- Fix Windows process termination using taskkill instead of killpg
- Add Windows-compatible npm detection and commands
- Fix Next.js SSR error with dynamic imports
- Improve cloud API error handling for local mode
- Add frontend connection retry mechanism with 5 retries
- Fix string formatting in prompt loading
- Add cognee CLI entry point alongside cognee-cli
Resolves Windows compatibility issues and improves error handling
across CLI and frontend components.
Updated installation instructions and usage examples in README.
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output format is same as the othere adpaters get_filtered_graph_data.
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Only minimal changes are made in the file for adapter for kuzu database.
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#1436
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Co-authored-by: hajdul88 <52442977+hajdul88@users.noreply.github.com>
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## Description
Temporary fix for MCP on main
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## Changes Made
Update Cognee version for MCP
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## DCO Affirmation
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## Description
The async LLM client fails with non-reasoning models like gpt-4o with
the error: `Completion error: litellm.BadRequestError: OpenAIException -
Unrecognized request argument supplied: reasoning_effort`.
This PR add a sets the `drop_params` config to True to drop all
unsupported model configs instead of catching with a try-except block.
Additionally, the `reasoning_effort` wasn't being set for the sync
client. It adds the parameter for both async & sync for consistency.
## Type of Change
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- [ ] Other (please specify):
## Changes Made
- Set `litellm.drop_params=True` to auto-drop unsupported parameters
- Changed `reasoning_effort` from `extra_body` to a direct parameter
- Add `reasoning_effort` to the sync client
- Removed redundant retry for `reasoning_effort`
## Testing
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feature works
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- [ ] All new and existing tests pass
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submitted already
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## DCO Affirmation
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## Description
Added auto tagging so that core team PRs always get the same lable
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## Changes Made
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## Description
Refactor BAML and LLMGateway to reduce code duplication and allow
dynamic response model generation for BAML
## DCO Affirmation
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the terms of the Topoteretes Developer Certificate of Origin.
- Set litellm.drop_params=True to auto-drop unsupported parameters
- Changed reasoning_effort from extra_body to direct parameter
- Added reasoning_effort to both async and sync methods
- Removed redundant retry logic for unsupported parameters
- Ensures compatibility with models that don't support reasoning_effort
This fixes errors when using models that don't support the reasoning_effort
parameter while maintaining the optimization for models that do support it.
- Added AdvancedPdfLoader class for enhanced PDF processing using the unstructured library.
- Integrated fallback mechanism to PyPdfLoader in case of unstructured library import failure or exceptions.
- Updated supported loaders to include AdvancedPdfLoader.
- Added unit tests for AdvancedPdfLoader to ensure functionality and error handling.
- Updated poetry.lock and pyproject.toml to include new dependencies and versions.
Signed-off-by: EricXiao <taoiaox@gmail.com>
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I Implemented Lexical Chunk Retriever In the LexicalRetriever class is
Inherite the BaseRetriever and The DocumentChunk are lazy loaded when
first time query is made because it save time during object
initialization
and the function get_context and the get_completion are Implemented same
as the ChunksRetriever the only diffrence is that the DocumentChunk are
converted to match the output type of the ChunksRetriever using function
get_own_properties in the utils.
## Type of Change
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- Added LexicalRetriever base class with customizable tokenizer & scorer
- Implemented caching of DocumentChunk tokens and payloads
- Added robust initialization with error handling and logging -
Implemented get_context with top_k ranking and optional scores
- Implemented get_completion consistent with BaseRetriever interface
- Added JaccardChunksRetriever demo using set/multiset Jaccard
similarity
- Support for stopwords and multiset frequency-aware similarity -
Integrated logging for initialization, scoring, and retrieval
## Testing
- Manual tests: initialized retriever, retrieved chunks with toy corpus
- Edge cases: empty corpus, empty query, scorer/tokenizer errors
- Verified Jaccard similarity results for single/multiset cases
- Code formatted and linted
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## Related Issues
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Relates to #1392
## Additional Notes
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Int the cognee/modules/chunking/models/DocumentChunk.py
don't remove the optional from is_part_of attributes.
## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin.
---------
Co-authored-by: Andrej Milicevic <milicevicandrej@yahoo.com>
Co-authored-by: Igor Ilic <30923996+dexters1@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
Co-authored-by: Igor Ilic <igorilic03@gmail.com>
Co-authored-by: Vasilije <8619304+Vasilije1990@users.noreply.github.com>
Co-authored-by: Boris <boris@topoteretes.com>
Co-authored-by: lxobr <122801072+lxobr@users.noreply.github.com>
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