LlamaIndex Data Ingestion & Indexing Strategy

Outline a LlamaIndex data ingestion and indexing strategy for M&A due diligence. Describe how you would handle various data types (PDF financial reports, web articles from Skyvern, internal company databases). Specify the types of indexes you would use (e.g., VectorStoreIndex, KeywordTableIndex) and why. Provide a Python snippet demonstrating basic document loading and index creation using LlamaIndex `SimpleDirectoryReader` and `VectorStoreIndex`.

```python
from llama_index.readers.simple_directory import SimpleDirectoryReader
from llama_index.core import VectorStoreIndex, Settings
from llama_index.llms.gemini import Gemini
from llama_index.embeddings.ollama import OllamaEmbedding

# Configure settings for Gemini LLM and Ollama embeddings
Settings.llm = Gemini(model="gemini-pro", temperature=0.0)
Settings.embed_model = OllamaEmbedding(model_name="nomic-embed-text")

# Simulate loading documents
documents = SimpleDirectoryReader(input_files=["path/to/financial_report.pdf", "path/to/market_analysis.txt"]).load_data()

# Create a vector index
index = VectorStoreIndex.from_documents(documents)
```