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Vector Db Init
ASecurityInteractively initializes the Vector DB plugin, configuring source manifests and vector_profiles.json for In-Process or Server mode.
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- Added September 2, 2026
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[](https://www.skillsdirectory.com/skills/richfrem-vector-db-init-agent-plugins-skills)---
name: vector-db-init
plugin: agent-memory
description: Interactively initializes the Vector DB plugin, configuring source manifests and vector_profiles.json for In-Process or Server mode.
allowed-tools: Bash, Read, Write
---
# Vector DB Initialization (`vector-db-init`)
Prepares the local environment and manifests for ChromaDB vector embeddings and semantic search.
## Contents
- [Critical Constraints](#critical-constraints)
- [Quick start](#quick-start)
- [Operational Parameters](#operational-parameters)
- [Workflow](#workflow)
- [Verification](#verification)
- [References](#references)
## Critical Constraints
1. **In-Process Default**: Operates directly on disk via `chroma_data_path`; no daemon required unless multi-process concurrency is needed.
2. **Dependency Install**: Requires `chromadb` and `sentence-transformers` available in the python environment.
3. **Manifest Sovereignty**: Configuration persists in `.agent/learning/vector_profiles.json`.
## Quick start
Execute automatic profile scaffolding for local vector storage:
```bash
python3 scripts/init.py
```
## Operational Parameters
Settings configured in `.agent/learning/vector_profiles.json`:
- `chroma_host`: Empty string for in-process direct disk mode; IP:port for server mode.
- `batch_size`: Embedding batch size (default: 1,000 files).
- `embedding_model`: `nomic-ai/nomic-embed-text-v1.5`.
- `parent_chunk_size`: 2,000 chars; `child_chunk_size`: 400 chars.
## Workflow
1. **Dependency Verification**: Ensure dependencies are installed in virtualenv.
2. **Target Discovery**: Identify project directories to index (e.g. `docs/`, `plugins/`).
3. **Manifest Generation**: Write `.agent/learning/vector_knowledge_manifest.json`.
4. **Scaffold Profile**: Run `python3 scripts/init.py` to create the profile configuration.
5. **Next Steps**: Guide user to run `vector-db-ingest` to index content.
## Verification
Confirm configuration file generation and validity:
```bash
test -f .agent/learning/vector_profiles.json && echo "Profile configured"
```
## References
- [acceptance-criteria.md](references/acceptance-criteria.md) — Acceptance criteria for vector database bootstrapping.
- [vector-db-bootstrap-guide.md](references/vector-db-bootstrap-guide.md) — Step-by-step initialization manual.
Files in this skill
- SKILL.md
- acceptance-criteria.md
- assets/resources/architecture_sequence.mmd
- assets/resources/deployment_model.mmd
- assets/resources/rag_design_choices.md
- assets/resources/stabilizers/README.md
- assets/resources/stabilizers/vector_consistency_check.md
- assets/vector_knowledge_manifest.json
- assets/vector_profiles.json
- evals/evals.json
- evals/results.tsv
- references/acceptance-criteria.md
- references/cheapest_models.json
- references/cheapest_models.md
- requirements.in
- requirements.txt
- scripts/init.py
- scripts/query.py
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