxcnbzsuunf2xkcy5juulhmzsmkq2v3sw3o3lgssb53cnu - valory/registration_abci:0.1.0:bafybeic2ynseiak7jpta7jfwuqwyp453b4p7lolr4wihxmpn633uekv5am - valory/reset_pause_abci:0.1.0:bafybeidzajbe3erygeh2xbd6lrjv7nsptznjuzrt24ykgvhgotdeyhfnba -- valory/subscription_abci:0.1.0:bafybeick4zicydp42ieum74wekcz25uim3uyt7vugzjgfzsriqhswlxfti -- valory/task_execution:0.1.0:bafybeid7kneedwb3hu2nlsjusxgkfq4mvnsobxsbuqpfwkxnaeor6ffbpi -- valory/task_submission_abci:0.1.0:bafybeidned5sysejuakraqa4ssmc76chqnvyu7qqg4aywzddsayyooqflm +- valory/subscription_abci:0.1.0:bafybeigaxq7m2dqv2huhg5jvb4jx3rysqwvvjj2xhojow3t3zzuwq2k4ie +- valory/task_execution:0.1.0:bafybeicthrgfdv6q56htrsradow445smojjk2zqqizm4cdxyfxfor22vyy +- valory/task_submission_abci:0.1.0:bafybeib4m2bwgchloqss3wotsx4rz7qqkwydaesiqkls2zq7zbtp6jtpsi - valory/termination_abci:0.1.0:bafybeie4zvjfxvdu7qrulmur3chpjz3kpj5m4bjsxvpk4gvj5zbyyayfaa - valory/transaction_settlement_abci:0.1.0:bafybeiaefgqbs7zsn5xe5kdwrujj7ivygkn3ujpw6crnvi3knvxw75qmja - valory/websocket_client:0.1.0:bafybeidwntmkk4b2ixq5454ycbkknclqx7a6vpn7aqpm2nw3duszqrxvta @@ -196,7 +196,7 @@ models: args: agent_mech_contract_addresses: ${list:["0xFf82123dFB52ab75C417195c5fDB87630145ae81"]} task_deadline: ${float:240.0} - file_hash_to_tools_json: ${list:[["bafybeifp6tn3ovhuz4oipy67ijfdm4y7t2o7en3xuggn6kh5wbwokxmczu",["claude-prediction-offline","claude-prediction-online"]],["bafybeiaodddyn4eruafqg5vldkkjfglj7jg76uvyi5xhi2cysktlu4w6r4",["openai-gpt-3.5-turbo-instruct","openai-gpt-3.5-turbo","openai-gpt-4"]],["bafybeiafdm3jctiz6wwo3rmo3vdubk7j7l5tumoxi5n5rc3x452mtkgyua",["stabilityai-stable-diffusion-v1-5","stabilityai-stable-diffusion-xl-beta-v2-2-2","stabilityai-stable-diffusion-512-v2-1","stabilityai-stable-diffusion-768-v2-1"]],["bafybeidpbnqbruzqlq424qt3i5dcvyqmcimshjilftabnrroujmjhdmteu",["transfer-native"]],["bafybeiglhy5epaytvt5qqdx77ld23ekouli53qrf2hjyebd5xghlunidfi",["prediction-online","prediction-offline"]]]} + file_hash_to_tools_json: ${list:[["bafybeicziwfw7nb7gaxso357hrvtdlv6f23grm2c2rlfngpz4vbvoz2bke",["openai-gpt-3.5-turbo-instruct","openai-gpt-3.5-turbo","openai-gpt-4"]],["bafybeibaalr745aqajcaijykactx2nmg3jviefvnvpuk5kkxlt2yeedc2q",["stabilityai-stable-diffusion-v1-5","stabilityai-stable-diffusion-xl-beta-v2-2-2","stabilityai-stable-diffusion-512-v2-1","stabilityai-stable-diffusion-768-v2-1"]],["bafybeideuyqn4uslp4ccanzd5tjladzotyi6tiwfxbouecw5ufzgyi4ryy",["transfer-native"]],["bafybeig7yntvhhfufaadhd43zr4loivpit43kwfgban72w43xz4u3tansi",["prediction-offline","prediction-online","prediction-online-summarized-info"]],["bafybeiautzxe3faq53ceogfjtfbml5373wvqpsk77c4k3hjivloblivuxy",["prediction-online-sme","prediction-offline-sme"]],["bafybeifp6tn3ovhuz4oipy67ijfdm4y7t2o7en3xuggn6kh5wbwokxmczu",["claude-prediction-online","claude-prediction-offline"]],["bafybeievl777e2425q7zy6qkt26luu2i6xzp4q6pquykntx2yzivy3iwum",["deepmind-optimization-strong","deepmind-optimization"]],["bafybeihsyxhchqgtdwsd53z4a2lswt6ri4fre2yg2bpjo36kwboilsp7ai",["prediction-sentence-embedding-conservative","prediction-sentence-embedding-bold"]],["bafybeial5a56vsowqu4suynnmv5pkt5iebkxtmpgrae57qzi7s6tg4vq6e",["prediction-online-sum-url-content"]]]} api_keys_json: ${list:[["openai", "dummy_api_key"],["stabilityai", "dummy_api_key"],["google_api_key", "dummy_api_key"],["google_engine_id", "dummy_api_key"]]} polling_interval: ${float:30.0} diff --git a/packages/valory/services/mech/service.yaml b/packages/valory/services/mech/service.yaml index 26f350a5..4d98d254 100644 --- a/packages/valory/services/mech/service.yaml +++ b/packages/valory/services/mech/service.yaml @@ -7,7 +7,7 @@ license: Apache-2.0 fingerprint: README.md: bafybeif7ia4jdlazy6745ke2k2x5yoqlwsgwr6sbztbgqtwvs3ndm2p7ba fingerprint_ignore_patterns: [] -agent: valory/mech:0.1.0:bafybeigpvlyzu2d6xjf2ij4gvmtefrcn5rmmzp237mpsb7ejdpuzskvspu +agent: valory/mech:0.1.0:bafybeiepdstu23qid2pcrugkpwtibh7jhlshmnmclwwbgjmefio27cp4im number_of_agents: 4 deployment: agent: diff --git a/packages/valory/skills/mech_abci/skill.yaml b/packages/valory/skills/mech_abci/skill.yaml index 7513e892..aedb1f16 100644 --- a/packages/valory/skills/mech_abci/skill.yaml +++ b/packages/valory/skills/mech_abci/skill.yaml @@ -23,10 +23,10 @@ skills: - valory/abstract_round_abci:0.1.0:bafybeiaqcl7h2famylusiffigwem7tevkcsyocdu5xd42jkmgq6kvowzgq - valory/registration_abci:0.1.0:bafybeic2ynseiak7jpta7jfwuqwyp453b4p7lolr4wihxmpn633uekv5am - valory/reset_pause_abci:0.1.0:bafybeidzajbe3erygeh2xbd6lrjv7nsptznjuzrt24ykgvhgotdeyhfnba -- valory/task_submission_abci:0.1.0:bafybeidned5sysejuakraqa4ssmc76chqnvyu7qqg4aywzddsayyooqflm +- valory/task_submission_abci:0.1.0:bafybeib4m2bwgchloqss3wotsx4rz7qqkwydaesiqkls2zq7zbtp6jtpsi - valory/termination_abci:0.1.0:bafybeie4zvjfxvdu7qrulmur3chpjz3kpj5m4bjsxvpk4gvj5zbyyayfaa - valory/transaction_settlement_abci:0.1.0:bafybeiaefgqbs7zsn5xe5kdwrujj7ivygkn3ujpw6crnvi3knvxw75qmja -- valory/subscription_abci:0.1.0:bafybeick4zicydp42ieum74wekcz25uim3uyt7vugzjgfzsriqhswlxfti +- valory/subscription_abci:0.1.0:bafybeigaxq7m2dqv2huhg5jvb4jx3rysqwvvjj2xhojow3t3zzuwq2k4ie behaviours: main: args: {} diff --git a/packages/valory/skills/subscription_abci/skill.yaml b/packages/valory/skills/subscription_abci/skill.yaml index fd350f3a..fa5d1058 100644 --- a/packages/valory/skills/subscription_abci/skill.yaml +++ b/packages/valory/skills/subscription_abci/skill.yaml @@ -166,7 +166,7 @@ dependencies: googlesearch-python: version: ==1.2.3 openai: - version: ==0.27.2 + version: ==1.11.0 py-multibase: version: ==1.0.3 py-multicodec: diff --git a/packages/valory/skills/task_execution/behaviours.py b/packages/valory/skills/task_execution/behaviours.py index d3959ea1..38beb317 100644 --- a/packages/valory/skills/task_execution/behaviours.py +++ b/packages/valory/skills/task_execution/behaviours.py @@ -48,6 +48,7 @@ from packages.valory.protocols.ipfs.dialogues import IpfsDialogue from packages.valory.protocols.ledger_api import LedgerApiMessage from packages.valory.skills.task_execution.models import Params +from packages.valory.skills.task_execution.utils.benchmarks import TokenCounterCallback from packages.valory.skills.task_execution.utils.ipfs import ( ComponentPackageLoader, get_ipfs_file_hash, @@ -291,8 +292,16 @@ def _handle_done_task(self, task_result: Any) -> None: } if task_result is not None: # task succeeded - deliver_msg, prompt, transaction = task_result - response = {**response, "result": deliver_msg, "prompt": prompt} + deliver_msg, prompt, transaction, counter_callback = task_result + cost_dict = {} + if counter_callback is not None: + cost_dict = cast(TokenCounterCallback, counter_callback).cost_dict + response = { + **response, + "result": deliver_msg, + "prompt": prompt, + "cost_dict": cost_dict, + } self._done_task["transaction"] = transaction self.context.logger.info(f"Task result for request {req_id}: {task_result}") @@ -371,6 +380,7 @@ def _prepare_task(self, task_data: Dict[str, Any]) -> None: task_data["tool_py"] = tool_py task_data["callable_method"] = callable_method task_data["api_keys"] = self.params.api_keys + task_data["counter_callback"] = TokenCounterCallback() future = self._submit_task(tool_task.execute, **task_data) executing_task = cast(Dict[str, Any], self._executing_task) executing_task["timeout_deadline"] = time.time() + self.params.task_deadline diff --git a/packages/valory/skills/task_execution/skill.yaml b/packages/valory/skills/task_execution/skill.yaml index 1ceb554a..73b2439b 100644 --- a/packages/valory/skills/task_execution/skill.yaml +++ b/packages/valory/skills/task_execution/skill.yaml @@ -7,11 +7,12 @@ license: Apache-2.0 aea_version: '>=1.0.0, <2.0.0' fingerprint: __init__.py: bafybeidqhvvlnthkbnmrdkdeyjyx2f2ab6z4xdgmagh7welqnh2v6wczx4 - behaviours.py: bafybeifa72egwarcmfneqmo3ak6wfygjc3i7hplxl6ptafb263vuaey3fm + behaviours.py: bafybeihprwot27csugwpoimsqcwprxu5bstqpvanot3nkmfgvhbr66ww4y dialogues.py: bafybeid4zxalqdlo5mw4yfbuf34hx4jp5ay5z6chm4zviwu4cj7fudtwca handlers.py: bafybeidbt5ezj74cgfogk3w4uw4si2grlnk5g54veyumw7g5yh6gdscywu models.py: bafybeihgclxctyltuehj2f4fzj26edptqugrrm4phd6ovuulezrqot6qo4 utils/__init__.py: bafybeiccdijaigu6e5p2iruwo5mkk224o7ywedc7nr6xeu5fpmhjqgk24e + utils/benchmarks.py: bafybeihdutp44ds4cupszbd34gsmcw6fsdda2tzkh5b27fpg65ejbpdvdm utils/ipfs.py: bafybeidinbdqkidix44ibz5hug7inkcbijooag53gr5mtbaa72tk335uqq utils/task.py: bafybeieuziu7owtk543z3umgmayhjh67klftk7vrhz24l6rlaii5lvkqh4 fingerprint_ignore_patterns: [] @@ -99,11 +100,15 @@ dependencies: googlesearch-python: version: ==1.2.3 openai: - version: ==0.27.2 + version: ==1.11.0 py-multibase: version: ==1.0.3 py-multicodec: version: ==0.2.1 pyyaml: version: <=6.0.1,>=3.10 + tiktoken: + version: ==0.5.1 + anthropic: + version: ==0.3.11 is_abstract: false diff --git a/packages/valory/skills/task_execution/utils/benchmarks.py b/packages/valory/skills/task_execution/utils/benchmarks.py new file mode 100644 index 00000000..8793c5c8 --- /dev/null +++ b/packages/valory/skills/task_execution/utils/benchmarks.py @@ -0,0 +1,105 @@ +# -*- 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. +# +# ------------------------------------------------------------------------------ +"""Benchmarking for tools.""" + +import logging +from typing import Any, Dict, Union + +import anthropic +import tiktoken +from tiktoken import Encoding + + +PRICE_NUM_TOKENS = 1000 + + +def encoding_for_model(model: str) -> Encoding: + """Get the encoding for a model.""" + return tiktoken.encoding_for_model(model) + + +def count_tokens(text: str, model: str) -> int: + """Count the number of tokens in a text.""" + if "claude" in model: + return anthropic.Anthropic().count_tokens(text) + + enc = encoding_for_model(model) + return len(enc.encode(text)) + + +class TokenCounterCallback: + """Callback to count the number of tokens used in a generation.""" + + TOKEN_PRICES = { + "gpt-3.5-turbo": {"input": 0.001, "output": 0.002}, + "gpt-4": {"input": 0.03, "output": 0.06}, + "gpt-4-turbo": {"input": 0.01, "output": 0.03}, + "claude-2": {"input": 0.008, "output": 0.024}, + } + + def __init__(self) -> None: + """Initialize the callback.""" + self.cost_dict: Dict[str, Union[int, float]] = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "input_cost": 0, + "output_cost": 0, + "total_cost": 0, + } + + @staticmethod + def token_to_cost(tokens: int, model: str, tokens_type: str) -> float: + """Converts a number of tokens to a cost in dollars.""" + return ( + tokens + / PRICE_NUM_TOKENS + * TokenCounterCallback.TOKEN_PRICES[model][tokens_type] + ) + + def calculate_cost(self, tokens_type: str, model: