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a/_modules/components/data_process/data_components.html +++ b/_modules/components/data_process/data_components.html @@ -440,7 +440,7 @@

Source code for components.data_process.data_components

retriever_output: Union[RetrieverOutput, List[RetrieverOutput]], deduplicate: bool = False, ) -> str: - r"""The retrieved documents from one or mulitple queries. + r"""The retrieved documents from one or multiple queries. Deduplicate is especially helpful when you used query expansion. """ """ diff --git a/_sources/contributor/contribute_to_code.rst.txt b/_sources/contributor/contribute_to_code.rst.txt new file mode 100644 index 00000000..86ebaea7 --- /dev/null +++ b/_sources/contributor/contribute_to_code.rst.txt @@ -0,0 +1,96 @@ +Contribute to Code +====================================== +This document will cover how you can contribute to lightRAG codebase. + +Set Up +^^^^^^^^^^^^^^^^^^^ +The current ``LightRAG`` code contribution supports `poetry `_ setup only. The team is working on optimizing the library and will get back to support more environment soon. +If you are only interested in using ``LightRAG`` as a package, please check our `installation guide `_. + +To set up ``poetry`` and contribute, please check the following steps: + +1. **Clone the Repository:** + + .. code-block:: bash + + git clone https://github.com/SylphAI-Inc/LightRAG + cd LightRAG + +2. **Configure API Keys:** + + Copy the example environment file and add your API keys: + + .. code-block:: bash + + cp .env.example .env + # example API keys: + # OPENAI_API_KEY=YOUR_API_KEY_IF_YOU_USE_OPENAI + # GROQ_API_KEY=YOUR_API_KEY_IF_YOU_USE_GROQ + # ANTHROPIC_API_KEY=YOUR_API_KEY_IF_YOU_USE_ANTHROPIC + # GOOGLE_API_KEY=YOUR_API_KEY_IF_YOU_USE_GOOGLE + # COHERE_API_KEY=YOUR_API_KEY_IF_YOU_USE_COHERE + # HF_TOKEN=YOUR_API_KEY_IF_YOU_USE_HF + +3. **Install Dependencies:** + + The ``./lightrag/pyproject.toml`` controls the dependencies for the ``LightRAG`` package. + Use Poetry to install the dependencies and set up the virtual environment: + + .. code-block:: bash + cd lightrag + poetry install + poetry shell + +Codebase Structure +^^^^^^^^^^^^^^^^^^^ +It is recommended to check our `LightRAG codebase structure `_ and current `API references `_ to familiarize yourself with the directories and paths before contributing. + +Code Examples +^^^^^^^^^^^^^^^^^^^ +We want to support you with our best. We have included code samples in the `tutorial `_ for you to refer to. + +We inlcude a list of potential samples(`We are working in progress to add more`): + +- `ModelClient integration `_. This document will help if you want to add new models not included in our codebase. +- `Retriever Integration `_. We provide different retrivers but you can create more. + +Code Tips +^^^^^^^^^^^^^^^^^^^ +* When writing code, it is appreciated to include any important docstrings and comments. Please refer to `documentation contribution guidelines <./contribute_to_document.html>`_ for standard docstrings. +* LightRAG is a Python library and if you could follow the `Google Python Style Guide `_, the codebase will be more consistent. + +Dependencies +^^^^^^^^^^^^^^^^^^^ +If you want to add any new dependencies to the package, please include them in your PR description to inform us. +Since we have already set up the testing automatic workflow in GitHub, please also set your new dependencies in +``./lightrag/pyproject.toml`` file ``[tool.poetry.group.test.dependencies]`` section to avoid dependency errors in our CI/CD workflow. + +In order to correctly add the dependency using ``poetry``, please run + +.. code-block:: bash + + poetry add --group test + +Testing +^^^^^^^^^^^^^^^^^^^ +After you update the code, please make sure your code is well tested before making a pull request. +There is a ``./lightrag/tests`` folder in the project directory to host your unit testing cases. + +You might need to install the testing packages using ``poetry``: + +For example: + +.. code-block:: bash + + poetry add --group test unittest + poetry add --group test pytest + poetry add --group test mypy + + +All the test scripts should start with ``test_``. For example, run the individual test for ``components`` with: + +.. code-block:: bash + + python lightrag/tests/test_components.py + + diff --git a/_sources/contributor/contribute_to_document.rst.txt b/_sources/contributor/contribute_to_document.rst.txt index 2f33a24d..3b1516cf 100644 --- a/_sources/contributor/contribute_to_document.rst.txt +++ b/_sources/contributor/contribute_to_document.rst.txt @@ -1,14 +1,11 @@ -Contribute to Document +Contribute to Documentation =============================================== .. contents:: :local: :depth: 2 -.. _Writing documentation: - -Writing Documentation ---------------------------- +.. _Documentation Contribution: - **User-Facing Documentation**: Found on the main docs site. These include tutorials, guides, and usage documentation meant for end users. - **Developer Documentation**: Located within the repository's READMEs and the ``docs/`` directory. These documents are more technical and intended for contributors and maintainers. @@ -35,14 +32,14 @@ Setup & Build Documentation **2. Install Necessary Packages** -LightRAG's documentation style is `pydata_sphinx_theme `_ (version: 0.15.3). +LightRAG's documentation style is `pydata_sphinx_theme `_. -Install by ``pip``: +.. Install by ``pip``: -.. code-block:: bash +.. .. code-block:: bash - cd docs - pip install -r requirements.txt +.. cd docs +.. pip install -r requirements.txt Install by ``poetry`` along with all other dependencies for LightRAG: @@ -107,6 +104,9 @@ To manually run these tests, run: make doctest +Documentation Dependencies +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +If your documentation requires any