[New RM] Add AzureAISearch

README.md

@@ -93,7 +93,7 @@ You could also install the source code which allows you to modify the behavior o
 Currently, our package support:
 
 - `OpenAIModel`, `AzureOpenAIModel`, `ClaudeModel`, `VLLMClient`, `TGIClient`, `TogetherClient`, `OllamaClient`, `GoogleModel`, `DeepSeekModel`, `GroqModel` as language model components
-- `YouRM`, `BingSearch`, `VectorRM`, `SerperRM`, `BraveRM`, `SearXNG`, `DuckDuckGoSearchRM`, `TavilySearchRM`, `GoogleSearch` as retrieval module components
+- `YouRM`, `BingSearch`, `VectorRM`, `SerperRM`, `BraveRM`, `SearXNG`, `DuckDuckGoSearchRM`, `TavilySearchRM`, `GoogleSearch`, and `AzureAISearch` as retrieval module components
 
 :star2: **PRs for integrating more language models into [knowledge_storm/lm.py](knowledge_storm/lm.py) and search engines/retrievers into [knowledge_storm/rm.py](knowledge_storm/rm.py) are highly appreciated!**
 

examples/storm_examples/run_storm_wiki_gpt.py

@@ -20,10 +20,11 @@
 """
 
 import os
+
 from argparse import ArgumentParser
 from knowledge_storm import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs
 from knowledge_storm.lm import OpenAIModel, AzureOpenAIModel
-from knowledge_storm.rm import YouRM, BingSearch, BraveRM, SerperRM, DuckDuckGoSearchRM, TavilySearchRM, SearXNG
+from knowledge_storm.rm import YouRM, BingSearch, BraveRM, SerperRM, DuckDuckGoSearchRM, TavilySearchRM, SearXNG, AzureAISearch
 from knowledge_storm.utils import load_api_key
 
 
@@ -72,6 +73,7 @@ def main(args):
 
     # STORM is a knowledge curation system which consumes information from the retrieval module.
     # Currently, the information source is the Internet and we use search engine API as the retrieval module.
+
     match args.retriever:
         case 'bing':
             rm = BingSearch(bing_search_api=os.getenv('BING_SEARCH_API_KEY'), k=engine_args.search_top_k)
@@ -87,8 +89,10 @@ def main(args):
             rm = TavilySearchRM(tavily_search_api_key=os.getenv('TAVILY_API_KEY'), k=engine_args.search_top_k, include_raw_content=True)
         case 'searxng':
             rm = SearXNG(searxng_api_key=os.getenv('SEARXNG_API_KEY'), k=engine_args.search_top_k)
+        case 'azure_ai_search':
+            rm = AzureAISearch(azure_ai_search_api_key=os.getenv('AZURE_AI_SEARCH_API_KEY'), k=engine_args.search_top_k)
         case _:
-             raise ValueError(f'Invalid retriever: {args.retriever}. Choose either "bing", "you", "brave", "duckduckgo", "serper", "tavily", or "searxng"')
+             raise ValueError(f'Invalid retriever: {args.retriever}. Choose either "bing", "you", "brave", "duckduckgo", "serper", "tavily", "searxng", or "azure_ai_search"')
 
     runner = STORMWikiRunner(engine_args, lm_configs, rm)
 
@@ -113,7 +117,7 @@ def main(args):
                         help='Maximum number of threads to use. The information seeking part and the article generation'
                              'part can speed up by using multiple threads. Consider reducing it if keep getting '
                              '"Exceed rate limit" error when calling LM API.')
-    parser.add_argument('--retriever', type=str, choices=['bing', 'you', 'brave', 'serper', 'duckduckgo', 'tavily', 'searxng'],
+    parser.add_argument('--retriever', type=str, choices=['bing', 'you', 'brave', 'serper', 'duckduckgo', 'tavily', 'searxng', 'azure_ai_search'],
                         help='The search engine API to use for retrieving information.')
     # stage of the pipeline
     parser.add_argument('--do-research', action='store_true',
@@ -138,4 +142,4 @@ def main(args):
     parser.add_argument('--remove-duplicate', action='store_true',
                         help='If True, remove duplicate content from the article.')
 
