feat(vertex/base.py): add PowerComponentTypes enum to represent power components
feat(vertex/base.py): add is_power_component attribute to Vertex class to determine if a vertex is a power component
feat(vertex/base.py): add get_result_dict method to Vertex class to return a dictionary with the result of the build process
feat(vertex/base.py): add get_built_result method to Vertex class to return the built result of a vertex
feat(vertex/base.py): add set_artifacts method to Vertex class
feat(vertex/base.py): add steps and steps_ran attributes to Vertex class to keep track of build steps
feat(vertex/base.py): add layer attribute to Vertex class to represent the layer of the vertex
feat(vertex/base.py): add set_top_level method to Vertex class to set the parent_is_top_level attribute
feat(vertex/base.py): add pinned attribute to Vertex class to indicate if the vertex is pinned
feat(vertex/base.py): add _reset method to Vertex class to reset the state of the vertex before building
feat(vertex/base.py): add build method to Vertex class to build the vertex and run build steps
feat(vertex/base.py): add get_requester_result method to Vertex class to get the result of the requester vertex
fix(vertex/base.py): fix add_edge method in Vertex class to check if the edge already exists before adding it
fix(vertex/base.py): fix __getstate__ method in Vertex class to include pinned attribute in the state
fix(vertex/base.py): fix _parse_data method in Vertex class to correctly set the pinned attribute
fix(vertex/base.py): fix _run method in Vertex class to handle different types of built objects and handle exceptions
Ollama embeddings are useful to enhance langflow's support of Ollama,
allowing users to run LLMs such as Mistral and LLama locally. Langchain
documentation can be found via [this
link](https://python.langchain.com/docs/integrations/text_embedding/ollama).
Changes:
- New `OllamaEmbeddingsComponent` class
- Associated documentation in the `Embeddings` section