new dependencies, please include it in the ``pyproject.toml`` under the root directory, include it in your PR description and let us know. Commit Changes ~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/_sources/contributor/contribution.rst.txt b/_sources/contributor/contribution.rst.txt new file mode 100644 index 00000000..5e5485f2 --- /dev/null +++ b/_sources/contributor/contribution.rst.txt @@ -0,0 +1,63 @@ +LightRAG Contribution Guide +======================================= +``LightRAG``'s contribution process is similar to most open source projects on GitHub. We encourage new project ideas and the communication between ``LightRAG`` team, developers and the broader community. +Please don't forget to join us on `Discord `_. + +Contribution Process +---------------------------- +You are always welcomed to contribute even if you've never participated in open source project before. +Here is the basic contribution process: + +Environment +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +When contributing, please note: +LightRAG separates the source code environment and documentation environment. + +* To activate the code environment, you should run ``poetry install`` and ``poetry shell`` under ``./lightrag``. The ``./lightrag/pyproject.toml`` contains the dependencies for the ``LightRAG`` package. + +* To activate the documentation environment, you can run ``poetry install`` and ``poetry shell`` under ``.``. The ``./pyproject.toml`` controls documentation dependencies. + +Find a direction to work on +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +The team builds ``LightRAG`` based on latest researches and product cases. But you might have your own task to apply ``LightRAG``. +Therefore, you can extend ``LightRAG`` and add any new features you believe will solve yours or others' problems. +If you don't have any idea yet, you can: + +* Check the `existing issues `_ and see if there is anyone you know how to fix or you'd love to fix. + +* Join us on `Discord `_. We are glad to discuss with you and know what you are interested in here. + +Figure out the scope of your change +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +**Small:** Most of the pull requests are small. If your change is small, such as fixing a line of bug, please go ahead to push it. + +**Big:** But if you are making a new feature, or planning to push a large change, it is recommended to contact us on `Discord `_ first. + +**Unknown:** If you have no idea how big it will be, we are here to help you. Please post your idea on `issues `_. We will read it carefully and get back to you. + +Add your code +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Please check our `code contribution guidelines <./contribute_to_code.html>`_ to work with code. + +Pull requests +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +**WIP PR:** If you are working on an in pull request that is not ready for review, you can create a PR with **"[WIP]"** to inform us that this PR is a draft **“work in progress”**. + +**Finished PR:** You can name your finished PR as **"[New Retriever Integration]"** for example. +We will carry out code review regularly and provide feedbacks as soon as possible. +Please iterate your PR with the feedbacks. We will try our best to reduce the revision workload on your side. +Once your PR is approved, we will merge the PR for you. +If you have any concerns about our feedbacks, please feel free to contact us on `Discord `_. + +Writing Documentation +---------------------------- +It is a good practice to submit your code with documentations to help the ``LightRAG`` team and other developers better understand your updates. +Please see our `documentation contribution guidelines <./contribute_to_document.html>`_ for more details on ``LightRAG`` documentation standard. + + + + +.. admonition:: Resources + :class: highlight + + \ No newline at end of file diff --git a/_sources/contributor/contribution_guide.rst.txt b/_sources/contributor/contribution_guide.rst.txt deleted file mode 100644 index bf6df950..00000000 --- a/_sources/contributor/contribution_guide.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -LightRAG Contribution Guide -====================================== - -#. Writing Documentation - -Please see our `documentation contribution guidelines <./contribute_to_document.html>`_ for more details. \ No newline at end of file diff --git a/_sources/contributor/index.rst.txt b/_sources/contributor/index.rst.txt index 2b2306e7..676f65d9 100644 --- a/_sources/contributor/index.rst.txt +++ b/_sources/contributor/index.rst.txt @@ -5,5 +5,7 @@ For Contributors .. toctree:: :maxdepth: 2 - contribution_guide - contribute_to_document \ No newline at end of file + contribution + contribute_to_code + contribute_to_document + .. version_control \ No newline at end of file diff --git a/_sources/contributor/version_control.rst.txt b/_sources/contributor/version_control.rst.txt new file mode 100644 index 00000000..456c7528 --- /dev/null +++ b/_sources/contributor/version_control.rst.txt @@ -0,0 +1,86 @@ +.. _release-guide: + +Release Version Control Guide +======================================= + +Overview +-------- +**The version will mainly be managed by the LightRAG team. But we are glad to share how we will release the latest version here.