Making it possible to use "certainty" as a parameter for the weaviate similarity_search (#1218)

Checking if weaviate similarity_search kwargs contains "certainty" and
use it accordingly. The minimal level of certainty must be a float, and
it is computed by normalized distance.

docs/modules/indexes/chain_examples/chat_vector_db.ipynb

@@ -268,12 +268,48 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fb44daa",
-   "metadata": {},
+   "source": [
+    "## Chat Vector DB with `search_distance`\n",
+    "If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "vectordbkwargs = {\"search_distance\": 0.9}"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0), vectorstore, return_source_documents=True)\n",
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
     "## Chat Vector DB with `map_reduce`\n",
     "We can also use different types of combine document chains with the Chat Vector DB chain."
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
@@ -486,7 +522,7 @@
    "source": [
     "chat_history = [(query, result[\"answer\"])]\n",
     "query = \"Did he mention who she suceeded\"\n",
-    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+    "result = qa({\"question\": query, \"chat_history\": chat_history})\n"
    ]
   }
  ],

langchain/vectorstores/weaviate.py

@@ -1,7 +1,7 @@
 """Wrapper around weaviate vector database."""
 from __future__ import annotations
 
-from typing import Any, Iterable, List, Optional
+from typing import Any, Dict, Iterable, List, Optional
 from uuid import uuid4
 
 from langchain.docstore.document import Document
@@ -78,7 +78,9 @@ class Weaviate(VectorStore):
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Document]:
         """Look up similar documents in weaviate."""
-        content = {"concepts": [query]}
+        content: Dict[str, Any] = {"concepts": [query]}
+        if kwargs.get("search_distance"):
+            content["certainty"] = kwargs.get("search_distance")
         query_obj = self._client.query.get(self._index_name, self._query_attrs)
         result = query_obj.with_near_text(content).with_limit(k).do()
         docs = []