code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] | Split a list into n (roughly) equal-sized chunks | split_list | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/model_video_general.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/model_video_general.py | Apache-2.0 |
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] | Split a list into n (roughly) equal-sized chunks | split_list | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/model_video_qa.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/model_video_qa.py | Apache-2.0 |
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] | Split a list into n (roughly) equal-sized chunks | split_list | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/model_vqa.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/model_vqa.py | Apache-2.0 |
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] | Split a list into n (roughly) equal-sized chunks | split_list | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/model_vqa_scienceqa.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/model_vqa_scienceqa.py | Apache-2.0 |
def annotate(prediction_set, caption_files, output_dir):
"""
Evaluates question and answer pairs using GPT-3
Returns a score for correctness.
"""
for file in caption_files:
key = file[:-5] # Strip file extension
qa_set = prediction_set[key]
question = qa_set['q']
answ... |
Evaluates question and answer pairs using GPT-3
Returns a score for correctness.
| annotate | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_1_correctness.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_1_correctness.py | Apache-2.0 |
def main():
"""
Main function to control the flow of the program.
"""
# Parse arguments.
args = parse_args()
file = args.pred_path
try:
pred_contents = json.load(file)
except:
pred_contents = read_jsonl(file)
# Dictionary to store the count of occurrences for each v... |
Main function to control the flow of the program.
| main | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_1_correctness.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_1_correctness.py | Apache-2.0 |
def annotate(prediction_set, caption_files, output_dir):
"""
Evaluates question and answer pairs using GPT-3 and
returns a score for detailed orientation.
"""
for file in caption_files:
key = file[:-5] # Strip file extension
qa_set = prediction_set[key]
question = qa_set['q']... |
Evaluates question and answer pairs using GPT-3 and
returns a score for detailed orientation.
| annotate | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_2_detailed_orientation.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_2_detailed_orientation.py | Apache-2.0 |
def main():
"""
Main function to control the flow of the program.
"""
# Parse arguments.
args = parse_args()
file = args.pred_path
try:
pred_contents = json.load(file)
except:
pred_contents = read_jsonl(file)
# Dictionary to store the count of occurrences for each v... |
Main function to control the flow of the program.
| main | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_2_detailed_orientation.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_2_detailed_orientation.py | Apache-2.0 |
def annotate(prediction_set, caption_files, output_dir):
"""
Evaluates question and answer pairs using GPT-3 and
returns a score for contextual understanding.
"""
for file in caption_files:
key = file[:-5] # Strip file extension
qa_set = prediction_set[key]
question = qa_set[... |
Evaluates question and answer pairs using GPT-3 and
returns a score for contextual understanding.
| annotate | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_3_context.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_3_context.py | Apache-2.0 |
def main():
"""
Main function to control the flow of the program.
"""
# Parse arguments.
args = parse_args()
file = args.pred_path
try:
pred_contents = json.load(file)
except:
pred_contents = read_jsonl(file)
# Dictionary to store the count of occurrences for each v... |
Main function to control the flow of the program.
| main | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_3_context.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_3_context.py | Apache-2.0 |
def annotate(prediction_set, caption_files, output_dir):
"""
Evaluates question and answer pairs using GPT-3 and
returns a score for temporal understanding.
"""
for file in caption_files:
key = file[:-5] # Strip file extension
qa_set = prediction_set[key]
question = qa_set['q... |
Evaluates question and answer pairs using GPT-3 and
returns a score for temporal understanding.
| annotate | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_4_temporal.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_4_temporal.py | Apache-2.0 |
def main():
"""
Main function to control the flow of the program.
"""
# Parse arguments.
args = parse_args()
file = args.pred_path
try:
pred_contents = json.load(file)
except:
pred_contents = read_jsonl(file)
# Dictionary to store the count of occurrences for each v... |
Main function to control the flow of the program.
| main | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_4_temporal.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_4_temporal.py | Apache-2.0 |
def annotate(prediction_set, caption_files, output_dir):
"""
Evaluates question and answer pairs using GPT-3 and
returns a score for consistency.
"""
for file in caption_files:
key = file[:-5] # Strip file extension
qa_set = prediction_set[key]
question1 = qa_set['q1']
... |
Evaluates question and answer pairs using GPT-3 and
returns a score for consistency.
| annotate | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_5_consistency.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_5_consistency.py | Apache-2.0 |
def main():
"""
Main function to control the flow of the program.
"""
# Parse arguments.
args = parse_args()
file = args.pred_path
try:
pred_contents = json.load(file)
except:
pred_contents = read_jsonl(file)
# Dictionary to store the count of occurrences for each v... |
Main function to control the flow of the program.
| main | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_benchmark_5_consistency.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_benchmark_5_consistency.py | Apache-2.0 |
def get_pred_idx(prediction, choices, options):
"""
Get the index (e.g. 2) from the prediction (e.g. 'C')
"""
if prediction in options[:len(choices)]:
return options.index(prediction)
else:
return random.choice(range(len(choices))) |
Get the index (e.g. 2) from the prediction (e.g. 'C')
| get_pred_idx | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_science_qa.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_science_qa.py | Apache-2.0 |
def annotate(prediction_set, caption_files, output_dir):
"""
Evaluates question and answer pairs using GPT-3
Returns a score for correctness.