** + +This guide outlines the process for releasing a new version of ``LightRAG``. +The workflow pipeline validates the version tag, builds the package, runs tests, publishes to PyPI, and creates a release on GitHub. The workflow is triggered by tags pushed to the **Release** branch. See `GitHub tags `_ for more details on version release tagging. + +Steps to Release a New Version +------------------------------ +1. Update the **./lightrag/pyproject.toml** version number and the latest dependencies before pushing a new release. Make sure to follow the `PEP 440 rules `_ to define the version, otherwise, the workflow will fail. For example: + + .. code-block:: python + + [tool.poetry] + name = "lightrag" + + version = "0.0.0-rc.1" + description = "The 'PyTorch' library for LLM applications. RAG=Retriever-Agent-Generator." + +2. Ensure your updates are the latest and correct. Update the version number following `Semantic Versioning `_. Here is a list of sample tags: + + .. code-block:: none + + Stable Release Tags: + v1.0.0 + v1.2.3 + + Pre-release Tags: + v1.0.0-alpha.1 + v1.0.0-beta.1 + v1.0.0-rc.1 + + Custom Pre-release Tags: + v1.0.0-test.1 + v1.1.0-dev.2 + v1.2.3-pre.3 + v2.0.0-exp.4 + v2.1.0-nightly.5 + +3. The workflow will be triggered when new releases are pushed to the **release** branch. Push the **./lightrag/pyproject.toml** to the release branch: + + .. code-block:: python + + git add lightrag/pyproject.toml + git commit -m "new version release" + git push origin release + + Since the workflow only processes **tags**, your file submission will not go through the version release workflow. + + Only the tags you pushed will get checked. + + To push the new version tag, please run: + + .. code-block:: python + + git tag -a vx.y.z -m "Release version x.y.z" + git push origin release + +4. To delete redundant local tags: + + .. code-block:: python + + git tags # list the existing tags + + git tag -d + git push origin --delete + +Important Notes +--------------- +- **Do Not Reuse Tags:** If you need to fix a problem and update the package, you must create a new version number. Duplicated tag number or version number won't work. Never reuse version numbers as this can lead to confusion and potential deployment issues. +- **Monitor the Workflow:** After pushing the tag, monitor the GitHub Actions workflow to ensure that it completes successfully. Check the ``"Actions"`` tab in the GitHub repository to see the progress and logs. + +Common Problems +----------------- +- If the workflow fails, review the logs for errors. Common issues might include: + + **Tag Validation:** If your tag validation failed, you should check if your pushed tag meets the standard. + + **Package Build:** Pay attention to the error during the package building and see if there is any bug. + +- If you encounter errors related to tagging (e.g., "tag already exists"), check that you're incrementing the version numbers correctly. diff --git a/_sources/developer_notes/agent.rst.txt b/_sources/developer_notes/agent.rst.txt index 9a39767c..90e46e91 100644 --- a/_sources/developer_notes/agent.rst.txt +++ b/_sources/developer_notes/agent.rst.txt @@ -353,8 +353,8 @@ Now, let's run the test function to see the agent in action. .. code-block:: python - test_react_agent(ModelClientType.GROQ(), llama3_model_kwargs) - test_react_agent(ModelClientType.OPENAI(), gpt_model_kwargs) + test_react_agent(ModelClientType.GROQ, llama3_model_kwargs) + test_react_agent(ModelClientType.OPENAI, gpt_model_kwargs) Our agent will show the core steps for developers via colored printout, including input_query, steps, and the final answer. The printout of the first query with llama3 is shown below (without the color here): diff --git a/_sources/developer_notes/text_splitter.rst.txt b/_sources/developer_notes/text_splitter.rst.txt index 75cd177e..0aa9ecb0 100644 --- a/_sources/developer_notes/text_splitter.rst.txt +++ b/_sources/developer_notes/text_splitter.rst.txt @@ -1,11 +1,11 @@ Text Splitter ------------------ +====================== .. .. admonition:: Author .. :class: highlight .. `Xiaoyi Gu `_ -In this tutorial, we will learn: +In this tutorial, we will discuss: #. TextSplitter Overview @@ -13,41 +13,51 @@ In this tutorial, we will learn: #. How to use it +#. Chunking Tips + +#. Integration with Other Document Types and Customization Tips + TextSplitter Overview -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +----------------------------- LLMs’s context window is limited and the performance often drops with very long and nonsense input. Shorter content is more manageable and fits memory constraint. -The goal of the text splitter is to chunk large data into smaller ones, potentially improving embedding and retrieving. +The goal of the ``TextSplitter`` is to chunk large data into smaller ones, potentially improving embedding and retrieving. The ``TextSplitter`` is designed to efficiently process and chunk **plain text**. It leverages configurable separators to facilitate the splitting of :obj:`document object ` into smaller manageable document chunks. How does it work -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +----------------------------- ``TextSplitter`` first utilizes ``split_by`` to specify the text-splitting criterion and breaks the long text into smaller texts. -Then we create a sliding window with length= ``chunk_size``. It moves at step= ``chunk_size`` - ``chunk_overlap``. +Then we create a sliding window with ``length= chunk_size``. It moves at ``step= chunk_size - chunk_overlap``. The texts inside each window will get merged to a smaller chunk. The generated chunks from the splitted text will be returned. -**Splitting Types** - +Splitting Types +^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``TextSplitter`` supports 2 types of splitting. +Here are sample examples and you will see the real output of ``TextSplitter`` in the usage section. -* **Type 1:** Specify the exact text splitting point such as space<" "> and periods<".">. It is intuitive, for example, split_by "word": +* **Type 1:** Specify the exact text splitting point such as space<" "> and periods<".">. E.g. if you set ``split_by = "word"``, you will get: :: "Hello, world!" -> ["Hello, " ,"world!"] -* **Type 2:** Use :class:`tokenizer `. It works as: +* **Type 2:** Use :class:`core.tokenizer.Tokenizer`. It works as: :: - "Hello, world!" -> ['Hello', ',', ' world', '!'] + "Hello, world!" -> ["Hello", ",", " world", "!"] + +Tokenization aligns with how models see text in the form of tokens (`Reference `_). +Consider using tokenization when your embedding model works better for tokens or you will input the data chunks to LLM models that are sensitive to token limit. + +.. note:: -This aligns with how models see text in the form of tokens (`Reference `_), -Tokenizer reflects the real token numbers the models take in and helps the developers control budgets. + Tokenizer reflects the real token numbers the models take in and helps the developers control budgets. -**Definitions** +Definitions +^^^^^^^^^^^^^^^^^^^^^^^^^^^ * **split_by** specifies the split rule, i.e. the smallest unit during splitting. We support ``"word"``, ``"sentence"``, ``"page"``, ``"passage"``, and ``"token"``. The splitter utilizes the corresponding separator from the ``SEPARATORS`` dictionary. For Type 1 splitting, we apply ``Python str.split()`` to break the text. @@ -55,14 +65,15 @@ For Type 1 splitting, we apply ``Python str.split()`` to break the text. * **SEPARATORS**: Maps ``split_by`` criterions to their exact text separators, e.g., spaces <" "> for "word" or periods <"."> for "sentence". .. note:: - For option ``token``, its separator is "" because we directly split by a tokenizer, instead of text point. + For option ``token``, its separator is "" because we directly split by a tokenizer, instead of specific text point. -* **chunk_size** is the the maximum number of units in each chunk. +* **chunk_size** is the the maximum number of units in each chunk. To figure out which ``chunk_size`` works best for you, you can firstly preprocess your raw data, select a range of the ``chunk_size`` and then run the evaluation on your use case with a bunch of queries. * **chunk_overlap** is the number of units that each chunk should overlap. Including context at the borders prevents sudden meaning shift in text between sentences/context, especially in sentiment analysis. Here are examples of how ``split_by``, ``chunk_size`` works with ``chunk_overlap``. -Document Text: + +Input Document Text: :: @@ -90,7 +101,7 @@ Document Text: - 2 - "Hello, this is l", "is lightrag.", "trag. Please implement your", "implement your splitter here." -When splitting by ``word`` with ``chunk_size`` = 5 and ``chunk_overlap`` = 2, +When splitting by ``word`` with ``chunk_size = 5`` and ``chunk_overlap = 2``, each chunk will repeat 2 words from the previous chunk. These 2 words are set by ``chunk_overlap``. This means each chunk has ``5-2=3`` word(split unit) difference compared with its previous. @@ -99,12 +110,15 @@ For example, the tokenizer transforms ``lightrag`` to ['l', 'igh', 'trag']. So t .. note:: ``chunk_overlap`` should always be smaller than ``chunk_size``, otherwise the window won't move and the splitting stucks. - When ``split_by`` = ``token``, the punctuation is considered as a token. + Our default tokenization model is ``cl100k_base``. If you use tokenization (``split_by`` = ``token``), the punctuations are also considered as tokens. How to use it -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +----------------------------- What you need is to specify the arguments and input your documents this way: +Split by word +^^^^^^^^^^^^^^^^^^ + .. code-block:: python from lightrag.components.data_process.text_splitter import TextSplitter @@ -134,13 +148,81 @@ What you need is to specify the arguments and input your documents this way: # Document(id=ca0af45b-4f88-49b5-97db-163da9868ea4, text='text. Even more text to ', meta_data=None, vector=[], parent_doc_id=doc1, order=1, score=None) # Document(id=e7b617b2-3927-4248-afce-ec0fc247ac8b, text='to illustrate.', meta_data=None, vector=[], parent_doc_id=doc1, order=2, score=None) +Split by token +^^^^^^^^^^^^^^^^^^ +.. code-block:: python + + from lightrag.components.data_process.text_splitter import TextSplitter + from lightrag.core.types import Document + import tiktoken + + # Configure the splitter settings + text_splitter = TextSplitter( + split_by="token", + chunk_size=5, + chunk_overlap=0 + ) + + doc = Document( + text="Example text. More example text. Even more text to illustrate.", + id = "doc1" + ) + + splitted_docs = (text_splitter.call(documents=[doc])) + + for doc in splitted_docs: + print(doc) + + # Output: + # Document(id=27cec433-b400-4f11-8871-e386e774d150, text='Example text. More example', meta_data=None, vector=[], parent_doc_id=doc1, order=0, score=None) + # Document(id=8905dc5f-8be5-4ca4-88b1-2ae492258b53, text=' text. Even more text', meta_data=None, vector=[], parent_doc_id=doc1, order=1, score=None) + # Document(id=ba8e1e23-82fb-4aa8-bfc5-e22084984bb9, text=' to illustrate.', meta_data=None, vector=[], parent_doc_id=doc1, order=2, score=None) + +Chunking Tips +----------------------------- +Choosing the proper chunking strategy involves considering several key factors: + +- **Content Type**: Adapt your chunking approach to matching the specific type of content, such as articles, books, social media posts, or genetic sequences. +- **Embedding Model**: Select a chunking method that aligns with your embedding model's training to optimize performance. For example, sentence-based splitting pairs well with `sentence-transformer `_ models, while token-based splitting is ideal for OpenAI's `text-embedding-ada-002 `_. +- **Query Dynamics**: The length and complexity of queries should influence your chunking strategy. Larger chunks may be better for shorter queries lacking detailed specifications and needing broad context, whereas longer queries(more specific) might have higher accuracy with finer granularity. +- **Application of Results**: The application, whether it be semantic search, question