rm.py

import logging
import os
from typing import Callable, Union, List

import dspy
import requests

from langchain_huggingface import HuggingFaceEmbeddings
from langchain_qdrant import Qdrant
from qdrant_client import QdrantClient

from .utils import WebPageHelper


class YouRM(dspy.Retrieve):
    def __init__(self, ydc_api_key=None, k=3, is_valid_source: Callable = None):
        super().__init__(k=k)
        if not ydc_api_key and not os.environ.get("YDC_API_KEY"):
            raise RuntimeError("You must supply ydc_api_key or set environment variable YDC_API_KEY")
        elif ydc_api_key:
            self.ydc_api_key = ydc_api_key
        else:
            self.ydc_api_key = os.environ["YDC_API_KEY"]
        self.usage = 0

        # If not None, is_valid_source shall be a function that takes a URL and returns a boolean.
        if is_valid_source:
            self.is_valid_source = is_valid_source
        else:
            self.is_valid_source = lambda x: True

    def get_usage_and_reset(self):
        usage = self.usage
        self.usage = 0

        return {'YouRM': usage}

    def forward(self, query_or_queries: Union[str, List[str]], exclude_urls: List[str] = []):
        """Search with You.com for self.k top passages for query or queries

        Args:
            query_or_queries (Union[str, List[str]]): The query or queries to search for.
            exclude_urls (List[str]): A list of urls to exclude from the search results.

        Returns:
            a list of Dicts, each dict has keys of 'description', 'snippets' (list of strings), 'title', 'url'
        """
        queries = (
            [query_or_queries]
            if isinstance(query_or_queries, str)
            else query_or_queries
        )
        self.usage += len(queries)
        collected_results = []
        for query in queries:
            try:
                headers = {"X-API-Key": self.ydc_api_key}
                results = requests.get(
                    f"https://api.ydc-index.io/search?query={query}",
                    headers=headers,
                ).json()

                authoritative_results = []
                for r in results['hits']:
                    if self.is_valid_source(r['url']) and r['url'] not in exclude_urls:
                        authoritative_results.append(r)
                if 'hits' in results:
                    collected_results.extend(authoritative_results[:self.k])
            except Exception as e:
                logging.error(f'Error occurs when searching query {query}: {e}')

        return collected_results


class BingSearch(dspy.Retrieve):
    def __init__(self, bing_search_api_key=None, k=3, is_valid_source: Callable = None,
                 min_char_count: int = 150, snippet_chunk_size: int = 1000, webpage_helper_max_threads=10,
                 mkt='en-US', language='en', **kwargs):
        """
        Params:
            min_char_count: Minimum character count for the article to be considered valid.
            snippet_chunk_size: Maximum character count for each snippet.
            webpage_helper_max_threads: Maximum number of threads to use for webpage helper.
            mkt, language, **kwargs: Bing search API parameters.
            - Reference: https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/reference/query-parameters
        """
        super().__init__(k=k)
        if not bing_search_api_key and not os.environ.get("BING_SEARCH_API_KEY"):
            raise RuntimeError(
                "You must supply bing_search_subscription_key or set environment variable BING_SEARCH_API_KEY")
        elif bing_search_api_key:
            self.bing_api_key = bing_search_api_key
        else:
            self.bing_api_key = os.environ["BING_SEARCH_API_KEY"]
        self.endpoint = "https://api.bing.microsoft.com/v7.0/search"
        self.params = {
            'mkt': mkt,
            "setLang": language,
            "count": k,
            **kwargs
        }
        self.webpage_helper = WebPageHelper(
            min_char_count=min_char_count,
            snippet_chunk_size=snippet_chunk_size,
            max_thread_num=webpage_helper_max_threads
        )
        self.usage = 0

        # If not None, is_valid_source shall be a function that takes a URL and returns a boolean.
        if is_valid_source:
            self.is_valid_source = is_valid_source
        else:
            self.is_valid_source = lambda x: True

    def get_usage_and_reset(self):
        usage = self.usage
        self.usage = 0

        return {'BingSearch': usage}

    def forward(self, query_or_queries: Union[str, List[str]], exclude_urls: List[str] = []):
        """Search with Bing for self.k top passages for query or queries

        Args:
            query_or_queries (Union[str, List[str]]): The query or queries to search for.
            exclude_urls (List[str]): A list of urls to exclude from the search results.

