Extend opensearch to better support existing instances (#2500) (#2509)

Closes #2500.

docs/modules/indexes/vectorstores/examples/opensearch.ipynb

@@ -175,7 +175,7 @@
     "docsearch = OpenSearchVectorSearch.from_texts(texts, embeddings, opensearch_url=\"http://localhost:9200\", is_appx_search=False)\n",
     "filter = {\"bool\": {\"filter\": {\"term\": {\"text\": \"smuggling\"}}}}\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = docsearch.similarity_search(\"What did the president say about Ketanji Brown Jackson\", search_type=\"painless_scripting\", space_type=\"cosineSimilarity\", pre_filter=filter)"
+    "docs = docsearch.similarity_search(\"What did the president say about Ketanji Brown Jackson\", search_type=\"painless_scripting\", space_type=\"cosinesimil\", pre_filter=filter)"
    ]
   },
   {
@@ -191,6 +191,30 @@
    "source": [
     "print(docs[0].page_content)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73264864",
+   "metadata": {},
+   "source": [
+    "#### Using a preexisting OpenSearch instance\n",
+    "\n",
+    "It's also possible to use a preexisting OpenSearch instance with documents that already have vectors present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82a23440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is just an example, you would need to change these values to point to another opensearch instance\n",
+    "docsearch = OpenSearchVectorSearch(index_name=\"index-*\", embedding_function=embeddings, opensearch_url=\"http://localhost:9200\")\n",
+    "\n",
+    "# you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadata\n",
+    "docs = docsearch.similarity_search(\"Who was asking about getting lunch today?\", search_type=\"script_scoring\", space_type=\"cosinesimil\", vector_field=\"message_embedding\", text_field=\"message\", metadata_field=\"message_metadata\")"
+   ]
   }
  ],
  "metadata": {

langchain/vectorstores/opensearch_vector_search.py

@@ -128,12 +128,15 @@ def _default_text_mapping(
 
 
 def _default_approximate_search_query(
-    query_vector: List[float], size: int = 4, k: int = 4
+    query_vector: List[float],
+    size: int = 4,
+    k: int = 4,
+    vector_field: str = "vector_field",
 ) -> Dict:
     """For Approximate k-NN Search, this is the default query."""
     return {
         "size": size,
-        "query": {"knn": {"vector_field": {"vector": query_vector, "k": k}}},
+        "query": {"knn": {vector_field: {"vector": query_vector, "k": k}}},
     }
 
 
@@ -141,6 +144,7 @@ def _default_script_query(
     query_vector: List[float],
     space_type: str = "l2",
     pre_filter: Dict = MATCH_ALL_QUERY,
+    vector_field: str = "vector_field",
 ) -> Dict:
     """For Script Scoring Search, this is the default query."""
     return {
@@ -151,7 +155,7 @@ def _default_script_query(
                     "source": "knn_score",
                     "lang": "knn",
                     "params": {
-                        "field": "vector_field",
+                        "field": vector_field,
                         "query_value": query_vector,
                         "space_type": space_type,
                     },
@@ -176,6 +180,7 @@ def _default_painless_scripting_query(
     query_vector: List[float],
     space_type: str = "l2Squared",
     pre_filter: Dict = MATCH_ALL_QUERY,
+    vector_field: str = "vector_field",
 ) -> Dict:
     """For Painless Scripting Search, this is the default query."""
     source = __get_painless_scripting_source(space_type, query_vector)
@@ -186,7 +191,7 @@ def _default_painless_scripting_query(
                 "script": {
                     "source": source,
                     "params": {
-                        "field": "vector_field",
+                        "field": vector_field,
                         "query_value": query_vector,
                     },
                 },
@@ -269,6 +274,15 @@ class OpenSearchVectorSearch(VectorStore):
         Returns:
             List of Documents most similar to the query.
 
+        Optional Args:
+            vector_field: Document field embeddings are stored in. Defaults to
+            "vector_field".
+            text_field: Document field the text of the document is stored in. Defaults
+            to "text".
+            metadata_field: Document field that metadata is stored in. Defaults to
+            "metadata".
+            Can be set to a special value "*" to include the entire document.
+
         Optional Args for Approximate Search:
             search_type: "approximate_search"; default: "approximate_search"
             size: number of results the query actually returns; default: 4
@@ -291,18 +305,27 @@ class OpenSearchVectorSearch(VectorStore):
         """
         embedding = self.embedding_function.embed_query(query)
         search_type = _get_kwargs_value(kwargs, "search_type", "approximate_search")
+        text_field = _get_kwargs_value(kwargs, "text_field", "text")
+        metadata_field = _get_kwargs_value(kwargs, "metadata_field", "metadata")
         if search_type == "approximate_search":
             size = _get_kwargs_value(kwargs, "size", 4)
-            search_query = _default_approximate_search_query(embedding, size, k)
+            vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
+            search_query = _default_approximate_search_query(
+                embedding, size, k, vector_field
+            )
         elif search_type == SCRIPT_SCORING_SEARCH:
             space_type = _get_kwargs_value(kwargs, "space_type", "l2")
             pre_filter = _get_kwargs_value(kwargs, "pre_filter", MATCH_ALL_QUERY)
-            search_query = _default_script_query(embedding, space_type, pre_filter)
+            vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
+            search_query = _default_script_query(
+                embedding, space_type, pre_filter, vector_field
+            )
         elif search_type == PAINLESS_SCRIPTING_SEARCH:
             space_type = _get_kwargs_value(kwargs, "space_type", "l2Squared")
             pre_filter = _get_kwargs_value(kwargs, "pre_filter", MATCH_ALL_QUERY)
+            vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
             search_query = _default_painless_scripting_query(
-                embedding, space_type, pre_filter
+                embedding, space_type, pre_filter, vector_field
             )
         else:
             raise ValueError("Invalid `search_type` provided as an argument")
@@ -310,7 +333,13 @@ class OpenSearchVectorSearch(VectorStore):
         response = self.client.search(index=self.index_name, body=search_query)
         hits = [hit["_source"] for hit in response["hits"]["hits"][:k]]
         documents = [
-            Document(page_content=hit["text"], metadata=hit["metadata"]) for hit in hits
+            Document(
+                page_content=hit[text_field],
+                metadata=hit
+                if metadata_field == "*" or metadata_field not in hit
+                else hit[metadata_field],
+            )
+            for hit in hits
         ]
         return documents