str, **kwargs: Any) -> None: + """Calculate the cost of a generation.""" + # Check if it its prompt or tokens are passed in + prompt_key = f"{tokens_type}_prompt" + token_key = f"{tokens_type}_tokens" + if prompt_key in kwargs: + tokens = count_tokens(kwargs[prompt_key], model) + elif token_key in kwargs: + tokens = kwargs[token_key] + else: + logging.warning(f"No {token_key}_tokens or {tokens_type}_prompt found.") + cost = self.token_to_cost(tokens, model, tokens_type) + self.cost_dict[token_key] += tokens + self.cost_dict[f"{tokens_type}_cost"] += cost + + def __call__(self, model: str, **kwargs: Any) -> None: + """Callback to count the number of tokens used in a generation.""" + if model not in list(TokenCounterCallback.TOKEN_PRICES.keys()): + raise ValueError(f"Model {model} not supported.") + try: + self.calculate_cost("input", model, **kwargs) + self.calculate_cost("output", model, **kwargs) + self.cost_dict["total_tokens"] = ( + self.cost_dict["input_tokens"] + self.cost_dict["output_tokens"] + ) + self.cost_dict["total_cost"] = ( + self.cost_dict["input_cost"] + self.cost_dict["output_cost"] + ) + except Exception as e: + logging.error(f"Error in TokenCounterCallback: {e}") diff --git a/packages/valory/skills/task_submission_abci/skill.yaml b/packages/valory/skills/task_submission_abci/skill.yaml index f96be887..c39ac630 100644 --- a/packages/valory/skills/task_submission_abci/skill.yaml +++ b/packages/valory/skills/task_submission_abci/skill.yaml @@ -171,7 +171,7 @@ dependencies: googlesearch-python: version: ==1.2.3 openai: - version: ==0.27.2 + version: ==1.11.0 py-multibase: version: ==1.0.3 py-multicodec: diff --git a/poetry.lock b/poetry.lock index b31e7ecb..5eb0da29 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,89 +1,89 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry and should not be changed by hand. 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a/tools/native_transfer_request/native_transfer_request.py +++ b/tools/native_transfer_request/native_transfer_request.py @@ -24,7 +24,28 @@ import ast from typing import Any, Dict, Optional, Tuple, cast -import openai +from openai import OpenAI + + +client: Optional[OpenAI] = None + +class OpenAIClientManager: + """Client context manager for OpenAI.""" + def __init__(self, api_key: str): + self.api_key = api_key + + def __enter__(self) -> OpenAI: + global client + if client is None: + client = OpenAI(api_key=self.api_key) + return client + + def __exit__(self, exc_type, exc_value, traceback) -> None: + global client + if client is not None: + client.close() + client = None + ENGINE = "gpt-3.5-turbo" @@ -53,24 +74,22 @@ def make_request_openai_request( prompt: str, - api_key: str, engine: str = ENGINE, max_tokens: Optional[int] = None, temperature: Optional[float] = None, ) -> str: """Make openai request.""" - openai.api_key = api_key max_tokens = max_tokens or MAX_TOKENS temperature = temperature or TEMPERATURE - moderation_result = openai.Moderation.create(prompt) - if moderation_result["results"][0]["flagged"]: + moderation_result = client.moderations.create(input=prompt) + if moderation_result.results[0].flagged: return "Moderation flagged the prompt as in violation of terms." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}, ] - response = openai.ChatCompletion.create( + response = client.chat.completions.create( model=engine, messages=messages, temperature=temperature, @@ -85,10 +104,10 @@ def make_request_openai_request( def native_transfer( prompt: str, api_key: str, -) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: +) -> Tuple[str, Optional[str], Optional[Dict[str, Any]], Any]: """Perform native transfer.""" tool_prompt = NATIVE_TRANSFER_PROMPT.format(user_prompt=prompt) - response = make_request_openai_request(prompt=tool_prompt, api_key=api_key) + response = make_request_openai_request(prompt=tool_prompt) try: # parse the response to get the transaction object string itself @@ -101,8 +120,7 @@ def native_transfer( "to": str(parsed_txs["to"]), "value": int(parsed_txs["wei_value"]), } - - return response, prompt, transaction + return response, prompt, transaction, None AVAILABLE_TOOLS = { @@ -110,14 +128,17 @@ def native_transfer( } -def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: +def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]], Any]: """Run the task""" - prompt = kwargs["prompt"] - api_key = kwargs["api_keys"]["openai"] - tool = cast(str, kwargs["tool"]).replace(TOOL_PREFIX, "") + with OpenAIClientManager(kwargs["api_keys"]["openai"]): + + prompt = kwargs["prompt"] + api_key = kwargs["api_keys"]["openai"] + tool = cast(str, kwargs["tool"]).replace(TOOL_PREFIX, "") - if tool not in AVAILABLE_TOOLS: - return f"No tool named `{kwargs['tool']}`", None, None + if tool not in AVAILABLE_TOOLS: + return f"No tool named `{kwargs['tool']}`", None, None, None - transaction_builder = AVAILABLE_TOOLS[tool] - return transaction_builder(prompt, api_key) + transaction_builder = AVAILABLE_TOOLS[tool] + response = transaction_builder(prompt, api_key) + return response diff --git a/tools/openai_request/openai_request.py b/tools/openai_request/openai_request.py index dc23ad21..3557558e 100644 --- a/tools/openai_request/openai_request.py +++ b/tools/openai_request/openai_request.py @@ -20,7 +20,26 @@ from typing import Any, Dict, Optional, Tuple -import openai +from openai import OpenAI + +client: Optional[OpenAI] = None + +class OpenAIClientManager: + """Client context manager for OpenAI.""" + def __init__(self, api_key: str): + self.api_key = api_key + + def __enter__(self) -> OpenAI: + global client + if client is None: + client = OpenAI(api_key=self.api_key) + return client + + def __exit__(self, exc_type, exc_value, traceback) -> None: + global client + if client is not None: + client.close() + client = None DEFAULT_OPENAI_SETTINGS = { @@ -35,44 +54,45 @@ ALLOWED_TOOLS = [PREFIX + value for values in ENGINES.values() for value in values] -def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: +def run(**kwargs) -> Tuple[Optional[str], Optional[Dict[str, Any]], Any]: """Run the task""" - openai.api_key = kwargs["api_keys"]["openai"] - max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) - temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - prompt = kwargs["prompt"] - tool = kwargs["tool"] - if tool not in ALLOWED_TOOLS: - return f"Tool {tool} is not in the list of supported tools.", None, None + with OpenAIClientManager(kwargs["api_keys"]["openai"]): + max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) + temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) + prompt = kwargs["prompt"] + tool = kwargs["tool"] + counter_callback = kwargs.get("counter_callback", None) + if tool not in ALLOWED_TOOLS: + return f"Tool {tool} is not in the list of supported tools.", None, None - engine = tool.replace(PREFIX, "") - moderation_result = openai.Moderation.create(prompt) - if moderation_result["results"][0]["flagged"]: - return "Moderation flagged the prompt as in violation of terms.", None, None + engine = tool.replace(PREFIX, "") + moderation_result = client.moderations.create(input=prompt) + if moderation_result.results[0].flagged: + return "Moderation flagged the prompt as in violation of terms.", None, None - if engine in ENGINES["chat"]: - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": prompt}, - ] - response = openai.ChatCompletion.create( - model=engine, - messages=messages, + if engine in ENGINES["chat"]: + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prompt}, + ] + response = client.chat.completions.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + n=1, + timeout=120, + stop=None, + ) + return response.choices[0].message.content, prompt, None + response = client.completions.create( + engine=engine, + prompt=prompt, temperature=temperature, max_tokens=max_tokens, - n=1, + top_p=1, + frequency_penalty=0, timeout=120, - stop=None, + presence_penalty=0, ) - return response.choices[0].message.content, prompt, None - response = openai.Completion.create( - engine=engine, - prompt=prompt, - temperature=temperature, - max_tokens=max_tokens, - top_p=1, - frequency_penalty=0, - timeout=120, - presence_penalty=0, - ) - return response.choices[0].text, prompt, None + return response.choices[0].text, prompt, counter_callback diff --git a/tools/optimization_by_prompting/optimization_by_prompting.py b/tools/optimization_by_prompting/optimization_by_prompting.py index 7682d479..40d6c0c7 100644 --- a/tools/optimization_by_prompting/optimization_by_prompting.py +++ b/tools/optimization_by_prompting/optimization_by_prompting.py @@ -21,7 +21,6 @@ import os from io import StringIO -from typing import Any, Dict, Optional, Tuple import re import json from concurrent.futures import Future, ThreadPoolExecutor @@ -31,13 +30,35 @@ from bs4 import BeautifulSoup from googleapiclient.discovery import build -import openai +from openai import OpenAI + import pandas as pd from langchain.chains import LLMChain -from langchain.llms import OpenAI +from langchain.llms import OpenAI as OpenAILLM from langchain.prompts import PromptTemplate from sklearn.metrics import roc_auc_score + +client: Optional[OpenAI] = None + +class OpenAIClientManager: + """Client context manager for OpenAI.""" + def __init__(self, api_key: str): + self.api_key = api_key + + def __enter__(self) -> OpenAI: + global client + if client is None: + client = OpenAI(api_key=self.api_key) + return client + + def __exit__(self, exc_type, exc_value, traceback) -> None: + global client + if client is not None: + client.close() + client = None + + # Provide several examples in order to backtest the resulted prompt EXAMPLES = """query;event "Will Apple release iphone 15 by 1 October 2023?";1 @@ -156,17 +177,22 @@ A valid example is: {{"p_yes": 0.5}} """ + def evaluate_prompt(prompt, df, llm): prompt += OUTPUT_FORMAT chain = LLMChain(llm=llm, prompt=prompt) probas = [] for row in df.itertuples(): - pred_chain = chain.run({"user_prompt": row.query, "additional_information": OUTPUT_FORMAT}) + pred_chain = chain.run( + {"user_prompt": row.query, "additional_information": OUTPUT_FORMAT} + ) try: dictionary_match = float(eval(pred_chain)["p_yes"]) except: - dictionary_match = float(eval(re.search(r'\{.*\}', pred_chain).group(0))["p_yes"]) + dictionary_match = float( + eval(re.search(r"\{.