# SambaParse SambaParse is a Python library that simplifies the process of extracting and processing unstructured data using the Unstructured.io API. It provides a convenient wrapper around the Unstructured.io CLI tool, allowing you to ingest data from various sources, perform partitioning, chunking, embedding, and load the processed data into a vector database. It's designed to be used within AI Starter kits and SN Apps, unifying our data ingestion and document intelligence platform. This allows us to keep our code base centralized for data ingestion kits. ## Prerequisites Before using SambaParse, make sure you have the following: - Docker installed on your machine (or access to another API server) - An Unstructured.io API key Before using SambaParse, make sure you have the following: - Create a `.env` file in the ai-starter-kit root directory (not in the parsing folder root): ```bash UNSTRUCTURED_API_KEY=your_api_key_here ``` ## Setup ### Pre Reqs Using pyenv to manage virtualenv's is recommended Mac install instructions. See pyenv-virtualenv repo for more detailed instructions. ```bash brew install pyenv-virtualenv ``` - Create a python venv using python version 3.10.12 ```bash pyenv install 3.10.12 pyenv virtualenv 3.10.12 sambaparse pyenv activate sambaparse ``` - Clone the ai-starter-kit repo and cd: ```bash git clone https://github.com/sambanova/ai-starter-kit ``` - cd into utils/parsing and pip install the requirements ```bash pip install -r requirements.txt ``` - cd into the unstructured-api foder and Install the unstructured-api make-file: ```bash cd unstructured-api ``` - Run ```bash make install ``` - Run The Web Server: ```bash make run-web-app ``` This script will start the Unstructured API server using the specified API key and expose it on port 8005. - Alternatively, if you have another Unstructured API server running on a different instance, make sure to update the `partition_endpoint` and `unstructured_port` values in the YAML configuration file accordingly. ## Usage 1. Import the `SambaParse` class from the `ai-starter-kit` library: ```python from utils.parsing.sambaparse import SambaParse ``` 2. Create a YAML configuration file (e.g., `config.yaml`) to specify the desired settings for the ingestion process. Here's the configuration for use cases 1 and 2 ie local files and folders: ```yaml processor: verbose: True output_dir: './output' num_processes: 2 sources: local: recursive: True confluence: api_token: 'your_confluence_api_token' user_email: 'your_email@example.com' url: 'https://your-confluence-url.atlassian.net' github: url: 'owner/repo' branch: 'main' google_drive: service_account_key: 'path/to/service_account_key.json' recursive: True drive_id: 'your_drive_id' partitioning: pdf_infer_table_structure: True skip_infer_table_types: [] strategy: 'auto' hi_res_model_name: 'yolox' ocr_languages: ['eng'] encoding: 'utf-8' fields_include: ['element_id', 'text', 'type', 'metadata', 'embeddings'] flatten_metadata: False metadata_exclude: [] metadata_include: [] partition_endpoint: 'http://localhost' unstructured_port: 8005 partition_by_api: True chunking: enabled: True strategy: 'basic' chunk_max_characters: 1500 chunk_overlap: 300 embedding: enabled: False provider: 'langchain-huggingface' model_name: 'intfloat/e5-large-v2' destination_connectors: enabled: False type: 'chroma' batch_size: 80 chroma: host: 'localhost' port: 8004 collection_name: 'snconf' tenant: 'default_tenant' database: 'default_database' qdrant: location: 'http://localhost:6333' collection_name: 'test' additional_processing: enabled: True extend_metadata: True replace_table_text: True table_text_key: 'text_as_html' return_langchain_docs: True convert_metadata_keys_to_string: True ``` Make sure to place the `config.yaml` file in the desired folder. 3. Create an instance of the `SambaParse` class, passing the path to the YAML configuration file: ```python sambaparse = SambaParse('path/to/config.yaml') ``` 4. Use the `run_ingest` method to process your data: - For a single file: ```python source_type = 'local' input_path = 'path/to/your/file.pdf' additional_metadata = {'key': 'value'} texts, metadata_list, langchain_docs = sambaparse.run_ingest(source_type, input_path=input_path, additional_metadata=additional_metadata) ``` - For a folder: ```python source_type = 'local' input_path = 'path/to/your/file.pdf' additional_metadata = {'key': 'value'} texts, metadata_list, langchain_docs = sambaparse.run_ingest(source_type, input_path=input_path, additional_metadata=additional_metadata) ``` - For Confluence: ```python source_type = 'confluence' additional_metadata = {'key': 'value'} texts, metadata_list, langchain_docs = sambaparse.run_ingest(source_type, additional_metadata=additional_metadata) ``` Note that for conflence you must enable embedding and destinatation connectors automatically ie Chroma and turn off additional processing (ie langchain), an example yaml to do that is below ```yaml processor: verbose: True output_dir: './output' num_processes: 2 sources: local: recursive: True confluence: api_token: 'your_confluence_api_token' user_email: 'your_email@example.com' url: 'https://your-confluence-url.atlassian.net' github: url: 'owner/repo' branch: 'main' google_drive: service_account_key: 'path/to/service_account_key.json' recursive: True drive_id: 'your_drive_id' partitioning: pdf_infer_table_structure: True skip_infer_table_types: [] strategy: 'auto' hi_res_model_name: 'yolox' ocr_languages: ['eng'] encoding: 'utf-8' fields_include: ['element_id', 'text', 'type', 'metadata', 'embeddings'] flatten_metadata: False metadata_exclude: [] metadata_include: [] partition_endpoint: 'http://localhost' unstructured_port: 8005 partition_by_api: True chunking: enabled: True strategy: 'basic' chunk_max_characters: 1500 chunk_overlap: 300 embedding: enabled: True provider: 'langchain-huggingface' model_name: 'intfloat/e5-large-v2' destination_connectors: enabled: True type: 'chroma' batch_size: 80 chroma: host: 'localhost' port: 8004 collection_name: 'snconf' tenant: 'default_tenant' database: 'default_database' qdrant: location: 'http://localhost:6333' collection_name: 'test' additional_processing: enabled: False extend_metadata: True replace_table_text: True table_text_key: 'text_as_html' return_langchain_docs: True convert_metadata_keys_to_string: True ``` In addition for confluence you will need to have a Chroma Server running on port 8004, you can do this by running the docker command below ```bash docker run -d --rm --name chromadb -v ./chroma:/chroma/chroma -e IS_PERSISTENT=TRUE -e ANONYMIZED_TELEMETRY=TRUE -p 8004:8000 chromadb/chroma:latest ``` The `run_ingest` method returns a tuple containing the extracted texts, metadata, and LangChain documents (if `return_langchain_docs` is set to `True` in the configuration). 5. Process the returned data as needed: - `texts`: A list of extracted text elements from the documents. - `metadata_list`: A list of metadata dictionaries for each text element. - `langchain_docs`: A list of LangChain `Document` objects, which combine the text and metadata. #### Configuration Options The YAML configuration file allows you to customize various aspects of the ingestion process. Here are some of the key options: - `processor`: Settings related to the processing of documents, such as the output directory and the number of processes to use. - `sources`: Configuration for different data sources, including local files, Confluence, GitHub, and Google Drive. - `partitioning`: Options for partitioning the documents, including the strategy, OCR languages, and API settings. - `chunking`: Settings for chunking the documents, such as enabling chunking, specifying the chunking strategy, and setting the maximum chunk size and overlap. - `embedding`: Options for embedding the documents, including enabling embedding, specifying the embedding provider, and setting the model name. - `additional_processing`: Configuration for additional processing steps, such as extending metadata, replacing table text, and returning LangChain documents. Make sure to review and modify the configuration file according to your specific requirements.