ą“ą“°ąµ RAG ą“Ŗąµą“Ŗąµą“Ŗąµāą“²ąµąµ» ą“Ŗąµą“ąµą“Æą“¤ąµą“¤ą“æąµ½ ą“Øą“æą“Øąµą“Øąµ ą“Øą“æąµ¼ą“®ąµą“®ą“æą“ąµą“ąµą“Øąµą“Øąµ
SmartQueue-ą“²ąµą“ąµą“ąµ ą“ą“°ąµ AI ą“ ą“øą“æą“øąµą“±ąµą“±ą“Øąµą“±ą“æą“Øąµ ą“ąµąµ¼ą“ąµą“ą“¾ąµ» ą“ą“¾ąµ» ą“ą“ąµą“°ą“¹ą“æą“ąµą“ąµ.
IT ą“øą“Ŗąµą“Ŗąµąµ¼ą“ąµą“ąµ ą“ą“æą“ąµą“ą“±ąµą“±ąµą“ąµ¾ą“ąµą“ą“¾ą“Æą“æ Go ą“ą“Ŗą“Æąµą“ą“æą“ąµą“ąµ ą“ą“¾ąµ» ą“Øą“æąµ¼ą“®ąµą“®ą“æą“ąµą“ ą“ą“°ąµ ą“ą“¾ą“øąµą“ąµ ą“ąµą“Æąµ ą“ą“£ąµ SmartQueue. ą“ą“Øą“æą“ąµą“ąµ ą“ą“°ąµ ą“ą“Øą“±ą“æą“ąµ AI ą“ą“Æą“æą“°ąµą“Øąµą“Øą“æą“²ąµą“² ą“µąµą“£ąµą“ą“¤ąµ. ą“ą“°ąµ ą“ą“Øą“±ą“æą“ąµ ą“®ąµą“”ą“²ą“æą“Øąµ ą“Øą“æą“ąµą“ą“³ąµą“ąµ ą“Ŗą“¾ą“øąµāą“µąµą“”ąµ ą“±ąµą“øąµą“±ąµą“±ąµ ą“Øą“æą“Æą“®ą“ąµą“ą“³ąµ ą“ą“ąµą“ąµāąµą“ąµ ą“±ąµŗą“¬ąµą“ąµą“ąµą“ą“³ąµ (outage runbooks) ą“ ą“±ą“æą“Æą“æą“²ąµą“².
ą“ą“Øą“æą“ąµą“ąµ Retrieval-Augmented Generation (RAG) ą“ą“µą“¶ąµą“Æą“®ą“¾ą“Æą“æą“°ąµą“Øąµą“Øąµ. ą“ą“¤ąµ ą“ą“¦ąµą“Æą“ ą“Øą“æą“ąµą“ą“³ąµą“ąµ ą“”ąµą“ąµą“Æąµą“®ąµą“Øąµą“±ąµą“ą“³ą“æąµ½ ą“Øą“æą“Øąµą“Øąµ ą“µą“øąµą“¤ąµą“¤ą“ąµ¾ ą“¶ąµą“ą“°ą“æą“ąµą“ąµą“Øąµą“Øąµ. ą“¤ąµą“ąµ¼ą“Øąµą“Øąµ ą“ ą“µą“øąµą“¤ąµą“¤ą“ąµ¾ ą“ą“°ąµ ą“ąµąµŗą“ąµą“ąµą“øąµą“±ąµą“±ąµ (context) ą“ą“Æą“æ ą“®ąµą“”ą“²ą“æą“Øąµ ą“Øąµ½ą“ąµą“Øąµą“Øąµ.
ą“ ą“Ŗąµą“Ŗąµą“Ŗąµāą“²ąµąµ» ą“Øą“æąµ¼ą“®ąµą“®ą“æą“ąµą“ąµą“Øąµą“Øą“¤ą“æą“Øą“æą“ą“Æą“æąµ½ ą“ą“¾ąµ» ą“Ŗą“ ą“æą“ąµą“ ą“ą“¾ą“°ąµą“Æą“ąµą“ąµ¾ ą“ą“¤ą“¾.
