MAJOR CHANGES:
- Replace complex uv-based Dockerfile with simple pip-only approach
- Add requirements.txt for standard Python dependency management
- Remove all uv commands that might trigger cache mount behavior
- Add .dockerignore for clean Railway build context
- Add nixpacks.toml to force Dockerfile usage (disable auto-detection)
- Update railway.json with explicit Docker configuration
PROBLEM SOLVED:
Railway 'Cache mount ID is not prefixed with cache key' error should be resolved
by eliminating all potential sources of cache mount directives.
DEPLOYMENT STRATEGY:
- Single-stage Docker build using standard pip
- Install graphiti-core from source with 'pip install .'
- Install MCP dependencies with 'pip install -r requirements.txt'
- No complex build tools or caching mechanisms
- Explicit Railway Docker configuration
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Co-Authored-By: Claude <noreply@anthropic.com>
The mcp_server/Dockerfile contained --mount=type=cache directives that Railway doesn't support.
Railway will now use the root Dockerfile which is optimized for Railway deployment without cache mounts.
Fixes: Cache mount ID is not prefixed with cache key error
🚀 Generated with Claude Code (https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix Docker cache mount issues that caused Railway build failures
- Add port argument support to MCP server for Railway compatibility
- Create Railway-optimized Dockerfile without cache mounts
- Add railway.json configuration for proper deployment
- Create comprehensive deployment and ChatGPT integration guides
- Add environment variable templates for Railway deployment
- Support Railway's PORT environment variable handling
- Ready for ChatGPT MCP SSE integration
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Co-Authored-By: Claude <noreply@anthropic.com>
Add important notice about Azure OpenAI v1 API opt-in requirement for structured outputs.
Without this opt-in, users encounter 404 errors when using client.beta.chat.completions.parse().
Includes link to Microsoft's official documentation on the v1 API migration.
Fixes ZEPAI-1634
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Co-authored-by: Claude <noreply@anthropic.com>
- Replace OpenAIClient with OpenAIGenericClient in Ollama documentation
- Add bash code block formatting for model installation commands
- Update API key placeholder from 'abc' to 'ollama' for clarity
- Add comment clarifying Ollama's OpenAI-compatible endpoint
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: Daniel Chalef <danielchalef@users.noreply.github.com>
* Rebased Neptune changes based on significant rework done
* Updated the README documentation
* Fixed linting and formatting
* Update README.md
Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
* Update graphiti_core/driver/neptune_driver.py
Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
* Update README.md
Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
* Addressed feedback from code review
* Updated the README documentation for clarity
* Updated the README and neptune_driver based on PR feedback
* Update node_db_queries.py
* bug: Fixed issue with missing call to create indicies for Neptune and added quickstart example
* chore: added pyright to ignore the attribute not in GrapHDriver
* Fixed quickstart with feedback from automated PR
---------
Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
Co-authored-by: Preston Rasmussen <109292228+prasmussen15@users.noreply.github.com>
feat: Add GitHub AI Moderator workflow
Add simple workflow configuration for GitHub's AI Moderator action to automatically detect spam in issues and comments.
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Co-authored-by: Claude <noreply@anthropic.com>
* gpt-5-mini and gpt-5-nano default
* bump version
* remove unused imports
* linter
* update
* disable neptune errors while we get a fixture in place
* update pyright
* revert non-structured completions
* fix typo
fix: respect search_methods configuration in edge_search and node_search
The search functions were ignoring the configured search_methods and
always executing all three methods (BM25, cosine similarity, and BFS).
This was causing unnecessary performance overhead - running 3x the
required computations when users only wanted specific search methods.
Now the functions build search tasks dynamically based on what's
actually configured in search_methods, only executing the requested
search algorithms.
This should significantly improve performance when users need only
specific search methods. Would appreciate your review on this approach\!
Fixes#788
