Weaviate | Respan Docs

Weaviate is an open-source AI-native vector database that stores both objects and vectors. It supports semantic search, hybrid search (combining BM25 and vector), generative search, and automatic vectorization with built-in ML model integrations. Respan gives you full observability over every collection operation, search, and generative query.

Set up Respan

Create an account at platform.respan.ai and grab an API key.

Run npx @respan/cli setup to set up with your coding agent.

Example projects

Python examples

Tracing

Setup

Install packages

$ pip install respan-ai opentelemetry-instrumentation-weaviate weaviate-client

Set environment variables

$ export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

RESPAN_API_KEY is used to export traces to Respan.

Initialize and run

1 import weaviate
2 import weaviate.classes.config as wc
3 from respan import Respan
4 from opentelemetry.instrumentation.weaviate import WeaviateInstrumentor
5 
6 respan = Respan(instrumentations=[WeaviateInstrumentor()])
7 
8 client = weaviate.connect_to_local()
9 
10 collection = client.collections.create(
11     name="Document",
12     vectorizer_config=wc.Configure.Vectorizer.text2vec_transformers(),
13     properties=[
14         wc.Property(name="title", data_type=wc.DataType.TEXT),
15         wc.Property(name="content", data_type=wc.DataType.TEXT),
16     ],
17 )
18 
19 collection.data.insert_many([
20     {"title": "AI Observability", "content": "Respan provides tracing for AI applications."},
21     {"title": "Vector Search", "content": "Weaviate supports semantic and hybrid search."},
22     {"title": "Embeddings", "content": "Embeddings capture semantic meaning as vectors."},
23 ])
24 
25 results = collection.query.near_text(query="How does AI tracing work?", limit=2)
26 for obj in results.objects:
27     print(f"{obj.properties['title']}: {obj.properties['content']}")
28 
29 client.close()
30 respan.flush()

View your trace

Open the Traces page to see your Weaviate operation spans with searches, hybrid queries, and generative results.

Configuration

Parameter	Type	Default	Description
`api_key`	`str \| None`	`None`	Falls back to `RESPAN_API_KEY` env var.
`base_url`	`str \| None`	`None`	Falls back to `RESPAN_BASE_URL` env var.
`instrumentations`	`list`	`[]`	Plugin instrumentations to activate (e.g. `WeaviateInstrumentor()`).
`customer_identifier`	`str \| None`	`None`	Default customer identifier for all spans.
`metadata`	`dict \| None`	`None`	Default metadata attached to all spans.
`environment`	`str \| None`	`None`	Environment tag (e.g. `"production"`).

Attributes

In Respan()

1 from respan import Respan
2 from opentelemetry.instrumentation.weaviate import WeaviateInstrumentor
3 
4 respan = Respan(
5     instrumentations=[WeaviateInstrumentor()],
6     customer_identifier="user_123",
7     metadata={"service": "rag-api", "version": "1.0.0"},
8 )

With propagate_attributes

1 from respan import Respan, propagate_attributes
2 from opentelemetry.instrumentation.weaviate import WeaviateInstrumentor
3 
4 respan = Respan(instrumentations=[WeaviateInstrumentor()])
5 
6 def search(user_id: str, query: str):
7     with propagate_attributes(
8         customer_identifier=user_id,
9         thread_identifier="conv_abc_123",
10         metadata={"plan": "pro"},
11     ):
12         return collection.query.near_text(query=query, limit=2)

Attribute	Type	Description
`customer_identifier`	`str`	Identifies the end user in Respan analytics.
`thread_identifier`	`str`	Groups related messages into a conversation.
`metadata`	`dict`	Custom key-value pairs. Merged with default metadata.