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-# IBM DB2 Vector Store Component - Developer Guidelines
-
-## Overview
-
-This document provides comprehensive guidelines for understanding, testing, and working with the IBM DB2 Vector Store component in Langflow.
-
-## Component Architecture
-
-### Files Structure
-
-```
-src/lfx/src/lfx/components/ibm/
-├── db2_vector.py # Main vector store component
-├── db2vs.py # DB2 vector store implementation
-├── db2_security.py # Security validation utilities
-└── __init__.py
-
-src/backend/tests/unit/components/ibm/
-├── test_db2_vector.py # Component integration tests
-├── test_db2vs.py # DB2VS class unit tests
-├── test_db2_security.py # Security validation tests
-└── __init__.py
-
-src/frontend/src/icons/IBM/db2/
-└── DB2.jsx # Component icon
-```
-
-### Component Hierarchy
-
-```
-DB2VectorStoreComponent (db2_vector.py)
- ↓ uses
-DB2VS (db2vs.py) - LangChain VectorStore implementation
- ↓ uses
-Security Validators (db2_security.py)
-```
-
-## Key Features
-
-### 1. Vector Store Operations
-- **Add Documents**: Ingest Data objects with embeddings
-- **Similarity Search**: Find similar documents using vector similarity
-- **MMR Search**: Maximum Marginal Relevance search for diverse results
-- **Similarity Score Threshold**: Filter results by similarity score
-- **Metadata Filtering**: Filter documents by metadata attributes
-
-### 2. Security Features
-- Database name validation (alphanumeric, underscore, max 128 chars)
-- Hostname validation (FQDN or IP address)
-- Port validation (1-65535)
-- SQL identifier validation (prevents injection)
-- Credential redaction in error messages
-
-### 3. Data Ingestion
-- Accepts `Data` objects only (Langflow standard)
-- Automatic metadata filtering for complex types
-- Duplicate detection (optional)
-- Batch processing support
-
-## Testing
-
-### Running Tests
-
-```bash
-# Run all DB2 component tests
-cd src/backend
-uv run pytest tests/unit/components/ibm/ -v
-
-# Run specific test file
-uv run pytest tests/unit/components/ibm/test_db2_vector.py -v
-
-# Run specific test
-uv run pytest tests/unit/components/ibm/test_db2_vector.py::test_similarity_search -v
-
-# Run with coverage
-uv run pytest tests/unit/components/ibm/ --cov=lfx.components.ibm --cov-report=html
-```
-
-### Test Coverage Summary
-
-#### test_db2_vector.py (20 tests)
-- Component metadata validation
-- Build vector store functionality
-- Search operations (similarity, MMR, similarity_score_threshold)
-- Data ingestion with various input types
-- Duplicate handling
-- Metadata filtering with complex data
-- Error handling
-
-#### test_db2vs.py (12 tests)
-- DB2VS class initialization
-- Table existence checking
-- Distance function selection
-- Add texts functionality
-- Delete operations
-- Dimension validation
-
-#### test_db2_security.py (9 tests)
-- Database name validation
-- Hostname validation
-- Port validation
-- SQL identifier validation
-- Error message sanitization
-
-**Total: 41 tests (38 passing, 3 skipped)**
-
-### Test Patterns
-
-#### 1. Component Testing Pattern
-```python
-def test_component_feature():
- # Arrange: Create component with mocked dependencies
- component = DB2VectorStoreComponent()
- component.embedding = MagicMock()
-
- # Act: Execute component method
- result = component.build_vector_store()
-
- # Assert: Verify behavior
- assert result is not None
-```
-
-#### 2. Integration Testing Pattern
-```python
-@patch("lfx.components.ibm.db2_vector.DB2VS")
-def test_integration_feature(mock_db2vs):
- # Setup mocks
- mock_instance = MagicMock()
- mock_db2vs.return_value = mock_instance
-
- # Test integration
- component = DB2VectorStoreComponent()
- component.build_vector_store()
-
- # Verify integration
- mock_db2vs.assert_called_once()
-```
-
-## Component Usage
-
-### Basic Usage
-
-```python
-from lfx.components.ibm import DB2VectorStoreComponent
-from langflow.schema import Data
-
-# Initialize component
-db2_component = DB2VectorStoreComponent()
-
-# Configure connection
-db2_component.database = "TESTDB"
-db2_component.hostname = "localhost"
-db2_component.port = 50000
-db2_component.username = "db2user"
-db2_component.password = "password"
-db2_component.collection_name = "my_vectors"
-
-# Set embedding model
-db2_component.embedding = embedding_model
-
-# Ingest data
-data = [
- Data(text="Document 1", metadata={"source": "file1.txt"}),
- Data(text="Document 2", metadata={"source": "file2.txt"})
-]
-db2_component.ingest_data = data
-
-# Build vector store
-vector_store = db2_component.build_vector_store()
-
-# Search
-db2_component.search_query = "search term"
-results = db2_component.search_documents()
-```
-
-### Search Types