answering, or summarization, dictates the appropriate chunking method, especially considering the limitations of content windows in large language models (LLMs). +- **System Integration**: Efficient chunking aligns with system capabilities. For example, `Full-Text Search:` Use larger chunks to allow algorithms to explore broader contexts effectively. For example, search books based on extensive excerpts or chapters. `Granular Search Systems:` Employ smaller chunks to precisely retrieve information relevant to user queries, such as retrieving specific instructions directly in response to a user’s question. For example, if a user asks, "How do I reset my password?". The system can retrieve a specific sentence or paragraph addressing that action directly. + + +Chunking Strategies +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Fixed-Size Chunking +"""""""""""""""""""""""""" + +- Ideal for content requiring uniform chunk sizes like genetic sequences or standardized data entries. This method, which involves splitting text into equal-sized word blocks, is simple and efficient but may compromise semantic coherence and risk breaking important contextual links. + +Content-Aware Chunking +"""""""""""""""""""""""""" + +- **Split by Sentence**: Proper for texts needing a deep understanding of complete sentences, such as academic articles or medical reports. This method maintains grammatical integrity and contextual flow. +- **Split by Passage**: Useful for maintaining the structure and coherence of large documents. Supports detailed tasks like question answering and summarization by focusing on specific text sections. +- **Split by Page**: Effective for large documents where each page contains distinct information, such as legal or academic texts, facilitating precise navigation and information extraction. + +Token-Based Splitting +"""""""""""""""""""""""""" + +- Beneficial for scenarios where embedding models have strict token limitations. This method divides text based on token count, optimizing compatibility with LLMs like GPT, though it may slow down processing due to model complexities. + +Upcoming Splitting Features +"""""""""""""""""""""""""""""""" + +- **Semantic Splitting**: Focuses on grouping texts by meaning rather than structure, enhancing the relevance for thematic searches or advanced contextual retrieval tasks. + +If you are using strategies not based on tokenizer, please be careful of your token length when you input the chunks to your model. + Integration with Other Document Types -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +---------------------------------------------------------- This functionality is ideal for segmenting texts into sentences, words, pages, or passages, which can then be processed further for NLP applications. -For **PDFs**, developers will need to extract the text before using the splitter. Libraries like ``PyPDF2`` or ``PDFMiner`` can be utilized for this purpose. +For `PDFs`, developers will need to extract the text before using the splitter. Libraries like ``PyPDF2`` or ``PDFMiner`` can be utilized for this purpose. ``LightRAG``'s future implementations will introduce splitters for ``JSON``, ``HTML``, ``markdown``, and ``code``. Customization Tips -~~~~~~~~~~~~~~~~~~~~~ -You can also customize the ``SEPARATORS``. For example, by defining ``SEPARATORS`` = {"question": "?"} and setting ``split_by`` = "question", the document will be split at each ``?``, ideal for processing text structured -as a series of questions. If you need to customize :class:`tokenizer `, please check `Reference `_. +----------------------------- +You can also customize the ``SEPARATORS``. For example, by defining ``SEPARATORS`` = ``{"question": "?"} ``and setting ``split_by = "question"``, the document will be split at each ``?``, ideal for processing text structured +as a series of questions. If you need to customize :class:`core.tokenizer.Tokenizer`, please check `Reference `_. diff --git a/_sources/get_started/community.rst.txt b/_sources/get_started/community.rst.txt index 2f529eb6..2e42e9e1 100644 --- a/_sources/get_started/community.rst.txt +++ b/_sources/get_started/community.rst.txt @@ -5,7 +5,7 @@ Community You can join our community on various platforms: -* `Discord `_ +* `Discord `_ * `GitHub Discussion `_ .. _discord-link: diff --git a/_sources/get_started/installation.rst.txt b/_sources/get_started/installation.rst.txt index 6c01c878..7ac7840b 100644 --- a/_sources/get_started/installation.rst.txt +++ b/_sources/get_started/installation.rst.txt @@ -1,6 +1,9 @@ Installation ============ + +.. _Installation: + LightRAG is available in Python. 1. Install LightRAG diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt index 7ed3b674..47da44ac 100644 --- a/_sources/index.rst.txt +++ b/_sources/index.rst.txt @@ -232,10 +232,10 @@ We are building a library that unites the two worlds, forming a healthy LLM appl .. resources/index -.. hide the for contributors now +.. .. toctree:: .. :glob: .. :maxdepth: 1 -.. :caption: For Contributors +.. :caption: Contributors .. :hidden: -.. contributor/index +.. contributor/index \ No newline at end of file diff --git a/apis/components/components.data_process.data_components.html b/apis/components/components.data_process.data_components.html index c97262a3..8c46a31b 100644 --- a/apis/components/components.data_process.data_components.html +++ b/apis/components/components.data_process.data_components.html @@ -525,7 +525,7 @@ diff --git a/contributor/contribution_guide.html b/contributor/contribution.html similarity index 65% rename from contributor/contribution_guide.html rename to contributor/contribution.html index 10c3aeb7..0dd87e81 100644 --- a/contributor/contribution_guide.html +++ b/contributor/contribution.html @@ -48,7 +48,7 @@ - + @@ -250,6 +250,10 @@ + +
- +

