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Mistral AI IndustrialKnowledgeAgent

IndustrialKnowledgeAgent

agentsnon_frameworkmistral-cookbookmistralindustrial_knowledge_agent
alph-notebooks/mistral-cookbook / IndustrialKnowledgeAgent.ipynb

IndustrialKnowledgeAgent: The Smart Industrial Equipment Knowledge Agent

Open In Colab

Problem Statement

In industrial settings, engineers and technicians often struggle to manage and retrieve comprehensive information about various equipment. This information is scattered across technical manuals, maintenance logs, safety protocols, troubleshooting guides, and parts inventories. The fragmented nature of this data makes it difficult to access and utilize effectively, leading to inefficiencies and potential safety risks. This problem requires an intelligent, adaptive solution to provide real-time, context-aware responses to queries.

Proposed Solution

To address these challenges, we propose an agentic workflow that integrates a Retrieval-Augmented Generation (RAG) system with a database querying system (FunctionCalling). This solution leverages LLMs (including structured output mechanism), embedding models and structured data retrieval to provide contextually relevant and precise information. The workflow is orchestrated by multiple agents, each with a specific role:

  1. RAGAgent: Utilizes LLMs and Embedding models to retrieve and generate contextually relevant information from technical documents.
  2. DatabaseQueryAgent: Handles precise and structured data retrieval from databases containing maintenance logs, technical specifications, parts inventories, and compliance records.
  3. WorkflowOrchestrator: Orchestrates interactions between the RAGSearchAgent and DatabaseAgent, ensuring seamless and efficient query resolution.

Dataset Details

PDF Documents

The PDF documents contain detailed information about various industrial equipment, categorized into:

  1. Technical Manuals: Operation and maintenance guides.
  2. Maintenance Guides: Routine and preventive maintenance tasks.
  3. Troubleshooting Guides: Solutions to common issues.
  4. Safety Protocols: Safety procedures and guidelines.

Databases

The databases contain structured information that complements the PDF documents:

  1. Compliance Database (compliance_db): Safety certifications and compliance statuses.
  2. Maintenance Database (maintenance_db): Logs of maintenance activities.
  3. Technical Specifications Database (technical_specifications_db): Detailed technical specifications.
  4. Parts Inventory and Compatibility Database (parts_inventory_compatibility_db): Information on parts, compatibility, and inventory status.

By integrating these datasets, the proposed agentic workflow aims to provide a comprehensive and efficient system for managing and retrieving industrial equipment information, ensuring that engineers and technicians have access to the most relevant and up-to-date information.

NOTE: Please note that all data used in this demonstration has been synthetically generated.

Technical Architecture:

Alt text

Installation

Installs the necessary Python packages for the IndusAgent system.

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Imports

Imports required libraries for LLM operations, data processing, vector database management, and utility functions.

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Download Data

Downloads the dataset from Google Drive, extracts it to a data directory, and sets up the working environment. The dataset contains CSV files for database operations and PDFs for document processing.

By the end of the process, you should be able to see the downloaded data, as shown in the image below.

Alt text

Download data from Google Drive

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From: https://drive.google.com/uc?id=1lwYSN6ry3JOA7pw3WAx72a_IXGqqmR8y
To: /content/data.zip
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✅ File downloaded: data.zip

Extract and setup data directory

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✅ Files extracted to: /content
📂 Current directory: /content/data
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📜 Extracted files: ['csv_data', 'pdf_data']

Set up environment variables

Sets up the Mistral API key as an environment variable for authentication.

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Initialize Mistral LLM and Qdrant Vector Database

Initializes the Mistral LLM client for text generation and Qdrant vector database client for similarity search operations.

