> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trychroma.com/llms.txt
> Use this file to discover all available pages before exploring further.

# General

> How to improve Chroma performance across single-node and distributed deployments.

## Python Thin Client

If you are running Chroma in client-server mode in a Python application, you may not need the full Chroma library. Instead, you can use the lightweight client-only library.

In this case, you can install the `chromadb-client` package **instead** of our `chromadb` package.

The `chromadb-client` package is a lightweight HTTP client for the server with a minimal dependency footprint.

<CodeGroup>
  ```terminal pip theme={null}
  pip install chromadb-client
  ```

  ```terminal poetry theme={null}
  poetry add chromadb-client
  ```

  ```terminal uv theme={null}
  uv pip install chromadb-client
  ```
</CodeGroup>

```python theme={null}
# Python
import chromadb
# Example setup of the client to connect to your chroma server
client = chromadb.HttpClient(host='localhost', port=8000)

# Or for async usage:
async def main():
    client = await chromadb.AsyncHttpClient(host='localhost', port=8000)
```

Note that the `chromadb-client` package is a subset of the full Chroma library and does not include all the dependencies. If you want to use the full Chroma library, you can install the `chromadb` package instead.

Most importantly, the thin-client package has no default embedding functions. If you `add()` documents without embeddings, you must have manually specified an embedding function and install the dependencies for it.

## Local vs API Embedding Models

Chroma's built-in embedding functions can be locally generated or generated
via an API, depending on the provider. Some local embedding functions are lightweight (such as BM25), but most are
heavy and require large libraries and model weights to be downloaded. If you are
building in a serverless environment, you should use a dedicated service to
generate the embedding.

This dedicated service can be self-hosted via
[HuggingFace](/integrations/embedding-models/hugging-face-server), or hosted by
someone such as the OpenAI, Bedrock, or Chroma Cloud embedding models.

## Warm-up queries

Infrequently used collections are moved to cold storage. The first time a
collection is queried, it will be slower than average because the system needs
to cache the data. Chroma users typically send a warm-up query to make the
collection warm. This helps end users avoid cold-start latency entirely.