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* feat(ibm): add DB2 Vector Store component Add comprehensive IBM DB2 Vector Store integration for Langflow. Components Added: - DB2VectorStoreComponent: Main vector store component - DB2VS: LangChain-compatible vector store implementation - DB2 security validators: Input validation and sanitization Features: - Vector similarity search (Similarity, MMR, Similarity Score Threshold) - Secure connection handling with input validation - Metadata filtering for complex data types - Duplicate detection support - Batch document ingestion Test Coverage: - 41 unit tests (38 passing, 3 skipped) - Component integration tests - Security validation tests - DB2VS class tests Documentation: - Comprehensive developer guidelines * refactor(db2): simplify component to match Chroma patterns - Remove complex data ingestion (CSV, DataFrame, dict, Message handling) - Add _add_documents_to_vector_store() method matching Chroma - Add similarity_score_threshold search type - Simplify data ingestion from 220 to 45 lines (80% reduction) - Add 5 new tests (similarity_search, mmr_search, search_with_different_types, duplicate_handling, metadata_filtering) - All 41 tests passing (38 passed, 3 skipped) BREAKING CHANGE: Component now only accepts Data objects for ingestion. * ci: fix CI failures - update component index and starter projects - Regenerate component_index.json with DB2 component - Update all starter projects with new component registry - Fix ruff formatting issues * ci: trigger CI re-run to fix merge issue * ci: add merge conflict resolution for component_index.json in autofix workflow Handle merge conflicts in the Update Component Index job by: - Automatically resolving component_index.json conflicts using --theirs strategy - Failing if non-generated files have conflicts (requires manual resolution) - Completing the merge after resolving generated file conflicts This prevents the workflow from failing when component_index.json has conflicts during PR updates, as this file is auto-generated and can be safely regenerated. * fix: Remove DB2SQLComponent reference from IBM components - Removed DB2SQLComponent from __init__.py as the db2_sql.py module doesn't exist - This fixes the test_all_modules_importable test failure - Fixes CI failure in Unit Tests - Python 3.14 - Group 5 * revert: Remove unrelated starter project JSON changes * revert: Remove unrelated CI workflow and formatting changes * docs: add DB2 Vector Store .mdx documentation following Chroma DB structure * fix: address CodeRabbit review comments - add __init__.py docstring and implement score threshold filtering - Added docstring to src/backend/tests/unit/components/__init__.py to fix Ruff INP001 error - Added FloatInput score_threshold parameter (default 0.5) to DB2 Vector Store component - Implemented threshold filtering in similarity_score_threshold search mode - Filters results to only include documents with relevance scores >= threshold * chore: remove auto-generated and unnecessary files from PR - Remove uv.lock (dependency lock file - auto-generated) - Remove src/frontend/package-lock.json (frontend lock file - auto-generated) - Remove src/lfx/src/lfx/_assets/component_index.json (auto-generated by CI) - Remove .secrets.baseline (security baseline - auto-generated) - Remove starter_projects JSON (auto-generated example) - Remove tweaks_builder.py (unrelated test helper) Per DEVELOPMENT.md guidelines, these files should not be committed by contributors. * chore: restore auto-generated files to keep PR focused on DB2 component * fix(ci): update auto-generated files and fix docs build - Update component index with new IBM DB2 components (363 components, 97 modules) - Update frontend package-lock.json - Fix docs build by removing reference to missing image in bundles-db2.mdx Fixes CI failures: - Update Component Index - Update Starter Projects - Test Docs Build * feat(db2): add SSL/TLS support to DB2 Vector Store component - Add SSL/TLS encryption support for DB2 database connections - Add SSL certificate validation and download functionality - Support local certificate files (.crt, .pem, .cer) and URLs - Add optional certificate password support for encrypted keystores - Implement automatic cleanup of temporary downloaded certificates - Add comprehensive error handling and logging for SSL connections - Update component_index.json with new SSL configuration inputs - Update package-lock.json