"""
for file in caption_files:
key = file[:-5] # Strip file extension
qa_set = prediction_set[key]
question = qa_set['q']
answ... |
Evaluates question and answer pairs using GPT-3
Returns a score for correctness.
| annotate | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_video_qa.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_video_qa.py | Apache-2.0 |
def main():
"""
Main function to control the flow of the program.
"""
# Parse arguments.
args = parse_args()
file = args.pred_path
try:
pred_contents = json.load(file)
except:
pred_contents = read_jsonl(file)
# Dictionary to store the count of occurrences for each v... |
Main function to control the flow of the program.
| main | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/eval/evaluate/evaluate_video_qa.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/eval/evaluate/evaluate_video_qa.py | Apache-2.0 |
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
... | Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the rand... | trunc_normal_ | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/model/cluster.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/model/cluster.py | Apache-2.0 |
def drop_path(x, drop_prob: float = 0., training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with dif... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
| drop_path | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/model/cluster.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/model/cluster.py | Apache-2.0 |
def index_points(points, idx):
"""Sample features following the index.
Returns:
new_points:, indexed points data, [B, S, C]
Args:
points: input points data, [B, N, C]
idx: sample index data, [B, S]
"""
device = points.device
B = points.shape[0]
view_shape = list(idx.... | Sample features following the index.
Returns:
new_points:, indexed points data, [B, S, C]
Args:
points: input points data, [B, N, C]
idx: sample index data, [B, S]
| index_points | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/model/cluster.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/model/cluster.py | Apache-2.0 |
def cluster_dpc_knn(token_dict, cluster_num, k=5, token_mask=None):
"""Cluster tokens with DPC-KNN algorithm.
Return:
idx_cluster (Tensor[B, N]): cluster index of each token.
cluster_num (int): actual cluster number. The same with
input cluster number
Args:
token_dict (di... | Cluster tokens with DPC-KNN algorithm.
Return:
idx_cluster (Tensor[B, N]): cluster index of each token.
cluster_num (int): actual cluster number. The same with
input cluster number
Args:
token_dict (dict): dict for token information
cluster_num (int): cluster number
... | cluster_dpc_knn | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/model/cluster.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/model/cluster.py | Apache-2.0 |
def merge_tokens(token_dict, idx_cluster, cluster_num, token_weight=None):
"""Merge tokens in the same cluster to a single cluster.
Implemented by torch.index_add(). Flops: B*N*(C+2)
Return:
out_dict (dict): dict for output token information
Args:
token_dict (dict): dict for input token... | Merge tokens in the same cluster to a single cluster.
Implemented by torch.index_add(). Flops: B*N*(C+2)
Return:
out_dict (dict): dict for output token information
Args:
token_dict (dict): dict for input token information
idx_cluster (Tensor[B, N]): cluster index of each token.
... | merge_tokens | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/model/cluster.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/model/cluster.py | Apache-2.0 |
def setup_for_distributed(is_master):
"""
This function disables printing when not in master process
"""
import builtins as __builtin__
builtin_print = __builtin__.print
def print(*args, **kwargs):
force = kwargs.pop("force", False)
if is_master or force:
builtin_pr... |
This function disables printing when not in master process
| setup_for_distributed | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/model/multimodal_encoder/utils.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/model/multimodal_encoder/utils.py | Apache-2.0 |
def download_cached_file(url, check_hash=True, progress=False):
"""
Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again.
If distributed, only the main process downloads the file, and the other processes wait for the file to be downloaded.
"""
def ... |
Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again.
If distributed, only the main process downloads the file, and the other processes wait for the file to be downloaded.
| download_cached_file | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/model/multimodal_encoder/utils.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/model/multimodal_encoder/utils.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.... | Input shape: Batch x Time x Channel
attention_mask: [bsz, q_len]
| forward | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/train/llama_flash_attn_monkey_patch.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/train/llama_flash_attn_monkey_patch.py | Apache-2.0 |
def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
output_dir: str):
"""Collects the state dict and dump to disk."""
if getattr(trainer.args, "tune_mm_mlp_adapter", False):
# Only save Adapter
keys_to_match = ['mm_projector', "ctm", "block"]
... | Collects the state dict and dump to disk. | safe_save_model_for_hf_trainer | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/train/train.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/train/train.py | Apache-2.0 |
def smart_tokenizer_and_embedding_resize(
special_tokens_dict: Dict,
tokenizer: transformers.PreTrainedTokenizer,
model: transformers.PreTrainedModel,
):
"""Resize tokenizer and embedding.
Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
... | Resize tokenizer and embedding.
Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
| smart_tokenizer_and_embedding_resize | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/train/train.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/train/train.py | Apache-2.0 |
def _add_speaker_and_signal(header, source, get_conversation=True):
"""Add speaker and start/end signal on each round."""
BEGIN_SIGNAL = "### "
END_SIGNAL = "\n"
conversation = header
for sentence in source:
from_str = sentence["from"]
if from_str.lower() == "human":
from... | Add speaker and start/end signal on each round. | _add_speaker_and_signal | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/train/train.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/train/train.py | Apache-2.0 |
def preprocess(
sources: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
"""
Given a list of sources, each is a conversation list. This transform:
1. Add signal '### ' at the beginning each sentence, with end signal '\n';
2. Concaten... |
Given a list of sources, each is a conversation list. This transform:
1. Add signal '### ' at the beginning each sentence, with end signal '
';
2. Concatenate conversations together;
3. Tokenize the concatenated conversation;
4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
... | preprocess | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/train/train.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/train/train.py | Apache-2.0 |
def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
data_args) -> Dict:
"""Make dataset and collator for supervised fine-tuning."""
train_dataset = LazySupervisedDataset(tokenizer=tokenizer, data_args=data_args)
data_collator = DataCollatorForSupervis... | Make dataset and collator for supervised fine-tuning. | make_supervised_data_module | python | PKU-YuanGroup/Chat-UniVi | ChatUniVi/train/train.py | https://github.com/PKU-YuanGroup/Chat-UniVi/blob/master/ChatUniVi/train/train.py | Apache-2.0 |
def __init__(self, ipc_socket, callback=None, quit_callback=None):
"""Create the wrapper.
