From 6ca958e6d307154f5d1fc99af1e48124a4ce17a1 Mon Sep 17 00:00:00 2001 From: Kerem Turgutlu Date: Fri, 19 Jun 2026 20:03:00 +0300 Subject: [PATCH] approx usage for interrupted --- cachy.jsonl | 1 + fastllm/_modidx.py | 10 +- fastllm/acomplete.py | 44 +- fastllm/anthropic.py | 8 +- fastllm/chat.py | 35 +- fastllm/gemini.py | 7 +- fastllm/openai_chat.py | 9 +- fastllm/openai_responses.py | 9 +- fastllm/types.py | 17 +- nbs/00_types.ipynb | 47 +- nbs/02_oai_responses.ipynb | 6 + nbs/03_oai_chat.ipynb | 6 + nbs/04_anthropic.ipynb | 6 +- nbs/05_gemini.ipynb | 5 + nbs/06_acomplete.ipynb | 181 ++++++- nbs/07_chat.ipynb | 986 +++++++++++++++++++++++------------- 16 files changed, 961 insertions(+), 416 deletions(-) diff --git a/cachy.jsonl b/cachy.jsonl index 25467ac..63385e8 100644 --- a/cachy.jsonl +++ b/cachy.jsonl @@ -371,3 +371,4 @@ {"key": "356444ea", "response": "event: response.created\ndata: {\"type\":\"response.created\",\"response\":{\"id\":\"resp_0caaa4c670396b3a006a2fb586dd4081998903f6718f88dd82\",\"object\":\"response\",\"created_at\":1781511558,\"status\":\"in_progress\",\"background\":false,\"completed_at\":null,\"error\":null,\"frequency_penalty\":0.0,\"incomplete_details\":null,\"instructions\":\"You can invoke lightweight fenced tools by emitting a 5-backtick markdown fence tagged `py` or `bash`.\\n\\nWhen you want code executed, output exactly one complete executable fence and then stop, always include leading and trailing new lines:\\n\\n**Code fence** (assistant emits):\\n\\n\\n\\n`````py\\n\\n`````\\n\\n\\nor\\n\\n\\n`````bash\\n\\n`````\\n\\n\\n\\nUser will respond with the code fence result:\\n\\n**Result fence** (user responds):\\n\\n\\n\\n`````result\\n\\n`````\\n\\n\\n\\n5 backticks throughout so regular markdown ``` ``` blocks the model emits inside its code/output remain unambiguous. 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'fastllm.acomplete.mk_client': ('acomplete.html#mk_client', 'fastllm/acomplete.py'), + 'fastllm.acomplete.resolve_api_vendor': ('acomplete.html#resolve_api_vendor', 'fastllm/acomplete.py')}, 'fastllm.anthropic': { 'fastllm.anthropic._ant_cc': ('anthropic.html#_ant_cc', 'fastllm/anthropic.py'), 'fastllm.anthropic._ant_part_type': ('anthropic.html#_ant_part_type', 'fastllm/anthropic.py'), 'fastllm.anthropic._sanid': ('anthropic.html#_sanid', 'fastllm/anthropic.py'), 'fastllm.anthropic.acollect_stream': ('anthropic.html#acollect_stream', 'fastllm/anthropic.py'), + 'fastllm.anthropic.approx_raw_usage': ('anthropic.html#approx_raw_usage', 'fastllm/anthropic.py'), 'fastllm.anthropic.cost': ('anthropic.html#cost', 'fastllm/anthropic.py'), 'fastllm.anthropic.delta_index_fn': ('anthropic.html#delta_index_fn', 'fastllm/anthropic.py'), 'fastllm.anthropic.denorm_assistant': ('anthropic.html#denorm_assistant', 'fastllm/anthropic.py'), @@ -112,6 +114,7 @@ 'fastllm.chat._apply_cache_idxs': ('chat.html#_apply_cache_idxs', 'fastllm/chat.py'), 'fastllm.chat._bytes2content': ('chat.html#_bytes2content', 'fastllm/chat.py'), 'fastllm.chat._call_func': ('chat.html#_call_func', 'fastllm/chat.py'), + 'fastllm.chat._chunk_text': ('chat.html#_chunk_text', 'fastllm/chat.py'), 'fastllm.chat._extract_tool_parts': ('chat.html#_extract_tool_parts', 'fastllm/chat.py'), 'fastllm.chat._handle_stop_reason': ('chat.html#_handle_stop_reason', 'fastllm/chat.py'), 'fastllm.chat._has_cache': ('chat.html#_has_cache', 'fastllm/chat.py'), @@ -156,6 +159,7 @@ 'fastllm.gemini': { 'fastllm.gemini._gem_filter_sch': ('gemini.html#_gem_filter_sch', 'fastllm/gemini.py'), 'fastllm.gemini._gem_part_type': ('gemini.html#_gem_part_type', 'fastllm/gemini.py'), 'fastllm.gemini.acollect_stream': ('gemini.html#acollect_stream', 'fastllm/gemini.py'), + 'fastllm.gemini.approx_raw_usage': ('gemini.html#approx_raw_usage', 'fastllm/gemini.py'), 'fastllm.gemini.cost': ('gemini.html#cost', 'fastllm/gemini.py'), 'fastllm.gemini.delta_index_fn': ('gemini.html#delta_index_fn', 'fastllm/gemini.py'), 'fastllm.gemini.denorm_assistant': ('gemini.html#denorm_assistant', 'fastllm/gemini.py'), @@ -181,6 +185,7 @@ 'fastllm.gemini.norm_tool_calls': ('gemini.html#norm_tool_calls', 'fastllm/gemini.py'), 'fastllm.gemini.norm_usage': ('gemini.html#norm_usage', 'fastllm/gemini.py')}, 'fastllm.openai_chat': { 'fastllm.openai_chat.acollect_stream': ('oai_chat.html#acollect_stream', 'fastllm/openai_chat.py'), + 'fastllm.openai_chat.approx_raw_usage': ('oai_chat.html#approx_raw_usage', 'fastllm/openai_chat.py'), 'fastllm.openai_chat.cost': ('oai_chat.html#cost', 'fastllm/openai_chat.py'), 'fastllm.openai_chat.delta_index_fn': ('oai_chat.html#delta_index_fn', 'fastllm/openai_chat.py'), 'fastllm.openai_chat.denorm_assistant': ('oai_chat.html#denorm_assistant', 'fastllm/openai_chat.py'), @@ -208,6 +213,8 @@ 'fastllm.openai_responses': { 'fastllm.openai_responses._sanid': ('oai_responses.html#_sanid', 'fastllm/openai_responses.py'), 'fastllm.openai_responses.acollect_stream': ( 'oai_responses.html#acollect_stream', 'fastllm/openai_responses.py'), + 'fastllm.openai_responses.approx_raw_usage': ( 'oai_responses.html#approx_raw_usage', + 'fastllm/openai_responses.py'), 'fastllm.openai_responses.cost': ('oai_responses.html#cost', 'fastllm/openai_responses.py'), 'fastllm.openai_responses.delta_index_fn': ( 'oai_responses.html#delta_index_fn', 'fastllm/openai_responses.py'), @@ -279,6 +286,7 @@ 'fastllm.types._fetch_url_partial': ('types.html#_fetch_url_partial', 'fastllm/types.py'), 'fastllm.types._trunc_strs': ('types.html#_trunc_strs', 'fastllm/types.py'), 'fastllm.types.approx_pricing': ('types.html#approx_pricing', 'fastllm/types.py'), + 'fastllm.types.approx_str_tokens': ('types.html#approx_str_tokens', 'fastllm/types.py'), 'fastllm.types.data_url': ('types.html#data_url', 'fastllm/types.py'), 'fastllm.types.display_list': ('types.html#display_list', 'fastllm/types.py'), 'fastllm.types.fn_schema': ('types.html#fn_schema', 'fastllm/types.py'), diff --git a/fastllm/acomplete.py b/fastllm/acomplete.py index afa3b4b..0c52867 100644 --- a/fastllm/acomplete.py +++ b/fastllm/acomplete.py @@ -3,8 +3,8 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/06_acomplete.ipynb. # %% auto #0 -__all__ = ['specs_path', 'ant_spec', 'oai_spec', 'gem_spec', 'vendor_mapping', 'api2spec', 'defaults', 'mk_client', - 'ContextWindowExceededError', 'acomplete'] +__all__ = ['specs_path', 'ant_spec', 'oai_spec', 'gem_spec', 'vendor_mapping', 'api2spec', 'defaults', 'resolve_api_vendor', + 'mk_client', 'ContextWindowExceededError', 'acomplete'] # %% ../nbs/06_acomplete.ipynb #f2f57253 import asyncio,json,httpx @@ -50,31 +50,35 @@ # %% ../nbs/06_acomplete.ipynb #77d27ea7 api2spec = {'openai':oai_spec, 'openai_chat':oai_spec, 'anthropic':ant_spec, 'gemini':gem_spec} -# %% ../nbs/06_acomplete.ipynb #79075d95 -@flexicache() -def mk_client(model=None, vendor_name=None, api_name=None, api_key=None, base_url=None, xtra_hdrs=None, - timeout=httpx.Timeout(connect=30, read=300, write=30, pool=10)): +# %% ../nbs/06_acomplete.ipynb #c714601e +def resolve_api_vendor(model=None, vendor_name=None, api_name=None, api_key=None, base_url=None): err_msg = f"please pass a valid one vendor: {', '.join(list(vendor_mapping))} or pass `api_name`,`base_url` and `api_key`" if vendor_name: override_base_url = base_url - try: - api_name, base_url, env_api_nm, *auth_json = vendor_mapping[vendor_name] - base_url = override_base_url or base_url - if auth_json and not api_key and not os.getenv(env_api_nm): - fn,keys = auth_json[0] # pyright: ignore[reportAssignmentType] - auth_fn = Path(fn).expanduser() - if auth_fn.exists(): api_key = nested_idx(json.loads(auth_fn.read_text()), *keys) - api_key = get_api_key(api_key, env_api_nm) + try: api_name,base_url,env_api_nm,*auth_json = vendor_mapping[vendor_name] except KeyError: raise ValueError(f"Unknown vendor '{vendor_name}', {err_msg}") - elif base_url and api_key: vendor_name, api_name = ifnone(vendor_name, 'custom'), ifnone(api_name, 'openai_chat') - elif (api_name:=infer_api_name(model)): base_url, vendor_name = None, api_name - else: raise ValueError(f"Model {model} can't be auto resolved, {err_msg}") + return api_name,vendor_name,override_base_url or base_url,env_api_nm,auth_json + if base_url and api_key: return ifnone(api_name, 'openai_chat'), ifnone(vendor_name, 'custom'), base_url, None, [] + if api_name:=infer_api_name(model): return api_name, api_name, None, None, [] + raise ValueError(f"Model {model} can't be auto resolved, {err_msg}") + +# %% ../nbs/06_acomplete.ipynb #2fd2bdeb +@flexicache() +def mk_client(model=None, vendor_name=None, api_name=None, api_key=None, base_url=None, xtra_hdrs=None, + timeout=httpx.Timeout(connect=30, read=300, write=30, pool=10)): + api_name,vendor_name,base_url,env_api_nm,auth_json = resolve_api_vendor(model, vendor_name, api_name, api_key, base_url) + if env_api_nm: + if auth_json and not api_key and not os.getenv(env_api_nm): + fn,keys = auth_json[0] + auth_fn = Path(fn).expanduser() + if auth_fn.exists(): api_key = nested_idx(json.loads(auth_fn.read_text()), *keys) + api_key = get_api_key(api_key, env_api_nm) api = api_registry.apis[api_name] - spec, hdrs = api2spec[api_name], api.get_hdrs(api_key) + spec,hdrs = api2spec[api_name], api.get_hdrs(api_key) cli = OpenAPIClient(spec, headers=merge(hdrs, ifnone(xtra_hdrs, {})), timeout=timeout) if base_url is not None: - for op in cli.ops: op.base_url = base_url # pyright: ignore[reportAttributeAccessIssue] - return cli, api_name, vendor_name + for op in cli.ops: op.base_url = base_url + return cli,api_name,vendor_name # %% ../nbs/06_acomplete.ipynb #df851a5c class ContextWindowExceededError(APIError): pass diff --git a/fastllm/anthropic.py b/fastllm/anthropic.py index 4f198ab..76be055 100644 --- a/fastllm/anthropic.py +++ b/fastllm/anthropic.py @@ -5,7 +5,7 @@ 'norm_sse_event', 'delta_index_fn', 'acollect_stream', 'denorm_tool_use', 'denorm_assistant', 'denorm_tool', 'denorm_msgs', 'denorm_tool_schs', 'denorm_tool_choice', 'denorm_reasoning', 'denorm_web_search', 'denorm_system', 'denorm_user', 'denorm_image', 'denorm_file', 'denorm_tool_result', 'mk_payload', - 'get_hdrs', 'cost'] + 'get_hdrs', 'approx_raw_usage', 'cost'] # %% ../nbs/04_anthropic.ipynb #02afd3d7 import json @@ -288,6 +288,10 @@ def get_hdrs(api_key=None): return {"x-api-key": get_api_key(api_key, 'ANTHROPIC_API_KEY'), "anthropic-version": "2023-06-01"} # %% ../nbs/04_anthropic.ipynb #0d03643a +def approx_raw_usage(pt, ct, cached_frac=0.8): + cached = int(pt*cached_frac) + return dict(usage=dict(input_tokens=pt-cached, output_tokens=ct, cache_read_input_tokens=cached)) + def cost(usage, m): raw = usage.raw in_tok = raw['input_tokens'] @@ -304,5 +308,5 @@ def cost(usage, m): # %% ../nbs/04_anthropic.ipynb #f7c0b989 api_registry.register('anthropic', norm_tool_calls=norm_tool_calls, norm_parts=norm_parts, norm_finish=norm_finish, norm_usage=norm_usage, - finalize_usage=finalize_usage, acollect_stream=acollect_stream, mk_payload=mk_payload, cost=cost, get_hdrs=get_hdrs, + finalize_usage=finalize_usage, acollect_stream=acollect_stream, mk_payload=mk_payload, approx_raw_usage=approx_raw_usage, cost=cost, get_hdrs=get_hdrs, op_path=('messages.messages_post','messages.messages_post')) diff --git a/fastllm/chat.py b/fastllm/chat.py index 7fb9cf2..daddf7b 100644 --- a/fastllm/chat.py +++ b/fastllm/chat.py @@ -381,7 +381,7 @@ def fmt(self): return f"\n\n{token_dtls_tag}{summ}\n\n`{self!r}`\n\n\n" # %% ../nbs/07_chat.ipynb #cb3d7e77 -class AsyncChat: +class AsyncChat(BasicRepr): def __init__( self, model:str, # LiteLLM compatible model name @@ -496,11 +496,34 @@ async def _alite_call_func(tc, tool_schemas, ns): return _mk_tool_result(await maybe_await(res)) # %% ../nbs/07_chat.ipynb #ee4fb755 +def _chunk_text(o): + if isinstance(o, dict): return ''.join(str(o.get(k) or '') for k in ('text','thinking','refusal')) + if isinstance(o, Part): return o.text or '' + return '' + @asave_iter -async def astream_with_complete(self, agen, postproc=noop): - async for chunk in agen: - if not isinstance(chunk, Completion): yield postproc(chunk) - self.value = chunk +async def astream_with_complete(self, chat, agen, postproc=noop): + out = [] + try: + async for chunk in agen: + if not isinstance(chunk, Completion): + out.append(_chunk_text(chunk)) + yield postproc(chunk) + self.value = chunk + except (GeneratorExit, asyncio.CancelledError): + api_name,vendor_name,*_ = resolve_api_vendor(chat.model, chat.vendor_name, chat.api_name, chat.api_key, chat.base_url) + api = api_registry.apis[api_name] + pt = approx_str_tokens([chat.turn_sysp, chat.turn_msgs, chat.tool_schemas]) + ct = approx_str_tokens(''.join(out)) + usage = api.norm_usage(api.approx_raw_usage(pt, ct)) + self.value = Completion(model=chat.model, + message=Msg(role="assistant", content=[Part('text', ''.join(out))]), + finish_reason=FinishReason.interrupted, + api_name=api_name, + vendor_name=vendor_name, + usage=usage) + chat._track(self.value) + raise # %% ../nbs/07_chat.ipynb #a049cf52 @patch @@ -519,7 +542,7 @@ async def _call(self:AsyncChat, msg=None, prefill=None, temp=None, think=None, s temperature=None if think else ifnone(temp,self.temp), **self.turn_kwargs) if stream: if self.prefill: yield _mk_prefill(self.prefill) - res = astream_with_complete(res, postproc=postproc) + res = astream_with_complete(self, res, postproc=postproc) async for chunk in res: yield chunk res = res.value self.turn_res, self.turn_msg = res, contents(res) diff --git a/fastllm/gemini.py b/fastllm/gemini.py index 68f6d66..9bf659c 100644 --- a/fastllm/gemini.py +++ b/fastllm/gemini.py @@ -5,7 +5,7 @@ 'acollect_stream', 'denorm_tool_use', 'denorm_assistant', 'denorm_tool', 'denorm_msgs', 'denorm_tool_schs', 'denorm_tool_choice', 'denorm_reasoning', 'denorm_web_search', 'denorm_system', 'denorm_user', 'denorm_image', 'denorm_audio', 'denorm_video', 'denorm_file', 'denorm_tool_result', 'mk_payload', - 'get_hdrs', 'cost'] + 'get_hdrs', 'approx_raw_usage', 'cost'] # %% ../nbs/05_gemini.ipynb #02afd3d7 import json @@ -269,6 +269,10 @@ def get_hdrs(api_key=None): return {"x-goog-api-key": get_api_key(api_key, 'GEMINI_API_KEY')} # %% ../nbs/05_gemini.ipynb #4ee3891f +def approx_raw_usage(pt, ct, cached_frac=0.8): + cached = int(pt*cached_frac) + return dict(usageMetadata=dict(promptTokenCount=pt, candidatesTokenCount=ct, totalTokenCount=pt+ct, cachedContentTokenCount=cached)) + def cost(usage, m): raw = usage.raw prompt_tot = raw.get('promptTokenCount', 0) @@ -301,6 +305,7 @@ def cost(usage, m): acollect_stream=acollect_stream, mk_payload=mk_payload, cost=cost, + approx_raw_usage=approx_raw_usage, get_hdrs=get_hdrs, op_path=('models.generate_content','models.stream_generate_content')) api_registry.register('gemini', **api_ns) diff --git a/fastllm/openai_chat.py b/fastllm/openai_chat.py index 5c58b20..08fabfa 100644 --- a/fastllm/openai_chat.py +++ b/fastllm/openai_chat.py @@ -4,7 +4,8 @@ __all__ = ['api_ns', 'norm_tool_calls', 'norm_finish', 'norm_parts', 'norm_sse_event', 'delta_index_fn', 'acollect_stream', 'denorm_tool_use', 'denorm_assistant', 'denorm_tool', 'denorm_msgs', 'denorm_tool_schs', 'denorm_tool_choice', 'denorm_reasoning', 'denorm_web_search', 'denorm_system', 'denorm_user', - 'denorm_image', 'denorm_audio', 'denorm_file', 'denorm_tool_result', 'mk_payload', 'get_hdrs', 'cost'] + 'denorm_image', 'denorm_audio', 'denorm_file', 'denorm_tool_result', 'mk_payload', 'get_hdrs', + 'approx_raw_usage', 'cost'] # %% ../nbs/03_oai_chat.ipynb #493e3606 import json @@ -194,6 +195,11 @@ def get_hdrs(api_key=None): return {"Authorization": f"Bearer {get_api_key(api_key, 'OPENAI_API_KEY')}"} # %% ../nbs/03_oai_chat.ipynb #f89e2bf6 +def approx_raw_usage(pt, ct, cached_frac=0.8): + cached = int(pt*cached_frac) + return dict(usage=dict(prompt_tokens=pt, completion_tokens=ct, total_tokens=pt+ct, + prompt_tokens_details=dict(cached_tokens=cached), completion_tokens_details=dict(reasoning_tokens=0))) + def cost(usage, m): raw = usage.raw pd,cd = raw.get('prompt_tokens_details') or {},raw.get('completion_tokens_details') or {} @@ -215,6 +221,7 @@ def cost(usage, m): acollect_stream=acollect_stream, mk_payload=mk_payload, cost=cost, + approx_raw_usage=approx_raw_usage, get_hdrs=get_hdrs, op_path=('chat.create_chat_completion','chat.create_chat_completion')) api_registry.register('openai_chat', **api_ns) diff --git a/fastllm/openai_responses.py b/fastllm/openai_responses.py index f0c40fa..725228e 100644 --- a/fastllm/openai_responses.py +++ b/fastllm/openai_responses.py @@ -4,7 +4,8 @@ __all__ = ['api_ns', 'norm_tool_call', 'norm_tool_calls', 'norm_usage', 'norm_finish', 'norm_parts', 'norm_sse_event', 'delta_index_fn', 'acollect_stream', 'denorm_tool_use', 'denorm_tool', 'denorm_assistant', 'denorm_msgs', 'denorm_tool_schs', 'denorm_tool_choice', 'denorm_reasoning', 'denorm_web_search', 'denorm_system', - 'denorm_user', 'denorm_image', 'denorm_file', 'denorm_tool_result', 'mk_payload', 'get_hdrs', 'cost'] + 'denorm_user', 'denorm_image', 'denorm_file', 'denorm_tool_result', 'mk_payload', 'get_hdrs', + 'approx_raw_usage', 'cost'] # %% ../nbs/02_oai_responses.ipynb #591f55b5 import json @@ -243,6 +244,11 @@ def get_hdrs(api_key=None): return {"Authorization": f"Bearer {get_api_key(api_key, 'OPENAI_API_KEY')}"} # %% ../nbs/02_oai_responses.ipynb #a907ffa8 +def approx_raw_usage(pt, ct, cached_frac=0.8): + cached = int(pt*cached_frac) + return dict(usage=dict(input_tokens=pt, output_tokens=ct, total_tokens=pt+ct, + input_tokens_details=dict(cached_tokens=cached), output_tokens_details=dict(reasoning_tokens=0))) + def cost(usage, m): raw = usage.raw cached = raw.get('input_tokens_details', {}).get('cached_tokens', 0) @@ -259,6 +265,7 @@ def cost(usage, m): acollect_stream=acollect_stream, mk_payload=mk_payload, cost=cost, + approx_raw_usage=approx_raw_usage, get_hdrs=get_hdrs, op_path=('responses.create_response','responses.create_response')) api_registry.register('openai', **api_ns) diff --git a/fastllm/types.py b/fastllm/types.py index 6abc4e8..6151ae8 100644 --- a/fastllm/types.py +++ b/fastllm/types.py @@ -6,10 +6,10 @@ __all__ = ['PartType', 'FinishReason', 'api_registry', 'model_prices_url', 'haik45', 'sonn45', 'sonn', 'sonn46', 'opus46', 'opus', 'gpt54', 'gpt54m', 'gpt55', 'codex54', 'codex54m', 'codex55', 'codex53spark', 'model_info_registry', 'modern_llm', 'deepseek_v4_common', 'mimo_v25_common', 'codex_pricing', 'Part', 'Msg', 'ToolCall', - 'display_list', 'Usage', 'Completion', 'APIRegistry', 'mk_completion', 'mk_tool_res_msg', 'fn_schema', - 'sys_text', 'part_txt', 'data_url', 'url_mime', 'payload_kwargs', 'get_api_key', 'resize_b64', - 'model_prices_meta', 'infer_api_name', 'get_model_meta', 'register_model_info', 'get_model_info', - 'get_model_pricing', 'approx_pricing'] + 'display_list', 'Usage', 'Completion', 'approx_str_tokens', 'APIRegistry', 'mk_completion', + 'mk_tool_res_msg', 'fn_schema', 'sys_text', 'part_txt', 'data_url', 'url_mime', 'payload_kwargs', + 'get_api_key', 'resize_b64', 'model_prices_meta', 'infer_api_name', 'get_model_meta', 'register_model_info', + 'get_model_info', 'get_model_pricing', 'approx_pricing'] # %% ../nbs/00_types.ipynb #b4d047fd import httpx, base64, io @@ -148,16 +148,19 @@ def _repr_markdown_(self: Completion): """ # %% ../nbs/00_types.ipynb #ce59e431 -FinishReason = str_enum('finish_reason', 'stop', 'tool_calls', 'length', 'content_filter') +FinishReason = str_enum('finish_reason', 'stop', 'tool_calls', 'length', 'content_filter', 'interrupted') + +# %% ../nbs/00_types.ipynb #c5a88e6f +def approx_str_tokens(o): return int(len(str(o))/3.4) + 1 # %% ../nbs/00_types.ipynb #fc681c52 class APIRegistry: def __init__(self): self.apis = {} - def register(self, name, finalize_usage=noop, **kwargs): self.apis[name] = SimpleNamespace(finalize_usage=finalize_usage, **kwargs) + def register(self, name, finalize_usage=noop, approx_raw_usage=noop, **kwargs): + self.apis[name] = SimpleNamespace(finalize_usage=finalize_usage, approx_raw_usage=approx_raw_usage, **kwargs) api_registry = APIRegistry() - # %% ../nbs/00_types.ipynb #d58a5f96 def mk_completion(resp, model, api_name, vendor_name): "Normalize an api response into Completion." diff --git a/nbs/00_types.ipynb b/nbs/00_types.ipynb index 683a6a9..0c34b8f 100644 --- a/nbs/00_types.ipynb +++ b/nbs/00_types.ipynb @@ -698,7 +698,47 @@ "outputs": [], "source": [ "#| export\n", - "FinishReason = str_enum('finish_reason', 'stop', 'tool_calls', 'length', 'content_filter')" + "FinishReason = str_enum('finish_reason', 'stop', 'tool_calls', 'length', 'content_filter', 'interrupted')" + ] + }, + { + "cell_type": "markdown", + "id": "751494ed", + "metadata": {}, + "source": [ + "## Token Estimation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5a88e6f", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def approx_str_tokens(o): return int(len(str(o))/3.4) + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef168d18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = {'hello': 42}; approx_str_tokens(d)" ] }, { @@ -719,9 +759,10 @@ "#| export\n", "class APIRegistry:\n", " def __init__(self): self.apis = {}\n", - " def register(self, name, finalize_usage=noop, **kwargs): self.apis[name] = SimpleNamespace(finalize_usage=finalize_usage, **kwargs)\n", + " def register(self, name, finalize_usage=noop, approx_raw_usage=noop, **kwargs):\n", + " self.apis[name] = SimpleNamespace(finalize_usage=finalize_usage, approx_raw_usage=approx_raw_usage, **kwargs)\n", "\n", - "api_registry = APIRegistry()\n" + "api_registry = APIRegistry()" ] }, { diff --git a/nbs/02_oai_responses.ipynb b/nbs/02_oai_responses.ipynb index 7f67fce..f2022d7 100644 --- a/nbs/02_oai_responses.ipynb +++ b/nbs/02_oai_responses.ipynb @@ -3718,6 +3718,11 @@ "outputs": [], "source": [ "#| export\n", + "def approx_raw_usage(pt, ct, cached_frac=0.8):\n", + " cached = int(pt*cached_frac)\n", + " return dict(usage=dict(input_tokens=pt, output_tokens=ct, total_tokens=pt+ct,\n", + " input_tokens_details=dict(cached_tokens=cached), output_tokens_details=dict(reasoning_tokens=0)))\n", + "\n", "def cost(usage, m):\n", " raw = usage.raw\n", " cached = raw.get('input_tokens_details', {}).get('cached_tokens', 0)\n", @@ -3764,6 +3769,7 @@ " acollect_stream=acollect_stream,\n", " mk_payload=mk_payload,\n", " cost=cost,\n", + " approx_raw_usage=approx_raw_usage,\n", " get_hdrs=get_hdrs,\n", " op_path=('responses.create_response','responses.create_response'))\n", "api_registry.register('openai', **api_ns)" diff --git a/nbs/03_oai_chat.ipynb b/nbs/03_oai_chat.ipynb index 61f4592..09a2b54 100644 --- a/nbs/03_oai_chat.ipynb +++ b/nbs/03_oai_chat.ipynb @@ -2193,6 +2193,11 @@ "outputs": [], "source": [ "#| export\n", + "def approx_raw_usage(pt, ct, cached_frac=0.8):\n", + " cached = int(pt*cached_frac)\n", + " return dict(usage=dict(prompt_tokens=pt, completion_tokens=ct, total_tokens=pt+ct,\n", + " prompt_tokens_details=dict(cached_tokens=cached), completion_tokens_details=dict(reasoning_tokens=0)))\n", + "\n", "def cost(usage, m):\n", " raw = usage.raw\n", " pd,cd = raw.get('prompt_tokens_details') or {},raw.get('completion_tokens_details') or {}\n", @@ -2244,6 +2249,7 @@ " acollect_stream=acollect_stream,\n", " mk_payload=mk_payload,\n", " cost=cost,\n", + " approx_raw_usage=approx_raw_usage,\n", " get_hdrs=get_hdrs, \n", " op_path=('chat.create_chat_completion','chat.create_chat_completion'))\n", "api_registry.register('openai_chat', **api_ns)" diff --git a/nbs/04_anthropic.ipynb b/nbs/04_anthropic.ipynb index 01ffa5d..430582e 100644 --- a/nbs/04_anthropic.ipynb +++ b/nbs/04_anthropic.ipynb @@ -2876,6 +2876,10 @@ "outputs": [], "source": [ "#| export\n", + "def approx_raw_usage(pt, ct, cached_frac=0.8):\n", + " cached = int(pt*cached_frac)\n", + " return dict(usage=dict(input_tokens=pt-cached, output_tokens=ct, cache_read_input_tokens=cached))\n", + "\n", "def cost(usage, m):\n", " raw = usage.raw\n", " in_tok = raw['input_tokens']\n", @@ -2940,7 +2944,7 @@ "source": [ "#| export\n", "api_registry.register('anthropic', norm_tool_calls=norm_tool_calls, norm_parts=norm_parts, norm_finish=norm_finish, norm_usage=norm_usage,\n", - " finalize_usage=finalize_usage, acollect_stream=acollect_stream, mk_payload=mk_payload, cost=cost, get_hdrs=get_hdrs,\n", + " finalize_usage=finalize_usage, acollect_stream=acollect_stream, mk_payload=mk_payload, approx_raw_usage=approx_raw_usage, cost=cost, get_hdrs=get_hdrs,\n", " op_path=('messages.messages_post','messages.messages_post'))" ] }, diff --git a/nbs/05_gemini.ipynb b/nbs/05_gemini.ipynb index 6959b0c..6ea0b18 100644 --- a/nbs/05_gemini.ipynb +++ b/nbs/05_gemini.ipynb @@ -3000,6 +3000,10 @@ "outputs": [], "source": [ "#| export\n", + "def approx_raw_usage(pt, ct, cached_frac=0.8):\n", + " cached = int(pt*cached_frac)\n", + " return dict(usageMetadata=dict(promptTokenCount=pt, candidatesTokenCount=ct, totalTokenCount=pt+ct, cachedContentTokenCount=cached))\n", + "\n", "def cost(usage, m):\n", " raw = usage.raw\n", " prompt_tot = raw.get('promptTokenCount', 0)\n", @@ -3062,6 +3066,7 @@ " acollect_stream=acollect_stream,\n", " mk_payload=mk_payload,\n", " cost=cost,\n", + " approx_raw_usage=approx_raw_usage,\n", " get_hdrs=get_hdrs,\n", " op_path=('models.generate_content','models.stream_generate_content'))\n", "api_registry.register('gemini', **api_ns)" diff --git a/nbs/06_acomplete.ipynb b/nbs/06_acomplete.ipynb index db649ca..ff83de8 100644 --- a/nbs/06_acomplete.ipynb +++ b/nbs/06_acomplete.ipynb @@ -237,35 +237,162 @@ { "cell_type": "code", "execution_count": null, - "id": "79075d95", + "id": "c714601e", "metadata": {}, "outputs": [], "source": [ "#| export\n", - "@flexicache()\n", - "def mk_client(model=None, vendor_name=None, api_name=None, api_key=None, base_url=None, xtra_hdrs=None,\n", - " timeout=httpx.Timeout(connect=30, read=300, write=30, pool=10)):\n", + "def resolve_api_vendor(model=None, vendor_name=None, api_name=None, api_key=None, base_url=None):\n", " err_msg = f\"please pass a valid one vendor: {', '.join(list(vendor_mapping))} or pass `api_name`,`base_url` and `api_key`\"\n", " if vendor_name:\n", " override_base_url = base_url\n", - " try:\n", - " api_name, base_url, env_api_nm, *auth_json = vendor_mapping[vendor_name]\n", - " base_url = override_base_url or base_url\n", - " if auth_json and not api_key and not os.getenv(env_api_nm):\n", - " fn,keys = auth_json[0] # pyright: ignore[reportAssignmentType]\n", - " auth_fn = Path(fn).expanduser()\n", - " if auth_fn.exists(): api_key = nested_idx(json.loads(auth_fn.read_text()), *keys)\n", - " api_key = get_api_key(api_key, env_api_nm)\n", + " try: api_name,base_url,env_api_nm,*auth_json = vendor_mapping[vendor_name]\n", " except KeyError: raise ValueError(f\"Unknown vendor '{vendor_name}', {err_msg}\")\n", - " elif base_url and api_key: vendor_name, api_name = ifnone(vendor_name, 'custom'), ifnone(api_name, 'openai_chat')\n", - " elif (api_name:=infer_api_name(model)): base_url, vendor_name = None, api_name\n", - " else: raise ValueError(f\"Model {model} can't be auto resolved, {err_msg}\")\n", + " return api_name,vendor_name,override_base_url or base_url,env_api_nm,auth_json\n", + " if base_url and api_key: return ifnone(api_name, 'openai_chat'), ifnone(vendor_name, 'custom'), base_url, None, []\n", + " if api_name:=infer_api_name(model): return api_name, api_name, None, None, []\n", + " raise ValueError(f\"Model {model} can't be auto resolved, {err_msg}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d22d445", + "metadata": {}, + "outputs": [], + "source": [ + "api_name,vendor_name,*_ = resolve_api_vendor('models/gemini-model')\n", + "test_eq(api_name, 'gemini'); test_eq(vendor_name, 'gemini')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fc960c7", + "metadata": {}, + "outputs": [], + "source": [ + "api_name,vendor_name,*_ = resolve_api_vendor('gpt-model')\n", + "test_eq(api_name, 'openai'); test_eq(vendor_name, 'openai')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98bdc45c", + "metadata": {}, + "outputs": [], + "source": [ + "api_name,vendor_name,*_ = resolve_api_vendor('claude-model')\n", + "test_eq(api_name, 'anthropic'); test_eq(vendor_name, 'anthropic')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2195819", + "metadata": {}, + "outputs": [], + "source": [ + "api_name,vendor_name,*_ = resolve_api_vendor('gpt-model', 'openai_chat')\n", + "test_eq(api_name, 'openai_chat'); test_eq(vendor_name, 'openai_chat')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad5d70b9", + "metadata": {}, + "outputs": [], + "source": [ + "api_name,vendor_name,*_ = resolve_api_vendor('gpt-codex', 'codex')\n", + "test_eq(api_name, 'openai'); test_eq(vendor_name, 'codex')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61516d6c", + "metadata": {}, + "outputs": [], + "source": [ + "api_name,vendor_name,*_ = resolve_api_vendor('kimi-2.5', 'fireworks_ai')\n", + "test_eq(api_name, 'openai_chat'); test_eq(vendor_name, 'fireworks_ai')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "553c518d", + "metadata": {}, + "outputs": [], + "source": [ + "api_name,vendor_name,*_ = resolve_api_vendor('my-vllm-model', api_name='openai_chat', base_url='https://api.custom.com', api_key='123')\n", + "test_eq(api_name, 'openai_chat'); test_eq(vendor_name, 'custom')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c84e7a2c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unknown vendor 'unk', please pass a valid one vendor: openai, anthropic, gemini, openai_chat, codex, moonshot, deepseek, mimo, openrouter, together, fireworks_ai, qwen, minimax or pass `api_name`,`base_url` and `api_key`\n" + ] + } + ], + "source": [ + "try: api_name,vendor_name,*_ = resolve_api_vendor('kimi-2.5', 'unk')\n", + "except Exception as e: print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd172306", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model unk can't be auto resolved, please pass a valid one vendor: openai, anthropic, gemini, openai_chat, codex, moonshot, deepseek, mimo, openrouter, together, fireworks_ai, qwen, minimax or pass `api_name`,`base_url` and `api_key`\n" + ] + } + ], + "source": [ + "try: api_name,vendor_name,*_ = resolve_api_vendor('unk')\n", + "except Exception as e: print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fd2bdeb", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "@flexicache()\n", + "def mk_client(model=None, vendor_name=None, api_name=None, api_key=None, base_url=None, xtra_hdrs=None,\n", + " timeout=httpx.Timeout(connect=30, read=300, write=30, pool=10)):\n", + " api_name,vendor_name,base_url,env_api_nm,auth_json = resolve_api_vendor(model, vendor_name, api_name, api_key, base_url)\n", + " if env_api_nm:\n", + " if auth_json and not api_key and not os.getenv(env_api_nm):\n", + " fn,keys = auth_json[0]\n", + " auth_fn = Path(fn).expanduser()\n", + " if auth_fn.exists(): api_key = nested_idx(json.loads(auth_fn.read_text()), *keys)\n", + " api_key = get_api_key(api_key, env_api_nm)\n", " api = api_registry.apis[api_name]\n", - " spec, hdrs = api2spec[api_name], api.get_hdrs(api_key)\n", + " spec,hdrs = api2spec[api_name], api.get_hdrs(api_key)\n", " cli = OpenAPIClient(spec, headers=merge(hdrs, ifnone(xtra_hdrs, {})), timeout=timeout)\n", " if base_url is not None:\n", - " for op in cli.ops: op.base_url = base_url # pyright: ignore[reportAttributeAccessIssue]\n", - " return cli, api_name, vendor_name" + " for op in cli.ops: op.base_url = base_url\n", + " return cli,api_name,vendor_name" ] }, { @@ -924,7 +1051,7 @@ "" ], "text/plain": [ - "Completion(model='MiniMax-M3', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today?', data={'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=165, completion_tokens=9, total_tokens=174, cached_tokens=114, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 51, 'output_tokens': 9, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 114, 'service_tier': 'standard'}), tool_calls=[], api_name='anthropic', vendor_name='minimax', raw={'deltas': [Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'message_start', 'message': {'id': '067b3e23180b1bff95a718cbfce604bb', 'type': 'message', 'role': 'assistant', 'content': [], 'model': 'MiniMax-M3', 'stop_reason': None, 'stop_sequence': None, 'usage': {'input_tokens': 51, 'output_tokens': 0, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 114, 'service_tier': 'standard'}, 'service_tier': 'standard'}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'ping'}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_start', 'index': 0, 'content_block': {'type': 'text', 'text': ''}}), Delta(text='Hello', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Hello'}}), Delta(text='! How can I help you today?', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': '! How can I help you today?'