Note:

  1. We will use our latest model, Mistral Small 3 for demonstration.
  2. You need to set up Qdrant Cloud or a Docker setup before proceeding. You can refer to the documentation for the setup instructions.
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System Prompts

The system uses three different types of prompts to guide the LLMs for response generation:

  1. PDF Summarization Prompt: summarization_prompt is used to create concise summaries of PDF documents.
  2. Response Generation Prompt: response_generation_prompt is used to generate responses based on retrieved context.
  3. Final Response Integration Prompt: final_response_generation_prompt is used to summarize responses from multiple sources - PDFs and different databases.
[8]

DataProcessor

The DataProcessor class is a comprehensive component that handles all data processing operations in the system. It manages both unstructured (PDFs) and structured (CSV) data, along with embedding generation and storage.

  • PDF document processing and text extraction using Mistral OCR.
  • CSV to database ingestion
  • Embedding generation and vector storage
  • Batch processing of documents and data

Main Components

1. Document Processing

  • get_categorized_filepaths: Walks through the directory structure to get categorized PDF file paths
  • parse_pdf: Extracts text from all pages of a PDF file using Mistral OCR.
  • process_single_pdf: Processes individual PDFs through the complete pipeline
  • process_documents: Handles sequential processing of multiple documents

2. Summarization and Embeddings

  • summarize: Generates concise summaries of text using the Mistral model
  • get_text_embedding: Creates text embeddings using Mistral's embedding model
  • qdrant_insert_embeddings: Stores embeddings with metadata in Qdrant vector database
  • process_and_store_embeddings: Handles batch processing of embeddings

3. Database Operations

  • insert_csv_to_table: Loads a single CSV file into a specified database table
  • insert_data_database: Handles multiple CSV files insertion into their respective tables
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RAGAgent

The RAGAgent class implements Retrieval-Augmented Generation (RAG) to provide intelligent search and response generation. It combines vector search capabilities with the LLM to give contextually relevant answers.

  • Query categorization and classification
  • Vector similarity search in Qdrant
  • Context-aware response generation
  • Document citation handling

Main Components

1. Query Processing

  • query_categorization: Classifies queries into predefined categories (technical manual, safety protocol, etc.)
  • query: Orchestrates the complete RAG pipeline from query to final response

2. Search and Retrieval

  • qdrant_search: Performs semantic search using query embeddings, filters results by document category and returns top-k most relevant documents.

3. Response Generation

  • generate_response: Creates natural language responses using retrieved context, uses LLM with specialized prompts, Provides citations to source documents.

Query Category Model

A Pydantic model that defines the structure for query categorization, used by RAGAgent to classify queries into relevant categories (technical_manual, safety_protocol, etc.).

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Database Query Tools

Defines database query function tools. These tools define the function calling interface for the DatabaseQueryAgent, enabling structured querying of different database tables.

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DatabaseQueryAgent

The DatabaseQueryAgent class manages interactions with the SQLite database, handling structured data queries through function calling on various databases containing maintenance logs, technical specifications, parts inventories, and compliance records. It provides specialized querying capabilities for different database tables.

  • Natural language query processing
  • Structured database querying
  • Function calling for query execution
  • JSON response formatting

Main Components

1. Table-Specific Queries

  • query_compliance: Retrieves filtered compliance records
  • query_maintenance: Accesses maintenance-related information
  • query_technical_specs: Fetches technical specifications
  • query_parts_inventory_compatibility: Retrieves parts and compatibility data

2. Query Processing

  • query: Processes natural language queries using function calling, Handles tool calls for appropriate database operations, Tracks database tool citations, Formats responses with query results.
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WorkflowOrchestrator

The WorkflowOrchestrator class orchestrates the interaction between RAGAgent and DatabaseQueryAgent to provide comprehensive responses by combining information from both structured and unstructured data sources.

  • Workflow orchestration and coordination
  • Response combination and integration
  • Final response summarization
  • Source citation management

Main Components

1. Workflow Execution

  • workflow: Manages the complete query processing pipeline, Coordinates responses from both agents, Generates final unified response, Maintains traceability through citations.

