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GitHub / Renatoelho / ContextDB
Vou mostrar como desenvolver um sistema para otimizar o uso de prompts em aplicações de inteligência artificial, utilizando um banco de dados de contexto. Este projeto utiliza Python e plataformas de gerenciamento de banco de dados.
JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Renatoelho%2FContextDB
Stars: 1
Forks: 0
Open Issues: 0
License: mit
Language: Python
Repo Size: 253 KB
Dependencies:
57
Created: 27 days ago
Updated: 18 days ago
Last pushed: 18 days ago
Last synced: 18 days ago
Topics: chatgpt, duckdb, ia, langchain, openia, prompt, python, vector-database
Files
Dependencies
- PyYAML ==6.0.1
- SQLAlchemy ==2.0.29
- aiohttp ==3.9.5
- aiosignal ==1.3.1
- annotated-types ==0.6.0
- anyio ==4.3.0
- async-timeout ==4.0.3
- attrs ==23.2.0
- certifi ==2024.2.2
- charset-normalizer ==3.3.2
- dataclasses-json ==0.6.4
- distro ==1.9.0
- duckdb ==0.10.2
- exceptiongroup ==1.2.1
- flake8 ==7.0.0
- frozenlist ==1.4.1
- greenlet ==3.0.3
- h11 ==0.14.0
- httpcore ==1.0.5
- httpx ==0.27.0
- idna ==3.7
- jsonpatch ==1.33
- jsonpointer ==2.4
- langchain ==0.1.16
- langchain-community ==0.0.34
- langchain-core ==0.1.45
- langchain-openai ==0.1.3
- langchain-text-splitters ==0.0.1
- langsmith ==0.1.49
- marshmallow ==3.21.1
- mccabe ==0.7.0
- multidict ==6.0.5
- mypy-extensions ==1.0.0
- numpy ==1.24.4
- openai ==1.23.2
- orjson ==3.10.1
- packaging ==23.2
- pandas ==2.0.3
- pycodestyle ==2.11.1
- pydantic ==2.7.0
- pydantic_core ==2.18.1
- pyflakes ==3.2.0
- python-dateutil ==2.9.0.post0
- python-dotenv ==1.0.1
- pytz ==2024.1
- regex ==2024.4.16
- requests ==2.31.0
- six ==1.16.0
- sniffio ==1.3.1
- tenacity ==8.2.3
- tiktoken ==0.6.0
- tqdm ==4.66.2
- typing-inspect ==0.9.0
- typing_extensions ==4.11.0
- tzdata ==2024.1
- urllib3 ==2.2.1
- yarl ==1.9.4