> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memic.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart (Python)
> Install, authenticate, upload a file, and search — in five minutes.
This guide takes you from zero to a working search query against your own
content. You'll need Python 3.10+ and a Memic API key from the
[dashboard](https://memic.ai/dashboard/api-keys).
## 1. Install the SDK
```bash theme={null}
pip install memic
```
## 2. Authenticate
All Memic API calls are authenticated with an API key. Get one from your
environment's API keys page in the dashboard, then set it as an environment
variable:
```bash theme={null}
export MEMIC_API_KEY="mk_live_..."
```
The SDK picks it up automatically. You can also pass it explicitly:
```python theme={null}
from memic import Memic
client = Memic(api_key="mk_live_...")
```
Every API key is bound to exactly one **environment** (e.g. `staging` or
`production`). Swap the key to swap environments — no code changes.
## 3. Verify your connection
```python theme={null}
from memic import Memic
client = Memic()
print(client.me())
# ApiKeyInfo(organization_name='...', project_name='...', environment_slug='production')
```
If this returns your org and project, you're authenticated correctly.
## 4. Upload a file
Memic's upload flow uses presigned URLs so large files go directly to storage
without passing through the API server. The SDK handles both steps for you:
```python theme={null}
result = client.files.upload("./product-handbook.pdf")
print(result.file_id)
```
The file is queued for processing (chunking, embedding, indexing). You can
poll for status:
```python theme={null}
status = client.files.get_status(result.file_id)
print(status.status) # pending → processing → ready
```
Typical processing time for a PDF is under 30 seconds.
## 5. Search your content
Once the file is `ready`, semantic search works immediately:
```python theme={null}
results = client.search("what is our refund policy")
for hit in results.semantic:
print(f"[{hit.score:.2f}] {hit.text[:200]}...")
print(f" from {hit.source.file_name}")
```
## 6. Chat over your content
Search returns passages; chat returns grounded answers:
```python theme={null}
response = client.chat("Summarize our refund policy in three bullet points")
print(response.answer)
for citation in response.citations:
print(f" → {citation.file_name} (p. {citation.page})")
```
## Next steps
Batch uploads, metadata, folder organization.
Filters, top-k tuning, hybrid retrieval.
Build a customer-facing chat over your docs.
Every endpoint, every parameter.