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]"
    ]
   },