community[minor]: Breebs docs retriever (#16578)

- **Description:** Implementation of breeb retriever with integration
tests ->
libs/community/tests/integration_tests/retrievers/test_breebs.py and
documentation (notebook) ->
docs/docs/integrations/retrievers/breebs.ipynb.
  - **Dependencies:** None

libs/community/langchain_community/retrievers/__init__.py

@@ -25,6 +25,7 @@ from langchain_community.retrievers.azure_cognitive_search import (
 )
 from langchain_community.retrievers.bedrock import AmazonKnowledgeBasesRetriever
 from langchain_community.retrievers.bm25 import BM25Retriever
+from langchain_community.retrievers.breebs import BreebsRetriever
 from langchain_community.retrievers.chaindesk import ChaindeskRetriever
 from langchain_community.retrievers.chatgpt_plugin_retriever import (
     ChatGPTPluginRetriever,
@@ -78,6 +79,7 @@ __all__ = [
     "ArceeRetriever",
     "ArxivRetriever",
     "AzureCognitiveSearchRetriever",
+    "BreebsRetriever",
     "ChatGPTPluginRetriever",
     "ChaindeskRetriever",
     "CohereRagRetriever",

libs/community/langchain_community/retrievers/breebs.py

@@ -0,0 +1,49 @@
+from typing import List
+
+import requests
+from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain_core.documents.base import Document
+from langchain_core.retrievers import BaseRetriever
+
+
+class BreebsRetriever(BaseRetriever):
+    """A retriever class for `Breebs`.
+
+    See https://www.breebs.com/ for more info.
+    Args:
+        breeb_key: The key to trigger the breeb
+        (specialized knowledge pill on a specific topic).
+
+    To retrieve the list of all available Breebs : you can call https://breebs.promptbreeders.com/web/listbreebs
+    """
+
+    breeb_key: str
+    url = "https://breebs.promptbreeders.com/knowledge"
+
+    def __init__(self, breeb_key: str):
+        super().__init__(breeb_key=breeb_key)
+        self.breeb_key = breeb_key
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        """Retrieve context for given query.
+        Note that for time being there is no score."""
+        r = requests.post(
+            self.url,
+            json={
+                "breeb_key": self.breeb_key,
+                "query": query,
+            },
+        )
+        if r.status_code != 200:
+            return []
+        else:
+            chunks = r.json()
+            return [
+                Document(
+                    page_content=chunk["content"],
+                    metadata={"source": chunk["source_url"], "score": 1},
+                )
+                for chunk in chunks
+            ]