refactor: remove DocumentEmbeddings from in memory vector store

.github/workflows/ci.yaml

@@ -4,7 +4,7 @@ name: Lint & Test
 on:
   pull_request:
     branches:
-      - main
+      - "**"
   push:
     branches:
       - main

rig-core/examples/calculator_chatbot.rs

@@ -2,10 +2,10 @@ use anyhow::Result;
 use rig::{
     cli_chatbot::cli_chatbot,
     completion::ToolDefinition,
-    embeddings::EmbeddingsBuilder,
+    embeddings::{DocumentEmbeddings, EmbeddingsBuilder},
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
     tool::{Tool, ToolEmbedding, ToolSet},
-    vector_store::{in_memory_store::InMemoryVectorStore, VectorStore},
+    vector_store::in_memory_store::InMemoryVectorStore,
 };
 use serde::{Deserialize, Serialize};
 use serde_json::json;
@@ -251,9 +251,20 @@ async fn main() -> Result<(), anyhow::Error> {
         .build()
         .await?;
 
-    let mut store = InMemoryVectorStore::default();
-    store.add_documents(embeddings).await?;
-    let index = store.index(embedding_model);
+    let index = InMemoryVectorStore::default()
+        .add_documents(
+            embeddings
+                .into_iter()
+                .map(
+                    |DocumentEmbeddings {
+                         id,
+                         document,
+                         embeddings,
+                     }| { (id, document, embeddings) },
+                )
+                .collect(),
+        )?
+        .index(embedding_model);
 
     // Create RAG agent with a single context prompt and a dynamic tool source
     let calculator_rag = openai_client

rig-core/examples/rag.rs

@@ -2,9 +2,9 @@ use std::env;
 
 use rig::{
     completion::Prompt,
-    embeddings::EmbeddingsBuilder,
+    embeddings::{DocumentEmbeddings, EmbeddingsBuilder},
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
-    vector_store::{in_memory_store::InMemoryVectorStore, VectorStore},
+    vector_store::in_memory_store::InMemoryVectorStore,
 };
 
 #[tokio::main]
@@ -15,20 +15,27 @@ async fn main() -> Result<(), anyhow::Error> {
 
     let embedding_model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
 
-    // Create vector store, compute embeddings and load them in the store
-    let mut vector_store = InMemoryVectorStore::default();
-
     let embeddings = EmbeddingsBuilder::new(embedding_model.clone())
         .simple_document("doc0", "Definition of a *flurbo*: A flurbo is a green alien that lives on cold planets")
         .simple_document("doc1", "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("doc2", "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.")
         .build()
         .await?;
 
-    vector_store.add_documents(embeddings).await?;
-
-    // Create vector store index
-    let index = vector_store.index(embedding_model);
+    let index = InMemoryVectorStore::default()
+        .add_documents(
+            embeddings
+                .into_iter()
+                .map(
+                    |DocumentEmbeddings {
+                         id,
+                         document,
+                         embeddings,
+                     }| { (id, document, embeddings) },
+                )
+                .collect(),
+        )?
+        .index(embedding_model);
 
     let rag_agent = openai_client.agent("gpt-4")
         .preamble("

rig-core/examples/rag_dynamic_tools.rs

@@ -1,10 +1,10 @@
 use anyhow::Result;
 use rig::{
     completion::{Prompt, ToolDefinition},
-    embeddings::EmbeddingsBuilder,
+    embeddings::{DocumentEmbeddings, EmbeddingsBuilder},
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
     tool::{Tool, ToolEmbedding, ToolSet},
-    vector_store::{in_memory_store::InMemoryVectorStore, VectorStore},
+    vector_store::in_memory_store::InMemoryVectorStore,
 };
 use serde::{Deserialize, Serialize};
 use serde_json::json;
@@ -150,9 +150,6 @@ async fn main() -> Result<(), anyhow::Error> {
 
     let embedding_model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
 
-    // Create vector store, compute tool embeddings and load them in the store
-    let mut vector_store = InMemoryVectorStore::default();
-
     let toolset = ToolSet::builder()
         .dynamic_tool(Add)
         .dynamic_tool(Subtract)
@@ -163,10 +160,20 @@ async fn main() -> Result<(), anyhow::Error> {
         .build()
         .await?;
 
