Input Processing

This outline describes the process of how user input is parsed and transformed into search queries within the Autoflow project.

1. Parsing User Input: The initial step involves parsing user input into a structured format that can be easily processed. This is achieved through the use of a parser library. The parser library breaks down the user’s input into meaningful components.

2. Transformation:

The parsed input is then transformed into a search query format. This involves:

• Normalizing Input: The parser converts the input to a consistent format, e.g., converting all text to lowercase, removing spaces, or handling special characters.
• Keyword Extraction: Keywords are extracted from the input to form the basis of the search query. This may involve identifying specific terms, phrases, or other relevant elements.
• Building the Search Query: Using the extracted keywords, a search query is constructed. This may involve appending specific operators, modifiers, or syntax depending on the search engine’s requirements.

Example:

// Input: "Find all articles about pandas in the last week"
          // Parsed Input: ["Find", "all", "articles", "about", "pandas", "in", "the", "last", "week"]
          // Transformed Search Query: "pandas articles published:last week"
          

3. Query Execution:

The transformed search query is then sent to the underlying search engine for execution. The search engine processes the query and returns relevant results.

4. Results Processing:

The results returned from the search engine are then processed and presented to the user in a user-friendly format. This may involve ranking, filtering, or highlighting relevant information.

5. Logging:

The entire process is logged to provide insights into user interactions and query patterns. This logging information helps in improving the system’s performance and efficiency.

Example Implementation:

def process_input(user_input):
              # Parse the user input
              parsed_input = parser.parse(user_input)
          
              # Transform the parsed input into a search query
              search_query = transform_input(parsed_input)
          
              # Execute the search query
              results = search_engine.execute_query(search_query)
          
              # Process the results and return them to the user
              return process_results(results)
          
          def transform_input(parsed_input):
              # Normalize the input
              normalized_input = normalize(parsed_input)
          
              # Extract keywords
              keywords = extract_keywords(normalized_input)
          
              # Build the search query
              search_query = build_search_query(keywords)
              
              return search_query
          
          # ... (Implementation of parser, normalize, extract_keywords, build_search_query, 
          #           search_engine, and process_results functions)
          

Example usage:

user_input = "Find articles about pandas in the last week"
          results = process_input(user_input)
          # ... (Process the results and display them to the user)
          

Note: The specific implementation details may vary depending on the chosen parser, search engine, and the specific requirements of the project.