RAG Knowledge Base
Retrieval-Augmented Generation pipeline for intelligent interview context
RAG System:
Enabled and operational
0
Total Embeddings
0
Source Types
3-small
Embedding Model
On
HyDE Search
Indexed Content by Source Type
Resumes
0 chunksJob Roles
0 chunksQuestions
0 chunksTranscripts
0 chunksAsync Video
0 chunksKnowledge
0 chunksCompanies
0 chunksATS Jobs
0 chunksATS Candidates
0 chunksAI Agents
0 chunksPipeline Config
| Chunk Size | 500 chars |
| Chunk Overlap | 50 chars |
| Top-K Results | 5 |
| Re-rank Top-K | 3 |
| Max Context | 2000 tokens |
| HyDE | Enabled |
| Embedding | text-embedding-3-small |
RAG Pipeline
Document Ingestion
10 source types: Resumes, Jobs, Transcripts, Videos, Knowledge & moreChunking
Sentence-boundary aware, 500 char chunksEmbedding
OpenAI text-embedding-3-smallpgvector Storage
Cosine similarity searchHyDE + Re-rank
Hypothetical docs, source-type boosting
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Re-indexing in progress...