From 258c9e9a95dd0ad07bc5aaa75fef16fe3bfeed67 Mon Sep 17 00:00:00 2001 From: Kylin Date: Thu, 12 Sep 2024 09:05:29 +0800 Subject: [PATCH] Merge official graphrag 0.3.4 (#25) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Bump micromatch from 4.0.5 to 4.0.8 in /docsite (#1013) Bumps [micromatch](https://github.com/micromatch/micromatch) from 4.0.5 to 4.0.8. - [Release notes](https://github.com/micromatch/micromatch/releases) - [Changelog](https://github.com/micromatch/micromatch/blob/4.0.8/CHANGELOG.md) - [Commits](https://github.com/micromatch/micromatch/compare/4.0.5...4.0.8) --- updated-dependencies: - dependency-name: micromatch dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump ruff from 0.5.7 to 0.6.2 (#1014) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.7 to 0.6.2. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.5.7...0.6.2) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Alonso Guevara * Ensure entity types to be str in prompt tune (#1015) * Fix weight casting during graph extraction (#1016) * Fix weight casting during graph extraction * Format * Format * Update developer guide (#1029) * Add missing config parameter for prompt tuning docs (#1017) * Improve search type hint (#1031) * update get_local_search_engine and get_global_search_engine return annotation * add semversioner file * reorder imports * fix pyright errors * revert change and ignore previous pyright error --------- Co-authored-by: wanhua.gu Co-authored-by: longyunfeigu <2514553187@qq.com> Co-authored-by: Alonso Guevara * Patch "past" dependency issues (#1033) * Patch "past" dependency issues * Semver * Release v0.3.2 (#1034) * Update VectorStoreSearchResult score value range (#937) update VectorStoreSearchResult score comment Co-authored-by: wanhua.gu Co-authored-by: Alonso Guevara * Add source URL to the package (#927) Co-authored-by: Josh Bradley Co-authored-by: Alonso Guevara * Fix/text unit code cleanup (#1040) * Optimized _build_text_unit_context function for improved time and space complexity Refactored the _build_text_unit_context function to enhance its performance and efficiency. Key optimizations include: 1. Set for Text Unit IDs: Replaced list-based membership checks with a set (text_unit_ids_set) to achieve constant-time complexity for membership checks, reducing overall time complexity. 2. Direct Attribute Removal: Utilized pop with a default value (None) to directly remove attributes entity_order and num_relationships from text units, minimizing overhead and avoiding potential KeyError. 3. Default Dictionary for Entity Orders: Implemented defaultdict for managing entity orders, simplifying the ranking process and improving readability. These improvements result in a more efficient function with better performance, especially when handling large datasets or numerous selected entities. The refactoring ensures that the core functionality remains unchanged while enhancing both time and space complexity. * Format * Ruff fixes * semver --------- Co-authored-by: arjun-234 Co-authored-by: Arjun D. <103405661+arjun-234@users.noreply.github.com> * Fix INIT_YAML embeddings default settings (#1039) Co-authored-by: Thanh Long Phan Co-authored-by: Alonso Guevara * Bump pytest-asyncio from 0.23.8 to 0.24.0 (#1022) Bumps [pytest-asyncio](https://github.com/pytest-dev/pytest-asyncio) from 0.23.8 to 0.24.0. - [Release notes](https://github.com/pytest-dev/pytest-asyncio/releases) - [Commits](https://github.com/pytest-dev/pytest-asyncio/compare/v0.23.8...v0.24.0) --- updated-dependencies: - dependency-name: pytest-asyncio dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Alonso Guevara * Bump json-repair from 0.26.0 to 0.28.4 (#1044) Bumps [json-repair](https://github.com/mangiucugna/json_repair) from 0.26.0 to 0.28.4. - [Release notes](https://github.com/mangiucugna/json_repair/releases) - [Commits](https://github.com/mangiucugna/json_repair/compare/0.26.0...v0.28.4) --- updated-dependencies: - dependency-name: json-repair dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump lancedb from 0.11.0 to 0.12.0 (#1024) Bumps [lancedb](https://github.com/lancedb/lancedb) from 0.11.0 to 0.12.0. - [Release notes](https://github.com/lancedb/lancedb/releases) - [Changelog](https://github.com/lancedb/lancedb/blob/main/release_process.md) - [Commits](https://github.com/lancedb/lancedb/compare/python-v0.11.0...python-v0.12.0) --- updated-dependencies: - dependency-name: lancedb dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump textual from 0.76.0 to 0.78.0 (#1038) Bumps [textual](https://github.com/Textualize/textual) from 0.76.0 to 0.78.0. - [Release notes](https://github.com/Textualize/textual/releases) - [Changelog](https://github.com/Textualize/textual/blob/main/CHANGELOG.md) - [Commits](https://github.com/Textualize/textual/compare/v0.76.0...v0.78.0) --- updated-dependencies: - dependency-name: textual dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Fix/entity extraction strategy (#1046) * fix strategy config in entity_extraction * update init content --------- Co-authored-by: KylinMountain * fix for issue 515 (#925) * fix for issue 515 * semver impact document --------- Co-authored-by: Kanishk Tyagi Co-authored-by: Alonso Guevara * docs: update manual_prompt_tuning.md (#963) paramater -> parameter Co-authored-by: Alonso Guevara * Update indexer_adapters.py (#895) Update the lines 71 and 72 before: entity_df["community"] = entity_df["community"].fillna(-1) entity_df["community"] = entity_df["community"].astype(int) after: entity_df.loc[:, "community"] = entity_df["community"].fillna(-1) entity_df.loc[:, "community"] = entity_df["community"].astype(int) Co-authored-by: Alonso Guevara * Fix circular dependency on prompt tune api (#1054) * Bump notebook from 7.2.1 to 7.2.2 (#1055) Bumps [notebook](https://github.com/jupyter/notebook) from 7.2.1 to 7.2.2. - [Release notes](https://github.com/jupyter/notebook/releases) - [Changelog](https://github.com/jupyter/notebook/blob/@jupyter-notebook/tree@7.2.2/CHANGELOG.md) - [Commits](https://github.com/jupyter/notebook/compare/@jupyter-notebook/tree@7.2.1...@jupyter-notebook/tree@7.2.2) --- updated-dependencies: - dependency-name: notebook dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Alonso Guevara * Bump jupyterlab from 4.2.4 to 4.2.5 (#1056) Bumps [jupyterlab](https://github.com/jupyterlab/jupyterlab) from 4.2.4 to 4.2.5. - [Release notes](https://github.com/jupyterlab/jupyterlab/releases) - [Changelog](https://github.com/jupyterlab/jupyterlab/blob/@jupyterlab/lsp@4.2.5/CHANGELOG.md) - [Commits](https://github.com/jupyterlab/jupyterlab/compare/@jupyterlab/lsp@4.2.4...@jupyterlab/lsp@4.2.5) --- updated-dependencies: - dependency-name: jupyterlab dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Update Prompt Tuning docs (#1057) * Update Prompt Tuning docs * Semver * Update bash example in docs for prompt tune (#1059) * Semver * Update bash command * Fix img for autotune (#1060) * Fix img for autotune * Add line breaks to tune docs * More line breaks * Fix img width (#1061) * Consistent config load_config (#1065) * Consistent config load_config - Provide a consistent way to load configuration - Resolve potential timestamp directories upfront upon config object creation - Add unit tests for resolving timestamp directories - Resolves #599 - Resolves #1049 * fix formatting issues * remove unnecessary path resolution * fix smoke tests * update prompts to use load_config * Update none checks * Update none checks * Update searching for config method signature * Update unit tests * fix formatting issues * fix setting base_dir to full paths when not using file system. (#1096) * fix setting base_dir to full paths when not using file system. * add general resolve_path * Clean and organize run index code (#1090) * Create entypoint for cli and api (#1067) * Add cli and api entrypoints for update index * Semver * Update docs * Run tests on feature branch main * Better /main handling in tests * Clean and organize run index code * Ruff fix * Pyright fix * Format fixes * Pyright fix * Format * Fix integ tests * Fix ruff * Reorganize and clean up * Load query from blob (#1095) * Moved query loading from file to helper function * added loading parquets from blob to function * resolved adlfs async error * debugging cleanup and small fixes * added connection string support * semversioner and ruff fixes * completed testing for merge with main * more ruff changes * fixed unbound vars warning * rewrote function to use storage utils * removed unused vars --------- Co-authored-by: Kenny Zhang * Update create_pipeline_config.py (#1108) * Update create_pipeline_config.py Order switched to ensure that user settings at runtime take precedence. * Updated semversioner. * release v0.3.3 (#1116) * Deep copy txt units on local search to avoid race conditions (#1118) * Deep copy txt units on local search to avoid race conditions * Format * Fix summarization including empty descriptions (#1124) * Fix summarization including empty descriptions * Update * Release v0.3.4 (#1125) --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Alonso Guevara Co-authored-by: Josh Bradley Co-authored-by: wanhua.gu Co-authored-by: longyunfeigu <2514553187@qq.com> Co-authored-by: Konstantin Gukov Co-authored-by: arjun-234 Co-authored-by: Arjun D. <103405661+arjun-234@users.noreply.github.com> Co-authored-by: TLP <104315397+TLongP@users.noreply.github.com> Co-authored-by: Thanh Long Phan Co-authored-by: fantom845 <77169323+fantom845@users.noreply.github.com> Co-authored-by: Kanishk Tyagi Co-authored-by: Ikko Eltociear Ashimine Co-authored-by: guangxiangdebizi <154864206+guangxiangdebizi@users.noreply.github.com> Co-authored-by: Derek Worthen Co-authored-by: KennyZhang1 <90438893+KennyZhang1@users.noreply.github.com> Co-authored-by: Kenny Zhang Co-authored-by: Doug Orbaker <107270698+dorbaker@users.noreply.github.com> --- .github/workflows/python-ci.yml | 10 +- .../workflows/python-integration-tests.yml | 8 +- .github/workflows/python-notebook-tests.yml | 9 +- .github/workflows/python-smoke-tests.yml | 8 +- .github/workflows/spellcheck.yml | 2 +- .semversioner/0.3.2.json | 42 ++ .semversioner/0.3.3.json | 66 +++ .semversioner/0.3.4.json | 14 + .../patch-20240821135138469990.json | 4 - .../patch-20240821235154401001.json | 4 - CHANGELOG.md | 35 ++ DEVELOPING.md | 12 +- docsite/_includes/page.njk | 4 +- docsite/img/auto-tune-diagram.png | Bin 0 -> 2683906 bytes .../posts/prompt_tuning/auto_prompt_tuning.md | 53 +- .../prompt_tuning/manual_prompt_tuning.md | 2 +- docsite/posts/prompt_tuning/overview.md | 8 +- docsite/yarn.lock | 10 +- graphrag/config/__init__.py | 9 + graphrag/config/config_file_loader.py | 17 +- graphrag/config/create_graphrag_config.py | 1 + graphrag/config/load_config.py | 66 +++ graphrag/config/logging.py | 8 +- ...olve_timestamp_path.py => resolve_path.py} | 41 +- graphrag/index/__main__.py | 24 +- graphrag/index/api.py | 26 +- graphrag/index/cli.py | 55 +-- graphrag/index/create_pipeline_config.py | 2 +- .../graph/extractors/graph/graph_extractor.py | 11 +- .../description_summary_extractor.py | 8 +- graphrag/index/init_content.py | 8 +- graphrag/index/run.py | 465 ------------------ graphrag/index/run/__init__.py | 8 + graphrag/index/run/cache.py | 17 + graphrag/index/run/postprocess.py | 50 ++ graphrag/index/run/profiling.py | 75 +++ graphrag/index/run/run.py | 245 +++++++++ graphrag/index/run/utils.py | 116 +++++ graphrag/index/run/workflow.py | 104 ++++ .../index/verbs/graph/merge/merge_graphs.py | 6 +- graphrag/prompt_tune/api.py | 2 +- graphrag/prompt_tune/cli.py | 9 +- .../generator/entity_extraction_prompt.py | 2 +- .../generator/entity_relationship.py | 4 +- graphrag/prompt_tune/loader/__init__.py | 2 - graphrag/prompt_tune/loader/config.py | 61 --- graphrag/query/api.py | 19 +- graphrag/query/cli.py | 192 +++----- graphrag/query/factories.py | 5 +- graphrag/query/indexer_adapters.py | 4 +- .../local_search/mixed_context.py | 54 +- graphrag/utils/__init__.py | 4 + graphrag/utils/storage.py | 53 ++ graphrag/vector_stores/base.py | 2 +- poetry.lock | 106 ++-- pyproject.toml | 18 +- .../timestamp_dirs/20240812-120000/empty.txt | 0 tests/unit/config/test_resolve_path.py | 42 ++ tests/unit/query/test_infer_data_dir.py | 32 -- 59 files changed, 1347 insertions(+), 917 deletions(-) create mode 100644 .semversioner/0.3.2.json create mode 100644 .semversioner/0.3.3.json create mode 100644 .semversioner/0.3.4.json delete mode 100644 .semversioner/next-release/patch-20240821135138469990.json delete mode 100644 .semversioner/next-release/patch-20240821235154401001.json create mode 100644 docsite/img/auto-tune-diagram.png create mode 100644 graphrag/config/load_config.py rename graphrag/config/{resolve_timestamp_path.py => resolve_path.py} (72%) delete mode 100644 graphrag/index/run.py create mode 100644 graphrag/index/run/__init__.py create mode 100644 graphrag/index/run/cache.py create mode 100644 graphrag/index/run/postprocess.py create mode 100644 graphrag/index/run/profiling.py create mode 100644 graphrag/index/run/run.py create mode 100644 graphrag/index/run/utils.py create mode 100644 graphrag/index/run/workflow.py delete mode 100644 graphrag/prompt_tune/loader/config.py create mode 100644 graphrag/utils/__init__.py create mode 100644 graphrag/utils/storage.py create mode 100644 tests/unit/config/fixtures/timestamp_dirs/20240812-120000/empty.txt create mode 100644 tests/unit/config/test_resolve_path.py delete mode 100644 tests/unit/query/test_infer_data_dir.py diff --git a/.github/workflows/python-ci.yml b/.github/workflows/python-ci.yml index 9529bf3271..c22ea27998 100644 --- a/.github/workflows/python-ci.yml +++ b/.github/workflows/python-ci.yml @@ -1,9 +1,13 @@ name: Python CI on: push: - branches: [main] + branches: + - "**/main" # Matches branches like feature/main + - "main" # Matches the main branch pull_request: - branches: [main] + branches: + - "**/main" + - "main" permissions: contents: read @@ -72,4 +76,4 @@ jobs: - name: Unit Test run: | - poetry run poe test_unit \ No newline at end of file + poetry run poe test_unit diff --git a/.github/workflows/python-integration-tests.yml b/.github/workflows/python-integration-tests.yml index eb58b13995..2dd49ab2a7 100644 --- a/.github/workflows/python-integration-tests.yml +++ b/.github/workflows/python-integration-tests.yml @@ -1,9 +1,13 @@ name: Python Integration Tests on: push: - branches: [main] + branches: + - "**/main" # Matches branches like feature/main + - "main" # Matches the main branch pull_request: - branches: [main] + branches: + - "**/main" + - "main" permissions: contents: read diff --git a/.github/workflows/python-notebook-tests.yml b/.github/workflows/python-notebook-tests.yml index 602410052e..014848874e 100644 --- a/.github/workflows/python-notebook-tests.yml +++ b/.github/workflows/python-notebook-tests.yml @@ -1,9 +1,13 @@ name: Python Notebook Tests on: push: - branches: [main] + branches: + - "**/main" # Matches branches like feature/main + - "main" # Matches the main branch pull_request: - branches: [main] + branches: + - "**/main" + - "main" permissions: contents: read @@ -64,7 +68,6 @@ jobs: poetry run python -m pip install gensim poetry install - - name: Notebook Test run: | poetry run poe test_notebook diff --git a/.github/workflows/python-smoke-tests.yml b/.github/workflows/python-smoke-tests.yml index 45c9ca8e56..0002b6dba2 100644 --- a/.github/workflows/python-smoke-tests.yml +++ b/.github/workflows/python-smoke-tests.yml @@ -1,9 +1,13 @@ name: Python Smoke Tests on: push: - branches: [main] + branches: + - "**/main" # Matches branches like feature/main + - "main" # Matches the main branch pull_request: - branches: [main] + branches: + - "**/main" + - "main" permissions: contents: read diff --git a/.github/workflows/spellcheck.yml b/.github/workflows/spellcheck.yml index bbf2be9dfc..6712d8f2a2 100644 --- a/.github/workflows/spellcheck.yml +++ b/.github/workflows/spellcheck.yml @@ -4,7 +4,7 @@ on: branches: [main] pull_request: paths: - - '**/*' + - "**/*" jobs: spellcheck: runs-on: ubuntu-latest diff --git a/.semversioner/0.3.2.json b/.semversioner/0.3.2.json new file mode 100644 index 0000000000..66791cca78 --- /dev/null +++ b/.semversioner/0.3.2.json @@ -0,0 +1,42 @@ +{ + "changes": [ + { + "description": "Add context data to query API responses.", + "type": "patch" + }, + { + "description": "Add missing config parameter documentation for prompt tuning", + "type": "patch" + }, + { + "description": "Add neo4j community notebook", + "type": "patch" + }, + { + "description": "Ensure entity types to be str when running prompt tuning", + "type": "patch" + }, + { + "description": "Fix weight casting during graph extraction", + "type": "patch" + }, + { + "description": "Patch \"past\" dependency issues", + "type": "patch" + }, + { + "description": "Update developer guide.", + "type": "patch" + }, + { + "description": "Update query type hints.", + "type": "patch" + }, + { + "description": "change-lancedb-placement", + "type": "patch" + } + ], + "created_at": "2024-08-26T23:43:01+00:00", + "version": "0.3.2" +} \ No newline at end of file diff --git a/.semversioner/0.3.3.json b/.semversioner/0.3.3.json new file mode 100644 index 0000000000..99726199c8 --- /dev/null +++ b/.semversioner/0.3.3.json @@ -0,0 +1,66 @@ +{ + "changes": [ + { + "description": "Add entrypoints for incremental indexing", + "type": "patch" + }, + { + "description": "Clean up and organize run index code", + "type": "patch" + }, + { + "description": "Consistent config loading. Resolves #99 and Resolves #1049", + "type": "patch" + }, + { + "description": "Fix circular dependency when running prompt tune api directly", + "type": "patch" + }, + { + "description": "Fix default settings for embedding", + "type": "patch" + }, + { + "description": "Fix img for auto tune", + "type": "patch" + }, + { + "description": "Fix img width", + "type": "patch" + }, + { + "description": "Fixed a bug in prompt tuning process", + "type": "patch" + }, + { + "description": "Refactor text unit build at local search", + "type": "patch" + }, + { + "description": "Update Prompt Tuning docs", + "type": "patch" + }, + { + "description": "Update create_pipeline_config.py", + "type": "patch" + }, + { + "description": "Update prompt tune command in docs", + "type": "patch" + }, + { + "description": "add querying from azure blob storage", + "type": "patch" + }, + { + "description": "fix setting base_dir to full paths when not using file system.", + "type": "patch" + }, + { + "description": "fix strategy config in entity_extraction", + "type": "patch" + } + ], + "created_at": "2024-09-10T19:51:24+00:00", + "version": "0.3.3" +} \ No newline at end of file diff --git a/.semversioner/0.3.4.json b/.semversioner/0.3.4.json new file mode 100644 index 0000000000..1c328d4d23 --- /dev/null +++ b/.semversioner/0.3.4.json @@ -0,0 +1,14 @@ +{ + "changes": [ + { + "description": "Deep copy txt units on local search to avoid race conditions", + "type": "patch" + }, + { + "description": "Fix summarization including empty descriptions", + "type": "patch" + } + ], + "created_at": "2024-09-11T22:31:58+00:00", + "version": "0.3.4" +} \ No newline at end of file diff --git a/.semversioner/next-release/patch-20240821135138469990.json b/.semversioner/next-release/patch-20240821135138469990.json deleted file mode 100644 index f6494e37f8..0000000000 --- a/.semversioner/next-release/patch-20240821135138469990.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "type": "patch", - "description": "change-lancedb-placement" -} diff --git a/.semversioner/next-release/patch-20240821235154401001.json b/.semversioner/next-release/patch-20240821235154401001.json deleted file mode 100644 index b544e564cf..0000000000 --- a/.semversioner/next-release/patch-20240821235154401001.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "type": "patch", - "description": "Add context data to query API responses." -} diff --git a/CHANGELOG.md b/CHANGELOG.md index 0139dcd3fb..87bbb82cde 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,41 @@ # Changelog Note: version releases in the 0.x.y range may introduce breaking changes. +## 0.3.4 + +- patch: Deep copy txt units on local search to avoid race conditions +- patch: Fix summarization including empty descriptions + +## 0.3.3 + +- patch: Add entrypoints for incremental indexing +- patch: Clean up and organize run index code +- patch: Consistent config loading. Resolves #99 and Resolves #1049 +- patch: Fix circular dependency when running prompt tune api directly +- patch: Fix default settings for embedding +- patch: Fix img for auto tune +- patch: Fix img width +- patch: Fixed a bug in prompt tuning process +- patch: Refactor text unit build at local search +- patch: Update Prompt Tuning docs +- patch: Update create_pipeline_config.py +- patch: Update prompt tune command in docs +- patch: add querying from azure blob storage +- patch: fix setting base_dir to full paths when not using file system. +- patch: fix strategy config in entity_extraction + +## 0.3.2 + +- patch: Add context data to query API responses. +- patch: Add missing config parameter documentation for prompt tuning +- patch: Add neo4j community notebook +- patch: Ensure entity types to be str when running prompt tuning +- patch: Fix weight casting during graph extraction +- patch: Patch "past" dependency issues +- patch: Update developer guide. +- patch: Update query type hints. +- patch: change-lancedb-placement + ## 0.3.1 - patch: Add preflight check to check LLM connectivity. diff --git a/DEVELOPING.md b/DEVELOPING.md index dd1763efa9..2bafd9cdcd 100644 --- a/DEVELOPING.md +++ b/DEVELOPING.md @@ -16,7 +16,7 @@ poetry install ``` -## Execute the Indexing Engine +## Executing the Indexing Engine ```sh poetry run poe index <...args> @@ -28,6 +28,13 @@ poetry run poe index <...args> poetry run poe query <...args> ``` +## Versioning + +We use [semversioner](https://github.com/raulgomis/semversioner) to automate and enforce semantic versioning in the release process. Our CI/CD pipeline checks that all PR's include a json file generated by semversioner. When submitting a PR, please run: +```shell +poetry run semversioner add-change -t patch -d "." +``` + # Azurite Some unit and smoke tests use Azurite to emulate Azure resources. This can be started by running: @@ -40,10 +47,9 @@ or by simply running `azurite` in the terminal if already installed globally. Se # Lifecycle Scripts -Our Python package utilizes Poetry to manage dependencies and [poethepoet](https://pypi.org/project/poethepoet/) to manage build scripts. +Our Python package utilizes Poetry to manage dependencies and [poethepoet](https://pypi.org/project/poethepoet/) to manage custom build scripts. Available scripts are: - - `poetry run poe index` - Run the Indexing CLI - `poetry run poe query` - Run the Query CLI - `poetry build` - This invokes `poetry build`, which will build a wheel file and other distributable artifacts. diff --git a/docsite/_includes/page.njk b/docsite/_includes/page.njk index b6f57bc96f..dbdb348898 100644 --- a/docsite/_includes/page.njk +++ b/docsite/_includes/page.njk @@ -107,10 +107,10 @@ title: GraphRAG {{link_to("/posts/prompt_tuning/overview/", "Prompt Tuning")}}
  • - {{link_to("/posts/prompt_tuning/auto_prompt_tuning/", "Automatic Templating")}} + {{link_to("/posts/prompt_tuning/auto_prompt_tuning/", "Auto Tuning")}}
  • - {{link_to("/posts/prompt_tuning/manual_prompt_tuning/", "Manual Prompt Tuning")}} + {{link_to("/posts/prompt_tuning/manual_prompt_tuning/", "Manual Tuning")}}
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+

+Figure 1: Auto Tuning Conceptual Diagram. +

## Prerequisites -Before running the automatic template generation make sure you have already initialized your workspace with the `graphrag.index --init` command. This will create the necessary configuration files and the default prompts. Refer to the [Init Documentation](/posts/config/init) for more information about the initialization process. +Before running auto tuning make sure you have already initialized your workspace with the `graphrag.index --init` command. This will create the necessary configuration files and the default prompts. Refer to the [Init Documentation](/posts/config/init) for more information about the initialization process. ## Usage You can run the main script from the command line with various options: ```bash -python -m graphrag.prompt_tune [--root ROOT] [--domain DOMAIN] [--method METHOD] [--limit LIMIT] [--language LANGUAGE] [--max-tokens MAX_TOKENS] [--chunk-size CHUNK_SIZE] [--no-entity-types] [--output OUTPUT] +python -m graphrag.prompt_tune [--root ROOT] [--domain DOMAIN] [--method METHOD] [--limit LIMIT] [--language LANGUAGE] \ +[--max-tokens MAX_TOKENS] [--chunk-size CHUNK_SIZE] [--n-subset-max N_SUBSET_MAX] [--k K] \ +[--min-examples-required MIN_EXAMPLES_REQUIRED] [--no-entity-types] [--output OUTPUT] ``` ## Command-Line Options +- `--config` (required): The path to the configuration file. This is required to load the data and model settings. + - `--root` (optional): The data project root directory, including the config files (YML, JSON, or .env). Defaults to the current directory. - `--domain` (optional): The domain related to your input data, such as 'space science', 'microbiology', or 'environmental news'. If left empty, the domain will be inferred from the input data. -- `--method` (optional): The method to select documents. Options are all, random, or top. Default is random. +- `--method` (optional): The method to select documents. Options are all, random, auto or top. Default is random. - `--limit` (optional): The limit of text units to load when using random or top selection. Default is 15. @@ -38,6 +49,12 @@ python -m graphrag.prompt_tune [--root ROOT] [--domain DOMAIN] [--method METHOD - `--chunk-size` (optional): The size in tokens to use for generating text units from input documents. Default is 200. +- `--n-subset-max` (optional): The number of text chunks to embed when using auto selection method. Default is 300. + +- `--k` (optional): The number of documents to select when using auto selection method. Default is 15. + +- `--min-examples-required` (optional): The minimum number of examples required for entity extraction prompts. Default is 2. + - `--no-entity-types` (optional): Use untyped entity extraction generation. We recommend using this when your data covers a lot of topics or it is highly randomized. - `--output` (optional): The folder to save the generated prompts. Default is "prompts". @@ -45,30 +62,46 @@ python -m graphrag.prompt_tune [--root ROOT] [--domain DOMAIN] [--method METHOD ## Example Usage ```bash -python -m graphrag.prompt_tune --root /path/to/project --domain "environmental news" --method random --limit 10 --language English --max-tokens 2048 --chunk-size 256 --no-entity-types --output /path/to/output +python -m graphrag.prompt_tune --root /path/to/project --config /path/to/settings.yaml --domain "environmental news" \ +--method random --limit 10 --language English --max-tokens 2048 --chunk-size 256 --min-examples-required 3 \ +--no-entity-types --output /path/to/output ``` or, with minimal configuration (suggested): ```bash -python -m graphrag.prompt_tune --root /path/to/project --no-entity-types +python -m graphrag.prompt_tune --root /path/to/project --config /path/to/settings.yaml --no-entity-types ``` ## Document Selection Methods -The auto template feature ingests the input data and then divides it into text units the size of the chunk size parameter. -After that, it uses one of the following selection methods to pick a sample to work with for template generation: +The auto tuning feature ingests the input data and then divides it into text units the size of the chunk size parameter. +After that, it uses one of the following selection methods to pick a sample to work with for prompt generation: - `random`: Select text units randomly. This is the default and recommended option. - `top`: Select the head n text units. - `all`: Use all text units for the generation. Use only with small datasets; this option is not usually recommended. +- `auto`: Embed text units in a lower-dimensional space and select the k nearest neighbors to the centroid. This is useful when you have a large dataset and want to select a representative sample. ## Modify Env Vars -After running auto-templating, you should modify the following environment variables (or config variables) to pick up the new prompts on your index run. Note: Please make sure to update the correct path to the generated prompts, in this example we are using the default "prompts" path. +After running auto tuning, you should modify the following environment variables (or config variables) to pick up the new prompts on your index run. Note: Please make sure to update the correct path to the generated prompts, in this example we are using the default "prompts" path. - `GRAPHRAG_ENTITY_EXTRACTION_PROMPT_FILE` = "prompts/entity_extraction.txt" - `GRAPHRAG_COMMUNITY_REPORT_PROMPT_FILE` = "prompts/community_report.txt" - `GRAPHRAG_SUMMARIZE_DESCRIPTIONS_PROMPT_FILE` = "prompts/summarize_descriptions.txt" + +or in your yaml config file: + +```yaml +entity_extraction: + prompt: "prompts/entity_extraction.txt" + +summarize_descriptions: + prompt: "prompts/summarize_descriptions.txt" + +community_reports: + prompt: "prompts/community_report.txt" +``` diff --git a/docsite/posts/prompt_tuning/manual_prompt_tuning.md b/docsite/posts/prompt_tuning/manual_prompt_tuning.md index 935840d253..b51b8c950c 100644 --- a/docsite/posts/prompt_tuning/manual_prompt_tuning.md +++ b/docsite/posts/prompt_tuning/manual_prompt_tuning.md @@ -44,7 +44,7 @@ Each of these prompts may be overridden by writing a custom prompt file in plain - **{record_delimiter}** - A delimiter for separating tuple instances. - **{completion_delimiter}** - An indicator for when generation is complete. -Note: there is additional paramater for the `Claim Description` that is used in claim extraction. +Note: there is additional parameter for the `Claim Description` that is used in claim extraction. The default value is `"Any claims or facts that could be relevant to information discovery."