Rename Databerry to Chaindesk (#7022)

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

docs/api_reference/api_reference.rst

@@ -1316,7 +1316,7 @@ Classes
     retrievers.azure_cognitive_search.AzureCognitiveSearchRetriever
     retrievers.chatgpt_plugin_retriever.ChatGPTPluginRetriever
     retrievers.contextual_compression.ContextualCompressionRetriever
-    retrievers.databerry.DataberryRetriever
+    retrievers.chaindesk.ChaindeskRetriever
     retrievers.docarray.DocArrayRetriever
     retrievers.docarray.SearchType
     retrievers.document_compressors.base.BaseDocumentCompressor

docs/docs_skeleton/vercel.json

@@ -137,8 +137,8 @@
       "destination": "/docs/ecosystem/integrations/ctransformers"
     },
     {
-      "source": "/en/latest/integrations/databerry.html",
-      "destination": "/docs/ecosystem/integrations/databerry"
+      "source": "/en/latest/integrations/chaindesk.html",
+      "destination": "/docs/ecosystem/integrations/chaindesk"
     },
     {
       "source": "/en/latest/integrations/databricks/databricks.html",
@@ -1329,8 +1329,8 @@
       "destination": "/docs/modules/data_connection/retrievers/integrations/cohere-reranker"
     },
     {
-      "source": "/en/latest/modules/indexes/retrievers/examples/databerry.html",
-      "destination": "/docs/modules/data_connection/retrievers/integrations/databerry"
+      "source": "/en/latest/modules/indexes/retrievers/examples/chaindesk.html",
+      "destination": "/docs/modules/data_connection/retrievers/integrations/chaindesk"
     },
     {
       "source": "/en/latest/modules/indexes/retrievers/examples/elastic_search_bm25.html",
@@ -2125,4 +2125,4 @@
       "destination": "/docs/:path*"
     }
   ]
-}
+}

docs/extras/ecosystem/integrations/chaindesk.mdx

@@ -1,17 +1,17 @@
-# Databerry
+# Chaindesk
 
->[Databerry](https://databerry.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
+>[Chaindesk](https://chaindesk.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
 
 
 ## Installation and Setup
 
-We need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url. 
-We need the [API Key](https://docs.databerry.ai/api-reference/authentication).
+We need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url. 
+We need the [API Key](https://docs.chaindesk.ai/api-reference/authentication).
 
 ## Retriever
 
-See a [usage example](/docs/modules/data_connection/retrievers/integrations/databerry.html).
+See a [usage example](/docs/modules/data_connection/retrievers/integrations/chaindesk.html).
 
 ```python
-from langchain.retrievers import DataberryRetriever
+from langchain.retrievers import ChaindeskRetriever
 ```

docs/extras/modules/data_connection/retrievers/integrations/chaindesk.ipynb

@@ -1,21 +1,31 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9fc6205b",
    "metadata": {},
    "source": [
-    "# Databerry\n",
+    "# Chaindesk\n",
     "\n",
-    ">[Databerry platform](https://docs.databerry.ai/introduction) brings data from anywhere (Datsources: Text, PDF, Word, PowerPpoint, Excel, Notion, Airtable, Google Sheets, etc..) into Datastores (container of multiple Datasources).\n",
-    "Then your Datastores can be connected to ChatGPT via Plugins or any other Large Langue Model (LLM) via the `Databerry API`.\n",
+    ">[Chaindesk platform](https://docs.chaindesk.ai/introduction) brings data from anywhere (Datsources: Text, PDF, Word, PowerPpoint, Excel, Notion, Airtable, Google Sheets, etc..) into Datastores (container of multiple Datasources).\n",
+    "Then your Datastores can be connected to ChatGPT via Plugins or any other Large Langue Model (LLM) via the `Chaindesk API`.\n",
     "\n",
-    "This notebook shows how to use [Databerry's](https://www.databerry.ai/) retriever.\n",
+    "This notebook shows how to use [Chaindesk's](https://www.chaindesk.ai/) retriever.\n",
     "\n",
-    "First, you will need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url. You need the [API Key](https://docs.databerry.ai/api-reference/authentication)."
+    "First, you will need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url. You need the [API Key](https://docs.chaindesk.ai/api-reference/authentication)."
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3697b9fd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "944e172b",
    "metadata": {},
@@ -34,7 +44,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.retrievers import DataberryRetriever"
+    "from langchain.retrievers import ChaindeskRetriever"
    ]
   },
   {
@@ -46,9 +56,9 @@
    },
    "outputs": [],
    "source": [
-    "retriever = DataberryRetriever(\n",
-    "    datastore_url=\"https://clg1xg2h80000l708dymr0fxc.databerry.ai/query\",\n",
-    "    # api_key=\"DATABERRY_API_KEY\", # optional if datastore is public\n",
+    "retriever = ChaindeskRetriever(\n",
+    "    datastore_url=\"https://clg1xg2h80000l708dymr0fxc.chaindesk.ai/query\",\n",
+    "    # api_key=\"CHAINDESK_API_KEY\", # optional if datastore is public\n",
     "    # top_k=10 # optional\n",
     ")"
    ]

