diff --git a/class_hierarchy_edges.csv b/class_hierarchy_edges.csv new file mode 100644 index 00000000..a9348645 --- /dev/null +++ b/class_hierarchy_edges.csv @@ -0,0 +1,68 @@ +Component,ListParser +Component,JsonParser +Component,YamlParser +Component,ToolManager +Component,Prompt +Component,ModelClient +Component,Retriever +Component,FunctionTool +Component,Tokenizer +Component,Generator +Component,Embedder +Component,BatchEmbedder +Component,Sequential +Component,FunComponent +Component,ReActAgent +Component,OutputParser +Component,TextSplitter +Component,DocumentSplitter +Component,ToEmbeddings +Component,RetrieverOutputToContextStr +Component,DefaultLLMJudge +Component,LLMAugmenter +Generic,LocalDB +Generic,Retriever +Generic,GeneratorOutput +Generic,Parameter +Generic,Sample +Generic,Sampler +Generic,RandomSampler +Generic,ClassSampler +ModelClient,CohereAPIClient +ModelClient,TransformersClient +ModelClient,GroqAPIClient +ModelClient,GoogleGenAIClient +ModelClient,OpenAIClient +ModelClient,AnthropicAPIClient +Retriever,BM25Retriever +Retriever,PostgresRetriever +Retriever,RerankerRetriever +Retriever,LLMRetriever +Retriever,FAISSRetriever +Enum,DataClassFormatType +Enum,ModelType +Enum,DistanceToOperator +Enum,OptionalPackages +DataClass,EmbedderOutput +DataClass,GeneratorOutput +DataClass,RetrieverOutput +DataClass,FunctionDefinition +DataClass,Function +DataClass,FunctionExpression +DataClass,FunctionOutput +DataClass,StepOutput +DataClass,Document +DataClass,DialogTurn +DataClass,Instruction +DataClass,GeneratorStatesRecord +DataClass,GeneratorCallRecord +Generator,CoTGenerator +Generator,CoTGeneratorWithJsonOutput +OutputParser,YamlOutputParser +OutputParser,JsonOutputParser +OutputParser,ListOutputParser +OutputParser,BooleanOutputParser +Optimizer,BootstrapFewShot +Optimizer,LLMOptimizer +Sampler,RandomSampler +Sampler,ClassSampler diff --git a/docs/source/_static/class_hierarchy.html b/docs/source/_static/class_hierarchy.html new file mode 100644 index 00000000..99997886 --- /dev/null +++ b/docs/source/_static/class_hierarchy.html @@ -0,0 +1,155 @@ + + + + + + + + + +
+

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+ + + + + + + diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css index 80443f28..be7a7db0 100644 --- a/docs/source/_static/css/custom.css +++ b/docs/source/_static/css/custom.css @@ -10,6 +10,20 @@ .theme-version { display: none; } +.bd-sidebar-primary { + width: 100%; /* Full width by default */ +} + +/* Adjust width for larger screens */ +@media (min-width: 768px) { /* Example breakpoint for tablets and larger */ + .bd-sidebar-primary { + width: 270px; /* Adjust the width for larger screens */ + } +} + +/* .copyright { + text-align: center; +} */ p { font-size: 0.9em; margin-bottom: 1.15rem; diff --git a/docs/source/developer_notes/base_data_class.rst b/docs/source/developer_notes/base_data_class.rst index 966d155e..92199d3d 100644 --- a/docs/source/developer_notes/base_data_class.rst +++ b/docs/source/developer_notes/base_data_class.rst @@ -12,7 +12,7 @@ Tensor wraps a multi-dimensional matrix to better support its operations and com In LLM applications, data constantly needs to interact with LLMs in the form of strings via prompt and be parsed back to structured data from LLMs' text prediction. :class:`core.base_data_class.DataClass` is designed to ease the data interaction with LLMs via prompt(input) and text prediction(output). -.. figure:: /_static/images/dataclass.png +.. figure:: ../_static/images/dataclass.png :align: center :alt: DataClass :width: 680px diff --git a/docs/source/developer_notes/class_hierarchy.rst b/docs/source/developer_notes/class_hierarchy.rst new file mode 100644 index 00000000..2e3d446a --- /dev/null +++ b/docs/source/developer_notes/class_hierarchy.rst @@ -0,0 +1,6 @@ +LightRAG Class Hierarchy Visualization +============================= + +.. raw:: html + + diff --git a/docs/source/developer_notes/evaluation.rst b/docs/source/developer_notes/evaluation.rst index 66fc17e4..9b9315eb 100644 --- a/docs/source/developer_notes/evaluation.rst +++ b/docs/source/developer_notes/evaluation.rst @@ -1,4 +1,4 @@ -A Guideline on LLM Evaluation +LLM Evaluation ==================================== .. admonition:: Author diff --git a/docs/source/developer_notes/optimizer.rst b/docs/source/developer_notes/optimizer.rst index e6201a16..d26f6ad0 100644 --- a/docs/source/developer_notes/optimizer.rst +++ b/docs/source/developer_notes/optimizer.rst @@ -1,2 +1,2 @@ -Optimizer - The Future of LLM applications -========================================================== \ No newline at end of file +Optimizer +========================================================== diff --git a/docs/source/index.rst b/docs/source/index.rst index e6e13499..9cd4d1e0 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -66,8 +66,6 @@ It is light, modular, and robust. return await self.generator.acall({"input_str": query}) - qa = SimpleQA() - answer = qa("What is LightRAG?") @@ -75,14 +73,22 @@ It is light, modular, and robust. Clarity and Simplicity ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Developers who are building real-world Large Language Model (LLM) applications are the real heroes. +As a library, we provide them the foundamental building blocks with 100% clarity and simplicity. -We understand that developers building real-world Large Language Model (LLM) applications are the real heroes. Just like AI researchers and engineers who build models on top of PyTorch, developers require **Maximum Flexibility and Customizability**: Each developer has unique data needs to build their own models/components, experiment with In-context Learning (ICL) or model finetuning, and deploy the LLM applications to production. This means the library must provide fundamental lower-level building blocks and strive for clarity and simplicity: +.. We support them with require **Maximum Flexibility and Customizability**: -- We maintain no more than two levels of subclasses. -- Each core abstract class is designed to be robust and flexible. -- We use 10X less code than other libraries to achieve 10X more robustness and flexibility. +.. Each developer has unique data needs to build their own models/components, experiment with In-context Learning (ICL) or model finetuning, and deploy the LLM applications to production. This means the library must provide fundamental lower-level building blocks and strive for clarity and simplicity: -Here is the printed out structure of ``qa``: +- Two foundamental and powerful base classes: ``component`` for the pipeline and ``DataClass`` for the data interaction with LLMs. +- We end up with less than two levels of subclasses. :doc:`developer_notes/class_hierarchy`. +- The result is a library with bare minimum abstraction with maximum flexibility and customizability. + +.. - We use 10X less code than other libraries to achieve 10X more robustness and flexibility. + +.. - `Class Hierarchy Visualization `_ + +Similar to PyTorch module, our ``component`` gives us a great visualization on the pipeline structure. .. code-block:: diff --git a/poetry.lock b/poetry.lock index c021f7d1..db792665 100644 --- a/poetry.lock +++ b/poetry.lock @@ -494,17 +494,17 @@ css = ["tinycss2 (>=1.1.0,<1.3)"] [[package]] name = "boto3" -version = "1.34.131" +version = "1.34.136" description = 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"nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"] -dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] +deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] +dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] +dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"] +dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] ftfy = ["ftfy"] @@ -5987,25 +6328,26 @@ natten = ["natten (>=0.14.6,<0.15.0)"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] optuna = ["optuna"] -quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"] +quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.4.4)", "urllib3 (<2.0.0)"] ray = ["ray[tune] (>=2.7.0)"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] +ruff = ["ruff (==0.4.4)"] sagemaker = ["sagemaker (>=2.31.0)"] sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"] serving = ["fastapi", "pydantic", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] -tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] -timm = ["timm"] +timm = ["timm (<=0.9.16)"] tokenizers = ["tokenizers (>=0.19,<0.20)"] torch = ["accelerate (>=0.21.0)", "torch"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17,<2.0)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] video = ["av (==9.2.0)", "decord (==0.6.