diff --git a/docs/config/yaml.md b/docs/config/yaml.md index 10973fd211..1885db1fe5 100644 --- a/docs/config/yaml.md +++ b/docs/config/yaml.md @@ -87,9 +87,9 @@ Our pipeline can ingest .csv, .txt, or .json data from an input location. See th - `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name. - `storage_account_blob_url` **str** - (blob only) The storage account blob URL to use. - `cosmosdb_account_blob_url` **str** - (cosmosdb only) The CosmosDB account blob URL to use. -- `file_type` **text|csv|json** - The type of input data to load. Default is `text` +- `type` **text|csv|json** - The type of input data to load. Default is `text` - `encoding` **str** - The encoding of the input file. Default is `utf-8` -- `file_pattern` **str** - A regex to match input files. Default is `.*\.csv$`, `.*\.txt$`, or `.*\.json$` depending on the specified `file_type`, but you can customize it if needed. +- `file_pattern` **str** - A regex to match input files. Default is `.*\.csv$`, `.*\.txt$`, or `.*\.json$` depending on the specified `type`, but you can customize it if needed. - `text_column` **str** - (CSV/JSON only) The text column name. If unset we expect a column named `text`. - `title_column` **str** - (CSV/JSON only) The title column name, filename will be used if unset. - `metadata` **list[str]** - (CSV/JSON only) The additional document attributes fields to keep. diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb index abcd7832fc..2a0c0f15de 100644 --- a/docs/examples_notebooks/api_overview.ipynb +++ b/docs/examples_notebooks/api_overview.ipynb @@ -28,11 +28,10 @@ "from pathlib import Path\n", "from pprint import pprint\n", "\n", + "import graphrag.api as api\n", "import pandas as pd\n", "from graphrag.config.load_config import load_config\n", - "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n", - "\n", - "import graphrag.api as api" + "from graphrag.index.typing.pipeline_run_result import PipelineRunResult" ] }, { diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb index 505c0fe1f3..5657770eaf 100644 --- a/docs/examples_notebooks/input_documents.ipynb +++ b/docs/examples_notebooks/input_documents.ipynb @@ -30,11 +30,10 @@ "from pathlib import Path\n", "from pprint import pprint\n", "\n", + "import graphrag.api as api\n", "import pandas as pd\n", "from graphrag.config.load_config import load_config\n", - "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n", - "\n", - "import graphrag.api as api" + "from graphrag.index.typing.pipeline_run_result import PipelineRunResult" ] }, { diff --git a/docs/index/inputs.md b/docs/index/inputs.md index 7f34370a70..458306bf49 100644 --- a/docs/index/inputs.md +++ b/docs/index/inputs.md @@ -116,7 +116,7 @@ settings.yaml ```yaml input: - file_type: text + type: text metadata: [title] chunks: @@ -194,7 +194,7 @@ settings.yaml ```yaml input: - file_type: json + type: json title_column: headline text_column: content diff --git a/packages/graphrag-cache/graphrag_cache/cache_factory.py b/packages/graphrag-cache/graphrag_cache/cache_factory.py index f8bbd65dcc..6e4bc32813 100644 --- a/packages/graphrag-cache/graphrag_cache/cache_factory.py +++ b/packages/graphrag-cache/graphrag_cache/cache_factory.py @@ -26,7 +26,7 @@ def register_cache( cache_initializer: Callable[..., Cache], scope: ServiceScope = "transient", ) -> None: - """Register a custom storage implementation. + """Register a custom cache implementation. Args ---- diff --git a/packages/graphrag-cache/pyproject.toml b/packages/graphrag-cache/pyproject.toml index e87f5d81e0..69b79de36b 100644 --- a/packages/graphrag-cache/pyproject.toml +++ b/packages/graphrag-cache/pyproject.toml @@ -23,12 +23,12 @@ authors = [ license = "MIT" readme = "README.md" license-files = ["LICENSE"] -requires-python = ">=3.10,<3.13" +requires-python = ">=3.11,<3.14" classifiers = [ "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] dependencies = [ "graphrag-common==2.7.0", diff --git a/packages/graphrag-chunking/graphrag_chunking/add_metadata.py b/packages/graphrag-chunking/graphrag_chunking/add_metadata.py deleted file mode 100644 index 3b02994d4c..0000000000 --- a/packages/graphrag-chunking/graphrag_chunking/add_metadata.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'prepend_metadata' function.""" - - -def add_metadata( - text: str, - metadata: dict, - delimiter: str = ": ", - line_delimiter: str = "\n", - append: bool = False, -) -> str: - """Add metadata to the given text, prepending by default. This utility writes the dict as rows of key/value pairs.""" - metadata_str = ( - line_delimiter.join(f"{k}{delimiter}{v}" for k, v in metadata.items()) - + line_delimiter - ) - return text + metadata_str if append else metadata_str + text diff --git a/packages/graphrag-chunking/graphrag_chunking/chunk_result.py b/packages/graphrag-chunking/graphrag_chunking/chunk_result.py deleted file mode 100644 index d9b3177511..0000000000 --- a/packages/graphrag-chunking/graphrag_chunking/chunk_result.py +++ /dev/null @@ -1,17 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""The ChunkResult dataclass.""" - -from dataclasses import dataclass - - -@dataclass -class ChunkResult: - """Result of chunking a document.""" - - text: str - index: int - start_char: int - end_char: int - token_count: int | None = None diff --git a/packages/graphrag-chunking/graphrag_chunking/chunker.py b/packages/graphrag-chunking/graphrag_chunking/chunker.py index 552b28a8a8..3e44ce46f4 100644 --- a/packages/graphrag-chunking/graphrag_chunking/chunker.py +++ b/packages/graphrag-chunking/graphrag_chunking/chunker.py @@ -4,9 +4,10 @@ """A module containing the 'Chunker' class.""" from abc import ABC, abstractmethod +from collections.abc import Callable from typing import Any -from graphrag_chunking.chunk_result import ChunkResult +from graphrag_chunking.text_chunk import TextChunk class Chunker(ABC): @@ -17,5 +18,7 @@ def __init__(self, **kwargs: Any) -> None: """Create a chunker instance.""" @abstractmethod - def chunk(self, text: str) -> list[ChunkResult]: + def chunk( + self, text: str, transform: Callable[[str], str] | None = None + ) -> list[TextChunk]: """Chunk method definition.""" diff --git a/packages/graphrag-chunking/graphrag_chunking/chunking_config.py b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py index 1bb5b84e44..69db1260a1 100644 --- a/packages/graphrag-chunking/graphrag_chunking/chunking_config.py +++ b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py @@ -30,7 +30,7 @@ class ChunkingConfig(BaseModel): description="The chunk overlap to use.", default=100, ) - prepend_metadata: bool = Field( - description="Prepend metadata into each chunk.", - default=False, + prepend_metadata: list[str] | None = Field( + description="Metadata fields from the source document to prepend on each chunk.", + default=None, ) diff --git a/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py b/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py index 3eb15befff..780de6a0bc 100644 --- a/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py +++ b/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py @@ -5,26 +5,28 @@ from collections.abc import Callable -from graphrag_chunking.chunk_result import ChunkResult +from graphrag_chunking.text_chunk import TextChunk def create_chunk_results( chunks: list[str], + transform: Callable[[str], str] | None = None, encode: Callable[[str], list[int]] | None = None, -) -> list[ChunkResult]: - """Create chunk results from a list of text chunks. The index assignments are 0-based and assume chunks we not stripped relative to the source text.""" +) -> list[TextChunk]: + """Create chunk results from a list of text chunks. The index assignments are 0-based and assume chunks were not stripped relative to the source text.""" results = [] start_char = 0 for index, chunk in enumerate(chunks): end_char = start_char + len(chunk) - 1 # 0-based indices - chunk = ChunkResult( - text=chunk, + result = TextChunk( + original=chunk, + text=transform(chunk) if transform else chunk, index=index, start_char=start_char, end_char=end_char, ) if encode: - chunk.token_count = len(encode(chunk.text)) - results.append(chunk) + result.token_count = len(encode(result.text)) + results.append(result) start_char = end_char + 1 return results diff --git a/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py index 91e68ccaf2..42d8a9a211 100644 --- a/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py +++ b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py @@ -9,9 +9,9 @@ import nltk from graphrag_chunking.bootstrap_nltk import bootstrap -from graphrag_chunking.chunk_result import ChunkResult from graphrag_chunking.chunker import Chunker from graphrag_chunking.create_chunk_results import create_chunk_results +from graphrag_chunking.text_chunk import TextChunk class SentenceChunker(Chunker): @@ -24,10 +24,14 @@ def __init__( self._encode = encode bootstrap() - def chunk(self, text) -> list[ChunkResult]: + def chunk( + self, text: str, transform: Callable[[str], str] | None = None + ) -> list[TextChunk]: """Chunk the text into sentence-based chunks.""" sentences = nltk.sent_tokenize(text.strip()) - results = create_chunk_results(sentences, encode=self._encode) + results = create_chunk_results( + sentences, transform=transform, encode=self._encode + ) # nltk sentence tokenizer may trim whitespace, so we need to adjust start/end chars for index, result in enumerate(results): txt = result.text diff --git a/packages/graphrag-chunking/graphrag_chunking/text_chunk.py b/packages/graphrag-chunking/graphrag_chunking/text_chunk.py new file mode 100644 index 0000000000..876832f734 --- /dev/null +++ b/packages/graphrag-chunking/graphrag_chunking/text_chunk.py @@ -0,0 +1,29 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""The TextChunk dataclass.""" + +from dataclasses import dataclass + + +@dataclass +class TextChunk: + """Result of chunking a document.""" + + original: str + """Raw original text chunk before any transformation.""" + + text: str + """The final text content of this chunk.""" + + index: int + """Zero-based index of this chunk within the source document.""" + + start_char: int + """Character index where the raw chunk text begins in the source document.""" + + end_char: int + """Character index where the raw chunk text ends in the source document.""" + + token_count: int | None = None + """Number of tokens in the final chunk text, if computed.""" diff --git a/packages/graphrag-chunking/graphrag_chunking/token_chunker.py b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py index 9a5e8ecfb0..41921a0415 100644 --- a/packages/graphrag-chunking/graphrag_chunking/token_chunker.py +++ b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py @@ -6,9 +6,9 @@ from collections.abc import Callable from typing import Any -from graphrag_chunking.chunk_result import ChunkResult from graphrag_chunking.chunker import Chunker from graphrag_chunking.create_chunk_results import create_chunk_results +from graphrag_chunking.text_chunk import TextChunk class TokenChunker(Chunker): @@ -28,7 +28,9 @@ def __init__( self._encode = encode self._decode = decode - def chunk(self, text: str) -> list[ChunkResult]: + def chunk( + self, text: str, transform: Callable[[str], str] | None = None + ) -> list[TextChunk]: """Chunk the text into token-based chunks.""" chunks = split_text_on_tokens( text, @@ -37,7 +39,7 @@ def chunk(self, text: str) -> list[ChunkResult]: encode=self._encode, decode=self._decode, ) - return create_chunk_results(chunks, encode=self._encode) + return create_chunk_results(chunks, transform=transform, encode=self._encode) def split_text_on_tokens( diff --git a/packages/graphrag-chunking/graphrag_chunking/transformers.py b/packages/graphrag-chunking/graphrag_chunking/transformers.py new file mode 100644 index 0000000000..5d6f11e0e6 --- /dev/null +++ b/packages/graphrag-chunking/graphrag_chunking/transformers.py @@ -0,0 +1,25 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A collection of useful built-in transformers you can use for chunking.""" + +from collections.abc import Callable +from typing import Any + + +def add_metadata( + metadata: dict[str, Any], + delimiter: str = ": ", + line_delimiter: str = "\n", + append: bool = False, +) -> Callable[[str], str]: + """Add metadata to the given text, prepending by default. This utility writes the dict as rows of key/value pairs.""" + + def transformer(text: str) -> str: + metadata_str = ( + line_delimiter.join(f"{k}{delimiter}{v}" for k, v in metadata.items()) + + line_delimiter + ) + return text + metadata_str if append else metadata_str + text + + return transformer diff --git a/packages/graphrag-input/README.md b/packages/graphrag-input/README.md new file mode 100644 index 0000000000..5dd6cb1497 --- /dev/null +++ b/packages/graphrag-input/README.md @@ -0,0 +1,72 @@ +# GraphRAG Inputs + +This package provides input document loading utilities for GraphRAG, supporting multiple file formats including CSV, JSON, JSON Lines, and plain text. + +## Supported File Types + +The following four standard file formats are supported out of the box: + +- **CSV** - Tabular data with configurable column mappings +- **JSON** - JSON files with configurable property paths +- **JSON Lines** - Line-delimited JSON records +- **Text** - Plain text files + +### Markitdown Support + +Additionally, we support the `InputType.MarkItDown` format, which uses the [MarkItDown](https://github.com/microsoft/markitdown) library to import any supported file type. The MarkItDown converter can handle a wide variety of file formats including Office documents, PDFs, HTML, and more. + +**Note:** Additional optional dependencies may need to be installed depending on the file type you're processing. The choice of converter is determined by MarkItDowns's processing logic, which primarily uses the file extension to select the appropriate converter. Please refer to the [MarkItDown repository](https://github.com/microsoft/markitdown) for installation instructions and detailed information about supported formats. + +## Examples + +Basic usage with the factory: +```python +from graphrag_input import create_input_reader, InputConfig, InputType +from graphrag_storage import StorageConfig, create_storage + +config = InputConfig( + type=InputType.Csv, + text_column="content", + title_column="title", +) +storage = create_storage(StorageConfig(base_dir="./input")) +reader = create_input_reader(config, storage) +documents = await reader.read_files() +``` + +Import a pdf with MarkItDown: + +```bash +pip install 'markitdown[pdf]' # required dependency for pdf processing +``` + +```python +from graphrag_input import create_input_reader, InputConfig, InputType +from graphrag_storage import StorageConfig, create_storage + +config = InputConfig( + type=InputType.MarkitDown, + file_pattern=".