Cassandra Vector Store, add metadata filtering + improvements (#9280)

This PR addresses a few minor issues with the Cassandra vector store
implementation and extends the store to support Metadata search.

Thanks to the latest cassIO library (>=0.1.0), metadata filtering is
available in the store.

Further,
- the "relevance" score is prevented from being flipped in the [0,1]
interval, thus ensuring that 1 corresponds to the closest vector (this
is related to how the underlying cassIO class returns the cosine
difference);
- bumped the cassIO package version both in the notebooks and the
pyproject.toml;
- adjusted the textfile location for the vector-store example after the
reshuffling of the Langchain repo dir structure;
- added demonstration of metadata filtering in the Cassandra vector
store notebook;
- better docstring for the Cassandra vector store class;
- fixed test flakiness and removed offending out-of-place escape chars
from a test module docstring;

To my knowledge all relevant tests pass and mypy+black+ruff don't
complain. (mypy gives unrelated errors in other modules, which clearly
don't depend on the content of this PR).

Thank you!
Stefano

---------

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

docs/extras/integrations/memory/cassandra_chat_message_history.ipynb

@@ -23,7 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"cassio>=0.0.7\""
+    "!pip install \"cassio>=0.1.0\""
    ]
   },
   {
@@ -155,7 +155,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/vectorstores/cassandra.ipynb

@@ -23,7 +23,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install \"cassio>=0.0.7\""
+    "!pip install \"cassio>=0.1.0\""
    ]
   },
   {
@@ -152,7 +152,9 @@
    "source": [
     "from langchain.document_loaders import TextLoader\n",
     "\n",
-    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
+    "SOURCE_FILE_NAME = \"../../modules/state_of_the_union.txt\"\n",
+    "\n",
+    "loader = TextLoader(SOURCE_FILE_NAME)\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "docs = text_splitter.split_documents(documents)\n",
@@ -197,7 +199,7 @@
     "#     table_name=table_name,\n",
     "# )\n",
     "\n",
-    "# docsearch_preexisting.similarity_search(query, k=2)"
+    "# docs = docsearch_preexisting.similarity_search(query, k=2)"
    ]
   },
   {
@@ -253,6 +255,51 @@
     "for i, doc in enumerate(found_docs):\n",
     "    print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da791c5f",
+   "metadata": {},
+   "source": [
+    "### Metadata filtering\n",
+    "\n",
+    "You can specify filtering on metadata when running searches in the vector store. By default, when inserting documents, the only metadata is the `\"source\"` (but you can customize the metadata at insertion time).\n",
+    "\n",
+    "Since only one files was inserted, this is just a demonstration of how filters are passed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93f132fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter = {\"source\": SOURCE_FILE_NAME}\n",
+    "filtered_docs = docsearch.similarity_search(query, filter=filter, k=5)\n",
+    "print(f\"{len(filtered_docs)} documents retrieved.\")\n",
+    "print(f\"{filtered_docs[0].page_content[:64]} ...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b413ec4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter = {\"source\": \"nonexisting_file.txt\"}\n",
+    "filtered_docs2 = docsearch.similarity_search(query, filter=filter)\n",
+    "print(f\"{len(filtered_docs2)} documents retrieved.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0fea764",
+   "metadata": {},
+   "source": [
+    "Please visit the [cassIO documentation](https://cassio.org/frameworks/langchain/about/) for more on using vector stores with Langchain."
+   ]
   }
  ],
  "metadata": {
@@ -271,7 +318,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,