}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_stop', 'index': 0}), Delta(text='', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=, usage=Usage(prompt_tokens=165, completion_tokens=9, total_tokens=174, cached_tokens=114, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 51, 'output_tokens': 9, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 114, 'service_tier': 'standard'}), raw={'type': 'message_delta', 'delta': {'stop_reason': 'end_turn'}, 'usage': {'input_tokens': 51, 'output_tokens': 9, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 114, 'service_tier': 'standard'}})]})" + "Completion(model='MiniMax-M3', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today?', data={'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=165, completion_tokens=9, total_tokens=174, cached_tokens=114, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 51, 'output_tokens': 9, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 114, 'service_tier': 'standard'}), tool_calls=[], api_name='anthropic', vendor_name='minimax', raw={'deltas': [Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'message_start', 'message': {'id': '06849c212dc22979a52daec346c00c21', 'type': 'message', 'role': 'assistant', 'content': [], 'model': 'MiniMax-M3', 'stop_reason': None, 'stop_sequence': None, 'usage': {'input_tokens': 51, 'output_tokens': 0, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 114, 'service_tier': 'standard'}, 'service_tier': 'standard'}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'ping'}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_start', 'index': 0, 'content_block': {'type': 'text', 'text': ''}}), Delta(text='Hello', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Hello'}}), Delta(text='! How can I help you today?', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': '! How can I help you today?'}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_stop', 'index': 0}), Delta(text='', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=, usage=Usage(prompt_tokens=165, completion_tokens=9, total_tokens=174, cached_tokens=114, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 51, 'output_tokens': 9, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 114, 'service_tier': 'standard'}), raw={'type': 'message_delta', 'delta': {'stop_reason': 'end_turn'}, 'usage': {'input_tokens': 51, 'output_tokens': 9, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 114, 'service_tier': 'standard'}})]})" ] }, "execution_count": null, @@ -9158,7 +9285,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m16:35:46 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1168 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" + "\u001b[92m19:07:12 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1191 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" ] } ], @@ -9191,7 +9318,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m16:35:47 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1168 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3.1-pro-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" + "\u001b[92m19:07:13 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1191 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3.1-pro-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" ] } ], @@ -9232,7 +9359,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m16:35:47 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1168 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" + "\u001b[92m19:07:13 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1191 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" ] } ], @@ -9265,7 +9392,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m16:35:48 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1168 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3.1-pro-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" + "\u001b[92m19:07:14 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1191 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3.1-pro-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" ] } ], @@ -9306,7 +9433,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m16:35:55 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1168 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" + "\u001b[92m19:07:14 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1191 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" ] } ], @@ -9350,7 +9477,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m16:36:05 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1168 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3.1-pro-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" + "\u001b[92m19:07:15 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1191 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3.1-pro-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" ] }, { @@ -9453,7 +9580,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m16:36:23 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1168 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" + "\u001b[92m19:07:16 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1191 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" ] } ], @@ -9473,7 +9600,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m16:36:25 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1168 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" + "\u001b[92m19:07:16 - LiteLLM:WARNING\u001b[0m: vertex_and_google_ai_studio_gemini.py:1191 - DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to function for Gemini 3+ (gemini-3-flash-preview) but are planned for removal in a future release. Move sampling guidance into the `system` instructions instead.\n" ] } ], diff --git a/nbs/07_chat.ipynb b/nbs/07_chat.ipynb index 766bd75..94cc12b 100644 --- a/nbs/07_chat.ipynb +++ b/nbs/07_chat.ipynb @@ -151,20 +151,20 @@ "text/markdown": [ "
\n", "\n", - "Hi there! How can I help you today?\n", + "Hello! How can I help you today?\n", "\n", "
\n", "\n", "- model: `models/gemini-3.1-pro-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=4, completion_tokens=10, total_tokens=216, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=202, raw={'promptTokenCount': 4, 'candidatesTokenCount': 10, 'totalTokenCount': 216, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 4}], 'thoughtsTokenCount': 202, 'serviceTier': 'standard'})`\n", + "- usage: `Usage(prompt_tokens=4, completion_tokens=9, total_tokens=282, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=269, raw={'promptTokenCount': 4, 'candidatesTokenCount': 9, 'totalTokenCount': 282, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 4}], 'thoughtsTokenCount': 269, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='models/gemini-3.1-pro-preview', message=Msg(role='assistant', content=[Part(type=, text='Hi there! How can I help you today?', data={'text': 'Hi there! 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How can I help you today?', data={'text': 'Hello! 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Is there something I can help you with today?', data={'type': 'text', 'text': 'Hi there! 👋 How are you doing? Is there something I can help you with today?'})]), finish_reason=, usage=Usage(prompt_tokens=10, completion_tokens=25, total_tokens=35, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 10, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 25, 'service_tier': 'standard', 'inference_geo': 'global'}), tool_calls=[], api_name='anthropic', vendor_name='anthropic', raw={'model': 'claude-sonnet-4-6', 'id': 'msg_012muna9Aeq9ZVx7yvr4nMu6', 'type': 'message', 'role': 'assistant', 'content': [{'type': 'text', 'text': 'Hi there! 👋 How are you doing? 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Is there something I can help you with today?'})]), finish_reason=, usage=Usage(prompt_tokens=10, completion_tokens=25, total_tokens=35, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 10, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 25, 'service_tier': 'standard', 'inference_geo': 'global'}), tool_calls=[], api_name='anthropic', vendor_name='anthropic', raw={'model': 'claude-sonnet-4-6', 'id': 'msg_01MEhZjix4K7ZzLPb1ZrhTkt', 'type': 'message', 'role': 'assistant', 'content': [{'type': 'text', 'text': 'Hi there! 👋 How are you doing? Is there something I can help you with today?'}], 'stop_reason': 'end_turn', 'stop_sequence': None, 'stop_details': None, 'usage': {'input_tokens': 10, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 25, 'service_tier': 'standard', 'inference_geo': 'global'}})" ] }, "metadata": {}, @@ -284,7 +284,7 @@ "" ], "text/plain": [ - "Completion(model='gpt-4.1', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today? 😊', data={'type': 'output_text', 'logprobs': [], 'text': 'Hello! How can I help you today? 😊', 'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=10, completion_tokens=11, total_tokens=21, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 10, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 11, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 21}), tool_calls=[], api_name='openai', vendor_name='openai', raw={'id': 'resp_0b7e387c4fbf962b0069f3033f0c8c8194a984ad62fcfdd3ca', 'object': 'response', 'created_at': 1777533759, 'status': 'completed', 'background': False, 'billing': {'payer': 'developer'}, 'completed_at': 1777533759, 'error': None, 'frequency_penalty': 0.0, 'incomplete_details': None, 'instructions': None, 'max_output_tokens': None, 'max_tool_calls': None, 'model': 'gpt-4.1-2025-04-14', 'moderation': None, 'output': [{'id': 'msg_0b7e387c4fbf962b0069f3033f7a68819498bbb23d44b58ab0', 'type': 'message', 'status': 'completed', 'content': [{'type': 'output_text', 'logprobs': [], 'text': 'Hello! How can I help you today? 😊', 'citations': []}], 'role': 'assistant'}], 'parallel_tool_calls': True, 'presence_penalty': 0.0, 'previous_response_id': None, 'prompt_cache_key': None, 'prompt_cache_retention': 'in_memory', 'reasoning': {'effort': None, 'summary': None}, 'safety_identifier': None, 'service_tier': 'default', 'store': True, 'temperature': 1.0, 'text': {'format': {'type': 'text'}, 'verbosity': 'medium'}, 'tool_choice': 'auto', 'tools': [], 'top_logprobs': 0, 'top_p': 1.0, 'truncation': 'disabled', 'usage': {'input_tokens': 10, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 11, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 21}, 'user': None, 'metadata': {}})" + "Completion(model='gpt-4.1', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today? 😊', data={'type': 'output_text', 'logprobs': [], 'text': 'Hello! How can I help you today? 😊', 'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=10, completion_tokens=11, total_tokens=21, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 10, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 11, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 21}), tool_calls=[], api_name='openai', vendor_name='openai', raw={'id': 'resp_0e956e55a1087aab006a2c03cc1970819d81582d4bcab3151f', 'object': 'response', 'created_at': 1781269452, 'status': 'completed', 'background': False, 'billing': {'payer': 'developer'}, 'completed_at': 1781269452, 'error': None, 'frequency_penalty': 0.0, 'incomplete_details': None, 'instructions': None, 'max_output_tokens': None, 'max_tool_calls': None, 'model': 'gpt-4.1-2025-04-14', 'moderation': None, 'output': [{'id': 'msg_0e956e55a1087aab006a2c03cc8650819db6c81395b4f244e0', 'type': 'message', 'status': 'completed', 'content': [{'type': 'output_text', 'logprobs': [], 'text': 'Hello! How can I help you today? 😊', 'citations': []}], 'role': 'assistant'}], 'parallel_tool_calls': True, 'presence_penalty': 0.0, 'previous_response_id': None, 'prompt_cache_key': None, 'prompt_cache_retention': 'in_memory', 'reasoning': {'context': None, 'effort': None, 'summary': None}, 'safety_identifier': None, 'service_tier': 'default', 'store': True, 'temperature': 1.0, 'text': {'format': {'type': 'text'}, 'verbosity': 'medium'}, 'tool_choice': 'auto', 'tools': [], 'top_logprobs': 0, 'top_p': 1.0, 'truncation': 'disabled', 'usage': {'input_tokens': 10, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 11, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 21}, 'user': None, 'metadata': {}})" ] }, "metadata": {}, @@ -310,20 +310,20 @@ "text/markdown": [ "
\n", "\n", - "Hi! How can I help you today?\n", + "Hi! 👋 How are you doing today? Is there anything I can help you with?\n", "\n", "
\n", "\n", "- model: `claude-fable-5`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=11, completion_tokens=29, total_tokens=40, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 11, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 29, 'output_tokens_details': {'thinking_tokens': 16}, 'service_tier': 'standard', 'inference_geo': 'global'})`\n", + "- usage: `Usage(prompt_tokens=11, completion_tokens=44, total_tokens=55, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 11, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 44, 'output_tokens_details': {'thinking_tokens': 16}, 'service_tier': 'standard', 'inference_geo': 'global'})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='claude-fable-5', message=Msg(role='assistant', content=[Part(type=, text='', data={'type': 'thinking', 'thinking': '', 'signature': 'CAIS5AEKYggOGAIqQMdeTO1HGw8ARWUNYXw+/URqM04JoteP3pDpOIxS/w41AiCrJxXSvsj6MWuJKF5GIYVPXsfRbsdykitmu5CjVTIyDmNsYXVkZS1mYWJsZS01OAFCCHRoaW5raW5nEgxVpBhDZb7h4x6u2fAaDEZBJb7ZlvHy5CtBnCIwD0BoiSmyrITD/k4eMkZ3SsGwGVW1bxFdJ8hUPzB2rRuIChqnvQeU8ZJ1Ax80+lmKKjB0+REmJVxalVgWrxucJ4Luo687A29wGyOHzvusn7TL2CLXsruvIrnyUp/qL3kH1HoYAQ=='}), Part(type=, text='Hi! How can I help you today?', data={'type': 'text', 'text': 'Hi! How can I help you today?'})]), finish_reason=, usage=Usage(prompt_tokens=11, completion_tokens=29, total_tokens=40, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 11, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 29, 'output_tokens_details': {'thinking_tokens': 16}, 'service_tier': 'standard', 'inference_geo': 'global'}), tool_calls=[], api_name='anthropic', vendor_name='anthropic', raw={'model': 'claude-fable-5', 'id': 'msg_01LkAjzAgeTiaQybj13JmFCZ', 'type': 'message', 'role': 'assistant', 'content': [{'type': 'thinking', 'thinking': '', 'signature': 'CAIS5AEKYggOGAIqQMdeTO1HGw8ARWUNYXw+/URqM04JoteP3pDpOIxS/w41AiCrJxXSvsj6MWuJKF5GIYVPXsfRbsdykitmu5CjVTIyDmNsYXVkZS1mYWJsZS01OAFCCHRoaW5raW5nEgxVpBhDZb7h4x6u2fAaDEZBJb7ZlvHy5CtBnCIwD0BoiSmyrITD/k4eMkZ3SsGwGVW1bxFdJ8hUPzB2rRuIChqnvQeU8ZJ1Ax80+lmKKjB0+REmJVxalVgWrxucJ4Luo687A29wGyOHzvusn7TL2CLXsruvIrnyUp/qL3kH1HoYAQ=='}, {'type': 'text', 'text': 'Hi! How can I help you today?'}], 'stop_reason': 'end_turn', 'stop_sequence': None, 'stop_details': None, 'usage': {'input_tokens': 11, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 29, 'output_tokens_details': {'thinking_tokens': 16}, 'service_tier': 'standard', 'inference_geo': 'global'}})" + "Completion(model='claude-fable-5', message=Msg(role='assistant', content=[Part(type=, text='', data={'type': 'thinking', 'thinking': '', 'signature': 'CAIS5AEKYggOGAIqQMdeTO1HGw8ARWUNYXw+/URqM04JoteP3pDpOIxS/w41AiCrJxXSvsj6MWuJKF5GIYVPXsfRbsdykitmu5CjVTIyDmNsYXVkZS1mYWJsZS01OAFCCHRoaW5raW5nEgwQ36RrElefhN+OwsQaDNxJQ4a3dMO/6WYsvCIwS90MlnaNyPx/oiKwLfOgiGf031N3VbC/zQqA5KDBo+HQ53xGKfSI84KITVqhL/i7KjCNxd7kb5WQVLjmmqiivrjPDNw1SfOY6z78U1mGKRJHVA0CKQhmHLdNAlLIE8DY/UkYAQ=='}), Part(type=, text='Hi! 👋 How are you doing today? Is there anything I can help you with?', data={'type': 'text', 'text': 'Hi! 👋 How are you doing today? Is there anything I can help you with?'})]), finish_reason=, usage=Usage(prompt_tokens=11, completion_tokens=44, total_tokens=55, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 11, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 44, 'output_tokens_details': {'thinking_tokens': 16}, 'service_tier': 'standard', 'inference_geo': 'global'}), tool_calls=[], api_name='anthropic', vendor_name='anthropic', raw={'model': 'claude-fable-5', 'id': 'msg_01QmJnyeS9MBCFE1x6UuUUqr', 'type': 'message', 'role': 'assistant', 'content': [{'type': 'thinking', 'thinking': '', 'signature': 'CAIS5AEKYggOGAIqQMdeTO1HGw8ARWUNYXw+/URqM04JoteP3pDpOIxS/w41AiCrJxXSvsj6MWuJKF5GIYVPXsfRbsdykitmu5CjVTIyDmNsYXVkZS1mYWJsZS01OAFCCHRoaW5raW5nEgwQ36RrElefhN+OwsQaDNxJQ4a3dMO/6WYsvCIwS90MlnaNyPx/oiKwLfOgiGf031N3VbC/zQqA5KDBo+HQ53xGKfSI84KITVqhL/i7KjCNxd7kb5WQVLjmmqiivrjPDNw1SfOY6z78U1mGKRJHVA0CKQhmHLdNAlLIE8DY/UkYAQ=='}, {'type': 'text', 'text': 'Hi! 👋 How are you doing today? Is there anything I can help you with?'}], 'stop_reason': 'end_turn', 'stop_sequence': None, 'stop_details': None, 'usage': {'input_tokens': 11, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 44, 'output_tokens_details': {'thinking_tokens': 16}, 'service_tier': 'standard', 'inference_geo': 'global'}})" ] }, "execution_count": null, @@ -367,7 +367,7 @@ "text/markdown": [ "
\n", "\n", - "Hello! How can I help?\n", + "Hi! How can I help?\n", "\n", "
\n", "\n", @@ -380,7 +380,7 @@ "
" ], "text/plain": [ - "Completion(model='gpt-5.4', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help?', data={'type': 'output_text', 'logprobs': [], 'text': 'Hello! How can I help?', 'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=9, completion_tokens=11, total_tokens=20, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 9, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 11, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 20}), tool_calls=[], api_name='openai', vendor_name='openai', raw={'id': 'resp_0761f45d9febd6d80069f30343bef48196836253a22bea9712', 'object': 'response', 'created_at': 1777533763, 'status': 'completed', 'background': False, 'billing': {'payer': 'developer'}, 'completed_at': 1777533764, 'error': None, 'frequency_penalty': 0.0, 'incomplete_details': None, 'instructions': None, 'max_output_tokens': None, 'max_tool_calls': None, 'model': 'gpt-5.4-2026-03-05', 'moderation': None, 'output': [{'id': 'msg_0761f45d9febd6d80069f303444c7081968cd478e0d25a0c5d', 'type': 'message', 'status': 'completed', 'content': [{'type': 'output_text', 'logprobs': [], 'text': 'Hello! How can I help?', 'citations': []}], 'phase': 'final_answer', 'role': 'assistant'}], 'parallel_tool_calls': True, 'presence_penalty': 0.0, 'previous_response_id': None, 'prompt_cache_key': None, 'prompt_cache_retention': 'in_memory', 'reasoning': {'effort': 'none', 'summary': None}, 'safety_identifier': None, 'service_tier': 'default', 'store': True, 'temperature': 1.0, 'text': {'format': {'type': 'text'}, 'verbosity': 'medium'}, 'tool_choice': 'auto', 'tools': [], 'top_logprobs': 0, 'top_p': 0.98, 'truncation': 'disabled', 'usage': {'input_tokens': 9, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 11, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 20}, 'user': None, 'metadata': {}})" + "Completion(model='gpt-5.4', message=Msg(role='assistant', content=[Part(type=, text='Hi! How can I help?', data={'type': 'output_text', 'logprobs': [], 'text': 'Hi! How can I help?', 'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=9, completion_tokens=11, total_tokens=20, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 9, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 11, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 20}), tool_calls=[], api_name='openai', vendor_name='openai', raw={'id': 'resp_0c7bcf60e173647b006a2c03d22e30819e92b2888703267353', 'object': 'response', 'created_at': 1781269458, 'status': 'completed', 'background': False, 'billing': {'payer': 'developer'}, 'completed_at': 1781269458, 'error': None, 'frequency_penalty': 0.0, 'incomplete_details': None, 'instructions': None, 'max_output_tokens': None, 'max_tool_calls': None, 'model': 'gpt-5.4-2026-03-05', 'moderation': None, 'output': [{'id': 'msg_0c7bcf60e173647b006a2c03d2b3c0819e978fd340cda7745c', 'type': 'message', 'status': 'completed', 'content': [{'type': 'output_text', 'logprobs': [], 'text': 'Hi! How can I help?', 'citations': []}], 'phase': 'final_answer', 'role': 'assistant'}], 'parallel_tool_calls': True, 'presence_penalty': 0.0, 'previous_response_id': None, 'prompt_cache_key': None, 'prompt_cache_retention': '24h', 'reasoning': {'context': 'current_turn', 'effort': 'none', 'summary': None}, 'safety_identifier': None, 'service_tier': 'default', 'store': True, 'temperature': 1.0, 'text': {'format': {'type': 'text'}, 'verbosity': 'medium'}, 'tool_choice': 'auto', 'tools': [], 'top_logprobs': 0, 'top_p': 0.98, 'truncation': 'disabled', 'usage': {'input_tokens': 9, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 11, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 20}, 'user': None, 'metadata': {}})" ] }, "execution_count": null, @@ -416,7 +416,7 @@ "" ], "text/plain": [ - "Completion(model='gpt-5.4-mini', message=Msg(role='assistant', content=[Part(type=, text='Hi there! How can I help you today?', data={'type': 'output_text', 'logprobs': [], 'text': 'Hi there! How can I help you today?', 'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=9, completion_tokens=14, total_tokens=23, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 9, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 14, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 23}), tool_calls=[], api_name='openai', vendor_name='openai', raw={'id': 'resp_059aee765534be990069f30347cdf0819d9d10412ca19f74d8', 'object': 'response', 'created_at': 1777533767, 'status': 'completed', 'background': False, 'billing': {'payer': 'developer'}, 'completed_at': 1777533768, 'error': None, 'frequency_penalty': 0.0, 'incomplete_details': None, 'instructions': None, 'max_output_tokens': None, 'max_tool_calls': None, 'model': 'gpt-5.4-mini-2026-03-17', 'moderation': None, 'output': [{'id': 'msg_059aee765534be990069f303484cfc819da96eee150eaaccae', 'type': 'message', 'status': 'completed', 'content': [{'type': 'output_text', 'logprobs': [], 'text': 'Hi there! How can I help you today?', 'citations': []}], 'phase': 'final_answer', 'role': 'assistant'}], 'parallel_tool_calls': True, 'presence_penalty': 0.0, 'previous_response_id': None, 'prompt_cache_key': None, 'prompt_cache_retention': 'in_memory', 'reasoning': {'effort': 'none', 'summary': None}, 'safety_identifier': None, 'service_tier': 'default', 'store': True, 'temperature': 1.0, 'text': {'format': {'type': 'text'}, 'verbosity': 'medium'}, 'tool_choice': 'auto', 'tools': [], 'top_logprobs': 0, 'top_p': 0.98, 'truncation': 'disabled', 'usage': {'input_tokens': 9, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 14, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 23}, 'user': None, 'metadata': {}})" + "Completion(model='gpt-5.4-mini', message=Msg(role='assistant', content=[Part(type=, text='Hi there! How can I help you today?', data={'type': 'output_text', 'logprobs': [], 'text': 'Hi there! How can I help you today?', 'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=9, completion_tokens=14, total_tokens=23, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 9, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 14, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 23}), tool_calls=[], api_name='openai', vendor_name='openai', raw={'id': 'resp_0dab6c2e90d38554006a2c03d3a2508191aa1bc0fcb7d8fe90', 'object': 'response', 'created_at': 1781269459, 'status': 'completed', 'background': False, 'billing': {'payer': 'developer'}, 'completed_at': 1781269460, 'error': None, 'frequency_penalty': 0.0, 'incomplete_details': None, 'instructions': None, 'max_output_tokens': None, 'max_tool_calls': None, 'model': 'gpt-5.4-mini-2026-03-17', 'moderation': None, 'output': [{'id': 'msg_0dab6c2e90d38554006a2c03d4346c819186bcea8d925b7768', 'type': 'message', 'status': 'completed', 'content': [{'type': 'output_text', 'logprobs': [], 'text': 'Hi there! How can I help you today?', 'citations': []}], 'phase': 'final_answer', 'role': 'assistant'}], 'parallel_tool_calls': True, 'presence_penalty': 0.0, 'previous_response_id': None, 'prompt_cache_key': None, 'prompt_cache_retention': '24h', 'reasoning': {'context': 'current_turn', 'effort': 'none', 'summary': None}, 'safety_identifier': None, 'service_tier': 'default', 'store': True, 'temperature': 1.0, 'text': {'format': {'type': 'text'}, 'verbosity': 'medium'}, 'tool_choice': 'auto', 'tools': [], 'top_logprobs': 0, 'top_p': 0.98, 'truncation': 'disabled', 'usage': {'input_tokens': 9, 'input_tokens_details': {'cached_tokens': 0}, 'output_tokens': 14, 'output_tokens_details': {'reasoning_tokens': 0}, 'total_tokens': 23}, 'user': None, 'metadata': {}})" ] }, "execution_count": null, @@ -448,14 +448,14 @@ "
\n", "\n", "```python\n", - "{ 'cache_read_input_token_cost': 1.6e-07,\n", - " 'input_cost_per_token': 9.5e-07,\n", + "{ 'cache_read_input_token_cost': 1e-07,\n", + " 'input_cost_per_token': 6e-07,\n", " 'litellm_provider': 'fireworks_ai',\n", " 'max_input_tokens': 262144,\n", " 'max_output_tokens': 262144,\n", " 'max_tokens': 262144,\n", " 'mode': 'chat',\n", - " 'output_cost_per_token': 4e-06,\n", + " 'output_cost_per_token': 3e-06,\n", " 'source': 'https://fireworks.ai/pricing',\n", " 'supports_function_calling': True,\n", " 'supports_reasoning': True,\n", @@ -467,14 +467,14 @@ "
