community[patch]: update for compatibility with latest Meilisearch version (#18970)

- **Description:** Updates Meilisearch vectorstore for compatibility
with v1.6 and above. Adds embedders settings and embedder_name which are
now required.

---------

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

docs/docs/integrations/vectorstores/meilisearch.ipynb

@@ -130,7 +130,14 @@
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_text_splitters import CharacterTextSplitter\n",
     "\n",
-    "embeddings = OpenAIEmbeddings()"
+    "embeddings = OpenAIEmbeddings()\n",
+    "embedders = {\n",
+    "    \"default\": {\n",
+    "        \"source\": \"userProvided\",\n",
+    "        \"dimensions\": 1536,\n",
+    "    }\n",
+    "}\n",
+    "embedder_name = \"default\""
    ]
   },
   {
@@ -152,7 +159,9 @@
    "outputs": [],
    "source": [
     "# Use Meilisearch vector store to store texts & associated embeddings as vector\n",
-    "vector_store = Meilisearch.from_texts(texts=texts, embedding=embeddings)"
+    "vector_store = Meilisearch.from_texts(\n",
+    "    texts=texts, embedding=embeddings, embedders=embedders, embedder_name=embedder_name\n",
+    ")"
    ]
   },
   {
@@ -188,11 +197,16 @@
     "docs = text_splitter.split_documents(documents)\n",
     "\n",
     "# Import documents & embeddings in the vector store\n",
-    "vector_store = Meilisearch.from_documents(documents=documents, embedding=embeddings)\n",
+    "vector_store = Meilisearch.from_documents(\n",
+    "    documents=documents,\n",
+    "    embedding=embeddings,\n",
+    "    embedders=embedders,\n",
+    "    embedder_name=embedder_name,\n",
+    ")\n",
     "\n",
     "# Search in our vector store\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = vector_store.similarity_search(query)\n",
+    "docs = vector_store.similarity_search(query, embedder_name=embedder_name)\n",
     "print(docs[0].page_content)"
    ]
   },
@@ -221,7 +235,11 @@
     "\n",
     "client = meilisearch.Client(url=\"http://127.0.0.1:7700\", api_key=\"***\")\n",
     "vector_store = Meilisearch(\n",
-    "    embedding=embeddings, client=client, index_name=\"langchain_demo\", text_key=\"text\"\n",
+    "    embedding=embeddings,\n",
+    "    embedders=embedders,\n",
+    "    client=client,\n",
+    "    index_name=\"langchain_demo\",\n",
+    "    text_key=\"text\",\n",
     ")\n",
     "vector_store.add_documents(documents)"
    ]
@@ -232,7 +250,7 @@
    "source": [
     "## Similarity Search with score\n",
     "\n",
-    "This specific method allows you to return the documents and the distance score of the query to them."
+    "This specific method allows you to return the documents and the distance score of the query to them. `embedder_name` is the name of the embedder that should be used for semantic search, defaults to \"default\"."
    ]
   },
   {
@@ -241,7 +259,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "docs_and_scores = vector_store.similarity_search_with_score(query)\n",
+    "docs_and_scores = vector_store.similarity_search_with_score(\n",
+    "    query, embedder_name=embedder_name\n",
+    ")\n",
     "docs_and_scores[0]"
    ]
   },
@@ -249,7 +269,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Similarity Search by vector"
+    "## Similarity Search by vector\n",
+    "`embedder_name` is the name of the embedder that should be used for semantic search, defaults to \"default\"."
    ]
   },
   {
@@ -259,7 +280,9 @@
    "outputs": [],
    "source": [
     "embedding_vector = embeddings.embed_query(query)\n",
-    "docs_and_scores = vector_store.similarity_search_by_vector(embedding_vector)\n",
+    "docs_and_scores = vector_store.similarity_search_by_vector(\n",
+    "    embedding_vector, embedder_name=embedder_name\n",
+    ")\n",
     "docs_and_scores[0]"
    ]
   },

