docs: Rig-rs integration

Signed-off-by: Anush008 <anushshetty90@gmail.com>

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

@@ -5,8 +5,8 @@ weight: 20
 
 ## Framework Integrations
 
-| Framework                             | Description                                                                                          |
+| Framework                                                     | Description                                                                                          |
-| ------------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| ------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
 | [AutoGen](/documentation/frameworks/autogen/)                 | Framework from Microsoft building LLM applications using multiple conversational agents.             |
 | [Canopy](/documentation/frameworks/canopy/)                   | Framework from Pinecone for building RAG applications using LLMs and knowledge bases.                |
 | [Cheshire Cat](/documentation/frameworks/cheshire-cat/)       | Framework to create personalized AI assistants using custom data.                                    |
@@ -23,8 +23,9 @@ weight: 20
 | [Mem0](/documentation/frameworks/mem0/)                       | Self-improving memory layer for LLM applications, enabling personalized AI experiences.              |
 | [MemGPT](/documentation/frameworks/memgpt/)                   | System to build LLM agents with long term memory & custom tools                                      |
 | [Pandas-AI](/documentation/frameworks/pandas-ai/)             | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language        |
+| [Rig-rs](/documentation/frameworks/rig-rs/)                   | Rust library for building scalable, modular, and ergonomic LLM-powered applications.                 |
 | [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search.             |
 | [Spring AI](/documentation/frameworks/spring-ai/)             | Java AI framework for building with Spring design principles such as portability and modular design. |
-| [Sycamore](/documentation/frameworks/sycamore/)             | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
+| [Sycamore](/documentation/frameworks/sycamore/)               | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
 | [txtai](/documentation/frameworks/txtai/)                     | Python library for semantic search, LLM orchestration and language model workflows.                  |
 | [Vanna AI](/documentation/frameworks/vanna-ai/)               | Python RAG framework for SQL generation and querying.                                                |

qdrant-landing/content/documentation/frameworks/rig-rs.md

@@ -0,0 +1,81 @@
+---
+title: Rig-rs
+---
+
+# Rig-rs
+
+[Rig](http://rig.rs) is a Rust library for building scalable, modular, and ergonomic LLM-powered applications. It has full support for LLM completion and embedding workflows with minimal boiler plate.
+
+Rig supports Qdrant as a vectorstore to ingest and search for documents semantically.
+
+## Installation
+
+```console
+cargo add rig-core rig-qdrant qdrant-client
+```
+
+## Usage
+
+Here's an example ingest and retrieve flow using Rig and Qdrant.
+
+```rust
+use qdrant_client::{
+    qdrant::{PointStruct, QueryPointsBuilder, UpsertPointsBuilder},
+    Payload, Qdrant,
+};
+use rig::{
+    embeddings::EmbeddingsBuilder,
+    providers::openai::{Client, TEXT_EMBEDDING_3_SMALL},
+    vector_store::VectorStoreIndex,
+};
+use rig_qdrant::QdrantVectorStore;
+use serde_json::json;
+
+const COLLECTION_NAME: &str = "rig-collection";
+
+// Initialize Qdrant client.
+let client = Qdrant::from_url("http://localhost:6334").build()?;
+// Initialize OpenAI client.
+let openai_client = Client::new("<OPENAI_API_KEY>");
+let model = openai_client.embedding_model(TEXT_EMBEDDING_3_SMALL);
+
+let documents = EmbeddingsBuilder::new(model.clone())
+    .simple_document("0981d983-a5f8-49eb-89ea-f7d3b2196d2e", "Definition of a *flurbo*: A flurbo is a green alien that lives on cold planets")
+    .simple_document("62a36d43-80b6-4fd6-990c-f75bb02287d1", "Definition of a *glarb-glarb*: A glarb-glarb is a ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.")
+    .simple_document("f9e17d59-32e5-440c-be02-b2759a654824", "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.")
+    .build()
+    .await?;
+
+let points: Vec<PointStruct> = documents
+    .into_iter()
+    .map(|d| {
+        let vec: Vec<f32> = d.embeddings[0].vec.iter().map(|&x| x as f32).collect();
+        PointStruct::new(
+            d.id,
+            vec,
+            Payload::try_from(json!({
+                "document": d.document,
+            }))
+            .unwrap(),
+        )
+    })
+    .collect();
+
+client
+    .upsert_points(UpsertPointsBuilder::new(COLLECTION_NAME, points))
+    .await?;
+
+let query_params = QueryPointsBuilder::new(COLLECTION_NAME).with_payload(true);
+let vector_store = QdrantVectorStore::new(client, model, query_params.build());
+
+let results = vector_store
+    .top_n::<serde_json::Value>("Define a glarb-glarb?", 1)
+    .await?;
+
+println!("Results: {:?}", results);
+```
+
+## Further reading
+
+- [Rig-rs Documentation](https://rig.rs)
+- [Source Code](https://github.com/0xPlaygrounds/rig)