searchflow 0.0.77

An assistant helping you to index webpages into structured datasets.

SearchFlow

SearchFlow is an assistant designed to help you index webpages into structured datasets. It leverages various tools and models to scrape, process, and store web content efficiently.

Features

• Web Scraping: Uses trafilatura for focused crawling and web scraping.
• Document Processing: Supports chunking and processing of various document types.
• Database Management: Manages projects, documents, and prompts using PostgreSQL.
• Vector Search: Utilizes vector search for document retrieval.
• LLM Integration: Integrates with language models for question answering and document grading.

Installation

To set up the development environment, use the provided Dockerfile and .devcontainer/devcontainer.json for a consistent development setup.

Prerequisites

• Docker
• Python 3.11 or higher

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/searchflow.git
   cd searchflow
   

2. Build the Docker container:

   docker build -t searchflow .
   

3. Run the Docker container:

   docker run -it -p 8501:8501 searchflow
   

Usage

Web Scraping

To scrape a website and index the links:

from searchflow.importers.webscraper import WebScraper
scraper = WebScraper(project_name="example_project")
scraper.get_all_links(base_url="https://example.com")

Document Processing

To upload and process files:

from searchflow.importers.file_importer import FileImporter
files = Files()
files.upload_file(document_data=[(b"file_content", "example.pdf")], project_name="example_project")

Vector Search

To perform a similarity search:

from searchflow.db.postgresql import DB
db = DB()
results = db.similarity_search(project_name="example_project", query="example query"