libs/community/langchain_community/vectorstores/meilisearch.py

@@ -65,8 +65,15 @@ class Meilisearch(VectorStore):
             # api_key is optional; provide it if your meilisearch instance requires it
             client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
             embeddings = OpenAIEmbeddings()
+            embedders = {
+                "theEmbedderName": {
+                    "source": "userProvided",
+                    "dimensions": "1536"
+                }
+            }
             vectorstore = Meilisearch(
                 embedding=embeddings,
+                embedders=embedders,
                 client=client,
                 index_name='langchain_demo',
                 text_key='text')
@@ -81,6 +88,8 @@ class Meilisearch(VectorStore):
         index_name: str = "langchain-demo",
         text_key: str = "text",
         metadata_key: str = "metadata",
+        *,
+        embedders: Optional[Dict[str, Any]] = None,
     ):
         """Initialize with Meilisearch client."""
         client = _create_client(client=client, url=url, api_key=api_key)
@@ -90,18 +99,24 @@ class Meilisearch(VectorStore):
         self._embedding = embedding
         self._text_key = text_key
         self._metadata_key = metadata_key
+        self._embedders = embedders
+        self._embedders_settings = self._client.index(
+            str(self._index_name)
+        ).update_embedders(embedders)
 
     def add_texts(
         self,
         texts: Iterable[str],
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
+        embedder_name: Optional[str] = "default",
         **kwargs: Any,
     ) -> List[str]:
         """Run more texts through the embedding and add them to the vector store.
 
         Args:
             texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
+            embedder_name: Name of the embedder. Defaults to "default".
             metadatas (Optional[List[dict]]): Optional list of metadata.
                 Defaults to None.
             ids Optional[List[str]]: Optional list of IDs.
@@ -128,7 +143,7 @@ class Meilisearch(VectorStore):
             docs.append(
                 {
                     "id": id,
-                    "_vectors": embedding,
+                    "_vectors": {f"{embedder_name}": embedding},
                     f"{self._metadata_key}": metadata,
                 }
             )
@@ -142,12 +157,14 @@ class Meilisearch(VectorStore):
         query: str,
         k: int = 4,
         filter: Optional[Dict[str, str]] = None,
+        embedder_name: Optional[str] = "default",
         **kwargs: Any,
     ) -> List[Document]:
         """Return meilisearch documents most similar to the query.
 
         Args:
             query (str): Query text for which to find similar documents.
+            embedder_name: Name of the embedder to be used. Defaults to "default".
             k (int): Number of documents to return. Defaults to 4.
             filter (Optional[Dict[str, str]]): Filter by metadata.
                 Defaults to None.
@@ -158,6 +175,7 @@ class Meilisearch(VectorStore):
         """
         docs_and_scores = self.similarity_search_with_score(
             query=query,
+            embedder_name=embedder_name,
             k=k,
             filter=filter,
             kwargs=kwargs,
@@ -169,12 +187,14 @@ class Meilisearch(VectorStore):
         query: str,
         k: int = 4,
         filter: Optional[Dict[str, str]] = None,
+        embedder_name: Optional[str] = "default",
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return meilisearch documents most similar to the query, along with scores.
 
         Args:
             query (str): Query text for which to find similar documents.
+            embedder_name: Name of the embedder to be used. Defaults to "default".
             k (int): Number of documents to return. Defaults to 4.
             filter (Optional[Dict[str, str]]): Filter by metadata.
                 Defaults to None.
@@ -187,6 +207,7 @@ class Meilisearch(VectorStore):
 
         docs = self.similarity_search_by_vector_with_scores(
             embedding=_query,
+            embedder_name=embedder_name,
             k=k,
             filter=filter,
             kwargs=kwargs,
@@ -196,6 +217,7 @@ class Meilisearch(VectorStore):
     def similarity_search_by_vector_with_scores(
         self,
         embedding: List[float],
+        embedder_name: Optional[str] = "default",
         k: int = 4,
         filter: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
@@ -204,6 +226,7 @@ class Meilisearch(VectorStore):
 