*\}", pred_chain).group(0))["p_yes"] + ) probas.append(dictionary_match) return probas @@ -183,9 +209,15 @@ def create_new_instructions(llm, instructions, score): return evaluations -def prompt_engineer(openai_api_key, init_instructions, instructions_format, iterations=3, model_name="gpt-3.5-turbo"): +def prompt_engineer( + openai_api_key, + init_instructions, + instructions_format, + iterations=3, + model_name="gpt-3.5-turbo", +): - llm = OpenAI(model_name=model_name, openai_api_key=openai_api_key) + llm = OpenAILLM(model_name=model_name, openai_api_key=openai_api_key) score_template = {"template": init_instructions, "score": 0.0} df = pd.read_csv(StringIO(EXAMPLES), sep=";") @@ -215,9 +247,7 @@ def prompt_engineer(openai_api_key, init_instructions, instructions_format, iter score = calculate_score(df) print(f"Score: {score}\n") if score > score_template["score"]: - print( - f"Best template score: {score} \nTemplate: {template}\n" - ) + print(f"Best template score: {score} \nTemplate: {template}\n") score_template["template"] = template score_template["score"] = score template = create_new_instructions( @@ -280,7 +310,10 @@ def process_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] + futures = [ + (executor.submit(requests.get, url, timeout=timeout), url) + for url in batch + ] yield futures @@ -296,7 +329,9 @@ def extract_texts(urls: List[str], num_words: int = 300) -> List[str]: result = future.result() if result.status_code != 200: continue - extracted_texts.append(extract_text(html=result.text, num_words=num_words)) + extracted_texts.append( + extract_text(html=result.text, num_words=num_words) + ) count += 1 if count >= max_allowed: stop = True @@ -304,38 +339,37 @@ def extract_texts(urls: List[str], num_words: int = 300) -> List[str]: except requests.exceptions.ReadTimeout: print(f"Request timed out: {url}.") except Exception as e: - print(f"An error occurred: {e}") + print(f"An error occurred: {e}") if stop: break return extracted_texts def fetch_additional_information( - prompt: str, - engine: str, - temperature: float, - max_tokens: int, - google_api_key: str, - google_engine: str, + prompt: str, + engine: str, + temperature: float, + max_tokens: int, + google_api_key: str, + google_engine: str, ) -> str: """Fetch additional information.""" url_query_prompt = URL_QUERY_PROMPT.format(user_prompt=prompt) - moderation_result = openai.Moderation.create(url_query_prompt) - if moderation_result["results"][0]["flagged"]: + moderation_result = client.moderations.create(input=url_query_prompt) + if moderation_result.results[0].flagged: return "" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": url_query_prompt}, ] - response = openai.ChatCompletion.create( + response = client.chat.completions.create( model=engine, messages=messages, temperature=temperature, max_tokens=max_tokens, n=1, timeout=90, - request_timeout=90, stop=None, ) json_data = json.loads(response.choices[0].message.content) @@ -348,20 +382,20 @@ def fetch_additional_information( return "\n".join(["- " + text for text in texts]) -def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: +def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]], Any]: """Run the task""" - tool = kwargs["tool"] - prompt = kwargs["prompt"] - max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) - temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - - openai_key = kwargs["api_keys"]["openai"] - openai.api_key = openai_key - if tool not in ALLOWED_TOOLS: - raise ValueError(f"Tool {tool} is not supported.") - - engine = TOOL_TO_ENGINE[tool] - additional_information = fetch_additional_information( + with OpenAIClientManager(kwargs["api_keys"]["openai"]): + tool = kwargs["tool"] + prompt = kwargs["prompt"] + max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) + temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) + + openai_key = kwargs["api_keys"]["openai"] + 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, temperature=temperature, @@ -370,28 +404,29 @@ def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: google_engine=kwargs["api_keys"]["google_engine_id"], ) - instructions = prompt_engineer(openai_key, PREDICTION_PROMPT_INSTRUCTIONS, PREDICTION_PROMPT_FORMAT) - instructions += PREDICTION_PROMPT_FORMAT - prediction_prompt = instructions.format( - user_prompt=prompt, additional_information=additional_information - ) - - moderation_result = openai.Moderation.create(prediction_prompt) - if moderation_result["results"][0]["flagged"]: - return "Moderation flagged the prompt as in violation of terms.", None, None - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": prediction_prompt}, - ] + instructions = prompt_engineer( + openai_key, PREDICTION_PROMPT_INSTRUCTIONS, PREDICTION_PROMPT_FORMAT + ) + instructions += PREDICTION_PROMPT_FORMAT + prediction_prompt = instructions.format( + user_prompt=prompt, additional_information=additional_information + ) - response = openai.ChatCompletion.create( - model=engine, - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - n=1, - timeout=150, - request_timeout=150, - stop=None, - ) - return response.choices[0].message.content, prediction_prompt, None + moderation_result = client.moderations.create(input=prediction_prompt) + if moderation_result.results[0].flagged: + return "Moderation flagged the prompt as in violation of terms.", None, None, None + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prediction_prompt}, + ] + + response = client.chat.completions.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + n=1, + timeout=150, + stop=None, + ) + return response.choices[0].message.content, prediction_prompt, None, None diff --git a/tools/prediction_request/prediction_request.py b/tools/prediction_request/prediction_request.py index 58183adf..0cb0c61d 100644 --- a/tools/prediction_request/prediction_request.py +++ b/tools/prediction_request/prediction_request.py @@ -27,7 +27,8 @@ from string import punctuation from typing import Any, Dict, Generator, List, Optional, Tuple, Callable -import openai +from openai import OpenAI + import requests import spacy from bs4 import BeautifulSoup @@ -37,6 +38,26 @@ from spacy.lang.en import STOP_WORDS from spacy.tokens import Doc, Span +client: Optional[OpenAI] = None + + +class OpenAIClientManager: + """Client context manager for OpenAI.""" + def __init__(self, api_key: str): + self.api_key = api_key + + def __enter__(self) -> OpenAI: + global client + if client is None: + client = OpenAI(api_key=self.api_key) + return client + + def __exit__(self, exc_type, exc_value, traceback) -> None: + global client + if client is not None: + client.close() + client = None + FrequenciesType = Dict[str, float] ScoresType = Dict[Span, float] @@ -237,21 +258,20 @@ def fetch_additional_information( ) -> str: """Fetch additional information.""" url_query_prompt = URL_QUERY_PROMPT.format(user_prompt=prompt) - moderation_result = openai.Moderation.create(url_query_prompt) - if moderation_result["results"][0]["flagged"]: + moderation_result = client.moderations.create(input=url_query_prompt) + if moderation_result.results[0].flagged: return "" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": url_query_prompt}, ] - response = openai.ChatCompletion.create( + response = client.chat.completions.create( model=engine, messages=messages, temperature=temperature, max_tokens=max_tokens, n=1, timeout=90, - request_timeout=90, stop=None, ) json_data = json.loads(response.choices[0].message.content) @@ -336,72 +356,71 @@ def summarize(text: str, compression_factor: float, vocab: str) -> str: return summary_text -def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: +def run(**kwargs) -> Tuple[Optional[str], Optional[Dict[str, Any]], Any]: """Run the task""" - tool = kwargs["tool"] - prompt = kwargs["prompt"] - max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) - temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - num_urls = kwargs.get("num_urls", DEFAULT_NUM_URLS[tool]) - num_words = kwargs.get("num_words", DEFAULT_NUM_WORDS[tool]) - compression_factor = kwargs.get("compression_factor", DEFAULT_COMPRESSION_FACTOR) - vocab = kwargs.get("vocab", DEFAULT_VOCAB) - counter_callback = kwargs.get("counter_callback", None) - api_keys = kwargs.get("api_keys", {}) - google_api_key = api_keys.get("google_api_key", None) - google_engine_id = api_keys.get("google_engine_id", None) - - openai.api_key = kwargs["api_keys"]["openai"] - if tool not in ALLOWED_TOOLS: - raise ValueError(f"Tool {tool} is not supported.") - - engine = TOOL_TO_ENGINE[tool] - if tool.startswith("prediction-online"): - additional_information, counter_callback = fetch_additional_information( - prompt, - engine, - temperature, - max_tokens, - google_api_key, - google_engine_id, - num_urls, - num_words, - counter_callback=counter_callback, - source_links=kwargs.get("source_links", None), - ) - else: - additional_information = "" - - if additional_information and tool == "prediction-online-summarized-info": - additional_information = summarize( - additional_information, compression_factor, vocab + with OpenAIClientManager(kwargs["api_keys"]["openai"]): + tool = kwargs["tool"] + prompt = kwargs["prompt"] + max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) + temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) + num_urls = kwargs.get("num_urls", DEFAULT_NUM_URLS[tool]) + num_words = kwargs.get("num_words", DEFAULT_NUM_WORDS[tool]) + compression_factor = kwargs.get("compression_factor", DEFAULT_COMPRESSION_FACTOR) + vocab = kwargs.get("vocab", DEFAULT_VOCAB) + counter_callback = kwargs.get("counter_callback", None) + api_keys = kwargs.get("api_keys", {}) + google_api_key = api_keys.get("google_api_key", None) + google_engine_id = api_keys.get("google_engine_id", None) + + if tool not in ALLOWED_TOOLS: + raise ValueError(f"Tool {tool} is not supported.") + + engine = TOOL_TO_ENGINE[tool] + if tool.startswith("prediction-online"): + additional_information, counter_callback = fetch_additional_information( + prompt, + engine, + temperature, + max_tokens, + google_api_key, + google_engine_id, + num_urls, + num_words, + counter_callback=counter_callback, + source_links=kwargs.get("source_links", None), + ) + else: + additional_information = "" + + if additional_information and tool == "prediction-online-summarized-info": + additional_information = summarize( + additional_information, compression_factor, vocab + ) + + prediction_prompt = PREDICTION_PROMPT.format( + user_prompt=prompt, additional_information=additional_information ) - - prediction_prompt = PREDICTION_PROMPT.format( - user_prompt=prompt, additional_information=additional_information - ) - moderation_result = openai.Moderation.create(prediction_prompt) - if moderation_result["results"][0]["flagged"]: - return "Moderation flagged the prompt as in