Pipeline of Legal Contract Analysis.

A sophisticated RAG-based system of automated legal contract analysis with local LLMs.

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Overview

The current project uses a production scale pipeline to analyse legal contracts of CUAD (Contract Understanding Atticus Dataset). It is powered by a LLM (Ollama + llama3.1:8b) and developed Retrieval-Augmented Generation (RAG) solution to provide enterprise-level contract intelligence ~80% average extraction accuracy on key clauses.

Core Capabilities

Clause Extraction | Extracts three critical clauses (verbatim) of a given clause:

• Termination (90% success rate)
• Confidentiality (70% success rate)
• Liability/Indemnification (78% success rate)

Contract Summarization | Prepares brief summaries 100-150 words summarizing:

• Contract purpose and parties
• Key obligations
• Financial terms
• Important conditions

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Key Features

Feature	Description
High-Accuracy Architecture Design	4-call design with specific task-LLM processing.
State-of-the-art RAG system	FAISS + langchain + mxbai-embed-large embeddings.
Hybrid Search	Hybrid search is a combination of semantic search, keyword matching, and section header detection.
Semantic Validation	Custom validation with primary/secondary key checking.
Aggressive Cleaning	The Aggressive Cleaning removes timely artifacts of pollution and semantic bleed.
Checkpoint System	Auto-saving 10 contracts between crashes.
Production Metrics	50 contracts successfully processed in ~5 hours.

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Quick Start

Prerequisites

• Python 3.8 or higher
• Ollama installed and running
• 8GB+ RAM recommended
• ~6GB disk space for models

Installation

1. Clone the repository

   git clone https://github.com/Ojas1584/Contract_Analysis.git
   cd Contract_Analysis

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Pull required Ollama models

   # For embeddings (semantic search)
   ollama pull mxbai-embed-large:latest
   
   # For text generation (clause extraction & summarization)
   ollama pull llama3.1:8b

Configuration

Edit the configuration block in contractsAnalysis.py:

# Configuration
PDF_DIR = "/path/to/your/contracts"  # Absolute path to PDF folder
MAX_CONTRACTS = 50
RESUME_FROM_CHECKPOINT = True
DEBUG_MODE = False

Approach & Methodology: An Iterative Design

The final pipeline (contractsAnalysis.py) was developed after multiple iterations of testing, optimization, and error analysis. The biggest problem was to adjust the trade-off between the processing speed and the extraction accuracy.

Step 1: Code 1 (2-Call Batch Model) ~ 1.5hrs

Hypothesis: One, batched prompt (a single call to Summary, a single call to all 3 Clauses) would have been the most efficient.

Result: Bad Extraction

• Accuracy: The test was run on a 7-file validation set with disastrous errors. The combination of complicated prompt would confuse the LLM and generate:
  1. Duplicate Clauses: Transfer of the Liability clause to the Confidentiality slot.
  2. Wrong Sections: Removing a section that is not relevant such as the “Work for Hire"
• Speed: This model was counter-intuitive as the slowest. The context with 25,000 + characters and the extremely sophisticated prompt took more time of the LLM to think than 4 simple prompts.

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Step 2: Code 2 (4-Call Model) ~ 2.5hrs

Hypothesis: The code 1 failures needed to be fixed by simplifying the task. I re-architected the pipeline to a 4-call pipeline (1 per task (Summary, Term, Conf, Liab). Every call would be searched by a specific RAG search.

Result: Partial Success

• Accuracy: This corrected the error of the Duplicate Clause. Nonetheless, there appeared new, less pronounced failures:
  1. Prompt Pollution: In the LLM, it would leak its own prompt instructions into the output (e.g., 1. Search the ENTIRE...).
  2. Semantic Bleed: The LLM would state that it did not find it but instead it would include incorrect (but helpful) text such as, However, I did find..., that would corrupt the data.
  3. Wrong Section: The v2 validation was too naive. It would pass a "Warranties" section for a "Liability" query because it saw the word "warranty."

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Step 3: Code 3 (4-Call Robust Model) ~ 5hrs

Hypothesis: The 4-call architecture was correct, but the validation and cleaning layers were too weak. The pipeline needed to be strict against specific, observed LLM failure modes.