-    main(parser.parse_args())
+    main(parser.parse_args())

knowledge_storm/rm.py

@@ -13,6 +13,8 @@
 
 from .utils import WebPageHelper
 
+from azure.core.credentials import AzureKeyCredential
+from azure.search.documents import SearchClient
 
 class YouRM(dspy.Retrieve):
     def __init__(self, ydc_api_key=None, k=3, is_valid_source: Callable = None):
@@ -77,7 +79,6 @@ def forward(
 
         return collected_results
 
-
 class BingSearch(dspy.Retrieve):
     def __init__(
         self,
@@ -1093,3 +1094,109 @@ def forward(
             collected_results.append(r)
 
         return collected_results
+
+class AzureAISearch(dspy.Retrieve):
+    """Retrieve information from custom queries using Azure AI Search. General Documentation can be found at: https://learn.microsoft.com/en-us/azure/search/search-create-service-portal. Python Documentation and examples can be found at https://learn.microsoft.com/en-us/python/api/overview/azure/search-documents-readme?view=azure-python. Requires pip install azure-search-documents"""
+
+    def __init__(
+        self, 
+        azure_ai_search_api_key=None, 
+        azure_ai_search_url=None, 
+        azure_ai_search_index_name=None, 
+        k=3, 
+        is_valid_source: Callable = None
+    ):
+        """
+        Params:
+            azure_ai_search_api_key: Azure AI Search API key. Check out https://learn.microsoft.com/en-us/azure/search/search-security-api-keys?tabs=rest-use%2Cportal-find%2Cportal-query
+                "API key" section
+            azure_ai_search_url: Custom Azure AI Search Endpoint URL. Check out https://learn.microsoft.com/en-us/azure/search/search-create-service-portal#name-the-service
+            azure_ai_search_index_name: Custom Azure AI Search Index Name. Check out https://learn.microsoft.com/en-us/azure/search/search-how-to-create-search-index?tabs=portal
+            k: Number of top results to retrieve.
+            is_valid_source: Optional function to filter valid sources.
+            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.
+        """
+        super().__init__(k=k)
+        if not azure_ai_search_api_key and not os.environ.get("AZURE_AI_SEARCH_API_KEY"):
+            raise RuntimeError(
+                "You must supply azure_ai_search_api_key or set environment variable AZURE_AI_SEARCH_API_KEY"
+            )
+        elif azure_ai_search_api_key:
+            self.azure_ai_search_api_key = azure_ai_search_api_key
+        else:
+            self.azure_ai_search_api_key = os.environ["AZURE_AI_SEARCH_API_KEY"]
+
+        if not azure_ai_search_url and not os.environ.get("AZURE_AI_SEARCH_URL"):
+            raise RuntimeError(
+                "You must supply azure_ai_search_url or set environment variable AZURE_AI_SEARCH_URL"
+            )
+        elif azure_ai_search_url:
+            self.azure_ai_search_url = azure_ai_search_url
+        else:
+            self.azure_ai_search_url = os.environ["AZURE_AI_SEARCH_URL"]
+
+        if not azure_ai_search_index_name and not os.environ.get("AZURE_AI_SEARCH_INDEX_NAME"):
+            raise RuntimeError(
+                "You must supply azure_ai_search_index_name or set environment variable AZURE_AI_SEARCH_INDEX_NAME"
+            )
+        elif azure_ai_search_index_name:
+            self.azure_ai_search_index_name = azure_ai_search_index_name
+        else:
+            self.azure_ai_search_index_name = os.environ["AZURE_AI_SEARCH_INDEX_NAME"]
+
+        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 {"AzureAISearch": usage}
+
+    def forward(
+        self, query_or_queries: Union[str, List[str]], exclude_urls: List[str] = []
+    ):
+        """Search with Azure Open AI 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 = []
+
+        client = SearchClient(self.azure_ai_search_url,self.azure_ai_search_index_name,AzureKeyCredential(self.azure_ai_search_api_key))
+        for query in queries:
+            try:
+                # https://learn.microsoft.com/en-us/python/api/azure-search-documents/azure.search.documents.searchclient?view=azure-python#azure-search-documents-searchclient-search
+                results = client.search(search_text=query, top=1)
+
+                for result in results:
+                    document = {
+                        "url": result['metadata_storage_path'],
+                        "title": result['title'],
+                        "description": "N/A",
+                        "snippets": [result['chunk']]
+                    }
+                    collected_results.append(document)
+            except Exception as e:
+                logging.error(f"Error occurs when searching query {query}: {e}")
+
+        return collected_results
+
+

requirements.txt

@@ -7,4 +7,5 @@ trafilatura
 langchain-huggingface
 qdrant-client
 langchain-qdrant
-numpy==1.26.4
+numpy==1.26.4
+azure-search-documents==11.5.1