" ], "text/plain": [ - "{'cache_read_input_token_cost': 1.6e-07,\n", - " 'input_cost_per_token': 9.5e-07,\n", + "{'cache_read_input_token_cost': 1e-07,\n", + " 'input_cost_per_token': 6e-07,\n", " 'litellm_provider': 'fireworks_ai',\n", " 'max_input_tokens': 262144,\n", " 'max_output_tokens': 262144,\n", " 'max_tokens': 262144,\n", " 'mode': 'chat',\n", - " 'output_cost_per_token': 4e-06,\n", + " 'output_cost_per_token': 3e-06,\n", " 'source': 'https://fireworks.ai/pricing',\n", " 'supports_function_calling': True,\n", " 'supports_response_schema': True,\n", @@ -573,7 +573,7 @@ " 'max_input_tokens': 1050000,\n", " 'max_output_tokens': 128000,\n", " 'max_tokens': 128000,\n", - " 'mode': {},\n", + " 'mode': None,\n", " 'output_cost_per_token': 1.5e-05,\n", " 'output_cost_per_token_above_272k_tokens': 2.25e-05,\n", " 'output_cost_per_token_batches': 7.5e-06,\n", @@ -615,7 +615,7 @@ " 'max_input_tokens': 1050000,\n", " 'max_output_tokens': 128000,\n", " 'max_tokens': 128000,\n", - " 'mode': {},\n", + " 'mode': None,\n", " 'output_cost_per_token': 1.5e-05,\n", " 'output_cost_per_token_above_272k_tokens': 2.25e-05,\n", " 'output_cost_per_token_flex': 7.5e-06,\n", @@ -1032,20 +1032,20 @@ "text/markdown": [ "
\n", "\n", - "Hey there! How can I help you today?\n", + "Hello! How can I help you today?\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=2, completion_tokens=10, total_tokens=37, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=25, raw={'promptTokenCount': 2, 'candidatesTokenCount': 10, 'totalTokenCount': 37, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 25})`\n", + "- usage: `Usage(prompt_tokens=2, completion_tokens=9, total_tokens=101, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=90, raw={'promptTokenCount': 2, 'candidatesTokenCount': 9, 'totalTokenCount': 101, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 90, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='Hey there! How can I help you today?', data={'text': 'Hey there! How can I help you today?', 'thoughtSignature': 'EqcBCqQBAQw51sd2uld7YR+piFHs+TpFjmhIuEEvCCWXkDQtyBqSGFZvkG0PORLkVf9E8tl+vTcxFaP0Dzw29zx5UueaOuJ05ySHxlsrwCGY2/SM6ClGOt2cDW22T7NU3a6LXiXeJ+G5hZNS9njalTsJmp3hXUN58leZwfJFMjvRZHia+ZZiOg/TD/2wB1QjLUElkTCyPEFBglsVqcSrxUmuD6l397fZA5M='})]), finish_reason=, usage=Usage(prompt_tokens=2, completion_tokens=10, total_tokens=37, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=25, raw={'promptTokenCount': 2, 'candidatesTokenCount': 10, 'totalTokenCount': 37, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 25}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': 'Hey there! How can I help you today?', 'thoughtSignature': 'EqcBCqQBAQw51sd2uld7YR+piFHs+TpFjmhIuEEvCCWXkDQtyBqSGFZvkG0PORLkVf9E8tl+vTcxFaP0Dzw29zx5UueaOuJ05ySHxlsrwCGY2/SM6ClGOt2cDW22T7NU3a6LXiXeJ+G5hZNS9njalTsJmp3hXUN58leZwfJFMjvRZHia+ZZiOg/TD/2wB1QjLUElkTCyPEFBglsVqcSrxUmuD6l397fZA5M='}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 2, 'candidatesTokenCount': 10, 'totalTokenCount': 37, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 25}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'dwPzaYi3GaulnsEPqa7L2QU'})" + "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today?', data={'text': 'Hello! How can I help you today?', 'thoughtSignature': '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'})]), finish_reason=, usage=Usage(prompt_tokens=2, completion_tokens=9, total_tokens=101, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=90, raw={'promptTokenCount': 2, 'candidatesTokenCount': 9, 'totalTokenCount': 101, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 90, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': 'Hello! How can I help you today?', 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 2, 'candidatesTokenCount': 9, 'totalTokenCount': 101, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 90, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': '1QMsaomwA-y8_uMP0M7GkAY'})" ] }, "execution_count": null, @@ -1089,20 +1089,20 @@ "text/markdown": [ "
\n", "\n", - "Hey there! How can I help you today?\n", + "Hello! How can I help you today?\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=2, completion_tokens=10, total_tokens=37, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=25, raw={'promptTokenCount': 2, 'candidatesTokenCount': 10, 'totalTokenCount': 37, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 25})`\n", + "- usage: `Usage(prompt_tokens=2, completion_tokens=9, total_tokens=101, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=90, raw={'promptTokenCount': 2, 'candidatesTokenCount': 9, 'totalTokenCount': 101, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 90, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='Hey there! How can I help you today?', data={'text': 'Hey there! How can I help you today?', 'thoughtSignature': 'EqcBCqQBAQw51sd2uld7YR+piFHs+TpFjmhIuEEvCCWXkDQtyBqSGFZvkG0PORLkVf9E8tl+vTcxFaP0Dzw29zx5UueaOuJ05ySHxlsrwCGY2/SM6ClGOt2cDW22T7NU3a6LXiXeJ+G5hZNS9njalTsJmp3hXUN58leZwfJFMjvRZHia+ZZiOg/TD/2wB1QjLUElkTCyPEFBglsVqcSrxUmuD6l397fZA5M='})]), finish_reason=, usage=Usage(prompt_tokens=2, completion_tokens=10, total_tokens=37, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=25, raw={'promptTokenCount': 2, 'candidatesTokenCount': 10, 'totalTokenCount': 37, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 25}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': 'Hey there! How can I help you today?', 'thoughtSignature': 'EqcBCqQBAQw51sd2uld7YR+piFHs+TpFjmhIuEEvCCWXkDQtyBqSGFZvkG0PORLkVf9E8tl+vTcxFaP0Dzw29zx5UueaOuJ05ySHxlsrwCGY2/SM6ClGOt2cDW22T7NU3a6LXiXeJ+G5hZNS9njalTsJmp3hXUN58leZwfJFMjvRZHia+ZZiOg/TD/2wB1QjLUElkTCyPEFBglsVqcSrxUmuD6l397fZA5M='}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 2, 'candidatesTokenCount': 10, 'totalTokenCount': 37, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 25}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'dwPzaYi3GaulnsEPqa7L2QU'})" + "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today?', data={'text': 'Hello! How can I help you today?', 'thoughtSignature': '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'})]), finish_reason=, usage=Usage(prompt_tokens=2, completion_tokens=9, total_tokens=101, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=90, raw={'promptTokenCount': 2, 'candidatesTokenCount': 9, 'totalTokenCount': 101, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 90, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': 'Hello! How can I help you today?', 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 2, 'candidatesTokenCount': 9, 'totalTokenCount': 101, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2}], 'thoughtsTokenCount': 90, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': '1QMsaomwA-y8_uMP0M7GkAY'})" ] }, "execution_count": null, @@ -1196,28 +1196,26 @@ "text/markdown": [ "
\n", "\n", - "The image shows an adorable **Cavalier King Charles Spaniel puppy**.\n", + "This image shows a very cute **Cavalier King Charles Spaniel puppy** lying in the grass.\n", "\n", "Here are the details:\n", "\n", - "* **The Puppy:** It has classic \"Blenheim\" coloring—a pearly white coat with chestnut-brown patches over its long, floppy ears and around its large, dark eyes. It is lying down, looking directly at the camera with its front paws stretched forward.\n", - "* **Flowers:** To the left of the puppy is a dense cluster of small, daisy-like purple flowers with yellow centers (likely asters).\n", - "* **Environment:** The puppy is resting on green grass. The background behind the puppy is dark and out of focus, featuring what looks like a wooden structure or a large flower pot.\n", - "\n", - "The overall scene is very sweet and focused on the puppy's expressive face.\n", + "* **The Puppy:** It has the classic \"Blenheim\" coloring, which consists of chestnut/reddish-brown markings on a white coat. It has long, floppy brown ears, large soulful dark eyes, and a white blaze running down its face. It is looking directly at the camera with a sweet expression, and one white paw is stretched out in front.\n", + "* **The Flowers:** On the left side of the image, there is a cluster of small purple daisy-like flowers (likely asters) with yellow centers. The puppy is partially tucked behind these flowers.\n", + "* **The Setting:** The puppy is lying on green grass. In the background on the right, there appears to be a dark wooden structure or a large flower pot. The lighting is soft and natural, suggesting an outdoor setting.\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=1087, completion_tokens=167, total_tokens=1873, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=619, raw={'promptTokenCount': 1087, 'candidatesTokenCount': 167, 'totalTokenCount': 1873, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 7}, {'modality': 'IMAGE', 'tokenCount': 1080}], 'thoughtsTokenCount': 619})`\n", + "- usage: `Usage(prompt_tokens=1087, completion_tokens=196, total_tokens=1827, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=544, raw={'promptTokenCount': 1087, 'candidatesTokenCount': 196, 'totalTokenCount': 1827, 'promptTokensDetails': [{'modality': 'IMAGE', 'tokenCount': 1080}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 544, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='The image shows an adorable **Cavalier King Charles Spaniel puppy**.\\n\\nHere are the details:\\n\\n* **The Puppy:** It has classic \"Blenheim\" coloring—a pearly white coat with chestnut-brown patches over its long, floppy ears and around its large, dark eyes. It is lying down, looking directly at the camera with its front paws stretched forward.\\n* **Flowers:** To the left of the puppy is a dense cluster of small, daisy-like purple flowers with yellow centers (likely asters).\\n* **Environment:** The puppy is resting on green grass. The background behind the puppy is dark and out of focus, featuring what looks like a wooden structure or a large flower pot.\\n\\nThe overall scene is very sweet and focused on the puppy\\'s expressive face.', data={'text': 'The image shows an adorable **Cavalier King Charles Spaniel puppy**.\\n\\nHere are the details:\\n\\n* **The Puppy:** It has classic \"Blenheim\" coloring—a pearly white coat with chestnut-brown patches over its long, floppy ears and around its large, dark eyes. It is lying down, looking directly at the camera with its front paws stretched forward.\\n* **Flowers:** To the left of the puppy is a dense cluster of small, daisy-like purple flowers with yellow centers (likely asters).\\n* **Environment:** The puppy is resting on green grass. 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It has long, floppy brown ears, large soulful dark eyes, and a white blaze running down its face. It is looking directly at the camera with a sweet expression, and one white paw is stretched out in front.\\n* **The Flowers:** On the left side of the image, there is a cluster of small purple daisy-like flowers (likely asters) with yellow centers. The puppy is partially tucked behind these flowers.\\n* **The Setting:** The puppy is lying on green grass. In the background on the right, there appears to be a dark wooden structure or a large flower pot. The lighting is soft and natural, suggesting an outdoor setting.', data={'text': 'This image shows a very cute **Cavalier King Charles Spaniel puppy** lying in the grass.\\n\\nHere are the details:\\n\\n* **The Puppy:** It has the classic \"Blenheim\" coloring, which consists of chestnut/reddish-brown markings on a white coat. It has long, floppy brown ears, large soulful dark eyes, and a white blaze running down its face. It is looking directly at the camera with a sweet expression, and one white paw is stretched out in front.\\n* **The Flowers:** On the left side of the image, there is a cluster of small purple daisy-like flowers (likely asters) with yellow centers. The puppy is partially tucked behind these flowers.\\n* **The Setting:** The puppy is lying on green grass. In the background on the right, there appears to be a dark wooden structure or a large flower pot. The lighting is soft and natural, suggesting an outdoor setting.', 'thoughtSignature': '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'})]), finish_reason=, usage=Usage(prompt_tokens=1087, completion_tokens=196, total_tokens=1827, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=544, raw={'promptTokenCount': 1087, 'candidatesTokenCount': 196, 'totalTokenCount': 1827, 'promptTokensDetails': [{'modality': 'IMAGE', 'tokenCount': 1080}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 544, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': 'This image shows a very cute **Cavalier King Charles Spaniel puppy** lying in the grass.\\n\\nHere are the details:\\n\\n* **The Puppy:** It has the classic \"Blenheim\" coloring, which consists of chestnut/reddish-brown markings on a white coat. It has long, floppy brown ears, large soulful dark eyes, and a white blaze running down its face. It is looking directly at the camera with a sweet expression, and one white paw is stretched out in front.\\n* **The Flowers:** On the left side of the image, there is a cluster of small purple daisy-like flowers (likely asters) with yellow centers. The puppy is partially tucked behind these flowers.\\n* **The Setting:** The puppy is lying on green grass. In the background on the right, there appears to be a dark wooden structure or a large flower pot. The lighting is soft and natural, suggesting an outdoor setting.', 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 1087, 'candidatesTokenCount': 196, 'totalTokenCount': 1827, 'promptTokensDetails': [{'modality': 'IMAGE', 'tokenCount': 1080}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 544, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': '1gMsaqDxJJqv_uMP39vXoAw'})" ] }, "execution_count": null, @@ -1242,35 +1240,30 @@ "\n", "
Thinking\n", "\n", - "The user wants me to identify what's in the image. Looking at the image, I can see a small puppy with brown and white fur lying on green grass. Next to the puppy are some purple flowers (likely asters or similar small purple flowers). The puppy appears to be a Cavalier King Charles Spaniel puppy, given its distinctive coloring - white face with brown ears and markings, large dark eyes, and that specific puppy-like appearance. It's lying down with its front paws extended on the grass.\n", - "\n", - "I should describe the image clearly and accurately. The image shows:\n", - "- A puppy (Cavalier King Charles Spaniel)\n", - "- Brown and white coloring\n", - "- Lying on green grass\n", - "- Purple flowers nearby (to the left of the puppy)\n", - "- Cute, looking at camera\n", + "The user is asking me to identify what's in the image. Looking at the image, I can see a small puppy with brown and white fur lying on green grass. The puppy appears to be a Cavalier King Charles Spaniel puppy, with distinctive brown ears, a white blaze on its face, and a brown patch over one eye. Next to the puppy, on the left side, there are purple flowers (looks like aster or daisy-like purple flowers) with yellow centers. The puppy is lying down with its front paws extended, looking directly at the camera with an adorable expression.\n", "\n", - "Let me provide a friendly, descriptive answer.\n", + "I should describe this clearly and accurately for the user.\n", "\n", "
\n", "\n", - "This image shows an adorable **Cavalier King Charles Spaniel puppy** lying on green grass. The puppy has the breed's characteristic **brown and white coat**, with large dark eyes, floppy brown ears, and a white blaze down the center of its face. It's resting with its front paws stretched out, looking directly at the camera. \n", + "This image shows an adorable **Cavalier King Charles Spaniel puppy** lying on green grass. The puppy has the breed's characteristic coloring—white fur with chestnut brown patches on its ears and over one eye, along with a black nose and dark, expressive eyes.\n", "\n", - "Next to the puppy (on the left side of the image) is a cluster of **small purple flowers**—likely asters or daisy-like blooms—growing in the grass. The overall scene is very cute and gives off a sweet, summery, outdoor vibe! 🐶🌸\n", + "Next to the puppy, on the left side, is a cluster of **small purple flowers** (they look like asters or daisy-like blooms) with yellow centers.\n", + "\n", + "The puppy is lying down with its front paws stretched out, looking directly at the camera with an innocent, sweet expression. It's a very cute, classic \"puppy portrait\" style photo!\n", "\n", "
\n", "\n", "- model: `accounts/fireworks/models/kimi-k2p6`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=107, completion_tokens=300, total_tokens=407, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'prompt_tokens': 107, 'total_tokens': 407, 'completion_tokens': 300, 'prompt_tokens_details': {'cached_tokens': 0}})`\n", + "- usage: `Usage(prompt_tokens=107, completion_tokens=260, total_tokens=367, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'prompt_tokens': 107, 'total_tokens': 367, 'completion_tokens': 260, 'prompt_tokens_details': {'cached_tokens': 0}})`\n", "\n", "
\n", "\n", "" ], "text/plain": [ - "Completion(model='accounts/fireworks/models/kimi-k2p6', message=Msg(role='assistant', content=[Part(type='thinking', text=\"The user wants me to identify what's in the image. Looking at the image, I can see a small puppy with brown and white fur lying on green grass. Next to the puppy are some purple flowers (likely asters or similar small purple flowers). The puppy appears to be a Cavalier King Charles Spaniel puppy, given its distinctive coloring - white face with brown ears and markings, large dark eyes, and that specific puppy-like appearance. It's lying down with its front paws extended on the grass.\\n\\nI should describe the image clearly and accurately. The image shows:\\n- A puppy (Cavalier King Charles Spaniel)\\n- Brown and white coloring\\n- Lying on green grass\\n- Purple flowers nearby (to the left of the puppy)\\n- Cute, looking at camera\\n\\nLet me provide a friendly, descriptive answer.\", data=None), Part(type='text', text=\"This image shows an adorable **Cavalier King Charles Spaniel puppy** lying on green grass. The puppy has the breed's characteristic **brown and white coat**, with large dark eyes, floppy brown ears, and a white blaze down the center of its face. It's resting with its front paws stretched out, looking directly at the camera. \\n\\nNext to the puppy (on the left side of the image) is a cluster of **small purple flowers**—likely asters or daisy-like blooms—growing in the grass. The overall scene is very cute and gives off a sweet, summery, outdoor vibe! 🐶🌸\", data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=107, completion_tokens=300, total_tokens=407, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'prompt_tokens': 107, 'total_tokens': 407, 'completion_tokens': 300, 'prompt_tokens_details': {'cached_tokens': 0}}), tool_calls=[], api_name='openai_chat', vendor_name='fireworks_ai', raw={'id': 'chatcmpl-1cde9440db084c7aaeb92b30e84a41b8', 'object': 'chat.completion', 'created': 1777533873, 'model': 'accounts/fireworks/models/kimi-k2p6', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': \"This image shows an adorable **Cavalier King Charles Spaniel puppy** lying on green grass. The puppy has the breed's characteristic **brown and white coat**, with large dark eyes, floppy brown ears, and a white blaze down the center of its face. It's resting with its front paws stretched out, looking directly at the camera. \\n\\nNext to the puppy (on the left side of the image) is a cluster of **small purple flowers**—likely asters or daisy-like blooms—growing in the grass. The overall scene is very cute and gives off a sweet, summery, outdoor vibe! 🐶🌸\", 'reasoning_content': \"The user wants me to identify what's in the image. Looking at the image, I can see a small puppy with brown and white fur lying on green grass. Next to the puppy are some purple flowers (likely asters or similar small purple flowers). The puppy appears to be a Cavalier King Charles Spaniel puppy, given its distinctive coloring - white face with brown ears and markings, large dark eyes, and that specific puppy-like appearance. It's lying down with its front paws extended on the grass.\\n\\nI should describe the image clearly and accurately. The image shows:\\n- A puppy (Cavalier King Charles Spaniel)\\n- Brown and white coloring\\n- Lying on green grass\\n- Purple flowers nearby (to the left of the puppy)\\n- Cute, looking at camera\\n\\nLet me provide a friendly, descriptive answer.\"}, 'finish_reason': 'stop', 'token_ids': None}], 'usage': {'prompt_tokens': 107, 'total_tokens': 407, 'completion_tokens': 300, 'prompt_tokens_details': {'cached_tokens': 0}}, 'prompt_token_ids': [163587, 2482, 163601, 45702, 1573, 306, 566, 4082, 30, 163602, 4017, 163603, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163605, 163604, 198, 163586, 163588, 69702, 163601, 163606]})" + "Completion(model='accounts/fireworks/models/kimi-k2p6', message=Msg(role='assistant', content=[Part(type='thinking', text=\"The user is asking me to identify what's in the image. Looking at the image, I can see a small puppy with brown and white fur lying on green grass. The puppy appears to be a Cavalier King Charles Spaniel puppy, with distinctive brown ears, a white blaze on its face, and a brown patch over one eye. Next to the puppy, on the left side, there are purple flowers (looks like aster or daisy-like purple flowers) with yellow centers. The puppy is lying down with its front paws extended, looking directly at the camera with an adorable expression.\\n\\nI should describe this clearly and accurately for the user.\", data=None), Part(type='text', text='This image shows an adorable **Cavalier King Charles Spaniel puppy** lying on green grass. The puppy has the breed\\'s characteristic coloring—white fur with chestnut brown patches on its ears and over one eye, along with a black nose and dark, expressive eyes.\\n\\nNext to the puppy, on the left side, is a cluster of **small purple flowers** (they look like asters or daisy-like blooms) with yellow centers.\\n\\nThe puppy is lying down with its front paws stretched out, looking directly at the camera with an innocent, sweet expression. It\\'s a very cute, classic \"puppy portrait\" style photo!', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=107, completion_tokens=260, total_tokens=367, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'prompt_tokens': 107, 'total_tokens': 367, 'completion_tokens': 260, 'prompt_tokens_details': {'cached_tokens': 0}}), tool_calls=[], api_name='openai_chat', vendor_name='fireworks_ai', raw={'id': 'chatcmpl-112a29b7e6cc4f7f8dcad3e58457ce60', 'object': 'chat.completion', 'created': 1781269469, 'model': 'accounts/fireworks/models/kimi-k2p6', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'This image shows an adorable **Cavalier King Charles Spaniel puppy** lying on green grass. The puppy has the breed\\'s characteristic coloring—white fur with chestnut brown patches on its ears and over one eye, along with a black nose and dark, expressive eyes.\\n\\nNext to the puppy, on the left side, is a cluster of **small purple flowers** (they look like asters or daisy-like blooms) with yellow centers.\\n\\nThe puppy is lying down with its front paws stretched out, looking directly at the camera with an innocent, sweet expression. It\\'s a very cute, classic \"puppy portrait\" style photo!', 'reasoning_content': \"The user is asking me to identify what's in the image. Looking at the image, I can see a small puppy with brown and white fur lying on green grass. The puppy appears to be a Cavalier King Charles Spaniel puppy, with distinctive brown ears, a white blaze on its face, and a brown patch over one eye. Next to the puppy, on the left side, there are purple flowers (looks like aster or daisy-like purple flowers) with yellow centers. The puppy is lying down with its front paws extended, looking directly at the camera with an adorable expression.\\n\\nI should describe this clearly and accurately for the user.\"}, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 107, 'total_tokens': 367, 'completion_tokens': 260, 'prompt_tokens_details': {'cached_tokens': 0}}})" ] }, "execution_count": null, @@ -1348,27 +1341,28 @@ "text/markdown": [ "
\n", "\n", - "This image is a serene landscape photograph, likely a composite or a high-quality stock photo, showing a view from a wooden deck overlooking a calm lake and mountains.\n", + "This image is a serene landscape photograph featuring a tranquil lake view from the perspective of a wooden deck.\n", + "\n", + "The image is composed of three main layers:\n", "\n", - "The image features several key elements:\n", - "* **Foreground:** A dark, weathered wooden deck or platform made of horizontal planks.\n", - "* **Middle ground:** A very still, blue lake that acts as a mirror, reflecting the trees and mountains.\n", - "* **Background:** A dense line of green evergreen trees along the shoreline, and beyond them, massive, snow-capped mountains under a bright, slightly hazy sky. \n", + "1. **Foreground:** In the immediate foreground, there are weathered, dark brown wooden planks of a deck or platform, leading the viewer's eye into the scene.\n", + "2. **Middle Ground:** The center of the image is a very still, blue lake. The water's surface is so calm that it perfectly reflects the trees and mountains from the background. A thin strip of yellowish-green grass lines the far shore.\n", + "3. **Background:** A dense forest of green trees stands at the base of massive, jagged mountains. The mountains are partially covered in snow or glaciers and have a hazy, ethereal blue appearance, fading into a bright, washed-out sky.\n", "\n", - "The overall composition creates a peaceful and majestic nature scene.\n", + "The overall atmosphere of the image is peaceful and dreamlike, characterized by soft lighting and a cool color palette of blues, greens, and earthy browns.\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=1086, completion_tokens=130, total_tokens=1806, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=590, raw={'promptTokenCount': 1086, 'candidatesTokenCount': 130, 'totalTokenCount': 1806, 'promptTokensDetails': [{'modality': 'IMAGE', 'tokenCount': 1080}, {'modality': 'TEXT', 'tokenCount': 6}], 'thoughtsTokenCount': 590, 'serviceTier': 'standard'})`\n", + "- usage: `Usage(prompt_tokens=1086, completion_tokens=202, total_tokens=2034, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=746, raw={'promptTokenCount': 1086, 'candidatesTokenCount': 202, 'totalTokenCount': 2034, 'promptTokensDetails': [{'modality': 'IMAGE', 'tokenCount': 1080}, {'modality': 'TEXT', 'tokenCount': 6}], 'thoughtsTokenCount': 746, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='This image is a serene landscape photograph, likely a composite or a high-quality stock photo, showing a view from a wooden deck overlooking a calm lake and mountains.\\n\\nThe image features several key elements:\\n* **Foreground:** A dark, weathered wooden deck or platform made of horizontal planks.\\n* **Middle ground:** A very still, blue lake that acts as a mirror, reflecting the trees and mountains.\\n* **Background:** A dense line of green evergreen trees along the shoreline, and beyond them, massive, snow-capped mountains under a bright, slightly hazy sky. \\n\\nThe overall composition creates a peaceful and majestic nature scene.', data={'text': 'This image is a serene landscape photograph, likely a composite or a high-quality stock photo, showing a view from a wooden deck overlooking a calm lake and mountains.\\n\\nThe image features several key elements:\\n* **Foreground:** A dark, weathered wooden deck or platform made of horizontal planks.\\n* **Middle ground:** A very still, blue lake that acts as a mirror, reflecting the trees and mountains.\\n* **Background:** A dense line of green evergreen trees along the shoreline, and beyond them, massive, snow-capped mountains under a bright, slightly hazy sky. \\n\\nThe overall composition creates a peaceful and majestic nature scene.', 'thoughtSignature': 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and beyond them, massive, snow-capped mountains under a bright, slightly hazy sky. \\n\\nThe overall composition creates a peaceful and majestic nature scene.', 'thoughtSignature': 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'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 1086, 'candidatesTokenCount': 130, 'totalTokenCount': 1806, 'promptTokensDetails': [{'modality': 'IMAGE', 'tokenCount': 1080}, {'modality': 'TEXT', 'tokenCount': 6}], 'thoughtsTokenCount': 590, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'LKMWapWPLujO_uMPpJu10AM'})" + "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text=\"This image is a serene landscape photograph featuring a tranquil lake view from the perspective of a wooden deck.\\n\\nThe image is composed of three main layers:\\n\\n1. **Foreground:** In the immediate foreground, there are weathered, dark brown wooden planks of a deck or platform, leading the viewer's eye into the scene.\\n2. **Middle Ground:** The center of the image is a very still, blue lake. The water's surface is so calm that it perfectly reflects the trees and mountains from the background. A thin strip of yellowish-green grass lines the far shore.\\n3. **Background:** A dense forest of green trees stands at the base of massive, jagged mountains. The mountains are partially covered in snow or glaciers and have a hazy, ethereal blue appearance, fading into a bright, washed-out sky.\\n\\nThe overall atmosphere of the image is peaceful and dreamlike, characterized by soft lighting and a cool color palette of blues, greens, and earthy browns.\", data={'text': \"This image is a serene landscape photograph featuring a tranquil lake view from the perspective of a wooden deck.\\n\\nThe image is composed of three main layers:\\n\\n1. **Foreground:** In the immediate foreground, there are weathered, dark brown wooden planks of a deck or platform, leading the viewer's eye into the scene.\\n2. **Middle Ground:** The center of the image is a very still, blue lake. The water's surface is so calm that it perfectly reflects the trees and mountains from the background. A thin strip of yellowish-green grass lines the far shore.\\n3. **Background:** A dense forest of green trees stands at the base of massive, jagged mountains. The mountains are partially covered in snow or glaciers and have a hazy, ethereal blue appearance, fading into a bright, washed-out sky.\\n\\nThe overall atmosphere of the image is peaceful and dreamlike, characterized by soft lighting and a cool color palette of blues, greens, and earthy browns.\", 'thoughtSignature': 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The water's surface is so calm that it perfectly reflects the trees and mountains from the background. A thin strip of yellowish-green grass lines the far shore.\\n3. **Background:** A dense forest of green trees stands at the base of massive, jagged mountains. The mountains are partially covered in snow or glaciers and have a hazy, ethereal blue appearance, fading into a bright, washed-out sky.\\n\\nThe overall atmosphere of the image is peaceful and dreamlike, characterized by soft lighting and a cool color palette of blues, greens, and earthy browns.\", 'thoughtSignature': 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'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 1086, 'candidatesTokenCount': 202, 'totalTokenCount': 2034, 'promptTokensDetails': [{'modality': 'IMAGE', 'tokenCount': 1080}, {'modality': 'TEXT', 'tokenCount': 6}], 'thoughtsTokenCount': 746, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': '3wMsauf7CNiv_PUPlsDV6Qc'})" ] }, "execution_count": null, @@ -1399,20 +1393,20 @@ "text/markdown": [ "