dependencies Security improvements: - Validate certificate paths and file permissions - Support system default CA certificates (recommended for IBM Cloud DB2) - Redact sensitive information in error messages - Clean up temporary files on connection failure This enhancement enables secure encrypted connections to DB2 databases, which is recommended for production environments. * [autofix.ci] apply automated fixes * refactor(db2): minimize metadata storage in DB2 vector store - Clear metadata before storage to reduce unnecessary data (file_path, filename, etc.) - Return only text content during retrieval for cleaner results - Optimize list comprehension for better performance (PERF401, RET504) * test: remove skipped tests from DB2 vector store test suite - Removed version compatibility tests for versions where component didn't exist - Changed file_names_mapping to return empty list for new component - Overrode base class version tests to prevent skips - Fixed linting issues (hardcoded passwords, nested with statements) - All 40 tests now pass with 0 skipped tests * fix(tests): update DB2 vector store test description format Fixes test_component_metadata assertion to match the actual component description format. The test was failing due to line wrapping differences in the multi-line description string. Fixes CI failure in PR #13163 * [autofix.ci] apply automated fixes * chore: trigger CI re-run to resolve flaky test failures * fix(tests): mark OpenAI-dependent test as api_key_required to prevent CI failures * restructure as new bundle package * [autofix.ci] apply automated fixes * fix(ibm): improve DB2 Vector Store component reliability and validation * fix: implement SSL/TLS toggle functionality for DB2 Vector Store - Add update_build_config method to dynamically show/hide SSL certificate fields - SSL certificate path and password fields now only visible when use_ssl is enabled - Improves UX by hiding irrelevant fields when SSL is disabled - Update component_index.json with new component configuration - All 38 existing tests pass --------- Co-authored-by: Dhruv Chaturvedi <dhruv_insights@mac.lkw-in.ibm.com> Co-authored-by: Dhruv Chaturvedi <dhruv_insights@Dhruvs-MacBook-Pro.local> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: priyanshu-krishnan1 <priyanshu.krishnan1@ibm.com> Co-authored-by: Eric Hare <ericrhare@gmail.com>
Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.
✨ Highlight features
- Visual builder interface to quickly get started and iterate.
- Source code access lets you customize any component using Python.
- Interactive playground to immediately test and refine your flows with step-by-step control.
- Multi-agent orchestration with conversation management and retrieval.
- Deploy as an API or export as JSON for Python apps.
- Deploy as an MCP server and turn your flows into tools for MCP clients.
- Observability with LangSmith, LangFuse and other integrations.
- Enterprise-ready security and scalability.
🖥️ Langflow Desktop
Langflow Desktop is the easiest way to get started with Langflow. All dependencies are included, so you don't need to manage Python environments or install packages manually. Available for Windows and macOS.
⚡️ Quickstart
Install locally (recommended)
Requires Python 3.10–3.13 and uv (recommended package manager).
Install
From a fresh directory, run:
uv pip install langflow -U
The latest Langflow package is installed. For more information, see Install and run the Langflow OSS Python package.
Run
To start Langflow, run:
uv run langflow run
Langflow starts at http://127.0.0.1:7860.
That's it! You're ready to build with Langflow! 🎉
📦 Other install options
Run from source
If you've cloned this repository and want to contribute, run this command from the repository root:
make run_cli
For more information, see DEVELOPMENT.md.
Docker
Start a Langflow container with default settings:
docker run -p 7860:7860 langflowai/langflow:latest
Langflow is available at http://localhost:7860/. For configuration options, see the Docker deployment guide.
🛡️ Security
For security information, see our Security Policy.
🚀 Deployment
Langflow is completely open source and you can deploy it to all major deployment clouds. To learn how to deploy Langflow, see our Langflow deployment guides.
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👋 Contribute
We welcome contributions from developers of all levels. If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.