retriever_output_to_context_str(retriever_output)

The retrieved documents from one or mulitple queries.

The retrieved documents from one or multiple queries.

@@ -560,7 +560,7 @@
retriever_output_to_context_str(retriever_output: RetrieverOutput | List[RetrieverOutput], deduplicate: bool = False) str[source]#
-

The retrieved documents from one or mulitple queries. +

The retrieved documents from one or multiple queries. Deduplicate is especially helpful when you used query expansion.

diff --git a/contributor/contribute_to_code.html b/contributor/contribute_to_code.html new file mode 100644 index 00000000..b94b65f6 --- /dev/null +++ b/contributor/contribute_to_code.html @@ -0,0 +1,583 @@ + + + + + + + + + + + Contribute to Code — LightRAG documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + +
+
+
+
+
+ +
+ +
+ + + + + +
+
+ + + +
+ + + + + + + + + + + + + +
+ +
+ + +
+
+ +
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+ +
+ +
+ + + + +
+ +
+ + +
+
+ + + + + +
+ +
+

Contribute to Code#

+

This document will cover how you can contribute to lightRAG codebase.

+
+

Set Up#

+

The current LightRAG code contribution supports poetry setup only. The team is working on optimizing the library and will get back to support more environment soon. +If you are only interested in using LightRAG as a package, please check our installation guide.

+

To set up poetry and contribute, please check the following steps:

+
    +
  1. Clone the Repository:

    +
    git clone https://github.com/SylphAI-Inc/LightRAG
    +cd LightRAG
    +
    +
    +
  2. +
  3. Configure API Keys:

    +

    Copy the example environment file and add your API keys:

    +
    cp .env.example .env
    +# example API keys:
    +# OPENAI_API_KEY=YOUR_API_KEY_IF_YOU_USE_OPENAI
    +# GROQ_API_KEY=YOUR_API_KEY_IF_YOU_USE_GROQ
    +# ANTHROPIC_API_KEY=YOUR_API_KEY_IF_YOU_USE_ANTHROPIC
    +# GOOGLE_API_KEY=YOUR_API_KEY_IF_YOU_USE_GOOGLE
    +# COHERE_API_KEY=YOUR_API_KEY_IF_YOU_USE_COHERE
    +# HF_TOKEN=YOUR_API_KEY_IF_YOU_USE_HF
    +
    +
    +
  4. +
  5. Install Dependencies:

    +
    +

    The ./lightrag/pyproject.toml controls the dependencies for the LightRAG package. +Use Poetry to install the dependencies and set up the virtual environment:

    +
    +
  6. +
+
+
+

Codebase Structure#

+

It is recommended to check our LightRAG codebase structure and current API references to familiarize yourself with the directories and paths before contributing.

+
+
+

Code Examples#

+

We want to support you with our best. We have included code samples in the tutorial for you to refer to.

+

We inlcude a list of potential samples(We are working in progress to add more):

+ +
+
+

Code Tips#

+ +
+
+

Dependencies#

+

If you want to add any new dependencies to the package, please include them in your PR description to inform us. +Since we have already set up the testing automatic workflow in GitHub, please also set your new dependencies in +./lightrag/pyproject.toml file [tool.poetry.group.test.dependencies] section to avoid dependency errors in our CI/CD workflow.

+

In order to correctly add the dependency using poetry, please run

+
poetry add --group test <package-name>
+
+
+
+
+

Testing#

+

After you update the code, please make sure your code is well tested before making a pull request. +There is a ./lightrag/tests folder in the project directory to host your unit testing cases.

+

You might need to install the testing packages using poetry:

+

For example:

+
poetry add --group test unittest
+poetry add --group test pytest
+poetry add --group test mypy
+
+
+

All the test scripts should start with test_. For example, run the individual test for components with:

+
python lightrag/tests/test_components.py
+
+
+
+
+ + +
+ + + + + +
+ +
+
+
+ +
+ + + +
+ + +
+
+ +
+ +
+
+
+ + + + + +
+ + +
+ + \ No newline at end of file diff --git a/contributor/contribute_to_document.html b/contributor/contribute_to_document.html index 66b85ea3..4d40991d 100644 --- a/contributor/contribute_to_document.html +++ b/contributor/contribute_to_document.html @@ -8,7 +8,7 @@ - Contribute to Document — LightRAG documentation + Contribute to Documentation — LightRAG documentation @@ -411,46 +411,36 @@
-
-

Contribute to Document#

+
+

Contribute to Documentation#

-
-

Writing Documentation#

-
@@ -524,13 +517,11 @@

Commit Changes

@@ -409,10 +413,56 @@

LightRAG Contribution Guide#

-
    -
  1. Writing Documentation

  2. -
-

Please see our documentation contribution guidelines for more details.

