Normalda Factory boy işlədirdim. amma hər dəfə yeni model yaradanda Factory-sini yazıb əlavə eləmək yerinə, hazır işi avtomatlaşdıran tool axtarıram
Читать полностью…28 Avqust saat Bakı vaxtı ilə saat 16:00 -da https://leaddev.com/ platformasında canlı panel müzakirədə çıxış edəcəm.
Mövzu, "How can AI actually help your teams?" olacaq. Ətraflı məlumat və qeydiyyat üçün https://leaddev.com/events/how-can-ai-actually-help-your-teams
Bu LeadDev platformasında sayca ikinci panelist kimi iştirakım olacaq. Çayınızı, kofenizi, pivənizi götürün qoşulun.
Salam axshaminiz xeyir. Reminder olaraq qeyd etmek isteyirdim ki, ayin 20sinnen sonra gonderilen CV ler qebul edilmeyecek, evaluation processi bashlandigi uchun.
Читать полностью…bele bir markdown duzeltdi. mene pdf ve ya makrdown lazim idi, istenilen text tipli fayl. onu knowledge base kimi istifade edirem
# API ConceptsЧитать полностью…
This page describes the high-level concepts of the LangGraph Cloud API. The conceptual guide of LangGraph (Python library) is [here](../../concepts/index.md).
## Data Models
The LangGraph Cloud API consists of a few core data models: [Assistants](#assistants), [Threads](#threads), [Runs](#runs), and [Cron Jobs](#cron-jobs).
### Assistants
An assistant is a configured instance of a [`CompiledGraph`][compiledgraph]. It abstracts the cognitive architecture of the graph and contains instance specific configuration and metadata. Multiple assistants can reference the same graph but can contain different configuration and metadata, which may differentiate the behavior of the assistants. An assistant (i.e. the graph) is invoked as part of a run.
The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the [API reference](../reference/api/api_ref.html#tag/assistantscreate) for more details.
#### Configuring Assistants
You can save custom assistants from the same graph to set different default prompts, models, and other configurations without changing a line of code in your graph. This allows you the ability to quickly test out different configurations without having to rewrite your graph every time, and also give users the flexibility to select different configurations when using your LangGraph application. See <a href="https://langchain-ai.github.io/langgraph/cloud/how-tos/cloud_examples/configuration_cloud/">this</a> how-to for information on how to configure a deployed graph.
### Threads
A thread contains the accumulated state of a group of runs. If a run is executed on a thread, then the [state][state] of the underlying graph of the assistant will be persisted to the thread. A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
The state of a thread at a particular point in time is called a checkpoint.
For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/low_level.md#checkpointer).
The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../reference/api/api_ref.html#tag/threadscreate) for more details.
### Runs
Hezewrat, bu repodaki docoumentationlari men nece pdf edim?
https://github.com/langchain-ai/langgraph/blob/main/docs/mkdocs.yml
Salam, Django ilə yazdığım proyket-də veb-də media folder-inin içində şəkillər görsənir, amma mobil tərəfdə görsənmir. səbəbi nə ola bilər?
Читать полностью…Hezerat, Pydantic ile bagli meslehetli tutorial ne var? videolar coxdur. meslehetli olanlardan isteyirem
Читать полностью…Oxford-da research eden bir qaqas JSON spesifikasiyasinin daha efficient olmasi ucun bele bir proyekt uzerinde calisir. Aranizda json-un hecmine onem verecek "hessas" proyektlerde isleyen varsa maraqli gele biler
https://jsonbinpack.sourcemeta.com/
Meqsed low power, iot ve s sahelerde limitli bandwidth ucun daha effektiv olmaqdir. Detalli benchmarklar ucun paper de yazilib https://arxiv.org/pdf/2201.03051
Salam, Django-da avtomatik mock data dolduran module bilirsiniz ? internetdə baxdıqlarım köhnə verisyalara aid olur.
Читать полностью…Bele bir kodla markdown eledim. chatgpt dedim meseleni, kodu yazdi menim ucun:
import osЧитать полностью…
import glob
def combine_markdown_files(source_dir, output_file):
# Find all markdown files recursively in the source directory
markdown_files = glob.glob(os.path.join(source_dir, '**/*.md'), recursive=True)
# Sort files to maintain a consistent order
markdown_files.sort()
with open(output_file, 'w', encoding='utf-8') as outfile:
for file in markdown_files:
with open(file, 'r', encoding='utf-8') as infile:
outfile.write(infile.read())
outfile.write('\n\n') # Add a newline between documents
print(f"Combined {len(markdown_files)} Markdown files into {output_file}")
if __name__ == "__main__":
# Define the directory where the markdown files are located and the output file name
source_directory = "docs"
output_filename = "combined_documentation.md"
# Combine the markdown files
combine_markdown_files(source_directory, output_filename)