community: Add support for SAP HANA Vector hnsw index creation and advanced filtering

docs/core_docs/docs/integrations/vectorstores/hanavector.mdx

@@ -35,12 +35,47 @@ import ExampleLoader from "@examples/indexes/vector_stores/hana_vector/fromDocs.
 
 <CodeBlock language="typescript">{ExampleLoader}</CodeBlock>
 
+## Creating an HNSW Vector Index
+
+A vector index can significantly speed up top-k nearest neighbor queries for vectors. Users can create a Hierarchical Navigable Small World (HNSW) vector index using the `create_hnsw_index` function.
+
+For more information about creating an index at the database level, such as parameters requirement, please refer to the [official documentation](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-vector-engine-guide/create-vector-index-statement-data-definition).
+
+import ExampleIndex from "@examples/indexes/vector_stores/hana_vector/createHnswIndex.ts";
+
+<CodeBlock language="typescript">{ExampleIndex}</CodeBlock>
+
 ## Basic Vectorstore Operations
 
 import ExampleBasic from "@examples/indexes/vector_stores/hana_vector/basics.ts";
 
 <CodeBlock language="typescript">{ExampleBasic}</CodeBlock>
 
+## Advanced filtering
+
+import { Table, Tr, Th, Td } from "@mdx-js/react";
+
+In addition to the basic value-based filtering capabilities, it is possible to use more advanced filtering. The table below shows the available filter operators.
+
+| Operator   | Semantic                                                                   |
+| ---------- | -------------------------------------------------------------------------- |
+| `$eq`      | Equality (==)                                                              |
+| `$ne`      | Inequality (!=)                                                            |
+| `$lt`      | Less than (<)                                                              |
+| `$lte`     | Less than or equal (<=)                                                    |
+| `$gt`      | Greater than (>)                                                           |
+| `$gte`     | Greater than or equal (>=)                                                 |
+| `$in`      | Contained in a set of given values (in)                                    |
+| `$nin`     | Not contained in a set of given values (not in)                            |
+| `$between` | Between the range of two boundary values                                   |
+| `$like`    | Text equality based on the "LIKE" semantics in SQL (using "%" as wildcard) |
+| `$and`     | Logical "and", supporting 2 or more operands                               |
+| `$or`      | Logical "or", supporting 2 or more operands                                |
+
+import ExampleAdvancedFilter from "@examples/indexes/vector_stores/hana_vector/advancedFiltering.ts";
+
+<CodeBlock language="typescript">{ExampleAdvancedFilter}</CodeBlock>
+
 ## Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)
 
 import ExampleChain from "@examples/indexes/vector_stores/hana_vector/chains.ts";

