docs: Integration docs for Langchain4J and LangchainGo (#571)

* docs: langchain4j, langchaingo

* docs: removed " bedrock.md

* Apply suggestions from code review

Co-authored-by: Mike Jang <michael@linuxexam.com>

* Rename LangchainGo.md to langchain-go.md

* docs: URL description update Langchain4J

* Update qdrant-landing/content/documentation/frameworks/langchain4j.md

Co-authored-by: Mike Jang <michael@linuxexam.com>

---------

Co-authored-by: Mike Jang <michael@linuxexam.com>

qdrant-landing/content/documentation/embeddings/bedrock.md

@@ -13,7 +13,7 @@ You'll need the following information from your AWS account:
 - Access key ID
 - Secret key
 
-To configure your credentials, review the following AWS article: [How do I create an AWS access key](https://repost.aws/knowledge-center/create-access-key)."
+To configure your credentials, review the following AWS article: [How do I create an AWS access key](https://repost.aws/knowledge-center/create-access-key).
 
 With the following code sample, you can generate embeddings using the [Titan Embeddings G1 - Text model](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) which produces sentence embeddings of size 1536.
 

qdrant-landing/content/documentation/frameworks/langchain-go.md

@@ -0,0 +1,67 @@
+---
+title: Langchain Go
+weight: 120
+---
+
+# Langchain Go
+
+[Langchain Go](https://tmc.github.io/langchaingo/docs/) is a framework for developing data-aware applications powered by language models in Go.
+
+You can use Qdrant as a vector store in Langchain Go.
+
+## Setup
+
+Install the `langchain-go` project dependency
+
+```bash
+go get -u github.com/tmc/langchaingo
+```
+
+## Usage
+
+Before you use the following code sample, customize the following values for your configuration:
+
+- `YOUR_QDRANT_REST_URL`: If you've set up Qdrant using the [Quick Start](/documentation/quick-start/) guide,
+  set this value to `http://localhost:6333`.
+- `YOUR_COLLECTION_NAME`: Use our [Collections](/documentation/concepts/collections) guide to create or
+  list collections.
+
+```go
+import (
+        "fmt"
+        "log"
+
+        "github.com/tmc/langchaingo/embeddings"
+        "github.com/tmc/langchaingo/llms/openai"
+        "github.com/tmc/langchaingo/vectorstores"
+        "github.com/tmc/langchaingo/vectorstores/qdrant"
+)
+
+ llm, err := openai.New()
+ if err != nil {
+  log.Fatal(err)
+ }
+
+ e, err := embeddings.NewEmbedder(llm)
+ if err != nil {
+  log.Fatal(err)
+ }
+
+ url, err := url.Parse("YOUR_QDRANT_REST_URL")
+ if err != nil {
+  log.Fatal(err)
+ }
+
+ store, err := qdrant.New(
+  qdrant.WithURL(*url),
+  qdrant.WithCollectionName("YOUR_COLLECTION_NAME"),
+  qdrant.WithEmbedder(e),
+ )
+ if err != nil {
+  log.Fatal(err)
+ }
+```
+
+## Further Reading
+
+- You can find usage examples of Langchain Go [here](https://github.com/tmc/langchaingo/tree/main/examples).

qdrant-landing/content/documentation/frameworks/langchain4j.md

@@ -0,0 +1,55 @@
+---
+title: Langchain4J
+weight: 110
+---
+
+# LangChain for Java
+
+LangChain for Java, also known as [Langchain4J](https://github.com/langchain4j/langchain4j), is a community port of [Langchain](https://www.langchain.com/) for building context-aware AI applications in Java
+
+You can use Qdrant as a vector store in Langchain4J through the [`langchain4j-qdrant`](https://central.sonatype.com/artifact/dev.langchain4j/langchain4j-qdrant) module.
+
+## Setup
+
+Add the `langchain4j-qdrant` to your project dependencies.
+
+```xml
+<dependency>
+    <groupId>dev.langchain4j</groupId>
+    <artifactId>langchain4j-qdrant</artifactId>
+    <version>VERSION</version>
+</dependency>
+```
+
+## Usage
+
+Before you use the following code sample, customize the following values for your configuration:
+
+- `YOUR_COLLECTION_NAME`: Use our [Collections](/documentation/concepts/collections) guide to create or
+  list collections.
+- `YOUR_HOST_URL`: Use the GRPC URL for your system. If you used the [Quick Start](/documentation/quick-start/) guide,
+  it may be http://localhost:6334. If you've deployed in the [Qdrant Cloud](/documentation/cloud/), you may have a
+  longer URL such as `https://example.location.cloud.qdrant.io:6334`.
+- `YOUR_API_KEY`: Substitute the API key associated with your configuration.
+```java
+import dev.langchain4j.store.embedding.EmbeddingStore;
+import dev.langchain4j.store.embedding.qdrant.QdrantEmbeddingStore;
+
+EmbeddingStore<TextSegment> embeddingStore =
+    QdrantEmbeddingStore.builder()
+        // Ensure the collection is configured with the appropriate dimensions
+        // of the embedding model.
+        // Reference https://qdrant.tech/documentation/concepts/collections/
+        .collectionName("YOUR_COLLECTION_NAME")
+        .host("YOUR_HOST_URL")
+        // GRPC port of the Qdrant server
+        .port(6334)
+        .apiKey("YOUR_API_KEY")
+        .build();
+```
+
+`QdrantEmbeddingStore` supports all the semantic features of Langchain4J. 
+
+## Further Reading
+
+- You can refer to the [Langchain4J examples](https://github.com/langchain4j/langchain4j-examples/) to get started.