LlamaIndex

Introduction to LlamaIndex

LlamaIndex is a versatile framework designed to facilitate the creation of AI-powered knowledge assistants by enabling large language models (LLMs) to interact seamlessly with diverse data sources. Whether you’re dealing with unstructured documents, APIs, or databases, LlamaIndex provides the tools to parse, index, and retrieve information efficiently. It’s particularly beneficial for enterprises aiming to integrate AI into their data workflows, offering both managed services through LlamaCloud and open-source libraries for custom implementations.

Key Features

• Data Parsing and Extraction: LlamaIndex excels at converting complex documents, including PDFs and spreadsheets, into structured data using its advanced parsing engine, LlamaParse. This process supports various extraction modes like Fast, Balanced, and Multi-modal, each tailored to different data complexities.
• Indexing and Retrieval: The framework supports multiple indexing strategies, such as VectorStoreIndex and TreeIndex, allowing for efficient data retrieval. Users can customize chunk sizes, response modes, and integrate with various vector stores like Chroma and Pinecone.
• Agent Framework: LlamaIndex’s agent system enables the orchestration of multi-step workflows, combining data retrieval, LLM processing, and external API interactions. This is ideal for building autonomous agents that can perform tasks like research, summarization, and decision-making.
• Extensive Integrations: With over 350 data connectors and integrations, including support for S3, Azure Blob Storage, Google Drive, and Confluence, LlamaIndex ensures seamless data ingestion from various sources.
• Observability and Evaluation: The platform offers tools to monitor and evaluate the performance of your LLM applications, helping to fine-tune and optimize workflows.

How to Use LlamaIndex

Getting started with LlamaIndex is straightforward. For Python users, installation is as simple as:

pip install llama-index

Once installed, you can begin by loading your documents and creating an index:

from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("Your question here")
print(response)

This basic setup allows you to parse documents, index them, and perform queries using a large language model. For more advanced configurations, such as integrating different vector stores or customizing chunk sizes, refer to the official documentation.

Pricing

LlamaIndex operates on a credit-based pricing model, where each action—such as parsing, indexing, or extracting—consumes a certain number of credits. The cost per 1,000 credits varies by region and service:

• North America: $1.00
• Europe: $1.50

Subscription plans include:

• Free: $0/month – Includes 10,000 credits, suitable for individual users with basic needs.
• Starter: $50/month – Includes 50,000 credits, supports up to 5 users, and allows integration with 5 external data sources.
• Pro: $500/month – Includes 500,000 credits, supports up to 10 users, and allows integration with 25 external data sources.
• Enterprise: Custom pricing – Tailored solutions with dedicated support, SaaS/VPC deployment options, and unlimited integrations.

Additional usage beyond the included credits is billed at a pay-as-you-go rate, with 1,000 credits costing $1.00 in North America and $1.50 in Europe.

Frequently Asked Questions (FAQ)

• How are credits consumed? Credits are consumed based on the parsing mode, model used, and whether files are cached. For instance, parsing a page with the OpenAI GPT-4 model costs 30 credits per page.
• How can I monitor my credit usage? Real-time usage data is available via the LlamaCloud dashboard, where you can view your quota and the number of requests made. Usage metadata is also included in every API call.
• Are documents stored after parsing? Documents are cached for 48 hours to avoid redundant parsing. After this period, they are permanently deleted. If you wish to avoid caching, you can set the ‘do_not_cache’ parameter to True during parsing.
• What are the limitations? The maximum file size for parsing is 300MB, with a maximum of 35 images extracted per page. Additionally, the maximum amount of text extracted per page is 64KB; content beyond this limit is ignored.
• Can I upgrade or downgrade my plan? Yes, you can upgrade or downgrade your subscription plan at any time through the LlamaCloud dashboard. Changes take effect immediately upon confirmation.