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

# Vector database integrations

> Auto-instrument ChromaDB, Pinecone, Qdrant, Milvus, Astra DB, and PostgreSQL vector operations with OpenTelemetry

OpenLIT instruments vector database clients to trace every query, upsert, delete, and index operation your application performs. You get full visibility into retrieval latency, collection names, operation types, and result counts — without changing your database code.

## Get started

<Steps>
  <Step title="Install OpenLIT">
    ```shell theme={null}
    pip install openlit
    ```
  </Step>

  <Step title="Install your vector database client">
    ```shell theme={null}
    # ChromaDB
    pip install chromadb

    # Pinecone
    pip install pinecone

    # Qdrant
    pip install qdrant-client

    # Milvus
    pip install pymilvus

    # Astra DB
    pip install astrapy
    ```
  </Step>

  <Step title="Initialize OpenLIT">
    Call `openlit.init()` before any vector database operations.

    <CodeGroup>
      ```python ChromaDB example theme={null}
      import openlit
      import chromadb

      openlit.init(otlp_endpoint="http://127.0.0.1:4318")

      client = chromadb.Client()
      collection = client.create_collection("documents")

      collection.add(
          documents=["OpenLIT makes AI observability easy"],
          ids=["doc1"]
      )

      results = collection.query(
          query_texts=["AI observability"],
          n_results=1
      )
      print(results)
      ```

      ```python Pinecone example theme={null}
      import openlit
      from pinecone import Pinecone

      openlit.init(otlp_endpoint="http://127.0.0.1:4318")

      pc = Pinecone(api_key="YOUR_API_KEY")
      index = pc.Index("my-index")

      index.upsert(
          vectors=[("vec1", [0.1, 0.2, 0.3])]
      )

      results = index.query(
          vector=[0.1, 0.2, 0.3],
          top_k=5
      )
      print(results)
      ```
    </CodeGroup>
  </Step>
</Steps>

## Supported vector databases

| Database              | Python            | TypeScript | Instrumentor key |
| --------------------- | ----------------- | ---------- | ---------------- |
| ChromaDB              | ✅ `chromadb`      | ✅          | `chroma`         |
| Pinecone              | ✅ `pinecone`      | ✅          | `pinecone`       |
| Qdrant                | ✅ `qdrant-client` | ✅          | `qdrant`         |
| Milvus                | ✅ `pymilvus`      | —          | `milvus`         |
| Astra DB              | ✅ `astrapy`       | —          | `astra`          |
| PostgreSQL (psycopg3) | ✅ `psycopg`       | —          | `psycopg`        |

## Span attributes captured

Vector database spans include the following attributes:

| Attribute               | Description                                         |
| ----------------------- | --------------------------------------------------- |
| `db.system`             | Database system name (e.g., `chromadb`, `pinecone`) |
| `db.operation`          | Operation type (e.g., `query`, `upsert`, `delete`)  |
| `db.collection.name`    | Collection or index name                            |
| `db.vector.query.top_k` | Number of results requested                         |
| `db.vector.dimension`   | Vector dimension                                    |
| `server.address`        | Database host                                       |
| `server.port`           | Database port                                       |

<Note>
  PostgreSQL (`psycopg3`) instrumentation traces SQL queries executed against pgvector-enabled tables using the same span attributes as other vector operations, plus standard database span attributes like `db.statement`.
</Note>

## Disabling specific databases

```python theme={null}
import openlit

# Instrument everything except Pinecone
openlit.init(
    otlp_endpoint="http://127.0.0.1:4318",
    disabled_instrumentors=["pinecone"]
)
```

***

<CardGroup cols={3}>
  <Card title="LLM provider integrations" href="/sdk/integrations/llm-providers" icon="microchip-ai">
    Trace LLM calls with token counts, cost, and latency
  </Card>

  <Card title="AI framework integrations" href="/sdk/integrations/ai-frameworks" icon="sitemap">
    Trace agent execution, tool calls, and chains
  </Card>

  <Card title="Destinations" href="/sdk/destinations/overview" icon="link">
    Send telemetry to Datadog, Grafana, New Relic, and other observability stacks
  </Card>
</CardGroup>