> For the complete documentation index, see [llms.txt](https://strata-ai.gitbook.io/strata-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://strata-ai.gitbook.io/strata-ai/high-level-guides/interactive-blocks.md).

# RAG Module

**Overview**

The **RAG (Retrieval-Augmented Generation)** module enhances the capabilities of Large Language Models (LLMs) by allowing them to reference external knowledge bases. This approach ensures:

* Improved precision and relevance in responses.
* Access to domain-specific knowledge without requiring model retraining.
* Practical and authoritative outputs.

#### **Features**

The RAG module in Strata AI provides the following functionalities:

1. **Data Input**:
   * Supports multiple file formats (e.g., PDF, DOCX, MD, CSV, TXT, PPT).
   * Handles Python objects directly.
2. **Retrieval**:
   * Supports Faiss, BM25, ChromaDB, ElasticSearch, and mixed retrieval methods.
3. **Post-Retrieval**:
   * Includes advanced re-ranking methods like LLM Rerank, ColbertRerank, CohereRerank, and ObjectRerank for accurate data prioritization.
4. **Data Updates**:
   * Allows addition and modification of text and Python objects.
5. **Data Storage and Recovery**:
   * Saves vectorized data to avoid re-vectorization during future queries.

***

#### **Preparation**

**Installation**

Install the RAG module using the following commands:

```
# From PyPI
pip install strataai[rag]

# From source
pip install -e .[rag]
```

> Note: Some modules, like ColbertRerank, require additional manual installation. For example, install `llama-index-postprocessor-colbert-rerank` for ColbertRerank.

**Embedding Configuration**

Set up embeddings in your configuration file:

```
# Example for OpenAI
embedding:
  api_type: "openai"
  base_url: "YOUR_BASE_URL"
  api_key: "YOUR_API_KEY"
  dimensions: "MODEL_DIMENSIONS"
```

You can also configure embeddings for Azure, Gemini, or Ollama. For additional types like HuggingFace or Bedrock, use the `embed_model` field in `from_docs` or `from_objs` functions.

**Optional: Omniparse Configuration**

To optimize PDF parsing, configure Omniparse:

```
omniparse:
  api_key: 'YOUR_API_KEY'
  base_url: 'YOUR_BASE_URL'
```

Omniparse is optional. If configured, it is used exclusively for PDF files.

***

#### **Key Functionalities**

**1. Data Input**

**Example 1.1: Files or Directories**

```
import asyncio
from strataai.rag.engines import SimpleEngine

async def main():
    engine = SimpleEngine.from_docs(input_files=["path/to/file.txt"])
    answer = await engine.aquery("What does Bob like?")
    print(answer)

if __name__ == "__main__":
    asyncio.run(main())
```

**Example 1.2: Python Objects**

```
from pydantic import BaseModel
from strataai.rag.engines import SimpleEngine

class Player(BaseModel):
    name: str
    goal: str

async def main():
    objs = [Player(name="Jeff", goal="Top One")]
    engine = SimpleEngine.from_objs(objs=objs)
    answer = await engine.aquery("What is Jeff's goal?")
    print(answer)

if __name__ == "__main__":
    asyncio.run(main())
```

***

**2. Retrieval**

**Example 2.1: Faiss Retrieval**

```
from strataai.rag.engines import SimpleEngine
from strataai.rag.schema import FAISSRetrieverConfig

async def main():
    engine = SimpleEngine.from_docs(
        input_files=["path/to/file.txt"],
        retriever_configs=[FAISSRetrieverConfig()]
    )
    answer = await engine.aquery("What does Bob like?")
    print(answer)

if __name__ == "__main__":
    asyncio.run(main())
```

**Example 2.2: Hybrid Retrieval**

```
from strataai.rag.schema import BM25RetrieverConfig

async def main():
    engine = SimpleEngine.from_docs(
        input_files=["path/to/file.txt"],
        retriever_configs=[FAISSRetrieverConfig(), BM25RetrieverConfig()]
    )
    answer = await engine.aquery("What does Bob like?")
    print(answer)

if __name__ == "__main__":
    asyncio.run(main())
```

***

**3. Post-Retrieval**

**Example 3.1: LLM Re-Ranking**

```
from strataai.rag.schema import LLMRankerConfig

async def main():
    engine = SimpleEngine.from_docs(
        input_files=["path/to/file.txt"],
        retriever_configs=[FAISSRetrieverConfig()],
        ranker_configs=[LLMRankerConfig()]
    )
    answer = await engine.aquery("What does Bob like?")
    print(answer)

if __name__ == "__main__":
    asyncio.run(main())
```

***

**4. Data Updates**

**Example 4.1: Add Text and Objects**

```
engine.add_docs(["path/to/new_file.txt"])
engine.add_objs([Player(name="Mike", goal="Top Three")])
```

***

**5. Data Storage and Recovery**

**Example 5.1: Persist and Reload**

```
persist_dir = "./tmp_storage"
engine = SimpleEngine.from_docs(input_files=["path/to/file.txt"])
engine.persist(persist_dir)

engine = SimpleEngine.from_index(persist_path=persist_dir)
answer = await engine.aquery("What does Bob like?")
print(answer)
```

***

####