*ipc_socket* is the pipe name. (Not including \\\\.\\pipe\\)
*callback(json_data)* is the function for recieving events.
*quit_callback* is called when the socket connection dies.
"""
... | Create the wrapper.
*ipc_socket* is the pipe name. (Not including \\.\pipe\)
*callback(json_data)* is the function for recieving events.
*quit_callback* is called when the socket connection dies.
| __init__ | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def send(self, data):
"""Send *data* to the pipe, encoded as JSON."""
try:
self.socket.send_bytes(json.dumps(data).encode('utf-8') + b'\n')
except OSError as ex:
if len(ex.args) == 1 and ex.args[0] == "handle is closed":
raise BrokenPipeError("handle is cl... | Send *data* to the pipe, encoded as JSON. | send | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def run(self):
"""Process pipe events. Do not run this directly. Use *start*."""
data = b''
try:
while True:
current_data = self.socket.recv_bytes(2048)
if current_data == b'':
break
data += current_data
... | Process pipe events. Do not run this directly. Use *start*. | run | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def __init__(self, ipc_socket, callback=None, quit_callback=None):
"""Create the wrapper.
*ipc_socket* is the path to the socket.
*callback(json_data)* is the function for recieving events.
*quit_callback* is called when the socket connection dies.
"""
self.ipc_socket = ... | Create the wrapper.
*ipc_socket* is the path to the socket.
*callback(json_data)* is the function for recieving events.
*quit_callback* is called when the socket connection dies.
| __init__ | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def send(self, data):
"""Send *data* to the socket, encoded as JSON."""
if self.socket is None:
raise BrokenPipeError("socket is closed")
self.socket.send(json.dumps(data).encode('utf-8') + b'\n') | Send *data* to the socket, encoded as JSON. | send | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def run(self):
"""Process socket events. Do not run this directly. Use *start*."""
data = b''
try:
while True:
current_data = self.socket.recv(1024)
if current_data == b'':
break
data += current_data
... | Process socket events. Do not run this directly. Use *start*. | run | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def __init__(self, ipc_socket, mpv_location=None, **kwargs):
"""
Create and start the MPV process. Will block until socket/pipe is available.
*ipc_socket* is the path to the Unix/Linux socket or name of the Windows pipe.
*mpv_location* is the path to mpv. If left unset it tries the one ... |
Create and start the MPV process. Will block until socket/pipe is available.
*ipc_socket* is the path to the Unix/Linux socket or name of the Windows pipe.
*mpv_location* is the path to mpv. If left unset it tries the one in the PATH.
All other arguments are forwarded to MPV as comman... | __init__ | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def __init__(self, ipc_socket, callback=None, quit_callback=None):
"""Create the wrapper.
*ipc_socket* is the path to the Unix/Linux socket or name of the Windows pipe.
*callback(event_name, data)* is the function for recieving events.
*quit_callback* is called when the socket connectio... | Create the wrapper.
*ipc_socket* is the path to the Unix/Linux socket or name of the Windows pipe.
*callback(event_name, data)* is the function for recieving events.
*quit_callback* is called when the socket connection to MPV dies.
| __init__ | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def event_callback(self, data):
"""Internal callback for recieving events from MPV."""
if "request_id" in data:
self.cid_result[data["request_id"]] = data
self.cid_wait[data["request_id"]].set()
elif "event" in data:
self.callback(data["event"], data) | Internal callback for recieving events from MPV. | event_callback | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def command(self, command, *args):
"""
Issue a command to MPV. Will block until completed or timeout is reached.
*command* is the name of the MPV command
All further arguments are forwarded to the MPV command.
Throws TimeoutError if timeout of 120 seconds is reached.
... |
Issue a command to MPV. Will block until completed or timeout is reached.
*command* is the name of the MPV command
All further arguments are forwarded to the MPV command.
Throws TimeoutError if timeout of 120 seconds is reached.
| command | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def run(self):
"""Process socket events. Do not run this directly. Use *start*."""
while True:
event = self.queue.get()
if event == "quit":
break
try:
event[0](*event[1])
except Exception:
log.error("EventHan... | Process socket events. Do not run this directly. Use *start*. | run | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def __init__(self, start_mpv=True, ipc_socket=None, mpv_location=None,
log_handler=None, loglevel=None, quit_callback=None, **kwargs):
"""
Create the interface to MPV and process instance.
*start_mpv* will start an MPV process if true. (Default: True)
*ipc_socket* is th... |
Create the interface to MPV and process instance.
*start_mpv* will start an MPV process if true. (Default: True)
*ipc_socket* is the path to the Unix/Linux socket or name of Windows pipe. (Default: Random Temp File)
*mpv_location* is the location of MPV for *start_mpv*. (Default: Use M... | __init__ | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def bind_event(self, name, callback):
"""
Bind a callback to an MPV event.