\n", "\n", - "Based on the text in the document, the author is **Jeremy Howard**, co-founder of **fast.ai**.\n", + "Based on the text in the document, the author is **Jeremy Howard**, co-founder of fast.ai.\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=541, completion_tokens=24, total_tokens=769, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=204, raw={'promptTokenCount': 541, 'candidatesTokenCount': 24, 'totalTokenCount': 769, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 9}, {'modality': 'IMAGE', 'tokenCount': 532}], 'thoughtsTokenCount': 204})`\n", + "- usage: `Usage(prompt_tokens=541, completion_tokens=23, total_tokens=667, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=103, raw={'promptTokenCount': 541, 'candidatesTokenCount': 23, 'totalTokenCount': 667, 'promptTokensDetails': [{'modality': 'IMAGE', 'tokenCount': 532}, {'modality': 'TEXT', 'tokenCount': 9}], 'thoughtsTokenCount': 103, 'serviceTier': 'standard'})`\n", "\n", "
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This simple fact has been observed by humans for thousands of years.\"', data={'text': 'The audio says: \"The sun rises in the east and sets in the west. 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This simple fact has been observed by humans for thousands of years.\"', 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 181, 'candidatesTokenCount': 30, 'totalTokenCount': 342, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 7}, {'modality': 'AUDIO', 'tokenCount': 174}], 'thoughtsTokenCount': 131, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'BQQsas1glpv-4w_knNGIDA'})" ] }, "execution_count": null, @@ -1583,20 +1577,20 @@ "text/markdown": [ "
\n", "\n", - "The audio says: \"The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.\"\n", + "The audio is saying: \"The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.\"\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=181, completion_tokens=30, total_tokens=367, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=156, raw={'promptTokenCount': 181, 'candidatesTokenCount': 30, 'totalTokenCount': 367, 'promptTokensDetails': [{'modality': 'AUDIO', 'tokenCount': 174}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 156, 'serviceTier': 'standard'})`\n", + "- usage: `Usage(prompt_tokens=181, completion_tokens=31, total_tokens=436, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=224, raw={'promptTokenCount': 181, 'candidatesTokenCount': 31, 'totalTokenCount': 436, 'promptTokensDetails': [{'modality': 'AUDIO', 'tokenCount': 174}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 224, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "
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This simple fact has been observed by humans for thousands of years.\"', 'thoughtSignature': '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'})]), finish_reason=, usage=Usage(prompt_tokens=181, completion_tokens=30, total_tokens=367, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=156, raw={'promptTokenCount': 181, 'candidatesTokenCount': 30, 'totalTokenCount': 367, 'promptTokensDetails': [{'modality': 'AUDIO', 'tokenCount': 174}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 156, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': 'The audio says: \"The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.\"', 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 181, 'candidatesTokenCount': 30, 'totalTokenCount': 367, 'promptTokensDetails': [{'modality': 'AUDIO', 'tokenCount': 174}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 156, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'I6QWau-IELLE1MkPxsbI0Aw'})" + "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='The audio is saying: \"The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.\"', data={'text': 'The audio is saying: \"The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.\"', 'thoughtSignature': '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'})]), finish_reason=, usage=Usage(prompt_tokens=181, completion_tokens=31, total_tokens=436, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=224, raw={'promptTokenCount': 181, 'candidatesTokenCount': 31, 'totalTokenCount': 436, 'promptTokensDetails': [{'modality': 'AUDIO', 'tokenCount': 174}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 224, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': 'The audio is saying: \"The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.\"', 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 181, 'candidatesTokenCount': 31, 'totalTokenCount': 436, 'promptTokensDetails': [{'modality': 'AUDIO', 'tokenCount': 174}, {'modality': 'TEXT', 'tokenCount': 7}], 'thoughtsTokenCount': 224, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'BwQsapLpIcPQ_uMPtPqaiAw'})" ] }, "execution_count": null, @@ -1630,20 +1624,20 @@ "text/markdown": [ "
\n", "\n", - "A photographer named Saeka Shimada takes a walk through Tokyo at night, using the Google Pixel 8 Pro's \"Video Boost\" and \"Night Sight\" features to capture vibrant and detailed video in low-light settings.\n", + "Saeka Shimada, a Tokyo-based photographer, takes viewers on a tour of Tokyo at night to showcase the Google Pixel 8 Pro’s new \"Video Boost\" feature. This feature enhances low-light videos with Night Sight to create higher-quality videos after they've been recorded. Saeka records videos throughout different Tokyo neighborhoods, including Sancha and Shibuya, and shows how the feature enhances her night-time videos.\n", "\n", "
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\n", "\n", "
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\n", "\n", - "A Tokyo-based photographer named Saeka Shimada explores the city's vibrant streets and alleys at night, demonstrating the \"Video Boost\" and \"Night Sight\" capabilities of the Google Pixel 8 Pro for high-quality low-light videography.\n", + "A Tokyo-based photographer named Saeka Shimada demonstrates the \"Video Boost\" feature on the Google Pixel 8 Pro. She explores different neighborhoods in Tokyo at night, capturing high-quality, low-light videos and photos to showcase the phone's enhanced \"Night Sight\" capabilities.\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=5210, completion_tokens=51, total_tokens=5766, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=505, raw={'promptTokenCount': 5210, 'candidatesTokenCount': 51, 'totalTokenCount': 5766, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 17}, {'modality': 'VIDEO', 'tokenCount': 5193}], 'thoughtsTokenCount': 505, 'serviceTier': 'standard'})`\n", + "- usage: `Usage(prompt_tokens=5210, completion_tokens=57, total_tokens=5633, cached_tokens=3537, cache_creation_tokens=0, reasoning_tokens=366, raw={'promptTokenCount': 5210, 'candidatesTokenCount': 57, 'totalTokenCount': 5633, 'cachedContentTokenCount': 3537, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 17}, {'modality': 'VIDEO', 'tokenCount': 5193}], 'cacheTokensDetails': [{'modality': 'VIDEO', 'tokenCount': 3526}, {'modality': 'TEXT', 'tokenCount': 11}], 'thoughtsTokenCount': 366, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "
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She explores different neighborhoods in Tokyo at night, capturing high-quality, low-light videos and photos to showcase the phone\\'s enhanced \"Night Sight\" capabilities.', 'thoughtSignature': 'EvIMCu8MAQw51sesf7mGCWw26xPQCSew7zimEG3pehRK1KFDBmUXK7/nMeLbISDTxYWQQc+0IBugNcS7AlvgfdRvdFtPfSUdyC0hOPFZ8gs8hTQa5BcrCQQGrwrh4KVXTsTlSk7nBOfFhReGi3ll5Ruuu6P+eKibxvEdXWJMlNkxw2FWuR9wgH2tO7hshcXbOHp1qHZenMdqIVAioGxCalfsj9Zk0HI/Y14BocTEzLJYRuLaaPf2ZRDRy5+1tiSsmPSXoTQ+EMlY1ueXTlW9RmSjnMZCSfVP0PmxJrIv8VWK6KbdTKmZFheHZmV1lB+KH3/F5OGIpxt/zpnykeCllTOj6VS+ZmCor7P3s1cn8JXwo5OyTub5r+TBlAer3ia3qr+s74NdEY2UV3+tvvwz5GZRxhFEAenp0aiU7NiCU4tn/YcJPEHsWghc8CZJh9XLoUMd7z4ZoDS1c7fW+GxOygHFeMse7qem4+B1QAsD3Fly4Qt6+gZvSpyiViWzJHTk7uw2T3kA86VjYLvgd0hGOoZSAWubJp6AaRdil1U8i4Jq2GPzhgcBgx9nXPu9vezIkqmHSxZuHN9HcUBUgwkuyPg1Zc4U7Ts+LuNAYWzkytm8u7DaIJVVcIRcZTxL/yYncNtQToOGZZQdtAmAAkuZEF92H/lCRUTxCBMTiq7Nr+Ump/H2ey8WSeqgb30PRkps9pC3TE/v5Z4Jx+4aaQ8ITyeyuDUmWDsavapIkAQW9zn7sBsUy8iSV1b26SiooaCAY1fOxF6ZJet4Unv4zAo0rU4Cgf5B7awbeW4c6zCp01rdA9/6Jdfug6xzRWQwIfNLkq1IAWLbaVYulSjv91NegcCHdOVjN0ZKbfHVaZ17bVRkUpLILIGrCdGf1d/EMhEOAY/M6u0MssE8VhngNgGGUnR07rocz8wj6ao9kEzLzkq9od+B47PRzeLeuvP+rmHiJH/jrzfodaHEKDsr9eK2UED7wEc9cuJU9U0zJ6BG3Wz0h9nKa1IWPqTua+pQoO9NoNf8qFS0L9sGzghjVPAkudHpbq3SyLa9IBwGw+XSAfLbqeiZqCG5TJ02JVU9dwLFoaEXZUje6BwZRsMCCX9o8nH9ex+mPFdk0JQJ71Yl2vjGIvYvzZOMmPBH8ToJVDVvnHcThrpKu1AyJkQfvbzmTcRiJeuvXhOtPJlB1enqr0fidZpyailqz7ZI07i+0qJAld1gXbbkRurLxHRFdZRK1cm3pgYXfqh4gF0Fw0OwnzjYihdq8YwyD5nQZj40N17uEjKyA00UsVz1/X1uxncrRm/TdO4hVIhT3pCDdMqN80MQnnS1TeLAzfSIhTEam3BHs/uaVZr+q2iHA+4/hhlT6RQIQorqt6Stfn8O4AstOqrEHXQxduevK4ilTqTk7mP9Kh6rNvPuq8wD9jaE6YcZr0HR84v+YWVbXJ3BxfQ9D7lzbQFBf8dRmrPBGQFSk7kvgC6CIQ1wpou/pV2LaBGfg6fkAu/lqA3PtIq8jk1ub+ehyENCeaoyxtiNlYl1Dmb2C3CXB2wWziE45PMiHAGBdOZ10fdjPFYBZt+YEoim9Fh7AHeDnXSqcGdbDHc/SFam2RGiurEfWBl/EJagWKMQV+GtifamZzsNJgnXst8bZhk0N1C2VcxbDbEE0Qzi/LULNHHNbzMyonVw/7Yje9226V2X28+muU0iMnkuGYPeyLcVcd1hnFNXR0h5JuCaCpRIQDGs5/EKyCItx7xb57/GyhV4GuiGCXHCoREkDIfbUWBGRUWlMgC8zJV8TXjir6cw85mT+AfeO3f6QOxUZVmKG4ZQpR7tByK86yh4p+vtRO4hp6a/6sa/e9UlS01qoH//BUgOHtH01x1Da9gpaPqWWTHGXQBsPv6GqnJTg6aznXu1m+28tnEH5nFBd/nFdQE2koqp0o+4tNZodmGVFZbqPAPgxrN4O5rcukYfMyf5h8pi34Bu0sezBvwyNEkT2W3iwoHhB/DvgAh+eL4Jb3N7+nF4PhN/RuzqF8IklD6NO5NJ6wITIly5d9cGa6X4lv+aJcfpS5ltu/CuKuoZC5hs/Ez0sRvRdjR4unyxLJDhN06cHAUZjBnb4MnITFxVmTSdLj9Gkv6fbniT9sCV2wNrDZDKcMAdv50LJSnJ5A6BAryZTa9t5eJSo28fS9JaVgxvFVHG20yKYxGpc8xZjxyORkodlYrF'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 5210, 'candidatesTokenCount': 57, 'totalTokenCount': 5633, 'cachedContentTokenCount': 3537, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 17}, {'modality': 'VIDEO', 'tokenCount': 5193}], 'cacheTokensDetails': [{'modality': 'VIDEO', 'tokenCount': 3526}, {'modality': 'TEXT', 'tokenCount': 11}], 'thoughtsTokenCount': 366, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'HQQsap64Iq2M_PUP2fe_wAI'})" ] }, "execution_count": null, @@ -1963,10 +1957,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "- [\"\\nI'll solve this step-by-step, using parallel calls where possible.\\n\\n\", '
\\n\\n```json\\n{\\n \"id\": \"toolu_01KjnQH2Nsz2viQ7XYpLW3Ta\",\\n \"call\": { \"function\": \"simple_add\", \"arguments\": { \"a\": 10, \"b\": 5 } },\\n \"result\": \"15\",\\n \"server\": false\\n}\\n```\\n\\n
', None]\n", - "- ['{\\n \"id\": \"toolu_01KjnQH2Nsz2viQ7XYpLW3Ta\",\\n \"call\": { \"function\": \"simple_add\", \"arguments\": { \"a\": 10, \"b\": 5 } },\\n \"result\": \"15\",\\n \"server\": false\\n}', '\\n\\n', '
\\n\\n```json\\n{\\n \"id\": \"toolu_01Koi2EZrGZsBbnQ13wuuvzY\",\\n \"call\": { \"function\": \"simple_add\", \"arguments\": { \"a\": 2, \"b\": 1 } },\\n \"result\": \"3\",\\n \"server\": false\\n}\\n```\\n\\n
']\n", - "- [None, '{\\n \"id\": \"toolu_01Koi2EZrGZsBbnQ13wuuvzY\",\\n \"call\": { \"function\": \"simple_add\", \"arguments\": { \"a\": 2, \"b\": 1 } },\\n \"result\": \"3\",\\n \"server\": false\\n}', '\\n\\nNow I need to multiply 15 * 3 before I can do the final division:\\n\\n']\n", - "- ['
\\n\\n```json\\n{\\n \"id\": \"toolu_0141NRaWUjmGtwxZjWkyiq6C\",\\n \"call\": { \"function\": \"multiply\", \"arguments\": { \"a\": 15, \"b\": 3 } },\\n \"result\": \"45\",\\n \"server\": false\\n}\\n```\\n\\n
', None, '{\\n \"id\": \"toolu_0141NRaWUjmGtwxZjWkyiq6C\",\\n \"call\": { \"function\": \"multiply\", \"arguments\": { \"a\": 15, \"b\": 3 } },\\n \"result\": \"45\",\\n \"server\": false\\n}']\n", + "- [\"\\nI'll solve this step-by-step, using parallel calls where possible.\\n\\n\", '
\\n\\n```json\\n{\\n \"id\": \"toolu_01KjnQH2Nsz2viQ7XYpLW3Ta\",\\n \"call\": { \"function\": \"simple_add\", \"arguments\": { \"a\": 10, \"b\": 5 } },\\n \"result\": \"15\",\\n \"server\": false\\n}\\n```\\n\\n
', None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- ['{\\n \"id\": \"toolu_01KjnQH2Nsz2viQ7XYpLW3Ta\",\\n \"call\": { \"function\": \"simple_add\", \"arguments\": { \"a\": 10, \"b\": 5 } },\\n \"result\": \"15\",\\n \"server\": false\\n}', '\\n\\n', '
\\n\\n```json\\n{\\n \"id\": \"toolu_01Koi2EZrGZsBbnQ13wuuvzY\",\\n \"call\": { \"function\": \"simple_add\", \"arguments\": { \"a\": 2, \"b\": 1 } },\\n \"result\": \"3\",\\n \"server\": false\\n}\\n```\\n\\n
']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- [None, '{\\n \"id\": \"toolu_01Koi2EZrGZsBbnQ13wuuvzY\",\\n \"call\": { \"function\": \"simple_add\", \"arguments\": { \"a\": 2, \"b\": 1 } },\\n \"result\": \"3\",\\n \"server\": false\\n}', '\\n\\nNow I need to multiply 15 * 3 before I can do the final division:\\n\\n']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- ['
\\n\\n```json\\n{\\n \"id\": \"toolu_0141NRaWUjmGtwxZjWkyiq6C\",\\n \"call\": { \"function\": \"multiply\", \"arguments\": { \"a\": 15, \"b\": 3 } },\\n \"result\": \"45\",\\n \"server\": false\\n}\\n```\\n\\n
', None, '{\\n \"id\": \"toolu_0141NRaWUjmGtwxZjWkyiq6C\",\\n \"call\": { \"function\": \"multiply\", \"arguments\": { \"a\": 15, \"b\": 3 } },\\n \"result\": \"45\",\\n \"server\": false\\n}']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "- [\"\\n\\n
Cache hit: 81.8% | Tokens: total=23,276 input=23,158 (+18,910 cached, 0 new) output=118 (reasoning 23)\\n\\n`Usage(prompt_tokens=3, completion_tokens=10, total_tokens=13, raw={'input_tokens': 3, 'cache_creation_input_tokens': 2079, 'cache_read_input_tokens': 2070, 'cache_creation': {'ephemeral_5m_input_tokens': 2079, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 10, 'service_tier': 'standard', 'inference_geo': 'global'})`\\n\\n
\\n\", None, None]\n" ] } @@ -2364,21 +2382,21 @@ "\n", "\n", "\n", - "🔧 simple_div({'b': 5, 'a': 45})\n", + "🔧 simple_div({'a': 45, 'b': 5})\n", "\n", "\n", "
\n", "\n", "- model: `models/gemini-3.1-pro-preview`\n", "- finish_reason: `tool_calls`\n", - "- usage: `Usage(prompt_tokens=205, completion_tokens=19, total_tokens=444, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=220, raw={'promptTokenCount': 205, 'candidatesTokenCount': 19, 'totalTokenCount': 444, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 205}], 'thoughtsTokenCount': 220, 'serviceTier': 'standard'})`\n", + "- usage: `Usage(prompt_tokens=205, completion_tokens=19, total_tokens=422, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=198, raw={'promptTokenCount': 205, 'candidatesTokenCount': 19, 'totalTokenCount': 422, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 205}], 'thoughtsTokenCount': 198, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "" ], "text/plain": [ - "Completion(model='models/gemini-3.1-pro-preview', message=Msg(role='assistant', content=[Part(type=, text=None, data={'thoughtSignature': 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total_tokens=444, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=220, raw={'promptTokenCount': 205, 'candidatesTokenCount': 19, 'totalTokenCount': 444, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 205}], 'thoughtsTokenCount': 220, 'serviceTier': 'standard'}), tool_calls=[ToolCall(id='e1uanikq', name='simple_div', arguments={'b': 5, 'a': 45}, server=False, extra={'thoughtSignature': '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'})], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'functionCall': {'name': 'simple_div', 'args': {'b': 5, 'a': 45}, 'id': 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How can I help you?', data=None)]) \n", - "\n", - "Msg(role='user', content=[Part(type=, text='Call some functions!', data=None)]) \n", - "\n", - "Msg(role='assistant', content=[Part(type=, text=\"I'll solve this step-by-step, using parallel calls where possible.\", data=None), Part(type=, text=None, data={'id': 'toolu_01KjnQH2Nsz2viQ7XYpLW3Ta', 'name': 'simple_add', 'arguments': {'a': 10, 'b': 5}}), Part(type=, text=None, data={'id': 'toolu_01Koi2EZrGZsBbnQ13wuuvzY', 'name': 'simple_add', 'arguments': {'a': 2, 'b': 1}})]) \n", - "\n", - "Msg(role='tool', content=[Part(type=, text='15', data={'id': 'toolu_01KjnQH2Nsz2viQ7XYpLW3Ta', 'name': 'simple_add'}), Part(type=, text='3', data={'id': 'toolu_01Koi2EZrGZsBbnQ13wuuvzY', 'name': 'simple_add'})]) \n", - "\n", - "Msg(role='assistant', content=[Part(type=, text='Now I need to multiply 15 * 3 before I can do the final division:', data=None), Part(type=, text=None, data={'id': 'toolu_0141NRaWUjmGtwxZjWkyiq6C', 'name': 'multiply', 'arguments': {'a': 15, 'b': 3}, 'cache_control': {'type': 'ephemeral'}})]) \n", - "\n", - "Msg(role='tool', content=[Part(type=, text='45', data={'id': 'toolu_0141NRaWUjmGtwxZjWkyiq6C', 'name': 'multiply'})]) \n", - "\n", - "Msg(role='assistant', content=[Part(type=, text='.', data={'cache_control': {'type': 'ephemeral'}})]) \n", - "\n", - "Msg(role='user', content=[Part(type=, text='again', data=None)]) \n", + "Msg(role='user', content=[Part(type=, text='Hello!', data={'cache_control': {'type': 'ephemeral'}})]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Msg(role='assistant', content=[Part(type=, text='Hi! How can I help you?', data=None)]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Msg(role='user', content=[Part(type=, text='Call some functions!', data=None)]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Msg(role='assistant', content=[Part(type=, text=\"I'll solve this step-by-step, using parallel calls where possible.\", data=None), Part(type=, text=None, data={'id': 'toolu_01KjnQH2Nsz2viQ7XYpLW3Ta', 'name': 'simple_add', 'arguments': {'a': 10, 'b': 5}}), Part(type=, text=None, data={'id': 'toolu_01Koi2EZrGZsBbnQ13wuuvzY', 'name': 'simple_add', 'arguments': {'a': 2, 'b': 1}})]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Msg(role='tool', content=[Part(type=, text='15', data={'id': 'toolu_01KjnQH2Nsz2viQ7XYpLW3Ta', 'name': 'simple_add'}), Part(type=, text='3', data={'id': 'toolu_01Koi2EZrGZsBbnQ13wuuvzY', 'name': 'simple_add'})]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Msg(role='assistant', content=[Part(type=, text='Now I need to multiply 15 * 3 before I can do the final division:', data=None), Part(type=, text=None, data={'id': 'toolu_0141NRaWUjmGtwxZjWkyiq6C', 'name': 'multiply', 'arguments': {'a': 15, 'b': 3}, 'cache_control': {'type': 'ephemeral'}})]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Msg(role='tool', content=[Part(type=, text='45', data={'id': 'toolu_0141NRaWUjmGtwxZjWkyiq6C', 'name': 'multiply'})]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Msg(role='assistant', content=[Part(type=, text='.', data={'cache_control': {'type': 'ephemeral'}})]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Msg(role='user', content=[Part(type=, text='again', data=None)]) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n" ] } @@ -3057,37 +3177,33 @@ "text/markdown": [ "
\n", "\n", - "# Multiplying 213127 × 451233\n", + "## Step-by-Step Solution\n", "\n", - "## Setting Up the Problem\n", + "I'll break this down using the distributive property:\n", "\n", - "I'll break 451233 into **450000 + 1233** to make this manageable.\n", + "**213127 × 451233 = 213127 × (450000 + 1233)**\n", "\n", - "$$213127 × 451233 = 213127 × 450000 + 213127 × 1233$$\n", - "\n", - "## Part 1: 213127 × 450000\n", + "### Part 1: 213127 × 450000\n", "\n", "First, 213127 × 45:\n", "- 213127 × 40 = 8,525,080\n", "- 213127 × 5 = 1,065,635\n", "- Sum: **9,590,715**\n", "\n", - "Then multiply by 10000:\n", - "$$213127 × 450000 = 95,907,150,000$$\n", + "Then × 10,000: **95,907,150,000**\n", "\n", - "## Part 2: 213127 × 1233\n", + "### Part 2: 213127 × 1233\n", "\n", "Break into 1000 + 233:\n", - "- **213127 × 1000** = 213,127,000\n", - "- **213127 × 233:**\n", + "- 213127 × 1000 = 213,127,000\n", + "- 213127 × 233:\n", " - 213127 × 200 = 42,625,400\n", " - 213127 × 33 = 7,033,191\n", - " - Sum: 49,658,591\n", + " - Sum: **49,658,591**\n", "\n", - "Add together:\n", - "$$213127 × 1233 = 213,127,000 + 49,658,591 = 262,785,591$$\n", + "Adding: 213,127,000 + 49,658,591 = **262,785,591**\n", "\n", - "## Final Addition\n", + "### Final Addition\n", "\n", "$$95,907,150,000 + 262,785,591 = \\boxed{96,169,935,591}$$\n", "\n", @@ -3095,14 +3211,14 @@ "\n", "- model: `claude-opus-4-8`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=22, completion_tokens=678, total_tokens=700, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 22, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 678, 'output_tokens_details': {'thinking_tokens': 299}, 'service_tier': 'standard', 'inference_geo': 'global'})`\n", + "- usage: `Usage(prompt_tokens=22, completion_tokens=577, total_tokens=599, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 22, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 577, 'output_tokens_details': {'thinking_tokens': 258}, 'service_tier': 'standard', 'inference_geo': 'global'})`\n", "\n", "\n", "\n", "
" ], "text/plain": [ - "Completion(model='claude-opus-4-8', message=Msg(role='assistant', content=[Part(type=, text='', data={'type': 'thinking', 'thinking': '', 'signature': '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'}), Part(type=, text=\"# Multiplying 213127 × 451233\\n\\n## Setting Up the Problem\\n\\nI'll break 451233 into **450000 + 1233** to make this manageable.\\n\\n$$213127 × 451233 = 213127 × 450000 + 213127 × 1233$$\\n\\n## Part 1: 213127 × 450000\\n\\nFirst, 213127 × 45:\\n- 213127 × 40 = 8,525,080\\n- 213127 × 5 = 1,065,635\\n- Sum: **9,590,715**\\n\\nThen multiply by 10000:\\n$$213127 × 450000 = 95,907,150,000$$\\n\\n## Part 2: 213127 × 1233\\n\\nBreak into 1000 + 233:\\n- **213127 × 1000** = 213,127,000\\n- **213127 × 233:**\\n - 213127 × 200 = 42,625,400\\n - 213127 × 33 = 7,033,191\\n - Sum: 49,658,591\\n\\nAdd together:\\n$$213127 × 1233 = 213,127,000 + 49,658,591 = 262,785,591$$\\n\\n## Final Addition\\n\\n$$95,907,150,000 + 262,785,591 = \\\\boxed{96,169,935,591}$$\", data={'type': 'text', 'text': \"# Multiplying 213127 × 451233\\n\\n## Setting Up the Problem\\n\\nI'll break 451233 into **450000 + 1233** to make this manageable.\\n\\n$$213127 × 451233 = 213127 × 450000 + 213127 × 1233$$\\n\\n## Part 1: 213127 × 450000\\n\\nFirst, 213127 × 45:\\n- 213127 × 40 = 8,525,080\\n- 213127 × 5 = 1,065,635\\n- Sum: **9,590,715**\\n\\nThen multiply by 10000:\\n$$213127 × 450000 = 95,907,150,000$$\\n\\n## Part 2: 213127 × 1233\\n\\nBreak into 1000 + 233:\\n- **213127 × 1000** = 213,127,000\\n- **213127 × 233:**\\n - 213127 × 200 = 42,625,400\\n - 213127 × 33 = 7,033,191\\n - Sum: 49,658,591\\n\\nAdd together:\\n$$213127 × 1233 = 213,127,000 + 49,658,591 = 262,785,591$$\\n\\n## Final Addition\\n\\n$$95,907,150,000 + 262,785,591 = \\\\boxed{96,169,935,591}$$\"})]), finish_reason=, usage=Usage(prompt_tokens=22, completion_tokens=678, total_tokens=700, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 22, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 678, 'output_tokens_details': {'thinking_tokens': 299}, 'service_tier': 'standard', 'inference_geo': 'global'}), tool_calls=[], api_name='anthropic', vendor_name='anthropic', raw={'model': 'claude-opus-4-8', 'id': 'msg_01JxZCqWVJjzhryxMMqBrRU9', 'type': 'message', 'role': 'assistant', 'content': [{'type': 'thinking', 'thinking': '', 'signature': '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'}, {'type': 'text', 'text': \"# Multiplying 213127 × 451233\\n\\n## Setting Up the Problem\\n\\nI'll break 451233 into **450000 + 1233** to make this manageable.