examples/src/indexes/vector_stores/hana_vector/advancedFiltering.ts

@@ -0,0 +1,210 @@
+import { OpenAIEmbeddings } from "@langchain/openai";
+import hanaClient from "hdb";
+import { Document } from "@langchain/core/documents";
+import {
+  HanaDB,
+  HanaDBArgs,
+} from "@langchain/community/vectorstores/hanavector";
+
+const connectionParams = {
+  host: process.env.HANA_HOST,
+  port: process.env.HANA_PORT,
+  user: process.env.HANA_UID,
+  password: process.env.HANA_PWD,
+};
+const client = hanaClient.createClient(connectionParams);
+
+// Connect to SAP HANA
+await new Promise<void>((resolve, reject) => {
+  client.connect((err: Error) => {
+    if (err) {
+      reject(err);
+    } else {
+      console.log("Connected to SAP HANA successfully.");
+      resolve();
+    }
+  });
+});
+
+const docs: Document[] = [
+  {
+    pageContent: "First",
+    metadata: { name: "adam", is_active: true, id: 1, height: 10.0 },
+  },
+  {
+    pageContent: "Second",
+    metadata: { name: "bob", is_active: false, id: 2, height: 5.7 },
+  },
+  {
+    pageContent: "Third",
+    metadata: { name: "jane", is_active: true, id: 3, height: 2.4 },
+  },
+];
+
+// Initialize embeddings
+const embeddings = new OpenAIEmbeddings();
+
+const args: HanaDBArgs = {
+  connection: client,
+  tableName: "testAdvancedFilters",
+};
+
+// Create a LangChain VectorStore interface for the HANA database and specify the table (collection) to use in args.
+const vectorStore = new HanaDB(embeddings, args);
+// need to initialize once an instance is created.
+await vectorStore.initialize();
+// Delete already existing documents from the table
+await vectorStore.delete({ filter: {} });
+await vectorStore.addDocuments(docs);
+
+// Helper function to print filter results
+function printFilterResult(result: Document[]) {
+  if (result.length === 0) {
+    console.log("<empty result>");
+  } else {
+    result.forEach((doc) => console.log(doc.metadata));
+  }
+}
+
+let advancedFilter;
+
+// Not equal
+advancedFilter = { id: { $ne: 1 } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"id":{"$ne":1}}
+{ name: 'bob', is_active: false, id: 2, height: 5.7 } 
+{ name: 'jane', is_active: true, id: 3, height: 2.4 }
+*/
+
+// Between range
+advancedFilter = { id: { $between: [1, 2] } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"id":{"$between":[1,2]}}
+{ name: 'adam', is_active: true, id: 1, height: 10 }
+{ name: 'bob', is_active: false, id: 2, height: 5.7 } */
+
+// In list
+advancedFilter = { name: { $in: ["adam", "bob"] } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"name":{"$in":["adam","bob"]}}
+{ name: 'adam', is_active: true, id: 1, height: 10 }
+{ name: 'bob', is_active: false, id: 2, height: 5.7 } */
+
+// Not in list
+advancedFilter = { name: { $nin: ["adam", "bob"] } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"name":{"$nin":["adam","bob"]}}
+{ name: 'jane', is_active: true, id: 3, height: 2.4 } */
+
+// Greater than
+advancedFilter = { id: { $gt: 1 } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"id":{"$gt":1}}
+{ name: 'bob', is_active: false, id: 2, height: 5.7 }
+{ name: 'jane', is_active: true, id: 3, height: 2.4 } */
+
+// Greater than or equal to
+advancedFilter = { id: { $gte: 1 } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"id":{"$gte":1}}
+{ name: 'adam', is_active: true, id: 1, height: 10 }
+{ name: 'bob', is_active: false, id: 2, height: 5.7 }
+{ name: 'jane', is_active: true, id: 3, height: 2.4 } */
+
+// Less than
+advancedFilter = { id: { $lt: 1 } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"id":{"$lt":1}}
+<empty result> */
+
+// Less than or equal to
+advancedFilter = { id: { $lte: 1 } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"id":{"$lte":1}}
+{ name: 'adam', is_active: true, id: 1, height: 10 } */
+
+// Text filtering with $like
+advancedFilter = { name: { $like: "a%" } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"name":{"$like":"a%"}}
+{ name: 'adam', is_active: true, id: 1, height: 10 } */
+
+advancedFilter = { name: { $like: "%a%" } };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"name":{"$like":"%a%"}}
+{ name: 'adam', is_active: true, id: 1, height: 10 }
+{ name: 'jane', is_active: true, id: 3, height: 2.4 } */
+
+// Combined filtering with $or
+advancedFilter = { $or: [{ id: 1 }, { name: "bob" }] };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"$or":[{"id":1},{"name":"bob"}]}
+{ name: 'adam', is_active: true, id: 1, height: 10 }
+{ name: 'bob', is_active: false, id: 2, height: 5.7 } */
+
+// Combined filtering with $and
+advancedFilter = { $and: [{ id: 1 }, { id: 2 }] };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"$and":[{"id":1},{"id":2}]}
+<empty result> */
+
+advancedFilter = { $or: [{ id: 1 }, { id: 2 }, { id: 3 }] };
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"$or":[{"id":1},{"id":2},{"id":3}]}
+{ name: 'adam', is_active: true, id: 1, height: 10 }
+{ name: 'bob', is_active: false, id: 2, height: 5.7 }
+{ name: 'jane', is_active: true, id: 3, height: 2.4 } */
+
+// You can also define a nested filter with $and and $or.
+advancedFilter = {
+  $and: [{ $or: [{ id: 1 }, { id: 2 }] }, { height: { $gte: 5.0 } }],
+};
+console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
+printFilterResult(
+  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
+);
+/* Filter: {"$and":[{"$or":[{"id":1},{"id":2}]},{"height":{"$gte":5.0}}]}
+{ name: 'adam', is_active: true, id: 1, height: 10 } 
+{ name: 'bob', is_active: false, id: 2, height: 5.7 } */
+
+// Disconnect from SAP HANA aft er the operations
+client.disconnect();