` diff --git a/docsite/posts/prompt_tuning/overview.md b/docsite/posts/prompt_tuning/overview.md index f71b167d65..4aae19c88f 100644 --- a/docsite/posts/prompt_tuning/overview.md +++ b/docsite/posts/prompt_tuning/overview.md @@ -17,10 +17,10 @@ The default prompts are the simplest way to get started with the GraphRAG system - [Claim Extraction](http://github.com/microsoft/graphrag/blob/main/graphrag/index/graph/extractors/claims/prompts.py) - [Community Reports](http://github.com/microsoft/graphrag/blob/main/graphrag/index/graph/extractors/community_reports/prompts.py) -## Auto Templating +## Auto Tuning -Auto Templating leverages your input data and LLM interactions to create domain adaptive templates for the generation of the knowledge graph. It is highly encouraged to run it as it will yield better results when executing an Index Run. For more details about how to use it, please refer to the [Auto Templating](/posts/prompt_tuning/auto_prompt_tuning) documentation. +Auto Tuning leverages your input data and LLM interactions to create domain adapted prompts for the generation of the knowledge graph. It is highly encouraged to run it as it will yield better results when executing an Index Run. For more details about how to use it, please refer to the [Auto Tuning](/posts/prompt_tuning/auto_prompt_tuning) documentation. -## Manual Configuration +## Manual Tuning -Manual configuration is an advanced use-case. Most users will want to use the Auto Templating feature instead. Details about how to use manual configuration are available in the [Manual Prompt Configuration](/posts/prompt_tuning/manual_prompt_tuning) documentation. +Manual tuning is an advanced use-case. Most users will want to use the Auto Tuning feature instead. Details about how to use manual configuration are available in the [Manual Tuning](/posts/prompt_tuning/manual_prompt_tuning) documentation. diff --git a/docsite/yarn.lock b/docsite/yarn.lock index ad5b51fa2c..0b44f6c362 100644 --- a/docsite/yarn.lock +++ b/docsite/yarn.lock @@ -512,7 +512,7 @@ __metadata: languageName: node linkType: hard -"braces@npm:^3.0.2, braces@npm:~3.0.2": +"braces@npm:^3.0.3, braces@npm:~3.0.2": version: 3.0.3 resolution: "braces@npm:3.0.3" dependencies: @@ -1677,12 +1677,12 @@ __metadata: linkType: hard "micromatch@npm:^4.0.4, micromatch@npm:^4.0.5": - version: 4.0.5 - resolution: "micromatch@npm:4.0.5" + version: 4.0.8 + resolution: "micromatch@npm:4.0.8" dependencies: - braces: "npm:^3.0.2" + braces: "npm:^3.0.3" picomatch: "npm:^2.3.1" - checksum: a749888789fc15cac0e03273844dbd749f9f8e8d64e70c564bcf06a033129554c789bb9e30d7566d7ff6596611a08e58ac12cf2a05f6e3c9c47c50c4c7e12fa2 + checksum: 6bf2a01672e7965eb9941d1f02044fad2bd12486b5553dc1116ff24c09a8723157601dc992e74c911d896175918448762df3b3fd0a6b61037dd1a9766ddfbf58 languageName: node linkType: hard diff --git a/graphrag/config/__init__.py b/graphrag/config/__init__.py index 118018a98f..fae2ba3a12 100644 --- a/graphrag/config/__init__.py +++ b/graphrag/config/__init__.py @@ -3,6 +3,7 @@ """The Indexing Engine default config package root.""" +from .config_file_loader import load_config_from_file, search_for_config_in_root_dir from .create_graphrag_config import ( create_graphrag_config, ) @@ -42,6 +43,8 @@ TextEmbeddingConfigInput, UmapConfigInput, ) +from .load_config import load_config +from .logging import enable_logging_with_config from .models import ( CacheConfig, ChunkingConfig, @@ -65,6 +68,7 @@ UmapConfig, ) from .read_dotenv import read_dotenv +from .resolve_path import resolve_path __all__ = [ "ApiKeyMissingError", @@ -119,5 +123,10 @@ "UmapConfig", "UmapConfigInput", "create_graphrag_config", + "enable_logging_with_config", + "load_config", + "load_config_from_file", "read_dotenv", + "resolve_path", + "search_for_config_in_root_dir", ] diff --git a/graphrag/config/config_file_loader.py b/graphrag/config/config_file_loader.py index 3f045cdc41..667fbe8807 100644 --- a/graphrag/config/config_file_loader.py +++ b/graphrag/config/config_file_loader.py @@ -9,13 +9,13 @@ import yaml -from . import create_graphrag_config +from .create_graphrag_config import create_graphrag_config from .models.graph_rag_config import GraphRagConfig _default_config_files = ["settings.yaml", "settings.yml", "settings.json"] -def resolve_config_path_with_root(root: str | Path) -> Path: +def search_for_config_in_root_dir(root: str | Path) -> Path | None: """Resolve the config path from the given root directory. Parameters @@ -26,13 +26,9 @@ def resolve_config_path_with_root(root: str | Path) -> Path: Returns ------- - Path - The resolved config file path. - - Raises - ------ - FileNotFoundError - If the config file is not found or cannot be resolved for the directory. + Path | None + returns a Path if there is a config in the root directory + Otherwise returns None. """ root = Path(root) @@ -44,8 +40,7 @@ def resolve_config_path_with_root(root: str | Path) -> Path: if (root / file).is_file(): return root / file - msg = f"Unable to resolve config file for parent directory: {root}" - raise FileNotFoundError(msg) + return None class ConfigFileLoader(ABC): diff --git a/graphrag/config/create_graphrag_config.py b/graphrag/config/create_graphrag_config.py index 2a0f72daab..a0e78952b6 100644 --- a/graphrag/config/create_graphrag_config.py +++ b/graphrag/config/create_graphrag_config.py @@ -429,6 +429,7 @@ def hydrate_parallelization_params( or defs.ENTITY_EXTRACTION_ENTITY_TYPES, max_gleanings=max_gleanings, prompt=reader.str("prompt", Fragment.prompt_file), + strategy=entity_extraction_config.get("strategy"), encoding_model=reader.str(Fragment.encoding_model), strategy=entity_extraction_config.get("strategy"), ) diff --git a/graphrag/config/load_config.py b/graphrag/config/load_config.py new file mode 100644 index 0000000000..6c0f9fe5c1 --- /dev/null +++ b/graphrag/config/load_config.py @@ -0,0 +1,66 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Default method for loading config.""" + +from pathlib import Path + +from .config_file_loader import load_config_from_file, search_for_config_in_root_dir +from .create_graphrag_config import create_graphrag_config +from .enums import ReportingType, StorageType +from .models.graph_rag_config import GraphRagConfig +from .resolve_path import resolve_path + + +def load_config( + root_dir: str | Path, + config_filepath: str | Path | None = None, + run_id: str | None = None, +) -> GraphRagConfig: + """Load configuration from a file or create a default configuration. + + If a config file is not found the default configuration is created. + + Parameters + ---------- + root_dir : str | Path + The root directory of the project. Will search for the config file in this directory. + config_filepath : str | Path | None + The path to the config file. + If None, searches for config file in root and + if not found creates a default configuration. + run_id : str | None + The run id to use for resolving timestamp_paths. + """ + root = Path(root_dir).resolve() + + # If user specified a config file path then it is required + if config_filepath: + config_path = (root / config_filepath).resolve() + if not config_path.exists(): + msg = f"Specified Config file not found: {config_path}" + raise FileNotFoundError(msg) + + # Else optional resolve the config path from the root directory if it exists + config_path = search_for_config_in_root_dir(root) + if config_path: + config = load_config_from_file(config_path) + else: + config = create_graphrag_config(root_dir=str(root)) + + config.storage.base_dir = str( + resolve_path( + config.storage.base_dir, + root if config.storage.type == StorageType.file else None, + run_id, + ) + ) + config.reporting.base_dir = str( + resolve_path( + config.reporting.base_dir, + root if config.reporting.type == ReportingType.file else None, + run_id, + ) + ) + + return config diff --git a/graphrag/config/logging.py b/graphrag/config/logging.py index 84d7369955..99ee459a27 100644 --- a/graphrag/config/logging.py +++ b/graphrag/config/logging.py @@ -8,7 +8,6 @@ from .enums import ReportingType from .models.graph_rag_config import GraphRagConfig -from .resolve_timestamp_path import resolve_timestamp_path def enable_logging(log_filepath: str | Path, verbose: bool = False) -> None: @@ -35,7 +34,7 @@ def enable_logging(log_filepath: str | Path, verbose: bool = False) -> None: def enable_logging_with_config( - config: GraphRagConfig, timestamp_value: str, verbose: bool = False + config: GraphRagConfig, verbose: bool = False ) -> tuple[bool, str]: """Enable logging to a file based on the config. @@ -56,10 +55,7 @@ def enable_logging_with_config( (True, str) if logging was enabled. """ if config.reporting.type == ReportingType.file: - log_path = resolve_timestamp_path( - Path(config.root_dir) / config.reporting.base_dir / "indexing-engine.log", - timestamp_value, - ) + log_path = Path(config.reporting.base_dir) / "indexing-engine.log" enable_logging(log_path, verbose) return (True, str(log_path)) return (False, "") diff --git a/graphrag/config/resolve_timestamp_path.py b/graphrag/config/resolve_path.py similarity index 72% rename from graphrag/config/resolve_timestamp_path.py rename to graphrag/config/resolve_path.py index 492f620158..bc4131b68c 100644 --- a/graphrag/config/resolve_timestamp_path.py +++ b/graphrag/config/resolve_path.py @@ -79,9 +79,9 @@ def _resolve_timestamp_path_with_dir( return _resolve_timestamp_path_with_value(path, timestamp_dirs[0].name) -def resolve_timestamp_path( +def _resolve_timestamp_path( path: str | Path, - pattern_or_timestamp_value: re.Pattern[str] | str = re.compile(r"^\d{8}-\d{6}$"), + pattern_or_timestamp_value: re.Pattern[str] | str | None = None, ) -> Path: r"""Timestamp path resolver. @@ -110,6 +110,43 @@ def resolve_timestamp_path( If the parent directory expecting to contain timestamp directories does not exist or is not a directory. Or if no timestamp directories are found in the parent directory that match the pattern. """ + if not pattern_or_timestamp_value: + pattern_or_timestamp_value = re.compile(r"^\d{8}-\d{6}$") if isinstance(pattern_or_timestamp_value, str): return _resolve_timestamp_path_with_value(path, pattern_or_timestamp_value) return _resolve_timestamp_path_with_dir(path, pattern_or_timestamp_value) + + +def resolve_path( + path_to_resolve: Path | str, + root_dir: Path | str | None = None, + pattern_or_timestamp_value: re.Pattern[str] | str | None = None, +) -> Path: + """Resolve the path. + + Resolves any timestamp variables by either using the provided timestamp value if string or + by looking up the latest available timestamp directory that matches the given pattern. + Resolves the path against the root directory if provided. + + Parameters + ---------- + path_to_resolve : Path | str + The path to resolve. + root_dir : Path | str | None default=None + The root directory to resolve the path from, if provided. + pattern_or_timestamp_value : re.Pattern[str] | str, default=None + The pattern to use to match the timestamp directories or the timestamp value to use. + If a string is provided, the path will be resolved with the given string value. + Otherwise, the path will be resolved with the latest available timestamp directory + that matches the given pattern. + + Returns + ------- + Path + The resolved path. + """ + if root_dir: + path_to_resolve = (Path(root_dir) / path_to_resolve).resolve() + else: + path_to_resolve = Path(path_to_resolve) + return _resolve_timestamp_path(path_to_resolve, pattern_or_timestamp_value) diff --git a/graphrag/index/__main__.py b/graphrag/index/__main__.py index 0530290a63..2215e3a1b0 100644 --- a/graphrag/index/__main__.py +++ b/graphrag/index/__main__.py @@ -63,32 +63,36 @@ help="Create an initial configuration in the given path.", action="store_true", ) - parser.add_argument( - "--overlay-defaults", - help="Overlay default configuration values on a provided configuration file (--config).", - action="store_true", - ) parser.add_argument( "--skip-validations", help="Skip any preflight validation. Useful when running no LLM steps.", action="store_true", ) + parser.add_argument( + "--update-index", + help="Update a given index run id, leveraging previous outputs and applying new indexes.", + # Only required if config is not defined + required=False, + default=None, + type=str, + ) args = parser.parse_args() - if args.overlay_defaults and not args.config: - parser.error("--overlay-defaults requires --config") + if args.resume and args.update_index: + msg = "Cannot resume and update a run at the same time." + raise ValueError(msg) index_cli( - root=args.root, + root_dir=args.root, verbose=args.verbose or False, resume=args.resume, + update_index_id=args.update_index, memprofile=args.memprofile or False, nocache=args.nocache or False, reporter=args.reporter, - config=args.config, + config_filepath=args.config, emit=args.emit, dryrun=args.dryrun or False, init=args.init or False, - overlay_defaults=args.overlay_defaults or False, skip_validations=args.skip_validations or False, ) diff --git a/graphrag/index/api.py b/graphrag/index/api.py index a58e832c9b..89cbdfa7f8 