*name* is the MPV event name.
*callback(event_data)* is the function to call.
"""
if name not in self.event_bindings:
self.event_bindings[name] = set()
self.event_bindings[name... |
Bind a callback to an MPV event.
*name* is the MPV event name.
*callback(event_data)* is the function to call.
| bind_event | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def on_event(self, name):
"""
Decorator to bind a callback to an MPV event.
@on_event(name)
def my_callback(event_data):
pass
"""
def wrapper(func):
self.bind_event(name, func)
return func
return wrapper |
Decorator to bind a callback to an MPV event.
@on_event(name)
def my_callback(event_data):
pass
| on_event | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def on_key_press(self, name):
"""
Decorator to bind a callback to an MPV keypress event.
@on_key_press(key_name)
def my_callback():
pass
"""
def wrapper(func):
self.bind_key_press(name, func)
return func
return wrapper |
Decorator to bind a callback to an MPV keypress event.
@on_key_press(key_name)
def my_callback():
pass
| on_key_press | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def bind_key_press(self, name, callback):
"""
Bind a callback to an MPV keypress event.
*name* is the key symbol.
*callback()* is the function to call.
"""
self.keybind_lock.acquire()
keybind_id = self.keybind_id
self.keybind_id += 1
self.keybind_... |
Bind a callback to an MPV keypress event.
*name* is the key symbol.
*callback()* is the function to call.
| bind_key_press | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def bind_property_observer(self, name, callback):
"""
Bind a callback to an MPV property change.
*name* is the property name.
*callback(name, data)* is the function to call.
Returns a unique observer ID needed to destroy the observer.
"""
self.observer_lock.acqu... |
Bind a callback to an MPV property change.
*name* is the property name.
*callback(name, data)* is the function to call.
Returns a unique observer ID needed to destroy the observer.
| bind_property_observer | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def property_observer(self, name):
"""
Decorator to bind a callback to an MPV property change.
@property_observer(property_name)
def my_callback(name, data):
pass
"""
def wrapper(func):
self.bind_property_observer(name, func)
return fu... |
Decorator to bind a callback to an MPV property change.
@property_observer(property_name)
def my_callback(name, data):
pass
| property_observer | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def wait_for_property(self, name):
"""
Waits for the value of a property to change.
*name* is the name of the property.
"""
event = threading.Event()
first_event = True
def handler(*_):
nonlocal first_event
if first_event == True:
... |
Waits for the value of a property to change.
*name* is the name of the property.
| wait_for_property | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def terminate(self, join=True):
"""Terminate the connection to MPV and process (if *start_mpv* is used)."""
if self.mpv_process:
self.mpv_process.stop()
if self.mpv_inter:
self.mpv_inter.stop(join)
self.event_handler.stop(join) | Terminate the connection to MPV and process (if *start_mpv* is used). | terminate | python | kjtsune/embyToLocalPlayer | utils/python_mpv_jsonipc.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/python_mpv_jsonipc.py | Apache-2.0 |
def get_series_single_season(self, ser_id, season_num, translations='', info_only=False):
"""
ser_id: Trakt ID, Trakt slug, or IMDB ID
translations: specific 2 digit country language code
return: [{.., ids:ep_ids}, ..] not standard ids_item, not type field
"""
trans = f'?... |
ser_id: Trakt ID, Trakt slug, or IMDB ID
translations: specific 2 digit country language code
return: [{.., ids:ep_ids}, ..] not standard ids_item, not type field
| get_series_single_season | python | kjtsune/embyToLocalPlayer | utils/trakt_api.py | https://github.com/kjtsune/embyToLocalPlayer/blob/master/utils/trakt_api.py | Apache-2.0 |
async def math_guardrail(
context: RunContextWrapper[None], agent: Agent, input: str | list[TResponseInputItem]
) -> GuardrailFunctionOutput:
"""This is an input guardrail function, which happens to call an agent to check if the input
is a math homework question.
"""
result = await Runner.run(guardr... | This is an input guardrail function, which happens to call an agent to check if the input
is a math homework question.
| math_guardrail | python | openai/openai-agents-python | examples/agent_patterns/input_guardrails.py | https://github.com/openai/openai-agents-python/blob/master/examples/agent_patterns/input_guardrails.py | MIT |
async def update_seat(
context: RunContextWrapper[AirlineAgentContext], confirmation_number: str, new_seat: str
) -> str:
"""
Update the seat for a given confirmation number.
Args:
confirmation_number: The confirmation number for the flight.
new_seat: The new seat to update to.
"""
... |
Update the seat for a given confirmation number.
Args:
confirmation_number: The confirmation number for the flight.
new_seat: The new seat to update to.
| update_seat | python | openai/openai-agents-python | examples/customer_service/main.py | https://github.com/openai/openai-agents-python/blob/master/examples/customer_service/main.py | MIT |
def get_weather(city: str) -> str:
"""Get the weather for a given city."""
print(f"[debug] get_weather called with city: {city}")
choices = ["sunny", "cloudy", "rainy", "snowy"]
return f"The weather in {city} is {random.choice(choices)}." | Get the weather for a given city. | get_weather | python | openai/openai-agents-python | examples/voice/static/main.py | https://github.com/openai/openai-agents-python/blob/master/examples/voice/static/main.py | MIT |
def compose(self) -> ComposeResult:
"""Create child widgets for the app."""