2. Response summarization

  • combine_and_summarize_responses: Merges and summarizes responses from both agents, Applies structured formatting to combined responses, Uses summarization prompts for coherent output.
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Initialize and Process Documents

Initializes the DataProcessor and processes PDF documents through the complete pipeline - from file ingestion to embedding storage.

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Processing PDFs: 100%|██████████| 12/12 [00:24<00:00,  2.07s/it]

Processed batch 1/2
Processed batch 2/2

Insert Data into Database tables.

Loads multiple CSV files into their corresponding database tables in SQLite.

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Successfully inserted data into compliance
Successfully inserted data into maintenance
Successfully inserted data into technical_specifications
Successfully inserted data into parts_inventory_compatibility

Initialise the Agents

Initializes the three core agents:

  • RAGAgent for document search and response
  • DatabaseQueryAgent for structured data querying.
  • WorkflowAgent for orchestrating responses.
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Example Queries

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Query: What are the troubleshooting steps for inaccurate machining in CNC Machine (Model X) and when was its last maintenance performed?
----------------------
Tool call: query_maintenance
Tool call parameters: {'filters': {'EquipmentName': 'CNC Machine', 'Model': 'Model X'}}
Tool call: query_technical_specs
Tool call parameters: {'filters': {'EquipmentName': 'CNC Machine', 'Model': 'Model X', 'SpecificationType': 'Accuracy'}}
------------Answer----------
### Troubleshooting Steps for Inaccurate Machining in CNC Machine (Model X)

To address inaccurate machining in the CNC Machine (Model X), follow these comprehensive troubleshooting steps:

1. **Check the Program**:
   - Ensure that the CNC program is correct and free of errors. Even minor errors in the code can lead to significant inaccuracies in machining.

2. **Inspect the Tooling**:
   - Verify that the correct tools are being used and that they are in good condition. Worn or damaged tools can cause inaccuracies. Replace tools as needed.

3. **Verify the Setup**:
   - Check the workpiece setup to ensure it is secure and correctly positioned. Any movement or misalignment can lead to machining inaccuracies.

4. **Calibrate the Machine**:
   - Regular calibration of the machine's axes and spindles can help maintain accuracy. If the machine hasn't been calibrated recently, it may be time to do so.

5. **Check for Wear and Tear**:
   - Inspect the machine for any signs of wear and tear, such as worn bearings or guides. These components can affect the machine's accuracy over time.

6. **Environmental Factors**:
   - Ensure that the machining environment is stable. Factors such as temperature, humidity, and vibration can affect the machine's performance.

7. **Machine Maintenance**:
   - Regular maintenance, including lubrication and cleaning, can help prevent inaccuracies. Ensure that the machine is well-maintained and that all scheduled maintenance tasks are completed on time.

8. **Software and Firmware**:
   - Ensure that the machine's software and firmware are up-to-date. Outdated software can sometimes cause inaccuracies.

9. **Backlash Compensation**:
   - Check the backlash compensation settings. Incorrect settings can lead to inaccuracies, especially in high-precision machining.

10. **Consult the Manual**:
    - Refer to the machine's manual for any model-specific troubleshooting steps or recommendations.

### Last Maintenance Performed

The last maintenance for the CNC Machine (Model X) was performed on September 1, 2023. This maintenance was preventive and included an oil change. The next scheduled maintenance is set for December 1, 2023. For more detailed information, refer to the Maintenance Log in the Appendices section of the machine's documentation.

### Important Safety Precautions

- Always ensure the machine is turned off and locked out before performing any maintenance or inspection tasks.
- Wear appropriate personal protective equipment (PPE) when handling tools and machinery.
- Follow the manufacturer's guidelines for tool replacement and machine calibration to avoid any potential hazards.

By following these steps and maintaining a regular maintenance schedule, you can help ensure the accuracy and reliability of the CNC Machine (Model X).