*\\.pdf$" +) +storage = create_storage(StorageConfig(base_dir="./input")) +reader = create_input_reader(config, storage) +documents = await reader.read_files() +``` + +YAML config example for above: +```yaml +input: + type: markitdown + file_pattern: ".*\\.pdf$$" +input_storage: + type: file + base_dir: "input" +``` + +Note that when specifying column names for data extraction, we can handle nested objects (e.g., in JSON) with dot notation: +```python +from graphrag_input import get_property + +data = {"user": {"profile": {"name": "Alice"}}} +name = get_property(data, "user.profile.name") # Returns "Alice" +``` diff --git a/packages/graphrag-input/graphrag_input/__init__.py b/packages/graphrag-input/graphrag_input/__init__.py new file mode 100644 index 0000000000..e742d7f06b --- /dev/null +++ b/packages/graphrag-input/graphrag_input/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""GraphRAG input document loading package.""" + +from graphrag_input.get_property import get_property +from graphrag_input.input_config import InputConfig +from graphrag_input.input_reader import InputReader +from graphrag_input.input_reader_factory import create_input_reader +from graphrag_input.input_type import InputType +from graphrag_input.text_document import TextDocument + +__all__ = [ + "InputConfig", + "InputReader", + "InputType", + "TextDocument", + "create_input_reader", + "get_property", +] diff --git a/packages/graphrag-input/graphrag_input/csv.py b/packages/graphrag-input/graphrag_input/csv.py new file mode 100644 index 0000000000..6c0f51dd3a --- /dev/null +++ b/packages/graphrag-input/graphrag_input/csv.py @@ -0,0 +1,38 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'CSVFileReader' model.""" + +import csv +import logging + +from graphrag_input.structured_file_reader import StructuredFileReader +from graphrag_input.text_document import TextDocument + +logger = logging.getLogger(__name__) + + +class CSVFileReader(StructuredFileReader): + """Reader implementation for csv files.""" + + def __init__(self, file_pattern: str | None = None, **kwargs): + super().__init__( + file_pattern=file_pattern if file_pattern is not None else ".*\\.csv$", + **kwargs, + ) + + async def read_file(self, path: str) -> list[TextDocument]: + """Read a csv file into a list of documents. + + Args: + - path - The path to read the file from. + + Returns + ------- + - output - list with a TextDocument for each row in the file. + """ + file = await self._storage.get(path, encoding=self._encoding) + + reader = csv.DictReader(file.splitlines()) + rows = list(reader) + return await self.process_data_columns(rows, path) diff --git a/packages/graphrag-input/graphrag_input/get_property.py b/packages/graphrag-input/graphrag_input/get_property.py new file mode 100644 index 0000000000..8c82d69885 --- /dev/null +++ b/packages/graphrag-input/graphrag_input/get_property.py @@ -0,0 +1,36 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Utility for retrieving properties from nested dictionaries.""" + +from typing import Any + + +def get_property(data: dict[str, Any], path: str) -> Any: + """Retrieve a property from a dictionary using dot notation. + + Parameters + ---------- + data : dict[str, Any] + The dictionary to retrieve the property from. + path : str + A dot-separated string representing the path to the property (e.g., "foo.bar.baz"). + + Returns + ------- + Any + The value at the specified path. + + Raises + ------ + KeyError + If the path does not exist in the dictionary. + """ + keys = path.split(".") + current = data + for key in keys: + if not isinstance(current, dict) or key not in current: + msg = f"Property '{path}' not found" + raise KeyError(msg) + current = current[key] + return current diff --git a/packages/graphrag-input/graphrag_input/hashing.py b/packages/graphrag-input/graphrag_input/hashing.py new file mode 100644 index 0000000000..915824cd9f --- /dev/null +++ b/packages/graphrag-input/graphrag_input/hashing.py @@ -0,0 +1,27 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Hashing utilities.""" + +from collections.abc import Iterable +from hashlib import sha512 +from typing import Any + + +def gen_sha512_hash(item: dict[str, Any], hashcode: Iterable[str]) -> str: + """Generate a SHA512 hash. + + Parameters + ---------- + item : dict[str, Any] + The dictionary containing values to hash. + hashcode : Iterable[str] + The keys to include in the hash. + + Returns + ------- + str + The SHA512 hash as a hexadecimal string. + """ + hashed = "".join([str(item[column]) for column in hashcode]) + return f"{sha512(hashed.encode('utf-8'), usedforsecurity=False).hexdigest()}" diff --git a/packages/graphrag-input/graphrag_input/input_config.py b/packages/graphrag-input/graphrag_input/input_config.py new file mode 100644 index 0000000000..792ce3f85a --- /dev/null +++ b/packages/graphrag-input/graphrag_input/input_config.py @@ -0,0 +1,40 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Parameterization settings for the default configuration.""" + +from pydantic import BaseModel, ConfigDict, Field + +from graphrag_input.input_type import InputType + + +class InputConfig(BaseModel): + """The default configuration section for Input.""" + + model_config = ConfigDict(extra="allow") + """Allow extra fields to support custom reader implementations.""" + + type: str = Field( + description="The input file type to use.", + default=InputType.Text, + ) + encoding: str | None = Field( + description="The input file encoding to use.", + default=None, + ) + file_pattern: str | None = Field( + description="The input file pattern to use.", + default=None, + ) + id_column: str | None = Field( + description="The input ID column to use.", + default=None, + ) + title_column: str | None = Field( + description="The input title column to use.", + default=None, + ) + text_column: str | None = Field( + description="The input text column to use.", + default=None, + ) diff --git a/packages/graphrag-input/graphrag_input/input_reader.py b/packages/graphrag-input/graphrag_input/input_reader.py new file mode 100644 index 0000000000..be95168336 --- /dev/null +++ b/packages/graphrag-input/graphrag_input/input_reader.py @@ -0,0 +1,75 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'InputReader' model.""" + +from __future__ import annotations + +import logging +import re +from abc import ABCMeta, abstractmethod +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from graphrag_storage import Storage + + from graphrag_input.text_document import TextDocument + +logger = logging.getLogger(__name__) + + +class InputReader(metaclass=ABCMeta): + """Provide a cache interface for the pipeline.""" + + def __init__( + self, + storage: Storage, + file_pattern: str, + encoding: str = "utf-8", + **kwargs, + ): + self._storage = storage + self._encoding = encoding + self._file_pattern = file_pattern + + async def read_files(self) -> list[TextDocument]: + """Load files from storage and apply a loader function based on file type. Process metadata on the results if needed.""" + files = list(self._storage.find(re.compile(self._file_pattern))) + if len(files) == 0: + msg = f"No {self._file_pattern} matches found in storage" + logger.warning(msg) + files = [] + + documents: list[TextDocument] = [] + + for file in files: + try: + documents.extend(await self.read_file(file)) + except Exception as e: # noqa: BLE001 (catching Exception is fine here) + logger.warning("Warning! Error loading file %s. Skipping...", file) + logger.warning("Error: %s", e) + + logger.info( + "Found %d %s files, loading %d", + len(files), + self._file_pattern, + len(documents), + ) + total_files_log = ( + f"Total number of unfiltered {self._file_pattern} rows: {len(documents)}" + ) + logger.info(total_files_log) + + return documents + + @abstractmethod + async def read_file(self, path: str) -> list[TextDocument]: + """Read a file into a list of documents. + + Args: + - path - The path to read the file from. + + Returns + ------- + - output - List with an entry for each document in the file. + """ diff --git a/packages/graphrag-input/graphrag_input/input_reader_factory.py b/packages/graphrag-input/graphrag_input/input_reader_factory.py new file mode 100644 index 0000000000..825665fa9d --- /dev/null +++ b/packages/graphrag-input/graphrag_input/input_reader_factory.py @@ -0,0 +1,90 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'InputReaderFactory' model.""" + +import logging +from collections.abc import Callable + +from graphrag_common.factory import Factory +from graphrag_common.factory.factory import ServiceScope +from graphrag_storage.storage import Storage + +from graphrag_input.input_config import InputConfig +from graphrag_input.input_reader import InputReader +from graphrag_input.input_type import InputType + +logger = logging.getLogger(__name__) + + +class InputReaderFactory(Factory[InputReader]): + """Factory for creating Input Reader instances.""" + + +input_reader_factory = InputReaderFactory() + + +def register_input_reader( + input_reader_type: str, + input_reader_initializer: Callable[..., InputReader], + scope: ServiceScope = "transient", +) -> None: + """Register a custom input reader implementation. + + Args + ---- + - input_reader_type: str + The input reader id to register. + - input_reader_initializer: Callable[..., InputReader] + The input reader initializer to register. + """ + input_reader_factory.register(input_reader_type, input_reader_initializer, scope) + + +def create_input_reader(config: InputConfig, storage: Storage) -> InputReader: + """Create an input reader implementation based on the given configuration. + + Args + ---- + - config: InputConfig + The input reader configuration to use. + - storage: Storage | None + The storage implementation to use for reading the files. + + Returns + ------- + InputReader + The created input reader implementation. + """ + config_model = config.model_dump() + input_strategy = config.type + + if input_strategy not in input_reader_factory: + match input_strategy: + case InputType.Csv: + from graphrag_input.csv import CSVFileReader + + register_input_reader(InputType.Csv, CSVFileReader) + case InputType.Text: + from graphrag_input.text import TextFileReader + + register_input_reader(InputType.Text, TextFileReader) + case InputType.Json: + from graphrag_input.json import JSONFileReader + + register_input_reader(InputType.Json, JSONFileReader) + case InputType.JsonLines: + from graphrag_input.jsonl import JSONLinesFileReader + + register_input_reader(InputType.JsonLines, JSONLinesFileReader) + case InputType.MarkItDown: + from graphrag_input.markitdown import MarkItDownFileReader + + register_input_reader(InputType.MarkItDown, MarkItDownFileReader) + case _: + msg = f"InputConfig.type '{input_strategy}' is not registered in the InputReaderFactory. Registered types: {', '.join(input_reader_factory.keys())}." + raise ValueError(msg) + + config_model["storage"] = storage + + return input_reader_factory.create(input_strategy, init_args=config_model) diff --git a/packages/graphrag-input/graphrag_input/input_type.py b/packages/graphrag-input/graphrag_input/input_type.py new file mode 100644 index 0000000000..39d0559c8e --- /dev/null +++ b/packages/graphrag-input/graphrag_input/input_type.py @@ -0,0 +1,25 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing input file type enum.""" + +from enum import StrEnum + + +class InputType(StrEnum): + """The input file type for the pipeline.""" + + Csv = "csv" + """The CSV input type.""" + Text = "text" + """The text input type.""" + Json = "json" + """The JSON input type.""" + JsonLines = "jsonl" + """The JSON Lines input type.""" + MarkItDown = "markitdown" + """The MarkItDown input type.""" + + def __repr__(self): + """Get a string representation.""" + return f'"{self.value}"' diff --git a/packages/graphrag-input/graphrag_input/json.py b/packages/graphrag-input/graphrag_input/json.py new file mode 100644 index 0000000000..1e4bdefe8d --- /dev/null +++ b/packages/graphrag-input/graphrag_input/json.py @@ -0,0 +1,38 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'JSONFileReader' model.""" + +import json +import logging + +from graphrag_input.structured_file_reader import StructuredFileReader +from graphrag_input.text_document import TextDocument + +logger = logging.getLogger(__name__) + + +class JSONFileReader(StructuredFileReader): + """Reader implementation for json files.""" + + def __init__(self, file_pattern: str | None = None, **kwargs): + super().