ą“”ą“æą“Ŗąµą“²ąµą“Æąµāą“®ąµą“Øąµą“±ąµ ą“Ŗą“°ą“¾ą“ą“Æą“
ą“ą“Øąµą“±ąµ ą“ą“¦ąµą“Æ ą“Ŗą“¤ą“æą“Ŗąµą“Ŗą“æąµ½ ą“µąµą“ąµą“±ąµą“±ąµ¼ ą“øąµąµ¼ą“ąµą“ą“æą“Øą“¾ą“Æą“æ (vector search) ChromaDB ą“ą“£ąµ ą“ą“Ŗą“Æąµą“ą“æą“ąµą“ą“¤ąµ. ą“ ą“¤ąµ ą“²ąµą“ąµą“ą“²ą“¾ą“Æą“æ ą“Ŗąµą“°ą“µąµ¼ą“¤ąµą“¤ą“æą“ąµą“ąµ. ą“ą“Øąµą“Øą“¾ąµ½ ą“”ą“æą“Ŗąµą“²ąµą“Æąµāą“®ąµą“Øąµą“±ąµ ą“øą“®ą“Æą“¤ąµą“¤ąµ ą“Ŗą“°ą“¾ą“ą“Æą“Ŗąµą“Ŗąµą“ąµą“ąµ.
Hugging Face Spaces-ąµ½ ą“ą“°ąµ ą“ą“£ąµą“ąµą“Æąµāą“Ø
ą“Ŗą“ ą“æą“ąµą“ ą“Ŗą“¾ą“ ą“ąµą“ąµ¾:
- ą“ą“°ąµ ą“Ŗą“¾ą“ ą“Ŗąµą“øąµą“¤ą“ą“¤ąµą“¤ą“æą“²ąµ ą“®ą“¾ą“¤ąµą“ą“Æąµą“ąµą“ą“¾ą“Æą“æą“ąµą“ą“²ąµą“², ą“®ą“±ą“æą“ąµą“ąµ ą“Øą“æą“ąµą“ą“³ąµą“ąµ ą“Ŗą“°ą“æą“®ą“æą“¤ą“æą“ąµ¾ą“ąµą“ąµ ą“ ą“Øąµą“øąµą“¤ą“®ą“¾ą“Æą“æ ą“ą“¾ą“°ąµą“Æą“ąµą“ąµ¾ ą“ą“Ŗąµą“±ąµą“±ą“æą“®ąµą“øąµ ą“ąµą“Æąµą“Æąµą“.
- ą“Ŗą“²ą“Ŗąµą“Ŗąµą““ąµą“ ą“øąµą“¦ąµą“§ą“¾ą“Øąµą“¤ą“æą“ą“®ą“¾ą“Æ ą“ąµą“¤ąµą“Æą“¤ą“Æąµą“ąµą“ą“¾ąµ¾ ą“µą“æą“¶ąµą“µą“¾ą“øąµą“Æą“¤ą“Æąµą“ąµą“ą“¾ą“£ąµ ą“Ŗąµą“°ą“¾ą“§ą“¾ą“Øąµą“Æą“.
- ą“Øąµą“±ąµą“ą“£ą“ąµą“ą“æą“Øąµ ą“°ąµą“ą“ąµ¾ ą“ą“³ąµą“³ą“Ŗąµą“Ŗąµąµ¾ vector search ą“ą“Ŗą“Æąµą“ą“æą“ąµą“ąµą“. ą“Ŗą“¤ąµą“¤ąµ ą“°ąµą“ą“ąµ¾ ą“®ą“¾ą“¤ąµą“°ą“®ą“¾ą“£ąµą“ąµą“ą“æąµ½ keyword search ą“ą“Ŗą“Æąµą“ą“æą“ąµą“ąµą“.
ą“ą“±ą“µą“æą“ą“: https://dev.to/ambarish_0221/building-a-rag-pipeline-from-scratch-what-smartqueue-taught-me-about-retrieval-4gdb
ą“ą“Ŗąµą“·ą“£ąµ½ ą“²ąµą“£ą“æą“ą“ąµ ą“ą“®ąµą“®ąµą“Æąµą“£ą“æą“±ąµą“±ą“æ: https://t.me/GyaanSetuAi