        Returns:
            a list of Dicts, each dict has keys of 'description', 'snippets' (list of strings), 'title', 'url'
        """
        queries = (
            [query_or_queries]
            if isinstance(query_or_queries, str)
            else query_or_queries
        )
        self.usage += len(queries)

        url_to_results = {}

        headers = {"Ocp-Apim-Subscription-Key": self.bing_api_key}

        for query in queries:
            try:
                results = requests.get(
                    self.endpoint,
                    headers=headers,
                    params={**self.params, 'q': query}
                ).json()

                for d in results['webPages']['value']:
                    if self.is_valid_source(d['url']) and d['url'] not in exclude_urls:
                        url_to_results[d['url']] = {'url': d['url'], 'title': d['name'], 'description': d['snippet']}
            except Exception as e:
                logging.error(f'Error occurs when searching query {query}: {e}')

        valid_url_to_snippets = self.webpage_helper.urls_to_snippets(list(url_to_results.keys()))
        collected_results = []
        for url in valid_url_to_snippets:
            r = url_to_results[url]
            r['snippets'] = valid_url_to_snippets[url]['snippets']
            collected_results.append(r)

        return collected_results


class VectorRM(dspy.Retrieve):
    """Retrieve information from custom documents using Qdrant.

    To be compatible with STORM, the custom documents should have the following fields:
        - content: The main text content of the document.
        - title: The title of the document.
        - url: The URL of the document. STORM use url as the unique identifier of the document, so ensure different
            documents have different urls.
        - description (optional): The description of the document.
    The documents should be stored in a CSV file.
    """

    def __init__(self,
                 collection_name: str,
                 embedding_model: str,
                 device: str = "mps",
                 k: int = 3,
                ):
        """
        Params:
            collection_name: Name of the Qdrant collection.
            embedding_model: Name of the Hugging Face embedding model.
            device: Device to run the embeddings model on, can be "mps", "cuda", "cpu".
            k: Number of top chunks to retrieve.
        """
        super().__init__(k=k)
        self.usage = 0
        # check if the collection is provided
        if not collection_name:
            raise ValueError("Please provide a collection name.")
        # check if the embedding model is provided
        if not embedding_model:
            raise ValueError("Please provide an embedding model.")

        model_kwargs = {"device": device}
        encode_kwargs = {"normalize_embeddings": True}
        self.model = HuggingFaceEmbeddings(
            model_name=embedding_model, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
        )

        self.collection_name = collection_name
        self.client = None
        self.qdrant = None

    def _check_collection(self):
        """
        Check if the Qdrant collection exists and create it if it does not.
        """
        if self.client is None:
            raise ValueError("Qdrant client is not initialized.")
        if self.client.collection_exists(collection_name=f"{self.collection_name}"):
            print(f"Collection {self.collection_name} exists. Loading the collection...")
            self.qdrant = Qdrant(
                client=self.client,
                collection_name=self.collection_name,
                embeddings=self.model,
            )
        else:
            raise ValueError(f"Collection {self.collection_name} does not exist. Please create the collection first.")

    def init_online_vector_db(self, url: str, api_key: str):
        """
        Initialize the Qdrant client that is connected to an online vector store with the given URL and API key.

        Args:
            url (str): URL of the Qdrant server.
            api_key (str): API key for the Qdrant server.
        """
        if api_key is None:
            if not os.getenv("QDRANT_API_KEY"):
                raise ValueError("Please provide an api key.")
            api_key = os.getenv("QDRANT_API_KEY")
        if url is None:
            raise ValueError("Please provide a url for the Qdrant server.")

        try:
            self.client = QdrantClient(url=url, api_key=api_key)
            self._check_collection()
        except Exception as e:
            raise ValueError(f"Error occurs when connecting to the server: {e}")

    def init_offline_vector_db(self, vector_store_path: str):
        """
        Initialize the Qdrant client that is connected to an offline vector store with the given vector store folder path.