__init__( + file_pattern=file_pattern if file_pattern is not None else ".*\\.json$", + **kwargs, + ) + + async def read_file(self, path: str) -> list[TextDocument]: + """Read a JSON file into a list of documents. + + Args: + - path - The path to read the file from. + + Returns + ------- + - output - list with a TextDocument for each row in the file. + """ + text = await self._storage.get(path, encoding=self._encoding) + as_json = json.loads(text) + # json file could just be a single object, or an array of objects + rows = as_json if isinstance(as_json, list) else [as_json] + return await self.process_data_columns(rows, path) diff --git a/packages/graphrag-input/graphrag_input/jsonl.py b/packages/graphrag-input/graphrag_input/jsonl.py new file mode 100644 index 0000000000..f038aafaa5 --- /dev/null +++ b/packages/graphrag-input/graphrag_input/jsonl.py @@ -0,0 +1,38 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'JSONLinesFileReader' model.""" + +import json +import logging + +from graphrag_input.structured_file_reader import StructuredFileReader +from graphrag_input.text_document import TextDocument + +logger = logging.getLogger(__name__) + + +class JSONLinesFileReader(StructuredFileReader): + """Reader implementation for json lines files.""" + + def __init__(self, file_pattern: str | None = None, **kwargs): + super().__init__( + file_pattern=file_pattern if file_pattern is not None else ".*\\.jsonl$", + **kwargs, + ) + + async def read_file(self, path: str) -> list[TextDocument]: + """Read a JSON lines file into a list of documents. + + This differs from standard JSON files in that each line is a separate JSON object. + + Args: + - path - The path to read the file from. + + Returns + ------- + - output - list with a TextDocument for each row in the file. + """ + text = await self._storage.get(path, encoding=self._encoding) + rows = [json.loads(line) for line in text.splitlines()] + return await self.process_data_columns(rows, path) diff --git a/packages/graphrag-input/graphrag_input/markitdown.py b/packages/graphrag-input/graphrag_input/markitdown.py new file mode 100644 index 0000000000..466a2ebdd9 --- /dev/null +++ b/packages/graphrag-input/graphrag_input/markitdown.py @@ -0,0 +1,49 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'TextFileReader' model.""" + +import logging +from io import BytesIO +from pathlib import Path + +from markitdown import MarkItDown, StreamInfo + +from graphrag_input.hashing import gen_sha512_hash +from graphrag_input.input_reader import InputReader +from graphrag_input.text_document import TextDocument + +logger = logging.getLogger(__name__) + + +class MarkItDownFileReader(InputReader): + """Reader implementation for any file type supported by markitdown. + + https://github.com/microsoft/markitdown + """ + + async def read_file(self, path: str) -> list[TextDocument]: + """Read a text file into a DataFrame of documents. + + Args: + - path - The path to read the file from. + + Returns + ------- + - output - list with a TextDocument for each row in the file. + """ + bytes = await self._storage.get(path, encoding=self._encoding, as_bytes=True) + md = MarkItDown() + result = md.convert_stream( + BytesIO(bytes), stream_info=StreamInfo(extension=Path(path).suffix) + ) + text = result.markdown + + document = TextDocument( + id=gen_sha512_hash({"text": text}, ["text"]), + title=result.title if result.title else str(Path(path).name), + text=text, + creation_date=await self._storage.get_creation_date(path), + raw_data=None, + ) + return [document] diff --git a/packages/graphrag-input/graphrag_input/structured_file_reader.py b/packages/graphrag-input/graphrag_input/structured_file_reader.py new file mode 100644 index 0000000000..45628ff94d --- /dev/null +++ b/packages/graphrag-input/graphrag_input/structured_file_reader.py @@ -0,0 +1,65 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'StructuredFileReader' model.""" + +import logging +from typing import Any + +from graphrag_input.get_property import get_property +from graphrag_input.hashing import gen_sha512_hash +from graphrag_input.input_reader import InputReader +from graphrag_input.text_document import TextDocument + +logger = logging.getLogger(__name__) + + +class StructuredFileReader(InputReader): + """Base reader implementation for structured files such as csv and json.""" + + def __init__( + self, + id_column: str | None = None, + title_column: str | None = None, + text_column: str = "text", + **kwargs, + ): + super().__init__(**kwargs) + self._id_column = id_column + self._title_column = title_column + self._text_column = text_column + + async def process_data_columns( + self, + rows: list[dict[str, Any]], + path: str, + ) -> list[TextDocument]: + """Process configured data columns from a list of loaded dicts.""" + documents = [] + for index, row in enumerate(rows): + # text column is required - harvest from dict + text = get_property(row, self._text_column) + # id is optional - harvest from dict or hash from text + id = ( + get_property(row, self._id_column) + if self._id_column + else gen_sha512_hash({"text": text}, ["text"]) + ) + # title is optional - harvest from dict or use filename + num = f" ({index})" if len(rows) > 1 else "" + title = ( + get_property(row, self._title_column) + if self._title_column + else f"{path}{num}" + ) + creation_date = await self._storage.get_creation_date(path) + documents.append( + TextDocument( + id=id, + title=title, + text=text, + creation_date=creation_date, + raw_data=row, + ) + ) + return documents diff --git a/packages/graphrag-input/graphrag_input/text.py b/packages/graphrag-input/graphrag_input/text.py new file mode 100644 index 0000000000..5d42924181 --- /dev/null +++ b/packages/graphrag-input/graphrag_input/text.py @@ -0,0 +1,43 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'TextFileReader' model.""" + +import logging +from pathlib import Path + +from graphrag_input.hashing import gen_sha512_hash +from graphrag_input.input_reader import InputReader +from graphrag_input.text_document import TextDocument + +logger = logging.getLogger(__name__) + + +class TextFileReader(InputReader): + """Reader implementation for text files.""" + + def __init__(self, file_pattern: str | None = None, **kwargs): + super().__init__( + file_pattern=file_pattern if file_pattern is not None else ".*\\.txt$", + **kwargs, + ) + + async def read_file(self, path: str) -> list[TextDocument]: + """Read a text file into a list of documents. + + Args: + - path - The path to read the file from. + + Returns + ------- + - output - list with a TextDocument for each row in the file. + """ + text = await self._storage.get(path, encoding=self._encoding) + document = TextDocument( + id=gen_sha512_hash({"text": text}, ["text"]), + title=str(Path(path).name), + text=text, + creation_date=await self._storage.get_creation_date(path), + raw_data=None, + ) + return [document] diff --git a/packages/graphrag-input/graphrag_input/text_document.py b/packages/graphrag-input/graphrag_input/text_document.py new file mode 100644 index 0000000000..a771f30d43 --- /dev/null +++ b/packages/graphrag-input/graphrag_input/text_document.py @@ -0,0 +1,59 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""TextDocument dataclass.""" + +import logging +from dataclasses import dataclass +from typing import Any + +from graphrag_input.get_property import get_property + +logger = logging.getLogger(__name__) + + +@dataclass +class TextDocument: + """The TextDocument holds relevant content for GraphRAG indexing.""" + + id: str + """Unique identifier for the document.""" + text: str + """The main text content of the document.""" + title: str + """The title of the document.""" + creation_date: str + """The creation date of the document, ISO-8601 format.""" + raw_data: dict[str, Any] | None = None + """Raw data from source document.""" + + def get(self, field: str, default_value: Any = None) -> Any: + """ + Get a single field from the TextDocument. + + Functions like the get method on a dictionary, returning default_value if the field is not found. + + Supports nested fields using dot notation. + + This takes a two step approach for flexibility: + 1. If the field is one of the standard text document fields (id, title, text, creation_date), just grab it directly. This accommodates unstructured text for example, which just has the standard fields. + 2. Otherwise. try to extract it from the raw_data dict. This allows users to specify any column from the original input file. + + """ + if field in ["id", "title", "text", "creation_date"]: + return getattr(self, field) + + raw = self.raw_data or {} + try: + return get_property(raw, field) + except KeyError: + return default_value + + def collect(self, fields: list[str]) -> dict[str, Any]: + """Extract data fields from a TextDocument into a dict.""" + data = {} + for field in fields: + value = self.get(field) + if value is not None: + data[field] = value + return data diff --git a/packages/graphrag-input/pyproject.toml b/packages/graphrag-input/pyproject.toml new file mode 100644 index 0000000000..0a7d6b269f --- /dev/null +++ b/packages/graphrag-input/pyproject.toml @@ -0,0 +1,44 @@ +[project] +name = "graphrag-input" +version = "2.7.0" +description = "Input document loading utilities for GraphRAG" +authors = [ + {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"}, + {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"}, + {name = "Chris Trevino", email = "chtrevin@microsoft.com"}, + {name = "David Tittsworth", email = "datittsw@microsoft.com"}, + {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"}, + {name = "Derek Worthen", email = "deworthe@microsoft.com"}, + {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"}, + {name = "Ha Trinh", email = "trinhha@microsoft.com"}, + {name = "Jonathan Larson", email = "jolarso@microsoft.com"}, + {name = "Josh Bradley", email = "joshbradley@microsoft.com"}, + {name = "Kate Lytvynets", email = "kalytv@microsoft.com"}, + {name = "Kenny Zhang", email = "zhangken@microsoft.com"}, + {name = "Mónica Carvajal"}, + {name = "Nathan Evans", email = "naevans@microsoft.com"}, + {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"}, + {name = "Sarah Smith", email = "smithsarah@microsoft.com"}, +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.11,<3.14" +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "graphrag-common==2.7.0", + "graphrag-storage==2.7.0", + "pydantic~=2.10", + "markitdown~=0.1.0" +] + +[project.urls] +Source = "https://github.com/microsoft/graphrag" + +[build-system] +requires = ["hatchling>=1.27.0,<2.0.0"] +build-backend = "hatchling.build" diff --git a/packages/graphrag/graphrag/cli/index.py b/packages/graphrag/graphrag/cli/index.py index 60b724ea98..94a40cde48 100644 --- a/packages/graphrag/graphrag/cli/index.py +++ b/packages/graphrag/graphrag/cli/index.py @@ -9,9 +9,11 @@ import warnings from pathlib import Path +from graphrag_cache.cache_type import CacheType + import graphrag.api as api from graphrag.callbacks.console_workflow_callbacks import ConsoleWorkflowCallbacks -from graphrag.config.enums import CacheType, IndexingMethod +from graphrag.config.enums import IndexingMethod from graphrag.config.load_config import load_config from graphrag.index.validate_config import validate_config_names from graphrag.utils.cli import redact @@ -102,7 +104,7 @@ def _run_index( ) if not cache: - config.cache.type = CacheType.none + config.cache.type = CacheType.Noop if not skip_validation: validate_config_names(config) diff --git a/packages/graphrag/graphrag/cli/initialize.py b/packages/graphrag/graphrag/cli/initialize.py index d29866e5c4..2eb5ef785e 100644 --- a/packages/graphrag/graphrag/cli/initialize.py +++ b/packages/graphrag/graphrag/cli/initialize.py @@ -63,7 +63,7 @@ def initialize_project_at( raise ValueError(msg) input_path = ( - root / (graphrag_config_defaults.input.storage.base_dir or "input") + root / (graphrag_config_defaults.input_storage.base_dir or "input") ).resolve() input_path.mkdir(parents=True, exist_ok=True) # using replace with custom tokens instead of format here because we have a placeholder for GRAPHRAG_API_KEY that is used later for .env overlay diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py index cab8732af2..cc07438c85 100644 --- a/packages/graphrag/graphrag/config/defaults.py +++ b/packages/graphrag/graphrag/config/defaults.py @@ -9,13 +9,13 @@ from graphrag_cache import CacheType from graphrag_chunking.chunk_strategy_type import ChunkerType +from graphrag_input import InputType