langchain/retrievers/__init__.py

@@ -1,8 +1,8 @@
 from langchain.retrievers.arxiv import ArxivRetriever
 from langchain.retrievers.azure_cognitive_search import AzureCognitiveSearchRetriever
+from langchain.retrievers.chaindesk import ChaindeskRetriever
 from langchain.retrievers.chatgpt_plugin_retriever import ChatGPTPluginRetriever
 from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
-from langchain.retrievers.databerry import DataberryRetriever
 from langchain.retrievers.docarray import DocArrayRetriever
 from langchain.retrievers.elastic_search_bm25 import ElasticSearchBM25Retriever
 from langchain.retrievers.kendra import AmazonKendraRetriever
@@ -36,7 +36,7 @@ __all__ = [
     "AzureCognitiveSearchRetriever",
     "ChatGPTPluginRetriever",
     "ContextualCompressionRetriever",
-    "DataberryRetriever",
+    "ChaindeskRetriever",
     "ElasticSearchBM25Retriever",
     "KNNRetriever",
     "LlamaIndexGraphRetriever",

langchain/retrievers/chaindesk.py

@@ -0,0 +1,92 @@
+from typing import Any, List, Optional
+
+import aiohttp
+import requests
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForRetrieverRun,
+    CallbackManagerForRetrieverRun,
+)
+from langchain.schema import BaseRetriever, Document
+
+
+class ChaindeskRetriever(BaseRetriever):
+    """Retriever that uses the Chaindesk API."""
+
+    datastore_url: str
+    top_k: Optional[int]
+    api_key: Optional[str]
+
+    def __init__(
+        self,
+        datastore_url: str,
+        top_k: Optional[int] = None,
+        api_key: Optional[str] = None,
+    ):
+        self.datastore_url = datastore_url
+        self.api_key = api_key
+        self.top_k = top_k
+
+    def _get_relevant_documents(
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
+        **kwargs: Any,
+    ) -> List[Document]:
+        response = requests.post(
+            self.datastore_url,
+            json={
+                "query": query,
+                **({"topK": self.top_k} if self.top_k is not None else {}),
+            },
+            headers={
+                "Content-Type": "application/json",
+                **(
+                    {"Authorization": f"Bearer {self.api_key}"}
+                    if self.api_key is not None
+                    else {}
+                ),
+            },
+        )
+        data = response.json()
+        return [
+            Document(
+                page_content=r["text"],
+                metadata={"source": r["source"], "score": r["score"]},
+            )
+            for r in data["results"]
+        ]
+
+    async def _aget_relevant_documents(
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        **kwargs: Any,
+    ) -> List[Document]:
+        async with aiohttp.ClientSession() as session:
+            async with session.request(
+                "POST",
+                self.datastore_url,
+                json={
+                    "query": query,
+                    **({"topK": self.top_k} if self.top_k is not None else {}),
+                },
+                headers={
+                    "Content-Type": "application/json",
+                    **(
+                        {"Authorization": f"Bearer {self.api_key}"}
+                        if self.api_key is not None
+                        else {}
+                    ),
+                },
+            ) as response:
+                data = await response.json()
+        return [
+            Document(
+                page_content=r["text"],
+                metadata={"source": r["source"], "score": r["score"]},
+            )
+            for r in data["results"]
+        ]