violation of terms.", None, None - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": prediction_prompt}, - ] - response = openai.ChatCompletion.create( - model=engine, - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - n=1, - timeout=150, - request_timeout=150, - stop=None, - ) - if counter_callback is not None: - counter_callback( - input_tokens=response["usage"]["prompt_tokens"], - output_tokens=response["usage"]["completion_tokens"], + moderation_result = client.moderations.create(input=prediction_prompt) + if moderation_result.results[0].flagged: + return "Moderation flagged the prompt as in violation of terms.", None, None + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prediction_prompt}, + ] + response = client.chat.completions.create( model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + n=1, + timeout=150, + stop=None, ) - return response.choices[0].message.content, prediction_prompt, counter_callback - return response.choices[0].message.content, prediction_prompt, None + if counter_callback is not None: + counter_callback( + input_tokens=response["usage"]["prompt_tokens"], + output_tokens=response["usage"]["completion_tokens"], + model=engine, + ) + return response.choices[0].message.content, prediction_prompt, counter_callback + return response.choices[0].message.content, prediction_prompt, None diff --git a/tools/prediction_request_claude/prediction_request_claude.py b/tools/prediction_request_claude/prediction_request_claude.py index 151a3318..41d699f8 100644 --- a/tools/prediction_request_claude/prediction_request_claude.py +++ b/tools/prediction_request_claude/prediction_request_claude.py @@ -253,7 +253,7 @@ def fetch_additional_information( return "\n".join(["- " + text for text in texts]), None -def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: +def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]], Any]: """Run the task""" tool = kwargs["tool"] prompt = kwargs["prompt"] diff --git a/tools/prediction_request_embedding/prediction_sentence_embedding.py b/tools/prediction_request_embedding/prediction_sentence_embedding.py index 186630b1..2d57e8fe 100644 --- a/tools/prediction_request_embedding/prediction_sentence_embedding.py +++ b/tools/prediction_request_embedding/prediction_sentence_embedding.py @@ -29,7 +29,8 @@ from bs4 import BeautifulSoup, NavigableString from googleapiclient.discovery import build -import openai +from openai import OpenAI + import requests from requests import Session import spacy @@ -38,6 +39,28 @@ from dateutil import parser +client: Optional[OpenAI] = None + + +class OpenAIClientManager: + """Client context manager for OpenAI.""" + def __init__(self, api_key: str): + self.api_key = api_key + + def __enter__(self) -> OpenAI: + global client + if client is None: + client = OpenAI(api_key=self.api_key) + return client + + def __exit__(self, exc_type, exc_value, traceback) -> None: + global client + if client is not None: + client.close() + client = None + + + NUM_URLS_EXTRACT = 5 MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4000 # Set the limit for cost efficiency WORDS_PER_TOKEN_FACTOR = 0.75 @@ -1029,8 +1052,8 @@ def fetch_additional_information( url_query_prompt = URL_QUERY_PROMPT.format(event_question=event_question) # Perform moderation check - moderation_result = openai.Moderation.create(url_query_prompt) - if moderation_result["results"][0]["flagged"]: + moderation_result = client.moderations.create(input=url_query_prompt) + if moderation_result.results[0].flagged: # return empty additional information if the prompt is flagged return "" @@ -1041,14 +1064,13 @@ def fetch_additional_information( ] # Fetch queries from the OpenAI engine - response = openai.ChatCompletion.create( + response = client.chat.completions.create( model=engine, messages=messages, temperature=temperature, # Override the default temperature parameter set for the engine max_tokens=max_compl_tokens, # Override the default max_compl_tokens parameter set for the engine n=1, timeout=90, - request_timeout=90, stop=None, ) @@ -1077,7 +1099,7 @@ def fetch_additional_information( return additional_informations -def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: +def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]], Any]: """ Run the task with the given arguments. @@ -1091,93 +1113,90 @@ def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: Returns: Tuple[str, Optional[Dict[str, Any]]]: The generated content and any additional data. """ + with OpenAIClientManager(kwargs["api_keys"]["openai"]): + tool = kwargs["tool"] + prompt = kwargs["prompt"] + max_compl_tokens = kwargs.get( + "max_tokens", DEFAULT_OPENAI_SETTINGS["max_compl_tokens"] + ) + temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - tool = kwargs["tool"] - prompt = kwargs["prompt"] - max_compl_tokens = kwargs.get( - "max_tokens", DEFAULT_OPENAI_SETTINGS["max_compl_tokens"] - ) - temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - - openai.api_key = kwargs["api_keys"]["openai"] - if tool not in ALLOWED_TOOLS: - raise ValueError(f"TOOL {tool} is not supported.") - - # Load the spacy model - download_spacy_model("en_core_web_md") - nlp = spacy.load("en_core_web_md") - - # Get the LLM engine to be used - engine = TOOL_TO_ENGINE[tool] - - # Extract the event question from the prompt - event_question = re.search(r"\"(.+?)\"", prompt).group(1) - if not event_question: - raise ValueError("No event question found in prompt.") - - # Get the tiktoken base encoding - enc = tiktoken.get_encoding("cl100k_base") + if tool not in ALLOWED_TOOLS: + raise ValueError(f"TOOL {tool} is not supported.") - # Calculate the maximum number of tokens and words that can be consumed by the additional information string - max_add_tokens = get_max_tokens_for_additional_information( - max_compl_tokens=max_compl_tokens, - prompt=prompt, - enc=enc, - ) - max_add_words = int(max_add_tokens * 0.75) + # Load the spacy model + download_spacy_model("en_core_web_md") + nlp = spacy.load("en_core_web_md") - # Fetch additional information - additional_information = fetch_additional_information( - event_question=event_question, - engine="gpt-3.5-turbo", - temperature=0.5, - max_compl_tokens=max_compl_tokens, - nlp=nlp, - max_add_words=max_add_words, - google_api_key=kwargs["api_keys"]["google_api_key"], - google_engine=kwargs["api_keys"]["google_engine_id"], - ) + # Get the LLM engine to be used + engine = TOOL_TO_ENGINE[tool] - # Truncate additional information to stay within the chat completion token limit of 4096 - additional_information = truncate_additional_information( - additional_information, - max_add_tokens, - enc=enc, - ) + # Extract the event question from the prompt + event_question = re.search(r"\"(.+?)\"", prompt).group(1) + if not event_question: + raise ValueError("No event question found in prompt.") - # Get the current utc timestamp - current_time_utc = datetime.now(timezone.utc) - formatted_time_utc = current_time_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" + # Get the tiktoken base encoding + enc = tiktoken.get_encoding("cl100k_base") - # Generate the prediction prompt - prediction_prompt = PREDICTION_PROMPT.format( - event_question=event_question, - user_prompt=prompt, - additional_information=additional_information, - timestamp=formatted_time_utc, - ) + # Calculate the maximum number of tokens and words that can be consumed by the additional information string + max_add_tokens = get_max_tokens_for_additional_information( + max_compl_tokens=max_compl_tokens, + prompt=prompt, + enc=enc, + ) + max_add_words = int(max_add_tokens * 0.75) + + # Fetch additional information + additional_information = fetch_additional_information( + event_question=event_question, + engine="gpt-3.5-turbo", + temperature=0.5, + max_compl_tokens=max_compl_tokens, + nlp=nlp, + max_add_words=max_add_words, + google_api_key=kwargs["api_keys"]["google_api_key"], + google_engine=kwargs["api_keys"]["google_engine_id"], + ) - # Perform moderation - moderation_result = openai.Moderation.create(prediction_prompt) - if moderation_result["results"][0]["flagged"]: - return "Moderation flagged the prompt as in violation of terms.", None, None + # Truncate additional information to stay within the chat completion token limit of 4096 + additional_information = truncate_additional_information( + additional_information, + max_add_tokens, + enc=enc, + ) - # Create messages for the OpenAI engine - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": prediction_prompt}, - ] + # Get the current utc timestamp + current_time_utc = datetime.now(timezone.utc) + formatted_time_utc = current_time_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" - # Generate the response - response = openai.ChatCompletion.create( - model=engine, - messages=messages, - temperature=temperature, - max_tokens=max_compl_tokens, - n=1, - timeout=150, - request_timeout=150, - stop=None, - ) + # Generate the prediction prompt + prediction_prompt = PREDICTION_PROMPT.format( + event_question=event_question, + user_prompt=prompt, + additional_information=additional_information, + timestamp=formatted_time_utc, + ) - return response.choices[0].message.content, prediction_prompt, None \ No newline at end of file + # Perform moderation + moderation_result = client.moderations.create(input=prediction_prompt) + if moderation_result.results[0].flagged: + return "Moderation flagged the prompt as in violation of terms.", None, None + + # Create messages for the OpenAI engine + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prediction_prompt}, + ] + + # Generate the response + response = client.chat.completions.