        Args:
            vector_store_path (str): Path to the vector store.
        """
        if vector_store_path is None:
            raise ValueError("Please provide a folder path.")

        try:
            self.client = QdrantClient(path=vector_store_path)
            self._check_collection()
        except Exception as e:
            raise ValueError(f"Error occurs when loading the vector store: {e}")

    def get_usage_and_reset(self):
        usage = self.usage
        self.usage = 0

        return {'VectorRM': usage}

    def get_vector_count(self):
        """
        Get the count of vectors in the collection.

        Returns:
            int: Number of vectors in the collection.
        """
        return self.qdrant.client.count(collection_name=self.collection_name)

    def forward(self, query_or_queries: Union[str, List[str]], exclude_urls: List[str]):
        """
        Search in your data for self.k top passages for query or queries.

        Args:
            query_or_queries (Union[str, List[str]]): The query or queries to search for.
            exclude_urls (List[str]): Dummy parameter to match the interface. Does not have any effect.

        Returns:
            a list of Dicts, each dict has keys of 'description', 'snippets' (list of strings), 'title', 'url'
        """
        queries = (
            [query_or_queries]
            if isinstance(query_or_queries, str)
            else query_or_queries
        )
        self.usage += len(queries)
        collected_results = []
        for query in queries:
            related_docs = self.qdrant.similarity_search_with_score(query, k=self.k)
            for i in range(len(related_docs)):
                doc = related_docs[i][0]
                collected_results.append({
                    'description': doc.metadata['description'],
                    'snippets': [doc.page_content],
                    'title': doc.metadata['title'],
                    'url': doc.metadata['url'],
                })

        return collected_results


class SerperRM(dspy.Retrieve):
    """Retrieve information from custom queries using Serper.dev."""

    def __init__(self, serper_search_api_key=None, query_params=None):
        """Args:
            serper_search_api_key str: API key to run serper, can be found by creating an account on https://serper.dev/
            query_params (dict or list of dict): parameters in dictionary or list of dictionaries that has a max size of 100 that will be used to query.
                Commonly used fields are as follows (see more information in https://serper.dev/playground):
                    q str: query that will be used with google search
                    type str: type that will be used for browsing google. Types are search, images, video, maps, places, etc.
                    gl str: Country that will be focused on for the search
                    location str: Country where the search will originate from. All locates can be found here: https://api.serper.dev/locations.
                    autocorrect bool: Enable autocorrect on the queries while searching, if query is misspelled, will be updated.
                    results int: Max number of results per page.
                    page int: Max number of pages per call.
                    tbs str: date time range, automatically set to any time by default.
                    qdr:h str: Date time range for the past hour.
                    qdr:d str: Date time range for the past 24 hours.
                    qdr:w str: Date time range for past week.
                    qdr:m str: Date time range for past month.
                    qdr:y str: Date time range for past year.
        """
        super().__init__()
        self.usage = 0
        self.query_params = query_params
        self.serper_search_api_key = serper_search_api_key
        if not self.serper_search_api_key and not os.environ.get('SERPER_API_KEY'):
            raise RuntimeError(
                'You must supply a serper_search_api_key param or set environment variable SERPER_API_KEY'
            )

        elif self.serper_search_api_key:
            self.serper_search_api_key = serper_search_api_key

        else:
            self.serper_search_api_key = os.environ['SERPER_API_KEY']

        self.base_url = 'https://google.serper.dev'

    def serper_runner(self, query_params):
        self.search_url = f'{self.base_url}/search'

        headers = {
            'X-API-KEY': self.serper_search_api_key,
            'Content-Type': 'application/json',
        }

        response = requests.request(
            'POST', self.search_url, headers=headers, json=query_params
        )

        if response == None:
            raise RuntimeError(
                f'Error had occured while running the search process.\n Error is {response.reason}, had failed with status code {response.status_code}'
            )

        return response.json()

    def get_usage_and_reset(self):
        usage = self.usage
        self.usage = 0
        return {'SerperRM': usage}

    def forward(self, query_or_queries: Union[str, List[str]], exclude_urls: List[str]):
        """
        Calls the API and searches for the query passed in.