-
-#### 1. Similarity Search
-```python
-db2_component.search_type = "Similarity"
-db2_component.number_of_results = 5
-results = db2_component.search_documents()
-```
-
-#### 2. MMR Search
-```python
-db2_component.search_type = "MMR"
-db2_component.number_of_results = 5
-results = db2_component.search_documents()
-```
-
-#### 3. Similarity Score Threshold
-```python
-db2_component.search_type = "Similarity score threshold"
-db2_component.search_score_threshold = 0.7
-results = db2_component.search_documents()
-```
-
-## Development Guidelines
-
-### Adding New Features
-
-1. **Update Component Class** (`db2_vector.py`)
- - Add input fields if needed
- - Implement feature logic
- - Update docstrings
-
-2. **Add Tests** (`test_db2_vector.py`)
- - Unit tests for new methods
- - Integration tests for workflows
- - Edge case coverage
-
-3. **Update Security** (if needed)
- - Add validators in `db2_security.py`
- - Add tests in `test_db2_security.py`
-
-### Code Style
-
-- Follow Langflow component patterns
-- Use type hints
-- Add comprehensive docstrings
-- Handle errors gracefully
-- Log important operations
-
-### Testing Requirements
-
-- All new features must have tests
-- Maintain >80% code coverage
-- Test both success and failure paths
-- Mock external dependencies (DB2, embeddings)
-- Use fixtures for common test data
-
-## Common Issues & Solutions
-
-### Issue 1: Import Errors
-**Problem**: `ModuleNotFoundError: No module named 'ibm_db'`
-
-**Solution**:
-```bash
-pip install ibm-db ibm-db-dbi
-```
-
-### Issue 2: Connection Failures
-**Problem**: Cannot connect to DB2 database
-
-**Solution**:
-- Verify hostname and port
-- Check credentials
-- Ensure DB2 server is running
-- Check firewall rules
-
-### Issue 3: Test Failures
-**Problem**: Tests fail with "No module named 'lfx'"
-
-**Solution**:
-```bash
-cd src/lfx
-uv sync
-uv run pytest
-```
-
-### Issue 4: Dimension Mismatch
-**Problem**: `ValueError: Embedding dimension mismatch`
-
-**Solution**:
-- Ensure embedding model dimension matches table dimension
-- Drop and recreate table if dimension changed
-- Use consistent embedding model
-
-## Performance Considerations
-
-### Indexing
-- DB2 automatically creates vector indexes
-- Index creation happens on first insert
-- Large datasets may take time to index
-
-### Batch Operations
-- Use batch inserts for large datasets
-- Recommended batch size: 100-1000 documents
-- Monitor memory usage
-
-### Search Optimization
-- Use appropriate search type for use case
-- Limit number of results
-- Use metadata filtering to reduce search space
-
-## Security Best Practices
-
-1. **Credentials Management**
- - Never hardcode credentials
- - Use environment variables or secrets management
- - Rotate credentials regularly
-
-2. **Input Validation**
- - All inputs are validated before use
- - SQL injection prevention built-in
- - Error messages sanitize sensitive data
-
-3. **Connection Security**
- - Use SSL/TLS for production
- - Implement connection pooling
- - Set appropriate timeouts
-
-## Troubleshooting
-
-### Enable Debug Logging
-```python
-import logging
-logging.basicConfig(level=logging.DEBUG)
-```
-
-### Check Component Status
-```python
-print(component.status) # Shows current operation status
-```
-
-### Verify Vector Store
-```python
-vector_store = component.build_vector_store()
-print(f"Collection: {vector_store.collection_name}")
-print(f"Dimension: {vector_store.dimension}")
-```
-
-## Contributing
-
-### Before Submitting PR
-
-1. Run all tests: `make unit_tests`
-2. Check code style: `make format_backend && make lint`
-3. Update documentation if needed
-4. Add tests for new features
-5. Ensure all tests pass
-
-### PR Guidelines
-
-- Follow semantic commit conventions
-- Reference related issues
-- Provide clear description
-- Include test results
-- Update CHANGELOG if applicable
-
-## Resources
-
-- [IBM DB2 Documentation](https://www.ibm.com/docs/en/db2)
-- [LangChain VectorStore Guide](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
-- [Langflow Component Development](https://docs.langflow.org/)
-
-## Support
-
-For issues or questions:
-1. Check this documentation
-2. Review existing tests for examples
-3. Check Langflow documentation
-4. Open an issue on GitHub
-
----
-
-**Last Updated**: 2026-05-20
-**Component Version**: 1.0.0
-**Langflow Version**: Compatible with 1.x
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diff --git a/docs/docs/Components/bundles-db2.mdx b/docs/docs/Components/bundles-db2.mdx
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+---
+title: IBM Db2
+slug: /bundles-db2
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **IBM Db2** bundle.