Result: Success

• Fix 1 (Semantic Bleed/Pollution): I wrote a new clean_llm_artifacts function to aggressively find and delete any prompt text or "helpful" LLM chatter, ensuring a clean "Not found" or pure verbatim text.
• Fix 2 (Wrong Section): I created a Primary vs. Secondary Keyword system in validate_extraction. To pass, a Liability extraction must contain a primary keyword (like "liability" or "indemnify"). This perfectly fixed the "Wrong Section" error, forcing the script to reject the "Warranties" section and correctly report "Not found."

This iterative process produced the final contractAnalysis.py script, which proved robust across the entire 50-contract dataset.

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Execution

# For a fresh start (recommended for first run)
# On Windows PowerShell:
Remove-Item -Recurse -Force output

# On macOS/Linux:
# rm -rf output/

# Run the pipeline
python contractsAnalysis.py

Expected Output:

• Processing time: ~6 minutes per contract
• Total runtime for 50 contracts: ~5 hours
• Checkpoint saved every 10 contracts
• Final CSV and JSON exports in output/ directory

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Architecture

Pipeline Flow

Technology Stack

• LLM: Llama 3.1 (8B parameters) via Ollama
• Embeddings: mxbai-embed-large (semantic understanding)
• Vector Store: FAISS (in-memory, 3660 chunks)
• Framework: LangChain (orchestration)
• PDF Processing: PyPDF2 (text extraction)
• Data Processing: Pandas (output formatting)

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Model Evolution & Comparison

Through rigorous testing with 4-5 "problematic" contracts, three pipeline architectures were evaluated to find the optimal balance between speed and accuracy:

Performance Comparison

Model	Architecture	Speed (4 - 5 file test)	Accuracy	Verdict
Code 1	2-Call	13.9 min	Critical failures	Bad
Code 2	4-Call	11.3 min	Subtle errors	Good
Code 3	4-Call Robust	11-12 min	All tests pass	Best

Critical Test Cases (Sampled from CUAD Dataset at random)

Failure Mode	Test File	Code 1 (2-Call)	Code 2 (4-Call)	Code 3 (4-Call)
Duplicate Clause	ArcaUsTreasury...	FAIL	PASS	PASS
Wrong Section	StampscomInc...	FAIL	FAIL	PASS
Prompt Pollution	Paperexchange...	PASS	FAIL	PASS
Semantic Bleed	MphaseTech...	PASS	FAIL	PASS

Key Findings

1. Code 1 (2-Call ): Counter-intuitively slower due to complex prompt overhead

   • Duplicated clauses across extraction tasks
   • Couldn't focus on individual clause requirements

2. Code 2 (4-Call ): Faster but still prone to LLM artifacts

   • Fixed duplication issues
   • Prompt pollution (leaked system prompts)
   • Semantic bleed ("Not found. However, I did find...")

3. Code 3 (4-Call Robust):

   • Passed all test cases
   • Advanced keyword validation
   • Aggressive artifact cleaning
   • Primary/secondary keyword verification

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Production Results

Final Metrics (50 Contracts)

Total Runtime:      318.8 minutes (~5 hours)
Avg per Contract:   6.4 minutes
Contracts Failed:   0 (100% completion rate)
Checkpoints Saved:  5 (every 10 contracts)

Extraction Success Rates

Clause Type	Found	Total	Success Rate	Notes
Termination	45	50	90.0%	Excellent coverage
Liability	39	50	78.0%	Good accuracy
Confidentiality	35	50	70.0%	Conservative validation

Quality Assurance

The validation system prioritizes precision over recall:

• Rejects clauses missing primary keywords
• Identifies semantic bleed and prompt pollution
• Returns "Not found" for genuinely absent clauses
• Never hallucinates content to fill empty slots

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Core Functions

Main Pipeline Components

Function	Purpose	Key Features
main()	Orchestrates entire pipeline	Checkpoint management, progress tracking
load_contracts()	PDF loading and text extraction	Unicode cleaning, empty file handling
create_vector_store()	One-time FAISS index creation	1000-char chunks, semantic embeddings
get_enriched_context()	Hybrid RAG retrieval	3-strategy search (semantic + keyword + header)
extract_single_clause()	Clause extraction engine	LLM call + cleaning + validation
generate_summary()	Contract summarization	Context gathering + LLM generation