Sources:
- Database Tools: query_technical_specs,query_maintenance
- PDF Sources: ./pdf_data/troubleshooting_guide/CNC_Machine_(Model X)_Troubleshooting_Guide.pdf,./pdf_data/troubleshooting_guide/Robotic_Arm_(Unit 7)_Troubleshooting_Guide.pdf,./pdf_data/troubleshooting_guide/Cooling_System_(Model Y)_Troubleshooting_Guide.pdf
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Query: What are the safety protocols for the Cooling System (Model Y), and when is its next scheduled maintenance?
----------------------
Tool call: query_compliance
Tool call parameters: {'filters': {'EquipmentName': 'Cooling System', 'Model': 'Model Y', 'ComplianceType': 'Safety'}}
Tool call: query_maintenance
Tool call parameters: {'filters': {'EquipmentName': 'Cooling System', 'Model': 'Model Y'}}
------------Answer----------
### Safety Protocols for Cooling System (Model Y)

The Cooling System (Model Y) is compliant with OSHA safety standards, issued on March 10, 2021, and is set to expire on March 10, 2026. The system is currently active and Alice Johnson is responsible for its compliance. Below are the detailed safety protocols and maintenance schedule for the Cooling System (Model Y).

#### Personal Protective Equipment (PPE)
- **Required PPE:**
  - Safety Glasses
  - Gloves
  - Ear Protection
  - Safety Shoes
- **PPE Usage:**
  - Always wear the required PPE when operating or maintaining the system.
  - Ensure PPE is in good condition and fits properly.

#### System Operation Safety
- **Pre-Operation Checks:**
  - Inspect System: Check for any visible damage or issues.
  - Check Coolant Levels: Ensure coolant levels are adequate.
  - Check Fans: Ensure fans are functioning properly.
- **During Operation:**
  - Monitor System: Keep a close eye on the system during operation.
  - Listen for Unusual Noises: Stop the system if you hear any unusual noises.
  - Check Cooling Performance: Ensure the system is cooling effectively.
- **Post-Operation Checks:**
  - Clean System: Clean the system and work area.
  - Inspect Components: Check components for wear and replace as needed.
  - Record Issues: Document any issues or anomalies observed during operation.

#### Emergency Procedures
- **System Failure:**
  1. Stop Operation: Immediately stop the system.
  2. Notify Supervisor: Inform your supervisor or maintenance team.
  3. Document Issue: Record the details of the issue for further investigation.
- **Injury:**
  1. Provide First Aid: Administer first aid as needed.
  2. Notify Safety Team: Inform the safety team or emergency services.
  3. Document Incident: Record the details of the incident for further investigation.
- **Fire:**
  1. Evacuate Area: Clear the area around the system.
  2. Use Fire Extinguisher: Use the appropriate fire extinguisher to put out the fire.
  3. Notify Safety Team: Inform the safety team or emergency services.

#### Hazardous Materials
- **Coolant:**
  - Handling: Wear gloves and safety glasses when handling coolant.
  - Disposal: Dispose of used coolant according to local regulations.
- **Lubricants:**
  - Handling: Wear gloves and safety glasses when handling lubricants.
  - Disposal: Dispose of used lubricants according to local regulations.

#### Environmental Safety
- **Ventilation:**
  - Ensure the work area has adequate ventilation to prevent the buildup of fumes and heat.
- **Noise Levels:**
  - Use ear protection to protect hearing from loud noises.
- **Waste Management:**
  - Dispose of waste materials according to local regulations.

### Next Scheduled Maintenance
The next scheduled maintenance for the Cooling System (Model Y) is on October 10, 2023. This maintenance is preventive in nature and involves filter cleaning. The maintenance is currently scheduled, and Alice Johnson is the responsible technician.

Sources:
- Database Tools: query_compliance,query_maintenance
- PDF Sources: ./pdf_data/safety_protocol/Cooling_System_(Model Y)_Safety_Protocol.pdf,./pdf_data/safety_protocol/CNC_Machine_(Model X)_Safety_Protocol.pdf,./pdf_data/safety_protocol/Robotic_Arm_(Unit 7)_Safety_Protocol.pdf