vectara-cli 0.1.0

A CLI tool for interacting with the Vectara platform, including advanced text processing and indexing features.

vectara-cli

vectara-cli is a Python package designed to interact with the Vectara platform, providing a command-line interface (CLI) and a set of APIs for indexing and querying documents, managing corpora, and performing advanced text analysis and processing tasks. This package is particularly useful for developers and data scientists working on search and information retrieval applications.

Features

• Indexing text and documents into Vectara corpora.
• Querying indexed documents.
• Creating and deleting corpora.
• Advanced text processing and analysis using pre-trained models (optional advanced package).

Installation

Basic Installation

The basic installation includes the core functionality for interacting with the Vectara platform.

pip install vectara-cli

Advanced Installation

The advanced installation includes additional dependencies for advanced text processing and analysis features. This requires PyTorch, Transformers, and Accelerate, which can be substantial in size.

pip install vectara-cli[advanced]

Ensure you have an appropriate PyTorch version installed for your system, especially if you're installing on a machine with GPU support. Refer to the official PyTorch installation guide for more details.

Usage

Basic Usage

To enhance the "Basic Usage" section of your README.md for the PyPi package, we'll include examples that cover all the functionalities provided by core.py. This will give users a comprehensive guide on how to utilize the VectaraClient class for various operations such as indexing text, querying documents, creating and deleting corpora, indexing documents from a folder, and handling document uploads.

Basic Usage

Setting Up a Vectara Client

First, initialize the Vectara client with your customer ID and API key. This client will be used for all subsequent operations.

from vectara_cli.core import VectaraClient

customer_id = 'your_customer_id'
api_key = 'your_api_key'
vectara_client = VectaraClient(customer_id, api_key)

Indexing a Document

To index a document, you need its corpus ID, a unique document ID, and the text you want to index. Optionally, you can include context, metadata in JSON format, and custom dimensions.

corpus_id = 'your_corpus_id'
document_id = 'unique_document_id'
text = 'This is the document text you want to index.'
context = 'Document context'
metadata_json = '{"author": "John Doe"}'

vectara_client.index_text(corpus_id, document_id, text, context, metadata_json)

Indexing Documents from a Folder

To index all documents from a specified folder into a corpus, provide the corpus ID and the folder path.

corpus_id = 'your_corpus_id'
folder_path = '/path/to/your/documents'

results = vectara_client.index_documents_from_folder(corpus_id, folder_path)
for document_id, success, extracted_text in results:
    if success:
        print(f"Successfully indexed document {document_id}.")
    else:
        print(f"Failed to index document {document_id}.")

Querying Documents

To query documents, specify your search query, the number of results you want to return, and the corpus ID.

query_text = 'search query'
num_results = 10  # Number of results to return
corpus_id = 'your_corpus_id'

results = vectara_client.query(query_text, num_results, corpus_id)
print(results)

Creating a Corpus

You can create a new corpus by specifying its ID, name, description, and other settings.

create_corpus_response = vectara_client.create_corpus(
    corpus_id=123456789,
    name="Example Corpus",
    description="This is an example corpus.",
    dtProvision=1234567890,
    enabled=True,
    swapQenc=False,
    swapIenc=False,
    textless=False,
    encrypted=False,
    encoderId="default",
    metadataMaxBytes=10000,
    customDimensions=[
        {"name": "dimension1", "description": "First custom dimension", "servingDefault": 1.0, "indexingDefault": 1.0}
    ],
    filterAttributes=[
        {"name": "filter1", "description": "First filter attribute", "indexed": True, "type": "FILTER_ATTRIBUTE_TYPE__UNDEFINED", "level": "FILTER_ATTRIBUTE_LEVEL__UNDEFINED"}
    ]
)
print(create_corpus_response)

Deleting a Corpus

To delete a corpus, you only need to provide its ID.

corpus_id = 'your_corpus_id'
response, success = vectara_client.delete_corpus(corpus_id)

if success:
    print("Corpus deleted successfully.")
else:
    print("Failed to delete corpus:", response)

Uploading a Document

To upload and index a document, specify the corpus ID, the path to the document, and optionally, a document ID and metadata.

corpus_id = 'your_corpus_id'
file_path = '/path/to/your/document.pdf'
document_id = 'unique_document_id'  # Optional
metadata = {"author": "Author Name", "title": "Document Title"}  # Optional

try:
    response, status = vectara_client.upload_document(corpus_id, file_path, document_id, metadata)
    print("Upload successful:", response)
except Exception as e:
    print("Upload failed:", str(e))

Advanced Usage

The advanced features allow you to enrich your indexes with additional information automatically. This should produce better results for retrieval.

Non-Commercial Advanced Rag Using Rebel

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Use the use the Rebel Class for advanced indexing. This will automatically extract named entities, key phrases, and other relevant information from your documents :

from vectara_cli.advanced.non_commercial.rebel import Rebel

folder_path = '/path/to/your/documents'
query_text = 'search query'
num_results = 10  # Number of results to return
# Initialize the Rebel instance for advanced non-commercial text processing
rebel = Rebel()

# Perform advanced indexing
corpus_id_1, corpus_id_2 = rebel.advanced_upsert_folder(vectara_client, corpus_id_1, corpus_id_2, folder_path)

# Vanilla Retrieval 
plain_results = vectara_client.query(query_text, num_results, corpus_id_1)
# Enhanced Retrieval
enhanced_results = vectara_client.query(query_text, num_results, corpus_id_2)

# Print Results
print("=== Plain Results ===")
for result in plain_results:
    print(f"Document ID: {result['documentIndex']}, Score: {result['score']}, Text: {result['text'][:100]}...")

print("\n=== Enhanced Results ===")
for result in enhanced_results:
    print(f"Document ID: {result['documentIndex']}, Score: {result['score']}, Text: {result['text'][:100]}...")

Contributing

Contributions to vectara-cli are welcome! Please refer to the contributing guidelines in the repository for more information on how to contribute.

License

vectara-cli is MIT licensed. See the LICENSE file for more details.

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This README provides a comprehensive guide for installing and using the vectara-cli package. For further information or assistance, please refer to the Vectara documentation or submit an issue on the GitHub repository.