-    vector_store.add_documents(embeddings).await?;
-
-    // Create vector store index
-    let index = vector_store.index(embedding_model);
+    let index = InMemoryVectorStore::default()
+        .add_documents(
+            embeddings
+                .into_iter()
+                .map(
+                    |DocumentEmbeddings {
+                         id,
+                         document,
+                         embeddings,
+                     }| { (id, document, embeddings) },
+                )
+                .collect(),
+        )?
+        .index(embedding_model);
 
     // Create RAG agent with a single context prompt and a dynamic tool source
     let calculator_rag = openai_client

rig-core/examples/vector_search.rs

@@ -3,7 +3,7 @@ use std::env;
 use rig::{
     embeddings::{DocumentEmbeddings, EmbeddingsBuilder},
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
-    vector_store::{in_memory_store::InMemoryVectorIndex, VectorStoreIndex},
+    vector_store::{in_memory_store::InMemoryVectorStore, VectorStoreIndex},
 };
 
 #[tokio::main]
@@ -21,13 +21,26 @@ async fn main() -> Result<(), anyhow::Error> {
         .build()
         .await?;
 
-    let index = InMemoryVectorIndex::from_embeddings(model, embeddings).await?;
+    let index = InMemoryVectorStore::default()
+        .add_documents(
+            embeddings
+                .into_iter()
+                .map(
+                    |DocumentEmbeddings {
+                         id,
+                         document,
+                         embeddings,
+                     }| { (id, document, embeddings) },
+                )
+                .collect(),
+        )?
+        .index(model);
 
     let results = index
-        .top_n::<DocumentEmbeddings>("What is a linglingdong?", 1)
+        .top_n::<String>("What is a linglingdong?", 1)
         .await?
         .into_iter()
-        .map(|(score, id, doc)| (score, id, doc.document))
+        .map(|(score, id, doc)| (score, id, doc))
         .collect::<Vec<_>>();
 
     println!("Results: {:?}", results);

rig-core/examples/vector_search_cohere.rs

@@ -3,7 +3,7 @@ use std::env;
 use rig::{
     embeddings::{DocumentEmbeddings, EmbeddingsBuilder},
     providers::cohere::{Client, EMBED_ENGLISH_V3},
-    vector_store::{in_memory_store::InMemoryVectorStore, VectorStore, VectorStoreIndex},
+    vector_store::{in_memory_store::InMemoryVectorStore, VectorStoreIndex},
 };
 
 #[tokio::main]
@@ -15,24 +15,33 @@ async fn main() -> Result<(), anyhow::Error> {
     let document_model = cohere_client.embedding_model(EMBED_ENGLISH_V3, "search_document");
     let search_model = cohere_client.embedding_model(EMBED_ENGLISH_V3, "search_query");
 
-    let mut vector_store = InMemoryVectorStore::default();
-
     let embeddings = EmbeddingsBuilder::new(document_model)
         .simple_document("doc0", "Definition of a *flurbo*: A flurbo is a green alien that lives on cold planets")
         .simple_document("doc1", "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("doc2", "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.")
         .build()
         .await?;
 
-    vector_store.add_documents(embeddings).await?;
-
-    let index = vector_store.index(search_model);
+    let index = InMemoryVectorStore::default()
+        .add_documents(
+            embeddings
+                .into_iter()
+                .map(
+                    |DocumentEmbeddings {
+                         id,
+                         document,
+                         embeddings,
+                     }| { (id, document, embeddings) },
+                )
+                .collect(),
+        )?
+        .index(search_model);
 
     let results = index
-        .top_n::<DocumentEmbeddings>("What is a linglingdong?", 1)
+        .top_n::<String>("What is a linglingdong?", 1)
         .await?
         .into_iter()
-        .map(|(score, id, doc)| (score, id, doc.document))
+        .map(|(score, id, doc)| (score, id, doc))
         .collect::<Vec<_>>();
 
     println!("Results: {:?}", results);