+
+## IBM Db2 Vector Store
+
+You can use the **IBM Db2 Vector Store** component to read and write to an IBM Db2 database using an instance of `DB2VS` vector store.
+Includes support for remote Db2 instances with enterprise-grade security and performance.
+
+
+About vector store instances
+
+
+
+
+
+When writing, the component can create a new table at the specified location.
+
+:::tip
+IBM Db2 Vector Store provides enterprise-grade vector search capabilities with built-in security validation and support for multiple distance strategies.
+:::
+
+
+
+### Use the IBM Db2 Vector Store component in a flow
+
+The following example flow uses one **IBM Db2 Vector Store** component for both reads and writes:
+
+
+
+* When writing, it splits `JSON` from a [**URL** component](/url) into chunks, computes embeddings with attached **Embedding Model** component, and then loads the chunks and embeddings into the Db2 vector store.
+To trigger writes, click **Run component** on the **IBM Db2 Vector Store** component.
+
+* When reading, it uses chat input to perform a similarity search on the vector store, and then print the search results to the chat.
+To trigger reads, open the **Playground** and enter a chat message.
+
+After running the flow once, you can click **Inspect Output** on each component to understand how the data transformed as it passed from component to component.
+
+### IBM Db2 Vector Store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the provider's documentation or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|------|------|-------------|
+| **Table Name** (`collection_name`) | String | Input parameter. The name of your Db2 table to store vectors. Default: `LANGFLOW_VECTORS`. The table will be created if it doesn't exist. |
+| **Database Name** (`database`) | String | Input parameter. Name of the Db2 database. Use a Generic-typed global variable or direct input. Credential-typed variables are not allowed for database names. |
+| **Hostname** (`hostname`) | String | Input parameter. Db2 server hostname or IP address. Use a Generic-typed global variable or direct input. |
+| **Port** (`port`) | Integer | Input parameter. Db2 server port. Default: `50000`. |
+| **Username** (`username`) | String | Input parameter. Db2 database username. Use a Generic-typed global variable or direct input. |
+| **Password** (`password`) | String | Input parameter. Db2 database password. This should use a Credential-typed global variable for security. |
+| **Ingest Data** (`ingest_data`) | JSON or Table | Input parameter. `JSON` or `Table` input containing the records to write to the vector store. Only relevant for writes. |
+| **Search Query** (`search_query`) | String | Input parameter. The query to use for vector search. Only relevant for reads. |
+| **Cache Vector Store** (`should_cache_vector_store`) | Boolean | Input parameter. If `true`, the component caches the vector store in memory for faster reads. Default: Enabled (`true`). |
+| **Embedding** (`embedding`) | Embeddings | Input parameter. The embedding function to use for the vector store. You must attach an **Embedding Model** component to generate embeddings for your data. |
+| **Allow Duplicates** (`allow_duplicates`) | Boolean | Input parameter. If `true` (default), writes don't check for existing duplicates in the collection, allowing you to store multiple copies of the same content. If `false`, writes won't add documents that match existing documents already present in the collection. Only relevant for writes.|
+| **Search Type** (`search_type`) | String | Input parameter. The type of search to perform: `Similarity`, `MMR`, or `similarity_score_threshold`. Only relevant for reads. |
+| **Number of Results** (`number_of_results`) | Integer | Input parameter. The number of search results to return. Default: `4`. Only relevant for reads. |
+| **Distance Strategy** (`distance_strategy`) | String | Input parameter. Distance calculation strategy: `COSINE`, `EUCLIDEAN_DISTANCE`, or `DOT_PRODUCT`. Default: `COSINE`. |
+
+## See also
+
+* [**Local DB** component](/components-bundle-components#vector-stores-bundle)
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