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_compl_tokens, + n=1, + timeout=150, + stop=None, + ) + return response.choices[0].message.content, prediction_prompt, None diff --git a/tools/prediction_request_sme/prediction_request_sme.py b/tools/prediction_request_sme/prediction_request_sme.py index 4809d270..20f4c67e 100644 --- a/tools/prediction_request_sme/prediction_request_sme.py +++ b/tools/prediction_request_sme/prediction_request_sme.py @@ -25,11 +25,35 @@ from typing import Any, Dict, Generator, List, Optional, Tuple, Callable from itertools import islice -import openai +from openai import OpenAI + import requests from bs4 import BeautifulSoup from googleapiclient.discovery import build + +client: Optional[OpenAI] = None + + +class OpenAIClientManager: + """Client context manager for OpenAI.""" + def __init__(self, api_key: str): + self.api_key = api_key + + def __enter__(self) -> OpenAI: + global client + if client is None: + client = OpenAI(api_key=self.api_key) + return client + + def __exit__(self, exc_type, exc_value, traceback) -> None: + global client + if client is not None: + client.close() + client = None + + + NUM_URLS_EXTRACT = 5 DEFAULT_NUM_WORDS: Dict[str, Optional[int]] = defaultdict(lambda: 300) DEFAULT_OPENAI_SETTINGS = { @@ -251,21 +275,20 @@ def fetch_additional_information( ) -> str: """Fetch additional information.""" url_query_prompt = URL_QUERY_PROMPT.format(user_prompt=prompt) - moderation_result = openai.Moderation.create(url_query_prompt) - if moderation_result["results"][0]["flagged"]: + moderation_result = client.moderations.create(input=url_query_prompt) + if moderation_result.results[0].flagged: return "" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": url_query_prompt}, ] - response = openai.ChatCompletion.create( + response = client.chat.completions.create( model=engine, messages=messages, temperature=temperature, max_tokens=max_tokens, n=1, timeout=90, - request_timeout=90, stop=None, ) json_data = json.loads(response.choices[0].message.content) @@ -303,14 +326,13 @@ def get_sme_role( {"role": "system", "content": system_prompt}, {"role": "user", "content": market_question}, ] - response = openai.ChatCompletion.create( + response = client.chat.completions.create( model=engine, messages=messages, temperature=temperature, max_tokens=max_tokens, n=1, timeout=150, - request_timeout=150, stop=None, ) generated_sme_roles = response.choices[0].message.content @@ -326,83 +348,82 @@ def get_sme_role( return sme["sme"], sme["sme_introduction"], None -def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: +def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]], Any]: """Run the task""" - tool = kwargs["tool"] - prompt = kwargs["prompt"] - max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) - temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - source_links = kwargs.get("source_links", None) - num_urls = kwargs.get("num_urls", NUM_URLS_EXTRACT) - num_words = kwargs.get("num_words", DEFAULT_NUM_WORDS) - counter_callback = kwargs.get("counter_callback", None) - api_keys = kwargs.get("api_keys", {}) - google_api_key = api_keys.get("google_api_key", None) - google_engine_id = api_keys.get("google_engine_id", None) - - openai.api_key = kwargs["api_keys"]["openai"] - if tool not in ALLOWED_TOOLS: - raise ValueError(f"Tool {tool} is not supported.") - - engine = TOOL_TO_ENGINE[tool] - - try: - sme, sme_introduction, counter_callback = get_sme_role( - engine, - temperature, - max_tokens, - prompt, - counter_callback=counter_callback, + with OpenAIClientManager(kwargs["api_keys"]["openai"]): + tool = kwargs["tool"] + prompt = kwargs["prompt"] + max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) + temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) + source_links = kwargs.get("source_links", None) + num_urls = kwargs.get("num_urls", NUM_URLS_EXTRACT) + num_words = kwargs.get("num_words", DEFAULT_NUM_WORDS) + counter_callback = kwargs.get("counter_callback", None) + api_keys = kwargs.get("api_keys", {}) + google_api_key = api_keys.get("google_api_key", None) + google_engine_id = api_keys.get("google_engine_id", None) + + if tool not in ALLOWED_TOOLS: + raise ValueError(f"Tool {tool} is not supported.") + + engine = TOOL_TO_ENGINE[tool] + + try: + sme, sme_introduction, counter_callback = get_sme_role( + engine, + temperature, + max_tokens, + prompt, + counter_callback=counter_callback, + ) + except Exception as e: + print(f"An error occurred during SME role creation: {e}") + print("Using default SME introduction.") + sme_introduction = "You are a helpful assistant." + + if tool.startswith("prediction-online"): + additional_information, counter_callback = fetch_additional_information( + prompt=prompt, + engine=engine, + temperature=temperature, + max_tokens=max_tokens, + google_api_key=google_api_key, + google_engine=google_engine_id, + num_urls=num_urls, + num_words=num_words, + counter_callback=counter_callback, + source_links=source_links, + ) + else: + additional_information = None + prediction_prompt = PREDICTION_PROMPT.format( + user_prompt=prompt, additional_information=additional_information ) - except Exception as e: - print(f"An error occurred during SME role creation: {e}") - print("Using default SME introduction.") - sme_introduction = "You are a helpful assistant." - - if tool.startswith("prediction-online"): - additional_information, counter_callback = fetch_additional_information( - prompt=prompt, - engine=engine, + moderation_result = client.moderations.create(input=prediction_prompt) + if moderation_result.results[0].flagged: + return ( + "Moderation flagged the prompt as in violation of terms.", + prediction_prompt, + None, + ) + messages = [ + {"role": "system", "content": sme_introduction}, + {"role": "user", "content": prediction_prompt}, + ] + response = client.chat.completions.create( + model=engine, + messages=messages, temperature=temperature, max_tokens=max_tokens, - google_api_key=google_api_key, - google_engine=google_engine_id, - num_urls=num_urls, - num_words=num_words, - counter_callback=counter_callback, - source_links=source_links, - ) - else: - additional_information = None - prediction_prompt = PREDICTION_PROMPT.format( - user_prompt=prompt, additional_information=additional_information - ) - moderation_result = openai.Moderation.create(prediction_prompt) - if moderation_result["results"][0]["flagged"]: - return ( - "Moderation flagged the prompt as in violation of terms.", - prediction_prompt, - None, - ) - messages = [ - {"role": "system", "content": sme_introduction}, - {"role": "user", "content": prediction_prompt}, - ] - response = openai.ChatCompletion.create( - model=engine, - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - n=1, - timeout=150, - request_timeout=150, - stop=None, - ) - if counter_callback is not None: - counter_callback( - input_tokens=response["usage"]["prompt_tokens"], - output_tokens=response["usage"]["completion_tokens"], - model=engine, + n=1, + timeout=150, + stop=None, ) - return response.choices[0].message.content, prediction_prompt, counter_callback - return response.choices[0].message.content, prediction_prompt, None + if counter_callback is not None: + counter_callback( + input_tokens=response["usage"]["prompt_tokens"], + output_tokens=response["usage"]["completion_tokens"], + model=engine, + ) + return response.choices[0].message.content, prediction_prompt, counter_callback + return response.choices[0].message.content, prediction_prompt, None diff --git a/tools/prediction_sum_url_content/prediction_sum_url_content.py b/tools/prediction_sum_url_content/prediction_sum_url_content.py index 63b12dfe..599df8a7 100644 --- a/tools/prediction_sum_url_content/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content/prediction_sum_url_content.py @@ -27,7 +27,8 @@ from bs4 import BeautifulSoup, NavigableString from googleapiclient.discovery import build -import openai +from openai import OpenAI + import requests from requests import Session import spacy @@ -38,8 +39,31 @@ from tqdm import tqdm from sentence_transformers import SentenceTransformer, util + +client: Optional[OpenAI] = None + + +class OpenAIClientManager: + """Client context manager for OpenAI.""" + def __init__(self, api_key: str): + self.api_key = api_key + + def __enter__(self) -> OpenAI: + global client + if client is None: + client = OpenAI(api_key=self.api_key) + return client + + def __exit__(self, exc_type, exc_value, traceback) -> None: + global client + if client is not None: + client.close() + client = None + + + NUM_URLS_EXTRACT = 5 -MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4096 # Set the limit for cost efficiency +MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4096 # Set the limit for cost efficiency WORDS_PER_TOKEN_FACTOR = 0.75 DEFAULT_OPENAI_SETTINGS = { "max_compl_tokens": 200, @@ -132,55 +156,163 @@ # Global constants for possible attribute names for release and update dates RELEASE_DATE_NAMES = [ - 'date', 'pubdate', 'publishdate', 'OriginalPublicationDate', - 'article:published_time', 'sailthru.date', 'article.published', - 'published-date', 'og:published_time', 'publication_date', - 'publishedDate', 'dc.date', 'DC.date', 'article:published', - 'article_date_original', 'cXenseParse:recs:publishtime', 'DATE_PUBLISHED', - 'pub-date', 'pub_date', 'datePublished', 'date_published', - 'time_published', 'article:published_date', 'parsely-pub-date', - 'publish-date', 'pubdatetime', 'published_time', 'publishedtime', - 'article_date', 'created_date', 'published_at', 'lastPublishedDate' - 'og:published_time', 'og:release_date', 'article:published_time', - 'og:publication_date', 'og:pubdate', 'article:publication_date', - 'product:availability_starts', 'product:release_date', 'event:start_date', - 'event:release_date', 'og:time_published', 'og:start_date', 'og:created', - 'og:creation_date', 'og:launch_date', 'og:first_published', - 'og:original_publication_date', 'article:published', 'article:pub_date', - 'news:published_time', 'news:publication_date', 'blog:published_time', - 'blog:publication_date', 'report:published_time', 'report:publication_date', - 'webpage:published_time', 'webpage:publication_date', 'post:published_time', - 'post:publication_date', 'item:published_time', 'item:publication_date' + "date", + "pubdate", + "publishdate", + "OriginalPublicationDate", + "article:published_time", + "sailthru.date", + "article.published", + "published-date", + "og:published_time", + "publication_date", + "publishedDate", + "dc.date", + "DC.date", + "article:published", + "article_date_original", + "cXenseParse:recs:publishtime", + "DATE_PUBLISHED", + "pub-date", + "pub_date", + "datePublished", + "date_published", + "time_published", + "article:published_date", + "parsely-pub-date", + "publish-date", + "pubdatetime", + "published_time", + "publishedtime", + "article_date", + "created_date", + "published_at", + "lastPublishedDate" "og:published_time", + "og:release_date", + "article:published_time", + "og:publication_date", + "og:pubdate", + "article:publication_date", + "product:availability_starts", + "product:release_date", + "event:start_date", + "event:release_date", + "og:time_published", + "og:start_date", + "og:created", + "og:creation_date", + "og:launch_date", + "og:first_published", + "og:original_publication_date", + "article:published", + "article:pub_date", + "news:published_time", + "news:publication_date", + "blog:published_time", + "blog:publication_date", + "report:published_time", + "report:publication_date", + "webpage:published_time", + "webpage:publication_date", + "post:published_time", + "post:publication_date", + "item:published_time", + "item:publication_date", ] UPDATE_DATE_NAMES = [ - 'lastmod', 'lastmodified', 'last-modified', 'updated', - 'dateModified', 'article:modified_time', 'modified_date', - 'article:modified', 'og:updated_time', 'mod_date', - 'modifiedDate', 'lastModifiedDate', 'lastUpdate', 'last_updated', - 'LastUpdated', 'UpdateDate', 'updated_date', 'revision_date', - 'sentry:revision', 'article:modified_date', 'date_updated', - 'time_updated', 'lastUpdatedDate', 'last-update-date', 'lastupdate', - 'dateLastModified', 'article:update_time', 'modified_time', - 'last_modified_date', 'date_last_modified', - 'og:updated_time', 'og:modified_time', 'article:modified_time', - 'og:modification_date', 'og:mod_time', 'article:modification_date', - 'product:availability_ends', 'product:modified_date', 'event:end_date', - 'event:updated_date', 'og:time_modified', 'og:end_date', 'og:last_modified', - 'og:modification_date', 'og:revision_date', 'og:last_updated', - 'og:most_recent_update', 'article:updated', 'article:mod_date', - 'news:updated_time', 'news:modification_date', 'blog:updated_time', - 'blog:modification_date', 'report:updated_time', 'report:modification_date', - 'webpage:updated_time', 'webpage:modification_date', 'post:updated_time', - 'post:modification_date', 'item:updated_time', 'item:modification_date' + "lastmod", + "lastmodified", + "last-modified", + "updated", + "dateModified", + "article:modified_time", + "modified_date", + "article:modified", + "og:updated_time", + "mod_date", + "modifiedDate", + "lastModifiedDate", + "lastUpdate", + "last_updated", + "LastUpdated", + "UpdateDate", + "updated_date", + "revision_date", + "sentry:revision", + "article:modified_date", + "date_updated", + "time_updated", + "lastUpdatedDate", + "last-update-date", + "lastupdate", + "dateLastModified", + "article:update_time", + "modified_time", + "last_modified_date", + "date_last_modified", + "og:updated_time", + "og:modified_time", + "article:modified_time", + "og:modification_date", + "og:mod_time", + "article:modification_date", + "product:availability_ends", + "product:modified_date", + "event:end_date", + "event:updated_date", + "og:time_modified", + "og:end_date", + "og:last_modified", + "og:modification_date", + "og:revision_date", + "og:last_updated", + "og:most_recent_update", + "article:updated", + "article:mod_date", + "news:updated_time", + "news:modification_date", + "blog:updated_time", + "blog:modification_date", + "report:updated_time", + "report:modification_date", + "webpage:updated_time", + "webpage:modification_date", + "post:updated_time", + "post:modification_date", + "item:updated_time", + "item:modification_date", ] # Global constant for HTML tags to remove HTML_TAGS_TO_REMOVE = [ - "script", "style", "header", "footer", "aside", "nav", "form", "button", - "iframe", "input", "textarea", "select", "option", "label", "fieldset", - "legend", "img", "audio", "video", "source", "track", "canvas", "svg", - "object", "param", "embed", "link" + "script", + "style", + "header", + "footer", + "aside", + "nav", + "form", + "button", + "iframe", + "input", + "textarea", + "select", + "option", + "label", + "fieldset", + "legend", + "img", + "audio", + "video", + "source", + "track", + "canvas", + "svg", + "object", + "param", + "embed", + "link", ] @@ -200,29 +332,28 @@ def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[s def extract_event_date(doc_question) -> str: - ''' + """ Extracts the event date from the event question if present. - + Args: doc_question (spaCy Doc): Document text as a spaCy Doc object. - + Returns: str: The event date in year-month-day format if present, otherwise None. - ''' - + """ + event_date_ymd = None # Extract the date from the event question if present for ent in doc_question.ents: - if ent.label_ == 'DATE': + if ent.label_ == "DATE": event_date_ymd = standardize_date(ent.text) # If event date not formatted as YMD or not found, return None - if not datetime.strptime(event_date_ymd, '%Y-%m-%d') or event_date_ymd is None: + if not datetime.strptime(event_date_ymd, "%Y-%m-%d") or event_date_ymd is None: return None else: return event_date_ymd - def get_max_tokens_for_additional_information( @@ -275,19 +406,21 @@ def truncate_additional_information( # Encode the string into tokens add_enc = enc.encode(additional_informations) len_add_enc = len(add_enc) - + # Truncate additional information string if token sum exceeds maximum allowed if len_add_enc <= max_add_tokens: return additional_informations else: - add_trunc_enc = add_enc[:-int(len_add_enc - max_add_tokens)] + add_trunc_enc = add_enc[: -int(len_add_enc - max_add_tokens)] return enc.decode(add_trunc_enc) -def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: int = 3) -> List[str]: +def get_urls_from_queries( + queries: List[str], api_key: str, engine: str, num: int = 3 +) -> List[str]: """ Fetch unique URLs from search engine queries, limiting the number of URLs per query. - + Args: queries (List[str]): List of search engine queries. api_key (str): API key for the search engine. @@ -296,7 +429,7 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in Raises: ValueError: If the number of URLs per query exceeds the maximum allowed. - + Returns: List[str]: Unique list of URLs, omitting PDF and download-related URLs. """ @@ -312,7 +445,7 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in query=query, api_key=api_key, engine=engine, - num=max_num_fetch # Limit the number of returned URLs per query + num=max_num_fetch, # Limit the number of returned URLs per query ) # Add only unique URLs up to 'num' per query, omitting PDF and 'download' URLs @@ -334,13 +467,13 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in def standardize_date(date_text): """ Standardizes a given date string to the format 'YYYY-MM-DD' or 'MM-DD' if possible. - + Args: date_text (str): The date string to be standardized. Raises: ValueError: If the date string cannot be parsed. - + Returns: str: The standardized date string if possible, otherwise None. """ @@ -348,19 +481,19 @@ def standardize_date(date_text): try: # Compile regex patterns for month and day month_regex = re.compile( - r'\b(?:Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?)\b', - re.IGNORECASE + r"\b(?:Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?)\b", + re.IGNORECASE, ) - day_regex = re.compile(r'\b\d{1,2}\b') - + day_regex = re.compile(r"\b\d{1,2}\b") + # Parse date_text using dateutil parser parsed_date = parser.parse(date_text) - + # Check if year, month, and day are in the original date_text month_exists = month_regex.search(date_text) is not None day_exists = day_regex.search(date_text) is not None year_exists = str(parsed_date.year) in date_text - + # Format the parsed date accordingly if year_exists and month_exists and day_exists: return parsed_date.strftime("%Y-%m-%d") @@ -400,23 +533,27 @@ def get_context_around_isolated_event_date( raise ValueError( f"The maximum number of words must be less than or equal to 300." ) - + contexts_list = [] len_doc_text = len(doc_text) - + # Extract the month and day from the event date event_date_md = event_date_ymd[5:] - + for ent in doc_text.ents: - if ent.label_ == 'DATE': + if ent.label_ == "DATE": standardized_date = standardize_date(ent.text) if standardized_date is None: continue - + # Check if the entity matches the target date - if standardized_date == event_date_ymd or standardized_date == event_date_md: + if ( + standardized_date == event_date_ymd + or standardized_date == event_date_md + ): sentence = next( - sent for sent in doc_text.sents + sent + for sent in doc_text.sents if sent.start <= ent.start and sent.end >= ent.end ) @@ -428,10 +565,14 @@ def get_context_around_isolated_event_date( while context_words < max_context: # Extend the context from the start of the sentence new_start = start_token - 1 - while new_start >= 0 and doc_text[new_start].is_sent_start is None: + while ( + new_start >= 0 and doc_text[new_start].is_sent_start is None + ): new_start -= 1 if new_start >= 0: - context_words += len(doc_text[new_start:start_token].text.split()) + context_words += len( + doc_text[new_start:start_token].text.split() + ) start_token = new_start # Break if max_context is reached @@ -440,10 +581,15 @@ def get_context_around_isolated_event_date( # Extend the context from the end of the sentence new_end = end_token + 1 - while new_end < len_doc_text and doc_text[new_end].sent == sentence.sent: + while ( + new_end < len_doc_text + and doc_text[new_end].sent == sentence.sent + ): new_end += 1 if new_end < len_doc_text: - context_words += len(doc_text[end_token:new_end].text.split()) + context_words += len( + doc_text[end_token:new_end].text.split() + ) end_token = new_end # Break if max_context is reached @@ -453,20 +599,17 @@ def get_context_around_isolated_event_date( # Break if max_context cannot be reached if new_end == len_doc_text and start_token <= 0: break - - context = doc_text[max(0, start_token):min(len_doc_text, end_token)].text + + context = doc_text[ + max(0, start_token) : min(len_doc_text, end_token) + ].text contexts_list.append(context) return contexts_list def extract_relevant_information( - text: str, - query_emb, - event_date: str, - model, - nlp, - max_words: int + text: str, query_emb, event_date: str, model, nlp, max_words: int ) -> str: """ Extract relevant information from website text based on a given event question. @@ -481,27 +624,30 @@ def extract_relevant_information( Returns: str: The relevant sentences extracted from the website text. - """ - + """ + # Constants for sentence length and number thresholds len_sentence_threshold = 5 num_sentences_threshold = 1000 - sentences = [] + sentences = [] event_date_sentences = [] seen = set() # Truncate text for performance optimization text = text[:50000] - + # Apply NLP pipeline to text doc_text = nlp(text) # Extract unique sentences for sent in doc_text.sents: sentence_text = sent.text - if len(sentence_text.split()) >= len_sentence_threshold and sentence_text not in seen: + if ( + len(sentence_text.split()) >= len_sentence_threshold + and sentence_text not in seen + ): sentences.append(sentence_text) - seen.add(sentence_text) + seen.add(sentence_text) sentences.extend(event_date_sentences) # Extract contextual sentences around event date occurences within too short sentences @@ -513,65 +659,73 @@ def extract_relevant_information( if not sentences: return "" - + # Limit