        Args:
            query_or_queries (Union[str, List[str]]): The query or queries to search for.
            exclude_urls (List[str]): Dummy parameter to match the interface. Does not have any effect.

        Returns:
            a list of dictionaries, each dictionary has keys of 'description', 'snippets' (list of strings), 'title', 'url'
        """
        queries = (
            [query_or_queries]
            if isinstance(query_or_queries, str)
            else query_or_queries
        )

        self.usage += len(queries)
        self.results = []
        collected_results = []
        for query in queries:
            if query == 'Queries:':
                continue
            query_params = self.query_params

            # All available parameters can be found in the playground: https://serper.dev/playground
            # Sets the json value for query to be the query that is being parsed.
            query_params['q'] = query

            # Sets the type to be search, can be images, video, places, maps etc that Google provides.
            query_params['type'] = 'search'

            self.result = self.serper_runner(query_params)
            self.results.append(self.result)

        # Array of dictionaries that will be used by Storm to create the jsons
        collected_results = []

        for result in self.results:
            try:
                # An array of dictionaries that contains the snippets, title of the document and url that will be used.
                organic_results = result.get('organic')

                knowledge_graph = result.get('knowledgeGraph')
                for organic in organic_results:
                    snippets = []
                    snippets.append(organic.get('snippet'))
                    if knowledge_graph != None:
                        collected_results.append(
                            {
                                'snippets': snippets,
                                'title': organic.get('title'),
                                'url': organic.get('link'),
                                'description': knowledge_graph.get('description'),
                            }
                        )
                    else:
                        # Common for knowledge graph to be None, set description to empty string
                        collected_results.append(
                            {
                                'snippets': snippets,
                                'title': organic.get('title'),
                                'url': organic.get('link'),
                                'description': '',
                            }
                        )
            except:
                continue

        return collected_results


class BraveRM(dspy.Retrieve):
    def __init__(self, brave_search_api_key=None, k=3, is_valid_source: Callable = None):
        super().__init__(k=k)
        if not brave_search_api_key and not os.environ.get("BRAVE_API_KEY"):
            raise RuntimeError("You must supply brave_search_api_key or set environment variable BRAVE_API_KEY")
        elif brave_search_api_key:
            self.brave_search_api_key = brave_search_api_key
        else:
            self.brave_search_api_key = os.environ["BRAVE_API_KEY"]
        self.usage = 0

        # If not None, is_valid_source shall be a function that takes a URL and returns a boolean.
        if is_valid_source:
            self.is_valid_source = is_valid_source
        else:
            self.is_valid_source = lambda x: True

    def get_usage_and_reset(self):
        usage = self.usage
        self.usage = 0

        return {'BraveRM': usage}

    def forward(self, query_or_queries: Union[str, List[str]], exclude_urls: List[str] = []):
        """Search with api.search.brave.com for self.k top passages for query or queries

        Args:
            query_or_queries (Union[str, List[str]]): The query or queries to search for.
            exclude_urls (List[str]): A list of urls to exclude from the search results.

        Returns:
            a list of Dicts, each dict has keys of 'description', 'snippets' (list of strings), 'title', 'url'
        """
        queries = (
            [query_or_queries]
            if isinstance(query_or_queries, str)
            else query_or_queries
        )
        self.usage += len(queries)
        collected_results = []
        for query in queries:
            try:
                headers = {
                    "Accept": "application/json",
                    "Accept-Encoding": "gzip",
                    "X-Subscription-Token": self.brave_search_api_key
                }
                response = requests.get(
                    f"https://api.search.brave.com/res/v1/web/search?result_filter=web&q={query}",
                    headers=headers,
                ).json()
                results = response.get('web', {}).get('results', [])

                for result in results:
                    collected_results.append(
                        {
                            'snippets': result.get('extra_snippets', []),
                            'title': result.get('title'),
                            'url': result.get('url'),
                            'description': result.get('description'),
                        }
                    )
            except Exception as e:
                logging.error(f'Error occurs when searching query {query}: {e}')

        return collected_results