examples/src/indexes/vector_stores/hana_vector/createHnswIndex.ts

@@ -0,0 +1,98 @@
+import hanaClient from "hdb";
+import {
+  HanaDB,
+  HanaDBArgs,
+} from "@langchain/community/vectorstores/hanavector";
+import { OpenAIEmbeddings } from "@langchain/openai";
+
+// table "test_fromDocs" is already created with the previous example.
+// Now, we will use this existing table to create indexes and perform similarity search.
+
+const connectionParams = {
+  host: process.env.HANA_HOST,
+  port: process.env.HANA_PORT,
+  user: process.env.HANA_UID,
+  password: process.env.HANA_PWD,
+};
+const client = hanaClient.createClient(connectionParams);
+
+// Connect to SAP HANA
+await new Promise<void>((resolve, reject) => {
+  client.connect((err: Error) => {
+    if (err) {
+      reject(err);
+    } else {
+      console.log("Connected to SAP HANA successfully.");
+      resolve();
+    }
+  });
+});
+
+// Initialize embeddings
+const embeddings = new OpenAIEmbeddings();
+
+// First instance using the existing table "test_fromDocs" (default: Cosine similarity)
+const argsCosine: HanaDBArgs = {
+  connection: client,
+  tableName: "test_fromDocs",
+};
+
+// Second instance using the existing table "test_fromDocs" but with L2 Euclidean distance
+const argsL2: HanaDBArgs = {
+  connection: client,
+  tableName: "test_fromDocs",
+  distanceStrategy: "euclidean", // Use Euclidean distance for this instance
+};
+
+// Initialize both HanaDB instances
+const vectorStoreCosine = new HanaDB(embeddings, argsCosine);
+const vectorStoreL2 = new HanaDB(embeddings, argsL2);
+
+// Create HNSW index with Cosine similarity (default)
+await vectorStoreCosine.createHnswIndex({
+  indexName: "hnsw_cosine_index",
+  efSearch: 400,
+  m: 50,
+  efConstruction: 150,
+});
+
+// Create HNSW index with Euclidean (L2) distance
+await vectorStoreL2.createHnswIndex({
+  indexName: "hnsw_l2_index",
+  efSearch: 400,
+  m: 50,
+  efConstruction: 150,
+});
+
+// Query text for similarity search
+const query = "What did the president say about Ketanji Brown Jackson";
+
+// Perform similarity search using the default Cosine index
+const docsCosine = await vectorStoreCosine.similaritySearch(query, 2);
+console.log("Cosine Similarity Results:");
+docsCosine.forEach((doc) => {
+  console.log("-".repeat(80));
+  console.log(doc.pageContent);
+});
+/*
+Cosine Similarity Results:
+----------------------------------------------------------------------
+One of the most serious constitutional ... 
+
+And I did that 4 days ago, when I ...
+----------------------------------------------------------------------
+As I said last year, especially ... 
+
+While it often appears that we never agree, that isn’t true...
+*/
+// Perform similarity search using Euclidean distance (L2 index)
+const docsL2 = await vectorStoreL2.similaritySearch(query, 2);
+console.log("Euclidean (L2) Distance Results:");
+docsL2.forEach((doc) => {
+  console.log("-".repeat(80));
+  console.log(doc.pageContent);
+});
+// The L2 distance results should be the same as cosine search results.
+
+// Disconnect from SAP HANA after the operations
+client.disconnect();