This project streamlines the flow of text data into a Chroma vector database, handling everything from preprocessing to embedding generation. It cuts down on repetitive work by updating only changed content and supports smart chunking for retrieval-heavy applications. The result is a smooth, reliable path for building search or RAG systems powered by Chroma.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for Chroma Integration you've just found your team — Let’s Chat. 👆👆
This tool prepares and transfers structured or unstructured text into a vector store, ensuring that embeddings always reflect the latest data. It solves the common challenge of keeping vector databases synchronized with evolving datasets. It’s ideal for developers building semantic search tools, RAG pipelines, or knowledge engines that rely on efficient, consistent vector storage.
- Pulls dataset records and prepares them for embedding.
- Optionally chunks long documents for more precise retrieval.
- Computes embeddings using providers like OpenAI or Cohere.
- Writes or updates records in Chroma based on the chosen strategy.
- Removes outdated entries to keep the database clean and relevant.
| Feature | Description |
|---|---|
| Smart Data Sync | Updates only the records that actually changed, reducing compute time. |
| Chunking Support | Splits long texts into smaller units for better indexing and retrieval. |
| Multiple Embedding Providers | Works with OpenAI, Cohere, and other embedding APIs. |
| Flexible Update Strategies | Supports add, upsert, or delta-based updates based on your needs. |
| Expiration Cleanup | Automatically deletes data that hasn’t been refreshed within a set period. |
| Chroma Compatibility | Works with simple, cloud, and enterprise Chroma deployments. |
| Field Name | Field Description |
|---|---|
| text | Main textual content prepared for embedding. |
| metadata | Supplemental information used to annotate the vector. |
| url | Optional unique identifier for tracking content changes. |
| checksum | Internal value for detecting modified records. |
| last_seen_at | Timestamp of most recent data refresh. |
Chroma Integration/
├── src/
│ ├── main.js
│ ├── embeddings/
│ │ ├── openai.js
│ │ └── cohere.js
│ ├── chroma/
│ │ ├── client.js
│ │ └── writer.js
│ ├── processing/
│ │ ├── chunker.js
│ │ ├── delta.js
│ │ └── checksum.js
│ └── config/
│ └── defaults.json
├── data/
│ ├── sample-input.json
│ └── sample-output.json
├── package.json
├── requirements.txt
└── README.md
- Researchers use it to turn large document collections into searchable vector indexes, enabling more accurate information retrieval.
- Teams maintaining knowledge bases use it to keep embeddings aligned with frequently updated content.
- AI engineers rely on it to power RAG systems that need clean, up-to-date embeddings.
- Content platforms leverage it to detect updated or stale entries, ensuring their search layer always reflects current data.
Does the tool support multiple Chroma environments? Yes, it works with simple, hosted, and enterprise setups, including deployments with tenants and custom databases.
Can I choose which fields become metadata? You can map dataset fields to metadata properties, giving you full control over what contextual information lands in Chroma.
What happens if a record hasn’t changed? Its vector remains untouched. Only its last seen timestamp updates, saving compute and storage overhead.
Can chunking be disabled? Absolutely. If your text is already short enough, chunking can be switched off for faster processing.
Primary Metric: Processes several hundred documents per minute when embeddings are computed in parallel, depending on provider limits.
Reliability Metric: Maintains consistent update stability with a high success rate, even under large or repeated ingestion cycles.
Efficiency Metric: Delta-based updates reduce unnecessary embedding generation by an estimated 60–80 percent in recurring ingestion workflows.
Quality Metric: Produces clean, well-structured vector entries with consistent metadata and near-complete dataset coverage.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
|
"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
|
"Exceptional results, clear communication, and flawless delivery. Syed
|