Cohere × Weaviate — Semantic RAG prototype for Cohere

Own your AI

Bring Cohere models to enterprise data with precise, low-latency retrieval

Weaviate is an open-source vector database that stores objects and vectors, enabling semantic search, hybrid filtering and retrieval-augmented generation (RAG). Below is a live prototype showing how Weaviate's semantic layer retrieves company knowledge to power Cohere-based assistants and RAG flows.

Paste a Cohere support or product question

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Simulated retrieval

This panel shows the top documents Weaviate would return (ranked by semantic similarity). Click Run to simulate a live RAG flow: retrieved evidence + a generated answer.

Results will appear here after you run the prototype.

Why this matters for Cohere

Combine Cohere's models with Weaviate's semantic search to reduce token usage, improve answer relevance, and control data locality for enterprise deployments.

Where it fits

Weaviate sits beside model providers (Cohere, OpenAI, HF) and serves as the retrieval layer for RAG and production assistants.

Fast proof-of-value

Index your product docs and release notes; show a measurable jump in answer relevance within a single pilot.

Quick integration

Sample Python: index documents with an integrated Cohere vectorizer and run a semantic query.

# Example (illustrative)
import weaviate
from weaviate.classes.config import Configure, DataType, Property

client = weaviate.connect_to_local()

client.collections.create(
  name="CohereDocs",
  properties=[Property(name="text", data_type=DataType.TEXT)],
  vector_config=Configure.Vectors.cohere(),  # use Cohere as the integrated vectorizer
)

# index docs
client.collections.get("CohereDocs").data.insert_many([
  {"text":"Cohere release notes: model X increases throughput."},
  {"text":"Onboarding guide: configuring model deployment."},
])

# semantic search
results = client.collections.get("CohereDocs").query.near_text(query="low-latency deployment guidance", limit=3)
print(results)

Why Weaviate

Open-source, hybrid search, integrated model providers, built-in RAG and reranking — useful when you need precise retrieval in production.

Architecture (how it slots into Cohere)

Weaviate indexes your enterprise data, uses an integrated Cohere vectorizer to generate embeddings, and serves retrieved context to Cohere models for generation. This keeps model inputs small and focused while improving answer relevance.

Ready to pilot a Cohere + Weaviate retrieval layer?

We can index your docs and show RAG improvements on real questions in under a week.