+

LightRAG’s contribution process is similar to most open source projects on GitHub. We encourage new project ideas and the communication between LightRAG team, developers and the broader community. +Please don’t forget to join us on Discord.

+
+

Contribution Process#

+

You are always welcomed to contribute even if you’ve never participated in open source project before. +Here is the basic contribution process:

+
+

Environment#

+

When contributing, please note: +LightRAG separates the source code environment and documentation environment.

+
    +
  • To activate the code environment, you should run poetry install and poetry shell under ./lightrag. The ./lightrag/pyproject.toml contains the dependencies for the LightRAG package.

  • +
  • To activate the documentation environment, you can run poetry install and poetry shell under .. The ./pyproject.toml controls documentation dependencies.

  • +
+
+
+

Find a direction to work on#

+

The team builds LightRAG based on latest researches and product cases. But you might have your own task to apply LightRAG. +Therefore, you can extend LightRAG and add any new features you believe will solve yours or others’ problems. +If you don’t have any idea yet, you can:

+
    +
  • Check the existing issues and see if there is anyone you know how to fix or you’d love to fix.

  • +
  • Join us on Discord. We are glad to discuss with you and know what you are interested in here.

  • +
+
+
+

Figure out the scope of your change#

+

Small: Most of the pull requests are small. If your change is small, such as fixing a line of bug, please go ahead to push it.

+

Big: But if you are making a new feature, or planning to push a large change, it is recommended to contact us on Discord first.

+

Unknown: If you have no idea how big it will be, we are here to help you. Please post your idea on issues. We will read it carefully and get back to you.

+
+
+

Add your code#

+

Please check our code contribution guidelines to work with code.

+
+
+

Pull requests#

+

WIP PR: If you are working on an in pull request that is not ready for review, you can create a PR with “[WIP]” to inform us that this PR is a draft “work in progress”.

+

Finished PR: You can name your finished PR as “[New Retriever Integration]” for example. +We will carry out code review regularly and provide feedbacks as soon as possible. +Please iterate your PR with the feedbacks. We will try our best to reduce the revision workload on your side. +Once your PR is approved, we will merge the PR for you. +If you have any concerns about our feedbacks, please feel free to contact us on Discord.

+
+
+
+

Writing Documentation#

+

It is a good practice to submit your code with documentations to help the LightRAG team and other developers better understand your updates. +Please see our documentation contribution guidelines for more details on LightRAG documentation standard.

+
@@ -432,6 +482,31 @@

LightRAG Contribution Guide +
diff --git a/contributor/index.html b/contributor/index.html index a4a63c4e..5044c264 100644 --- a/contributor/index.html +++ b/contributor/index.html @@ -404,9 +404,26 @@

For Contributors#

+

How to use it#

What you need is to specify the arguments and input your documents this way:

+
+

Split by word#

from lightrag.components.data_process.text_splitter import TextSplitter
 from lightrag.core.types import Document
 
@@ -597,16 +610,88 @@ 

How to use it +

Split by token#

+
from lightrag.components.data_process.text_splitter import TextSplitter
+from lightrag.core.types import Document
+import tiktoken
+
+# Configure the splitter settings
+text_splitter = TextSplitter(
+    split_by="token",
+    chunk_size=5,
+    chunk_overlap=0
+)
+
+doc = Document(
+    text="Example text. More example text. Even more text to illustrate.",
+    id = "doc1"
+    )
+
+splitted_docs = (text_splitter.call(documents=[doc]))
+
+for doc in splitted_docs:
+    print(doc)
+
+# Output:
+# Document(id=27cec433-b400-4f11-8871-e386e774d150, text='Example text. More example', meta_data=None, vector=[], parent_doc_id=doc1, order=0, score=None)
+# Document(id=8905dc5f-8be5-4ca4-88b1-2ae492258b53, text=' text. Even more text', meta_data=None, vector=[], parent_doc_id=doc1, order=1, score=None)
+# Document(id=ba8e1e23-82fb-4aa8-bfc5-e22084984bb9, text=' to illustrate.', meta_data=None, vector=[], parent_doc_id=doc1, order=2, score=None)
+
+
+

+
+
+

Chunking Tips#

+

Choosing the proper chunking strategy involves considering several key factors:

+
    +
  • Content Type: Adapt your chunking approach to matching the specific type of content, such as articles, books, social media posts, or genetic sequences.

  • +
  • Embedding Model: Select a chunking method that aligns with your embedding model’s training to optimize performance. For example, sentence-based splitting pairs well with sentence-transformer models, while token-based splitting is ideal for OpenAI’s text-embedding-ada-002.

  • +
  • Query Dynamics: The length and complexity of queries should influence your chunking strategy. Larger chunks may be better for shorter queries lacking detailed specifications and needing broad context, whereas longer queries(more specific) might have higher accuracy with finer granularity.

  • +
  • Application of Results: The application, whether it be semantic search, question answering, or summarization, dictates the appropriate chunking method, especially considering the limitations of content windows in large language models (LLMs).

  • +
  • System Integration: Efficient chunking aligns with system capabilities. For example, Full-Text Search: Use larger chunks to allow algorithms to explore broader contexts effectively. For example, search books based on extensive excerpts or chapters. Granular Search Systems: Employ smaller chunks to precisely retrieve information relevant to user queries, such as retrieving specific instructions directly in response to a user’s question. For example, if a user asks, “How do I reset my password?”. The system can retrieve a specific sentence or paragraph addressing that action directly.