Quality Control Functions

Function	Purpose	Key Features
clean_llm_artifacts()	Output sanitization	Removes "Here is...", prompt pollution, semantic bleed
validate_extraction()	5-step validation	Length check, keyword verification, "not found" detection
extract_party_names()	Regex party extraction	Identifies "Company Inc.", "LLC", "Ltd." patterns
extract_financial_terms()	Regex financial extraction	Finds $X,XXX, X% patterns

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Usage Examples

Basic Usage

# Run with default settings
python contractAnalysis.py

Resume from Checkpoint

# The pipeline automatically detects and resumes from checkpoints
# If checkpoint found: "Resuming from contract 30/50"
# If no checkpoint: " No checkpoint found. Starting from scratch."
python contractAnalysis.py

Inspect Results

import pandas as pd

# Load the results
df = pd.read_csv('output/contract_analysis_YYYYMMDD_HHMMSS.csv')

# View summary statistics
print(f"Total contracts: {len(df)}")
print(f"Termination found: {(df['termination_clause'] != 'Not found').sum()}")
print(f"Confidentiality found: {(df['confidentiality_clause'] != 'Not found').sum()}")
print(f"Liability found: {(df['liability_clause'] != 'Not found').sum()}")

# View first contract
print(df.iloc[0]['summary'])

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Performance & Optimization

Runtime Analysis

Full 50-Contract Run: ~5 hours

Total Runtime:      318.8 minutes (5 hours 19 minutes)
Per Contract:       ~6.4 minutes average
Bottleneck:         LLM inference (4 calls × ~90s each)
Total LLM Calls:    200 calls (50 contracts × 4 calls)

Why So Long? Understanding the Trade-offs

Factor	Impact	Notes
Local LLM	Slow	No GPU acceleration, CPU-only inference
4-Call Architecture	Slow	But necessary for 90% accuracy
Quality > Speed	Accurate	Precision-focused design
Large Context	Slow	25,000 char context per call

What You Get for the Wait

The 5-hour runtime delivers:

• Zero API costs (completely free)
• 90% extraction accuracy (production-grade)
• Full data privacy (nothing sent to external servers)
• Reproducible results (same model, same outputs)
• No rate limits (process 1000s of contracts)

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Sample Output

Source: 2ThemartComInc...pdf

Contract Summary Example

This agreement outlines the partnership between i-Escrow and 2TheMart, with the purpose of creating a co-branded escrow service. The key obligations of each party include providing content for the co-branded site, adhering to trademark usage policies, and ensuring compliance with California Escrow Law. Financial terms specify that 2TheMart will receive 0.025% of the average transaction size as advertising payments, while i-Escrow retains all rights to its intellectual property. The agreement has a one-year term, renewable for successive one-year periods upon mutual written agreement. Termination can occur due to breach, change in company structure, or bankruptcy, with either party providing written notice.

Extracted Clauses Example

Termination Clause:

8.1 TERM. The term of this Agreement shall continue for one (1) year following the Launch Date, unless earlier terminated as provided herein. This Agreement may be renewed for any number of successive one (1) year terms by mutual written agreement of the parties prior to the conclusion of the term of this Agreement. A party wishing to renew this Agreement shall give the other party notice thereof ...

Confidentiality Clause:

9. CONFIDENTIALITY AND PROPRIETARY INFORMATION

9.1 DEFINITIONS.

For purposes of this Section 9, "Confidential Information" means all confidential and proprietary information disclosed by one party to the other party under this Agreement, including without limitation trade secrets, know-how, business practices, technical data, product plans, designs, specifications, source code, object code, soft...

Liability Clause:

7. DISCLAIMER OF WARRANTIES.

EACH PARTY PROVIDES ALL MATERIALS AND SERVICES TO THE OTHER PARTY "AS IS." EACH PARTY DISCLAIMS ALL WARRANTIES AND CONDITIONS, EXPRESS, IMPLIED OR STATUTORY, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. Each party acknowledges that it has not entered into this Agreement in relianc...

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Development Setup

# Clone and setup
git clone https://github.com/Ojas1584/Contract_Analysis.git
cd Contract_Analysis
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

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Acknowledgments

• CUAD Dataset: Contract Understanding Atticus Dataset(https://www.atticusprojectai.org/cuad)
• Ollama: Local LLM inference
• LangChain: RAG framework
• FAISS: Vector similarity search

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Built with ❤️ using Ollama, LangChain, and FAISS