feat(dspy): add vector, hybrid and fulltext search support in azure ai search module

docs/docs/deep-dive/retrieval_models_clients/Azure.mdx

@@ -22,32 +22,59 @@ The constructor initializes an instance of the `AzureAISearchRM` class and sets
 - `search_api_key` (str): The API key for accessing the Azure AI Search service.
 - `search_index_name` (str): The name of the search index in the Azure AI Search service.
 - `field_text` (str): The name of the field containing text content in the search index. This field will be mapped to the "content" field in the dsp framework.
+- `field_vector` (Optional[str]): The name of the field containing vector content in the search index.
 - `k` (int, optional): The default number of top passages to retrieve. Defaults to 3.
+- `azure_openai_client` (Optional[openai.AzureOpenAI]): An instance of the AzureOpenAI client. Either openai_client or embedding_func must be provided. Defaults to None.
+- `openai_embed_model` (Optional[str]): The name of the OpenAI embedding model. Defaults to "text-embedding-ada-002".
+- `embedding_func` (Optional[Callable]): A function for generating embeddings. Either openai_client or embedding_func must be provided. Defaults to None.
 - `semantic_ranker` (bool, optional): Whether to use semantic ranking. Defaults to False.
 - `filter` (str, optional): Additional filter query. Defaults to None.
 - `query_language` (str, optional): The language of the query. Defaults to "en-Us".
 - `query_speller` (str, optional): The speller mode. Defaults to "lexicon".
 - `use_semantic_captions` (bool, optional): Whether to use semantic captions. Defaults to False.
 - `query_type` (Optional[QueryType], optional): The type of query. Defaults to QueryType.FULL.
 - `semantic_configuration_name` (str, optional): The name of the semantic configuration. Defaults to None.
+- `is_vector_search` (Optional[bool]): Whether to enable vector search. Defaults to False.
+- `is_hybrid_search` (Optional[bool]): Whether to enable hybrid search. Defaults to False.
+- `is_fulltext_search` (Optional[bool]): Whether to enable fulltext search. Defaults to True.
+- `vector_filter_mode` (Optional[VectorFilterMode]): The vector filter mode. Defaults to None.
 
-Available Query Types:
 
-    SIMPLE
+**Available Query Types:**
+
+- SIMPLE
     """Uses the simple query syntax for searches. Search text is interpreted using a simple query
     #: language that allows for symbols such as +, * and "". Queries are evaluated across all
     #: searchable fields by default, unless the searchFields parameter is specified."""
-    FULL
+- FULL
     """Uses the full Lucene query syntax for searches. Search text is interpreted using the Lucene
     #: query language which allows field-specific and weighted searches, as well as other advanced
     #: features."""
-    SEMANTIC
+- SEMANTIC
     """Best suited for queries expressed in natural language as opposed to keywords. Improves
     #: precision of search results by re-ranking the top search results using a ranking model trained
     #: on the Web corpus.""
 
     More Details: https://learn.microsoft.com/en-us/azure/search/search-query-overview
 
+**Available Vector Filter Mode:**
+
+- POST_FILTER = "postFilter"
+    """The filter will be applied after the candidate set of vector results is returned. Depending on
+    #: the filter selectivity, this can result in fewer results than requested by the parameter 'k'."""
+
+- PRE_FILTER = "preFilter"
+    """The filter will be applied before the search query."""
+
+    More Details: https://learn.microsoft.com/en-us/azure/search/vector-search-filters
+
+**Note**
+
+- The `AzureAISearchRM` client allows you to perform Vector search, Hybrid search, or Full text search.
+- By default, the `AzureAISearchRM` client uses the Azure OpenAI Client for generating embeddings. If you want to use something else, you can provide your custom embedding_func, but either the openai_client or embedding_func must be provided.
+- If you need to enable semantic search, either with vector, hybrid, or full text search, then set the `semantic_ranker` flag to True.
+- If `semantic_ranker` is True, always set the `query_type` to QueryType.SEMANTIC and always provide the `semantic_configuration_name`.
+
 Example of the AzureAISearchRM constructor:
 
 ```python
@@ -56,14 +83,22 @@ AzureAISearchRM(
     search_api_key: str,
     search_index_name: str,
     field_text: str,
+    field_vector: Optional[str] = None,
     k: int = 3,
+    azure_openai_client: Optional[openai.AzureOpenAI] = None,
+    openai_embed_model: Optional[str] = "text-embedding-ada-002",
+    embedding_func: Optional[Callable] = None,
     semantic_ranker: bool = False,
     filter: str = None,
     query_language: str = "en-Us",
     query_speller: str = "lexicon",
     use_semantic_captions: bool = False,
     query_type: Optional[QueryType] = QueryType.FULL,
-    semantic_configuration_name: str = None
+    semantic_configuration_name: str = None,
+    is_vector_search: Optional[bool] = False,
+    is_hybrid_search: Optional[bool] = False,
+    is_fulltext_search: Optional[bool] = True,
+    vector_filter_mode: Optional[VectorFilterMode.PRE_FILTER] = None
 )
 ```
 
@@ -128,6 +163,31 @@ for result in retrieval_response:
     print("Text:", result.long_text, "\n")
 ```
 
+3. Example of Semantic Hybrid Search.
+
+```python
+from dspy.retrieve.azureaisearch_rm import AzureAISearchRM
+
+azure_search = AzureAISearchRM(
+    search_service_name="search_service_name",
+    search_api_key="search_api_key",
+    search_index_name="search_index_name",
+    field_text="field_text",
+    field_vector="field_vector",
+    k=3,
+    azure_openai_client="azure_openai_client",
+    openai_embed_model="text-embedding-ada-002"
+    semantic_ranker=True,
+    query_type=QueryType.SEMANTIC,
+    semantic_configuration_name="semantic_configuration_name",
+    is_hybrid_search=True,
+)
+
+retrieval_response = azure_search("What is Thermodynamics", k=3)
+for result in retrieval_response:
+    print("Text:", result.long_text, "\n")
+```
+
 ***
 
 <AuthorDetails name="Prajapati Harishkumar Kishorkumar"/>