Codestral Embed Playground for Code embeddings

A Codestral Embed Playground for Code embeddings embeddings playground is a UI to test embeddings for search/RAG—generate vectors, run similarity checks, and do quick eval-style retrieval tests without building an indexing pipeline.

Prompt testing and evaluations for retrieval: “does this embed model group the right things together?” and “does it retrieve the right items for my queries?”

Yes. Bring your API keys. LangFast routes requests through our proxy.

To prevent abuse, apply fair-use limits, and let you save test sets, reuse comparisons, and share results with your team.

Nearest-neighbor relevance, semantic clustering, and whether similarity scores separate “correct” vs “distractor” documents for your real queries.

Create a small gold set: queries + expected relevant docs. Embed them, run similarity ranking, and check if the right docs land in the top results.

Yes. Run the same gold set and compare hit rate, ranking quality, and cost/latency.

Typically cosine similarity is the baseline for embedding comparisons. If you need a specific metric, use the exported call and compute it in your stack.

Yes. Embeddings often fail because chunking is wrong. Compare retrieval quality across different chunk sizes and overlaps using the same gold set.

Yes. Use cross-language query/doc pairs and evaluate whether the model retrieves correctly across languages.

Yes—export to cURL/JS/JSON so engineering can reproduce embedding calls and plug them into your indexing workflow.

LangFast is free to use with some basic features. You need to provide your own API keys to run models and use the app. When you add your API keys, you pay the model provider (e.g., OpenAI) for the credits/tokens you use. LangFast premium features can be unlocked with a one-time purchase.

Wait for reset or add paid usage to continue running embedding evaluations.

No. We don’t train on your prompts or data. Sharing is opt-in and retention is configurable.

Requests route to model providers. See the Data & Privacy page for processing regions and details.

LangChain helps you build RAG systems. LangFast helps you choose the embedding model and validate retrieval behavior before you implement.

Those tools manage datasets, evals, and tracing in workflows. LangFast is a quick bench for interactive embedding tests and model comparisons.