         Args:
             embedding (List[float]): Embedding to look up similar documents.
+            embedder_name: Name of the embedder to be used. Defaults to "default".
             k (int): Number of documents to return. Defaults to 4.
             filter (Optional[Dict[str, str]]): Filter by metadata.
                 Defaults to None.
@@ -214,7 +237,13 @@ class Meilisearch(VectorStore):
         """
         docs = []
         results = self._client.index(str(self._index_name)).search(
-            "", {"vector": embedding, "limit": k, "filter": filter}
+            "",
+            {
+                "vector": embedding,
+                "hybrid": {"semanticRatio": 1.0, "embedder": embedder_name},
+                "limit": k,
+                "filter": filter,
+            },
         )
 
         for result in results["hits"]:
@@ -233,12 +262,14 @@ class Meilisearch(VectorStore):
         embedding: List[float],
         k: int = 4,
         filter: Optional[Dict[str, str]] = None,
+        embedder_name: Optional[str] = "default",
         **kwargs: Any,
     ) -> List[Document]:
         """Return meilisearch documents most similar to embedding vector.
 
         Args:
             embedding (List[float]): Embedding to look up similar documents.
+            embedder_name: Name of the embedder to be used. Defaults to "default".
             k (int): Number of documents to return. Defaults to 4.
             filter (Optional[Dict[str, str]]): Filter by metadata.
                 Defaults to None.
@@ -249,6 +280,7 @@ class Meilisearch(VectorStore):
         """
         docs = self.similarity_search_by_vector_with_scores(
             embedding=embedding,
+            embedder_name=embedder_name,
             k=k,
             filter=filter,
             kwargs=kwargs,
@@ -268,6 +300,8 @@ class Meilisearch(VectorStore):
         ids: Optional[List[str]] = None,
         text_key: Optional[str] = "text",
         metadata_key: Optional[str] = "metadata",
+        embedders: Dict[str, Any] = {},
+        embedder_name: Optional[str] = "default",
         **kwargs: Any,
     ) -> Meilisearch:
         """Construct Meilisearch wrapper from raw documents.
@@ -288,21 +322,25 @@ class Meilisearch(VectorStore):
                 # The environment should be the one specified next to the API key
                 # in your Meilisearch console
                 client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
-                embeddings = OpenAIEmbeddings()
+                embedding = OpenAIEmbeddings()
+                embedders: Embedders index setting.
+                embedder_name: Name of the embedder. Defaults to "default".
                 docsearch = Meilisearch.from_texts(
                     client=client,
-                    embeddings=embeddings,
+                    embedding=embedding,
                 )
         """
         client = _create_client(client=client, url=url, api_key=api_key)
 
         vectorstore = cls(
             embedding=embedding,
+            embedders=embedders,
             client=client,
             index_name=index_name,
         )
         vectorstore.add_texts(
             texts=texts,
+            embedder_name=embedder_name,
             metadatas=metadatas,
             ids=ids,
             text_key=text_key,

libs/community/tests/integration_tests/vectorstores/test_meilisearch.py

@@ -1,5 +1,6 @@
 """Test Meilisearch functionality."""
-from typing import TYPE_CHECKING, Generator
+
+from typing import TYPE_CHECKING, Any, Dict, Generator
 
 import pytest
 import requests
@@ -33,6 +34,16 @@ class TestMeilisearchVectorSearch:
             timeout=10,
         )
 
+    @pytest.fixture
+    def new_embedders(self) -> Dict[str, Dict[str, Any]]:
+        return {
+            "default": {
+                "source": "userProvided",
+                # Dimension defined in FakeEmbeddings as [float(1.0)] * 9 + [float(0.0)]
+                "dimensions": 10,
+            }
+        }
+
     @pytest.fixture(autouse=True)
     def setup(self) -> None:
         self.delete_all_indexes()
@@ -63,12 +74,14 @@ class TestMeilisearchVectorSearch:
         # Wait for the last task to be completed
         client.wait_for_task(tasks.results[0].uid)
 