from graphrag_storage import StorageType from graphrag.config.embeddings import default_embeddings from graphrag.config.enums import ( AsyncType, AuthType, - InputFileType, ModelType, NounPhraseExtractorType, ReportingType, @@ -64,7 +64,7 @@ class ChunkingDefaults: size: int = 1200 overlap: int = 100 encoding_model: str = ENCODING_MODEL - prepend_metadata: bool = False + prepend_metadata: None = None @dataclass @@ -226,23 +226,22 @@ class StorageDefaults: @dataclass -class InputStorageDefaults(StorageDefaults): - """Default values for input storage.""" +class InputDefaults: + """Default values for input.""" - base_dir: str | None = DEFAULT_INPUT_BASE_DIR + type: ClassVar[InputType] = InputType.Text + encoding: str | None = None + file_pattern: None = None + id_column: None = None + title_column: None = None + text_column: None = None @dataclass -class InputDefaults: - """Default values for input.""" +class InputStorageDefaults(StorageDefaults): + """Default values for input storage.""" - storage: InputStorageDefaults = field(default_factory=InputStorageDefaults) - file_type: ClassVar[InputFileType] = InputFileType.text - encoding: str = "utf-8" - file_pattern: str = "" - text_column: str = "text" - title_column: None = None - metadata: None = None + base_dir: str | None = DEFAULT_INPUT_BASE_DIR @dataclass @@ -311,7 +310,7 @@ class LocalSearchDefaults: @dataclass -class OutputDefaults(StorageDefaults): +class OutputStorageDefaults(StorageDefaults): """Default values for output.""" base_dir: str | None = DEFAULT_OUTPUT_BASE_DIR @@ -362,7 +361,7 @@ class SummarizeDescriptionsDefaults: @dataclass -class UpdateIndexOutputDefaults(StorageDefaults): +class UpdateOutputStorageDefaults(StorageDefaults): """Default values for update index output.""" base_dir: str | None = DEFAULT_UPDATE_OUTPUT_BASE_DIR @@ -389,13 +388,14 @@ class GraphRagConfigDefaults: models: dict = field(default_factory=dict) reporting: ReportingDefaults = field(default_factory=ReportingDefaults) - storage: StorageDefaults = field(default_factory=StorageDefaults) - output: OutputDefaults = field(default_factory=OutputDefaults) - update_index_output: UpdateIndexOutputDefaults = field( - default_factory=UpdateIndexOutputDefaults + input_storage: InputStorageDefaults = field(default_factory=InputStorageDefaults) + output_storage: OutputStorageDefaults = field(default_factory=OutputStorageDefaults) + update_output_storage: UpdateOutputStorageDefaults = field( + default_factory=UpdateOutputStorageDefaults ) cache: CacheDefaults = field(default_factory=CacheDefaults) input: InputDefaults = field(default_factory=InputDefaults) + embed_text: EmbedTextDefaults = field(default_factory=EmbedTextDefaults) chunking: ChunkingDefaults = field(default_factory=ChunkingDefaults) snapshots: SnapshotsDefaults = field(default_factory=SnapshotsDefaults) diff --git a/packages/graphrag/graphrag/config/enums.py b/packages/graphrag/graphrag/config/enums.py index 855f062f27..9685ebb07b 100644 --- a/packages/graphrag/graphrag/config/enums.py +++ b/packages/graphrag/graphrag/config/enums.py @@ -8,57 +8,6 @@ from enum import Enum -class CacheType(str, Enum): - """The cache configuration type for the pipeline.""" - - file = "file" - """The file cache configuration type.""" - memory = "memory" - """The memory cache configuration type.""" - none = "none" - """The none cache configuration type.""" - blob = "blob" - """The blob cache configuration type.""" - cosmosdb = "cosmosdb" - """The cosmosdb cache configuration type""" - - def __repr__(self): - """Get a string representation.""" - return f'"{self.value}"' - - -class InputFileType(str, Enum): - """The input file type for the pipeline.""" - - csv = "csv" - """The CSV input type.""" - text = "text" - """The text input type.""" - json = "json" - """The JSON input type.""" - - def __repr__(self): - """Get a string representation.""" - return f'"{self.value}"' - - -class StorageType(str, Enum): - """The output type for the pipeline.""" - - file = "file" - """The file output type.""" - memory = "memory" - """The memory output type.""" - blob = "blob" - """The blob output type.""" - cosmosdb = "cosmosdb" - """The cosmosdb output type""" - - def __repr__(self): - """Get a string representation.""" - return f'"{self.value}"' - - class VectorStoreType(str, Enum): """The supported vector store types.""" diff --git a/packages/graphrag/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py index 086228bf55..df075ada0e 100644 --- a/packages/graphrag/graphrag/config/init_content.py +++ b/packages/graphrag/graphrag/config/init_content.py @@ -46,13 +46,10 @@ tokens_per_minute: null requests_per_minute: null -### Input settings ### +### Document processing settings ### input: - storage: - type: {graphrag_config_defaults.input.storage.type} # [file, blob, cosmosdb] - base_dir: "{graphrag_config_defaults.input.storage.base_dir}" - file_type: {graphrag_config_defaults.input.file_type.value} # [csv, text, json] + type: {graphrag_config_defaults.input.type.value} # [csv, text, json, jsonl] chunking: type: {graphrag_config_defaults.chunking.type} @@ -60,24 +57,28 @@ overlap: {graphrag_config_defaults.chunking.overlap} encoding_model: {graphrag_config_defaults.chunking.encoding_model} -### Output/storage settings ### +### Storage settings ### ## If blob storage is specified in the following four sections, ## connection_string and container_name must be provided -output: - type: {graphrag_config_defaults.output.type} # [file, blob, cosmosdb] - base_dir: "{graphrag_config_defaults.output.base_dir}" +input_storage: + type: {graphrag_config_defaults.input_storage.type} # [file, blob, cosmosdb] + base_dir: "{graphrag_config_defaults.input_storage.base_dir}" -cache: - type: {graphrag_config_defaults.cache.type} # [json, memory, none] - storage: - type: {graphrag_config_defaults.cache.storage.type} # [file, blob, cosmosdb] - base_dir: "{graphrag_config_defaults.cache.storage.base_dir}" +output_storage: + type: {graphrag_config_defaults.output_storage.type} # [file, blob, cosmosdb] + base_dir: "{graphrag_config_defaults.output_storage.base_dir}" reporting: type: {graphrag_config_defaults.reporting.type.value} # [file, blob] base_dir: "{graphrag_config_defaults.reporting.base_dir}" +cache: + type: {graphrag_config_defaults.cache.type} # [json, memory, none] + storage: + type: {graphrag_config_defaults.cache.storage.type} # [file, blob, cosmosdb] + base_dir: "{graphrag_config_defaults.cache.storage.base_dir}" + vector_store: type: {vector_store_defaults.type} db_uri: {vector_store_defaults.db_uri} diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py index f79537cd04..2e6efd4461 100644 --- a/packages/graphrag/graphrag/config/models/graph_rag_config.py +++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py @@ -9,12 +9,12 @@ from devtools import pformat from graphrag_cache import CacheConfig from graphrag_chunking.chunking_config import ChunkingConfig +from graphrag_input import InputConfig from graphrag_storage import StorageConfig, StorageType from pydantic import BaseModel, Field, model_validator -import graphrag.config.defaults as defs from graphrag.config.defaults import graphrag_config_defaults -from graphrag.config.enums import VectorStoreType +from graphrag.config.enums import ReportingType, VectorStoreType from graphrag.config.models.basic_search_config import BasicSearchConfig from graphrag.config.models.cluster_graph_config import ClusterGraphConfig from graphrag.config.models.community_reports_config import CommunityReportsConfig @@ -24,7 +24,6 @@ from graphrag.config.models.extract_graph_config import ExtractGraphConfig from graphrag.config.models.extract_graph_nlp_config import ExtractGraphNLPConfig from graphrag.config.models.global_search_config import GlobalSearchConfig -from graphrag.config.models.input_config import InputConfig from graphrag.config.models.language_model_config import LanguageModelConfig from graphrag.config.models.local_search_config import LocalSearchConfig from graphrag.config.models.prune_graph_config import PruneGraphConfig @@ -107,22 +106,22 @@ def _validate_rate_limiter_services(self) -> None: ) """The input configuration.""" - def _validate_input_pattern(self) -> None: - """Validate the input file pattern based on the specified type.""" - if len(self.input.file_pattern) == 0: - if self.input.file_type == defs.InputFileType.text: - self.input.file_pattern = ".*\\.txt$" - else: - self.input.file_pattern = f".*\\.{self.input.file_type.value}$" + input_storage: StorageConfig = Field( + description="The input storage configuration.", + default=StorageConfig( + base_dir=graphrag_config_defaults.input_storage.base_dir, + ), + ) + """The input storage configuration.""" def _validate_input_base_dir(self) -> None: """Validate the input base directory.""" - if self.input.storage.type == StorageType.File: - if not self.input.storage.base_dir: + if self.input_storage.type == StorageType.File: + if not self.input_storage.base_dir: msg = "input storage base directory is required for file input storage. Please rerun `graphrag init` and set the input storage configuration." raise ValueError(msg) - self.input.storage.base_dir = str( - Path(self.input.storage.base_dir).resolve() + self.input_storage.base_dir = str( + Path(self.input_storage.base_dir).resolve() ) chunking: ChunkingConfig = Field( @@ -140,7 +139,7 @@ def _validate_input_base_dir(self) -> None: output: StorageConfig = Field( description="The output configuration.", default=StorageConfig( - base_dir=graphrag_config_defaults.output.base_dir, + base_dir=graphrag_config_defaults.output_storage.base_dir, ), ) """The output configuration.""" @@ -153,22 +152,22 @@ def _validate_output_base_dir(self) -> None: raise ValueError(msg) self.output.base_dir = str(Path(self.output.base_dir).resolve()) - update_index_output: StorageConfig = Field( + update_output_storage: StorageConfig = Field( description="The output configuration for the updated index.", default=StorageConfig( - base_dir=graphrag_config_defaults.update_index_output.base_dir, + base_dir=graphrag_config_defaults.update_output_storage.base_dir, ), ) """The output configuration for the updated index.""" - def _validate_update_index_output_base_dir(self) -> None: - """Validate the update index output base directory.""" - if self.update_index_output.type == StorageType.File: - if not self.update_index_output.base_dir: - msg = "update_index_output base directory is required for file output. Please rerun `graphrag init` and set the update_index_output configuration." + def _validate_update_output_storage_base_dir(self) -> None: + """Validate the update output base directory.""" + if self.update_output_storage.type == StorageType.File: + if not self.update_output_storage.base_dir: + msg = "update_output_storage base directory is required for file output. Please rerun `graphrag init` and set the update_output_storage configuration." raise ValueError(msg) - self.update_index_output.base_dir = str( - Path(self.update_index_output.base_dir).resolve() + self.update_output_storage.base_dir = str( + Path(self.update_output_storage.base_dir).resolve() ) cache: CacheConfig = Field( @@ -184,7 +183,7 @@ def _validate_update_index_output_base_dir(self) -> None: def _validate_reporting_base_dir(self) -> None: """Validate the reporting base directory.""" - if self.reporting.type == defs.ReportingType.file: + if self.reporting.type == ReportingType.file: if self.reporting.base_dir.strip() == "": msg = "Reporting base directory is required for file reporting. Please rerun `graphrag init` and set the reporting configuration." raise ValueError(msg) @@ -318,11 +317,10 @@ def get_language_model_config(self, model_id: str) -> LanguageModelConfig: @model_validator(mode="after") def _validate_model(self): """Validate the model configuration.""" - self._validate_input_pattern() self._validate_input_base_dir() self._validate_reporting_base_dir() self._validate_output_base_dir() - self._validate_update_index_output_base_dir() + self._validate_update_output_storage_base_dir() self._validate_vector_store_db_uri() self._validate_factories() return self diff --git a/packages/graphrag/graphrag/config/models/input_config.py b/packages/graphrag/graphrag/config/models/input_config.py deleted file mode 100644 index c3c30f6302..0000000000 --- a/packages/graphrag/graphrag/config/models/input_config.