project-lyra/neomem/neomem/memory/utils.py

import hashlib
import re

from neomem.configs.prompts import FACT_RETRIEVAL_PROMPT


def get_fact_retrieval_messages(message):
    return FACT_RETRIEVAL_PROMPT, f"Input:\n{message}"


def parse_messages(messages):
    response = ""
    for msg in messages:
        if msg["role"] == "system":
            response += f"system: {msg['content']}\n"
        if msg["role"] == "user":
            response += f"user: {msg['content']}\n"
        if msg["role"] == "assistant":
            response += f"assistant: {msg['content']}\n"
    return response


def format_entities(entities):
    if not entities:
        return ""

    formatted_lines = []
    for entity in entities:
        simplified = f"{entity['source']} -- {entity['relationship']} -- {entity['destination']}"
        formatted_lines.append(simplified)

    return "\n".join(formatted_lines)


def remove_code_blocks(content: str) -> str:
    """
    Removes enclosing code block markers ```[language] and ``` from a given string.

    Remarks:
    - The function uses a regex pattern to match code blocks that may start with ``` followed by an optional language tag (letters or numbers) and end with ```.
    - If a code block is detected, it returns only the inner content, stripping out the markers.
    - If no code block markers are found, the original content is returned as-is.
    """
    pattern = r"^```[a-zA-Z0-9]*\n([\s\S]*?)\n```$"
    match = re.match(pattern, content.strip())
    match_res=match.group(1).strip() if match else content.strip()
    return re.sub(r"<think>.*?</think>", "", match_res, flags=re.DOTALL).strip()



def extract_json(text):
    """
    Extracts JSON content from a string, removing enclosing triple backticks and optional 'json' tag if present.
    If no code block is found, returns the text as-is.
    """
    text = text.strip()
    match = re.search(r"```(?:json)?\s*(.*?)\s*```", text, re.DOTALL)
    if match:
        json_str = match.group(1)
    else:
        json_str = text  # assume it's raw JSON
    return json_str


def get_image_description(image_obj, llm, vision_details):
    """
    Get the description of the image
    """

    if isinstance(image_obj, str):
        messages = [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "A user is providing an image. Provide a high level description of the image and do not include any additional text.",
                    },
                    {"type": "image_url", "image_url": {"url": image_obj, "detail": vision_details}},
                ],
            },
        ]
    else:
        messages = [image_obj]

    response = llm.generate_response(messages=messages)
    return response


def parse_vision_messages(messages, llm=None, vision_details="auto"):
    """
    Parse the vision messages from the messages
    """
    returned_messages = []
    for msg in messages:
        if msg["role"] == "system":
            returned_messages.append(msg)
            continue

        # Handle message content
        if isinstance(msg["content"], list):
            # Multiple image URLs in content
            description = get_image_description(msg, llm, vision_details)
            returned_messages.append({"role": msg["role"], "content": description})
        elif isinstance(msg["content"], dict) and msg["content"].get("type") == "image_url":
            # Single image content
            image_url = msg["content"]["image_url"]["url"]
            try:
                description = get_image_description(image_url, llm, vision_details)
                returned_messages.append({"role": msg["role"], "content": description})
            except Exception:
                raise Exception(f"Error while downloading {image_url}.")
        else:
            # Regular text content
            returned_messages.append(msg)

    return returned_messages


def process_telemetry_filters(filters):
    """
    Process the telemetry filters
    """
    if filters is None:
        return {}

    encoded_ids = {}
    if "user_id" in filters:
        encoded_ids["user_id"] = hashlib.md5(filters["user_id"].encode()).hexdigest()
    if "agent_id" in filters:
        encoded_ids["agent_id"] = hashlib.md5(filters["agent_id"].encode()).hexdigest()
    if "run_id" in filters:
        encoded_ids["run_id"] = hashlib.md5(filters["run_id"].encode()).hexdigest()

    return list(filters.keys()), encoded_ids


def sanitize_relationship_for_cypher(relationship) -> str:
    """Sanitize relationship text for Cypher queries by replacing problematic characters."""
    char_map = {
        "...": "_ellipsis_",
        "…": "_ellipsis_",
        "。": "_period_",
        "，": "_comma_",
        "；": "_semicolon_",
        "：": "_colon_",
        "！": "_exclamation_",
        "？": "_question_",
        "（": "_lparen_",
        "）": "_rparen_",
        "【": "_lbracket_",
        "】": "_rbracket_",
        "《": "_langle_",
        "》": "_rangle_",
        "'": "_apostrophe_",
        '"': "_quote_",
        "\\": "_backslash_",
        "/": "_slash_",
        "|": "_pipe_",
        "&": "_ampersand_",
        "=": "_equals_",
        "+": "_plus_",
        "*": "_asterisk_",
        "^": "_caret_",
        "%": "_percent_",
        "$": "_dollar_",
        "#": "_hash_",
        "@": "_at_",
        "!": "_bang_",
        "?": "_question_",
        "(": "_lparen_",
        ")": "_rparen_",
        "[": "_lbracket_",
        "]": "_rbracket_",
        "{": "_lbrace_",
        "}": "_rbrace_",
        "<": "_langle_",
        ">": "_rangle_",
    }

    # Apply replacements and clean up
    sanitized = relationship
    for old, new in char_map.items():
        sanitized = sanitized.replace(old, new)

    return re.sub(r"_+", "_", sanitized).strip("_")