-    def test_meilisearch(self) -> None:
+    def test_meilisearch(self, new_embedders: Dict[str, Any]) -> None:
         """Test end to end construction and search."""
         texts = ["foo", "bar", "baz"]
         vectorstore = Meilisearch.from_texts(
             texts=texts,
             embedding=FakeEmbeddings(),
+            embedders=new_embedders,
+            embedder_name=list(new_embedders)[0],
             url=TEST_MEILI_HTTP_ADDR,
             api_key=TEST_MEILI_MASTER_KEY,
             index_name=INDEX_NAME,
@@ -77,12 +90,14 @@ class TestMeilisearchVectorSearch:
         output = vectorstore.similarity_search("foo", k=1)
         assert output == [Document(page_content="foo")]
 
-    def test_meilisearch_with_client(self) -> None:
+    def test_meilisearch_with_client(self, new_embedders: Dict[str, Any]) -> None:
         """Test end to end construction and search."""
         texts = ["foo", "bar", "baz"]
         vectorstore = Meilisearch.from_texts(
             texts=texts,
             embedding=FakeEmbeddings(),
+            embedders=new_embedders,
+            embedder_name=list(new_embedders)[0],
             client=self.client(),
             index_name=INDEX_NAME,
         )
@@ -90,13 +105,15 @@ class TestMeilisearchVectorSearch:
         output = vectorstore.similarity_search("foo", k=1)
         assert output == [Document(page_content="foo")]
 
-    def test_meilisearch_with_metadatas(self) -> None:
+    def test_meilisearch_with_metadatas(self, new_embedders: Dict[str, Any]) -> None:
         """Test end to end construction and search."""
         texts = ["foo", "bar", "baz"]
         metadatas = [{"page": i} for i in range(len(texts))]
         docsearch = Meilisearch.from_texts(
             texts=texts,
             embedding=FakeEmbeddings(),
+            embedders=new_embedders,
+            embedder_name=list(new_embedders)[0],
             url=TEST_MEILI_HTTP_ADDR,
             api_key=TEST_MEILI_MASTER_KEY,
             index_name=INDEX_NAME,
@@ -109,13 +126,17 @@ class TestMeilisearchVectorSearch:
         assert output[0].metadata["page"] == 0
         assert output == [Document(page_content="foo", metadata={"page": 0})]
 
-    def test_meilisearch_with_metadatas_with_scores(self) -> None:
+    def test_meilisearch_with_metadatas_with_scores(
+        self, new_embedders: Dict[str, Any]
+    ) -> None:
         """Test end to end construction and scored search."""
         texts = ["foo", "bar", "baz"]
         metadatas = [{"page": str(i)} for i in range(len(texts))]
         docsearch = Meilisearch.from_texts(
             texts=texts,
             embedding=FakeEmbeddings(),
+            embedders=new_embedders,
+            embedder_name=list(new_embedders)[0],
             url=TEST_MEILI_HTTP_ADDR,
             api_key=TEST_MEILI_MASTER_KEY,
             index_name=INDEX_NAME,
@@ -123,9 +144,11 @@ class TestMeilisearchVectorSearch:
         )
         self._wait_last_task()
         output = docsearch.similarity_search_with_score("foo", k=1)
-        assert output == [(Document(page_content="foo", metadata={"page": "0"}), 9.0)]
+        assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
 
-    def test_meilisearch_with_metadatas_with_scores_using_vector(self) -> None:
+    def test_meilisearch_with_metadatas_with_scores_using_vector(
+        self, new_embedders: Dict[str, Any]
+    ) -> None:
         """Test end to end construction and scored search, using embedding vector."""
         texts = ["foo", "bar", "baz"]
         metadatas = [{"page": str(i)} for i in range(len(texts))]
@@ -134,6 +157,8 @@ class TestMeilisearchVectorSearch:
         docsearch = Meilisearch.from_texts(
             texts=texts,
             embedding=FakeEmbeddings(),
+            embedders=new_embedders,
+            embedder_name=list(new_embedders)[0],
             url=TEST_MEILI_HTTP_ADDR,
             api_key=TEST_MEILI_MASTER_KEY,
             index_name=INDEX_NAME,
@@ -144,4 +169,4 @@ class TestMeilisearchVectorSearch:
         output = docsearch.similarity_search_by_vector_with_scores(
             embedding=embedded_query, k=1
         )
-        assert output == [(Document(page_content="foo", metadata={"page": "0"}), 9.0)]
+        assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]