\\n\\n$$213127 × 451233 = 213127 × 450000 + 213127 × 1233$$\\n\\n## Part 1: 213127 × 450000\\n\\nFirst, 213127 × 45:\\n- 213127 × 40 = 8,525,080\\n- 213127 × 5 = 1,065,635\\n- Sum: **9,590,715**\\n\\nThen multiply by 10000:\\n$$213127 × 450000 = 95,907,150,000$$\\n\\n## Part 2: 213127 × 1233\\n\\nBreak into 1000 + 233:\\n- **213127 × 1000** = 213,127,000\\n- **213127 × 233:**\\n - 213127 × 200 = 42,625,400\\n - 213127 × 33 = 7,033,191\\n - Sum: 49,658,591\\n\\nAdd together:\\n$$213127 × 1233 = 213,127,000 + 49,658,591 = 262,785,591$$\\n\\n## Final Addition\\n\\n$$95,907,150,000 + 262,785,591 = \\\\boxed{96,169,935,591}$$\"}], 'stop_reason': 'end_turn', 'stop_sequence': None, 'stop_details': None, 'usage': {'input_tokens': 22, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 678, 'output_tokens_details': {'thinking_tokens': 299}, 'service_tier': 'standard', 'inference_geo': 'global'}})" + "Completion(model='claude-opus-4-8', message=Msg(role='assistant', content=[Part(type=, text='', data={'type': 'thinking', 'thinking': '', 'signature': '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'}), Part(type=, text=\"## Step-by-Step Solution\\n\\nI'll break this down using the distributive property:\\n\\n**213127 × 451233 = 213127 × (450000 + 1233)**\\n\\n### Part 1: 213127 × 450000\\n\\nFirst, 213127 × 45:\\n- 213127 × 40 = 8,525,080\\n- 213127 × 5 = 1,065,635\\n- Sum: **9,590,715**\\n\\nThen × 10,000: **95,907,150,000**\\n\\n### Part 2: 213127 × 1233\\n\\nBreak into 1000 + 233:\\n- 213127 × 1000 = 213,127,000\\n- 213127 × 233:\\n - 213127 × 200 = 42,625,400\\n - 213127 × 33 = 7,033,191\\n - Sum: **49,658,591**\\n\\nAdding: 213,127,000 + 49,658,591 = **262,785,591**\\n\\n### Final Addition\\n\\n$$95,907,150,000 + 262,785,591 = \\\\boxed{96,169,935,591}$$\", data={'type': 'text', 'text': \"## Step-by-Step Solution\\n\\nI'll break this down using the distributive property:\\n\\n**213127 × 451233 = 213127 × (450000 + 1233)**\\n\\n### Part 1: 213127 × 450000\\n\\nFirst, 213127 × 45:\\n- 213127 × 40 = 8,525,080\\n- 213127 × 5 = 1,065,635\\n- Sum: **9,590,715**\\n\\nThen × 10,000: **95,907,150,000**\\n\\n### Part 2: 213127 × 1233\\n\\nBreak into 1000 + 233:\\n- 213127 × 1000 = 213,127,000\\n- 213127 × 233:\\n - 213127 × 200 = 42,625,400\\n - 213127 × 33 = 7,033,191\\n - Sum: **49,658,591**\\n\\nAdding: 213,127,000 + 49,658,591 = **262,785,591**\\n\\n### Final Addition\\n\\n$$95,907,150,000 + 262,785,591 = \\\\boxed{96,169,935,591}$$\"})]), finish_reason=, usage=Usage(prompt_tokens=22, completion_tokens=577, total_tokens=599, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 22, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 577, 'output_tokens_details': {'thinking_tokens': 258}, 'service_tier': 'standard', 'inference_geo': 'global'}), tool_calls=[], api_name='anthropic', vendor_name='anthropic', raw={'model': 'claude-opus-4-8', 'id': 'msg_01RGW57dVeXuQ3qq1x6Lh9WV', 'type': 'message', 'role': 'assistant', 'content': [{'type': 'thinking', 'thinking': '', 'signature': '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'}, {'type': 'text', 'text': \"## Step-by-Step Solution\\n\\nI'll break this down using the distributive property:\\n\\n**213127 × 451233 = 213127 × (450000 + 1233)**\\n\\n### Part 1: 213127 × 450000\\n\\nFirst, 213127 × 45:\\n- 213127 × 40 = 8,525,080\\n- 213127 × 5 = 1,065,635\\n- Sum: **9,590,715**\\n\\nThen × 10,000: **95,907,150,000**\\n\\n### Part 2: 213127 × 1233\\n\\nBreak into 1000 + 233:\\n- 213127 × 1000 = 213,127,000\\n- 213127 × 233:\\n - 213127 × 200 = 42,625,400\\n - 213127 × 33 = 7,033,191\\n - Sum: **49,658,591**\\n\\nAdding: 213,127,000 + 49,658,591 = **262,785,591**\\n\\n### Final Addition\\n\\n$$95,907,150,000 + 262,785,591 = \\\\boxed{96,169,935,591}$$\"}], 'stop_reason': 'end_turn', 'stop_sequence': None, 'stop_details': None, 'usage': {'input_tokens': 22, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 577, 'output_tokens_details': {'thinking_tokens': 258}, 'service_tier': 'standard', 'inference_geo': 'global'}})" ] }, "execution_count": null, @@ -3271,14 +3387,14 @@ "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=3, completion_tokens=9, total_tokens=77, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65})`\n", + "- usage: `Usage(prompt_tokens=3, completion_tokens=9, total_tokens=79, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=67, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 79, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 67, 'serviceTier': 'standard'})`\n", "\n", "\n", "\n", "" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today?', data={'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=77, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'deltas': [Delta(text='Hello! How can I help', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=None, usage=Usage(prompt_tokens=3, completion_tokens=6, total_tokens=74, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 6, 'totalTokenCount': 74, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}), raw={'candidates': [{'content': {'parts': [{'text': 'Hello! How can I help'}], 'role': 'model'}, 'index': 0}], 'usageMetadata': {'promptTokenCount': 3, 'candidatesTokenCount': 6, 'totalTokenCount': 74, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'CQTzadesKYqG7M8P1f_ygQM'}), Delta(text=' you today?', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=None, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=77, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}), raw={'candidates': [{'content': {'parts': [{'text': ' you today?'}], 'role': 'model'}, 'index': 0}], 'usageMetadata': {'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'CQTzadesKYqG7M8P1f_ygQM'}), Delta(text='', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=77, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}), raw={'candidates': [{'content': {'parts': [{'text': '', 'thoughtSignature': 'EtUCCtICAQw51sc8/USM17eSlXy084QBR3CvImgSYQMu4SIX3flFPO/w1ef8S0o4xE5JqO2vwswVeid9RMyQuQv/0ZjISj6LRH+btu0vcO3p3wPfGPaqoZ9JcXA4V8Kmho4YA3j/lcgj8i5xDh5SNakjbkf/b9EH0GUYSFZ3Pg2R+vKaGlAuyXTYMybnzTPpICp7QUemOZWTVyw5KWoNsSlXmn6RuIvb7Gr3k5iD3OnW+Jj7rwkFSvvIHW4zg9FMjQTJyacLumzENToROGNSjUD2HHuMP8NcoFLbI1fUFwqIIIZOJ9RNOFNJzoS/rwzF1nplG9qmujIrII2LFV5XbF1BP7Cgc7LCD7uQqpnfdrZ0uBFTsry+4+LFcxqiqNEY127EDSHp1CQt4VtMV6wRStufenoouBvQJN8nRsaLiw4XW0KxDYJjsX+XDIh7F5xU/KCztRLH67M='}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'CQTzadesKYqG7M8P1f_ygQM'})]})" + "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today?', data={'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=79, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=67, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 79, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 67, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'deltas': [Delta(text='Hello! How can I', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=None, usage=Usage(prompt_tokens=3, completion_tokens=5, total_tokens=75, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=67, raw={'promptTokenCount': 3, 'candidatesTokenCount': 5, 'totalTokenCount': 75, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 67, 'serviceTier': 'standard'}), raw={'candidates': [{'content': {'parts': [{'text': 'Hello! 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"Here's the current weather in **Istanbul, Turkey** 🌤️:\n", + "Here's the current weather for **Istanbul, Türkiye** on Friday, June 12, 2026:\n", "\n", - "- **Condition:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")Istanbul is currently mostly sunny.\n", - "- **Temperature:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")63°F (≈17°C)\n", - "- **Feels Like:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")RealFeel® is 60°F, based on 52% humidity, 12% cloud cover, and winds of 11 mph.\n", - "- **Wind:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")ENE at 11 mph, with gusts up to 14 mph.\n", - "- **Humidity:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")52%\n", - "- **Visibility:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")15 miles\n", - "- **UV Index:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")0.8 (Low)\n", + "☀️ **Sunny & Warm**\n", "\n", - "**Looking ahead:** [*](https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251 \"Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather\")Rainy weather is expected Thursday evening through Friday morning.\n", + "- [*](https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251 \"Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather\")**Conditions:** Plenty of sun with a steady afternoon breeze\n", + "- [*](https://weather.yahoo.com/tr/istanbul/istanbul/ \"Istanbul, TR Weather Forecast, Conditions, and Maps – Yahoo Weather\")**High:** 83°F (28°C) | **Low:** 63°F (17°C)\n", + "- [*](https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251 \"Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather\")**Tonight:** Clear to partly cloudy, with a low of 67°F\n", + "- [*](https://www.wunderground.com/weather/tr/istanbul \"Istanbul, Türkiye Weather Conditions | Weather Underground\")**Wind:** NE at 10 to 15 mph\n", + "- [*](https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251 \"Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather\")**Air Quality:** Poor — the air has reached a high level of pollution and is unhealthy for sensitive groups. It's advised to reduce time spent outside if you experience difficulty breathing or throat irritation.\n", "\n", - "Overall, it's a pleasant spring day in Istanbul! 🌿" + "Overall, it's a beautiful sunny day in Istanbul, but do be mindful of the air quality if you're planning to spend time outdoors!" ] } ], @@ -3341,19 +3455,17 @@ "text/markdown": [ "
\n", "\n", - "Here's the current weather in **Istanbul, Turkey** 🌤️:\n", + "Here's the current weather for **Istanbul, Türkiye** on Friday, June 12, 2026:\n", "\n", - "- **Condition:** Istanbul is currently mostly sunny.\n", - "- **Temperature:** 63°F (≈17°C)\n", - "- **Feels Like:** RealFeel® is 60°F, based on 52% humidity, 12% cloud cover, and winds of 11 mph.\n", - "- **Wind:** ENE at 11 mph, with gusts up to 14 mph.\n", - "- **Humidity:** 52%\n", - "- **Visibility:** 15 miles\n", - "- **UV Index:** 0.8 (Low)\n", + "☀️ **Sunny & Warm**\n", "\n", - "**Looking ahead:** Rainy weather is expected Thursday evening through Friday morning.\n", + "- **Conditions:** Plenty of sun with a steady afternoon breeze\n", + "- **High:** 83°F (28°C) | **Low:** 63°F (17°C)\n", + "- **Tonight:** Clear to partly cloudy, with a low of 67°F\n", + "- **Wind:** NE at 10 to 15 mph\n", + "- **Air Quality:** Poor — the air has reached a high level of pollution and is unhealthy for sensitive groups. It's advised to reduce time spent outside if you experience difficulty breathing or throat irritation.\n", "\n", - "Overall, it's a pleasant spring day in Istanbul! 🌿\n", + "Overall, it's a beautiful sunny day in Istanbul, but do be mindful of the air quality if you're planning to spend time outdoors!\n", "\n", "🔧 web_search({'query': 'Istanbul weather today'})\n", "\n", @@ -3362,14 +3474,14 @@ "\n", "- model: `claude-sonnet-4-6`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=11316, completion_tokens=358, total_tokens=11674, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 11316, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'output_tokens': 358, 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}})`\n", + "- usage: `Usage(prompt_tokens=10302, completion_tokens=336, total_tokens=10638, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 10302, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'output_tokens': 336, 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}})`\n", "\n", "\n", "\n", "
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It's advised\", thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 11, 'delta': {'type': 'text_delta', 'text': \" of pollution and is unhealthy for sensitive groups. 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"Here's the current weather in **Istanbul, Turkey** 🌤️:\n", + "Here's the current weather for **Istanbul, Türkiye** on Friday, June 12, 2026:\n", "\n", - "- **Condition:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")Istanbul is currently mostly sunny.\n", - "- **Temperature:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")63°F (≈17°C)\n", - "- **Feels Like:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")RealFeel® is 60°F, based on 52% humidity, 12% cloud cover, and winds of 11 mph.\n", - "- **Wind:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")ENE at 11 mph, with gusts up to 14 mph.\n", - "- **Humidity:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")52%\n", - "- **Visibility:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")15 miles\n", - "- **UV Index:** [*](https://www.accuweather.com/en/tr/istanbul/318251/current-weather/318251 \"Istanbul, Istanbul, Türkiye Current Weather | AccuWeather\")0.8 (Low)\n", + "☀️ **Sunny & Warm**\n", "\n", - "**Looking ahead:** [*](https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251 \"Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather\")Rainy weather is expected Thursday evening through Friday morning.\n", + "- [*](https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251 \"Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather\")**Conditions:** Plenty of sun with a steady afternoon breeze\n", + "- [*](https://weather.yahoo.com/tr/istanbul/istanbul/ \"Istanbul, TR Weather Forecast, Conditions, and Maps – Yahoo Weather\")**High:** 83°F (28°C) | **Low:** 63°F (17°C)\n", + "- [*](https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251 \"Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather\")**Tonight:** Clear to partly cloudy, with a low of 67°F\n", + "- [*](https://www.wunderground.com/weather/tr/istanbul \"Istanbul, Türkiye Weather Conditions | Weather Underground\")**Wind:** NE at 10 to 15 mph\n", + "- [*](https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251 \"Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather\")**Air Quality:** Poor — the air has reached a high level of pollution and is unhealthy for sensitive groups. It's advised to reduce time spent outside if you experience difficulty breathing or throat irritation.\n", "\n", - "Overall, it's a pleasant spring day in Istanbul! 🌿" + "Overall, it's a beautiful sunny day in Istanbul, but do be mindful of the air quality if you're planning to spend time outdoors!" ] } ], @@ -3432,19 +3542,17 @@ "text/markdown": [ "
\n", "\n", - "Here's the current weather in **Istanbul, Turkey** 🌤️:\n", + "Here's the current weather for **Istanbul, Türkiye** on Friday, June 12, 2026:\n", "\n", - "- **Condition:** Istanbul is currently mostly sunny.\n", - "- **Temperature:** 63°F (≈17°C)\n", - "- **Feels Like:** RealFeel® is 60°F, based on 52% humidity, 12% cloud cover, and winds of 11 mph.\n", - "- **Wind:** ENE at 11 mph, with gusts up to 14 mph.\n", - "- **Humidity:** 52%\n", - "- **Visibility:** 15 miles\n", - "- **UV Index:** 0.8 (Low)\n", + "☀️ **Sunny & Warm**\n", "\n", - "**Looking ahead:** Rainy weather is expected Thursday evening through Friday morning.\n", + "- **Conditions:** Plenty of sun with a steady afternoon breeze\n", + "- **High:** 83°F (28°C) | **Low:** 63°F (17°C)\n", + "- **Tonight:** Clear to partly cloudy, with a low of 67°F\n", + "- **Wind:** NE at 10 to 15 mph\n", + "- **Air Quality:** Poor — the air has reached a high level of pollution and is unhealthy for sensitive groups. It's advised to reduce time spent outside if you experience difficulty breathing or throat irritation.\n", "\n", - "Overall, it's a pleasant spring day in Istanbul! 🌿\n", + "Overall, it's a beautiful sunny day in Istanbul, but do be mindful of the air quality if you're planning to spend time outdoors!\n", "\n", "🔧 web_search({'query': 'Istanbul weather today'})\n", "\n", @@ -3453,14 +3561,14 @@ "\n", "- model: `claude-sonnet-4-6`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=11316, completion_tokens=358, total_tokens=11674, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 11316, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'output_tokens': 358, 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}})`\n", + "- usage: `Usage(prompt_tokens=10302, completion_tokens=336, total_tokens=10638, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 10302, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'output_tokens': 336, 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}})`\n", "\n", "\n", "\n", "
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', 'url': 'https://www.wunderground.com/weather/tr/istanbul', 'title': 'Istanbul, Türkiye Weather Conditions | Weather Underground', 'encrypted_index': 'Eo8BCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDNb/RIMHFuvJUemL+hoMCi6jRSzTxFjSfop4IjCErVOnL9hf4b8FODC+2B0iaYeRIgJ/QhRMPcP3XnLItihQtTXDrZMQVDHBFsYDUVEqE7zsTEb4k8u7WRqQILG4H8CWwRsYBA=='}}}), Delta(text='**Wind:** NE at 10 to 15 mph', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 9, 'delta': {'type': 'text_delta', 'text': '**Wind:** NE at 10 to 15 mph'}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_stop', 'index': 9}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_start', 'index': 10, 'content_block': {'type': 'text', 'text': ''}}), Delta(text='\\n- ', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 10, 'delta': {'type': 'text_delta', 'text': '\\n- '}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_stop', 'index': 10}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_start', 'index': 11, 'content_block': {'citations': [], 'type': 'text', 'text': ''}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=[{'type': 'web_search_result_location', 'cited_text': 'The air has reached a high level of pollution and is unhealthy for sensitive groups. Reduce time spent outside if you are feeling symptoms such as dif...', 'url': 'https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251', 'title': 'Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather', 'encrypted_index': 'EpEBCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDO9MBPlVZyeCJqfwGBoMD7JAVOcsQyumZ3HtIjCDhRD//ykMPXBFc4yu2Zk0P/6U5Y1YOeiiFHX2TvwFcSlGoy/V3uu1WenVXuT7OEsqFZW0yFVsIe2fFexOp9LSnY4nK0IZjxgE'}], server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 11, 'delta': {'type': 'citations_delta', 'citation': {'type': 'web_search_result_location', 'cited_text': 'The air has reached a high level of pollution and is unhealthy for sensitive groups. Reduce time spent outside if you are feeling symptoms such as dif...', 'url': 'https://www.accuweather.com/en/tr/istanbul/318251/weather-forecast/318251', 'title': 'Istanbul, Istanbul, Türkiye Weather Forecast | AccuWeather', 'encrypted_index': 'EpEBCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDO9MBPlVZyeCJqfwGBoMD7JAVOcsQyumZ3HtIjCDhRD//ykMPXBFc4yu2Zk0P/6U5Y1YOeiiFHX2TvwFcSlGoy/V3uu1WenVXuT7OEsqFZW0yFVsIe2fFexOp9LSnY4nK0IZjxgE'}}}), Delta(text='**Air', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 11, 'delta': {'type': 'text_delta', 'text': '**Air'}}), Delta(text=' Quality:** Poor — the air has reached a high level', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 11, 'delta': {'type': 'text_delta', 'text': ' Quality:** Poor — the air has reached a high level'}}), Delta(text=\" of pollution and is unhealthy for sensitive groups. It's advised\", thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 11, 'delta': {'type': 'text_delta', 'text': \" of pollution and is unhealthy for sensitive groups. It's advised\"}}), Delta(text=' to reduce time spent outside if you experience difficulty breathing or throat irritation.', thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 11, 'delta': {'type': 'text_delta', 'text': ' to reduce time spent outside if you experience difficulty breathing or throat irritation.'}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_stop', 'index': 11}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_start', 'index': 12, 'content_block': {'type': 'text', 'text': ''}}), Delta(text=\"\\n\\nOverall, it's a beautiful sunny day in Istanbul, but\", thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 12, 'delta': {'type': 'text_delta', 'text': \"\\n\\nOverall, it's a beautiful sunny day in Istanbul, but\"}}), Delta(text=\" do be mindful of the air quality if you're planning to spend time outdoors!\", thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_delta', 'index': 12, 'delta': {'type': 'text_delta', 'text': \" do be mindful of the air quality if you're planning to spend time outdoors!\"}}), Delta(text=None, thinking=None, refusal='', tool_calls=[], citations=None, server_tool_result=None, finish_reason=None, usage=None, raw={'type': 'content_block_stop', 'index': 12}), Delta(text='', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=, usage=Usage(prompt_tokens=10302, completion_tokens=336, total_tokens=10638, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 10302, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'output_tokens': 336, 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}}), raw={'type': 'message_delta', 'delta': {'stop_reason': 'end_turn', 'stop_sequence': None, 'stop_details': None}, 'usage': {'input_tokens': 10302, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'output_tokens': 336, 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}}})]})" ] }, "execution_count": null, @@ -3540,14 +3648,11 @@ "text/markdown": [ "
\n", "\n", - "I will use the `simple_add` tool to calculate the sums of the two pairs of numbers provided.\n", - "\n", - "1. First, I'll add 5,478,954,793 and 547,982,745.\n", - "2. Then, I'll add 5,479,749,754 and 9,875,438,979.\n", + "I will use the `simple_add` tool to calculate the sums of the two pairs of numbers you provided.\n", "\n", "\n", "\n", - "🔧 simple_add({'b': 547982745, 'a': 5478954793})\n", + "🔧 simple_add({'a': 5478954793, 'b': 547982745})\n", "\n", "\n", "\n", @@ -3558,14 +3663,14 @@ "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `tool_calls`\n", - "- usage: `Usage(prompt_tokens=160, completion_tokens=170, total_tokens=518, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=188, raw={'promptTokenCount': 160, 'candidatesTokenCount': 170, 'totalTokenCount': 518, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 160}], 'thoughtsTokenCount': 188})`\n", + "- usage: `Usage(prompt_tokens=160, completion_tokens=95, total_tokens=420, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=165, raw={'promptTokenCount': 160, 'candidatesTokenCount': 95, 'totalTokenCount': 420, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 160}], 'thoughtsTokenCount': 165, 'serviceTier': 'standard'})`\n", "\n", "\n", "\n", "
" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text=\"I will use the `simple_add` tool to calculate the sums of the two pairs of numbers provided.\\n\\n1. First, I'll add 5,478,954,793 and 547,982,745.\\n2. Then, I'll add 5,479,749,754 and 9,875,438,979.\\n\\n\", data={'text': \"I will use the `simple_add` tool to calculate the sums of the two pairs of numbers provided.\\n\\n1. First, I'll add 5,478,954,793 and 547,982,745.\\n2. 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First, I'll add 5,478,954,793 and 547,982,745.\\n2. Then, I'll add 5,479,749,754 and 9,875,438,979.\\n\\n\"}, {'functionCall': {'name': 'simple_add', 'args': {'b': 547982745, 'a': 5478954793}, 'id': 'oxwvx1fm'}, 'thoughtSignature': 'EscDCsQDAQw51scPHdv+D5BX7JWdLzz3Bv8tsKFRuAJe2UkTFZ+NZKzNsLtmQBiia+/r4HJEUptq1zQB0q9HToX0qzCUqyNAbDLY76KxMeW9jpsnUvh6ZjPM5sDD7fAafF7cjdApNMsihPqIZBAZjAlFPcp1c/50MObH5f1q7hO7fgDS4iSJ3Q3FfbAYWnJ4nlA2peVMu/6WFcKZh1wcZCIuN6iFCj6nhH+6RKkaFRaM0b6XCmpti6qldSeZx+qtHmo+lzr1tct4Gz/CITDI7gRJ3qfLYV2u45jOhKzdd1t6gQ39XLJ93j0xd0AwpzcdZLbHWqwWJCQ43nNzhJ7IQTAWOSyPgKDnlAMHq2PTEoXBYkMBApCZ1x+HncBzt77kQrTTe7sWGVmD5boVnYAIFPFGXOULP5tDZ+nog+Fg8NV10vaFKlHVf+VDzFnVWxT259LN12ykGtBilfpTXiKCV12RAZwhuL7vXXHrsBGg5HNVImcXqgMvwf/rtQlJeop+9bEcAiU48hMFMzumOrCmmHD3HgxpYLW7T3vtDmbNdKCDqVtIwO4Rp5HE6GudRWmq8iC2UnyQglUXoXVnxIZW7eYYDsGAYrYgZ1A='}, {'functionCall': {'name': 'simple_add', 'args': {'a': 5479749754, 'b': 9875438979}, 'id': 'yzzmpqg3'}}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0, 'finishMessage': 'Model generated function call(s).'}], 'usageMetadata': {'promptTokenCount': 160, 'candidatesTokenCount': 170, 'totalTokenCount': 518, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 160}], 'thoughtsTokenCount': 188}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'VgTzabqIKqT1kdUPv4nAoQg'})" + "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='I will use the `simple_add` tool to calculate the sums of the two pairs of numbers you provided.\\n\\n', data={'text': 'I will use the `simple_add` tool to calculate the sums of the two pairs of numbers you provided.\\n\\n'}), Part(type=, text=None, data={'thoughtSignature': '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', 'id': 'ac6tzkyu', 'name': 'simple_add', 'arguments': {'a': 5478954793, 'b': 547982745}, 'server': False}), Part(type=, text=None, data={'id': '2oiaspzk', 'name': 'simple_add', 'arguments': {'a': 5479749754, 'b': 9875438979}, 'server': False})]), finish_reason=, usage=Usage(prompt_tokens=160, completion_tokens=95, total_tokens=420, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=165, raw={'promptTokenCount': 160, 'candidatesTokenCount': 95, 'totalTokenCount': 420, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 160}], 'thoughtsTokenCount': 165, 'serviceTier': 'standard'}), tool_calls=[ToolCall(id='ac6tzkyu', name='simple_add', arguments={'a': 5478954793, 'b': 547982745}, server=False, extra={'thoughtSignature': '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'}), ToolCall(id='2oiaspzk', name='simple_add', arguments={'a': 5479749754, 'b': 9875438979}, server=False, extra={})], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': 'I will use the `simple_add` tool to calculate the sums of the two pairs of numbers you provided.\\n\\n'}, {'functionCall': {'name': 'simple_add', 'args': {'a': 5478954793, 'b': 547982745}, 'id': 'ac6tzkyu'}, 'thoughtSignature': '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'}, {'functionCall': {'name': 'simple_add', 'args': {'a': 5479749754, 'b': 9875438979}, 'id': '2oiaspzk'}}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0, 'finishMessage': 'Model generated function call(s).'}], 'usageMetadata': {'promptTokenCount': 160, 'candidatesTokenCount': 95, 'totalTokenCount': 420, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 160}], 'thoughtsTokenCount': 165, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'RgQsar-cGLHZjMcP0fyKkQI'})" ] }, "execution_count": null, @@ -3596,13 +3701,13 @@ "text/markdown": [ "
\n", "\n", - "🔧 **simple_add**(`{'b': 547982745, 'a': 5478954793}`)\n", + "🔧 **simple_add**(`{'a': 5478954793, 'b': 547982745}`)\n", "\n", "
\n", "\n", - "- id: `oxwvx1fm`\n", + "- id: `ac6tzkyu`\n", "- server: `False`\n", - "- extra: `{'thoughtSignature': 'EscDCsQDAQw51scPHdv+D5BX7JWdLzz3Bv8tsKFRuAJe2UkTFZ+NZKzNsLtmQBiia+/r4HJEUptq1zQB0q9HToX0qzCUqyNAbDLY...'}`\n", + "- extra: `{'thoughtSignature': 'Ep8DCpwDAQw51sclZpJLASuscv81mLG39ngAucoEl4MsKgTLthQexz2NqaHyp5FCMgvATMSMDJy/fgX0BwGkOv2/nRefobDe92ap...'}`\n", "\n", "
\n", "\n", @@ -3610,7 +3715,7 @@ "\n", "
\n", "\n", - "- id: `yzzmpqg3`\n", + "- id: `2oiaspzk`\n", "- server: `False`\n", "- extra: `{}`\n", "\n", @@ -3619,13 +3724,13 @@ "
" ], "text/plain": [ - "Markdown(🔧 **simple_add**(`{'b': 547982745, 'a': 5478954793}`)\n", + "Markdown(🔧 **simple_add**(`{'a': 5478954793, 'b': 547982745}`)\n", "\n", "
\n", "\n", - "- id: `oxwvx1fm`\n", + "- id: `ac6tzkyu`\n", "- server: `False`\n", - "- extra: `{'thoughtSignature': 'EscDCsQDAQw51scPHdv+D5BX7JWdLzz3Bv8tsKFRuAJe2UkTFZ+NZKzNsLtmQBiia+/r4HJEUptq1zQB0q9HToX0qzCUqyNAbDLY...'}`\n", + "- extra: `{'thoughtSignature': 'Ep8DCpwDAQw51sclZpJLASuscv81mLG39ngAucoEl4MsKgTLthQexz2NqaHyp5FCMgvATMSMDJy/fgX0BwGkOv2/nRefobDe92ap...'}`\n", "\n", "
\n", "\n", @@ -3633,7 +3738,7 @@ "\n", "
\n", "\n", - "- id: `yzzmpqg3`\n", + "- id: `2oiaspzk`\n", "- server: `False`\n", "- extra: `{}`\n", "\n", @@ -3675,16 +3780,13 @@ "\n", "**Part** (`text`)\n", "\n", - "I will use the `simple_add` tool to calculate the sums of the two pairs of numbers provided.\n", - "\n", - "1. First, I'll add 5,478,954,793 and 547,982,745.\n", - "2. Then, I'll add 5,479,749,754 and 9,875,438,979.\n", + "I will use the `simple_add` tool to calculate the sums of the two pairs of numbers you provided.\n", "\n", "\n", "\n", "
\n", "\n", - "- data: `{'text': \"I will use the `simple_add` tool to calculate the sums of the two pairs of numbers provided.\\n\\n1. First, I'll add 5,478,954,793 and 547,982,745.\\n2. Then, I'll add 5,479,749,754 and 9,875,438,979.\\n\\n\"}`\n", + "- data: `{'text': 'I will use the `simple_add` tool to calculate the sums of the two pairs of numbers you provided.\\n\\n'}`\n", "\n", "
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They are found on every continent except Australia and Antarctica.\n", - "\n", - "**Appearance:** Otters have long, slim bodies and relatively short limbs. Their most striking features are powerful webbed feet for swimming and seal-like abilities for holding their breath underwater. Most have sharp claws, and all except the sea otter have long, muscular tails.\n", + "**What they are:**\n", + "Otters are carnivorous mammals in the subfamily Lutrinae, and all 14 extant species are semiaquatic, living in both freshwater and marine environments. They are found on every continent except Australia and Antarctica.\n", "\n", - "**Fur:** Otters have the densest fur of any animal — as many as a million hairs per square inch in places.\n", + "**Physical traits:**\n", + "Otters are distinguished by their long, slim bodies, powerful webbed feet for swimming, and their dense fur, which keeps them warm and buoyant in water. In fact, otters have the densest fur of any animal — as many as a million hairs per square inch in places.\n", "\n", - "**Diet:** All otters are expert hunters that eat fish, crustaceans, and other critters. Sea otters have an ingenious method to open shellfish — a sea otter will float on its back, place a rock on its chest, then smash the mollusk down on it until it breaks open.\n", + "**Behavior:**\n", + "They are playful animals, engaging in activities like sliding into water on natural slides and playing with stones. A sea otter will float on its back, place a rock on its chest, then smash a mollusk down on it to break it open. When it's time to nap, sea otters entangle themselves in kelp so they don't float away, and sometimes intertwine their feet with another otter to stay together.\n", "\n", - "**Behavior:** They are playful animals, engaging in activities like sliding into water on natural slides and playing with stones. When it's time to nap, sea otters entangle themselves in kelp so they don't float away, and they sometimes intertwine their feet with another sea otter to stay together.\n", + "**Diet:**\n", + "All otters are expert hunters that eat fish, crustaceans, and other critters.\n", "\n", - "**Lifespan:** Otters can live up to 16 years.\n", + "**Lifespan & young:**\n", + "They can live up to 16 years. Baby otters, called pups or kittens, stay with their mothers until they're up to a year old, or until she has another litter.\n", "\n", - "**Conservation:** Otters and their mustelid relatives were once hunted extensively for their fur, many to the point of near extinction. Despite regulations designed to protect them, many species remain at risk from pollution and habitat loss.\n", + "**Conservation:**\n", + "Otters and their relatives were once hunted extensively for their fur, many to the point of near extinction. Despite regulations designed to protect them, many species remain at risk from pollution and habitat loss.\n", "\n", "🔧 web_search({'query': 'otters facts overview'})\n", "\n", @@ -4250,14 +4374,14 @@ "\n", "- model: `claude-sonnet-4-6`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=16866, completion_tokens=591, total_tokens=17457, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 16866, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 591, 'service_tier': 'standard', 'inference_geo': 'global', 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}})`\n", + "- usage: `Usage(prompt_tokens=15288, completion_tokens=591, total_tokens=15879, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 15288, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 591, 'service_tier': 'standard', 'inference_geo': 'global', 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}})`\n", "\n", "