100644 --- a/graphrag/index/api.py +++ b/graphrag/index/api.py @@ -8,9 +8,7 @@ Backwards compatibility is not guaranteed at this time. """ -from graphrag.config.enums import CacheType -from graphrag.config.models.graph_rag_config import GraphRagConfig -from graphrag.config.resolve_timestamp_path import resolve_timestamp_path +from graphrag.config import CacheType, GraphRagConfig from .cache.noop_pipeline_cache import NoopPipelineCache from .create_pipeline_config import create_pipeline_config @@ -24,8 +22,10 @@ async def build_index( config: GraphRagConfig, - run_id: str, - memory_profile: bool, + run_id: str = "", + is_resume_run: bool = False, + is_update_run: bool = False, + memory_profile: bool = False, progress_reporter: ProgressReporter | None = None, emit: list[str] | None = None, ) -> list[PipelineRunResult]: @@ -37,6 +37,10 @@ async def build_index( The configuration. run_id : str The run id. Creates a output directory with this name. + is_resume_run : bool default=False + Whether to resume a previous index run. + is_update_run : bool default=False + Whether to update a previous index run. memory_profile : bool Whether to enable memory profiling. progress_reporter : ProgressReporter | None default=None @@ -50,11 +54,10 @@ async def build_index( list[PipelineRunResult] The list of pipeline run results """ - try: - resolve_timestamp_path(config.storage.base_dir, run_id) - resume = True - except ValueError as _: - resume = False + if is_resume_run and is_update_run: + msg = "Cannot resume and update a run at the same time." + raise ValueError(msg) + pipeline_config = create_pipeline_config(config) pipeline_cache = ( NoopPipelineCache() if config.cache.type == CacheType.none is None else None @@ -67,7 +70,8 @@ async def build_index( cache=pipeline_cache, progress_reporter=progress_reporter, emit=([TableEmitterType(e) for e in emit] if emit is not None else None), - is_resume_run=resume, + is_resume_run=is_resume_run, + is_update_run=is_update_run, ): outputs.append(output) if progress_reporter: diff --git a/graphrag/index/cli.py b/graphrag/index/cli.py index 6dda401ca8..4e99f8e7bf 100644 --- a/graphrag/index/cli.py +++ b/graphrag/index/cli.py @@ -11,13 +11,7 @@ import warnings from pathlib import Path -from graphrag.config import create_graphrag_config -from graphrag.config.config_file_loader import ( - load_config_from_file, - resolve_config_path_with_root, -) -from graphrag.config.enums import CacheType -from graphrag.config.logging import enable_logging_with_config +from graphrag.config import CacheType, enable_logging_with_config, load_config from .api import build_index from .graph.extractors.claims.prompts import CLAIM_EXTRACTION_PROMPT @@ -103,60 +97,49 @@ def handle_signal(signum, _): def index_cli( - root: str, + root_dir: str, init: bool, verbose: bool, resume: str | None, + update_index_id: str | None, memprofile: bool, nocache: bool, reporter: str | None, - config: str | None, + config_filepath: str | None, emit: str | None, dryrun: bool, - overlay_defaults: bool, skip_validations: bool, ): """Run the pipeline with the given config.""" progress_reporter = load_progress_reporter(reporter or "rich") info, error, success = _logger(progress_reporter) - run_id = resume or time.strftime("%Y%m%d-%H%M%S") + run_id = resume or update_index_id or time.strftime("%Y%m%d-%H%M%S") if init: - _initialize_project_at(root, progress_reporter) + _initialize_project_at(root_dir, progress_reporter) sys.exit(0) - if overlay_defaults or config: - config_path = ( - Path(root) / config if config else resolve_config_path_with_root(root) - ) - default_config = load_config_from_file(config_path) - else: - try: - config_path = resolve_config_path_with_root(root) - default_config = load_config_from_file(config_path) - except FileNotFoundError: - default_config = create_graphrag_config(root_dir=root) + root = Path(root_dir).resolve() + config = load_config(root, config_filepath, run_id) if nocache: - default_config.cache.type = CacheType.none + config.cache.type = CacheType.none - enabled_logging, log_path = enable_logging_with_config( - default_config, run_id, verbose - ) + enabled_logging, log_path = enable_logging_with_config(config, verbose) if enabled_logging: info(f"Logging enabled at {log_path}", True) else: info( - f"Logging not enabled for config {_redact(default_config.model_dump())}", + f"Logging not enabled for config {_redact(config.model_dump())}", True, ) if skip_validations: - validate_config_names(progress_reporter, default_config) + validate_config_names(progress_reporter, config) info(f"Starting pipeline run for: {run_id}, {dryrun=}", verbose) info( - f"Using default configuration: {_redact(default_config.model_dump())}", + f"Using default configuration: {_redact(config.model_dump())}", verbose, ) @@ -170,11 +153,13 @@ def index_cli( outputs = asyncio.run( build_index( - default_config, - run_id, - memprofile, - progress_reporter, - pipeline_emit, + config=config, + run_id=run_id, + is_resume_run=bool(resume), + is_update_run=bool(update_index_id), + memory_profile=memprofile, + progress_reporter=progress_reporter, + emit=pipeline_emit, ) ) encountered_errors = any( diff --git a/graphrag/index/create_pipeline_config.py b/graphrag/index/create_pipeline_config.py index 22dba20029..3743c91fa8 100644 --- a/graphrag/index/create_pipeline_config.py +++ b/graphrag/index/create_pipeline_config.py @@ -274,7 +274,7 @@ def _get_embedding_settings( # strategy = settings.resolved_strategy() # get the default strategy strategy.update({ - "vector_store": {**vector_store_settings, **(vector_store_params or {})} + "vector_store": {**(vector_store_params or {}), **vector_store_settings} }) # update the default strategy with the vector store settings # This ensures the vector store config is part of the strategy and not the global config return { diff --git a/graphrag/index/graph/extractors/graph/graph_extractor.py b/graphrag/index/graph/extractors/graph/graph_extractor.py index f1ba0011f9..49ca671a72 100644 --- a/graphrag/index/graph/extractors/graph/graph_extractor.py +++ b/graphrag/index/graph/extractors/graph/graph_extractor.py @@ -4,7 +4,6 @@ """A module containing 'GraphExtractionResult' and 'GraphExtractor' models.""" import logging -import numbers import re import traceback from collections.abc import Mapping @@ -248,11 +247,11 @@ async def _process_results( target = clean_str(record_attributes[2].upper()) edge_description = clean_str(record_attributes[3]) edge_source_id = clean_str(str(source_doc_id)) - weight = ( - float(record_attributes[-1]) - if isinstance(record_attributes[-1], numbers.Number) - else 1.0 - ) + try: + weight = float(record_attributes[-1]) + except ValueError: + weight = 1.0 + if source not in graph.nodes(): graph.add_node( source, diff --git a/graphrag/index/graph/extractors/summarize/description_summary_extractor.py b/graphrag/index/graph/extractors/summarize/description_summary_extractor.py index 011090654c..9319dba401 100644 --- a/graphrag/index/graph/extractors/summarize/description_summary_extractor.py +++ b/graphrag/index/graph/extractors/summarize/description_summary_extractor.py @@ -67,7 +67,7 @@ async def __call__( result = "" if len(descriptions) == 0: result = "" - if len(descriptions) == 1: + elif len(descriptions) == 1: result = descriptions[0] else: result = await self._summarize_descriptions(items, descriptions) @@ -87,7 +87,11 @@ async def _summarize_descriptions( if not isinstance(descriptions, list): descriptions = [descriptions] - # Iterate over descriptions, adding all until the max input tokens is reached + # Sort description lists + if len(descriptions) > 1: + descriptions = sorted(descriptions) + + # Iterate over descriptions, adding all until the max input tokens is reached usable_tokens = self._max_input_tokens - num_tokens_from_string( self._summarization_prompt ) diff --git a/graphrag/index/init_content.py b/graphrag/index/init_content.py index 6314217c1c..c63a6578fa 100644 --- a/graphrag/index/init_content.py +++ b/graphrag/index/init_content.py @@ -38,6 +38,8 @@ ## parallelization: override the global parallelization settings for embeddings async_mode: {defs.ASYNC_MODE.value} # or asyncio # target: {defs.EMBEDDING_TARGET.value} # or all + # batch_size: {defs.EMBEDDING_BATCH_SIZE} # the number of documents to send in a single request + # batch_max_tokens: {defs.EMBEDDING_BATCH_MAX_TOKENS} # the maximum number of tokens to send in a single request llm: api_key: ${{GRAPHRAG_API_KEY}} type: {defs.EMBEDDING_TYPE.value} # or azure_openai_embedding @@ -52,8 +54,6 @@ # max_retry_wait: {defs.LLM_MAX_RETRY_WAIT} # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times # concurrent_requests: {defs.LLM_CONCURRENT_REQUESTS} # the number of parallel inflight requests that may be made - # batch_size: {defs.EMBEDDING_BATCH_SIZE} # the number of documents to send in a single request - # batch_max_tokens: {defs.EMBEDDING_BATCH_MAX_TOKENS} # the maximum number of tokens to send in a single request @@ -89,8 +89,8 @@ # container_name: entity_extraction: - ## strategy: - ## type: + ## strategy: fully override the entity extraction strategy. + ## type: one of graph_intelligence, graph_intelligence_json and nltk ## llm: override the global llm settings for this task ## parallelization: override the global parallelization settings for this task ## async_mode: override the global async_mode settings for this task diff --git a/graphrag/index/run.py b/graphrag/index/run.py deleted file mode 100644 index b179f12c39..0000000000 --- a/graphrag/index/run.py +++ /dev/null @@ -1,465 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""Different methods to run the pipeline.""" - -import gc -import json -import logging -import time -import traceback -from collections.abc import AsyncIterable -from dataclasses import asdict -from io import BytesIO -from pathlib import Path -from string import Template -from typing import cast - -import pandas as pd -from datashaper import ( - DEFAULT_INPUT_NAME, - MemoryProfile, - Workflow, - WorkflowCallbacks, - WorkflowCallbacksManager, - WorkflowRunResult, -) - -from .cache import InMemoryCache, PipelineCache, load_cache -from .config import ( - PipelineBlobCacheConfig, - PipelineBlobReportingConfig, - PipelineBlobStorageConfig, - PipelineCacheConfigTypes, - PipelineConfig, - PipelineFileCacheConfig, - PipelineFileReportingConfig, - PipelineFileStorageConfig, - PipelineInputConfigTypes, - PipelineMemoryCacheConfig, - PipelineReportingConfigTypes, - PipelineStorageConfigTypes, - PipelineWorkflowReference, - PipelineWorkflowStep, -) -from .context import PipelineRunContext, PipelineRunStats -from .emit import TableEmitterType, create_table_emitters -from .input import load_input -from .load_pipeline_config import load_pipeline_config -from .progress import NullProgressReporter, ProgressReporter -from .reporting import ( - ConsoleWorkflowCallbacks, - ProgressWorkflowCallbacks, - load_pipeline_reporter, -) -from .storage import MemoryPipelineStorage, PipelineStorage, load_storage -from .typing import PipelineRunResult - -# Register all verbs -from .verbs import * # noqa -from .workflows import ( - VerbDefinitions, - WorkflowDefinitions, - create_workflow, - load_workflows, -) - -log = logging.getLogger(__name__) - - -async def run_pipeline_with_config( - config_or_path: PipelineConfig | str, - workflows: list[PipelineWorkflowReference] | None = None, - dataset: pd.DataFrame | None = None, - storage: PipelineStorage | None = None, - cache: PipelineCache | None = None, - callbacks: WorkflowCallbacks | None = None, - progress_reporter: ProgressReporter | None = None, - input_post_process_steps: list[PipelineWorkflowStep] | None = None, - additional_verbs: VerbDefinitions | None = None, - additional_workflows: WorkflowDefinitions | None = None, - emit: list[TableEmitterType] | None = None, - memory_profile: bool = False, - run_id: str | None = None, - is_resume_run: bool = False, - **_kwargs: dict, -) -> AsyncIterable[PipelineRunResult]: - """Run a pipeline with the given config. - - Args: - - config_or_path - The config to run the pipeline with - - workflows - The workflows to run (this overrides the config) - - dataset - The dataset to run the pipeline on (this overrides the config) - - storage - The storage to use for the pipeline (this overrides the config) - - cache - The cache to use for the pipeline (this overrides the config) - - reporter - The reporter to use for the pipeline (this overrides the config) - - input_post_process_steps - The post process steps to run on the input data (this overrides the config) - - additional_verbs - The custom verbs to use for the pipeline. - - additional_workflows - The custom workflows to use for the pipeline. - - emit - The table emitters to use for the pipeline. - - memory_profile - Whether or not to profile the memory. - - run_id - The run id to start or resume from. - """ - if isinstance(config_or_path, str): - log.info("Running pipeline with config %s", config_or_path) - else: - log.info("Running pipeline") - - run_id = run_id or time.strftime("%Y%m%d-%H%M%S") - config = load_pipeline_config(config_or_path) - config = _apply_substitutions(config, run_id) - root_dir = config.root_dir - - def _create_storage(config: PipelineStorageConfigTypes | None) -> PipelineStorage: - return load_storage( - config - or PipelineFileStorageConfig(base_dir=str(Path(root_dir or "") / "output")) - ) - - def _create_cache(config: PipelineCacheConfigTypes | None) -> PipelineCache: - return load_cache(config or PipelineMemoryCacheConfig(), root_dir=root_dir) - - def _create_reporter( - config: PipelineReportingConfigTypes | None, - ) -> WorkflowCallbacks | None: - return load_pipeline_reporter(config, root_dir) if config else None - - async def _create_input( - config: PipelineInputConfigTypes | None, - ) -> pd.DataFrame | None: - if config is None: - return None - - return await load_input(config, progress_reporter, root_dir) - - def _create_postprocess_steps( - config: PipelineInputConfigTypes | None, - ) -> list[PipelineWorkflowStep] | None: - return config.post_process if config is not None else None - - progress_reporter = progress_reporter or NullProgressReporter() - storage = storage or _create_storage(config.storage) - cache = cache or _create_cache(config.cache) - callbacks = callbacks or _create_reporter(config.reporting) - dataset = dataset if dataset is not None else await _create_input(config.input) - post_process_steps = input_post_process_steps or _create_postprocess_steps( - config.input - ) - workflows = workflows or config.workflows - - if dataset is None: - msg = "No dataset provided!" - raise ValueError(msg) - - async for table in run_pipeline( - workflows=workflows, - dataset=dataset, - storage=storage, - cache=cache, - callbacks=callbacks, - input_post_process_steps=post_process_steps, - memory_profile=memory_profile, - additional_verbs=additional_verbs, - additional_workflows=additional_workflows, - progress_reporter=progress_reporter, - emit=emit, - is_resume_run=is_resume_run, - ): - yield table - - -async def run_pipeline( - workflows: list[PipelineWorkflowReference], - dataset: pd.DataFrame, - storage: PipelineStorage | None = None, - cache: PipelineCache | None = None, - callbacks: WorkflowCallbacks | None = None, - progress_reporter: ProgressReporter | None = None, - input_post_process_steps: list[PipelineWorkflowStep] | None = None, - additional_verbs: VerbDefinitions | None = None, - additional_workflows: WorkflowDefinitions | None = None, - emit: list[TableEmitterType] | None = None, - memory_profile: bool = False, - is_resume_run: bool = False, - **_kwargs: dict, -) -> AsyncIterable[PipelineRunResult]: - """Run the pipeline. - - Args: - - workflows - The workflows to run - - dataset - The dataset to run the pipeline on, specifically a dataframe with the following columns at a minimum: - - id - The id of the document - - text - The text of the document - - title - The title of the document - These must exist after any post process steps are run if there are any! - - storage - The storage to use for the pipeline - - cache - The cache to use for the pipeline - - reporter - The reporter to use for the pipeline - - input_post_process_steps - The post process steps to run on the input data - - additional_verbs - The custom verbs to use for the pipeline - - additional_workflows - The custom workflows to use for the pipeline - - debug - Whether or not to run in debug mode - Returns: - - output - An iterable of workflow results as they complete running, as well as any errors that occur - """ - start_time = time.time() - stats = PipelineRunStats() - storage = storage or MemoryPipelineStorage() - cache = cache or InMemoryCache() - progress_reporter = progress_reporter or NullProgressReporter() - callbacks = callbacks or ConsoleWorkflowCallbacks() - callbacks = _create_callback_chain(callbacks, progress_reporter) - emit = emit or [TableEmitterType.Parquet] - emitters = create_table_emitters( - emit, - storage, - lambda e, s, d: cast(WorkflowCallbacks, callbacks).on_error( - "Error emitting table", e, s, d - ), - ) - loaded_workflows = load_workflows( - workflows, - additional_verbs=additional_verbs, - additional_workflows=additional_workflows, - memory_profile=memory_profile, - ) - workflows_to_run = loaded_workflows.workflows - workflow_dependencies = loaded_workflows.dependencies - - context = _create_run_context(storage, cache, stats) - - if len(emitters) == 0: - log.info( - "No emitters provided. No table outputs will be generated. This is probably not correct." - ) - - async def dump_stats() -> None: - await storage.set( - "stats.json", json.dumps(asdict(stats), indent=4, ensure_ascii=False) - ) - - async def load_table_from_storage(name: str) -> pd.DataFrame: - if not await storage.has(name): - msg = f"Could not find {name} in storage!" - raise ValueError(msg) - try: - log.info("read table from storage: %s", name) - return pd.read_parquet(BytesIO(await storage.get(name, as_bytes=True))) - except Exception: - log.exception("error loading table from storage: %s", name) - raise - - async def inject_workflow_data_dependencies(workflow: Workflow) -> None: - workflow.add_table(DEFAULT_INPUT_NAME, dataset) - deps = workflow_dependencies[workflow.name] - log.info("dependencies for %s: %s", workflow.name, deps) - for id in deps: - workflow_id = f"workflow:{id}" - table = await load_table_from_storage(f"{id}.parquet") - workflow.add_table(workflow_id, table) - - async def write_workflow_stats( - workflow: Workflow, - workflow_result: WorkflowRunResult, - workflow_start_time: float, - ) -> None: - for vt in workflow_result.verb_timings: - stats.workflows[workflow.name][f"{vt.index}_{vt.verb}"] = vt.timing - - workflow_end_time = time.time() - stats.workflows[workflow.name]["overall"] = ( - workflow_end_time - workflow_start_time - ) - stats.total_runtime = time.time() - start_time - await dump_stats() - - if workflow_result.memory_profile is not None: - await _save_profiler_stats( - storage, workflow.name, workflow_result.memory_profile - ) - - log.debug( - "first row of %s => %s", workflow_name, workflow.output().iloc[0].to_json() - ) - - async def emit_workflow_output(workflow: Workflow) -> pd.DataFrame: - output = cast(pd.DataFrame, workflow.output()) - for emitter in emitters: - await emitter.emit(workflow.name, output) - return output - - dataset = await _run_post_process_steps( - input_post_process_steps, dataset, context, callbacks - ) - - # Make sure the incoming data is valid - _validate_dataset(dataset) - - log.info("Final # of rows loaded: %s", len(dataset)) - stats.num_documents = len(dataset) - last_workflow = "input" - - try: - await dump_stats() - - for workflow_to_run in workflows_to_run: - # Try to flush out any intermediate dataframes - gc.collect() - - workflow = workflow_to_run.workflow - workflow_name: str = workflow.name - last_workflow = workflow_name - - log.info("Running workflow: %s...", workflow_name) - - if is_resume_run and await storage.has( - f"{workflow_to_run.workflow.name}.parquet" - ): - log.info("Skipping %s because it already exists", workflow_name) - continue - - stats.workflows[workflow_name] = {"overall": 0.0} - await inject_workflow_data_dependencies(workflow) - - workflow_start_time = time.time() - result = await workflow.run(context, callbacks) - await write_workflow_stats(workflow, result, workflow_start_time) - - # Save the output from the workflow - output = await emit_workflow_output(workflow) - yield PipelineRunResult(workflow_name, output, None) - output = None - workflow.dispose() - workflow = None - - stats.total_runtime = time.time() - start_time - await dump_stats() - except Exception as e: - log.exception("error running workflow %s", last_workflow) - cast(WorkflowCallbacks, callbacks).on_error( - "Error running pipeline!", e, traceback.format_exc() - ) - yield PipelineRunResult(last_workflow, None, [e]) - - -def _create_callback_chain( - callbacks: WorkflowCallbacks | None, progress: ProgressReporter | None -) -> WorkflowCallbacks: - """Create a callbacks manager.""" - manager = WorkflowCallbacksManager() - if callbacks is not None: - manager.register(callbacks) - if progress is not None: - manager.register(ProgressWorkflowCallbacks(progress)) - return manager - - -async def _save_profiler_stats( - storage: PipelineStorage, workflow_name: str, profile: MemoryProfile -): - """Save the profiler stats to the storage.""" - await storage.set( - f"{workflow_name}_profiling.peak_stats.csv", - profile.peak_stats.to_csv(index=True), - ) - - await storage.set( - f"{workflow_name}_profiling.snapshot_stats.csv", - profile.snapshot_stats.to_csv(index=True), - ) - - await storage.set( - f"{workflow_name}_profiling.time_stats.csv", - profile.time_stats.to_csv(index=True), - ) - - await storage.set( - f"{workflow_name}_profiling.detailed_view.csv", - profile.detailed_view.to_csv(index=True), - ) - - -async def _run_post_process_steps( - post_process: list[PipelineWorkflowStep] | None, - dataset: pd.DataFrame, - context: PipelineRunContext, - callbacks: WorkflowCallbacks, -) -> pd.DataFrame: - """Run the pipeline. - - Args: - - post_process - The post process steps to run - - dataset - The dataset to run the steps on - - context - The pipeline run context - Returns: - - output - The dataset after running the post process steps - """ - if post_process is not None and len(post_process) > 0: - input_workflow = create_workflow( - "Input Post Process", - post_process, - ) - input_workflow.add_table(DEFAULT_INPUT_NAME, dataset) - await input_workflow.run( - context=context, - callbacks=callbacks, - ) - dataset = cast(pd.DataFrame, input_workflow.output()) - return dataset - - -def _validate_dataset(dataset: pd.DataFrame): - """Validate the dataset for the pipeline. - - Args: - - dataset - The dataset to validate - """ - if not isinstance(dataset, pd.DataFrame): - msg = "Dataset must be a pandas dataframe!" - raise TypeError(msg) - - -def _apply_substitutions(config: PipelineConfig, run_id: str) -> PipelineConfig: - substitutions = {"timestamp": run_id} - - if ( - isinstance( - config.storage, PipelineFileStorageConfig | PipelineBlobStorageConfig - ) - and config.storage.base_dir - ): - config.storage.base_dir = Template(config.storage.base_dir).substitute( - substitutions - ) - if ( - isinstance(config.cache, PipelineFileCacheConfig | PipelineBlobCacheConfig) - and config.cache.base_dir - ): - config.cache.base_dir = Template(config.cache.base_dir).substitute( - substitutions - ) - - if ( - isinstance( - config.reporting, PipelineFileReportingConfig | PipelineBlobReportingConfig - ) - and config.reporting.base_dir - ): - config.reporting.base_dir = Template(config.reporting.base_dir).substitute( - substitutions - ) - - return config - - -def _create_run_context( - storage: PipelineStorage, - cache: PipelineCache, - stats: PipelineRunStats, -) -> PipelineRunContext: - """Create the run context for the pipeline.""" - return PipelineRunContext( - stats=stats, - cache=cache, - storage=storage, - ) diff --git a/graphrag/index/run/__init__.py b/graphrag/index/run/__init__.py new file mode 100644 index 0000000000..afb43acd8e --- /dev/null +++ b/graphrag/index/run/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Run module for GraphRAG.""" + +from graphrag.index.run.run import run_pipeline, run_pipeline_with_config + +__all__ = ["run_pipeline", "run_pipeline_with_config"] diff --git a/graphrag/index/run/cache.py b/graphrag/index/run/cache.py new file mode 100644 index 0000000000..0b77576ef4 --- /dev/null +++ b/graphrag/index/run/cache.py @@ -0,0 +1,17 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Cache functions for the GraphRAG update module.""" + +from graphrag.index.cache import load_cache +from graphrag.index.cache.pipeline_cache import PipelineCache +from graphrag.index.config.cache import ( + PipelineCacheConfigTypes, + PipelineMemoryCacheConfig, +) + + +def _create_cache( + config: PipelineCacheConfigTypes | None, root_dir: str +) -> PipelineCache: + return load_cache(config or PipelineMemoryCacheConfig(), root_dir=root_dir) diff --git a/graphrag/index/run/postprocess.py b/graphrag/index/run/postprocess.py new file mode 100644 index 0000000000..240df8e435 --- /dev/null +++ b/graphrag/index/run/postprocess.py @@ -0,0 +1,50 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Post Processing functions for the GraphRAG run module.""" + +from typing import cast + +import pandas as pd +from datashaper import DEFAULT_INPUT_NAME, WorkflowCallbacks + +from graphrag.index.config.input import PipelineInputConfigTypes +from graphrag.index.config.workflow import PipelineWorkflowStep +from graphrag.index.context import PipelineRunContext +from graphrag.index.workflows.load import create_workflow + + +def _create_postprocess_steps( + config: PipelineInputConfigTypes | None, +) -> list[PipelineWorkflowStep] | None: + """Retrieve the post process steps for the pipeline.""" + return config.post_process if config is not None else None + + +async def _run_post_process_steps( + post_process: list[PipelineWorkflowStep] | None, + dataset: pd.DataFrame, + context: PipelineRunContext, + callbacks: WorkflowCallbacks, +) -> pd.DataFrame: + """Run the pipeline. + + Args: + - post_process - The post process steps to run + - dataset - The dataset to run the steps on + - context - The pipeline run context + Returns: + - output - The dataset after running the post process steps + """ + if post_process: + input_workflow = create_workflow( + "Input Post Process", + post_process, + ) + input_workflow.add_table(DEFAULT_INPUT_NAME, dataset) + await input_workflow.run( + context=context, + callbacks=callbacks, + ) + dataset = cast(pd.DataFrame, input_workflow.output()) + return dataset diff --git a/graphrag/index/run/profiling.py b/graphrag/index/run/profiling.py new file mode 100644 index 0000000000..8640f027fd --- /dev/null +++ b/graphrag/index/run/profiling.py @@ -0,0 +1,75 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Profiling functions for the GraphRAG run module.""" + +import json +import logging +import time +from dataclasses import asdict + +from datashaper import MemoryProfile, Workflow, WorkflowRunResult + +from graphrag.index.context import PipelineRunStats +from graphrag.index.storage.typing import PipelineStorage + +log = logging.getLogger(__name__) + + +async def _save_profiler_stats( + storage: PipelineStorage, workflow_name: str, profile: MemoryProfile +): + """Save the profiler stats to the storage.""" + await storage.set( + f"{workflow_name}_profiling.peak_stats.csv", + profile.peak_stats.to_csv(index=True), + ) + + await storage.set( + f"{workflow_name}_profiling.snapshot_stats.csv", + profile.snapshot_stats.to_csv(index=True), + ) + + await storage.set( + f"{workflow_name}_profiling.time_stats.csv", + profile.time_stats.to_csv(index=True), + ) + + await storage.set( + f"{workflow_name}_profiling.detailed_view.csv", + profile.detailed_view.to_csv(index=True), + ) + + +async def _dump_stats(stats: PipelineRunStats, storage: PipelineStorage) -> None: + """Dump the stats to the storage.""" + await storage.set( + "stats.json", json.dumps(asdict(stats), indent=4, ensure_ascii=False) + ) + + +async def _write_workflow_stats( + workflow: Workflow, + workflow_result: WorkflowRunResult, + workflow_start_time: float, + start_time: float, + stats: PipelineRunStats, + storage: PipelineStorage, +) -> None: + """Write the workflow stats to the storage.""" + for vt in workflow_result.verb_timings: + stats.workflows[workflow.name][f"{vt.index}_{vt.verb}"] = vt.timing + + workflow_end_time = time.time() + stats.workflows[workflow.name]["overall"] = workflow_end_time - workflow_start_time + stats.total_runtime = time.time() - start_time + await _dump_stats(stats, storage) + + if workflow_result.memory_profile is not None: + await _save_profiler_stats( + storage, workflow.name, workflow_result.memory_profile + ) + + log.debug( + "first row of %s => %s", workflow.name, workflow.output().iloc[0].to_json() + ) diff --git a/graphrag/index/run/run.py b/graphrag/index/run/run.py new file mode 100644 index 0000000000..55ce71eac6 --- /dev/null +++ b/graphrag/index/run/run.py @@ -0,0 +1,245 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Different methods to run the pipeline.""" + +import gc +import logging +import time +import traceback +from collections.abc import AsyncIterable +from typing import cast + +import pandas as pd +from datashaper import WorkflowCallbacks + +from graphrag.index.cache import PipelineCache +from graphrag.index.config import ( + PipelineConfig, + PipelineWorkflowReference, + PipelineWorkflowStep, +) +from graphrag.index.emit import TableEmitterType, create_table_emitters +from graphrag.index.load_pipeline_config import load_pipeline_config +from graphrag.index.progress import NullProgressReporter, ProgressReporter +from graphrag.index.reporting import ( + ConsoleWorkflowCallbacks, +) +from graphrag.index.run.cache import _create_cache +from graphrag.index.run.postprocess import ( + _create_postprocess_steps, + _run_post_process_steps, +) +from graphrag.index.run.profiling import _dump_stats +from graphrag.index.run.utils import ( + _apply_substitutions, + _create_input, + _create_reporter, + _create_run_context, + _validate_dataset, +) +from graphrag.index.run.workflow import ( + _create_callback_chain, + _process_workflow, +) +from graphrag.index.storage import PipelineStorage +from graphrag.index.typing import PipelineRunResult + +# Register all verbs +from graphrag.index.verbs import * # noqa +from graphrag.index.workflows import ( + VerbDefinitions, + WorkflowDefinitions, + load_workflows, +) +from graphrag.utils.storage import _create_storage + +log = logging.getLogger(__name__) + + +async def run_pipeline_with_config( + config_or_path: PipelineConfig | str, + workflows: list[PipelineWorkflowReference] | None = None, + dataset: pd.DataFrame | None = None, + storage: PipelineStorage | None = None, + cache: PipelineCache | None = None, + callbacks: WorkflowCallbacks | None = None, + progress_reporter: ProgressReporter | None = None, + input_post_process_steps: list[PipelineWorkflowStep] | None = None, + additional_verbs: VerbDefinitions | None = None, + additional_workflows: WorkflowDefinitions | None = None, + emit: list[TableEmitterType] | None = None, + memory_profile: bool = False, + run_id: str | None = None, + is_resume_run: bool = False, + is_update_run: bool = False, + **_kwargs: dict, +) -> AsyncIterable[PipelineRunResult]: + """Run a pipeline with the given config. + + Args: + - config_or_path - The config to run the pipeline with + - workflows - The workflows to run (this overrides the config) + - dataset - The dataset to run the pipeline on (this overrides the config) + - storage - The storage to use for the pipeline (this overrides the config) + - cache - The cache to use for the pipeline (this overrides the config) + - reporter - The reporter to use for the pipeline (this overrides the config) + - input_post_process_steps - The post process steps to run on the input data (this overrides the config) + - additional_verbs - The custom verbs to use for the pipeline. + - additional_workflows - The custom workflows to use for the pipeline. + - emit - The table emitters to use for the pipeline. + - memory_profile - Whether or not to profile the memory. + - run_id - The run id to start or resume from. + """ + if isinstance(config_or_path, str): + log.info("Running pipeline with config %s", config_or_path) + else: + log.info("Running pipeline") + + run_id = run_id or time.strftime("%Y%m%d-%H%M%S") + config = load_pipeline_config(config_or_path) + config = _apply_substitutions(config, run_id) + root_dir = config.root_dir or "" + + progress_reporter = progress_reporter or NullProgressReporter() + storage = storage or _create_storage(config.storage, root_dir=root_dir) + cache = cache or _create_cache(config.cache, root_dir) + callbacks = callbacks or _create_reporter(config.reporting, root_dir) + dataset = ( + dataset + if dataset is not None + else await _create_input(config.input, progress_reporter, root_dir) + ) + + if is_update_run: + # TODO: Filter dataset to only include new data (this should be done in the input module) + pass + post_process_steps = input_post_process_steps or _create_postprocess_steps( + config.input + ) + workflows = workflows or config.workflows + + if dataset is None: + msg = "No dataset provided!" + raise ValueError(msg) + + async for table in run_pipeline( + workflows=workflows, + dataset=dataset, + storage=storage, + cache=cache, + callbacks=callbacks, + input_post_process_steps=post_process_steps, + memory_profile=memory_profile, + additional_verbs=additional_verbs, + additional_workflows=additional_workflows, + progress_reporter=progress_reporter, + emit=emit, + is_resume_run=is_resume_run, + ): + yield table + + +async def run_pipeline( + workflows: list[PipelineWorkflowReference], + dataset: pd.DataFrame, + storage: PipelineStorage | None = None, + cache: PipelineCache | None = None, + callbacks: WorkflowCallbacks | None = None, + progress_reporter: ProgressReporter | None = None, + input_post_process_steps: list[PipelineWorkflowStep] | None = None, + additional_verbs: VerbDefinitions | None = None, + additional_workflows: WorkflowDefinitions | None = None, + emit: list[TableEmitterType] | None = None, + memory_profile: bool = False, + is_resume_run: bool = False, + **_kwargs: dict, +) -> AsyncIterable[PipelineRunResult]: + """Run the pipeline. + + Args: + - workflows - The workflows to run + - dataset - The dataset to run the pipeline on, specifically a dataframe with the following columns at a minimum: + - id - The id of the document + - text - The text of the document + - title - The title of the document + These must exist after any post process steps are run if there are any! + - storage - The storage to use for the pipeline + - cache - The cache to use for the pipeline + - reporter - The reporter to use for the pipeline + - input_post_process_steps - The post process steps to run on the input data + - additional_verbs - The custom verbs to use for the pipeline + - additional_workflows - The custom workflows to use for the pipeline + - debug - Whether or not to run in debug mode + Returns: + - output - An iterable of workflow results as they complete running, as well as any errors that occur + """ + start_time = time.time() + + context = _create_run_context(storage=storage, cache=cache, stats=None) + + progress_reporter = progress_reporter or NullProgressReporter() + callbacks = callbacks or ConsoleWorkflowCallbacks() + callbacks = _create_callback_chain(callbacks, progress_reporter) + emit = emit or [TableEmitterType.Parquet] + emitters = create_table_emitters( + emit, + context.storage, + lambda e, s, d: cast(WorkflowCallbacks, callbacks).on_error( + "Error emitting table", e, s, d + ), + ) + loaded_workflows = load_workflows( + workflows, + additional_verbs=additional_verbs, + additional_workflows=additional_workflows, + memory_profile=memory_profile, + ) + workflows_to_run = loaded_workflows.workflows + workflow_dependencies = loaded_workflows.dependencies + + if len(emitters) == 0: + log.info( + "No emitters provided. No table outputs will be generated. This is probably not correct." + ) + + dataset = await _run_post_process_steps( + input_post_process_steps, dataset, context, callbacks + ) + + # Make sure the incoming data is valid + _validate_dataset(dataset) + + log.info("Final # of rows loaded: %s", len(dataset)) + context.stats.num_documents = len(dataset) + last_workflow = "input" + + try: + await _dump_stats(context.stats, context.storage) + + for workflow_to_run in workflows_to_run: + # Try to flush out any intermediate dataframes + gc.collect() + + last_workflow = workflow_to_run.workflow.name + result = await _process_workflow( + workflow_to_run.workflow, + context, + callbacks, + emitters, + workflow_dependencies, + dataset, + start_time, + is_resume_run, + ) + if result: + yield result + + context.stats.total_runtime = time.time() - start_time + await _dump_stats(context.stats, context.storage) + except Exception as e: + log.exception("error running workflow %s", last_workflow) + cast(WorkflowCallbacks, callbacks).on_error( + "Error running pipeline!", e, traceback.format_exc() + ) + yield PipelineRunResult(last_workflow, None, [e]) diff --git a/graphrag/index/run/utils.py b/graphrag/index/run/utils.py new file mode 100644 index 0000000000..7791913883 --- /dev/null +++ b/graphrag/index/run/utils.py @@ -0,0 +1,116 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Utility functions for the GraphRAG run module.""" + +import logging +from string import Template +from typing import Any + +import pandas as pd +from datashaper import ( + WorkflowCallbacks, +) + +from graphrag.index.cache.memory_pipeline_cache import InMemoryCache +from graphrag.index.cache.pipeline_cache import PipelineCache +from graphrag.index.config.cache import ( + PipelineBlobCacheConfig, + PipelineFileCacheConfig, +) +from graphrag.index.config.input import PipelineInputConfigTypes +from graphrag.index.config.pipeline import PipelineConfig +from graphrag.index.config.reporting import ( + PipelineBlobReportingConfig, + PipelineFileReportingConfig, + PipelineReportingConfigTypes, +) +from graphrag.index.config.storage import ( + PipelineBlobStorageConfig, + PipelineFileStorageConfig, +) +from graphrag.index.context import PipelineRunContext, PipelineRunStats +from graphrag.index.input import load_input +from graphrag.index.progress.types import ProgressReporter +from graphrag.index.reporting import load_pipeline_reporter +from graphrag.index.storage.memory_pipeline_storage import MemoryPipelineStorage +from graphrag.index.storage.typing import PipelineStorage + +log = logging.getLogger(__name__) + + +def _create_reporter( + config: PipelineReportingConfigTypes | None, root_dir: str +) -> WorkflowCallbacks | None: + """Create the reporter for the pipeline.""" + return load_pipeline_reporter(config, root_dir) if config else None + + +async def _create_input( + config: PipelineInputConfigTypes | None, + progress_reporter: ProgressReporter | None, + root_dir: str, +) -> pd.DataFrame | None: + """Load the input for the pipeline.""" + if config is None: + return None + + return await load_input(config, progress_reporter, root_dir) + + +def _validate_dataset(dataset: Any): + """Validate the dataset for the pipeline. + + Args: + - dataset - The dataset to validate + """ + if not isinstance(dataset, pd.DataFrame): + msg = "Dataset must be a pandas dataframe!" + raise TypeError(msg) + + +def _apply_substitutions(config: PipelineConfig, run_id: str) -> PipelineConfig: + """Apply the substitutions to the configuration.""" + substitutions = {"timestamp": run_id} + + if ( + isinstance( + config.storage, PipelineFileStorageConfig | PipelineBlobStorageConfig + ) + and config.storage.base_dir + ): + config.storage.base_dir = Template(config.storage.base_dir).substitute( + substitutions + ) + if ( + isinstance(config.cache, PipelineFileCacheConfig | PipelineBlobCacheConfig) + and config.cache.base_dir + ): + config.cache.base_dir = Template(config.cache.base_dir).substitute( + substitutions + ) + + if ( + isinstance( + config.reporting, PipelineFileReportingConfig | PipelineBlobReportingConfig + ) + and config.reporting.base_dir + ): + config.reporting.base_dir = Template(config.reporting.base_dir).substitute( + substitutions + ) + + return config + + +def _create_run_context( + storage: PipelineStorage | None, + cache: PipelineCache | None, + stats: PipelineRunStats | None, +) -> PipelineRunContext: + """Create the run context for the pipeline.""" + return PipelineRunContext( + stats=stats or PipelineRunStats(), + cache=cache or InMemoryCache(), + storage=storage or MemoryPipelineStorage(), + ) diff --git a/graphrag/index/run/workflow.py b/graphrag/index/run/workflow.py new file mode 100644 index 0000000000..43d232a356 --- /dev/null +++ b/graphrag/index/run/workflow.py @@ -0,0 +1,104 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Workflow functions for the GraphRAG update module.""" + +import logging +import time +from typing import cast + +import pandas as pd +from datashaper import ( + DEFAULT_INPUT_NAME, + Workflow, + WorkflowCallbacks, + WorkflowCallbacksManager, +) + +from graphrag.index.context import PipelineRunContext +from graphrag.index.emit.table_emitter import TableEmitter +from graphrag.index.progress.types import ProgressReporter +from graphrag.index.reporting.progress_workflow_callbacks import ( + ProgressWorkflowCallbacks, +) +from graphrag.index.run.profiling import _write_workflow_stats +from graphrag.index.storage.typing import PipelineStorage +from graphrag.index.typing import PipelineRunResult +from graphrag.utils.storage import _load_table_from_storage + +log = logging.getLogger(__name__) + + +async def _inject_workflow_data_dependencies( + workflow: Workflow, + workflow_dependencies: dict[str, list[str]], + dataset: pd.DataFrame, + storage: PipelineStorage, +) -> None: + """Inject the data dependencies into the workflow.""" + workflow.add_table(DEFAULT_INPUT_NAME, dataset) + deps = workflow_dependencies[workflow.name] + log.info("dependencies for %s: %s", workflow.name, deps) + for id in deps: + workflow_id = f"workflow:{id}" + table = await _load_table_from_storage(f"{id}.parquet", storage) + workflow.add_table(workflow_id, table) + + +async def _emit_workflow_output( + workflow: Workflow, emitters: list[TableEmitter] +) -> pd.DataFrame: + """Emit the workflow output.""" + output = cast(pd.DataFrame, workflow.output()) + for emitter in emitters: + await emitter.emit(workflow.name, output) + return output + + +def _create_callback_chain( + callbacks: WorkflowCallbacks | None, progress: ProgressReporter | None +) -> WorkflowCallbacks: + """Create a callbacks manager.""" + manager = WorkflowCallbacksManager() + if callbacks is not None: + manager.register(callbacks) + if progress is not None: + manager.register(ProgressWorkflowCallbacks(progress)) + return manager + + +async def _process_workflow( + workflow: Workflow, + context: PipelineRunContext, + callbacks: WorkflowCallbacks, + emitters: list[TableEmitter], + workflow_dependencies: dict[str, list[str]], + dataset: pd.DataFrame, + start_time: float, + is_resume_run: bool, +): + workflow_name = workflow.name + if is_resume_run and await context.storage.has(f"{workflow_name}.parquet"): + log.info("Skipping %s because it already exists", workflow_name) + return None + + context.stats.workflows[workflow_name] = {"overall": 0.0} + await _inject_workflow_data_dependencies( + workflow, workflow_dependencies, dataset, context.storage + ) + + workflow_start_time = time.time() + result = await workflow.run(context, callbacks) + await _write_workflow_stats( + workflow, + result, + workflow_start_time, + start_time, + context.stats, + context.storage, + ) + + # Save the output from the workflow + output = await _emit_workflow_output(workflow, emitters) + workflow.dispose() + return PipelineRunResult(workflow_name, output, None) diff --git a/graphrag/index/verbs/graph/merge/merge_graphs.py b/graphrag/index/verbs/graph/merge/merge_graphs.py index 8ab3fa47f7..a551e4cee7 100644 --- a/graphrag/index/verbs/graph/merge/merge_graphs.py +++ b/graphrag/index/verbs/graph/merge/merge_graphs.py @@ -129,7 +129,11 @@ def merge_edges( if not target_graph.has_edge(source, target): target_graph.add_edge(source, target, **(edge_data or {})) else: - merge_attributes(target_graph.edges[(source, target)], edge_data, edge_ops) + merge_attributes( + target_graph.edges[(source, target)], # noqa + edge_data, + edge_ops, + ) def merge_attributes( diff --git a/graphrag/prompt_tune/api.py b/graphrag/prompt_tune/api.py index 4bbcb5d7dc..f0295ecfe2 100644 --- a/graphrag/prompt_tune/api.py +++ b/graphrag/prompt_tune/api.py @@ -18,7 +18,6 @@ from graphrag.index.llm import load_llm from graphrag.index.progress import PrintProgressReporter -from .cli import DocSelectionType from .generator import ( MAX_TOKEN_COUNT, create_community_summarization_prompt, @@ -36,6 +35,7 @@ MIN_CHUNK_SIZE, load_docs_in_chunks, ) +from .types import DocSelectionType @validate_call diff --git a/graphrag/prompt_tune/cli.py b/graphrag/prompt_tune/cli.py index eb8ff6f49f..92cb581c90 100644 --- a/graphrag/prompt_tune/cli.py +++ b/graphrag/prompt_tune/cli.py @@ -5,11 +5,11 @@ from pathlib import Path +from graphrag.config import load_config from graphrag.index.progress import PrintProgressReporter from graphrag.prompt_tune.generator import MAX_TOKEN_COUNT from graphrag.prompt_tune.loader import ( MIN_CHUNK_SIZE, - read_config_parameters, ) from . import api @@ -53,11 +53,12 @@ async def prompt_tune( - min_examples_required: The minimum number of examples required for entity extraction prompts. """ reporter = PrintProgressReporter("") - graph_config = read_config_parameters(root, reporter, config) + root_path = Path(root).resolve() + graph_config = load_config(root_path, config) prompts = await api.generate_indexing_prompts( config=graph_config, - root=root, + root=str(root_path), chunk_size=chunk_size, limit=limit, selection_method=selection_method, @@ -70,7 +71,7 @@ async def prompt_tune( k=k, ) - output_path = Path(output) + output_path = (root_path / output).resolve() if output_path: reporter.info(f"Writing prompts to {output_path}") output_path.mkdir(parents=True, exist_ok=True) diff --git a/graphrag/prompt_tune/generator/entity_extraction_prompt.py b/graphrag/prompt_tune/generator/entity_extraction_prompt.py index b2192c0705..806e310915 100644 --- a/graphrag/prompt_tune/generator/entity_extraction_prompt.py +++ b/graphrag/prompt_tune/generator/entity_extraction_prompt.py @@ -54,7 +54,7 @@ def create_entity_extraction_prompt( else UNTYPED_GRAPH_EXTRACTION_PROMPT ) if isinstance(entity_types, list): - entity_types = ", ".join(entity_types) + entity_types = ", ".join(map(str, entity_types)) tokens_left = ( max_token_count diff --git a/graphrag/prompt_tune/generator/entity_relationship.py b/graphrag/prompt_tune/generator/entity_relationship.py index 72ecb5f4da..2733e3bd74 100644 --- a/graphrag/prompt_tune/generator/entity_relationship.py +++ b/graphrag/prompt_tune/generator/entity_relationship.py @@ -34,7 +34,9 @@ async def generate_entity_relationship_examples( if entity_types: entity_types_str = ( - entity_types if isinstance(entity_types, str) else ", ".join(entity_types) + entity_types + if isinstance(entity_types, str) + else ", ".join(map(str, entity_types)) ) messages = [ diff --git a/graphrag/prompt_tune/loader/__init__.py b/graphrag/prompt_tune/loader/__init__.py index 94e64cbe87..bc8026e92d 100644 --- a/graphrag/prompt_tune/loader/__init__.py +++ b/graphrag/prompt_tune/loader/__init__.py @@ -3,12 +3,10 @@ """Fine-tuning config and data loader module.""" -from .config import read_config_parameters from .input import MIN_CHUNK_OVERLAP, MIN_CHUNK_SIZE, load_docs_in_chunks __all__ = [ "MIN_CHUNK_OVERLAP", "MIN_CHUNK_SIZE", "load_docs_in_chunks", - "read_config_parameters", ] diff --git a/graphrag/prompt_tune/loader/config.py b/graphrag/prompt_tune/loader/config.py deleted file mode 100644 index 350feacd79..0000000000 --- a/graphrag/prompt_tune/loader/config.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""Config loading, parsing and handling module.""" - -from pathlib import Path - -from graphrag.config import create_graphrag_config -from graphrag.index.progress.types import ProgressReporter - - -def read_config_parameters( - root: str, reporter: ProgressReporter, config: str | None = None -): - """Read the configuration parameters from the settings file or environment variables. - - Parameters - ---------- - - root: The root directory where the parameters are. - - reporter: The progress reporter. - - config: The path to the settings file. - """ - _root = Path(root) - settings_yaml = ( - Path(config) - if config and Path(config).suffix in [".yaml", ".yml"] - else _root / "settings.yaml" - ) - if not settings_yaml.exists(): - settings_yaml = _root / "settings.yml" - if settings_yaml.exists(): - reporter.info(f"Reading settings from {settings_yaml}") - with settings_yaml.open("rb") as file: - import yaml - - data = yaml.safe_load(file.read().decode(encoding="utf-8", errors="strict")) - return create_graphrag_config(data, root) - - settings_json = ( - Path(config) - if config and Path(config).suffix == ".json" - else _root / "settings.json" - ) - if settings_yaml.exists(): - reporter.info(f"Reading settings from {settings_yaml}") - with settings_yaml.open("rb") as file: - import yaml - - data = yaml.safe_load(file.read().decode(encoding="utf-8", errors="strict")) - return create_graphrag_config(data, root) - - if settings_json.exists(): - reporter.info(f"Reading settings from {settings_json}") - with settings_json.open("rb") as file: - import json - - data = json.loads(file.read().decode(encoding="utf-8", errors="strict")) - return create_graphrag_config(data, root) - - reporter.info("Reading settings from environment variables") - return create_graphrag_config(root_dir=root) diff --git a/graphrag/query/api.py b/graphrag/query/api.py index c374260218..d4983d9a89 100644 --- a/graphrag/query/api.py +++ b/graphrag/query/api.py @@ -24,8 +24,7 @@ import pandas as pd from pydantic import validate_call -from graphrag.config.models.graph_rag_config import GraphRagConfig -from graphrag.config.resolve_timestamp_path import resolve_timestamp_path +from graphrag.config import GraphRagConfig from graphrag.index.progress.types import PrintProgressReporter from graphrag.model.entity import Entity from graphrag.query.structured_search.base import SearchResult # noqa: TCH001 @@ -140,7 +139,7 @@ async def global_search_streaming( get_context_data = True async for stream_chunk in search_result: if get_context_data: - context_data = _reformat_context_data(stream_chunk) + context_data = _reformat_context_data(stream_chunk) # type: ignore yield context_data get_context_data = False else: @@ -149,7 +148,6 @@ async def global_search_streaming( @validate_call(config={"arbitrary_types_allowed": True}) async def local_search( - root_dir: str | None, config: GraphRagConfig, nodes: pd.DataFrame, entities: pd.DataFrame, @@ -196,9 +194,8 @@ async def local_search( _entities = read_indexer_entities(nodes, entities, community_level) - base_dir = Path(str(root_dir)) / config.storage.base_dir - resolved_base_dir = resolve_timestamp_path(base_dir) - lancedb_dir = resolved_base_dir / "lancedb" + lancedb_dir = Path(config.storage.base_dir) / "lancedb" + vector_store_args.update({"db_uri": str(lancedb_dir)}) description_embedding_store = _get_embedding_description_store( entities=_entities, @@ -227,7 +224,6 @@ async def local_search( @validate_call(config={"arbitrary_types_allowed": True}) async def local_search_streaming( - root_dir: str | None, config: GraphRagConfig, nodes: pd.DataFrame, entities: pd.DataFrame, @@ -271,9 +267,8 @@ async def local_search_streaming( _entities = read_indexer_entities(nodes, entities, community_level) - base_dir = Path(str(root_dir)) / config.storage.base_dir - resolved_base_dir = resolve_timestamp_path(base_dir) - lancedb_dir = resolved_base_dir / "lancedb" + lancedb_dir = lancedb_dir = Path(config.storage.base_dir) / "lancedb" + vector_store_args.update({"db_uri": str(lancedb_dir)}) description_embedding_store = _get_embedding_description_store( entities=_entities, @@ -301,7 +296,7 @@ async def local_search_streaming( get_context_data = True async for stream_chunk in search_result: if get_context_data: - context_data = _reformat_context_data(stream_chunk) + context_data = _reformat_context_data(stream_chunk) # type: ignore yield context_data get_context_data = False else: diff --git a/graphrag/query/cli.py b/graphrag/query/cli.py index ed7dc4566a..872a4d0993 100644 --- a/graphrag/query/cli.py +++ b/graphrag/query/cli.py @@ -4,7 +4,6 @@ """Command line interface for the query module.""" import asyncio -import re import sys from pathlib import Path @@ -12,10 +11,12 @@ from graphrag.config import ( GraphRagConfig, - create_graphrag_config, + load_config, + resolve_path, ) -from graphrag.config.resolve_timestamp_path import resolve_timestamp_path +from graphrag.index.create_pipeline_config import create_pipeline_config from graphrag.index.progress import PrintProgressReporter +from graphrag.utils.storage import _create_storage, _load_table_from_storage from . import api @@ -25,7 +26,7 @@ def run_global_search( config_filepath: str | None, data_dir: str | None, - root_dir: str | None, + root_dir: str, community_level: int, response_type: str, streaming: bool, @@ -35,20 +36,27 @@ def run_global_search( Loads index files required for global search and calls the Query API. """ - data_dir, root_dir, config = _configure_paths_and_settings( - data_dir, root_dir, config_filepath - ) - data_path = Path(data_dir) - - final_nodes: pd.DataFrame = pd.read_parquet( - data_path / "create_final_nodes.parquet" - ) - final_entities: pd.DataFrame = pd.read_parquet( - data_path / "create_final_entities.parquet" - ) - final_community_reports: pd.DataFrame = pd.read_parquet( - data_path / "create_final_community_reports.parquet" + root = Path(root_dir).resolve() + config = load_config(root, config_filepath) + + if data_dir: + config.storage.base_dir = str(resolve_path(data_dir, root)) + + dataframe_dict = _resolve_parquet_files( + root_dir=root_dir, + config=config, + parquet_list=[ + "create_final_nodes.parquet", + "create_final_entities.parquet", + "create_final_community_reports.parquet", + ], + optional_list=[], ) + final_nodes: pd.DataFrame = dataframe_dict["create_final_nodes"] + final_entities: pd.DataFrame = dataframe_dict["create_final_entities"] + final_community_reports: pd.DataFrame = dataframe_dict[ + "create_final_community_reports" + ] # call the Query API if streaming: @@ -98,7 +106,7 @@ async def run_streaming_search(): def run_local_search( config_filepath: str | None, data_dir: str | None, - root_dir: str | None, + root_dir: str, community_level: int, response_type: str, streaming: bool, @@ -108,26 +116,32 @@ def run_local_search( Loads index files required for local search and calls the Query API. """ - data_dir, root_dir, config = _configure_paths_and_settings( - data_dir, root_dir, config_filepath - ) - data_path = Path(data_dir) - - final_nodes = pd.read_parquet(data_path / "create_final_nodes.parquet") - final_community_reports = pd.read_parquet( - data_path / "create_final_community_reports.parquet" - ) - final_text_units = pd.read_parquet(data_path / "create_final_text_units.parquet") - final_relationships = pd.read_parquet( - data_path / "create_final_relationships.parquet" - ) - final_entities = pd.read_parquet(data_path / "create_final_entities.parquet") - final_covariates_path = data_path / "create_final_covariates.parquet" - final_covariates = ( - pd.read_parquet(final_covariates_path) - if final_covariates_path.exists() - else None + root = Path(root_dir).resolve() + config = load_config(root, config_filepath) + + if data_dir: + config.storage.base_dir = str(resolve_path(data_dir, root)) + + dataframe_dict = _resolve_parquet_files( + root_dir=root_dir, + config=config, + parquet_list=[ + "create_final_nodes.parquet", + "create_final_community_reports.parquet", + "create_final_text_units.parquet", + "create_final_relationships.parquet", + "create_final_entities.parquet", + ], + optional_list=["create_final_covariates.parquet"], ) + final_nodes: pd.DataFrame = dataframe_dict["create_final_nodes"] + final_community_reports: pd.DataFrame = dataframe_dict[ + "create_final_community_reports" + ] + final_text_units: pd.DataFrame = dataframe_dict["create_final_text_units"] + final_relationships: pd.DataFrame = dataframe_dict["create_final_relationships"] + final_entities: pd.DataFrame = dataframe_dict["create_final_entities"] + final_covariates: pd.DataFrame | None = dataframe_dict["create_final_covariates"] # call the Query API if streaming: @@ -137,7 +151,6 @@ async def run_streaming_search(): context_data = None get_context_data = True async for stream_chunk in api.local_search_streaming( - root_dir=root_dir, config=config, nodes=final_nodes, entities=final_entities, @@ -163,7 +176,6 @@ async def run_streaming_search(): # not streaming response, context_data = asyncio.run( api.local_search( - root_dir=root_dir, config=config, nodes=final_nodes, entities=final_entities, @@ -182,75 +194,33 @@ async def run_streaming_search(): return response, context_data -def _configure_paths_and_settings( - data_dir: str | None, - root_dir: str | None, - config_filepath: str | None, -) -> tuple[str, str | None, GraphRagConfig]: - config = _create_graphrag_config(root_dir, config_filepath) - if data_dir is None