LlamaIndex

Ingest legal documents from diverse sources with native connectors. LlamaIndex simplifies loading unstructured data for downstream processing.

• Multi-Source Ingestion: Connect to PDFs, Word documents, APIs, SQL databases, and cloud storage.
• Legal Format Parsing: Handles complex layouts, footnotes, and multi-column text common in contracts and briefs.
• Incremental Loading: Efficiently update indexes with new case law or regulatory filings without full re-indexing.

Choose the optimal index for your legal retrieval task. Different structures offer unique trade-offs between latency, cost, and semantic fidelity.

• Vector Store Index: Generates embeddings for dense semantic search, ideal for finding conceptually similar precedents.
• Summary Index: Creates hierarchical summaries of long contracts for top-level Q&A.
• Keyword Table Index: Enables precise Boolean and keyword matching for exact statutory text retrieval.
• Knowledge Graph Index: Extracts entities and relationships to build a structured legal knowledge graph for multi-hop reasoning.

Define sophisticated retrieval logic by composing multiple indexes and query transformations. This enables complex legal reasoning pipelines.

• Sub-Question Query Engine: Decomposes a complex legal query into sub-questions, queries different indexes, and synthesizes a final answer.
• Router Query Engine: Dynamically selects the most relevant index (e.g., vector vs. keyword) based on the incoming query's intent.
• Recursive Retrieval: Chains retrieval steps, using an initial document summary to locate the most relevant sub-section for detailed extraction.

Control how retrieved context is presented to the LLM for final answer generation. These modules are critical for managing context window limits and citation accuracy.

• Refine: Iteratively feeds retrieved nodes to the LLM, refining an initial answer with new context. Useful for sequential analysis of long contracts.
• Compact and Accumulate: Compresses retrieved text chunks to fit within the context window before generation, reducing token costs.
• Tree Summarize: Recursively summarizes long document sets bottom-up, enabling Q&A over massive case law libraries.

Rigorously measure the quality of your legal RAG pipeline. Built-in evaluation tools are essential for ensuring high citation fidelity and answer correctness.

• Faithfulness Evaluator: Checks if the generated answer is fully supported by the retrieved source documents, mitigating hallucination.
• Relevancy Evaluator: Assesses whether the retrieved context is actually pertinent to the user's legal query.
• Correctness Evaluator: Compares the generated answer against a ground-truth reference to measure factual accuracy.
• Batch Evaluation: Run evaluations across a large dataset of legal question-answer pairs to benchmark pipeline performance.

Gain deep visibility into every step of the retrieval and generation process. Integrated tracing is vital for debugging complex legal reasoning chains.

• Event-Driven Architecture: Hooks into every stage—from document parsing and embedding to LLM calls and response synthesis.
• Integration with Observability Tools: Seamlessly connects to Arize Phoenix, Langfuse, and other LLM monitoring platforms.
• Latency Tracking: Pinpoint performance bottlenecks in the retrieval pipeline to optimize for sub-second legal research queries.