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'page_age': '3 weeks ago'}, {'type': 'web_search_result', 'title': 'Seven Quick Facts About River Otters - National Forest Foundation', 'url': 'https://www.nationalforests.org/article/seven-quick-facts-about-river-otters/', 'encrypted_content': 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'page_age': 'May 27, 2024'}], 'caller': {'type': 'direct'}}), Part(type=, text=\"Here's a brief overview of otters:\\n\\n**What they are:** \", data={'type': 'text', 'text': \"Here's a brief overview of otters:\\n\\n**What they are:** \"}), Part(type=, text='Otters are carnivorous mammals in the subfamily Lutrinae, and all 14 extant species are semiaquatic, living in both freshwater and marine environments.', data={'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters are carnivorous mammals in the subfamily Lutrinae. The 14 extant otter species are all semiaquatic, both freshwater and marine. ', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'EpEBCioIDxgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDG2M3iTDMKCEbQU0ixoMEkQctKir3rE+q0VxIjCr62otvRrh1qtXwwsIsbdPWR8nrzSQIGPG5OPkoSoljuMU6pT5cs6+cB/P8B99AzcqFdNonKukHKnf8MuUsYvz41fLhuo3YBgE'}], 'type': 'text', 'text': 'Otters are carnivorous mammals in the subfamily Lutrinae, and all 14 extant species are semiaquatic, living in both freshwater and marine environments.'}), Part(type=, text=' ', data={'type': 'text', 'text': ' '}), Part(type=, text='They are found on every continent except Australia and Antarctica.', data={'citations': [{'type': 'web_search_result_location', 'cited_text': 'The charismatic otter, a member of the weasel family, is found on every continent except Australia and Antarctica. ', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'EpABCioIDxgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDLTNpB75ibhbCw8vqhoMjgJ+kE0+Bogaghh3IjBWgVuM631lZ5vk8210vC4knoCO4+iUqst5he1Z/Jy5bHqiTPiexaijrLvwzVqhZZYqFIGxuzi4/FZbaEgd/AqICI/VrizIGAQ='}], 'type': 'text', 'text': 'They are found on every continent except Australia and Antarctica.'}), Part(type=, text='\\n\\n**Appearance:** ', data={'type': 'text', 'text': '\\n\\n**Appearance:** '}), Part(type=, text='Otters have long, slim bodies and relatively short limbs. Their most striking features are powerful webbed feet for swimming and seal-like abilities for holding their breath underwater. Most have sharp claws, and all except the sea otter have long, muscular tails.', data={'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters have long, slim bodies and relatively short limbs. Their most striking anatomical features are the powerful webbed feet used to swim, and their...', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'EpEBCioIDxgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDD+I6pJ70k00jWffJhoMzPPlAkixCFR/Xe7hIjBCOOsyTqsvhxaFxLWHTgPnPgnl74mgneeGY4uAR5m5dsyQcTJMboI50oGff56at6IqFfrX4cIQHkLD1FKyaObNCoyIiHMqohgE'}], 'type': 'text', 'text': 'Otters have long, slim bodies and relatively short limbs. Their most striking features are powerful webbed feet for swimming and seal-like abilities for holding their breath underwater. 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'page_age': '3 weeks ago'}, {'type': 'web_search_result', 'title': 'Seven Quick Facts About River Otters - National Forest Foundation', 'url': 'https://www.nationalforests.org/article/seven-quick-facts-about-river-otters/', 'encrypted_content': 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'page_age': 'December 3, 2025'}, {'type': 'web_search_result', 'title': 'Otter Animal Facts - Lutrinae - A-Z Animals', 'url': 'https://a-z-animals.com/animals/otter/', 'encrypted_content': 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'page_age': 'May 27, 2024'}], 'caller': {'type': 'direct'}}, {'type': 'text', 'text': \"Here's a brief overview of otters:\\n\\n**What they are:** \"}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters are carnivorous mammals in the subfamily Lutrinae. The 14 extant otter species are all semiaquatic, both freshwater and marine. ', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'EpEBCioIDxgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDG2M3iTDMKCEbQU0ixoMEkQctKir3rE+q0VxIjCr62otvRrh1qtXwwsIsbdPWR8nrzSQIGPG5OPkoSoljuMU6pT5cs6+cB/P8B99AzcqFdNonKukHKnf8MuUsYvz41fLhuo3YBgE'}], 'type': 'text', 'text': 'Otters are carnivorous mammals in the subfamily Lutrinae, and all 14 extant species are semiaquatic, living in both freshwater and marine environments.'}, {'type': 'text', 'text': ' '}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'The charismatic otter, a member of the weasel family, is found on every continent except Australia and Antarctica. ', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'EpABCioIDxgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDLTNpB75ibhbCw8vqhoMjgJ+kE0+Bogaghh3IjBWgVuM631lZ5vk8210vC4knoCO4+iUqst5he1Z/Jy5bHqiTPiexaijrLvwzVqhZZYqFIGxuzi4/FZbaEgd/AqICI/VrizIGAQ='}], 'type': 'text', 'text': 'They are found on every continent except Australia and Antarctica.'}, {'type': 'text', 'text': '\\n\\n**Appearance:** '}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters have long, slim bodies and relatively short limbs. Their most striking anatomical features are the powerful webbed feet used to swim, and their...', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'EpEBCioIDxgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDD+I6pJ70k00jWffJhoMzPPlAkixCFR/Xe7hIjBCOOsyTqsvhxaFxLWHTgPnPgnl74mgneeGY4uAR5m5dsyQcTJMboI50oGff56at6IqFfrX4cIQHkLD1FKyaObNCoyIiHMqohgE'}], 'type': 'text', 'text': 'Otters have long, slim bodies and relatively short limbs. Their most striking features are powerful webbed feet for swimming and seal-like abilities for holding their breath underwater. Most have sharp claws, and all except the sea otter have long, muscular tails.'}, {'type': 'text', 'text': '\\n\\n**Fur:** '}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters have the densest fur of any animal—as many as a million hairs per square inch in places. 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'page_age': 'May 4, 2021'}, {'type': 'web_search_result', 'title': 'Otter Fact Sheet | Blog | Nature | PBS', 'url': 'https://www.pbs.org/wnet/nature/blog/otter-fact-sheet/', 'encrypted_content': 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'page_age': 'February 11, 2026'}, {'type': 'web_search_result', 'title': 'Seven Quick Facts About River Otters — National Forest Foundation', 'url': 'https://www.nationalforests.org/article/seven-quick-facts-about-river-otters/', 'encrypted_content': 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'page_age': 'March 30, 2026'}, {'type': 'web_search_result', 'title': 'Otters • Chesapeake Bay Foundation', 'url': 'https://www.cbf.org/nature/otters/', 'encrypted_content': 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The 14 extant otter species are all semiaquatic, both freshwater and marine. ', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'EpEBCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDN7KLZobpSqdbB4rOxoMmuEvIMYJNRidIr1DIjBW+pwET11cTmpsWBHU3ybbjz6Ak7N9w2pS0r3cZgjzHWFwlR4d5AcJcDj0YgzxiVMqFdcezAcezknRhhRnmdp6j6dGZ8l2dRgE'}], 'type': 'text', 'text': 'Otters are carnivorous mammals in the subfamily Lutrinae, and all 14 extant species are semiaquatic, living in both freshwater and marine environments.'}), Part(type=, text=' ', data={'type': 'text', 'text': ' '}), Part(type=, text='They are found on every continent except Australia and Antarctica.', data={'citations': [{'type': 'web_search_result_location', 'cited_text': 'The charismatic otter, a member of the weasel family, is found on every continent except Australia and Antarctica. ', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'EpABCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDBirHOIFFlF1e3tMpRoMFXA/lTbhC8ayfsuyIjBRS2zZpzOaKf8KOblBLB0/D5uqgTbIsalUrfQdaoAaM8Khd+1UK3mdJ73Zw1bB9UYqFEI65OU2OZRH3SZKlTLR8eLqnsCaGAQ='}], 'type': 'text', 'text': 'They are found on every continent except Australia and Antarctica.'}), Part(type=, text='\\n\\n**Physical traits:**\\n', data={'type': 'text', 'text': '\\n\\n**Physical traits:**\\n'}), Part(type=, text='Otters are distinguished by their long, slim bodies, powerful webbed feet for swimming, and their dense fur, which keeps them warm and buoyant in water.', data={'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters are distinguished by their long, slim bodies, powerful webbed feet for swimming, and their dense fur, which keeps them warm and buoyant in wate...', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'Eo8BCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDKXgVFloT930aNTvrhoMlJVkZhqj0ajXCXzoIjDM2vHEh9CIVtgLrBGHJBawH0FzOl0ECRj72rIQsoHvnnCkWJZshwoaekS/dHzeFm0qE3ZWiWEV4KAtV5BVlseXTu8WBtoYBA=='}], 'type': 'text', 'text': 'Otters are distinguished by their long, slim bodies, powerful webbed feet for swimming, and their dense fur, which keeps them warm and buoyant in water.'}), Part(type=, text=' In fact, ', data={'type': 'text', 'text': ' In fact, '}), Part(type=, text='otters have the densest fur of any animal — as many as a million hairs per square inch in places.', data={'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters have the densest fur of any animal—as many as a million hairs per square inch in places. 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', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'Eo8BCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDH0Fi1i6HX0M22F1YBoM2UOTDhnnTz9CJFTyIjBay2TYScDFgUnqukVkBtKW75R7IK6c2G+ZDFr9CqHtF3BwP0JnFCs4B5wDSTX055IqEzETM/H07CHBCrHFbmkD3pctXE4YBA=='}], 'type': 'text', 'text': 'They are playful animals, engaging in activities like sliding into water on natural slides and playing with stones.'}), Part(type=, text=' ', data={'type': 'text', 'text': ' '}), Part(type=, text=\"A sea otter will float on its back, place a rock on its chest, then smash a mollusk down on it to break it open. When it's time to nap, sea otters entangle themselves in kelp so they don't float away, and sometimes intertwine their feet with another otter to stay together.\", data={'citations': [{'type': 'web_search_result_location', 'cited_text': 'A sea otter will float on its back, place a rock on its chest, then smash the mollusk down on it until it breaks open. 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'page_age': 'April 27, 2026'}, {'type': 'web_search_result', 'title': 'Otters, facts and information | National Geographic', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'encrypted_content': 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'page_age': 'May 4, 2021'}, {'type': 'web_search_result', 'title': 'Otter Fact Sheet | Blog | Nature | PBS', 'url': 'https://www.pbs.org/wnet/nature/blog/otter-fact-sheet/', 'encrypted_content': 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'page_age': 'February 11, 2026'}, {'type': 'web_search_result', 'title': 'Seven Quick Facts About River Otters — National Forest Foundation', 'url': 'https://www.nationalforests.org/article/seven-quick-facts-about-river-otters/', 'encrypted_content': 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'page_age': 'March 30, 2026'}, {'type': 'web_search_result', 'title': 'Otters • Chesapeake Bay Foundation', 'url': 'https://www.cbf.org/nature/otters/', 'encrypted_content': 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'page_age': 'December 3, 2025'}, {'type': 'web_search_result', 'title': 'River Otter | National Geographic Kids', 'url': 'https://kids.nationalgeographic.com/animals/mammals/facts/river-otter', 'encrypted_content': 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'page_age': 'October 6, 2023'}], 'caller': {'type': 'direct'}}, {'type': 'text', 'text': \"Here's a brief overview of otters:\\n\\n**What they are:**\\n\"}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters are carnivorous mammals in the subfamily Lutrinae. The 14 extant otter species are all semiaquatic, both freshwater and marine. ', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'EpEBCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDN7KLZobpSqdbB4rOxoMmuEvIMYJNRidIr1DIjBW+pwET11cTmpsWBHU3ybbjz6Ak7N9w2pS0r3cZgjzHWFwlR4d5AcJcDj0YgzxiVMqFdcezAcezknRhhRnmdp6j6dGZ8l2dRgE'}], 'type': 'text', 'text': 'Otters are carnivorous mammals in the subfamily Lutrinae, and all 14 extant species are semiaquatic, living in both freshwater and marine environments.'}, {'type': 'text', 'text': ' '}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'The charismatic otter, a member of the weasel family, is found on every continent except Australia and Antarctica. ', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'EpABCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDBirHOIFFlF1e3tMpRoMFXA/lTbhC8ayfsuyIjBRS2zZpzOaKf8KOblBLB0/D5uqgTbIsalUrfQdaoAaM8Khd+1UK3mdJ73Zw1bB9UYqFEI65OU2OZRH3SZKlTLR8eLqnsCaGAQ='}], 'type': 'text', 'text': 'They are found on every continent except Australia and Antarctica.'}, {'type': 'text', 'text': '\\n\\n**Physical traits:**\\n'}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters are distinguished by their long, slim bodies, powerful webbed feet for swimming, and their dense fur, which keeps them warm and buoyant in wate...', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'Eo8BCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDKXgVFloT930aNTvrhoMlJVkZhqj0ajXCXzoIjDM2vHEh9CIVtgLrBGHJBawH0FzOl0ECRj72rIQsoHvnnCkWJZshwoaekS/dHzeFm0qE3ZWiWEV4KAtV5BVlseXTu8WBtoYBA=='}], 'type': 'text', 'text': 'Otters are distinguished by their long, slim bodies, powerful webbed feet for swimming, and their dense fur, which keeps them warm and buoyant in water.'}, {'type': 'text', 'text': ' In fact, '}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters have the densest fur of any animal—as many as a million hairs per square inch in places. ', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'Eo8BCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDII7gMjgNgePP7Wc8hoMyLtGkUTiloiawzngIjBJ2f/eTB+PzCiLjghtaO2AwX9V5VEfJ5VN8g1Odc/EQ6sJL4m9TE+q/5QmH2Bvg2sqE5lvnwaCNsZxe3qWnTIA9M9ewcUYBA=='}], 'type': 'text', 'text': 'otters have the densest fur of any animal — as many as a million hairs per square inch in places.'}, {'type': 'text', 'text': '\\n\\n**Behavior:**\\n'}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'They are playful animals, engaging in activities like sliding into water on natural slides and playing with stones. ', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'Eo8BCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDH0Fi1i6HX0M22F1YBoM2UOTDhnnTz9CJFTyIjBay2TYScDFgUnqukVkBtKW75R7IK6c2G+ZDFr9CqHtF3BwP0JnFCs4B5wDSTX055IqEzETM/H07CHBCrHFbmkD3pctXE4YBA=='}], 'type': 'text', 'text': 'They are playful animals, engaging in activities like sliding into water on natural slides and playing with stones.'}, {'type': 'text', 'text': ' '}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'A sea otter will float on its back, place a rock on its chest, then smash the mollusk down on it until it breaks open. When it’s time to nap, sea otte...', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'EpMBCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDHOa1y76M4FvAJ8YKRoMQvQ6WhaD7tPd2w/uIjDdTRPvFU8S6EJ0V7xVv5mgpBza7d4Dk6zKk/mgxXdRDP/EtQNwB52dyV/LPJ2bIc0qF+PCvn0MoiFH8+1oHH1e+z3EcDpFLJVZGAQ='}], 'type': 'text', 'text': \"A sea otter will float on its back, place a rock on its chest, then smash a mollusk down on it to break it open. When it's time to nap, sea otters entangle themselves in kelp so they don't float away, and sometimes intertwine their feet with another otter to stay together.\"}, {'type': 'text', 'text': '\\n\\n**Diet:**\\n'}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'All otters are expert hunters that eat fish, crustaceans, and other critters. ', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'EpABCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDJbCTbLNyM4hA7Ne8hoMgnUErTUsql1icIHIIjCSQBZ3jWbCQDv0KVseRXpyztRDItlHUqSEQfsSq8C6GZRj86bwHEg527A4SXLlp/QqFKdblHl4DzmVZrGxSi7vtujU3gbIGAQ='}], 'type': 'text', 'text': 'All otters are expert hunters that eat fish, crustaceans, and other critters.'}, {'type': 'text', 'text': '\\n\\n**Lifespan & young:**\\n'}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'They can live up to 16 years, with their diet mainly consisting of fish and sometimes frogs, birds, or shellfish, depending on the species. ', 'url': 'https://en.wikipedia.org/wiki/Otter', 'title': 'Otter - Wikipedia', 'encrypted_index': 'EpABCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDKZrLsQhaUSv/iqs5xoMLmp3flfVHxi+IR5zIjCWP4k57h5kyCxVuZP6USVEMMXjU19xOFUC3n7fQSxyF0jFXrg/op/3CnUZeJoTcVYqFO/FxrCaOZghi2uRDJ9arDZWvTFkGAQ='}], 'type': 'text', 'text': 'They can live up to 16 years.'}, {'type': 'text', 'text': ' '}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'Baby otters, called pups or kittens, stay with their mothers until they’re up to a year old, or until she has another litter. ', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'Eo8BCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDBWNA7ZwRkhbUma5AxoMrFF1M+xZFrGgTfP3IjCCAefAxGCVzog932gwrqtiGgx9kYvyQo58XePBJO4CZUzfG5zjl5ut8of33BBf1UgqEyhPqvmNGEHj498NU+bNkH9I2gIYBA=='}], 'type': 'text', 'text': \"Baby otters, called pups or kittens, stay with their mothers until they're up to a year old, or until she has another litter.\"}, {'type': 'text', 'text': '\\n\\n**Conservation:**\\n'}, {'citations': [{'type': 'web_search_result_location', 'cited_text': 'Otters and their mustelid relatives were once hunted extensively for their fur, many to the point of near extinction. Despite regulations designed to ...', 'url': 'https://www.nationalgeographic.com/animals/mammals/facts/otters-1', 'title': 'Otters, facts and information | National Geographic', 'encrypted_index': 'EpMBCioIEBgCIiQ4ODk4YTFkYy0yMTNkLTRhNmYtOTljYi03ZTBlNTUzZDc0NWISDIVMHfmNkAXCNnHanhoMQf/SHt28XaqC5Q1JIjBri9akp3dtS6EU553LhtkQdMX0ZmFSLfKH0dd423oDQ4fgsKVgx6wd/6O74LsO6RcqF+n0KINfyiOxtjaRNO1Kx/AnYK5ayfTBGAQ='}], 'type': 'text', 'text': 'Otters and their relatives were once hunted extensively for their fur, many to the point of near extinction. Despite regulations designed to protect them, many species remain at risk from pollution and habitat loss.'}], 'stop_reason': 'end_turn', 'stop_sequence': None, 'stop_details': None, 'usage': {'input_tokens': 15288, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 591, 'service_tier': 'standard', 'inference_geo': 'global', 'server_tool_use': {'web_search_requests': 1, 'web_fetch_requests': 0}}})" ] }, "execution_count": null, @@ -4319,26 +4443,27 @@ "text/markdown": [ "
\n", "\n", - "Otters are semiaquatic carnivorous mammals belonging to the weasel family (**Mustelidae**). There are 13 known species, ranging from the small Asian small-clawed otter to the 6-foot-long giant river otter.\n", + "Otters are semi-aquatic carnivorous mammals belonging to the weasel family (**mustelids**). There are 13 different species found all over the world, ranging from small river otters to the large sea otter.\n", "\n", "### Key Facts:\n", - "* **Physical Traits:** They have long, streamlined bodies, webbed feet for swimming, and the **thickest fur of any animal** to keep them warm without a layer of blubber.\n", - "* **Habitat:** They live on every continent except Australia and Antarctica, residing in both freshwater (rivers and lakes) and saltwater (coastal areas).\n", - "* **Diet:** They primarily eat fish, crabs, and shellfish. Some species, like sea otters, are famous for using stones as tools to crack open shells.\n", - "* **Social Behavior:** Known for being highly playful, they often slide down mud banks or \"raft\" (float in groups) in the ocean while holding hands to avoid drifting apart.\n", + "* **Physical Features:** They have long, slim bodies, webbed feet for swimming, and powerful tails. Sea otters have the **thickest fur** of any animal—up to one million hairs per square inch—which keeps them warm since they lack blubber.\n", + "* **Diet:** They are skilled hunters that primarily eat fish, crabs, mussels, and frogs. Some species use rocks as \"tools\" to crack open shellfish.\n", + "* **Habitat:** They live in both freshwater (rivers and lakes) and marine environments (coastal oceans).\n", + "* **Behavior:** Known for being playful, they often slide down muddy banks or play with stones. Sea otters are famous for **holding hands** while sleeping to keep from drifting apart in the current.\n", + "* **Conservation:** Most otter species are currently considered threatened or endangered due to habitat loss, pollution, and historical hunting for their fur.\n", "\n", "
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\n", "\n", - "Otters are playful, semiaquatic carnivorous mammals belonging to the weasel family (**Mustelidae**). There are **13 extant species** found across the globe, except in Australia and Antarctica.\n", + "Otters are semi-aquatic carnivorous mammals belonging to the **Mustelidae** family (which also includes weasels, badgers, and wolverines). \n", "\n", - "### **Key Characteristics**\n", - "* **Habitat:** They live in both freshwater (rivers, lakes) and saltwater (coastal oceans) environments.\n", - "* **Physical Traits:** They have long, streamlined bodies, webbed feet, and powerful tails for swimming. Their fur is among the densest in the animal kingdom, providing insulation since they lack a layer of blubber.\n", - "* **Diet:** They are active hunters that primarily eat fish, crustaceans, frogs, and shellfish.\n", - "* **Behavior:** Known for being highly social and intelligent, they are famous for \"sliding\" on mud or snow and playing with stones. Sea otters are one of the few mammals known to use **tools**, often using rocks to crack open shells on their bellies.\n", + "Here is a very brief breakdown:\n", "\n", - "### **Types of Otters**\n", - "* **River Otters:** Found in freshwater systems, they are more agile on land than their marine cousins.\n", - "* **Sea Otters:** The heaviest species, they spend almost their entire lives in the ocean, often sleeping in groups called \"rafts\" while wrapped in kelp to keep from drifting away.\n", - "* **Giant Otters:** Found in South America, these can grow up to 6 feet long and are known for being particularly vocal and territorial.\n", + "* **Types:** There are 13 species, generally divided into **river otters** (freshwater) and **sea otters** (marine).\n", + "* **Key Feature:** They have incredibly dense, waterproof fur—sea otters have up to one million hairs per square inch, the thickest of any animal.\n", + "* **Behavior:** Known for being highly social and playful. Sea otters often hold paws while sleeping (called \"rafting\") so they don't drift apart.\n", + "* **Diet:** They primarily eat fish, crustaceans, and mollusks. Some species use rocks as tools to crack open shells.\n", + "* **Habitat:** They are found on every continent except Australia and Antarctica, living in rivers, lakes, and coastal oceans.\n", "\n", "
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Thinking\n", "\n", - "We need to interpret the user's question: \"what did I just say?\" The user previously said \"hi\" and I responded \"hello\". Now they ask what they just said. This could be a test of memory, or a playful question, or maybe they are checking if I recall the conversation. Since I'm an AI, I can recall the immediate prior message. The user's last message before the current one was \"hi\". So I should answer that they said \"hi\". But also note that after my \"hello\", they asked \"what did I just say?\" So the \"just say\" refers to their previous utterance. I'll respond accordingly.\n", + "We need to interpret the user's question: \"what did I just say?\" The user previously said \"hi\" and then the assistant responded \"hello\". Now user asks \"what did I just say?\" Possibly user is testing memory or being playful. As an AI, I should note that the user said \"hi\" just before. But careful: the user's last message is \"what did I just say?\" referring to their previous message. So answer: \"You said 'hi'.\" However, the user might be referring to the entire exchange? I'll answer directly.\n", "\n", "
\n", "\n", - "You just said **\"hi\"** before I responded with \"hello.\" Then you asked, \"what did I just say?\" — which is your current message. So, to be precise: the last thing you said before this question was \"hi.\"\n", + "You said \"hi\".\n", "\n", "
\n", "\n", "- model: `deepseek-v4-flash`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=16, completion_tokens=185, total_tokens=201, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=133, raw={'prompt_tokens': 16, 'completion_tokens': 185, 'total_tokens': 201, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 133}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 16})`\n", + "- usage: `Usage(prompt_tokens=16, completion_tokens=122, total_tokens=138, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=116, raw={'prompt_tokens': 16, 'completion_tokens': 122, 'total_tokens': 138, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 116}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 16})`\n", "\n", "