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""Parameterization settings for the default configuration.""" - -from graphrag_storage import StorageConfig -from pydantic import BaseModel, Field - -import graphrag.config.defaults as defs -from graphrag.config.defaults import graphrag_config_defaults -from graphrag.config.enums import InputFileType - - -class InputConfig(BaseModel): - """The default configuration section for Input.""" - - storage: StorageConfig = Field( - description="The storage configuration to use for reading input documents.", - default=StorageConfig( - base_dir=graphrag_config_defaults.input.storage.base_dir, - ), - ) - file_type: InputFileType = Field( - description="The input file type to use.", - default=graphrag_config_defaults.input.file_type, - ) - encoding: str = Field( - description="The input file encoding to use.", - default=defs.graphrag_config_defaults.input.encoding, - ) - file_pattern: str = Field( - description="The input file pattern to use.", - default=graphrag_config_defaults.input.file_pattern, - ) - text_column: str = Field( - description="The input text column to use.", - default=graphrag_config_defaults.input.text_column, - ) - title_column: str | None = Field( - description="The input title column to use.", - default=graphrag_config_defaults.input.title_column, - ) - metadata: list[str] | None = Field( - description="The document attribute columns to use.", - default=graphrag_config_defaults.input.metadata, - ) diff --git a/packages/graphrag/graphrag/data_model/schemas.py b/packages/graphrag/graphrag/data_model/schemas.py index 93ede46191..c0926b9bb7 100644 --- a/packages/graphrag/graphrag/data_model/schemas.py +++ b/packages/graphrag/graphrag/data_model/schemas.py @@ -64,7 +64,7 @@ N_TOKENS = "n_tokens" CREATION_DATE = "creation_date" -METADATA = "metadata" +RAW_DATA = "raw_data" # the following lists define the final content and ordering of columns in the data model parquet outputs ENTITIES_FINAL_COLUMNS = [ @@ -155,5 +155,5 @@ TEXT, TEXT_UNIT_IDS, CREATION_DATE, - METADATA, + RAW_DATA, ] diff --git a/packages/graphrag/graphrag/index/input/__init__.py b/packages/graphrag/graphrag/index/input/__init__.py deleted file mode 100644 index 15177c91db..0000000000 --- a/packages/graphrag/graphrag/index/input/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""The Indexing Engine input package root.""" diff --git a/packages/graphrag/graphrag/index/input/csv.py b/packages/graphrag/graphrag/index/input/csv.py deleted file mode 100644 index 810ffdd03c..0000000000 --- a/packages/graphrag/graphrag/index/input/csv.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'CSVFileReader' model.""" - -import logging -from io import BytesIO - -import pandas as pd - -from graphrag.index.input.input_reader import InputReader -from graphrag.index.input.util import process_data_columns - -logger = logging.getLogger(__name__) - - -class CSVFileReader(InputReader): - """Reader implementation for csv files.""" - - async def read_file(self, path: str) -> pd.DataFrame: - """Read a csv file into a DataFrame of documents. - - Args: - - path - The path to read the file from. - - Returns - ------- - - output - DataFrame with a row for each document in the file. - """ - buffer = BytesIO(await self._storage.get(path, as_bytes=True)) - data = pd.read_csv(buffer, encoding=self._config.encoding) - data = process_data_columns(data, self._config, path) - creation_date = await self._storage.get_creation_date(path) - data["creation_date"] = data.apply(lambda _: creation_date, axis=1) - return data diff --git a/packages/graphrag/graphrag/index/input/factory.py b/packages/graphrag/graphrag/index/input/factory.py deleted file mode 100644 index bc3b8112c8..0000000000 --- a/packages/graphrag/graphrag/index/input/factory.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'InputReaderFactory' model.""" - -import logging - -from graphrag_common.factory import Factory - -from graphrag.config.enums import InputFileType -from graphrag.index.input.csv import CSVFileReader -from graphrag.index.input.input_reader import InputReader -from graphrag.index.input.json import JSONFileReader -from graphrag.index.input.text import TextFileReader - -logger = logging.getLogger(__name__) - - -class InputReaderFactory(Factory[InputReader]): - """Factory for creating Input Reader instances.""" - - -input_reader_factory = InputReaderFactory() -input_reader_factory.register(InputFileType.text, TextFileReader) -input_reader_factory.register(InputFileType.csv, CSVFileReader) -input_reader_factory.register(InputFileType.json, JSONFileReader) diff --git a/packages/graphrag/graphrag/index/input/input_reader.py b/packages/graphrag/graphrag/index/input/input_reader.py deleted file mode 100644 index 18dc713dfd..0000000000 --- a/packages/graphrag/graphrag/index/input/input_reader.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'InputReader' model.""" - -from __future__ import annotations - -import logging -import re -from abc import ABCMeta, abstractmethod -from typing import TYPE_CHECKING - -import pandas as pd - -if TYPE_CHECKING: - from graphrag_storage import Storage - - from graphrag.config.models.input_config import InputConfig - -logger = logging.getLogger(__name__) - - -class InputReader(metaclass=ABCMeta): - """Provide a cache interface for the pipeline.""" - - def __init__(self, storage: Storage, config: InputConfig, **kwargs): - self._storage = storage - self._config = config - - async def read_files(self) -> pd.DataFrame: - """Load files from storage and apply a loader function based on file type. Process metadata on the results if needed.""" - files = list(self._storage.find(re.compile(self._config.file_pattern))) - - if len(files) == 0: - msg = f"No {self._config.file_type} files found in {self._config.storage.base_dir}" - raise ValueError(msg) - - files_loaded = [] - - for file in files: - try: - files_loaded.append(await self.read_file(file)) - except Exception as e: # noqa: BLE001 (catching Exception is fine here) - logger.warning("Warning! Error loading file %s. Skipping...", file) - logger.warning("Error: %s", e) - - logger.info( - "Found %d %s files, loading %d", - len(files), - self._config.file_type, - len(files_loaded), - ) - result = pd.concat(files_loaded) - total_files_log = ( - f"Total number of unfiltered {self._config.file_type} rows: {len(result)}" - ) - logger.info(total_files_log) - # Convert metadata columns to strings and collapse them into a JSON object - if self._config.metadata: - if all(col in result.columns for col in self._config.metadata): - # Collapse the metadata columns into a single JSON object column - result["metadata"] = result[self._config.metadata].apply( - lambda row: row.to_dict(), axis=1 - ) - else: - value_error_msg = ( - "One or more metadata columns not found in the DataFrame." - ) - raise ValueError(value_error_msg) - - result[self._config.metadata] = result[self._config.metadata].astype(str) - - return result - - @abstractmethod - async def read_file(self, path: str) -> pd.DataFrame: - """Read a file into a DataFrame of documents. - - Args: - - path - The path to read the file from. - - Returns - ------- - - output - DataFrame with a row for each document in the file. - """ diff --git a/packages/graphrag/graphrag/index/input/json.py b/packages/graphrag/graphrag/index/input/json.py deleted file mode 100644 index e9e1a7ab04..0000000000 --- a/packages/graphrag/graphrag/index/input/json.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'JSONFileReader' model.""" - -import json -import logging - -import pandas as pd - -from graphrag.index.input.input_reader import InputReader -from graphrag.index.input.util import process_data_columns - -logger = logging.getLogger(__name__) - - -class JSONFileReader(InputReader): - """Reader implementation for json files.""" - - async def read_file(self, path: str) -> pd.DataFrame: - """Read a JSON file into a DataFrame of documents. - - Args: - - path - The path to read the file from. - - Returns - ------- - - output - DataFrame with a row for each document in the file. - """ - text = await self._storage.get(path, encoding=self._config.encoding) - as_json = json.loads(text) - # json file could just be a single object, or an array of objects - rows = as_json if isinstance(as_json, list) else [as_json] - data = pd.DataFrame(rows) - data = process_data_columns(data, self._config, path) - creation_date = await self._storage.get_creation_date(path) - data["creation_date"] = data.apply(lambda _: creation_date, axis=1) - - return data diff --git a/packages/graphrag/graphrag/index/input/text.py b/packages/graphrag/graphrag/index/input/text.py deleted file mode 100644 index f9e2d7779e..0000000000 --- a/packages/graphrag/graphrag/index/input/text.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'TextFileReader' model.""" - -import logging -from pathlib import Path - -import pandas as pd - -from graphrag.index.input.input_reader import InputReader -from graphrag.index.utils.hashing import gen_sha512_hash - -logger = logging.getLogger(__name__) - - -class TextFileReader(InputReader): - """Reader implementation for text files.""" - - async def read_file(self, path: str) -> pd.DataFrame: - """Read a text file into a DataFrame of documents. - - Args: - - path - The path to read the file from. - - Returns - ------- - - output - DataFrame with a row for each document in the file. - """ - text = await self._storage.get(path, encoding=self._config.encoding) - new_item = {"text": text} - new_item["id"] = gen_sha512_hash(new_item, new_item.keys()) - new_item["title"] = str(Path(path).name) - new_item["creation_date"] = await self._storage.get_creation_date(path) - return pd.DataFrame([new_item]) diff --git a/packages/graphrag/graphrag/index/input/util.py b/packages/graphrag/graphrag/index/input/util.py deleted file mode 100644 index 2780909167..0000000000 --- a/packages/graphrag/graphrag/index/input/util.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""Shared column processing for structured input files.""" - -import logging - -import pandas as pd - -from graphrag.config.models.input_config import InputConfig -from graphrag.index.utils.hashing import gen_sha512_hash - -logger = logging.getLogger(__name__) - - -def process_data_columns( - documents: pd.DataFrame, config: InputConfig, path: str -) -> pd.DataFrame: - """Process configured data columns of a DataFrame.""" - if "id" not in documents.columns: - documents["id"] = documents.apply( - lambda x: gen_sha512_hash(x, x.keys()), axis=1 - ) - if config.text_column is not None and "text" not in documents.columns: - if config.text_column not in documents.columns: - logger.warning( - "text_column %s not found in csv file %s", - config.text_column, - path, - ) - else: - documents["text"] = documents.apply(lambda x: x[config.text_column], axis=1) - if config.title_column is not None: - if config.title_column not in documents.columns: - logger.warning( - "title_column %s not found in csv file %s", - config.title_column, - path, - ) - else: - documents["title"] = documents.apply( - lambda x: x[config.title_column], axis=1 - ) - else: - documents["title"] = documents.apply(lambda _: path, axis=1) - return documents diff --git a/packages/graphrag/graphrag/index/operations/prune_graph.py b/packages/graphrag/graphrag/index/operations/prune_graph.py index 6eb6b60065..11a5335b4e 100644 --- a/packages/graphrag/graphrag/index/operations/prune_graph.py +++ b/packages/graphrag/graphrag/index/operations/prune_graph.py @@ -50,7 +50,8 @@ def prune_graph( graph.remove_nodes_from([ node for node, data in graph.nodes(data=True) - if data[schemas.NODE_FREQUENCY] < min_node_freq + if schemas.NODE_FREQUENCY not in data + or data[schemas.NODE_FREQUENCY] < min_node_freq ]) if max_node_freq_std is not None: upper_threshold = _get_upper_threshold_by_std( diff --git a/packages/graphrag/graphrag/index/run/run_pipeline.py b/packages/graphrag/graphrag/index/run/run_pipeline.py index d4f0af20b6..f82e774423 100644 --- a/packages/graphrag/graphrag/index/run/run_pipeline.py +++ b/packages/graphrag/graphrag/index/run/run_pipeline.py @@ -35,7 +35,7 @@ async def run_pipeline( input_documents: pd.DataFrame | None = None, ) -> AsyncIterable[PipelineRunResult]: """Run all workflows using a simplified pipeline.""" - input_storage = create_storage(config.input.storage) + input_storage = create_storage(config.input_storage) output_storage = create_storage(config.output) cache = create_cache(config.cache) @@ -49,7 +49,7 @@ async def run_pipeline( if is_update_run: logger.info("Running incremental indexing.") - update_storage = create_storage(config.update_index_output) + update_storage = create_storage(config.update_output_storage) # we use this to store the new subset index, and will merge its content with the previous index update_timestamp = time.strftime("%Y%m%d-%H%M%S") timestamped_storage = update_storage.child(update_timestamp) diff --git a/packages/graphrag/graphrag/index/run/utils.py b/packages/graphrag/graphrag/index/run/utils.py index 9c77d16de6..040b77a45c 100644 --- a/packages/graphrag/graphrag/index/run/utils.py +++ b/packages/graphrag/graphrag/index/run/utils.py @@ -53,7 +53,7 @@ def get_update_storages( ) -> tuple[Storage, Storage, Storage]: """Get storage objects for the update index run.""" output_storage = create_storage(config.output) - update_storage = create_storage(config.update_index_output) + update_storage = create_storage(config.update_output_storage) timestamped_storage = update_storage.child(timestamp) delta_storage = timestamped_storage.child("delta") previous_storage = timestamped_storage.child("previous") diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index fce186e0c6..1b1d9b48a9 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -3,14 +3,14 @@ """A module containing run_workflow method definition.""" -import json import logging from typing import Any, cast import pandas as pd -from graphrag_chunking.add_metadata import add_metadata from graphrag_chunking.chunker import Chunker from graphrag_chunking.chunker_factory import create_chunker +from graphrag_chunking.transformers import add_metadata +from graphrag_input import TextDocument from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.config.models.graph_rag_config import GraphRagConfig @@ -54,7 +54,7 @@ def create_base_text_units( callbacks: WorkflowCallbacks, tokenizer: Tokenizer, chunker: Chunker, - prepend_metadata: bool | None = False, + prepend_metadata: list[str] | None = None, ) -> pd.DataFrame: """All the steps to transform base text_units.""" documents.sort_values(by=["id"], ascending=[True], inplace=True) @@ -66,15 +66,27 @@ def create_base_text_units( logger.info("Starting chunking process for %d documents", total_rows) def chunker_with_logging(row: pd.Series, row_index: int) -> Any: - row["chunks"] = [chunk.text for chunk in chunker.chunk(row["text"])] - - metadata = row.get("metadata", None) - if prepend_metadata and metadata is not None: - metadata = json.loads(metadata) if isinstance(metadata, str) else metadata - row["chunks"] = [ - add_metadata(chunk, metadata, line_delimiter=".