  • +
+
+

Chunking Strategies#

+
+

Fixed-Size Chunking#

+
    +
  • Ideal for content requiring uniform chunk sizes like genetic sequences or standardized data entries. This method, which involves splitting text into equal-sized word blocks, is simple and efficient but may compromise semantic coherence and risk breaking important contextual links.

  • +
+
+
+

Content-Aware Chunking#

+
    +
  • Split by Sentence: Proper for texts needing a deep understanding of complete sentences, such as academic articles or medical reports. This method maintains grammatical integrity and contextual flow.

  • +
  • Split by Passage: Useful for maintaining the structure and coherence of large documents. Supports detailed tasks like question answering and summarization by focusing on specific text sections.

  • +
  • Split by Page: Effective for large documents where each page contains distinct information, such as legal or academic texts, facilitating precise navigation and information extraction.

  • +
+
+
+

Token-Based Splitting#

+
    +
  • Beneficial for scenarios where embedding models have strict token limitations. This method divides text based on token count, optimizing compatibility with LLMs like GPT, though it may slow down processing due to model complexities.

  • +
+
+
+

Upcoming Splitting Features#

+
    +
  • Semantic Splitting: Focuses on grouping texts by meaning rather than structure, enhancing the relevance for thematic searches or advanced contextual retrieval tasks.

  • +
+

If you are using strategies not based on tokenizer, please be careful of your token length when you input the chunks to your model.

+
+
+

Integration with Other Document Types#

This functionality is ideal for segmenting texts into sentences, words, pages, or passages, which can then be processed further for NLP applications. -For PDFs, developers will need to extract the text before using the splitter. Libraries like PyPDF2 or PDFMiner can be utilized for this purpose. +For PDFs, developers will need to extract the text before using the splitter. Libraries like PyPDF2 or PDFMiner can be utilized for this purpose. LightRAG’s future implementations will introduce splitters for JSON, HTML, markdown, and code.

-
-

Customization Tips#

-

You can also customize the SEPARATORS. For example, by defining SEPARATORS = {“question”: “?”} and setting split_by = “question”, the document will be split at each ?, ideal for processing text structured -as a series of questions. If you need to customize tokenizer, please check Reference.

+
+

Customization Tips#

+

You can also customize the SEPARATORS. For example, by defining SEPARATORS = {"question": "?"} ``and setting ``split_by = "question", the document will be split at each ?, ideal for processing text structured +as a series of questions. If you need to customize core.tokenizer.Tokenizer, please check Reference.

@@ -657,12 +742,28 @@

Customization Tips diff --git a/get_started/installation.html b/get_started/installation.html index 010d5d5a..3dbb03cd 100644 --- a/get_started/installation.html +++ b/get_started/installation.html @@ -423,7 +423,7 @@

Installation#

-

LightRAG is available in Python.

+

LightRAG is available in Python.

1. Install LightRAG#

To install the package, run:

diff --git a/objects.inv b/objects.inv index 27a54af209950b1efb9873858aef238bd6268bb8..4af2c25bf71cff2987826746add9d2c9e8af4f02 100644 GIT binary patch delta 26891 zcmYg%V{|4>)NO2A6Wg|J+qR8Ko+J}Hd15CM+qP|+6DJc-eDi+aU3cAoUA=a9RiCO; zbp5d~pDa*a;NfH`<1nB565%Ma| zi6HPGauh~at82AfN~}H*tU>%${0S8>o#N&-kJU`rcQO3*7gsek%FE67rm$_INHTD4AC$t46N`Am~OBOn1?g#f|KswCU??wwR0A*lWR-uEH5V*^az)sSzZ$J^WBjE;MohD!7wrJIiZU3&J znb%s{y6)WWf%Pk!y{Hh|?T`Qlff50ZpHQncEt(yX57=Ls#i5SdP@%Y?;~vpYq!}29 z1nXzwDd%9qk2oHscb2&`U&);^)N-Ge8|W_16PnTm%1?byP#{6Gh19FpUd0AUro6qk zlj20pyALTFBsM#ILVN5f9sKI`Nnuvd{R0;9L61;lV_CerMYDdk(+IpaH^c!_+UT{^ zx82lkCM;s=S)}V8Uov30J3D!AU`6!L|Dem58~#h~C;R)dbGn4-$kEYNk?ddg;+b)zhY; z{~9u2i?j*%i^V8`>A5j_5ZgUM&~la?EIFvMGp-O zoRkyu?^LKEHsgFoBXNrSu0TT;*dbsMh+ZTJ>Hq~W#=w$&Y2$xmPWyp~av)h;KHyHL zA2*}-Hktn*!s6edkk%mU>TQ&RDuwuSgIYejgRA?ALQ-cXbdDd@;&g6SXLEWpSc111 zjS%3t1~G-p?!CBr-|r1uK*uu3(3vk^4tut${!SVdIPqi+RHnT-^uFUHzz$r#n!hr` zX8}62>&XRD@ZGib+*d*G4@?l7hqc5@{E-ujE-tdfj(KojBXks5{2~ZSakz~s+oTj! 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