\n", "\n", "" ], "text/plain": [ - "Completion(model='deepseek-v4-flash', message=Msg(role='assistant', content=[Part(type='thinking', text='We need to interpret the user\\'s question: \"what did I just say?\" The user previously said \"hi\" and I responded \"hello\". Now they ask what they just said. This could be a test of memory, or a playful question, or maybe they are checking if I recall the conversation. Since I\\'m an AI, I can recall the immediate prior message. The user\\'s last message before the current one was \"hi\". So I should answer that they said \"hi\". But also note that after my \"hello\", they asked \"what did I just say?\" So the \"just say\" refers to their previous utterance. I\\'ll respond accordingly.', data=None), Part(type='text', text='You just said **\"hi\"** before I responded with \"hello.\" Then you asked, \"what did I just say?\" — which is your current message. So, to be precise: the last thing you said before this question was \"hi.\"', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=16, completion_tokens=185, total_tokens=201, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=133, raw={'prompt_tokens': 16, 'completion_tokens': 185, 'total_tokens': 201, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 133}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 16}), tool_calls=[], api_name='openai_chat', vendor_name='deepseek', raw={'id': '6836529b-e1df-4644-8546-cd2f271640b2', 'object': 'chat.completion', 'created': 1778751762, 'model': 'deepseek-v4-flash', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'You just said **\"hi\"** before I responded with \"hello.\" Then you asked, \"what did I just say?\" — which is your current message. So, to be precise: the last thing you said before this question was \"hi.\"', 'reasoning_content': 'We need to interpret the user\\'s question: \"what did I just say?\" The user previously said \"hi\" and I responded \"hello\". Now they ask what they just said. This could be a test of memory, or a playful question, or maybe they are checking if I recall the conversation. Since I\\'m an AI, I can recall the immediate prior message. The user\\'s last message before the current one was \"hi\". So I should answer that they said \"hi\". But also note that after my \"hello\", they asked \"what did I just say?\" So the \"just say\" refers to their previous utterance. I\\'ll respond accordingly.'}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 16, 'completion_tokens': 185, 'total_tokens': 201, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 133}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 16}, 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402'})" + "Completion(model='deepseek-v4-flash', message=Msg(role='assistant', content=[Part(type='thinking', text='We need to interpret the user\\'s question: \"what did I just say?\" The user previously said \"hi\" and then the assistant responded \"hello\". Now user asks \"what did I just say?\" Possibly user is testing memory or being playful. As an AI, I should note that the user said \"hi\" just before. But careful: the user\\'s last message is \"what did I just say?\" referring to their previous message. So answer: \"You said \\'hi\\'.\" However, the user might be referring to the entire exchange? I\\'ll answer directly.', data=None), Part(type='text', text='You said \"hi\".', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=16, completion_tokens=122, total_tokens=138, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=116, raw={'prompt_tokens': 16, 'completion_tokens': 122, 'total_tokens': 138, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 116}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 16}), tool_calls=[], api_name='openai_chat', vendor_name='deepseek', raw={'id': '1c5c3675-7a9a-48d1-a65c-cd9bfc2d00d1', 'object': 'chat.completion', 'created': 1781269655, 'model': 'deepseek-v4-flash', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'You said \"hi\".', 'reasoning_content': 'We need to interpret the user\\'s question: \"what did I just say?\" The user previously said \"hi\" and then the assistant responded \"hello\". Now user asks \"what did I just say?\" Possibly user is testing memory or being playful. As an AI, I should note that the user said \"hi\" just before. But careful: the user\\'s last message is \"what did I just say?\" referring to their previous message. So answer: \"You said \\'hi\\'.\" However, the user might be referring to the entire exchange? I\\'ll answer directly.'}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 16, 'completion_tokens': 122, 'total_tokens': 138, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 116}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 16}, 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402'})" ] }, "execution_count": null, @@ -5297,7 +5442,7 @@ "\n", "\n", "\n", - "The result of **3 + 5** is **8**.\n", + "The result of adding 3 and 5 is **8**.\n", "\n", "
\n", "\n", @@ -5310,7 +5455,7 @@ "" ], "text/plain": [ - "Completion(model='deepseek-v4-flash', message=Msg(role='assistant', content=[Part(type='thinking', text='The result is 8.', data=None), Part(type='text', text='The result of **3 + 5** is **8**.', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=404, completion_tokens=20, total_tokens=424, cached_tokens=384, cache_creation_tokens=0, reasoning_tokens=6, raw={'prompt_tokens': 404, 'completion_tokens': 20, 'total_tokens': 424, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 20}), tool_calls=[], api_name='openai_chat', vendor_name='deepseek', raw={'id': '151c175c-de1d-4d34-823a-e686c057cc24', 'object': 'chat.completion', 'created': 1778752479, 'model': 'deepseek-v4-flash', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'The result of **3 + 5** is **8**.', 'reasoning_content': 'The result is 8.'}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 404, 'completion_tokens': 20, 'total_tokens': 424, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 20}, 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402'})" + "Completion(model='deepseek-v4-flash', message=Msg(role='assistant', content=[Part(type='thinking', text='The result is 8.', data=None), Part(type='text', text='The result of adding 3 and 5 is **8**.', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=404, completion_tokens=20, total_tokens=424, cached_tokens=384, cache_creation_tokens=0, reasoning_tokens=6, raw={'prompt_tokens': 404, 'completion_tokens': 20, 'total_tokens': 424, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 20}), tool_calls=[], api_name='openai_chat', vendor_name='deepseek', raw={'id': '78b49bd0-f61c-4b0c-ba38-97f00286577a', 'object': 'chat.completion', 'created': 1781269659, 'model': 'deepseek-v4-flash', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'The result of adding 3 and 5 is **8**.', 'reasoning_content': 'The result is 8.'}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 404, 'completion_tokens': 20, 'total_tokens': 424, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 20}, 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402'})" ] }, "execution_count": null, @@ -5333,9 +5478,9 @@ "data": { "text/plain": [ "[Msg(role='user', content=[Part(type=, text='add 3 + 5', data=None)]),\n", - " Msg(role='assistant', content=[Part(type='thinking', text='The user wants to add 3 and 5. Let me use the simple_add tool.', data=None), Part(type='tool_use', text=None, data={'index': 0, 'type': 'function', 'id': 'call_00_rwRVHrYx0Peh7oykHVde6698', 'name': 'simple_add', 'arguments': {'a': 3, 'b': 5}, 'server': False})]),\n", - " Msg(role='tool', content=[Part(type=, text='8', data={'id': 'call_00_rwRVHrYx0Peh7oykHVde6698', 'name': 'simple_add', 'arguments': {'a': 3, 'b': 5}, 'server': False})]),\n", - " Msg(role='assistant', content=[Part(type='thinking', text='The result is 8.', data=None), Part(type='text', text='The result of **3 + 5** is **8**.', data={'citations': []})])]" + " Msg(role='assistant', content=[Part(type='thinking', text=\"The user wants to add 3 and 5. I'll use the simple_add tool.\", data=None), Part(type='tool_use', text=None, data={'index': 0, 'type': 'function', 'id': 'call_00_vy7Tm45vEJJ4U5vHlFfJ0905', 'name': 'simple_add', 'arguments': {'a': 3, 'b': 5}, 'server': False})]),\n", + " Msg(role='tool', content=[Part(type=, text='8', data={'id': 'call_00_vy7Tm45vEJJ4U5vHlFfJ0905', 'name': 'simple_add', 'arguments': {'a': 3, 'b': 5}, 'server': False})]),\n", + " Msg(role='assistant', content=[Part(type='thinking', text='The result is 8.', data=None), Part(type='text', text='The result of adding 3 and 5 is **8**.', data={'citations': []})])]" ] }, "execution_count": null, @@ -5384,20 +5529,20 @@ "\n", "
\n", "\n", - "The result of **10 + 2** is **12**.\n", + "The result of adding 10 and 2 is **12**.\n", "\n", "
\n", "\n", "- model: `deepseek-v4-flash`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=494, completion_tokens=20, total_tokens=514, cached_tokens=384, cache_creation_tokens=0, reasoning_tokens=6, raw={'prompt_tokens': 494, 'completion_tokens': 20, 'total_tokens': 514, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 110})`\n", + "- usage: `Usage(prompt_tokens=502, completion_tokens=20, total_tokens=522, cached_tokens=384, cache_creation_tokens=0, reasoning_tokens=6, raw={'prompt_tokens': 502, 'completion_tokens': 20, 'total_tokens': 522, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 118})`\n", "\n", "
\n", "\n", "" ], "text/plain": [ - "Completion(model='deepseek-v4-flash', message=Msg(role='assistant', content=[Part(type='thinking', text='The result is 12.', data=None), Part(type='text', text='The result of **10 + 2** is **12**.', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=494, completion_tokens=20, total_tokens=514, cached_tokens=384, cache_creation_tokens=0, reasoning_tokens=6, raw={'prompt_tokens': 494, 'completion_tokens': 20, 'total_tokens': 514, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 110}), tool_calls=[], api_name='openai_chat', vendor_name='deepseek', raw={'id': '6fb7f82d-7ee1-4273-a702-c4937b2d8446', 'object': 'chat.completion', 'created': 1778752502, 'model': 'deepseek-v4-flash', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'The result of **10 + 2** is **12**.', 'reasoning_content': 'The result is 12.'}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 494, 'completion_tokens': 20, 'total_tokens': 514, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 110}, 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402'})" + "Completion(model='deepseek-v4-flash', message=Msg(role='assistant', content=[Part(type='thinking', text='The result is 12.', data=None), Part(type='text', text='The result of adding 10 and 2 is **12**.', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=502, completion_tokens=20, total_tokens=522, cached_tokens=384, cache_creation_tokens=0, reasoning_tokens=6, raw={'prompt_tokens': 502, 'completion_tokens': 20, 'total_tokens': 522, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 118}), tool_calls=[], api_name='openai_chat', vendor_name='deepseek', raw={'id': 'dfc8be6f-24bb-42af-af03-f7c73ed4f042', 'object': 'chat.completion', 'created': 1781269662, 'model': 'deepseek-v4-flash', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'The result of adding 10 and 2 is **12**.', 'reasoning_content': 'The result is 12.'}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 502, 'completion_tokens': 20, 'total_tokens': 522, 'prompt_tokens_details': {'cached_tokens': 384}, 'completion_tokens_details': {'reasoning_tokens': 6}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 118}, 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402'})" ] }, "execution_count": null, @@ -5419,13 +5564,13 @@ "data": { "text/plain": [ "[Msg(role='user', content=[Part(type=, text='add 3 + 5', data=None)]),\n", - " Msg(role='assistant', content=[Part(type='tool_use', text=None, data={'index': 0, 'type': 'function', 'id': 'call_00_rwRVHrYx0Peh7oykHVde6698', 'name': 'simple_add', 'arguments': {'a': 3, 'b': 5}, 'server': False})]),\n", - " Msg(role='tool', content=[Part(type=, text='8', data={'id': 'call_00_rwRVHrYx0Peh7oykHVde6698', 'name': 'simple_add', 'arguments': {'a': 3, 'b': 5}, 'server': False})]),\n", - " Msg(role='assistant', content=[Part(type='text', text='The result of **3 + 5** is **8**.', data={'citations': []})]),\n", + " Msg(role='assistant', content=[Part(type='tool_use', text=None, data={'index': 0, 'type': 'function', 'id': 'call_00_vy7Tm45vEJJ4U5vHlFfJ0905', 'name': 'simple_add', 'arguments': {'a': 3, 'b': 5}, 'server': False})]),\n", + " Msg(role='tool', content=[Part(type=, text='8', data={'id': 'call_00_vy7Tm45vEJJ4U5vHlFfJ0905', 'name': 'simple_add', 'arguments': {'a': 3, 'b': 5}, 'server': False})]),\n", + " Msg(role='assistant', content=[Part(type='text', text='The result of adding 3 and 5 is **8**.', data={'citations': []})]),\n", " Msg(role='user', content=[Part(type=, text='what is 10 + 2', data=None)]),\n", - " Msg(role='assistant', content=[Part(type='thinking', text='The user wants to add 10 and 2.', data=None), Part(type='tool_use', text=None, data={'index': 0, 'type': 'function', 'id': 'call_00_AjMaDAV0jy0PC8gNuVuC5090', 'name': 'simple_add', 'arguments': {'a': 10, 'b': 2}, 'server': False})]),\n", - " Msg(role='tool', content=[Part(type=, text='12', data={'id': 'call_00_AjMaDAV0jy0PC8gNuVuC5090', 'name': 'simple_add', 'arguments': {'a': 10, 'b': 2}, 'server': False})]),\n", - " Msg(role='assistant', content=[Part(type='thinking', text='The result is 12.', data=None), Part(type='text', text='The result of **10 + 2** is **12**.', data={'citations': []})])]" + " Msg(role='assistant', content=[Part(type='thinking', text='The user wants to add 10 and 2. Let me use the simple_add function.', data=None), Part(type='tool_use', text=None, data={'index': 0, 'type': 'function', 'id': 'call_00_3VKeBGea4PcODYkrf6B83285', 'name': 'simple_add', 'arguments': {'a': 10, 'b': 2}, 'server': False})]),\n", + " Msg(role='tool', content=[Part(type=, text='12', data={'id': 'call_00_3VKeBGea4PcODYkrf6B83285', 'name': 'simple_add', 'arguments': {'a': 10, 'b': 2}, 'server': False})]),\n", + " Msg(role='assistant', content=[Part(type='thinking', text='The result is 12.', data=None), Part(type='text', text='The result of adding 10 and 2 is **12**.', data={'citations': []})])]" ] }, "execution_count": null, @@ -5517,31 +5662,77 @@ "
\n", "\n", "```python\n", - "def fib(n):\n", - " # base cases\n", - " if n <= 1:\n", + "# Importing required module for efficient computations\n", + "from functools import lru_cache\n", + "\n", + "# Option 1: Recursive with memoization (most elegant for reading)\n", + "@lru_cache(maxsize=None)\n", + "def fib(n: int) -> int:\n", + " \"\"\"\n", + " Returns the n-th Fibonacci number (0-indexed: fib(0) = 0, fib(1) = 1).\n", + " Uses memoization to achieve O(n) time complexity.\n", + " \"\"\"\n", + " if n < 2:\n", " return n\n", - " # iterative approach for efficiency: O(n) time, O(1) space\n", + " return fib(n - 1) + fib(n - 2)\n", + "\n", + "\n", + "# Option 2: Iterative (most efficient for space)\n", + "def fib_iterative(n: int) -> int:\n", + " \"\"\"\n", + " Returns the n-th Fibonacci number iteratively.\n", + " O(n) time, O(1) space.\n", + " \"\"\"\n", + " if n < 2:\n", + " return n\n", + " \n", " a, b = 0, 1\n", " for _ in range(2, n + 1):\n", " a, b = b, a + b\n", + " \n", " return b\n", - "```\n", "\n", - "This implements the Fibonacci sequence: `fib(0)=0`, `fib(1)=1`, `fib(2)=1`, `fib(3)=2`, etc. The iterative method avoids the exponential blowup of naive recursion.\n", + "\n", + "# Option 3: Closed-form / Binet's formula (interesting but imprecise for large n)\n", + "import math\n", + "\n", + "def fib_binet(n: int) -> int:\n", + " \"\"\"\n", + " Returns the n-th Fibonacci number using Binet's formula.\n", + " Note: Due to floating-point precision, this is only exact for n <= 70.\n", + " \"\"\"\n", + " sqrt5 = math.sqrt(5)\n", + " phi = (1 + sqrt5) / 2\n", + " psi = (1 - sqrt5) / 2\n", + " \n", + " return int((phi**n - psi**n) / sqrt5)\n", + "\n", + "\n", + "# Demonstration\n", + "if __name__ == \"__main__\":\n", + " n = 30\n", + " print(f\"fib({n}) = {fib(n)}\") # Uses memoized recursion\n", + " print(f\"fib_iterative({n}) = {fib_iterative(n)}\")\n", + " print(f\"fib_binet({n}) = {fib_binet(n)}\") # Matches for n=30\n", + " \n", + " # Show the sequence\n", + " print(\"\\nFibonacci sequence (first 10):\")\n", + " for i in range(10):\n", + " print(f\"fib({i}) = {fib(i)}\")\n", + "```\n", "\n", "
\n", "\n", "- model: `deepseek-v4-flash`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=9, completion_tokens=127, total_tokens=136, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'prompt_tokens': 9, 'completion_tokens': 127, 'total_tokens': 136, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 9})`\n", + "- usage: `Usage(prompt_tokens=8, completion_tokens=468, total_tokens=476, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'prompt_tokens': 8, 'completion_tokens': 468, 'total_tokens': 476, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 8})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='deepseek-v4-flash', message=Msg(role='assistant', content=[Part(type='text', text='```python\\ndef fib(n):\\n # base cases\\n if n <= 1:\\n return n\\n # iterative approach for efficiency: O(n) time, O(1) space\\n a, b = 0, 1\\n for _ in range(2, n + 1):\\n a, b = b, a + b\\n return b\\n```\\n\\nThis implements the Fibonacci sequence: `fib(0)=0`, `fib(1)=1`, `fib(2)=1`, `fib(3)=2`, etc. The iterative method avoids the exponential blowup of naive recursion.', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=9, completion_tokens=127, total_tokens=136, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'prompt_tokens': 9, 'completion_tokens': 127, 'total_tokens': 136, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 9}), tool_calls=[], api_name='openai_chat', vendor_name='deepseek', raw={'id': '00f0fc81-9919-4f0f-886e-605d86f83c92', 'object': 'chat.completion', 'created': 1778685370, 'model': 'deepseek-v4-flash', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '\\ndef fib(n):\\n # base cases\\n if n <= 1:\\n return n\\n # iterative approach for efficiency: O(n) time, O(1) space\\n a, b = 0, 1\\n for _ in range(2, n + 1):\\n a, b = b, a + b\\n return b\\n```\\n\\nThis implements the Fibonacci sequence: `fib(0)=0`, `fib(1)=1`, `fib(2)=1`, `fib(3)=2`, etc. The iterative method avoids the exponential blowup of naive recursion.', 'reasoning_content': None}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 9, 'completion_tokens': 127, 'total_tokens': 136, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 9}, 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402'})" + "Completion(model='deepseek-v4-flash', message=Msg(role='assistant', content=[Part(type='text', text='```python\\n# Importing required module for efficient computations\\nfrom functools import lru_cache\\n\\n# Option 1: Recursive with memoization (most elegant for reading)\\n@lru_cache(maxsize=None)\\ndef fib(n: int) -> int:\\n \"\"\"\\n Returns the n-th Fibonacci number (0-indexed: fib(0) = 0, fib(1) = 1).\\n Uses memoization to achieve O(n) time complexity.\\n \"\"\"\\n if n < 2:\\n return n\\n return fib(n - 1) + fib(n - 2)\\n\\n\\n# Option 2: Iterative (most efficient for space)\\ndef fib_iterative(n: int) -> int:\\n \"\"\"\\n Returns the n-th Fibonacci number iteratively.\\n O(n) time, O(1) space.\\n \"\"\"\\n if n < 2:\\n return n\\n \\n a, b = 0, 1\\n for _ in range(2, n + 1):\\n a, b = b, a + b\\n \\n return b\\n\\n\\n# Option 3: Closed-form / Binet\\'s formula (interesting but imprecise for large n)\\nimport math\\n\\ndef fib_binet(n: int) -> int:\\n \"\"\"\\n Returns the n-th Fibonacci number using Binet\\'s formula.\\n Note: Due to floating-point precision, this is only exact for n <= 70.\\n \"\"\"\\n sqrt5 = math.sqrt(5)\\n phi = (1 + sqrt5) / 2\\n psi = (1 - sqrt5) / 2\\n \\n return int((phi**n - psi**n) / sqrt5)\\n\\n\\n# Demonstration\\nif __name__ == \"__main__\":\\n n = 30\\n print(f\"fib({n}) = {fib(n)}\") # Uses memoized recursion\\n print(f\"fib_iterative({n}) = {fib_iterative(n)}\")\\n print(f\"fib_binet({n}) = {fib_binet(n)}\") # Matches for n=30\\n \\n # Show the sequence\\n print(\"\\\\nFibonacci sequence (first 10):\")\\n for i in range(10):\\n print(f\"fib({i}) = {fib(i)}\")\\n```', data={'citations': []})]), finish_reason='stop', usage=Usage(prompt_tokens=8, completion_tokens=468, total_tokens=476, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'prompt_tokens': 8, 'completion_tokens': 468, 'total_tokens': 476, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 8}), tool_calls=[], api_name='openai_chat', vendor_name='deepseek', raw={'id': '4f16a643-09ba-47eb-b8da-a414c92bd5a3', 'object': 'chat.completion', 'created': 1781269663, 'model': 'deepseek-v4-flash', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '\\n# Importing required module for efficient computations\\nfrom functools import lru_cache\\n\\n# Option 1: Recursive with memoization (most elegant for reading)\\n@lru_cache(maxsize=None)\\ndef fib(n: int) -> int:\\n \"\"\"\\n Returns the n-th Fibonacci number (0-indexed: fib(0) = 0, fib(1) = 1).\\n Uses memoization to achieve O(n) time complexity.\\n \"\"\"\\n if n < 2:\\n return n\\n return fib(n - 1) + fib(n - 2)\\n\\n\\n# Option 2: Iterative (most efficient for space)\\ndef fib_iterative(n: int) -> int:\\n \"\"\"\\n Returns the n-th Fibonacci number iteratively.\\n O(n) time, O(1) space.\\n \"\"\"\\n if n < 2:\\n return n\\n \\n a, b = 0, 1\\n for _ in range(2, n + 1):\\n a, b = b, a + b\\n \\n return b\\n\\n\\n# Option 3: Closed-form / Binet\\'s formula (interesting but imprecise for large n)\\nimport math\\n\\ndef fib_binet(n: int) -> int:\\n \"\"\"\\n Returns the n-th Fibonacci number using Binet\\'s formula.\\n Note: Due to floating-point precision, this is only exact for n <= 70.\\n \"\"\"\\n sqrt5 = math.sqrt(5)\\n phi = (1 + sqrt5) / 2\\n psi = (1 - sqrt5) / 2\\n \\n return int((phi**n - psi**n) / sqrt5)\\n\\n\\n# Demonstration\\nif __name__ == \"__main__\":\\n n = 30\\n print(f\"fib({n}) = {fib(n)}\") # Uses memoized recursion\\n print(f\"fib_iterative({n}) = {fib_iterative(n)}\")\\n print(f\"fib_binet({n}) = {fib_binet(n)}\") # Matches for n=30\\n \\n # Show the sequence\\n print(\"\\\\nFibonacci sequence (first 10):\")\\n for i in range(10):\\n print(f\"fib({i}) = {fib(i)}\")\\n```', 'reasoning_content': None}, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 8, 'completion_tokens': 468, 'total_tokens': 476, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 8}, 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402'})" ] }, "execution_count": null, @@ -5630,7 +5821,7 @@ "text/markdown": [ "
\n", "\n", - "In a quiet town nestled between rolling hills, there lived a robot named Pixel. Pixel was built to help with chores, but he longed for a friend. Every day, he watched children play withResponse was cut off at token limit.\n", + "Spark was a little robot with a gleaming silver shell and a bright blue eye that blinked like a star. He roamed the city’s quiet streets every morning, picking up stray pieces of litterResponse was cut off at token limit.\n", "\n", "
\n", "\n", @@ -5643,7 +5834,7 @@ "
" ], "text/plain": [ - "Completion(model='gpt-4.1', message=Msg(role='assistant', content=[Part(type=, text='In a quiet town nestled between rolling hills, there lived a robot named Pixel. Pixel was built to help with chores, but he longed for a friend. Every day, he watched children play with', data={'type': 'output_text', 'logprobs': [], 'text': 'In a quiet town nestled between rolling hills, there lived a robot named Pixel. Pixel was built to help with chores, but he longed for a friend. 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\n", "\n", - "- data: `{'type': 'function_call', 'status': 'completed', 'call_id': 'call_B51xXWFg10YkHqkJVMODTx4i', 'id': 'fc_0e2920bf05b71dc30069f311b880888191a42435c7c037419e', 'name': 'async_add', 'arguments': {'a': 5, 'b': 7}, 'server': False}`\n", + "- data: `{'type': 'function_call', 'status': 'completed', 'call_id': 'call_Rd60uIGNk8JHc1NrOaTSaKKQ', 'id': 'fc_0da992fc5b25d8b2006a2c04bac2dc81a19b1e0a8f8bfafe95', 'name': 'async_add', 'arguments': {'a': 5, 'b': 7}, 'server': False}`\n", "\n", "
\n", "\n", @@ -6216,7 +6413,7 @@ "\n", "
\n", "\n", - "- data: `{'id': 'fc_0e2920bf05b71dc30069f311b880888191a42435c7c037419e', 'name': 'async_add', 'arguments': {'a': 5, 'b': 7}, 'server': False}`\n", + "- data: `{'id': 'fc_0da992fc5b25d8b2006a2c04bac2dc81a19b1e0a8f8bfafe95', 'name': 'async_add', 'arguments': {'a': 5, 'b': 7}, 'server': False}`\n", "\n", "
\n", "\n", @@ -6248,11 +6445,11 @@ "\n", "**Part** (`text`)\n", "\n", - "I used the tool to calculate 5 + 7, and the result is 12. If you have more calculations or questions, feel free to ask!\n", + "I used a calculation tool to determine the sum of 5 and 7. The result is 12. If you need more calculations or have other questions, please let me know!\n", "\n", "
\n", "\n", - "- data: `{'type': 'output_text', 'logprobs': [], 'text': 'I used the tool to calculate 5 + 7, and the result is 12. If you have more calculations or questions, feel free to ask!', 'citations': []}`\n", + "- data: `{'type': 'output_text', 'logprobs': [], 'text': 'I used a calculation tool to determine the sum of 5 and 7. The result is 12. If you need more calculations or have other questions, please let me know!', 'citations': []}`\n", "\n", "
\n", "\n", @@ -6291,7 +6488,7 @@ "\n", "
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\n", "\n", @@ -6309,7 +6506,7 @@ "\n", "
\n", "\n", - "- data: `{'id': 'fc_0e2920bf05b71dc30069f311b880888191a42435c7c037419e', 'name': 'async_add', 'arguments': {'a': 5, 'b': 7}, 'server': False}`\n", + "- data: `{'id': 'fc_0da992fc5b25d8b2006a2c04bac2dc81a19b1e0a8f8bfafe95', 'name': 'async_add', 'arguments': {'a': 5, 'b': 7}, 'server': False}`\n", "\n", "
\n", "\n", @@ -6341,11 +6538,11 @@ "\n", "**Part** (`text`)\n", "\n", - "I used the tool to calculate 5 + 7, and the result is 12. If you have more calculations or questions, feel free to ask!\n", + "I used a calculation tool to determine the sum of 5 and 7. The result is 12. If you need more calculations or have other questions, please let me know!\n", "\n", "
\n", "\n", - "- data: `{'type': 'output_text', 'logprobs': [], 'text': 'I used the tool to calculate 5 + 7, and the result is 12. If you have more calculations or questions, feel free to ask!', 'citations': []}`\n", + "- data: `{'type': 'output_text', 'logprobs': [], 'text': 'I used a calculation tool to determine the sum of 5 and 7. The result is 12. If you need more calculations or have other questions, please let me know!', 'citations': []}`\n", "\n", "
\n", "\n", @@ -6379,7 +6576,7 @@ "text/markdown": [ "
\n", "\n", - "In a quiet town nestled between rolling hills, there lived a robot named Pixel. Pixel was built to help with chores, but he longed for a friend. Every day, he watched children play withResponse was cut off at token limit.\n", + "Spark was a little robot with a gleaming silver shell and a bright blue eye that blinked like a star. He roamed the city’s quiet streets every morning, picking up stray pieces of litterResponse was cut off at token limit.\n", "\n", "
\n", "\n", @@ -6392,7 +6589,7 @@ "
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He roamed the city’s quiet streets every morning, picking up stray pieces of litter', data={'type': 'output_text', 'logprobs': [], 'text': 'Spark was a little robot with a gleaming silver shell and a bright blue eye that blinked like a star. 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Pixel was built to help with chores, but he longed for a friend. Every day, he watched children play with\n", + "Spark was a little robot with a gleaming silver shell and a bright blue eye that blinked like a star. He roamed the city’s quiet streets every morning, picking up stray pieces of litter\n", "\n", "
\n", "\n", - "- data: `{'type': 'output_text', 'logprobs': [], 'text': 'In a quiet town nestled between rolling hills, there lived a robot named Pixel. Pixel was built to help with chores, but he longed for a friend. Every day, he watched children play with', 'citations': []}`\n", + "- data: `{'type': 'output_text', 'logprobs': [], 'text': 'Spark was a little robot with a gleaming silver shell and a bright blue eye that blinked like a star. He roamed the city’s quiet streets every morning, picking up stray pieces of litter', 'citations': []}`\n", "\n", "
\n", "\n", @@ -6448,7 +6645,7 @@ "" ], "text/plain": [ - "Msg(role='assistant', content=[Part(type=, text='In a quiet town nestled between rolling hills, there lived a robot named Pixel. Pixel was built to help with chores, but he longed for a friend. Every day, he watched children play with', data={'type': 'output_text', 'logprobs': [], 'text': 'In a quiet town nestled between rolling hills, there lived a robot named Pixel. Pixel was built to help with chores, but he longed for a friend. Every day, he watched children play with', 'citations': []}), Part(type=, text='Response was cut off at token limit.', data=None)])" + "Msg(role='assistant', content=[Part(type=, text='Spark was a little robot with a gleaming silver shell and a bright blue eye that blinked like a star. He roamed the city’s quiet streets every morning, picking up stray pieces of litter', data={'type': 'output_text', 'logprobs': [], 'text': 'Spark was a little robot with a gleaming silver shell and a bright blue eye that blinked like a star. He roamed the city’s quiet streets every morning, picking up stray pieces of litter', 'citations': []}), Part(type=, text='Response was cut off at token limit.', data=None)])" ] }, "execution_count": null, @@ -6492,7 +6689,7 @@ "" ], "text/plain": [ - "Completion(model='claude-opus-4-5', message=Msg(role='assistant', content=[Part(type=, text='AI server provider content filter was applied to this request', data=None)]), finish_reason='refusal', usage=Usage(prompt_tokens=30, completion_tokens=4, total_tokens=34, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 30, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 4, 'service_tier': 'standard', 'inference_geo': 'not_available'}), tool_calls=[], api_name='anthropic', vendor_name='anthropic', raw={'model': 'claude-opus-4-5-20251101', 'id': 'msg_019rg4aYqPzPqtsiYjmzVJWZ', 'type': 'message', 'role': 'assistant', 'content': [], 'stop_reason': 'refusal', 'stop_sequence': None, 'stop_details': None, 'usage': {'input_tokens': 30, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 4, 'service_tier': 'standard', 'inference_geo': 'not_available'}})" + "Completion(model='claude-opus-4-5', message=Msg(role='assistant', content=[Part(type=, text='AI server provider content filter was applied to this request', data=None)]), finish_reason='refusal', usage=Usage(prompt_tokens=30, completion_tokens=4, total_tokens=34, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=0, raw={'input_tokens': 30, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 4, 'service_tier': 'standard', 'inference_geo': 'not_available'}), tool_calls=[], api_name='anthropic', vendor_name='anthropic', raw={'model': 'claude-opus-4-5-20251101', 'id': 'msg_018PURsxcUDPtnFDExw4dZra', 'type': 'message', 'role': 'assistant', 'content': [], 'stop_reason': 'refusal', 'stop_sequence': None, 'stop_details': {'type': 'refusal', 'category': 'bio', 'explanation': None}, 'usage': {'input_tokens': 30, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'cache_creation': {'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}, 'output_tokens': 4, 'service_tier': 'standard', 'inference_geo': 'not_available'}})" ] }, "execution_count": null, @@ -6593,8 +6790,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "🔧 async_add\n", - "The sum of 5 and 7 is 12. I have successfully completed the calculation." + "🔧 async_add\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 + 7 is 12." ] } ], @@ -6615,20 +6818,20 @@ "text/markdown": [ "
