Adding Vectara Self Query Retriever - feature request (#1249)

### Pull Request for Issue #1246 

**Description**,
This pull request addresses issue #1246, which proposes the addition of
a self-query retriever according to the LangChain Vectara integration.
The self-query retriever aims to empower users with the ability to
perform queries directly within the Vectara component(vector store).

**Changes Made**
I have added one more file under
`src\backend\langflow\components\retrievers` which contains a new
VectaraSelfQueryRetriverComponent class

**Files Added:** VectaraSelfQueryRetriever.py

**langchain documentation for this component:**

https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query

src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py

@@ -0,0 +1,70 @@
+from typing import List
+from langflow import CustomComponent
+import json
+from langchain.schema import BaseRetriever
+from langchain.schema.vectorstore import VectorStore
+from langchain.base_language import BaseLanguageModel
+from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain.chains.query_constructor.base import AttributeInfo
+
+
+class VectaraSelfQueryRetriverComponent(CustomComponent):
+    """
+    A custom component for implementing Vectara Self Query Retriever using a vector store.
+    """
+
+    display_name: str = "Vectara Self Query Retriever for Vectara Vector Store"
+    description: str = "Implementation of Vectara Self Query Retriever"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query"
+    )
+    beta = True
+
+    field_config = {
+        "code": {"show": True},
+        "vectorstore": {
+            "display_name": "Vector Store", 
+            "info": "Input Vectara Vectore Store"
+            },
+        "llm": {
+            "display_name": "LLM", 
+            "info": "For self query retriever"
+            },
+        "document_content_description":{
+            "display_name": "Document Content Description", 
+            "info": "For self query retriever",
+            },
+        "metadata_field_info": {
+            "display_name": "Metadata Field Info", 
+            "info": "Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\nExample input: {\"name\":\"speech\",\"description\":\"what name of the speech\",\"type\":\"string or list[string]\"}.\nThe keys should remain constant(name, description, type)",
+            },
+    }
+
+    def build(
+        self,
+        vectorstore: VectorStore,
+        document_content_description: str,
+        llm: BaseLanguageModel,
+        metadata_field_info: List[str],
+    ) -> BaseRetriever:
+        
+        metadata_field_obj = []
+
+        for meta in metadata_field_info:
+            meta_obj = json.loads(meta)
+            if 'name' not in meta_obj or 'description' not in meta_obj or 'type' not in meta_obj :
+                raise Exception('Incorrect metadata field info format.')
+            attribute_info = AttributeInfo(
+                name = meta_obj['name'],
+                description = meta_obj['description'],
+                type = meta_obj['type'],
+            )
+            metadata_field_obj.append(attribute_info)
+
+        return SelfQueryRetriever.from_llm(
+            llm,
+            vectorstore, 
+            document_content_description, 
+            metadata_field_obj, 
+            verbose=True
+        )