> ## Documentation Index
> Fetch the complete documentation index at: https://r2r-patch-fix-ingestion.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Developer Quickstart

> Getting started with SciPhi Cloud

This guide shows developers how to:

1. Ingest files into your SciPhi Cloud
2. Search over the ingested files
3. Request or stream a RAG (Retrieval-Augmented Generation) response
4. Use the RAG Agent for more complex, interactive queries

Be sure to complete the [deployment instructions](/sciphi-cloud/deploy) before continuing with this guide. If you would prefer to start interacting with your deployment from the cloud, then [refer here](/sciphi-cloud/projects).

### Installing the R2R CLI &/or SDK

PyPi is currently the primary supported method for installing the R2R client. Similarly, the javascript client can be installed through npm, though this does not yet include a CLI.

<Tabs>
  <Tab title="Python SDK + CLI">
    ```bash
    pip install r2r
    ```
  </Tab>

  <Tab title="JS SDK">
    ```bash
    npm install r2r-js
    ```
  </Tab>
</Tabs>

We are actively working on building more lightweight CLI options. Refer to the dedicated documentation sections for further information about the currently available options:

<CardGroup cols={3}>
  <Card title="API Reference" icon="message-code" href="/api-reference/introduction" />

  <Card title="Python SDK" icon="python" href="/documentation/python-sdk" />

  <Card title="Javascript SDK" icon="js" href="/documentation/js-sdk" />
</CardGroup>

The remainder of this quickstart will proceed with CLI commands, but all of these commands are easily reproduced inside of the Javascript or Python SDK.

### Set your environment variable

Start by clicking the copy icon next to your deployed pipeline URL (`https://sciphi-9ab60cab-0ff5-4cea-9d41-80f6d7607c3e-qwpin2swwa-ue.a.run.app` in the image below) and then exporting this into your local environment variable:

<Frame>
  <img src="https://mintlify.s3-us-west-1.amazonaws.com/r2r-patch-fix-ingestion/images/deployment.png" style={{ borderRadius: '0.5rem' }} />
</Frame>

```bash
export SCIPHI_CLOUD_URL=https://sciphi-7c961437-...YOUR_PIPELINE...-ue.a.run.app
```

Next, check that you are able to communicate with the deployment from your command line:

```bash
curl $SCIPHI_CLOUD_URL/v2/health
# {"results":{"response":"ok"}}
```

### Ingesting file(s)

The following command ingests a default sample file `r2r/examples/data/aristotle.txt`:

```bash
r2r --base-url=$SCIPHI_CLOUD_URL ingest-sample-file
```

To ingest your own files, replace the file path with your desired file or directory:

```bash
r2r --base-url=$SCIPHI_CLOUD_URL ingest-files /path/to/my/files
```

### Executing a search

Perform a search query:

```bash
r2r --base-url=$SCIPHI_CLOUD_URL search --query="who was aristotle?" --use-hybrid-search
```

### RAG Response

Generate a RAG response:

```bash
r2r --base-url=$SCIPHI_CLOUD_URL rag --query="who was aristotle?" --use-hybrid-search
```

### Stream a RAG Response

Stream a RAG response:

<Tabs>
  <Tab title="CLI">
    ```bash
    r2r --base-url=$SCIPHI_CLOUD_URL rag --query="who was aristotle?" --stream --use-hybrid-search
    ```
  </Tab>

  <Tab title="Python">
    ```python
    rag_results = client.rag(
        query="Who is John?",
        vector_search_settings={"use_hybrid_search": True},
        rag_generation_config=GenerationConfig(model="openai/gpt-4o-mini", temperature=0.0, stream=True)
    )

    for chunk in rag_results:
        print(chunk, end='', flush=True)
    ```
  </Tab>

  <Tab title="Curl">
    ```bash
    curl -X POST $SCIPHI_CLOUD_URL/v2/rag \
      -H "Content-Type: application/json" \
      -d '{
        "query": "who was aristotle?",
        "vector_search_settings": {
          "use_vector_search": true,
          "search_filters": {},
          "search_limit": 10,
          "use_hybrid_search": true
        },
        "kg_search_settings": {
          "use_kg_search": false
        },
        "rag_generation_config": {
          "model": "openai/gpt-4o",
          "stream": true
        }
      }' \
      --no-buffer
    ```
  </Tab>
</Tabs>

### Using the RAG Agent

The RAG Agent provides a more interactive and intelligent way to query your knowledge base. Here's an example of how to use it:

```python
from r2r import R2RClient

client = R2RClient(base_url=os.environ.get("SCIPHI_CLOUD_URL"))

messages = [
    {"role": "user", "content": "What was Aristotle's main contribution to philosophy?"},
    {"role": "assistant", "content": "Aristotle made numerous significant contributions to philosophy, but one of his main contributions was in the field of logic and reasoning. He developed a system of formal logic, which is considered the first comprehensive system of its kind in Western philosophy. This system, often referred to as Aristotelian logic or term logic, provided a framework for deductive reasoning and laid the groundwork for scientific thinking."},
    {"role": "user", "content": "Can you elaborate on how this influenced later thinkers?"}
]

result = client.agent(
    messages=messages,
    vector_search_settings={"use_hybrid_search":True},
    rag_generation_config={"model": "openai/gpt-4o", "temperature": 0.7}
)

print(result['choices'][0]['message']['content'])
```

This example shows how to use the RAG Agent for a multi-turn conversation about Aristotle's contributions to philosophy.

Remember to replace `$SCIPHI_CLOUD_URL` with your actual deployed pipeline URL in all examples.