\n", "\n", - "The sum of 5 and 7 is 12. I have successfully completed the calculation.\n", + "5 + 7 is 12.\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=155, completion_tokens=20, total_tokens=266, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=91, raw={'promptTokenCount': 155, 'candidatesTokenCount': 20, 'totalTokenCount': 266, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 155}], 'thoughtsTokenCount': 91})`\n", + "- usage: `Usage(prompt_tokens=155, completion_tokens=9, total_tokens=248, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=84, raw={'promptTokenCount': 155, 'candidatesTokenCount': 9, 'totalTokenCount': 248, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 155}], 'thoughtsTokenCount': 84, 'serviceTier': 'standard'})`\n", "\n", "
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" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='The sum of 5 and 7 is 12. I have successfully completed the calculation.', data={'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=155, completion_tokens=20, total_tokens=266, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=91, raw={'promptTokenCount': 155, 'candidatesTokenCount': 20, 'totalTokenCount': 266, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 155}], 'thoughtsTokenCount': 91}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'deltas': [Delta(text='The sum of 5 and 7 is 12. 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7 is 12.', data={'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=155, completion_tokens=9, total_tokens=248, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=84, raw={'promptTokenCount': 155, 'candidatesTokenCount': 9, 'totalTokenCount': 248, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 155}], 'thoughtsTokenCount': 84, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'deltas': [Delta(text='5 + 7 is 12.', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=None, usage=Usage(prompt_tokens=155, completion_tokens=9, total_tokens=248, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=84, raw={'promptTokenCount': 155, 'candidatesTokenCount': 9, 'totalTokenCount': 248, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 155}], 'thoughtsTokenCount': 84, 'serviceTier': 'standard'}), raw={'candidates': [{'content': {'parts': [{'text': '5 + 7 is 12.'}], 'role': 'model'}, 'index': 0}], 'usageMetadata': {'promptTokenCount': 155, 'candidatesTokenCount': 9, 'totalTokenCount': 248, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 155}], 'thoughtsTokenCount': 84, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'vgQsatjaCaLSjMcPmbfOkQI'}), Delta(text='', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=, usage=Usage(prompt_tokens=155, completion_tokens=9, total_tokens=248, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=84, raw={'promptTokenCount': 155, 'candidatesTokenCount': 9, 'totalTokenCount': 248, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 155}], 'thoughtsTokenCount': 84, 'serviceTier': 'standard'}), raw={'candidates': [{'content': {'parts': [{'text': '', 'thoughtSignature': 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usage: `Usage(prompt_tokens=3, completion_tokens=9, total_tokens=77, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65})`\n", + "- usage: `Usage(prompt_tokens=3, completion_tokens=9, total_tokens=55, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=43, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 55, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 43, 'serviceTier': 'standard'})`\n", "\n", "\n", "\n", "" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today?', data={'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=77, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'deltas': [Delta(text='Hello! How can I', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=None, usage=Usage(prompt_tokens=3, completion_tokens=5, total_tokens=73, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 5, 'totalTokenCount': 73, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}), raw={'candidates': [{'content': {'parts': [{'text': 'Hello! How can I'}], 'role': 'model'}, 'index': 0}], 'usageMetadata': {'promptTokenCount': 3, 'candidatesTokenCount': 5, 'totalTokenCount': 73, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'gRLzacDtIt6A3boP9vCWsAg'}), Delta(text=' help you today?', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=None, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=77, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}), raw={'candidates': [{'content': {'parts': [{'text': ' help you today?'}], 'role': 'model'}, 'index': 0}], 'usageMetadata': {'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'gRLzacDtIt6A3boP9vCWsAg'}), Delta(text='', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=77, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=65, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}), raw={'candidates': [{'content': {'parts': [{'text': '', 'thoughtSignature': 'EtQCCtECAQw51sfRXDOcN9iujtIsIdL/3Hqy9Ppa7GABoJXxMd00zUZUs4rcHJs815F1BZP0RlKbRrxtSACPJBb5ypxaKrzijIymPV7n9FynodoT6/B7wJquuHXD6rIvPy9/nssqrWAcBA5fJOdjXRtfM3tMLhIcl6Np3L87f6KeOwgS/npqJLikxKJxHFukl1cRw2COc3gqKfksPcAwPydBUcegmji3elck26EZmqzqO8+jCETceWkThUCxmg9jM9oWI3JmmrOFSKZ9/IcIFf4kuz/xeFxzPbdh/PQW1GMHndLy/PErTkRIwu5HtcZQYAZWcwqB3ob6ulYi0NdDWl9Y1SeMCa911GpG1W3iOro46AZcpe/+eEj16TFCqReGU6nD2MSHx9iNcGhTu919tAW5BGw1sKfZV5PFltMBAzQTRvplLakXAwsdxE/jPheVo6PZj9VtXQ=='}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 77, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 65}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'gRLzacDtIt6A3boP9vCWsAg'})]})" + "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='Hello! How can I help you today?', data={'citations': []})]), finish_reason=, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=55, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=43, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 55, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 43, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'deltas': [Delta(text='Hello', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=None, usage=Usage(prompt_tokens=3, completion_tokens=1, total_tokens=47, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=43, raw={'promptTokenCount': 3, 'candidatesTokenCount': 1, 'totalTokenCount': 47, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 43, 'serviceTier': 'standard'}), raw={'candidates': [{'content': {'parts': [{'text': 'Hello'}], 'role': 'model'}, 'index': 0}], 'usageMetadata': {'promptTokenCount': 3, 'candidatesTokenCount': 1, 'totalTokenCount': 47, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 43, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'xAQsatbrJbKgjMcPodSF6Q8'}), Delta(text='! How can I help you today?', thinking='', refusal='', tool_calls=[], citations=[], server_tool_result=None, finish_reason=None, usage=Usage(prompt_tokens=3, completion_tokens=9, total_tokens=55, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=43, raw={'promptTokenCount': 3, 'candidatesTokenCount': 9, 'totalTokenCount': 55, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 3}], 'thoughtsTokenCount': 43, 'serviceTier': 'standard'}), raw={'candidates': [{'content': {'parts': [{'text': '! 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'candidatesTokenCount': 16, 'totalTokenCount': 467, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 161}], 'thoughtsTokenCount': 290})`\n", + "- usage: `Usage(prompt_tokens=161, completion_tokens=16, total_tokens=573, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=396, raw={'promptTokenCount': 161, 'candidatesTokenCount': 16, 'totalTokenCount': 573, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 161}], 'thoughtsTokenCount': 396, 'serviceTier': 'standard'})`\n", "\n", "\n", "\n", "" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text=None, data={'thoughtSignature': 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api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'functionCall': {'name': 'stop_tool', 'args': {'msg': 'halt'}, 'id': 'j17z2fn1'}, 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0, 'finishMessage': 'Model generated function call(s).'}], 'usageMetadata': {'promptTokenCount': 161, 'candidatesTokenCount': 16, 'totalTokenCount': 573, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 161}], 'thoughtsTokenCount': 396, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'xQQsapyvLICbjMcPsKeysAQ'})" ] }, "metadata": {}, @@ -7202,14 +7405,14 @@ "\n", "
\n", "\n", - "- data: `{'id': '5yijuot9', 'name': 'stop_tool', 'arguments': {'msg': 'halt'}, 'server': False}`\n", + "- data: `{'id': 'j17z2fn1', 'name': 'stop_tool', 'arguments': {'msg': 'halt'}, 'server': False}`\n", "\n", "
\n", "\n", "" ], "text/plain": [ - "Part(type=, text='Can not continue: halt', data={'id': '5yijuot9', 'name': 'stop_tool', 'arguments': {'msg': 'halt'}, 'server': False})" + "Part(type=, text='Can not continue: halt', data={'id': 'j17z2fn1', 'name': 'stop_tool', 'arguments': {'msg': 'halt'}, 'server': False})" ] }, "metadata": {}, @@ -7220,20 +7423,20 @@ "text/markdown": [ "
\n", "\n", - "I have called the `stop_tool` with the message 'halt' as requested. The tool returned a message indicating that it cannot continue. As a result, I have not yet called `simple_add(1,2)`. To complete your request, I will need to call the `simple_add` tool in the next turn.\n", + "I have called the `stop_tool` with the message 'halt' as requested. To complete your request, I still need to call the `simple_add(1, 2)` tool. Since I have used my tool calls for this turn, please send another message so I can proceed with the addition.\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=240, completion_tokens=70, total_tokens=674, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=364, raw={'promptTokenCount': 240, 'candidatesTokenCount': 70, 'totalTokenCount': 674, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 240}], 'thoughtsTokenCount': 364})`\n", + "- usage: `Usage(prompt_tokens=240, completion_tokens=64, total_tokens=853, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=549, raw={'promptTokenCount': 240, 'candidatesTokenCount': 64, 'totalTokenCount': 853, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 240}], 'thoughtsTokenCount': 549, 'serviceTier': 'standard'})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text=\"I have called the `stop_tool` with the message 'halt' as requested. The tool returned a message indicating that it cannot continue. As a result, I have not yet called `simple_add(1,2)`. To complete your request, I will need to call the `simple_add` tool in the next turn.\", data={'text': \"I have called the `stop_tool` with the message 'halt' as requested. The tool returned a message indicating that it cannot continue. As a result, I have not yet called `simple_add(1,2)`. To complete your request, I will need to call the `simple_add` tool in the next turn.\", 'thoughtSignature': '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'})]), finish_reason=, usage=Usage(prompt_tokens=240, completion_tokens=70, total_tokens=674, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=364, raw={'promptTokenCount': 240, 'candidatesTokenCount': 70, 'totalTokenCount': 674, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 240}], 'thoughtsTokenCount': 364}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': \"I have called the `stop_tool` with the message 'halt' as requested. The tool returned a message indicating that it cannot continue. As a result, I have not yet called `simple_add(1,2)`. To complete your request, I will need to call the `simple_add` tool in the next turn.\", 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 240, 'candidatesTokenCount': 70, 'totalTokenCount': 674, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 240}], 'thoughtsTokenCount': 364}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'ixLzaaT8ArOVkdUP2oKSgAw'})" + "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text=\"I have called the `stop_tool` with the message 'halt' as requested. To complete your request, I still need to call the `simple_add(1, 2)` tool. Since I have used my tool calls for this turn, please send another message so I can proceed with the addition.\", data={'text': \"I have called the `stop_tool` with the message 'halt' as requested. To complete your request, I still need to call the `simple_add(1, 2)` tool. Since I have used my tool calls for this turn, please send another message so I can proceed with the addition.\", 'thoughtSignature': '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'})]), finish_reason=, usage=Usage(prompt_tokens=240, completion_tokens=64, total_tokens=853, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=549, raw={'promptTokenCount': 240, 'candidatesTokenCount': 64, 'totalTokenCount': 853, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 240}], 'thoughtsTokenCount': 549, 'serviceTier': 'standard'}), tool_calls=[], api_name='gemini', vendor_name='gemini', raw={'candidates': [{'content': {'parts': [{'text': \"I have called the `stop_tool` with the message 'halt' as requested. To complete your request, I still need to call the `simple_add(1, 2)` tool. Since I have used my tool calls for this turn, please send another message so I can proceed with the addition.\", 'thoughtSignature': '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'}], 'role': 'model'}, 'finishReason': 'STOP', 'index': 0}], 'usageMetadata': {'promptTokenCount': 240, 'candidatesTokenCount': 64, 'totalTokenCount': 853, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 240}], 'thoughtsTokenCount': 549, 'serviceTier': 'standard'}, 'modelVersion': 'gemini-3-flash-preview', 'responseId': 'yQQsat_CEOCNjMcPleuB-A8'})" ] }, "metadata": {}, @@ -7306,12 +7509,12 @@ "text/markdown": [ "
\n", "\n", - "A circle is a set of all points in a plane that are equidistant from a fixed point called the center.\n", + "A circle is a set of all points in a plane that are equidistant from a fixed central point.\n", "\n", "
" ], "text/plain": [ - "Markdown(A circle is a set of all points in a plane that are equidistant from a fixed point called the center.)" + "Markdown(A circle is a set of all points in a plane that are equidistant from a fixed central point.)" ] }, "metadata": {}, @@ -7344,7 +7547,7 @@ "\n", "```json\n", "{\n", - " \"id\": \"9og31y3r\",\n", + " \"id\": \"pel3g85m\",\n", " \"server\": false,\n", " \"call\": {\n", " \"function\": \"simple_add\",\n", @@ -7359,12 +7562,14 @@ "\n", "\n", "\n", - "The population of Brisbane's metro area in 2026 is estimated to be approximately **2,600,000** (specifically around 2,599,740), while the result of adding 1 + 1 is **2**.\n", + "According to the latest estimates for 2026, the population of **Greater Brisbane** is approximately **2,600,000** (metro area), though the Australian Bureau of Statistics recorded the population as **2,833,500** as of mid-2025.\n", + "\n", + "Additionally, the result of adding **1 + 1** is **2**.\n", "\n", - "As promised, here is a joke for you:\n", + "Now, as promised, here is a joke for you:\n", "\n", - "**Why did the math book look so sad?**\n", - "Because it had too many problems!\n", + "**Why don't scientists trust atoms?**\n", + "Because they make up everything!\n", "\n", "" ], @@ -7378,7 +7583,7 @@ "\n", "```json\n", "{\n", - " \"id\": \"9og31y3r\",\n", + " \"id\": \"pel3g85m\",\n", " \"server\": false,\n", " \"call\": {\n", " \"function\": \"simple_add\",\n", @@ -7393,12 +7598,14 @@ "\n", "\n", "\n", - "The population of Brisbane's metro area in 2026 is estimated to be approximately **2,600,000** (specifically around 2,599,740), while the result of adding 1 + 1 is **2**.\n", + "According to the latest estimates for 2026, the population of **Greater Brisbane** is approximately **2,600,000** (metro area), though the Australian Bureau of Statistics recorded the population as **2,833,500** as of mid-2025.\n", + "\n", + "Additionally, the result of adding **1 + 1** is **2**.\n", "\n", - "As promised, here is a joke for you:\n", + "Now, as promised, here is a joke for you:\n", "\n", - "**Why did the math book look so sad?**\n", - "Because it had too many problems!)" + "**Why don't scientists trust atoms?**\n", + "Because they make up everything!)" ] }, "metadata": {}, @@ -7446,24 +7653,28 @@ "text/markdown": [ "
\n", "\n", - "The population of Greater Brisbane is estimated to be approximately **2,599,740** (about 2.6 million) in 2026. As for your math problem, 1 + 1 equals **2**.\n", + "The results of your request are as follows:\n", + "\n", + "* **Brisbane's Population:** As of 2026, the estimated metro area population of Greater Brisbane is approximately **2,600,000**.\n", + "* **Simple Add (1+1):** The result is **2**.\n", + "\n", + "And here is a joke for you:\n", "\n", - "And here is your joke:\n", - "**Why did the math book look so sad?**\n", - "Because it had too many problems!\n", + "Why did the student get upset when their teacher called them average?\n", + "*It was a mean thing to say!*\n", "\n", "
\n", "\n", "- model: `models/gemini-3-flash-preview`\n", "- finish_reason: `stop`\n", - "- usage: `Usage(prompt_tokens=245, completion_tokens=78, total_tokens=467, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=144, raw={'promptTokenCount': 245, 'candidatesTokenCount': 78, 'totalTokenCount': 467, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 245}], 'thoughtsTokenCount': 144, 'server_tool_use': {'google_search': 1}})`\n", + "- usage: `Usage(prompt_tokens=155, completion_tokens=100, total_tokens=447, cached_tokens=0, cache_creation_tokens=0, reasoning_tokens=192, raw={'promptTokenCount': 155, 'candidatesTokenCount': 100, 'totalTokenCount': 447, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 155}], 'thoughtsTokenCount': 192, 'serviceTier': 'standard', 'server_tool_use': {'google_search': 1}})`\n", "\n", "
\n", "\n", "
" ], "text/plain": [ - "Completion(model='models/gemini-3-flash-preview', message=Msg(role='assistant', content=[Part(type=, text='', data={'thoughtSignature': 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', 'toolResponse': {'toolType': 'GOOGLE_SEARCH_WEB', 'response': {'search_suggestions': '\\n
\\n
\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
\\n
\\n
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', 'toolResponse': {'toolType': 'GOOGLE_SEARCH_WEB', 'response': {'search_suggestions': '\\n
\\n
\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
\\n
\\n
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', 'toolResponse': {'toolType': 'GOOGLE_SEARCH_WEB', 'response': {'search_suggestions': '\\n
\\n
\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
\\n
\\n
\\n Brisbane population 2026\\n
\\n
\\n'}, 'id': 'aorwgpbi'}})])" + "Msg(role='assistant', content=[Part(type=, text=None, data={'id': '246k7m4y', 'name': 'simple_add', 'arguments': {'b': 1, 'a': 1}, 'server': False}), Part(type=, text='', data={'thoughtSignature': 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'toolResponse': {'toolType': 'GOOGLE_SEARCH_WEB', 'response': {'search_suggestions': '\\n
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\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
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\\nBrisbane population 2026\\ncurrent population of Brisbane Australia\\n
\\n
'}, 'id': 'whkxtsw7'}})])" ] }, "execution_count": null, @@ -7580,7 +7791,28 @@ "
\n", "\n", "Sure! Let me run both at the same time!\n", - "- ⏳ web_search(query="Brisbane population 2025") ⏳\n", + "- ⏳ web_search(query="Brisbane population 2026") ⏳\n", + "\n", + "\n", + "
\n", + "web_search(query="Brisbane population 2026")→"Server tool call executed."\n", + "\n", + "```json\n", + "{\n", + " \"id\": \"srvtoolu_01Xv3qp4fE4HvtHXyekWWFcK\",\n", + " \"server\": true,\n", + " \"call\": {\n", + " \"function\": \"web_search\",\n", + " \"arguments\": {\n", + " \"query\": \"Brisbane population 2026\"\n", + " }\n", + " },\n", + " \"result\": \"Server tool call executed.\"\n", + "}\n", + "```\n", + "\n", + "
\n", + "\n", "\n", "- ⏳ simple_add(a="1", b="1") ⏳\n", "\n", @@ -7590,7 +7822,7 @@ "\n", "```json\n", "{\n", - " \"id\": \"toolu_01Ya4zhBCUi13Kxdder51wCi\",\n", + " \"id\": \"toolu_01SsR5KXsuHWtbJYbbxyL1Sy\",\n", " \"server\": false,\n", " \"call\": {\n", " \"function\": \"simple_add\",\n", @@ -7608,23 +7840,51 @@ "Both results are in! Here's a summary:\n", "\n", "**🌏 Brisbane's Population:**\n", - "[*](https://en.wikipedia.org/wiki/Brisbane \"Brisbane - Wikipedia\")According to the Australian Bureau of Statistics, the population of Greater Brisbane is **2,833,524** as of June 2025, making it the third-largest city in Australia. More locally, [*](https://www.brisbane.qld.gov.au/business/tools-and-resources/brisbane-s-key-economic-facts \"Brisbane's key economic facts | Brisbane City Council\")as of June 2025, **1.38 million** people call the City of Brisbane (the LGA) home.\n", + "Estimates vary by source and whether they refer to the city proper or the greater metro area, but here's what the data says:\n", + "\n", + "- [*](https://worldpopulationreview.com/cities/australia/brisbane \"Brisbane Population 2026\")Brisbane's 2026 population is estimated at **2,484,138**.\n", + "- [*](https://www.macrotrends.net/global-metrics/cities/206170/brisbane/population \"Brisbane, Australia Metro Area Population (1950-2026) | MacroTrends\")The metro area population of Brisbane in 2025 was **2,568,000**, a 1.26% increase from 2024.\n", + "- [*](https://worldpopulationreview.com/cities/australia/brisbane \"Brisbane Population 2026\")Brisbane is the capital city of Queensland, Australia, and is known as the **third most populous city** in the entire continent.\n", + "- [*](https://worldpopulationreview.com/cities/australia/brisbane \"Brisbane Population 2026\")Brisbane has grown by 39,248 in the last year, representing a **1.61% annual change**.\n", + "\n", + "---\n", "\n", - "**➕ 1 + 1 = 2**\n", - "The `simple_add` tool confirms that 1 + 1 = **2**. Groundbreaking stuff! 😄\n", + "**🔢 1 + 1 = 2** ✅ (The calculator confirms it — math still works!)\n", "\n", "---\n", "\n", - "And now, as promised, a joke:\n", + "**😄 And now, a joke:**\n", "\n", - "🤔 **Why don't scientists trust atoms?**\n", - "*Because they make up everything!* 😄\n", + "Why don't scientists trust atoms?\n", + "\n", + "*Because they make up everything!* 🥁\n", "\n", "
" ], "text/plain": [ "Markdown(Sure! Let me run both at the same time!\n", - "- ⏳ web_search(query="Brisbane population 2025") ⏳\n", + "- ⏳ web_search(query="Brisbane population 2026") ⏳\n", + "\n", + "\n", + "
\n", + "web_search(query="Brisbane population 2026")→"Server tool call executed."\n", + "\n", + "```json\n", + "{\n", + " \"id\": \"srvtoolu_01Xv3qp4fE4HvtHXyekWWFcK\",\n", + " \"server\": true,\n", + " \"call\": {\n", + " \"function\": \"web_search\",\n", + " \"arguments\": {\n", + " \"query\": \"Brisbane population 2026\"\n", + " }\n", + " },\n", + " \"result\": \"Server tool call executed.\"\n", + "}\n", + "```\n", + "\n", + "
\n", + "\n", "\n", "- ⏳ simple_add(a="1", b="1") ⏳\n", "\n", @@ -7634,7 +7894,7 @@ "\n", "```json\n", "{\n", - " \"id\": \"toolu_01Ya4zhBCUi13Kxdder51wCi\",\n", + " \"id\": \"toolu_01SsR5KXsuHWtbJYbbxyL1Sy\",\n", " \"server\": false,\n", " \"call\": {\n", " \"function\": \"simple_add\",\n", @@ -7652,17 +7912,24 @@ "Both results are in! Here's a summary:\n", "\n", "**🌏 Brisbane's Population:**\n", - "[*](https://en.wikipedia.org/wiki/Brisbane \"Brisbane - Wikipedia\")According to the Australian Bureau of Statistics, the population of Greater Brisbane is **2,833,524** as of June 2025, making it the third-largest city in Australia. More locally, [*](https://www.brisbane.qld.gov.au/business/tools-and-resources/brisbane-s-key-economic-facts \"Brisbane's key economic facts | Brisbane City Council\")as of June 2025, **1.38 million** people call the City of Brisbane (the LGA) home.\n", + "Estimates vary by source and whether they refer to the city proper or the greater metro area, but here's what the data says:\n", + "\n", + "- [*](https://worldpopulationreview.com/cities/australia/brisbane \"Brisbane Population 2026\")Brisbane's 2026 population is estimated at **2,484,138**.\n", + "- [*](https://www.macrotrends.net/global-metrics/cities/206170/brisbane/population \"Brisbane, Australia Metro Area Population (1950-2026) | MacroTrends\")The metro area population of Brisbane in 2025 was **2,568,000**, a 1.26% increase from 2024.\n", + "- [*](https://worldpopulationreview.com/cities/australia/brisbane \"Brisbane Population 2026\")Brisbane is the capital city of Queensland, Australia, and is known as the **third most populous city** in the entire continent.\n", + "- [*](https://worldpopulationreview.com/cities/australia/brisbane \"Brisbane Population 2026\")Brisbane has grown by 39,248 in the last year, representing a **1.61% annual change**.\n", + "\n", + "---\n", "\n", - "**➕ 1 + 1 = 2**\n", - "The `simple_add` tool confirms that 1 + 1 = **2**. Groundbreaking stuff! 😄\n", + "**🔢 1 + 1 = 2** ✅ (The calculator confirms it — math still works!)\n", "\n", "---\n", "\n", - "And now, as promised, a joke:\n", + "**😄 And now, a joke:**\n", + "\n", + "Why don't scientists trust atoms?\n", "\n", - "🤔 **Why don't scientists trust atoms?**\n", - "*Because they make up everything!* 😄)" + "*Because they make up everything!* 🥁)" ] }, "metadata": {}, @@ -7735,21 +8002,21 @@ "
\n", "\n", "\n", - "- ⏳ async_add(a="5", b="7") ⏳\n", + "- ⏳ async_add(b="7", a="5") ⏳\n", "\n", "\n", "
\n", - "async_add(a="5", b="7")→"12"\n", + "async_add(b="7", a="5")→"12"\n", "\n", "```json\n", "{\n", - " \"id\": \"z81fhcm3\",\n", + " \"id\": \"vknmfylt\",\n", " \"server\": false,\n", " \"call\": {\n", " \"function\": \"async_add\",\n", " \"arguments\": {\n", - " \"a\": \"5\",\n", - " \"b\": \"7\"\n", + " \"b\": \"7\",\n", + " \"a\": \"5\"\n", " }\n", " },\n", " \"result\": \"12\"\n", @@ -7758,27 +8025,27 @@ "\n", "
\n", "\n", - "The sum of 5 and 7 is 12. I have successfully completed the calculation.\n", + "5 + 7 is 12.\n", "\n", "
" ], "text/plain": [ "Markdown(\n", - "- ⏳ async_add(a="5", b="7") ⏳\n", + "- ⏳ async_add(b="7", a="5") ⏳\n", "\n", "\n", "
\n", - "async_add(a="5", b="7")→"12"\n", + "async_add(b="7", a="5")→"12"\n", "\n", "```json\n", "{\n", - " \"id\": \"z81fhcm3\",\n", + " \"id\": \"vknmfylt\",\n", " \"server\": false,\n", " \"call\": {\n", " \"function\": \"async_add\",\n", " \"arguments\": {\n", - " \"a\": \"5\",\n", - " \"b\": \"7\"\n", + " \"b\": \"7\",\n", + " \"a\": \"5\"\n", " }\n", " },\n", " \"result\": \"12\"\n", @@ -7787,7 +8054,7 @@ "\n", "
\n", "\n", - "The sum of 5 and 7 is 12. I have successfully completed the calculation.)" + "5 + 7 is 12.)" ] }, "metadata": {}, @@ -11048,6 +11315,33 @@ "_ = await adisplay_stream(res)" ] }, + { + "cell_type": "markdown", + "id": "07db3a5e", + "metadata": {}, + "source": [ + "#### Interruption" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aad1d14d", + "metadata": {}, + "outputs": [], + "source": [ + "chat = AsyncChat(model)\n", + "chat.turn_sysp,chat.turn_msgs = '',mk_msgs(\"Count from 1 to 1000, with each number on its own line.\")\n", + "raw = await c(chat.turn_msgs, stream=True)\n", + "rs = astream_with_complete(chat, raw)\n", + "it = aiter(rs)\n", + "await anext(it)\n", + "try: await it.aclose()\n", + "except GeneratorExit: pass\n", + "test_eq(chat.use.total_tokens > 0, True)\n", + "test_eq(rs.value.finish_reason, FinishReason.interrupted)" + ] + }, { "cell_type": "markdown", "id": "105e1701",