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Overview
Components
Pipeline Setup
How to Use
Use Cases
Best Practices
Troubleshooting
Advanced Features

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Pipeline

Pipeline Components

🌐 Web Hook

Receives uploaded code files via HTTP

📄 Data – Parser

Extracts structured text from uploaded files

🔧 Preprocessor – Code

Parses and tokenizes source code

🧠 Embedding – Sentence Transformer

Converts code chunks to vector embeddings

📊 Vector Store – Qdrant

Stores and indexes code embeddings for semantic search

Detailed Component Configuration

1. Web Hook

Purpose: Receives uploaded code files through HTTP requests

Configuration:

Method: PUT
Query Parameters: type, token
Headers:
- Content-Type: Automatically set based on file type
- Authorization: Your API key (found in the webhook URL)
Body: File upload (supports various code file formats)

Response: JSON containing processed objects with embedded code chunks

2. Data – Parser

Purpose: Extracts structured text, tables, or other relevant information from uploaded files for downstream processing

Description: The data parser node processes uploaded files and converts them into a format that can be consumed by the code preprocessor. It handles various file formats and extracts the raw text content needed for code analysis.

3. Preprocessor – Code

Purpose: Parses and tokenizes source code, extracting functions, classes, and comments to prepare code for analysis, embedding, or search

Configuration Options

Setting	Default	Options	Effect
Code Splitter Profile	Auto	Auto, C/C++, Python, JavaScript, TypeScript	Determines parsing rules and syntax highlighting
Maximum String Length	512	Configurable character limit	Controls chunk size for downstream processing

Supported Languages

Auto: Automatic language detection based on file extension
C/C++: C and C++ source code
Python: Python source code
JavaScript: JavaScript source code
TypeScript: TypeScript source code

4. Embedding – Sentence Transformer

Purpose: Converts text into high-dimensional vector embeddings using advanced transformer models

Configuration:

Model (Default: miniLM): Determines embedding quality, speed, and domain specialization

Available Models

Model	Full Name	Use Case
miniLM	`sentence-transformers/multi-qa-MiniLM-L6-cos-v1`	General use embeddings, good performance
miniAll	`sentence-transformers/all-MiniLM-L6-v2`	High-quality general purpose
mpnet	`sentence-transformers/multi-qa-mpnet-base-cos-v1`	Excellent quality, slower processing
Custom	Any Hugging Face model	Specialized requirements

5. Vector Store – Qdrant

Purpose: Stores and retrieves vector embeddings for similarity search

Host Configuration

Type	Description	Use Case
Local	Self-hosted Qdrant server	Development, private deployment
Cloud	Qdrant Cloud instance	Production, managed service

Local Qdrant Parameters

Parameter	Default	Description
Host	localhost	Your local Qdrant server hostname
Port	6333	Qdrant server port number
Collection	APARAVI	Vector collection name

Qdrant Cloud Parameters

Parameter	Default	Description
Host	–	Cloud instance URL (e.g., `<instance>.<region>.qdrant.io`)
Port	443	HTTPS port for cloud connections
API Key	Required	Authentication key for cloud access
Collection	APARAVI	Cloud collection name

Pipeline Setup Instructions

Step 1: Create the Pipeline

Start with Web Hook

Add the Web Hook connector as your source

Add Data Parser

Connect Web Hook’s Data output to Data-Parser

Add Code Preprocessor

Connect Web Hook’s Text output to Preprocessor-Code

Connect Parser to Preprocessor

Link Data-Parser output to Preprocessor-Code

Add Embedding

Connect Preprocessor-Code to Embedding-Transformer

Add Vector Store

Connect Embedding-Transformer to Qdrant

Step 2: Configure Components

Preprocessor – Code Configuration

Code Splitter Profile: Choose “Auto” for automatic language detection
Maximum String Length: Set to 512 (or adjust based on your needs)

Embedding Configuration

Model: Select “miniLM” for general-purpose code embedding
Alternative: Use “mpnet” for higher quality embeddings

Qdrant Configuration

For Cloud Setup:

Set Type to “Cloud”
Enter your Qdrant Cloud host URL
Provide your API key
Use default collection name “APARAVI”

Step 3: Run and Test

Start the Pipeline: Run your configured pipeline
Get Webhook URL: Copy the webhook URL from the Project Log
Test with Talend API Tester:
- Method: PUT
- URL: Your webhook URL
- Query Parameters: Add type and token
- Headers: Content-Type (auto-set), Authorization (your API key)
- Body: Upload a code file
Verify Success: Look for “200 OK” response
Check Qdrant: View code chunks as points in your vector store

How to Use the Pipeline

Prepare Your Code Files

Ensure your code files have proper extensions for language detection:

.py for Python
.js for JavaScript
.ts for TypeScript
.c, .cpp, .h for C/C++

Upload via API

Use the webhook URL to upload your code files. The system will:

Parse the uploaded file
Detect the programming language
Extract functions, classes, and comments
Create vector embeddings
Store in Qdrant for search

Query Your Codebase

Once processed, you can search your code using:

Semantic search queries
Function descriptions
Code patterns
Natural language questions

Use Cases

🔍 Code Search and Discovery

Semantic Code Search: Find code by meaning, not just keywords
Function Discovery: Locate similar functions across codebase
Pattern Recognition: Identify common coding patterns

📚 Code Analysis and Documentation

Code Summarization: Generate summaries of code functions
Documentation Generation: Auto-generate code documentation
Code Review Assistance: Identify potential issues or improvements

🤖 AI-Powered Development

Code Completion: Build intelligent code completion systems
Refactoring Suggestions: Identify code that could be improved
Knowledge Base: Create searchable code knowledge bases

Best Practices

Code Preprocessing

Language Detection: Use “Auto” for mixed-language codebases
Chunk Size: Balance between 256-1024 characters for optimal embedding
File Types: Support all major programming languages

Embedding Selection

miniLM: Best for general-purpose code embedding
mpnet: Use for higher quality when processing time allows
Custom Models: Consider domain-specific models for specialized code

Vector Store Management

Collection Organization: Use separate collections for different projects
Score Thresholds: Start with 0.7-0.8 for strict matching
Regular Updates: Re-index codebase after significant changes

Troubleshooting

Common Issues

Language Detection: Ensure file extensions match expected languages
Chunk Size: Adjust maximum string length if chunks are too large/small
Qdrant Connection: Verify host, port, and API key for cloud instances
Embedding Quality: Try different models if search results are poor

Performance Optimization

Batch Processing: Process multiple files in sequence
Model Selection: Choose faster models for large codebases
Vector Storage: Use cloud Qdrant for better scalability

Advanced Features

Multi-Language Support

The pipeline automatically detects and processes code in multiple programming languages:

🔧 Language Processing

C/C++: Full syntax parsing and function extraction
Python: Class, function, and comment extraction
JavaScript/TypeScript: Modern ES6+ syntax support
Auto-Detection: Intelligent language identification

Semantic Search Capabilities

🔍 Search Features

Function Similarity: Find functions with similar purposes
Code Pattern Matching: Identify common coding patterns
Documentation Search: Search through code comments and docstrings
Cross-Language Search: Find similar patterns across different languages

Transform Your Codebase: This pipeline transforms your codebase into a searchable, AI-ready knowledge base that enables intelligent code discovery and analysis.