\n") - for chunk in row["chunks"] - ] + if prepend_metadata: + # create a standard text document for metadata plucking + # ignore any additional fields in case the input dataframe has extra columns + document = TextDocument( + id=row["id"], + title=row["title"], + text=row["text"], + creation_date=row["creation_date"], + raw_data=row["raw_data"], + ) + metadata = document.collect(prepend_metadata) + transformer = add_metadata( + metadata=metadata, line_delimiter=".\n" + ) # delim with . for back-compat older indexes + else: + transformer = None + + row["chunks"] = [ + chunk.text for chunk in chunker.chunk(row["text"], transform=transformer) + ] + tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row diff --git a/packages/graphrag/graphrag/index/workflows/create_final_documents.py b/packages/graphrag/graphrag/index/workflows/create_final_documents.py index f12560bab6..554fbc4254 100644 --- a/packages/graphrag/graphrag/index/workflows/create_final_documents.py +++ b/packages/graphrag/graphrag/index/workflows/create_final_documents.py @@ -67,7 +67,7 @@ def create_final_documents( rejoined["id"] = rejoined["id"].astype(str) rejoined["human_readable_id"] = rejoined.index - if "metadata" not in rejoined.columns: - rejoined["metadata"] = pd.Series(dtype="object") + if "raw_data" not in rejoined.columns: + rejoined["raw_data"] = pd.Series(dtype="object") return rejoined.loc[:, DOCUMENTS_FINAL_COLUMNS] diff --git a/packages/graphrag/graphrag/index/workflows/load_input_documents.py b/packages/graphrag/graphrag/index/workflows/load_input_documents.py index 228ca4d0c4..0a5aa65454 100644 --- a/packages/graphrag/graphrag/index/workflows/load_input_documents.py +++ b/packages/graphrag/graphrag/index/workflows/load_input_documents.py @@ -6,10 +6,9 @@ import logging import pandas as pd +from graphrag_input import InputReader, create_input_reader from graphrag.config.models.graph_rag_config import GraphRagConfig -from graphrag.index.input.factory import InputReaderFactory -from graphrag.index.input.input_reader import InputReader from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput from graphrag.utils.storage import write_table_to_storage @@ -22,13 +21,15 @@ async def run_workflow( context: PipelineRunContext, ) -> WorkflowFunctionOutput: """Load and parse input documents into a standard format.""" - input_reader = InputReaderFactory().create( - config.input.file_type, - {"storage": context.input_storage, "config": config.input}, - ) + input_reader = create_input_reader(config.input, context.input_storage) output = await load_input_documents(input_reader) + if len(output) == 0: + msg = "Error reading documents, please see logs." + logger.error(msg) + raise ValueError(msg) + logger.info("Final # of rows loaded: %s", len(output)) context.stats.num_documents = len(output) @@ -39,4 +40,4 @@ async def run_workflow( async def load_input_documents(input_reader: InputReader) -> pd.DataFrame: """Load and parse input documents into a standard format.""" - return await input_reader.read_files() + return pd.DataFrame(await input_reader.read_files()) diff --git a/packages/graphrag/graphrag/index/workflows/load_update_documents.py b/packages/graphrag/graphrag/index/workflows/load_update_documents.py index e68471eae7..1cab6cabfe 100644 --- a/packages/graphrag/graphrag/index/workflows/load_update_documents.py +++ b/packages/graphrag/graphrag/index/workflows/load_update_documents.py @@ -6,11 +6,11 @@ import logging import pandas as pd +from graphrag_input.input_reader import InputReader +from graphrag_input.input_reader_factory import create_input_reader from graphrag_storage import Storage from graphrag.config.models.graph_rag_config import GraphRagConfig -from graphrag.index.input.factory import InputReaderFactory -from graphrag.index.input.input_reader import InputReader from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput from graphrag.index.update.incremental_index import get_delta_docs @@ -24,10 +24,7 @@ async def run_workflow( context: PipelineRunContext, ) -> WorkflowFunctionOutput: """Load and parse update-only input documents into a standard format.""" - input_reader = InputReaderFactory().create( - config.input.file_type, - {"storage": context.input_storage, "config": config.input}, - ) + input_reader = create_input_reader(config.input, context.input_storage) output = await load_update_documents( input_reader, context.previous_storage, @@ -50,7 +47,7 @@ async def load_update_documents( previous_storage: Storage, ) -> pd.DataFrame: """Load and parse update-only input documents into a standard format.""" - input_documents = await input_reader.read_files() + input_documents = pd.DataFrame(await input_reader.read_files()) # previous storage is the output of the previous run # we'll use this to diff the input from the prior delta_documents = await get_delta_docs(input_documents, previous_storage) diff --git a/packages/graphrag/graphrag/index/workflows/prune_graph.py b/packages/graphrag/graphrag/index/workflows/prune_graph.py index da4e2bc835..5653eef49b 100644 --- a/packages/graphrag/graphrag/index/workflows/prune_graph.py +++ b/packages/graphrag/graphrag/index/workflows/prune_graph.py @@ -58,6 +58,7 @@ def prune_graph( """Prune a full graph based on graph statistics.""" # create a temporary graph to prune, then turn it back into dataframes graph = create_graph(relationships, edge_attr=["weight"], nodes=entities) + pruned = prune_graph_operation( graph, min_node_freq=pruning_config.min_node_freq, diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py index a9ef358a30..2b4e2b1861 100644 --- a/packages/graphrag/graphrag/prompt_tune/loader/input.py +++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py @@ -10,11 +10,11 @@ import pandas as pd from graphrag_cache.noop_cache import NoopCache from graphrag_chunking.chunker_factory import create_chunker +from graphrag_input import create_input_reader from graphrag_storage import create_storage from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks from graphrag.config.models.graph_rag_config import GraphRagConfig -from graphrag.index.input.factory import InputReaderFactory from graphrag.index.operations.embed_text.run_embed_text import ( run_embed_text, ) @@ -63,14 +63,11 @@ async def load_docs_in_chunks( ) tokenizer = get_tokenizer(embeddings_llm_settings) chunker = create_chunker(config.chunking, tokenizer.encode, tokenizer.decode) - input_storage = create_storage(config.input.storage) - input_reader = InputReaderFactory().create( - config.input.file_type, - {"storage": input_storage, "config": config.input}, - ) + input_storage = create_storage(config.input_storage) + input_reader = create_input_reader(config.input, input_storage) dataset = await input_reader.read_files() chunks_df = create_base_text_units( - documents=dataset, + documents=pd.DataFrame(dataset), callbacks=NoopWorkflowCallbacks(), tokenizer=tokenizer, chunker=chunker, diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml index 5c2cb687d7..769634bcb3 100644 --- a/packages/graphrag/pyproject.toml +++ b/packages/graphrag/pyproject.toml @@ -41,6 +41,7 @@ dependencies = [ "environs~=11.0", "graphrag-cache==2.7.0", "graphrag-common==2.7.0", + "graphrag-input==2.7.0", "graphrag-storage==2.7.0", "graspologic-native~=1.2", "json-repair~=0.30", diff --git a/pyproject.toml b/pyproject.toml index 1f4ab1d8c7..b2ec49f4c1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,6 +55,7 @@ members = ["packages/*"] [tool.uv.sources] graphrag-chunking = { workspace = true } graphrag-common = { workspace = true } +graphrag-input = { workspace = true } graphrag-storage = { workspace = true } graphrag-cache = { workspace = true } @@ -75,6 +76,7 @@ _semversioner_update_graphrag_chunking_toml_version = "update-toml update --file _semversioner_update_graphrag_common_toml_version = "update-toml update --file packages/graphrag-common/pyproject.toml --path project.version --value $(uv run semversioner current-version)" _semversioner_update_graphrag_storage_toml_version = "update-toml update --file packages/graphrag-storage/pyproject.toml --path project.version --value $(uv run semversioner current-version)" _semversioner_update_graphrag_cache_toml_version = "update-toml update --file packages/graphrag-cache/pyproject.toml --path project.version --value $(uv run semversioner current-version)" +_semversioner_update_graphrag_input_toml_version = "update-toml update --file packages/graphrag-input/pyproject.toml --path project.version --value $(uv run semversioner current-version)" _semversioner_update_workspace_dependency_versions = "python -m scripts.update_workspace_dependency_versions" semversioner_add = "semversioner add-change" coverage_report = 'coverage report --omit "**/tests/**" --show-missing' @@ -110,6 +112,7 @@ sequence = [ '_semversioner_update_graphrag_toml_version', '_semversioner_update_graphrag_common_toml_version', '_semversioner_update_graphrag_chunking_toml_version', + '_semversioner_update_graphrag_input_toml_version', '_semversioner_update_graphrag_storage_toml_version', '_semversioner_update_graphrag_cache_toml_version', '_semversioner_update_workspace_dependency_versions', diff --git a/tests/fixtures/azure/config.json b/tests/fixtures/azure/config.json index 91d9fc51d0..774228d0ad 100644 --- a/tests/fixtures/azure/config.json +++ b/tests/fixtures/azure/config.json @@ -1,6 +1,6 @@ { "input_path": "./tests/fixtures/azure", - "input_file_type": "text", + "input_type": "text", "index_method": "standard", "workflow_config": { "skip_assert": true, diff --git a/tests/fixtures/azure/settings.yml b/tests/fixtures/azure/settings.yml index 80ba02e56b..b220786d88 100644 --- a/tests/fixtures/azure/settings.yml +++ b/tests/fixtures/azure/settings.yml @@ -14,7 +14,7 @@ input: connection_string: ${LOCAL_BLOB_STORAGE_CONNECTION_STRING} container_name: azurefixture base_dir: input - file_type: text + type: text cache: diff --git a/tests/fixtures/min-csv/config.json b/tests/fixtures/min-csv/config.json index 88d38f1e61..90b7c26061 100644 --- a/tests/fixtures/min-csv/config.json +++ b/tests/fixtures/min-csv/config.json @@ -1,6 +1,6 @@ { "input_path": "./tests/fixtures/min-csv", - "input_file_type": "text", + "input_type": "text", "index_method": "standard", "workflow_config": { "load_input_documents": { @@ -73,7 +73,7 @@ 15 ], "nan_allowed_columns": [ - "metadata" + "raw_data" ], "max_runtime": 30, "expected_artifacts": ["documents.parquet"] diff --git a/tests/fixtures/min-csv/settings.yml b/tests/fixtures/min-csv/settings.yml index 82066e753c..c6ebb2248e 100644 --- a/tests/fixtures/min-csv/settings.yml +++ b/tests/fixtures/min-csv/settings.yml @@ -35,7 +35,8 @@ vector_store: overwrite: True input: - file_type: csv + type: csv + encoding: utf-8-sig snapshots: embeddings: true \ No newline at end of file diff --git a/tests/fixtures/text/config.json b/tests/fixtures/text/config.json index 6248a08044..cc69e523ec 100644 --- a/tests/fixtures/text/config.json +++ b/tests/fixtures/text/config.json @@ -1,6 +1,6 @@ { "input_path": "./tests/fixtures/text", - "input_file_type": "text", + "input_type": "text", "index_method": "fast", "workflow_config": { "load_input_documents": { @@ -72,7 +72,7 @@ 1 ], "nan_allowed_columns": [ - "metadata" + "raw_data" ], "max_runtime": 30, "expected_artifacts": ["documents.parquet"] diff --git a/tests/smoke/test_fixtures.py b/tests/smoke/test_fixtures.py index 53205c7c09..7d43b0140a 100644 --- a/tests/smoke/test_fixtures.py +++ b/tests/smoke/test_fixtures.py @@ -126,7 +126,7 @@ class TestIndexer: def __run_indexer( self, root: Path, - input_file_type: str, + input_type: str, index_method: str, ): command = [ @@ -232,7 +232,7 @@ def __run_query(self, root: Path, query_config: dict[str, str]): def test_fixture( self, input_path: str, - input_file_type: str, + input_type: str, index_method: str, workflow_config: dict[str, dict[str, Any]], query_config: list[dict[str, str]], @@ -248,7 +248,7 @@ def test_fixture( dispose = asyncio.run(prepare_azurite_data(input_path, azure)) print("running indexer") - self.__run_indexer(root, input_file_type, index_method) + self.