and root_dir is None: - msg = "Either data_dir or root_dir must be provided." - raise ValueError(msg) - if data_dir is None: - base_dir = Path(str(root_dir)) / config.storage.base_dir - data_dir = str(resolve_timestamp_path(base_dir)) - return data_dir, root_dir, config - - -def _infer_data_dir(root: str) -> str: - output = Path(root) / "output" - # use the latest data-run folder - if output.exists(): - expr = re.compile(r"\d{8}-\d{6}") - filtered = [f for f in output.iterdir() if f.is_dir() and expr.match(f.name)] - folders = sorted(filtered, key=lambda f: f.name, reverse=True) - if len(folders) > 0: - folder = folders[0] - return str((folder / "artifacts").absolute()) - msg = f"Could not infer data directory from root={root}" - raise ValueError(msg) - - -def _create_graphrag_config( - root: str | None, - config_filepath: str | None, -) -> GraphRagConfig: - """Create a GraphRag configuration.""" - return _read_config_parameters(root or "./", config_filepath) - - -def _read_config_parameters(root: str, config: str | None): - _root = Path(root) - settings_yaml = ( - Path(config) - if config and Path(config).suffix in [".yaml", ".yml"] - else _root / "settings.yaml" - ) - if not settings_yaml.exists(): - settings_yaml = _root / "settings.yml" - - if settings_yaml.exists(): - reporter.info(f"Reading settings from {settings_yaml}") - with settings_yaml.open( - "rb", - ) as file: - import yaml - - data = yaml.safe_load(file.read().decode(encoding="utf-8", errors="strict")) - return create_graphrag_config(data, root) - - settings_json = ( - Path(config) - if config and Path(config).suffix == ".json" - else _root / "settings.json" - ) - if settings_json.exists(): - reporter.info(f"Reading settings from {settings_json}") - with settings_json.open("rb") as file: - import json - - data = json.loads(file.read().decode(encoding="utf-8", errors="strict")) - return create_graphrag_config(data, root) - - reporter.info("Reading settings from environment variables") - return create_graphrag_config(root_dir=root) +def _resolve_parquet_files( + root_dir: str, + config: GraphRagConfig, + parquet_list: list[str], + optional_list: list[str], +) -> dict[str, pd.DataFrame]: + """Read parquet files to a dataframe dict.""" + dataframe_dict = {} + pipeline_config = create_pipeline_config(config) + storage_obj = _create_storage(root_dir=root_dir, config=pipeline_config.storage) + for parquet_file in parquet_list: + df_key = parquet_file.split(".")[0] + df_value = asyncio.run( + _load_table_from_storage(name=parquet_file, storage=storage_obj) + ) + dataframe_dict[df_key] = df_value + + # for optional parquet files, set the dict entry to None instead of erroring out if it does not exist + for optional_file in optional_list: + file_exists = asyncio.run(storage_obj.has(optional_file)) + df_key = optional_file.split(".")[0] + if file_exists: + df_value = asyncio.run( + _load_table_from_storage(name=optional_file, storage=storage_obj) + ) + dataframe_dict[df_key] = df_value + else: + dataframe_dict[df_key] = None + + return dataframe_dict diff --git a/graphrag/query/factories.py b/graphrag/query/factories.py index 8b6d58fb7e..f976efdae9 100644 --- a/graphrag/query/factories.py +++ b/graphrag/query/factories.py @@ -21,6 +21,7 @@ from graphrag.query.llm.oai.chat_openai import ChatOpenAI from graphrag.query.llm.oai.embedding import OpenAIEmbedding from graphrag.query.llm.oai.typing import OpenaiApiType +from graphrag.query.structured_search.base import BaseSearch from graphrag.query.structured_search.global_search.community_context import ( GlobalCommunityContext, ) @@ -108,7 +109,7 @@ def get_local_search_engine( covariates: dict[str, list[Covariate]], response_type: str, description_embedding_store: BaseVectorStore, -) -> LocalSearch: +) -> BaseSearch: """Create a local search engine based on data + configuration.""" llm = get_llm(config) text_embedder = get_text_embedder(config) @@ -159,7 +160,7 @@ def get_global_search_engine( reports: list[CommunityReport], entities: list[Entity], response_type: str, -): +) -> BaseSearch: """Create a global search engine based on data + configuration.""" token_encoder = tiktoken.get_encoding(config.encoding_model) gs_config = config.global_search diff --git a/graphrag/query/indexer_adapters.py b/graphrag/query/indexer_adapters.py index 4ce90d9215..dba4712bb4 100644 --- a/graphrag/query/indexer_adapters.py +++ b/graphrag/query/indexer_adapters.py @@ -68,8 +68,8 @@ def read_indexer_reports( report_df = final_community_reports entity_df = final_nodes entity_df = _filter_under_community_level(entity_df, community_level) - entity_df["community"] = entity_df["community"].fillna(-1) - entity_df["community"] = entity_df["community"].astype(int) + entity_df.loc[:, "community"] = entity_df["community"].fillna(-1) + entity_df.loc[:, "community"] = entity_df["community"].astype(int) entity_df = entity_df.groupby(["title"]).agg({"community": "max"}).reset_index() entity_df["community"] = entity_df["community"].astype(str) diff --git a/graphrag/query/structured_search/local_search/mixed_context.py b/graphrag/query/structured_search/local_search/mixed_context.py index 117101e913..e0608e4bd2 100644 --- a/graphrag/query/structured_search/local_search/mixed_context.py +++ b/graphrag/query/structured_search/local_search/mixed_context.py @@ -3,6 +3,7 @@ """Algorithms to build context data for local search prompt.""" import logging +from copy import deepcopy from typing import Any import pandas as pd @@ -309,42 +310,36 @@ def _build_text_unit_context( context_name: str = "Sources", ) -> tuple[str, dict[str, pd.DataFrame]]: """Rank matching text units and add them to the context window until it hits the max_tokens limit.""" - if len(selected_entities) == 0 or len(self.text_units) == 0: + if not selected_entities or not self.text_units: return ("", {context_name.lower(): pd.DataFrame()}) - selected_text_units = list[TextUnit]() - # for each matching text unit, rank first by the order of the entities that match it, then by the number of matching relationships - # that the text unit has with the matching entities + selected_text_units = [] + text_unit_ids_set = set() + for index, entity in enumerate(selected_entities): - if entity.text_unit_ids: - for text_id in entity.text_unit_ids: - if ( - text_id not in [unit.id for unit in selected_text_units] - and text_id in self.text_units - ): - selected_unit = self.text_units[text_id] - num_relationships = count_relationships( - selected_unit, entity, self.relationships - ) - if selected_unit.attributes is None: - selected_unit.attributes = {} - selected_unit.attributes["entity_order"] = index - selected_unit.attributes["num_relationships"] = ( - num_relationships - ) - selected_text_units.append(selected_unit) + for text_id in entity.text_unit_ids or []: + if text_id not in text_unit_ids_set and text_id in self.text_units: + text_unit_ids_set.add(text_id) + selected_unit = deepcopy(self.text_units[text_id]) + num_relationships = count_relationships( + selected_unit, entity, self.relationships + ) + if selected_unit.attributes is None: + selected_unit.attributes = {} + selected_unit.attributes["entity_order"] = index + selected_unit.attributes["num_relationships"] = num_relationships + selected_text_units.append(selected_unit) - # sort selected text units by ascending order of entity order and descending order of number of relationships selected_text_units.sort( key=lambda x: ( - x.attributes["entity_order"], # type: ignore - -x.attributes["num_relationships"], # type: ignore + x.attributes["entity_order"], + -x.attributes["num_relationships"], ) ) for unit in selected_text_units: - del unit.attributes["entity_order"] # type: ignore - del unit.attributes["num_relationships"] # type: ignore + unit.attributes.pop("entity_order", None) + unit.attributes.pop("num_relationships", None) context_text, context_data = build_text_unit_context( text_units=selected_text_units, @@ -362,8 +357,8 @@ def _build_text_unit_context( ) context_key = context_name.lower() if context_key not in context_data: + candidate_context_data["in_context"] = False context_data[context_key] = candidate_context_data - context_data[context_key]["in_context"] = False else: if ( "id" in candidate_context_data.columns @@ -371,12 +366,11 @@ def _build_text_unit_context( ): candidate_context_data["in_context"] = candidate_context_data[ "id" - ].isin( # cspell:disable-line - context_data[context_key]["id"] - ) + ].isin(context_data[context_key]["id"]) context_data[context_key] = candidate_context_data else: context_data[context_key]["in_context"] = True + return (str(context_text), context_data) def _build_local_context( diff --git a/graphrag/utils/__init__.py b/graphrag/utils/__init__.py new file mode 100644 index 0000000000..4d2f3e2a27 --- /dev/null +++ b/graphrag/utils/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Util functions for the GraphRAG run module.""" diff --git a/graphrag/utils/storage.py b/graphrag/utils/storage.py new file mode 100644 index 0000000000..087b5a2939 --- /dev/null +++ b/graphrag/utils/storage.py @@ -0,0 +1,53 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Storage functions for the GraphRAG run module.""" + +import logging +from io import BytesIO +from pathlib import Path + +import pandas as pd + +from graphrag.index.config.storage import ( + PipelineFileStorageConfig, + PipelineStorageConfigTypes, +) +from graphrag.index.storage import load_storage +from graphrag.index.storage.typing import PipelineStorage + +log = logging.getLogger(__name__) + + +def _create_storage( + config: PipelineStorageConfigTypes | None, root_dir: str +) -> PipelineStorage: + """Create the storage for the pipeline. + + Parameters + ---------- + config : PipelineStorageConfigTypes + The storage configuration. + root_dir : str + The root directory. + + Returns + ------- + PipelineStorage + The pipeline storage. + """ + return load_storage( + config or PipelineFileStorageConfig(base_dir=str(Path(root_dir) / "output")) + ) + + +async def _load_table_from_storage(name: str, storage: PipelineStorage) -> pd.DataFrame: + if not await storage.has(name): + msg = f"Could not find {name} in storage!" + raise ValueError(msg) + try: + log.info("read table from storage: %s", name) + return pd.read_parquet(BytesIO(await storage.get(name, as_bytes=True))) + except Exception: + log.exception("error loading table from storage: %s", name) + raise diff --git a/graphrag/vector_stores/base.py b/graphrag/vector_stores/base.py index 38f0e584fc..d4c61a43b2 100644 --- a/graphrag/vector_stores/base.py +++ b/graphrag/vector_stores/base.py @@ -34,7 +34,7 @@ class VectorStoreSearchResult: """Document that was found.""" score: float - """Similarity score between 0 and 1. Higher is more similar.""" + """Similarity score between -1 and 1. 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license = "MIT" readme = "README.md" packages = [{ include = "graphrag" }] +[tool.poetry.urls] +"Source" = "https://github.com/microsoft/graphrag" + [tool.poetry-dynamic-versioning] enable = true style = "pep440" @@ -44,7 +48,7 @@ datashaper = "^0.0.49" # Vector Stores azure-search-documents = "^11.4.0" -lancedb = "^0.11.0" +lancedb = "^0.12.0" # Event Loops uvloop = { version = "^0.20.0", markers = "platform_system != 'Windows'" } @@ -79,7 +83,7 @@ tenacity = "^9.0.0" swifter = "^1.4.0" pydantic = "^2" rich = "^13.6.0" -textual = "^0.76.0" +textual = "^0.78.0" devtools = "^0.12.2" typing-extensions = "^4.12.2" @@ -87,8 +91,9 @@ typing-extensions = "^4.12.2" #Azure azure-storage-blob = "^12.22.0" azure-identity = "^1.17.1" -json-repair = "^0.26.0" +json-repair = "^0.28.4" +future = "^1.0.0" [tool.poetry.group.dev.dependencies] coverage = "^7.6.0" ipykernel = "^6.29.4" @@ -97,9 +102,9 @@ nbconvert = "^7.16.3" poethepoet = "^0.27.0" pyright = "^1.1.371" pytest = "^8.3.2" -pytest-asyncio 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file mode 100644 index 0000000000..bd82d28288 --- /dev/null +++ b/tests/unit/config/test_resolve_path.py @@ -0,0 +1,42 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +from pathlib import Path + +from graphrag.config.resolve_path import resolve_path + + +def test_resolve_path_no_timestamp_with_run_id(): + path = Path("path/to/data") + result = resolve_path(path, pattern_or_timestamp_value="20240812-121000") + assert result == path + + +def test_resolve_path_no_timestamp_without_run_id(): + path = Path("path/to/data") + result = resolve_path(path) + assert result == path + + +def test_resolve_path_with_timestamp_and_run_id(): + path = Path("some/path/${timestamp}/data") + expected = Path("some/path/20240812/data") + result = resolve_path(path, pattern_or_timestamp_value="20240812") + assert result == expected + + +def test_resolve_path_with_timestamp_and_inferred_directory(): + cwd = Path(__file__).parent + path = cwd / "fixtures/timestamp_dirs/${timestamp}/data" + expected = cwd / "fixtures/timestamp_dirs/20240812-120000/data" + result = resolve_path(path) + assert result == expected + + +def test_resolve_path_absolute(): + cwd = Path(__file__).parent + path = "fixtures/timestamp_dirs/${timestamp}/data" + expected = cwd / "fixtures/timestamp_dirs/20240812-120000/data" + result = resolve_path(path, cwd) + assert result == expected + assert result.is_absolute() diff --git a/tests/unit/query/test_infer_data_dir.py b/tests/unit/query/test_infer_data_dir.py deleted file mode 100644 index c950eb35ae..0000000000 --- a/tests/unit/query/test_infer_data_dir.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License -from pathlib import Path - -import pytest - -from graphrag.query.cli import _infer_data_dir - - -def test_infer_data_dir(): - root = "./tests/unit/query/data/defaults" - result = Path(_infer_data_dir(root)) - assert result.parts[-2] == "20240812-121000" - - -def test_infer_data_dir_ignores_hidden_files(): - """A hidden file, starting with '.', will naturally be selected as latest data directory.""" - root = "./tests/unit/query/data/hidden" - result = Path(_infer_data_dir(root)) - assert result.parts[-2] == "20240812-121000" - - -def test_infer_data_dir_ignores_non_numeric(): - root = "./tests/unit/query/data/non-numeric" - result = Path(_infer_data_dir(root)) - assert result.parts[-2] == "20240812-121000" - - -def test_infer_data_dir_throws_on_no_match(): - root = "./tests/unit/query/data/empty" - with pytest.raises(ValueError): # noqa PT011 (this is what is actually thrown...) - _infer_data_dir(root)