Harrison/new search (#4359)

Co-authored-by: Jiaping(JP) Zhang <vincentzhangv@gmail.com>

docs/modules/indexes/retrievers/examples/vectorstore-retriever.ipynb

@@ -25,18 +25,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "9fbcc58f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exiting: Cleaning up .chroma directory\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.vectorstores import FAISS\n",
@@ -74,6 +66,7 @@
    "id": "79b783de",
    "metadata": {},
    "source": [
+    "## Maximum Marginal Relevance Retrieval\n",
     "By default, the vectorstore retriever uses similarity search. If the underlying vectorstore support maximum marginal relevance search, you can specify that as the search type."
    ]
   },
@@ -97,11 +90,42 @@
     "docs = retriever.get_relevant_documents(\"what did he say abotu ketanji brown jackson\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2d958271",
+   "metadata": {},
+   "source": [
+    "## Similarity Score Threshold Retrieval\n",
+    "\n",
+    "You can also a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d4272ad8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = db.as_retriever(search_type=\"similarity_score_threshold\", search_kwargs={\"score_threshold\": .5})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "438e761d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = retriever.get_relevant_documents(\"what did he say abotu ketanji brown jackson\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c23b7698",
    "metadata": {},
    "source": [
+    "## Specifying top k\n",
     "You can also specify search kwargs like `k` to use when doing retrieval."
    ]
   },
@@ -171,7 +195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

langchain/vectorstores/base.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import asyncio
+import warnings
 from abc import ABC, abstractmethod
 from functools import partial
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TypeVar
@@ -116,6 +117,16 @@ class VectorStore(ABC):
         """Return docs and relevance scores in the range [0, 1].
 
         0 is dissimilar, 1 is most similar.
+
+        Args:
+            query: input text
+            k: Number of Documents to return. Defaults to 4.
+            **kwargs: kwargs to be passed to similarity search. Should include:
+                score_threshold: Optional, a floating point value between 0 to 1 to
+                    filter the resulting set of retrieved docs
+
+        Returns:
+            List of Tuples of (doc, similarity_score)
         """
         docs_and_similarities = self._similarity_search_with_relevance_scores(
             query, k=k, **kwargs
@@ -124,10 +135,23 @@ class VectorStore(ABC):
             similarity < 0.0 or similarity > 1.0
             for _, similarity in docs_and_similarities
         ):
-            raise ValueError(
+            warnings.warn(
                 "Relevance scores must be between"
                 f" 0 and 1, got {docs_and_similarities}"
             )
+
+        score_threshold = kwargs.get("score_threshold")
+        if score_threshold is not None:
+            docs_and_similarities = [
+                (doc, similarity)
+                for doc, similarity in docs_and_similarities
+                if similarity >= score_threshold
+            ]
+            if len(docs_and_similarities) == 0:
+                warnings.warn(
+                    f"No relevant docs were retrieved using the relevance score\
+                          threshold {score_threshold}"
+                )
         return docs_and_similarities
 
     def _similarity_search_with_relevance_scores(
@@ -324,13 +348,29 @@ class VectorStoreRetriever(BaseRetriever, BaseModel):
         """Validate search type."""
         if "search_type" in values:
             search_type = values["search_type"]
-            if search_type not in ("similarity", "mmr"):
+            if search_type not in ("similarity", "similarity_score_threshold", "mmr"):
                 raise ValueError(f"search_type of {search_type} not allowed.")
+            if search_type == "similarity_score_threshold":
+                score_threshold = values["search_kwargs"].get("score_threshold")
+                if (score_threshold is None) or (
+                    not isinstance(score_threshold, float)
+                ):
+                    raise ValueError(
+                        "`score_threshold` is not specified with a float value(0~1) "
+                        "in `search_kwargs`."
+                    )
         return values
 
     def get_relevant_documents(self, query: str) -> List[Document]:
         if self.search_type == "similarity":
             docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
+        elif self.search_type == "similarity_score_threshold":
+            docs_and_similarities = (
+                self.vectorstore.similarity_search_with_relevance_scores(
+                    query, **self.search_kwargs
+                )
+            )
+            docs = [doc for doc, _ in docs_and_similarities]
         elif self.search_type == "mmr":
             docs = self.vectorstore.max_marginal_relevance_search(
                 query, **self.search_kwargs