__run_indexer(root, input_type, index_method) print("indexer complete") if dispose is not None: diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 4dbefa4cf3..dd68b0ab6f 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -27,7 +27,7 @@ def setup_method(self, method): bootstrap() def test_basic_functionality(self): - """Test basic sentence splitting without metadata""" + """Test basic sentence splitting""" input = "This is a test. Another sentence. And a third one!" chunker = create_chunker(ChunkingConfig(type=ChunkerType.Sentence)) chunks = chunker.chunk(input) diff --git a/tests/unit/chunking/test_prepend_metadata.py b/tests/unit/chunking/test_prepend_metadata.py index 99f99c3093..74f5a698e5 100644 --- a/tests/unit/chunking/test_prepend_metadata.py +++ b/tests/unit/chunking/test_prepend_metadata.py @@ -1,14 +1,15 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -from graphrag_chunking.add_metadata import add_metadata +from graphrag_chunking.transformers import add_metadata def test_add_metadata_one_row(): """Test prepending metadata to chunks""" chunks = ["This is a test.", "Another sentence."] metadata = {"message": "hello"} - results = [add_metadata(chunk, metadata) for chunk in chunks] + transformer = add_metadata(metadata) + results = [transformer(chunk) for chunk in chunks] assert results[0] == "message: hello\nThis is a test." assert results[1] == "message: hello\nAnother sentence." @@ -17,7 +18,8 @@ def test_add_metadata_one_row_append(): """Test prepending metadata to chunks""" chunks = ["This is a test.", "Another sentence."] metadata = {"message": "hello"} - results = [add_metadata(chunk, metadata, append=True) for chunk in chunks] + transformer = add_metadata(metadata, append=True) + results = [transformer(chunk) for chunk in chunks] assert results[0] == "This is a test.message: hello\n" assert results[1] == "Another sentence.message: hello\n" @@ -26,7 +28,8 @@ def test_add_metadata_multiple_rows(): """Test prepending metadata to chunks""" chunks = ["This is a test.", "Another sentence."] metadata = {"message": "hello", "tag": "first"} - results = [add_metadata(chunk, metadata) for chunk in chunks] + transformer = add_metadata(metadata) + results = [transformer(chunk) for chunk in chunks] assert results[0] == "message: hello\ntag: first\nThis is a test." assert results[1] == "message: hello\ntag: first\nAnother sentence." @@ -35,9 +38,7 @@ def test_add_metadata_custom_delimiters(): """Test prepending metadata to chunks""" chunks = ["This is a test.", "Another sentence."] metadata = {"message": "hello", "tag": "first"} - results = [ - add_metadata(chunk, metadata, delimiter="-", line_delimiter="_") - for chunk in chunks - ] + transformer = add_metadata(metadata, delimiter="-", line_delimiter="_") + results = [transformer(chunk) for chunk in chunks] assert results[0] == "message-hello_tag-first_This is a test." assert results[1] == "message-hello_tag-first_Another sentence." diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py index 586cd1e18e..c02285125a 100644 --- a/tests/unit/config/utils.py +++ b/tests/unit/config/utils.py @@ -17,7 +17,6 @@ ) from graphrag.config.models.global_search_config import GlobalSearchConfig from graphrag.config.models.graph_rag_config import GraphRagConfig -from graphrag.config.models.input_config import InputConfig from graphrag.config.models.language_model_config import LanguageModelConfig from graphrag.config.models.local_search_config import LocalSearchConfig from graphrag.config.models.prune_graph_config import PruneGraphConfig @@ -29,6 +28,7 @@ from graphrag.config.models.vector_store_config import VectorStoreConfig from graphrag_cache import CacheConfig from graphrag_chunking.chunking_config import ChunkingConfig +from graphrag_input import InputConfig from graphrag_storage import StorageConfig from pydantic import BaseModel @@ -143,13 +143,11 @@ def assert_cache_configs(actual: CacheConfig, expected: CacheConfig) -> None: def assert_input_configs(actual: InputConfig, expected: InputConfig) -> None: - assert_storage_config(actual.storage, expected.storage) - assert actual.file_type == expected.file_type + assert actual.type == expected.type assert actual.encoding == expected.encoding assert actual.file_pattern == expected.file_pattern assert actual.text_column == expected.text_column assert actual.title_column == expected.title_column - assert actual.metadata == expected.metadata def assert_text_embedding_configs( @@ -338,8 +336,8 @@ def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) -> assert_vector_store_configs(actual.vector_store, expected.vector_store) assert_reporting_configs(actual.reporting, expected.reporting) assert_storage_config(actual.output, expected.output) - - assert_storage_config(actual.update_index_output, expected.update_index_output) + assert_storage_config(actual.input_storage, expected.input_storage) + assert_storage_config(actual.update_output_storage, expected.update_output_storage) assert_cache_configs(actual.cache, expected.cache) assert_input_configs(actual.input, expected.input) diff --git a/tests/unit/indexing/input/data/one-html/input.html b/tests/unit/indexing/input/data/one-html/input.html new file mode 100644 index 0000000000..dbf8badc70 --- /dev/null +++ b/tests/unit/indexing/input/data/one-html/input.html @@ -0,0 +1,8 @@ + + +Test + + +Hi how are you today? + + \ No newline at end of file diff --git a/tests/unit/indexing/input/data/one-jsonl/input.jsonl b/tests/unit/indexing/input/data/one-jsonl/input.jsonl new file mode 100644 index 0000000000..2866aed408 --- /dev/null +++ b/tests/unit/indexing/input/data/one-jsonl/input.jsonl @@ -0,0 +1,3 @@ +{ "title": "Hello", "text": "Hi how are you today?"} +{ "title": "Goodbye", "text": "I'm outta here"} +{ "title": "Adios", "text": "See you later"} \ No newline at end of file diff --git a/tests/unit/indexing/input/test_csv_loader.py b/tests/unit/indexing/input/test_csv_loader.py index b0dc645e1b..1a84d82676 100644 --- a/tests/unit/indexing/input/test_csv_loader.py +++ b/tests/unit/indexing/input/test_csv_loader.py @@ -1,82 +1,56 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -from graphrag.config.enums import InputFileType -from graphrag.config.models.input_config import InputConfig -from graphrag.index.input.factory import InputReaderFactory +from graphrag_input import InputConfig, InputType, create_input_reader from graphrag_storage import StorageConfig, create_storage async def test_csv_loader_one_file(): config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/one-csv", - ), - file_type=InputFileType.csv, + type=InputType.Csv, file_pattern=".*\\.csv$", ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/one-csv", + ) ) - assert documents.shape == (2, 4) - assert documents["title"].iloc[0] == "input.csv" + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 2 + assert documents[0].title == "input.csv (0)" + assert documents[0].raw_data == { + "title": "Hello", + "text": "Hi how are you today?", + } + assert documents[1].title == "input.csv (1)" async def test_csv_loader_one_file_with_title(): config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/one-csv", - ), - file_type=InputFileType.csv, - file_pattern=".*\\.csv$", + type=InputType.Csv, title_column="title", ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() - ) - assert documents.shape == (2, 4) - assert documents["title"].iloc[0] == "Hello" - - -async def test_csv_loader_one_file_with_metadata(): - config = InputConfig( - storage=StorageConfig( + storage = create_storage( + StorageConfig( base_dir="tests/unit/indexing/input/data/one-csv", - ), - file_type=InputFileType.csv, - file_pattern=".*\\.csv$", - title_column="title", - metadata=["title"], - ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() + ) ) - print(documents) - assert documents.shape == (2, 5) - assert documents["metadata"][0] == {"title": "Hello"} + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 2 + assert documents[0].title == "Hello" async def test_csv_loader_multiple_files(): config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/multiple-csvs", - ), - file_type=InputFileType.csv, - file_pattern=".*\\.csv$", + type=InputType.Csv, ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/multiple-csvs", + ) ) - assert documents.shape == (4, 4) + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 4 diff --git a/tests/unit/indexing/input/test_json_loader.py b/tests/unit/indexing/input/test_json_loader.py index 3959096b1c..d20d64ad2d 100644 --- a/tests/unit/indexing/input/test_json_loader.py +++ b/tests/unit/indexing/input/test_json_loader.py @@ -1,100 +1,71 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -from graphrag.config.enums import InputFileType -from graphrag.config.models.input_config import InputConfig -from graphrag.index.input.factory import InputReaderFactory +from graphrag_input import InputConfig, InputType, create_input_reader from graphrag_storage import StorageConfig, create_storage async def test_json_loader_one_file_one_object(): config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/one-json-one-object", - ), - file_type=InputFileType.json, + type=InputType.Json, file_pattern=".*\\.json$", ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/one-json-one-object", + ) ) - assert documents.shape == (1, 4) - assert documents["title"].iloc[0] == "input.json" + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 1 + assert documents[0].title == "input.json" + assert documents[0].raw_data == { + "title": "Hello", + "text": "Hi how are you today?", + } async def test_json_loader_one_file_multiple_objects(): config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/one-json-multiple-objects", - ), - file_type=InputFileType.json, - file_pattern=".*\\.json$", + type=InputType.Json, ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/one-json-multiple-objects", + ) ) - print(documents) - assert documents.shape == (3, 4) - assert documents["title"].iloc[0] == "input.json" + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 3 + assert documents[0].title == "input.json (0)" + assert documents[1].title == "input.json (1)" async def test_json_loader_one_file_with_title(): config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/one-json-one-object", - ), - file_type=InputFileType.json, - file_pattern=".*\\.json$", + type=InputType.Json, title_column="title", ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() - ) - assert documents.shape == (1, 4) - assert documents["title"].iloc[0] == "Hello" - - -async def test_json_loader_one_file_with_metadata(): - config = InputConfig( - storage=StorageConfig( + storage = create_storage( + StorageConfig( base_dir="tests/unit/indexing/input/data/one-json-one-object", - ), - file_type=InputFileType.json, - file_pattern=".*\\.json$", - title_column="title", - metadata=["title"], + ) ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() - ) - assert documents.shape == (1, 5) - assert documents["metadata"][0] == {"title": "Hello"} + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 1 + assert documents[0].title == "Hello" async def test_json_loader_multiple_files(): config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/multiple-jsons", - ), - file_type=InputFileType.json, - file_pattern=".*\\.json$", + type=InputType.Json, ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/multiple-jsons", + ) ) - assert documents.shape == (4, 4) + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 4 diff --git a/tests/unit/indexing/input/test_jsonl_loader.py b/tests/unit/indexing/input/test_jsonl_loader.py new file mode 100644 index 0000000000..dd56094290 --- /dev/null +++ b/tests/unit/indexing/input/test_jsonl_loader.py @@ -0,0 +1,42 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +from graphrag_input import InputConfig, InputType, create_input_reader +from graphrag_storage import StorageConfig, create_storage + + +async def test_jsonl_loader_one_file_multiple_objects(): + config = InputConfig( + type=InputType.JsonLines, + file_pattern=".*\\.jsonl$", + ) + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/one-jsonl", + ) + ) + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 3 + assert documents[0].title == "input.jsonl (0)" + assert documents[0].raw_data == { + "title": "Hello", + "text": "Hi how are you today?", + } + assert documents[1].title == "input.jsonl (1)" + + +async def test_jsonl_loader_one_file_with_title(): + config = InputConfig( + type=InputType.JsonLines, + title_column="title", + ) + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/one-jsonl", + ) + ) + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 3 + assert documents[0].title == "Hello" diff --git a/tests/unit/indexing/input/test_markitdown_loader.py b/tests/unit/indexing/input/test_markitdown_loader.py new file mode 100644 index 0000000000..c32e36dbb7 --- /dev/null +++ b/tests/unit/indexing/input/test_markitdown_loader.py @@ -0,0 +1,26 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +from graphrag_input import InputConfig, InputType, create_input_reader +from graphrag_storage import StorageConfig, create_storage + + +# these tests just confirm we can load files with MarkItDown, +# and use html specifically because it requires no additional dependency installation +async def test_markitdown_loader_one_file(): + config = InputConfig( + type=InputType.MarkItDown, + file_pattern=".