Add JavaScript/TypeScript integration to LangChain documentation

genai-tools/index.md

@@ -20,7 +20,7 @@ FalkorDB provides powerful tools and integrations for building intelligent GenAI
 
 - [GraphRAG-SDK](./graphrag-sdk.md): Build intelligent GraphRAG applications with FalkorDB and LLMs.
 - [AG2](./ag2.md): Build multi-agent AI systems with AG2 (formerly AutoGen) and FalkorDB GraphRAG.
-- [LangChain](./langchain.md): Integration with LangChain for AI agents with memory.
+- [LangChain](./langchain.md): Integration with LangChain for AI agents with memory (Python and JavaScript/TypeScript).
 - [LangGraph](./langgraph.md): Build stateful, multi-actor agentic applications with LangGraph.
 - [LlamaIndex](./llamaindex.md): Simplify development of LLM-powered applications with LlamaIndex.
 - [GraphRAG Toolkit](./graphrag-toolkit.md): AWS GraphRAG Toolkit integration for building knowledge graph applications.

genai-tools/langchain.md

@@ -9,22 +9,30 @@ parent: "GenAI Tools"
 
 FalkorDB is integrated with [LangChain](https://www.langchain.com/), bringing powerful graph database capabilities to AI-driven applications. This integration enables the creation of AI agents with memory, enhancing their ability to retain state and context across interactions.
 
+The FalkorDB LangChain integration is available for both **Python** and **JavaScript/TypeScript** environments, making it easy to build intelligent applications in your preferred language.
+
 ## Resources
 
-- 🔗 [FalkorDBQAChain Documentation](https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa)  
+- 🔗 [FalkorDBQAChain Documentation (Python)](https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa)
+- 📦 [@falkordb/langchain-ts Package (JavaScript/TypeScript)](https://www.npmjs.com/package/@falkordb/langchain-ts)
+- 💻 [FalkorDB-Langchain-js Repository](https://github.com/FalkorDB/FalkorDB-Langchain-js)
 - 📓 [Blog: Build AI Agents with Memory – LangChain + FalkorDB](https://www.falkordb.com/blog/building-ai-agents-with-memory-langchain/)
 
-## Installation
+---
+
+## Python Integration
+
+### Installation
 
 Install LangChain with FalkorDB support:
 
 ```bash
 pip install langchain langchain-community falkordb
 ```
 
-## Quick Start
+### Quick Start
 
-### 1. Connect to FalkorDB
+#### 1. Connect to FalkorDB
 
 ```python
 from langchain_community.graphs import FalkorDBGraph
@@ -39,7 +47,7 @@ graph = FalkorDBGraph(
 )
 ```
 
-### 2. Create a Knowledge Graph from Text
+#### 2. Create a Knowledge Graph from Text
 
 ```python
 from langchain.chains import GraphCypherQAChain
@@ -56,7 +64,7 @@ chain = GraphCypherQAChain.from_llm(
 )
 ```
 
-### 3. Query the Graph
+#### 3. Query the Graph
 
 ```python
 # Ask natural language questions
@@ -68,9 +76,9 @@ response = chain.run("What other movies did they act in?")
 print(response)
 ```
 
-## Advanced Usage
+### Advanced Usage
 
-### Using Graph Memory for Conversational AI
+#### Using Graph Memory for Conversational AI
 
 ```python
 from langchain.memory import ConversationGraphMemory
@@ -96,7 +104,7 @@ conversation.predict(input="I work as a software engineer")
 conversation.predict(input="What do you know about me?")
 ```
 
-### Custom Cypher Generation
+#### Custom Cypher Generation
 
 ```python
 from langchain.chains.graph_qa.cypher import GraphCypherQAChain
@@ -131,7 +139,7 @@ chain = GraphCypherQAChain.from_llm(
 response = chain.run("Find all products in the electronics category")
 ```
 
-### Loading Data into the Graph
+#### Loading Data into the Graph
 
 ```python
 from langchain_community.document_loaders import TextLoader
@@ -156,7 +164,7 @@ vector_store = FalkorDBVector.from_documents(
 )
 ```
 
-### Graph RAG Pattern
+#### Graph RAG Pattern
 
 ```python
 from langchain.chains import RetrievalQA
@@ -183,6 +191,101 @@ response = qa_chain.run("What are the key features of our product?")
 print(response)
 ```
 