the number of sentences for performance optimization sentences = sentences[:num_sentences_threshold] - + # Encode event question calculate similarity scores sent_emb = model.encode(sentences) similarities = util.dot_score(query_emb, sent_emb)[0].cpu().tolist() # Extract top relevant sentences relevant_sentences = [ - sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.4 + sent + for sent, sim in sorted( + zip(sentences, similarities), key=lambda x: x[1], reverse=True + ) + if sim > 0.4 ] - + if not relevant_sentences: return "" - + # Truncate text to fit max_words limit - output = ' '.join(relevant_sentences[:20]) - output_words = output.split(' ') + output = " ".join(relevant_sentences[:20]) + output_words = output.split(" ") if len(output_words) > max_words: - output = ' '.join(output_words[:max_words]) + output = " ".join(output_words[:max_words]) return output -def get_date(soup): +def get_date(soup): """ Retrieves the release and modification dates from the soup object containing the HTML tree. - + Args: soup (BeautifulSoup): The BeautifulSoup object for the webpage. - + Returns: str: A string representing the release and modification dates. """ - + release_date = "unknown" modified_date = "unknown" # Search for an update or modified date in the meta tags for name in UPDATE_DATE_NAMES: - meta_tag = soup.find("meta", {"name": name}) or soup.find("meta", {"property": name}) + meta_tag = soup.find("meta", {"name": name}) or soup.find( + "meta", {"property": name} + ) if meta_tag: modified_date = meta_tag.get("content", "") break - + # If not found, then look for release or publication date for name in RELEASE_DATE_NAMES: - meta_tag = soup.find("meta", {"name": name}) or soup.find("meta", {"property": name}) + meta_tag = soup.find("meta", {"name": name}) or soup.find( + "meta", {"property": name} + ) if meta_tag: release_date = meta_tag.get("content", "") break - + # Fallback to using the first time tag if neither release nor modified dates are found if release_date == "unknown" and modified_date == "unknown": time_tag = soup.find("time") if time_tag: release_date = time_tag.get("datetime", "") - + return f"({release_date}, {modified_date})" @@ -604,7 +758,7 @@ def extract_text( if not html: raise ValueError("HTML is empty.") - + soup = BeautifulSoup(html, "html.parser") # Get the date of the website @@ -614,8 +768,8 @@ def extract_text( # Remove unnecessary tags to clean up text for element in soup(HTML_TAGS_TO_REMOVE): - element.replace_with(NavigableString(' ')) - + element.replace_with(NavigableString(" ")) + # Extract and clean text text = soup.get_text() lines = (line.strip() for line in text.splitlines()) @@ -644,7 +798,7 @@ def process_in_batches( ) -> Generator[None, None, List[Tuple[Future, str]]]: """ Process URLs in batches using a generator and thread pool executor. - + Args: urls (List[str]): List of URLs to process. batch_size (int, optional): Size of the processing batch_size. Default is 5. @@ -660,7 +814,7 @@ def process_in_batches( if batch_size <= 0: raise ValueError("The 'batch_size' size must be greater than zero.") - + if timeout <= 0: raise ValueError("The 'timeout' must be greater than zero.") @@ -669,7 +823,7 @@ def process_in_batches( # User-Agent headers headers = { - 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:109.0) Gecko/20100101 Firefox/117.0' + "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:109.0) Gecko/20100101 Firefox/117.0" } session.headers.update(headers) @@ -678,19 +832,32 @@ def process_in_batches( # Loop through the URLs in batch_size of size 'batch_size' for i in range(0, len(urls), batch_size): batch = urls[i : i + batch_size] - + # Submit the batch of URLs for processing futures = [] for url in batch: try: # Submit a HEAD request to the url and check Content-Type - head_future = executor.submit(session.head, url, headers=headers, timeout=timeout, allow_redirects=True) + head_future = executor.submit( + session.head, + url, + headers=headers, + timeout=timeout, + allow_redirects=True, + ) head_response = head_future.result() - if 'text/html' not in head_response.headers.get('Content-Type', ''): + if "text/html" not in head_response.headers.get("Content-Type", ""): continue else: # Submit a GET request to the url - futures.append((executor.submit(session.get, url, headers=headers, timeout=timeout), url)) + futures.append( + ( + executor.submit( + session.get, url, headers=headers, timeout=timeout + ), + url, + ) + ) except Exception as e: print(f"An error occurred: {e}") @@ -705,7 +872,7 @@ def extract_texts( ) -> List[str]: """ Extract texts from a list of URLs using BERT and Spacy. - + Args: urls (List[str]): List of URLs to extract text from. event_question (str): Event-related question for text extraction. @@ -714,17 +881,17 @@ def extract_texts( Raises: ValueError: If the event date could not be extracted from the event question. Timeout: If the request timed out. - + Returns: List[str]: List of extracted texts. """ # Maximum number of allowed extractions max_allowed = 25 - + # Initialize empty list for storing extracted texts extracted_texts = [] - + # Initialize count and stop flag count = 0 stop = False @@ -734,14 +901,16 @@ def extract_texts( event_date = extract_event_date(doc_question) # Initialize Sentence Transformer model - model = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1') - + model = SentenceTransformer("sentence-transformers/multi-qa-distilbert-cos-v1") + # Create sentence embeddings for event question with Sentence Transformer query_emb = model.encode(event_question) if event_date is None: - raise ValueError(f"Could not extract precise event date from event question: {event_question}") - + raise ValueError( + f"Could not extract precise event date from event question: {event_question}" + ) + # Process URLs in batches for batch in process_in_batches(urls=urls): for future, url in tqdm(batch, desc="Processing URLs"): @@ -760,10 +929,10 @@ def extract_texts( nlp=nlp, max_words=max_words_per_url, ) - + # Delete the result object to free memory del result - + # Append the extracted text if available and increment the count if extracted_text: # extracted_texts.append(f"{url}\n{extracted_text}") @@ -774,18 +943,18 @@ def extract_texts( if count >= max_allowed: stop = True break - + except requests.exceptions.Timeout: print(f"Request for {url} timed out.") - + except Exception as e: print(f"An error occurred: {e}") traceback.print_exc() # Print stack trace for debugging - + # Break if the maximum number of extractions is reached if stop: break - + return extracted_texts @@ -802,7 +971,7 @@ def fetch_additional_information( """ Get urls from a web search and extract relevant information based on an event question. - + Args: event_question (str): The question related to the event. max_add_words (int): The maximum number of words allowed for the additional information. @@ -812,19 +981,19 @@ def fetch_additional_information( engine (str): The openai engine. Defaults to "gpt-3.5-turbo". temperature (float): The temperature parameter for the engine. Defaults to 1.0. max_compl_tokens (int): The maximum number of tokens for the engine's response. - + Returns: str: The relevant information fetched from all the URLs concatenated. """ # Create URL query prompt url_query_prompt = URL_QUERY_PROMPT.format(event_question=event_question) - + # Perform moderation check - moderation_result = openai.Moderation.create(url_query_prompt) - if moderation_result["results"][0]["flagged"]: + moderation_result = client.moderations.create(input=url_query_prompt) + if moderation_result.results[0].flagged: return "Moderation flagged the prompt as in violation of terms.", None - + # Create messages for the OpenAI engine messages = [ {"role": "system", "content": "You are a helpful assistant."}, @@ -832,17 +1001,16 @@ def fetch_additional_information( ] # Fetch queries from the OpenAI engine - response = openai.ChatCompletion.create( + response = client.chat.completions.create( model=engine, messages=messages, - temperature=temperature, # Override the default temperature parameter set for the engine - max_tokens=max_compl_tokens, # Override the default max_compl_tokens parameter set for the engine + temperature=temperature, # Override the default temperature parameter set for the engine + max_tokens=max_compl_tokens, # Override the default max_compl_tokens parameter set for the engine n=1, timeout=90, - request_timeout=90, stop=None, ) - + # Parse the response content print(f"RESPONSE: {response}") json_data = json.loads(response.choices[0].message.content) @@ -851,14 +1019,13 @@ def fetch_additional_information( for query in json_data["queries"]: print(f"query: {query}\n") - # Get URLs from queries urls = get_urls_from_queries( json_data["queries"], api_key=google_api_key, engine=google_engine, ) - + # Get max number of words per URL max_words_per_url = max_add_words // len(urls) if len(urls) > 0 else 0 @@ -876,7 +1043,7 @@ def fetch_additional_information( return additional_informations -def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: +def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]], Any]: """ Run the task with the given arguments. @@ -891,113 +1058,114 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: Returns: Tuple[str, Optional[Dict[str, Any]]]: The generated content and any additional data. """ + with OpenAIClientManager(kwargs["api_keys"]["openai"]): + tool = kwargs["tool"] + prompt = kwargs["prompt"] + max_compl_tokens = kwargs.get( + "max_tokens", DEFAULT_OPENAI_SETTINGS["max_compl_tokens"] + ) + temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - tool = kwargs["tool"] - prompt = kwargs["prompt"] - max_compl_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_compl_tokens"]) - temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - - - openai.api_key = kwargs["api_keys"]["openai"] - if tool not in ALLOWED_TOOLS: - raise ValueError(f"TOOL {tool} is not supported.") - - - # Print the settings - print(f"MECH TOOL: {tool}") - print(f"PROMPT: {prompt}") - print(f"MAX OPENAI RETURN TOKENS: {max_compl_tokens}") - print(f"LLM TEMPERATURE: {temperature}") - - # Load the spacy model - nlp = spacy.load("en_core_web_sm") - - # Get the LLM engine to be used - engine = TOOL_TO_ENGINE[tool] - print(f"ENGINE: {engine}") - - # Extract the event question from the prompt - event_question = re.search(r"\"(.+?)