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

# LlamaIndex

> Build production RAG pipelines and index custom data using Neosantara and LlamaIndex.

[LlamaIndex](https://www.llamaindex.ai/?utm_source=neosantara-docs\&utm_medium=referral) is a data framework designed to connect private and enterprise data to large language models through Retrieval-Augmented Generation (RAG). Neosantara provides OpenAI-compatible endpoints for both text generation and vector embeddings.

## Setup

Install the LlamaIndex packages and OpenAI-compatible adapters:

```bash theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
pip install -U llama-index llama-index-llms-openai-like llama-index-embeddings-openai-like
```

<CodeGroup>
  ```bash Bash / zsh icon="terminal" theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
  export NEOSANTARA_API_KEY="nsk_your_api_key_here"
  ```

  ```env .env icon="file-code" theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
  NEOSANTARA_API_KEY=nsk_your_api_key_here
  ```

  ```powershell PowerShell icon="terminal" theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
  $env:NEOSANTARA_API_KEY="nsk_your_api_key_here"
  ```
</CodeGroup>

## Global Settings Configuration

Use the LlamaIndex `Settings` module to set both the generation LLM and the embedding provider globally.

```python theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
import os
from llama_index.core import Settings
from llama_index.llms.openai_like import OpenAILike
from llama_index.embeddings.openai_like import OpenAILikeEmbedding

Settings.llm = OpenAILike(
    model="deepseek-v4.1-flash",
    api_key=os.environ["NEOSANTARA_API_KEY"],
    api_base="https://api.neosantara.xyz/v1",
    temperature=0.2,
    max_tokens=1024,
    is_chat_model=True
)

Settings.embed_model = OpenAILikeEmbedding(
    model="text-embedding-3-small",
    api_key=os.environ["NEOSANTARA_API_KEY"],
    api_base="https://api.neosantara.xyz/v1"
)
```

## End-to-End RAG Pipeline Example

The following script creates an in-memory vector index from text documents and queries the index with synthesized context:

```python theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
import os
from llama_index.core import Document, VectorStoreIndex, Settings
from llama_index.llms.openai_like import OpenAILike
from llama_index.embeddings.openai_like import OpenAILikeEmbedding

# 1. Configure generation and embedding models
Settings.llm = OpenAILike(
    model="deepseek-v4.1-flash",
    api_key=os.environ["NEOSANTARA_API_KEY"],
    api_base="https://api.neosantara.xyz/v1",
    is_chat_model=True
)

Settings.embed_model = OpenAILikeEmbedding(
    model="text-embedding-3-small",
    api_key=os.environ["NEOSANTARA_API_KEY"],
    api_base="https://api.neosantara.xyz/v1"
)

# 2. Define source documents
documents = [
    Document(text="Neosantara is Indonesia's AI Gateway providing Rupiah PAYG billing and multi-provider routing."),
    Document(text="The Free tier rate limits are 10 RPM, 30,000 ITPM, and 8,000 OTPM with a minimum deposit floor of IDR 50,000."),
    Document(text="The Indonesian UU PDP No. 27/2022 automated PII guardrail is enabled per-request via the X-Guard: on header.")
]

# 3. Build vector index
index = VectorStoreIndex.from_documents(documents)

# 4. Instantiate query engine and ask questions
query_engine = index.as_query_engine(similarity_top_k=2)
response = query_engine.query("How do developers enable Indonesian PII redaction on Neosantara?")

print("Answer:", response)
```

## Streaming Responses in LlamaIndex

For conversational chat interfaces or interactive CLIs, enable streaming on the query engine:

```python theme={"theme":{"light":"ayu-dark","dark":"catppuccin-latte"}}
query_engine = index.as_query_engine(streaming=True)
streaming_response = query_engine.query("What are the rate limits for the Free tier?")

for text in streaming_response.response_gen:
    print(text, end="", flush=True)
print()
```

## Recommended RAG Model Pairings

| Workflow                              | Embedding Model          | Generation Model (LLM) |
| :------------------------------------ | :----------------------- | :--------------------- |
| **High Throughput & Low Cost**        | `text-embedding-3-small` | `gemini-3.8-flash`     |
| **Deep Synthesis & Long Documents**   | `text-embedding-3-small` | `deepseek-v4.1-flash`  |
| **High Precision & Legal Compliance** | `text-embedding-3-large` | `claude-4.5-sonnet`    |

## Next Steps

* [Embeddings Capability Documentation](/en/gateway/capabilities/embeddings)
* [UU PDP Guardrails Documentation](/en/guides/guardrails)
* [LiteLLM Integration Guide](/en/integrations/litellm)


## Related topics

- [Embeddings](/en/gateway/capabilities/embeddings.md)
- [Data Guardrails & UU PDP](/en/guides/guardrails.md)
- [LiteLLM](/en/integrations/litellm.md)
- [Model Catalog](/en/gateway/models.md)