--- title: "azure-search-vector-samples vs cherche" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azure-azure-search-vector-samples-vs-raphaelsty-cherche" tools: ["azure-azure-search-vector-samples", "raphaelsty-cherche"] --- # azure-search-vector-samples vs cherche *GraphCanon updated Aug 23, 2026* ## Verdict Pick azure-search-vector-samples if azure-search-vector-samples offers Jupyter Notebook examples for implementing vector search with Azure AI Search services; pick cherche if cherche is a Python library for implementing neural search capabilities. [azure-search-vector-samples](https://azure.microsoft.com/products/search) reports 911 GitHub stars, 378 forks, and 65 open issues, last pushed Aug 9, 2026. [cherche](https://github.com/raphaelsty/cherche) has 332 stars, 14 forks, and 4 open issues, last pushed Jun 1, 2024. Figures are from public GitHub metadata via [azure-search-vector-samples's repository](https://github.com/Azure/azure-search-vector-samples) and [cherche's repository](https://github.com/raphaelsty/cherche). | | [azure-search-vector-samples](/tools/azure-azure-search-vector-samples.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Tagline | Code samples for vector search capabilities in Azure AI Search | Neural Search | | Stars | 911 | 332 | | Forks | 378 | 14 | | Open issues | 65 | 4 | | Language | Jupyter Notebook | Python | | Adopt for | azure-search-vector-samples offers Jupyter Notebook examples for implementing vector search with Azure AI Search services | Cherche is a Python library for implementing neural search capabilities. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Vector Databases | Data & Retrieval, Evaluation & Observability, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [azure-search-vector-samples](/tools/azure-azure-search-vector-samples.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 13d | 812d | | Open issues (now) | 65 | 4 | | Stars delta | +1 (30d) | 0 (30d) | | Open issues delta | +5 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/azure-azure-search-vector-samples/trust.md) | [trust report](/tools/raphaelsty-cherche/trust.md) | ## Shared compatibility - **Python**: [azure-search-vector-samples](/tools/azure-azure-search-vector-samples.md) - Python runtime; [cherche](/tools/raphaelsty-cherche.md) - Python runtime ## Decision facts: azure-search-vector-samples - **Adopt for:** azure-search-vector-samples offers Jupyter Notebook examples for implementing vector search with Azure AI Search services ## Decision facts: cherche - **Adopt for:** Cherche is a Python library for implementing neural search capabilities. ## Choose when ### Choose azure-search-vector-samples if… - azure-search-vector-samples is primarily Jupyter Notebook; cherche is Python. - Tags unique to azure-search-vector-samples: azure, azurecognitivesearch, embeddings, vector-search. - When developing applications that require advanced semantic search functionalities on unstructured data within the Microsoft ecosystem, as it integrates seamlessly with Azure resources ### Choose cherche if… - cherche is primarily Python; azure-search-vector-samples is Jupyter Notebook. - Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning. - Also covers Data & Retrieval, Evaluation & Observability. - Cherche is a Python library for implementing neural search capabilities. ## When NOT to use azure-search-vector-samples - When working in non-Microsoft cloud environments due to its tight integration with Azure services - For users who require real-time processing capabilities, as Azure AI Search might not be optimized for low-latency queries compared to specialized vector databases ## When NOT to use cherche - Last GitHub push was 815 days ago (dormant maintenance, Jun 1, 2024). Validate activity before betting a new project on cherche. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between azure-search-vector-samples and cherche? azure-search-vector-samples: Code samples for vector search capabilities in Azure AI Search. cherche: Neural Search. See the comparison table for live GitHub stats and shared categories. ### When should I choose azure-search-vector-samples over cherche? Choose azure-search-vector-samples over cherche when azure-search-vector-samples is primarily Jupyter Notebook; cherche is Python; Tags unique to azure-search-vector-samples: azure, azurecognitivesearch, embeddings, vector-search; When developing applications that require advanced semantic search functionalities on unstructured data within the Microsoft ecosystem, as it integrates seamlessly with Azure resources. ### When should I choose cherche over azure-search-vector-samples? Choose cherche over azure-search-vector-samples when cherche is primarily Python; azure-search-vector-samples is Jupyter Notebook; Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning; Also covers Data & Retrieval, Evaluation & Observability; Cherche is a Python library for implementing neural search capabilities. ### When should I avoid azure-search-vector-samples? When working in non-Microsoft cloud environments due to its tight integration with Azure services For users who require real-time processing capabilities, as Azure AI Search might not be optimized for low-latency queries compared to specialized vector databases ### When should I avoid cherche? Last GitHub push was 815 days ago (dormant maintenance, Jun 1, 2024). Validate activity before betting a new project on cherche. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is azure-search-vector-samples or cherche more popular on GitHub? azure-search-vector-samples has more GitHub stars (911 vs 332). Stars measure visibility, not whether either tool fits your constraints. ### Are azure-search-vector-samples and cherche open source? Yes - both are open-source projects on GitHub (azure-search-vector-samples: MIT, cherche: MIT). ### Where can I find alternatives to azure-search-vector-samples or cherche? GraphCanon lists graph-backed alternatives at [azure-search-vector-samples alternatives](/tools/azure-azure-search-vector-samples/alternatives) and [cherche alternatives](/tools/raphaelsty-cherche/alternatives) ([azure-search-vector-samples markdown twin](/tools/azure-azure-search-vector-samples/alternatives.md), [cherche markdown twin](/tools/raphaelsty-cherche/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/azure-azure-search-vector-samples-vs-raphaelsty-cherche.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, azure-search-vector-samples or cherche? azure-search-vector-samples: Active. cherche: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for azure-search-vector-samples and cherche? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [azure-search-vector-samples trust report](/tools/azure-azure-search-vector-samples/trust); [cherche trust report](/tools/raphaelsty-cherche/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=azure-azure-search-vector-samples`](/api/graphcanon/graph?tool=azure-azure-search-vector-samples) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_