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# 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: '[email protected]'
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: '[email protected]'
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. |