with Container():
yield Header(id="session-display")
yield AudioStatusIndicator(id="status-indicator")
yield RichLog(id="bottom-pane", wrap=True, highlight=True, markup=True) | Create child widgets for the app. | compose | python | openai/openai-agents-python | examples/voice/streamed/main.py | https://github.com/openai/openai-agents-python/blob/master/examples/voice/streamed/main.py | MIT |
def get_weather(city: str) -> str:
"""Get the weather for a given city."""
print(f"[debug] get_weather called with city: {city}")
choices = ["sunny", "cloudy", "rainy", "snowy"]
return f"The weather in {city} is {random.choice(choices)}." | Get the weather for a given city. | get_weather | python | openai/openai-agents-python | examples/voice/streamed/my_workflow.py | https://github.com/openai/openai-agents-python/blob/master/examples/voice/streamed/my_workflow.py | MIT |
def __init__(self, secret_word: str, on_start: Callable[[str], None]):
"""
Args:
secret_word: The secret word to guess.
on_start: A callback that is called when the workflow starts. The transcription
is passed in as an argument.
"""
self._input_his... |
Args:
secret_word: The secret word to guess.
on_start: A callback that is called when the workflow starts. The transcription
is passed in as an argument.
| __init__ | python | openai/openai-agents-python | examples/voice/streamed/my_workflow.py | https://github.com/openai/openai-agents-python/blob/master/examples/voice/streamed/my_workflow.py | MIT |
def as_tool(
self,
tool_name: str | None,
tool_description: str | None,
custom_output_extractor: Callable[[RunResult], Awaitable[str]] | None = None,
) -> Tool:
"""Transform this agent into a tool, callable by other agents.
This is different from handoffs in two ways... | Transform this agent into a tool, callable by other agents.
This is different from handoffs in two ways:
1. In handoffs, the new agent receives the conversation history. In this tool, the new agent
receives generated input.
2. In handoffs, the new agent takes over the conversation. I... | as_tool | python | openai/openai-agents-python | src/agents/agent.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/agent.py | MIT |
async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> str | None:
"""Get the system prompt for the agent."""
if isinstance(self.instructions, str):
return self.instructions
elif callable(self.instructions):
if inspect.iscoroutinefunction(self.inst... | Get the system prompt for the agent. | get_system_prompt | python | openai/openai-agents-python | src/agents/agent.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/agent.py | MIT |
async def get_mcp_tools(self) -> list[Tool]:
"""Fetches the available tools from the MCP servers."""
convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False)
return await MCPUtil.get_all_function_tools(self.mcp_servers, convert_schemas_to_strict) | Fetches the available tools from the MCP servers. | get_mcp_tools | python | openai/openai-agents-python | src/agents/agent.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/agent.py | MIT |
async def get_all_tools(self, run_context: RunContextWrapper[Any]) -> list[Tool]:
"""All agent tools, including MCP tools and function tools."""
mcp_tools = await self.get_mcp_tools()
async def _check_tool_enabled(tool: Tool) -> bool:
if not isinstance(tool, FunctionTool):
... | All agent tools, including MCP tools and function tools. | get_all_tools | python | openai/openai-agents-python | src/agents/agent.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/agent.py | MIT |
def __init__(self, output_type: type[Any], strict_json_schema: bool = True):
"""
Args:
output_type: The type of the output.
strict_json_schema: Whether the JSON schema is in strict mode. We **strongly** recommend
setting this to True, as it increases the likelihoo... |
Args:
output_type: The type of the output.
strict_json_schema: Whether the JSON schema is in strict mode. We **strongly** recommend
setting this to True, as it increases the likelihood of correct JSON input.
| __init__ | python | openai/openai-agents-python | src/agents/agent_output.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/agent_output.py | MIT |
def json_schema(self) -> dict[str, Any]:
"""The JSON schema of the output type."""
if self.is_plain_text():
raise UserError("Output type is plain text, so no JSON schema is available")
return self._output_schema | The JSON schema of the output type. | json_schema | python | openai/openai-agents-python | src/agents/agent_output.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/agent_output.py | MIT |
def validate_json(self, json_str: str) -> Any:
"""Validate a JSON string against the output type. Returns the validated object, or raises
a `ModelBehaviorError` if the JSON is invalid.
"""
validated = _json.validate_json(json_str, self._type_adapter, partial=False)
if self._is_wr... | Validate a JSON string against the output type. Returns the validated object, or raises
a `ModelBehaviorError` if the JSON is invalid.
| validate_json | python | openai/openai-agents-python | src/agents/agent_output.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/agent_output.py | MIT |
def to_call_args(self, data: BaseModel) -> tuple[list[Any], dict[str, Any]]:
"""
Converts validated data from the Pydantic model into (args, kwargs), suitable for calling
the original function.
"""
positional_args: list[Any] = []
keyword_args: dict[str, Any] = {}
... |
Converts validated data from the Pydantic model into (args, kwargs), suitable for calling
the original function.
| to_call_args | python | openai/openai-agents-python | src/agents/function_schema.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/function_schema.py | MIT |
def generate_func_documentation(
func: Callable[..., Any], style: DocstringStyle | None = None
) -> FuncDocumentation:
"""
Extracts metadata from a function docstring, in preparation for sending it to an LLM as a tool.
Args:
func: The function to extract documentation from.
style: The s... |
Extracts metadata from a function docstring, in preparation for sending it to an LLM as a tool.