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -6522,4 +6864,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = ">=3.11, <4.0" -content-hash = "4129281871725e73ccb571ca9a71a96c2c575b583bd6eb3eea649c1a96c8d9f2" +content-hash = "0be4e6e956c9ecb269263f17c19dfa5506ae361a3a999c8e11fdae1ad46b3bcf" diff --git a/pyproject.toml b/pyproject.toml index 894932bd..e2b148d0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,8 @@ torch = "^2.3.1" flagembedding = "^1.2.10" # cohere = "^5.5.7" openai = "^1.34.0" +networkx = "^3.3" +pyvis = "^0.3.2" [tool.poetry.group.dev.dependencies] @@ -50,6 +52,8 @@ langchain = "^0.2.5" langchain-community = "^0.2.5" langchain-openai = "^0.1.8" pydot = "^2.0.0" +matplotlib = "^3.9.0" +pyvis = "^0.3.2" [tool.poetry.group.doc.dependencies] diff --git a/test_pyvis.py b/test_pyvis.py new file mode 100644 index 00000000..39f72639 --- /dev/null +++ b/test_pyvis.py @@ -0,0 +1,15 @@ +from pyvis.network import Network +import networkx as nx + +# Create a simple NetworkX graph +G = nx.DiGraph() +G.add_edges_from([(1, 2), (2, 3), (3, 4)]) + +# Create a Pyvis Network +net = Network(notebook=False, width="100%", height="100%", directed=True) + +# Add nodes and edges from NetworkX to Pyvis +net.from_nx(G) + +# Save the network to an HTML file +net.show("test_network.html") diff --git a/visualize.py b/visualize.py index c3aca3f8..971d6e65 100644 --- a/visualize.py +++ b/visualize.py @@ -1,6 +1,8 @@ import os import ast import pydot +import networkx as nx +from pyvis.network import Network def get_class_name(node): @@ -28,6 +30,56 @@ def get_classes_from_file(file_path): return classes +def get_class_hierarchy_nx(classes): + graph = nx.DiGraph() + excluded_bases = { + "ComponentWithSuperInit", + "ComponentMissSuperInit", + "Memory", + "ObjectTypes", + } + + for cls in classes: + for base in cls.bases: + try: + base_name = get_class_name(base) + if base_name not in excluded_bases and cls.name not in excluded_bases: + print(f"Adding edge from {base_name} to {cls.name}") + graph.add_edge(base_name, cls.name) + except AttributeError as e: + print(f"Error processing {cls.name}: {base}, {e}") + pass + + return graph + + +def visualize_class_hierarchy_2(graph, filename="class_hierarchy.html"): + # Create a Pyvis Network + # Create a Pyvis Network + filename = filename.replace(".png", ".html") + print(filename) + net = Network(notebook=False, width="100%", height="100%", directed=True) + + # Add nodes and edges to the Pyvis network from the NetworkX graph + for node1, node2 in graph.edges(): + net.add_node(node1) + net.add_node(node2) + net.add_edge(node1, node2) + print("Nodes and edges added to the Pyvis network") + print(net) + + # Show graph + net.show(filename, local=True) + print(f"Class hierarchy saved as {filename}") + + +def save_edges_to_file(graph, filename="class_hierarchy_edges.csv"): + with open(filename, "w") as file: + for node1, node2 in graph.edges(): + file.write(f"{node1},{node2}\n") + print(f"Edges saved to {filename}") + + def get_class_hierarchy(classes): graph = pydot.Dot(graph_type="digraph") excluded_bases = { @@ -52,6 +104,41 @@ def get_class_hierarchy(classes): return graph +# def visualize_class_hierarchy_nx(graph, filename="class_hierarchy.png"): + +# filename = filename.replace(".png", ".html") + +# pos = nx.spring_layout(graph) # You can choose other layouts as needed + +# plt.figure(figsize=(10, 8)) +# nx.draw( +# graph, +# pos, +# with_labels=True, +# node_size=1500, +# node_color="skyblue", +# font_size=10, +# font_color="black", +# font_weight="bold", +# edge_color="#666666", +# linewidths=2, +# arrows=True, +# arrowstyle="->", +# arrowsize=10, +# ) + +# plt.title("LightRAG Class Hierarchy") +# plt.tight_layout() + +# # Convert to HTML using mpld3 +# html_str = mpld3.fig_to_html(plt.gcf()) + +# with open(filename, "w") as file: +# file.write(html_str) + +# print(f"Class hierarchy saved as {filename}") + + def visualize_class_hierarchy(graph, filename="class_hierarchy.png"): dpi = 800 graph.set_graph_defaults(dpi=str(dpi)) @@ -119,7 +206,13 @@ def lang_chain_paths(): all_classes.extend(process_directory(path)) # Generate the class hierarchy graph - graph = get_class_hierarchy(all_classes) + # graph = get_class_hierarchy(all_classes) + + # use nx + graph = get_class_hierarchy_nx(all_classes) # Visualize the class hierarchy - visualize_class_hierarchy(graph, filename=graph_name) + # visualize_class_hierarchy(graph, filename=graph_name) + + # visualize_class_hierarchy_2(graph, filename=graph_name) + save_edges_to_file(graph, filename="class_hierarchy_edges.csv")