+---
+
+## JavaScript/TypeScript Integration
+
+FalkorDB also provides a JavaScript/TypeScript integration for LangChain applications through the [@falkordb/langchain-ts](https://www.npmjs.com/package/@falkordb/langchain-ts) package.
+
+### Installation
+
+```bash
+npm install @falkordb/langchain-ts falkordb langchain @langchain/openai
+```
+
+### Quick Start (JS/TS)
+
+```typescript
+import { FalkorDBGraph } from "@falkordb/langchain-ts";
+import { ChatOpenAI } from "@langchain/openai";
+import { GraphCypherQAChain } from "@langchain/community/chains/graph_qa/cypher";
+
+// Initialize FalkorDB connection
+const graph = await FalkorDBGraph.initialize({
+  host: "localhost",
+  port: 6379,
+  graph: "movies"
+});
+
+// Set up the language model
+const model = new ChatOpenAI({ temperature: 0 });
+
+// Create and populate the graph
+await graph.query(
+  "CREATE (a:Actor {name:'Bruce Willis'})" +
+  "-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})"
+);
+
+// Refresh the graph schema
+await graph.refreshSchema();
+
+// Create a graph QA chain
+const chain = GraphCypherQAChain.fromLLM({
+  llm: model,
+  graph: graph as any,
+});
+
+// Ask questions about your graph
+const response = await chain.run("Who played in Pulp Fiction?");
+console.log(response);
+// Output: Bruce Willis played in Pulp Fiction.
+
+await graph.close();
+```
+
+### Key Features (JS/TS)
+
+- **Natural Language Querying**: Convert questions to Cypher queries automatically
+- **Schema Management**: Automatic schema refresh and retrieval
+- **Type Safety**: Full TypeScript support with type definitions
+- **Promise-based API**: Modern async/await patterns
+
+### API Reference (JS/TS)
+
+#### `FalkorDBGraph.initialize(config)`
+
+Create and initialize a new FalkorDB connection.
+
+**Config Options:**
+- `host` (string): Database host (default: "localhost")
+- `port` (number): Database port (default: 6379)
+- `graph` (string): Graph name to use
+- `url` (string): Alternative connection URL format
+- `enhancedSchema` (boolean): Enable enhanced schema details
+
+#### `query(query: string)`
+
+Execute a Cypher query on the graph.
+
+```typescript
+const result = await graph.query(
+  "MATCH (n:Person) RETURN n.name LIMIT 10"
+);
+```
+
+#### `refreshSchema()`
+
+Update the graph schema information.
+
+```typescript
+await graph.refreshSchema();
+console.log(graph.getSchema());
+```
+
+For more detailed JavaScript/TypeScript examples and documentation, see the [LangChain JS/TS Integration Guide](/integration/langchain-js.html) and the [@falkordb/langchain-ts repository](https://github.com/FalkorDB/FalkorDB-Langchain-js).
+
+---
+
 ## Use Cases
 
 - **Conversational AI with Memory**: Build chatbots that remember user context across sessions

integration/index.md

@@ -15,6 +15,7 @@ Learn how to leverage FalkorDB's flexible APIs and SDKs to build high-performanc
 ## Topics in This Section
 
 - [REST API](./rest.md): Learn how to interact with FalkorDB using its REST API for seamless integration with your applications.
+- [LangChain JS/TS](./langchain-js.md): Integrate FalkorDB with LangChain JavaScript/TypeScript applications for AI-powered graph querying.
 - [Kafka Connect](./kafka-connect.md): Learn how to interact with FalkorDB using Kafka Connect sink to replicate data from third-party applications.
 - [Apache Jena](./jena.md): Learn how to use FalkorDB with Apache Jena via the jena-falkordb-adapter.
 - [BOLT protocol support](./bolt-support.md): Learn how to connect to FalkorDB using the BOLT protocol.

integration/langchain-js.md

@@ -11,6 +11,8 @@ parent: "Integration"
 
 The [@falkordb/langchain-ts](https://www.npmjs.com/package/@falkordb/langchain-ts) package enables developers to integrate FalkorDB with LangChain applications. The integration allows applications to accept natural language questions, generate Cypher queries automatically, retrieve relevant context from the graph database, and return responses in natural language.
 
+> **Note:** FalkorDB also provides a Python integration for LangChain. See the [LangChain Python Integration](/genai-tools/langchain.html) for Python-specific documentation.
+
 ## Installation
 
 ### Step 1