Args:
func: The function to extract documentation from.
style: The style of the docstring to use for parsing. If not provided, we will attempt to
auto-detect the style.
Returns:
... | generate_func_documentation | python | openai/openai-agents-python | src/agents/function_schema.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/function_schema.py | MIT |
def function_schema(
func: Callable[..., Any],
docstring_style: DocstringStyle | None = None,
name_override: str | None = None,
description_override: str | None = None,
use_docstring_info: bool = True,
strict_json_schema: bool = True,
) -> FuncSchema:
"""
Given a python function, extract... |
Given a python function, extracts a `FuncSchema` from it, capturing the name, description,
parameter descriptions, and other metadata.
Args:
func: The function to extract the schema from.
docstring_style: The style of the docstring to use for parsing. If not provided, we will
a... | function_schema | python | openai/openai-agents-python | src/agents/function_schema.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/function_schema.py | MIT |
def input_guardrail(
func: _InputGuardrailFuncSync[TContext_co]
| _InputGuardrailFuncAsync[TContext_co]
| None = None,
*,
name: str | None = None,
) -> (
InputGuardrail[TContext_co]
| Callable[
[_InputGuardrailFuncSync[TContext_co] | _InputGuardrailFuncAsync[TContext_co]],
In... |
Decorator that transforms a sync or async function into an `InputGuardrail`.
It can be used directly (no parentheses) or with keyword args, e.g.:
@input_guardrail
def my_sync_guardrail(...): ...
@input_guardrail(name="guardrail_name")
async def my_async_guardrail(...): ...
... | input_guardrail | python | openai/openai-agents-python | src/agents/guardrail.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/guardrail.py | MIT |
def output_guardrail(
func: _OutputGuardrailFuncSync[TContext_co]
| _OutputGuardrailFuncAsync[TContext_co]
| None = None,
*,
name: str | None = None,
) -> (
OutputGuardrail[TContext_co]
| Callable[
[_OutputGuardrailFuncSync[TContext_co] | _OutputGuardrailFuncAsync[TContext_co]],
... |
Decorator that transforms a sync or async function into an `OutputGuardrail`.
It can be used directly (no parentheses) or with keyword args, e.g.:
@output_guardrail
def my_sync_guardrail(...): ...
@output_guardrail(name="guardrail_name")
async def my_async_guardrail(...): ...
... | output_guardrail | python | openai/openai-agents-python | src/agents/guardrail.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/guardrail.py | MIT |
def handoff(
agent: Agent[TContext],
tool_name_override: str | None = None,
tool_description_override: str | None = None,
on_handoff: OnHandoffWithInput[THandoffInput] | OnHandoffWithoutInput | None = None,
input_type: type[THandoffInput] | None = None,
input_filter: Callable[[HandoffInputData],... | Create a handoff from an agent.
Args:
agent: The agent to handoff to, or a function that returns an agent.
tool_name_override: Optional override for the name of the tool that represents the handoff.
tool_description_override: Optional override for the description of the tool that
... | handoff | python | openai/openai-agents-python | src/agents/handoffs.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/handoffs.py | MIT |
def to_input_item(self) -> TResponseInputItem:
"""Converts this item into an input item suitable for passing to the model."""
if isinstance(self.raw_item, dict):
# We know that input items are dicts, so we can ignore the type error
return self.raw_item # type: ignore
eli... | Converts this item into an input item suitable for passing to the model. | to_input_item | python | openai/openai-agents-python | src/agents/items.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/items.py | MIT |
def to_input_items(self) -> list[TResponseInputItem]:
"""Convert the output into a list of input items suitable for passing to the model."""
# We happen to know that the shape of the Pydantic output items are the same as the
# equivalent TypedDict input items, so we can just convert each one.
... | Convert the output into a list of input items suitable for passing to the model. | to_input_items | python | openai/openai-agents-python | src/agents/items.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/items.py | MIT |
def extract_last_content(cls, message: TResponseOutputItem) -> str:
"""Extracts the last text content or refusal from a message."""
if not isinstance(message, ResponseOutputMessage):
return ""
last_content = message.content[-1]
if isinstance(last_content, ResponseOutputText)... | Extracts the last text content or refusal from a message. | extract_last_content | python | openai/openai-agents-python | src/agents/items.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/items.py | MIT |
def extract_last_text(cls, message: TResponseOutputItem) -> str | None:
"""Extracts the last text content from a message, if any. Ignores refusals."""
if isinstance(message, ResponseOutputMessage):
last_content = message.content[-1]
if isinstance(last_content, ResponseOutputText)... | Extracts the last text content from a message, if any. Ignores refusals. | extract_last_text | python | openai/openai-agents-python | src/agents/items.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/items.py | MIT |
def input_to_new_input_list(
cls, input: str | list[TResponseInputItem]
) -> list[TResponseInputItem]:
"""Converts a string or list of input items into a list of input items."""
if isinstance(input, str):
return [
{
"content": input,
... | Converts a string or list of input items into a list of input items. | input_to_new_input_list | python | openai/openai-agents-python | src/agents/items.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/items.py | MIT |
def text_message_outputs(cls, items: list[RunItem]) -> str:
"""Concatenates all the text content from a list of message output items."""
text = ""
for item in items:
if isinstance(item, MessageOutputItem):
text += cls.text_message_output(item)
return text | Concatenates all the text content from a list of message output items. | text_message_outputs | python | openai/openai-agents-python | src/agents/items.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/items.py | MIT |
def text_message_output(cls, message: MessageOutputItem) -> str:
"""Extracts all the text content from a single message output item."""