\"", prompt).group(1) - if not event_question: - raise ValueError("No event question found in prompt.") - print(f"EVENT_QUESTION: {event_question}") - print() - - # Get the tiktoken base encoding - enc = tiktoken.get_encoding("cl100k_base") - - # Calculate the maximum number of tokens and words that can be consumed by the additional information string - max_add_tokens = get_max_tokens_for_additional_information( - max_compl_tokens=max_compl_tokens, - prompt=prompt, - enc=enc, - ) - max_add_words = int(max_add_tokens * 0.75) + if tool not in ALLOWED_TOOLS: + raise ValueError(f"TOOL {tool} is not supported.") - # Fetch additional information - additional_information = ( - fetch_additional_information( - event_question=event_question, - engine=engine, - temperature=temperature, + # Print the settings + print(f"MECH TOOL: {tool}") + print(f"PROMPT: {prompt}") + print(f"MAX OPENAI RETURN TOKENS: {max_compl_tokens}") + print(f"LLM TEMPERATURE: {temperature}") + + # Load the spacy model + nlp = spacy.load("en_core_web_sm") + + # Get the LLM engine to be used + engine = TOOL_TO_ENGINE[tool] + print(f"ENGINE: {engine}") + + # Extract the event question from the prompt + event_question = re.search(r"\"(.+?)\"", prompt).group(1) + if not event_question: + raise ValueError("No event question found in prompt.") + print(f"EVENT_QUESTION: {event_question}") + print() + + # Get the tiktoken base encoding + enc = tiktoken.get_encoding("cl100k_base") + + # Calculate the maximum number of tokens and words that can be consumed by the additional information string + max_add_tokens = get_max_tokens_for_additional_information( max_compl_tokens=max_compl_tokens, - nlp=nlp, - max_add_words=max_add_words, - google_api_key=kwargs["api_keys"]["google_api_key"], - google_engine=kwargs["api_keys"]["google_engine_id"], + prompt=prompt, + enc=enc, + ) + max_add_words = int(max_add_tokens * 0.75) + + # Fetch additional information + additional_information = ( + fetch_additional_information( + event_question=event_question, + engine=engine, + temperature=temperature, + max_compl_tokens=max_compl_tokens, + nlp=nlp, + max_add_words=max_add_words, + google_api_key=kwargs["api_keys"]["google_api_key"], + google_engine=kwargs["api_keys"]["google_engine_id"], + ) + if tool == "prediction-online-sum-url-content" + else "" ) - if tool == "prediction-online-sum-url-content" - else "" - ) - # Truncate additional information to stay within the chat completion token limit of 4096 - additional_information = truncate_additional_information( - additional_information, max_add_tokens, enc=enc, - ) + # Truncate additional information to stay within the chat completion token limit of 4096 + additional_information = truncate_additional_information( + additional_information, + max_add_tokens, + enc=enc, + ) - # Get the current utc timestamp - current_time_utc = datetime.now(timezone.utc) - formatted_time_utc = current_time_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" - - # Extract event date and format it to ISO 8601 with UTC timezone and 23:59:59 time - doc_question = nlp(event_question) - raw_event_date = extract_event_date(doc_question) - parsed_event_date = datetime.strptime(raw_event_date, "%Y-%m-%d") - final_event_date = parsed_event_date.replace(hour=23, minute=59, second=59, microsecond=0, tzinfo=timezone.utc) - formatted_event_date = final_event_date.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" - - # Generate the prediction prompt - prediction_prompt = PREDICTION_PROMPT.format( - event_question=event_question, - user_prompt=prompt, - timepoint=formatted_event_date, - additional_information=additional_information, - timestamp=formatted_time_utc, - ) - print(f"\nPREDICTION PROMPT: {prediction_prompt}\n") + # Get the current utc timestamp + current_time_utc = datetime.now(timezone.utc) + formatted_time_utc = current_time_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" - # Perform moderation - moderation_result = openai.Moderation.create(prediction_prompt) - if moderation_result["results"][0]["flagged"]: - return "Moderation flagged the prompt as in violation of terms.", None - - # Create messages for the OpenAI engine - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": prediction_prompt}, - ] - - # Generate the response - response = openai.ChatCompletion.create( - model=engine, - messages=messages, - temperature=temperature, - max_tokens=max_compl_tokens, - n=1, - timeout=150, - request_timeout=150, - stop=None, - ) - print(f"RESPONSE: {response}") + # Extract event date and format it to ISO 8601 with UTC timezone and 23:59:59 time + doc_question = nlp(event_question) + raw_event_date = extract_event_date(doc_question) + parsed_event_date = datetime.strptime(raw_event_date, "%Y-%m-%d") + final_event_date = parsed_event_date.replace( + hour=23, minute=59, second=59, microsecond=0, tzinfo=timezone.utc + ) + formatted_event_date = final_event_date.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" - return response.choices[0].message.content, None + # Generate the prediction prompt + prediction_prompt = PREDICTION_PROMPT.format( + event_question=event_question, + user_prompt=prompt, + timepoint=formatted_event_date, + additional_information=additional_information, + timestamp=formatted_time_utc, + ) + print(f"\nPREDICTION PROMPT: {prediction_prompt}\n") + + # Perform moderation + moderation_result = client.moderations.create(input=prediction_prompt) + if moderation_result.results[0].flagged: + return "Moderation flagged the prompt as in violation of terms.", None, None + + # Create messages for the OpenAI engine + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prediction_prompt}, + ] + + # Generate the response + response = client.chat.completions.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_compl_tokens, + n=1, + timeout=150, + stop=None, + ) + print(f"RESPONSE: {response}") + return response.choices[0].message.content, None, None diff --git a/tools/sme_generation_request/sme_generation_request.py b/tools/sme_generation_request/sme_generation_request.py index 28c77ba6..46e47aba 100644 --- a/tools/sme_generation_request/sme_generation_request.py +++ b/tools/sme_generation_request/sme_generation_request.py @@ -2,7 +2,28 @@ import json from typing import Any, Dict, Generator, List, Optional, Tuple -import openai +from openai import OpenAI + +client: Optional[OpenAI] = None + + +class OpenAIClientManager: + """Client context manager for OpenAI.""" + def __init__(self, api_key: str): + self.api_key = api_key + + def __enter__(self) -> OpenAI: + global client + if client is None: + client = OpenAI(api_key=self.api_key) + return client + + def __exit__(self, exc_type, exc_value, traceback) -> None: + global client + if client is not None: + client.close() + client = None + DEFAULT_OPENAI_SETTINGS = { "max_tokens": 500, @@ -56,50 +77,52 @@ task question: "{question}" """ -def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: + +def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]], Any]: """Generate SME roles for a given market question Raises: ValueError: _description_ Returns: - Tuple[str, Optional[Dict[str, Any]]]: str is the generated SME roles, it can be loaded with `json.loads` - to get a list of dict. The dict has two keys: "sme" and "sme_introduction". + Tuple[str, Optional[Dict[str, Any]]]: str is the generated SME roles, it can be loaded with `json.loads` + to get a list of dict. The dict has two keys: "sme" and "sme_introduction". The value of "sme" is the SME role name, and the value of "sme_introduction" is the introduction of the SME role. """ - tool = kwargs["tool"] - # prompt is the actual question - prompt = kwargs["prompt"] - max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) - temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - - if tool not in ALLOWED_TOOLS: - raise ValueError(f"tool must be one of {ALLOWED_TOOLS}") - - engine = TOOL_TO_ENGINE[tool] - - market_question = SME_GENERATION_MARKET_PROMPT.format(question=prompt) - system_prompt = SME_GENERATION_SYSTEM_PROMPT - - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": market_question}, - ] - response = openai.ChatCompletion.create( - model=engine, - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - n=1, - timeout=150, - request_timeout=150, - stop=None, - ) - - generated_sme_roles = response.choices[0].message.content - # check whether the generated_sme_roles is valid json - try: - generated_sme_roles = json.loads(generated_sme_roles) - except json.decoder.JSONDecodeError as e: - return f"Failed to generate SME roles due to {e}", json.dumps(messages), None - return response.choices[0].message.content, json.dumps(messages), None \ No newline at end of file + with OpenAIClientManager(kwargs["api_keys"]["openai"]): + tool = kwargs["tool"] + # prompt is the actual question + prompt = kwargs["prompt"] + max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) + temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) + + if tool not in ALLOWED_TOOLS: + raise ValueError(f"tool must be one of {ALLOWED_TOOLS}") + + engine = TOOL_TO_ENGINE[tool] + + market_question = SME_GENERATION_MARKET_PROMPT.format(question=prompt) + system_prompt = SME_GENERATION_SYSTEM_PROMPT + + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": market_question}, + ] + response = client.chat.completions.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + n=1, + timeout=150, + stop=None, + ) + + generated_sme_roles = response.choices[0].message.content + # check whether the generated_sme_roles is valid json + try: + generated_sme_roles = json.loads(generated_sme_roles) + except json.decoder.JSONDecodeError as e: + return f"Failed to generate SME roles due to {e}", json.dumps(messages), None, None + + return response.choices[0].message.content, json.dumps(messages), None, None diff --git a/tools/stability_ai_request/stabilityai_request.py b/tools/stability_ai_request/stabilityai_request.py index 968a1e79..be7e6a1b 100644 --- a/tools/stability_ai_request/stabilityai_request.py +++ b/tools/stability_ai_request/stabilityai_request.py @@ -113,4 +113,8 @@ def run(**kwargs: Any) -> Tuple[str, Optional[str], Optional[Dict[str, Any]]]: ) if response.status_code == 200: return json.dumps(response.json()), None, None - return (f"Error: Non-200 response ({response.status_code}): {response.text}", None, None) + return ( + f"Error: Non-200 response ({response.status_code}): {response.text}", + None, + None, + ) diff --git a/tox.ini b/tox.ini index 80437ca9..25718a95 100644 --- a/tox.ini +++ b/tox.ini @@ -307,6 +307,12 @@ exclude=(.*_pb2|.*custom_types) [mypy-aea.*] ignore_missing_imports = True +[mypy-anthropic.*] +ignore_missing_imports = True + +[mypy-tiktoken.*] +ignore_missing_imports = True + [mypy-packages.open_aea.*] ignore_errors=True