*\\.html$", + ) + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/one-html", + ) + ) + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 1 + # markitdown will extract the title and body from the HTML if present and clean them + assert documents[0].title == "Test" + assert documents[0].text == "Hi how are you today?" + assert documents[0].raw_data is None diff --git a/tests/unit/indexing/input/test_text_document.py b/tests/unit/indexing/input/test_text_document.py new file mode 100644 index 0000000000..d71c515bf5 --- /dev/null +++ b/tests/unit/indexing/input/test_text_document.py @@ -0,0 +1,76 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +import pytest +from graphrag_input import get_property + + +def test_get_property_single_level(): + data = {"foo": "bar"} + assert get_property(data, "foo") == "bar" + + +def test_get_property_two_levels(): + data = {"foo": {"bar": "baz"}} + assert get_property(data, "foo.bar") == "baz" + + +def test_get_property_three_levels(): + data = {"a": {"b": {"c": "value"}}} + assert get_property(data, "a.b.c") == "value" + + +def test_get_property_returns_dict(): + data = {"foo": {"bar": {"baz": "qux"}}} + result = get_property(data, "foo.bar") + assert result == {"baz": "qux"} + + +def test_get_property_missing_key_raises(): + data = {"foo": "bar"} + with pytest.raises(KeyError): + get_property(data, "missing") + + +def test_get_property_missing_nested_key_raises(): + data = {"foo": {"bar": "baz"}} + with pytest.raises(KeyError): + get_property(data, "foo.missing") + + +def test_get_property_non_dict_intermediate_raises(): + data = {"foo": "bar"} + with pytest.raises(KeyError): + get_property(data, "foo.bar") + + +def test_get_property_empty_dict_raises(): + data = {} + with pytest.raises(KeyError): + get_property(data, "foo") + + +def test_get_property_with_none_value(): + data = {"foo": None} + assert get_property(data, "foo") is None + + +def test_get_property_with_list_value(): + data = {"foo": [1, 2, 3]} + assert get_property(data, "foo") == [1, 2, 3] + + +def test_get_property_list_intermediate_raises(): + data = {"foo": [{"bar": "baz"}]} + with pytest.raises(KeyError): + get_property(data, "foo.bar") + + +def test_get_property_numeric_value(): + data = {"count": 42} + assert get_property(data, "count") == 42 + + +def test_get_property_boolean_value(): + data = {"enabled": True} + assert get_property(data, "enabled") is True diff --git a/tests/unit/indexing/input/test_text_loader.py b/tests/unit/indexing/input/test_text_loader.py new file mode 100644 index 0000000000..482593a270 --- /dev/null +++ b/tests/unit/indexing/input/test_text_loader.py @@ -0,0 +1,36 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +from graphrag_input import InputConfig, InputType, create_input_reader +from graphrag_storage import StorageConfig, create_storage + + +async def test_text_loader_one_file(): + config = InputConfig( + type=InputType.Text, + file_pattern=".*\\.txt$", + ) + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/one-txt", + ) + ) + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 1 + assert documents[0].title == "input.txt" + assert documents[0].raw_data is None + + +async def test_text_loader_multiple_files(): + config = InputConfig( + type=InputType.Text, + ) + storage = create_storage( + StorageConfig( + base_dir="tests/unit/indexing/input/data/multiple-txts", + ) + ) + reader = create_input_reader(config, storage) + documents = await reader.read_files() + assert len(documents) == 2 diff --git a/tests/unit/indexing/input/test_txt_loader.py b/tests/unit/indexing/input/test_txt_loader.py deleted file mode 100644 index 57ded82507..0000000000 --- a/tests/unit/indexing/input/test_txt_loader.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -from graphrag.config.enums import InputFileType -from graphrag.config.models.input_config import InputConfig -from graphrag.index.input.factory import InputReaderFactory -from graphrag_storage import StorageConfig, create_storage - - -async def test_txt_loader_one_file(): - config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/one-txt", - ), - file_type=InputFileType.text, - file_pattern=".*\\.txt$", - ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() - ) - assert documents.shape == (1, 4) - assert documents["title"].iloc[0] == "input.txt" - - -async def test_txt_loader_one_file_with_metadata(): - config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/one-txt", - ), - file_type=InputFileType.text, - file_pattern=".*\\.txt$", - metadata=["title"], - ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() - ) - assert documents.shape == (1, 5) - # unlike csv, we cannot set the title to anything other than the filename - assert documents["metadata"][0] == {"title": "input.txt"} - - -async def test_txt_loader_multiple_files(): - config = InputConfig( - storage=StorageConfig( - base_dir="tests/unit/indexing/input/data/multiple-txts", - ), - file_type=InputFileType.text, - file_pattern=".*\\.txt$", - ) - storage = create_storage(config.storage) - documents = ( - await InputReaderFactory() - .create(config.file_type, {"storage": storage, "config": config}) - .read_files() - ) - assert documents.shape == (2, 4) diff --git a/tests/verbs/data/communities.parquet b/tests/verbs/data/communities.parquet index d85403d6b6..76f255886c 100644 Binary files a/tests/verbs/data/communities.parquet and b/tests/verbs/data/communities.parquet differ diff --git a/tests/verbs/data/community_reports.parquet b/tests/verbs/data/community_reports.parquet index 26b9daba09..3998d1f203 100644 Binary files a/tests/verbs/data/community_reports.parquet and b/tests/verbs/data/community_reports.parquet differ diff --git a/tests/verbs/data/covariates.parquet b/tests/verbs/data/covariates.parquet index 844c414f73..0160267dd2 100644 Binary files a/tests/verbs/data/covariates.parquet and b/tests/verbs/data/covariates.parquet differ diff --git a/tests/verbs/data/documents.parquet b/tests/verbs/data/documents.parquet index a846fecfce..8176b28f34 100644 Binary files a/tests/verbs/data/documents.parquet and b/tests/verbs/data/documents.parquet differ diff --git a/tests/verbs/data/entities.parquet b/tests/verbs/data/entities.parquet index 0807395a7f..0d7d047d05 100644 Binary files a/tests/verbs/data/entities.parquet and b/tests/verbs/data/entities.parquet differ diff --git a/tests/verbs/data/relationships.parquet b/tests/verbs/data/relationships.parquet index e88d218270..b96143352a 100644 Binary files a/tests/verbs/data/relationships.parquet and b/tests/verbs/data/relationships.parquet differ diff --git a/tests/verbs/data/text_units.parquet b/tests/verbs/data/text_units.parquet index cdaa9b2b07..88368a9f05 100644 Binary files a/tests/verbs/data/text_units.parquet and b/tests/verbs/data/text_units.parquet differ diff --git a/tests/verbs/data/text_units_metadata.parquet b/tests/verbs/data/text_units_metadata.parquet deleted file mode 100644 index 9b6b44342a..0000000000 Binary files a/tests/verbs/data/text_units_metadata.parquet and /dev/null differ diff --git a/tests/verbs/data/text_units_metadata_included_chunk.parquet b/tests/verbs/data/text_units_metadata_included_chunk.parquet deleted file mode 100644 index ca2495975e..0000000000 Binary files a/tests/verbs/data/text_units_metadata_included_chunk.parquet and /dev/null differ diff --git a/tests/verbs/test_create_base_text_units.py b/tests/verbs/test_create_base_text_units.py index 89018e1609..77546c4c88 100644 --- a/tests/verbs/test_create_base_text_units.py +++ b/tests/verbs/test_create_base_text_units.py @@ -10,7 +10,6 @@ compare_outputs, create_test_context, load_test_table, - update_document_metadata, ) @@ -20,26 +19,18 @@ async def test_create_base_text_units(): context = await create_test_context() config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG) # type: ignore + config.chunking.prepend_metadata = ["title"] await run_workflow(config, context) actual = await load_table_from_storage("text_units", context.output_storage) - compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"]) - - -async def test_create_base_text_units_metadata(): - expected = load_test_table("text_units_metadata") + print("EXPECTED") + print(expected.columns) + print(expected) - context = await create_test_context() - - config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG) # type: ignore - config.input.metadata = ["title"] - config.chunking.prepend_metadata = True + print("ACTUAL") + print(actual.columns) + print(actual) - await update_document_metadata(config.input.metadata, context) - - await run_workflow(config, context) - - actual = await load_table_from_storage("text_units", context.output_storage) - compare_outputs(actual, expected, ["text", "document_id", "n_tokens"]) + compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"]) diff --git a/tests/verbs/test_create_community_reports.py b/tests/verbs/test_create_community_reports.py index 561f54108b..d479120ce2 100644 --- a/tests/verbs/test_create_community_reports.py +++ b/tests/verbs/test_create_community_reports.py @@ -4,15 +4,14 @@ from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS -from graphrag.index.workflows.create_community_reports import ( - run_workflow, -) -from graphrag.utils.storage import load_table_from_storage - from graphrag.index.operations.summarize_communities.community_reports_extractor import ( CommunityReportResponse, FindingModel, ) +from graphrag.index.workflows.create_community_reports import ( + run_workflow, +) +from graphrag.utils.storage import load_table_from_storage from .util import ( DEFAULT_MODEL_CONFIG, diff --git a/tests/verbs/test_create_final_documents.py b/tests/verbs/test_create_final_documents.py index 05f4b60691..32084466e7 100644 --- a/tests/verbs/test_create_final_documents.py +++ b/tests/verbs/test_create_final_documents.py @@ -13,7 +13,6 @@ compare_outputs, create_test_context, load_test_table, - update_document_metadata, ) @@ -34,26 +33,3 @@ async def test_create_final_documents(): for column in DOCUMENTS_FINAL_COLUMNS: assert column in actual.columns - - -async def test_create_final_documents_with_metadata_column(): - context = await create_test_context( - storage=["text_units"], - ) - - config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG) # type: ignore - config.input.metadata = ["title"] - - # simulate the metadata construction during initial input loading - await update_document_metadata(config.input.metadata, context) - - expected = await load_table_from_storage("documents", context.output_storage) - - await run_workflow(config, context) - - actual = await load_table_from_storage("documents", context.output_storage) - - compare_outputs(actual, expected) - - for column in DOCUMENTS_FINAL_COLUMNS: - assert column in actual.columns diff --git a/tests/verbs/test_prune_graph.py b/tests/verbs/test_prune_graph.py index 426230161a..78c6391d50 100644 --- a/tests/verbs/test_prune_graph.py +++ b/tests/verbs/test_prune_graph.py @@ -28,4 +28,4 @@ async def test_prune_graph(): nodes_actual = await load_table_from_storage("entities", context.output_storage) - assert len(nodes_actual) == 21 + assert len(nodes_actual) == 29 diff --git a/tests/verbs/util.py b/tests/verbs/util.py index 6dc6667210..e025dde65d 100644 --- a/tests/verbs/util.py +++ b/tests/verbs/util.py @@ -5,7 +5,7 @@ import pandas as pd from graphrag.index.run.utils import create_run_context from graphrag.index.typing.context import PipelineRunContext -from graphrag.utils.storage import load_table_from_storage, write_table_to_storage +from graphrag.utils.storage import write_table_to_storage from pandas.testing import assert_series_equal pd.set_option("display.max_columns", None) @@ -87,12 +87,3 @@ def compare_outputs( print("Actual:") print(actual[column]) raise - - -async def update_document_metadata(metadata: list[str], context: PipelineRunContext): - """Takes the default documents and adds the configured metadata columns for later parsing by the text units and final documents workflows.""" - documents = await load_table_from_storage("documents", context.output_storage) - documents["metadata"] = documents[metadata].apply(lambda row: row.to_dict(), axis=1) - await write_table_to_storage( - documents, "documents", context.output_storage - ) # write to the runtime context storage only diff --git a/unified-search-app/app/app_logic.py b/unified-search-app/app/app_logic.py index a573b9daa5..dc64e0e77c 100644 --- a/unified-search-app/app/app_logic.py +++ b/unified-search-app/app/app_logic.py @@ -7,6 +7,7 @@ import logging from typing import TYPE_CHECKING +import graphrag.api as api import streamlit as st from knowledge_loader.data_sources.loader import ( create_datasource, @@ -17,8 +18,6 @@ from state.session_variables import SessionVariables from ui.search import display_search_result -import graphrag.api as api - if TYPE_CHECKING: import pandas as pd diff --git a/uv.lock b/uv.lock index 94c8626cc5..72a6db6f11 100644 --- a/uv.lock +++ b/uv.lock @@ -1,9 +1,13 @@ version = 1 revision = 1 -requires-python = ">=3.11, <3.13" +requires-python = ">=3.11, <3.14" resolution-markers = [ - "python_full_version >= '3.12'", - "python_full_version < '3.12'", + "python_full_version >= '3.13' and sys_platform == 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform == 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] [manifest] @@ -12,6 +16,7 @@ members 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