text = ""
for item in message.raw_item.content:
if isinstance(item, ResponseOutputText):
text += item.text
return text | Extracts all the text content from a single message output item. | text_message_output | python | openai/openai-agents-python | src/agents/items.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/items.py | MIT |
def tool_call_output_item(
cls, tool_call: ResponseFunctionToolCall, output: str
) -> FunctionCallOutput:
"""Creates a tool call output item from a tool call and its output."""
return {
"call_id": tool_call.call_id,
"output": output,
"type": "function_call... | Creates a tool call output item from a tool call and its output. | tool_call_output_item | python | openai/openai-agents-python | src/agents/items.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/items.py | MIT |
async def on_agent_start(
self, context: RunContextWrapper[TContext], agent: Agent[TContext]
) -> None:
"""Called before the agent is invoked. Called each time the current agent changes."""
pass | Called before the agent is invoked. Called each time the current agent changes. | on_agent_start | python | openai/openai-agents-python | src/agents/lifecycle.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/lifecycle.py | MIT |
async def on_agent_end(
self,
context: RunContextWrapper[TContext],
agent: Agent[TContext],
output: Any,
) -> None:
"""Called when the agent produces a final output."""
pass | Called when the agent produces a final output. | on_agent_end | python | openai/openai-agents-python | src/agents/lifecycle.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/lifecycle.py | MIT |
async def on_start(self, context: RunContextWrapper[TContext], agent: Agent[TContext]) -> None:
"""Called before the agent is invoked. Called each time the running agent is changed to this
agent."""
pass | Called before the agent is invoked. Called each time the running agent is changed to this
agent. | on_start | python | openai/openai-agents-python | src/agents/lifecycle.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/lifecycle.py | MIT |
async def on_end(
self,
context: RunContextWrapper[TContext],
agent: Agent[TContext],
output: Any,
) -> None:
"""Called when the agent produces a final output."""
pass | Called when the agent produces a final output. | on_end | python | openai/openai-agents-python | src/agents/lifecycle.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/lifecycle.py | MIT |
async def on_handoff(
self,
context: RunContextWrapper[TContext],
agent: Agent[TContext],
source: Agent[TContext],
) -> None:
"""Called when the agent is being handed off to. The `source` is the agent that is handing
off to this agent."""
pass | Called when the agent is being handed off to. The `source` is the agent that is handing
off to this agent. | on_handoff | python | openai/openai-agents-python | src/agents/lifecycle.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/lifecycle.py | MIT |
def resolve(self, override: ModelSettings | None) -> ModelSettings:
"""Produce a new ModelSettings by overlaying any non-None values from the
override on top of this instance."""
if override is None:
return self
changes = {
field.name: getattr(override, field.nam... | Produce a new ModelSettings by overlaying any non-None values from the
override on top of this instance. | resolve | python | openai/openai-agents-python | src/agents/model_settings.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/model_settings.py | MIT |
async def run_demo_loop(agent: Agent[Any], *, stream: bool = True) -> None:
"""Run a simple REPL loop with the given agent.
This utility allows quick manual testing and debugging of an agent from the
command line. Conversation state is preserved across turns. Enter ``exit``
or ``quit`` to stop the loop... | Run a simple REPL loop with the given agent.
This utility allows quick manual testing and debugging of an agent from the
command line. Conversation state is preserved across turns. Enter ``exit``
or ``quit`` to stop the loop.
Args:
agent: The starting agent to run.
stream: Whether to s... | run_demo_loop | python | openai/openai-agents-python | src/agents/repl.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/repl.py | MIT |
def final_output_as(self, cls: type[T], raise_if_incorrect_type: bool = False) -> T:
"""A convenience method to cast the final output to a specific type. By default, the cast
is only for the typechecker. If you set `raise_if_incorrect_type` to True, we'll raise a
TypeError if the final output is... | A convenience method to cast the final output to a specific type. By default, the cast
is only for the typechecker. If you set `raise_if_incorrect_type` to True, we'll raise a
TypeError if the final output is not of the given type.
Args:
cls: The type to cast the final output to.
... | final_output_as | python | openai/openai-agents-python | src/agents/result.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/result.py | MIT |
def to_input_list(self) -> list[TResponseInputItem]:
"""Creates a new input list, merging the original input with all the new items generated."""
original_items: list[TResponseInputItem] = ItemHelpers.input_to_new_input_list(self.input)
new_items = [item.to_input_item() for item in self.new_item... | Creates a new input list, merging the original input with all the new items generated. | to_input_list | python | openai/openai-agents-python | src/agents/result.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/result.py | MIT |
def last_response_id(self) -> str | None:
"""Convenience method to get the response ID of the last model response."""
if not self.raw_responses:
return None
return self.raw_responses[-1].response_id | Convenience method to get the response ID of the last model response. | last_response_id | python | openai/openai-agents-python | src/agents/result.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/result.py | MIT |
def cancel(self) -> None:
"""Cancels the streaming run, stopping all background tasks and marking the run as
complete."""
self._cleanup_tasks() # Cancel all running tasks
self.is_complete = True # Mark the run as complete to stop event streaming
# Optionally, clear the event q... | Cancels the streaming run, stopping all background tasks and marking the run as
complete. | cancel | python | openai/openai-agents-python | src/agents/result.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/result.py | MIT |
def _create_error_details(self) -> RunErrorDetails:
"""Return a `RunErrorDetails` object considering the current attributes of the class."""
return RunErrorDetails(
input=self.input,
new_items=self.new_items,
raw_responses=self.raw_responses,
last_agent=se... | Return a `RunErrorDetails` object considering the current attributes of the class. | _create_error_details | python | openai/openai-agents-python | src/agents/result.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/result.py | MIT |
async def run(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
*,
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
previo... | Run a workflow starting at the given agent. The agent will run in a loop until a final
output is generated. The loop runs like so:
1. The agent is invoked with the given input.
2. If there is a final output (i.e. the agent produces something of type
`agent.output_type`, the loop term... | run | python | openai/openai-agents-python | src/agents/run.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/run.py | MIT |
def run_sync(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
*,
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
previou... | Run a workflow synchronously, starting at the given agent. Note that this just wraps the
`run` method, so it will not work if there's already an event loop (e.g. inside an async
function, or in a Jupyter notebook or async context like FastAPI). For those cases, use
the `run` method instead.
... | run_sync | python | openai/openai-agents-python | src/agents/run.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/run.py | MIT |
def run_streamed(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem],
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
previous_respo... | Run a workflow starting at the given agent in streaming mode. The returned result object
contains a method you can use to stream semantic events as they are generated.
The agent will run in a loop until a final output is generated. The loop runs like so:
1. The agent is invoked with the given i... | run_streamed | python | openai/openai-agents-python | src/agents/run.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/run.py | MIT |
def ensure_strict_json_schema(
schema: dict[str, Any],
) -> dict[str, Any]:
"""Mutates the given JSON schema to ensure it conforms to the `strict` standard
that the OpenAI API expects.
"""
if schema == {}:
return _EMPTY_SCHEMA
return _ensure_strict_json_schema(schema, path=(), root=schem... | Mutates the given JSON schema to ensure it conforms to the `strict` standard
that the OpenAI API expects.
| ensure_strict_json_schema | python | openai/openai-agents-python | src/agents/strict_schema.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/strict_schema.py | MIT |
def function_tool(
func: ToolFunction[...],
*,
name_override: str | None = None,
description_override: str | None = None,
docstring_style: DocstringStyle | None = None,
use_docstring_info: bool = True,
failure_error_function: ToolErrorFunction | None = None,
strict_mode: bool = True,
... | Overload for usage as @function_tool (no parentheses). | function_tool | python | openai/openai-agents-python | src/agents/tool.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/tool.py | MIT |
def function_tool(
func: ToolFunction[...] | None = None,
*,
name_override: str | None = None,
description_override: str | None = None,
docstring_style: DocstringStyle | None = None,
use_docstring_info: bool = True,
failure_error_function: ToolErrorFunction | None = default_tool_error_functi... |
Decorator to create a FunctionTool from a function. By default, we will:
1. Parse the function signature to create a JSON schema for the tool's parameters.
2. Use the function's docstring to populate the tool's description.
3. Use the function's docstring to populate argument descriptions.
The docs... | function_tool | python | openai/openai-agents-python | src/agents/tool.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/tool.py | MIT |
def maybe_reset_tool_choice(
cls, agent: Agent[Any], tool_use_tracker: AgentToolUseTracker, model_settings: ModelSettings
) -> ModelSettings:
"""Resets tool choice to None if the agent has used tools and the agent's reset_tool_choice
flag is True."""
if agent.reset_tool_choice is Tr... | Resets tool choice to None if the agent has used tools and the agent's reset_tool_choice
flag is True. | maybe_reset_tool_choice | python | openai/openai-agents-python | src/agents/_run_impl.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/_run_impl.py | MIT |
def remove_all_tools(handoff_input_data: HandoffInputData) -> HandoffInputData:
"""Filters out all tool items: file search, web search and function calls+output."""
history = handoff_input_data.input_history
new_items = handoff_input_data.new_items
filtered_history = (
_remove_tool_types_from_... | Filters out all tool items: file search, web search and function calls+output. | remove_all_tools | python | openai/openai-agents-python | src/agents/extensions/handoff_filters.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/extensions/handoff_filters.py | MIT |
def get_main_graph(agent: Agent) -> str:
"""
Generates the main graph structure in DOT format for the given agent.
Args:
agent (Agent): The agent for which the graph is to be generated.
Returns:
str: The DOT format string representing the graph.
"""
parts = [
"""
di... |
Generates the main graph structure in DOT format for the given agent.
Args:
agent (Agent): The agent for which the graph is to be generated.
Returns:
str: The DOT format string representing the graph.
| get_main_graph | python | openai/openai-agents-python | src/agents/extensions/visualization.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/extensions/visualization.py | MIT |
def get_all_nodes(
agent: Agent, parent: Agent | None = None, visited: set[str] | None = None
) -> str:
"""
Recursively generates the nodes for the given agent and its handoffs in DOT format.
Args:
agent (Agent): The agent for which the nodes are to be generated.
Returns:
str: The ... |
Recursively generates the nodes for the given agent and its handoffs in DOT format.
Args:
agent (Agent): The agent for which the nodes are to be generated.
Returns:
str: The DOT format string representing the nodes.
| get_all_nodes | python | openai/openai-agents-python | src/agents/extensions/visualization.py | https://github.com/openai/openai-agents-python/blob/master/src/agents/extensions/visualization.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.