Korvus can perform the entire RAG pipeline including embedding generation, vector search, keyword search, re-ranking and text-generation in on SQL query.
Korvus will build a SQL query that performs search, builds the context, formats the prompt, and performs text-generation all at once. It builds on syntax already used previously in the Vector Search guide.
Pipelines are required to perform RAG. See Pipelines for more information on using Pipelines.
This section will assume we have previously ran the following code:
content_copy
const collection = korvus.newCollection("test_rag_collection");
const pipeline = korvus.newPipeline("v1", {
text: {
splitter: { model: "recursive_character" },
semantic_search: {
model: "mixedbread-ai/mxbai-embed-large-v1",
},
full_text_search: { configuration: "english" },
},
});
await collection.add_pipeline(pipeline);
content_copy
collection = Collection("test_rag_collection")
pipeline = Pipeline(
"v1",
{
"text": {
"splitter": {"model": "recursive_character"},
"semantic_search": {
"model": "mixedbread-ai/mxbai-embed-large-v1",
},
"full_text_search": {"configuration": "english"},
},
},
)
await collection.add_pipeline(pipeline);
content_copy
let mut collection = Collection::new("test_rag_collection", None)?;
let mut pipeline = Pipeline::new(
"v1",
Some(
serde_json::json!(
{
"text": {
"splitter": {"model": "recursive_character"},
"semantic_search": {
"model": "mixedbread-ai/mxbai-embed-large-v1",
},
"full_text_search": {"configuration": "english"},
},
}
)
.into(),
),
)?;
collection.add_pipeline(&mut pipeline).await?;
content_copy
CollectionC * collection = korvus_collectionc_new("test_rag_collection", NULL);
PipelineC *pipeline = korvus_pipelinec_new("v1", "{\
\"text\": {\
\"splitter\": {\"model\": \"recursive_character\"},\
\"semantic_search\": {\
\"model\": \"mixedbread-ai/mxbai-embed-large-v1\"\
},\
\"full_text_search\": {\"configuration\": \"english\"}\
}\
}");
korvus_collectionc_add_pipeline(collection, pipeline);
This creates a Pipeline that is capable of full text search and semantic search on the text of documents.
The RAG method will automatically perform full text and semantic search for us using the same syntax as Vector Search.
content_copy
const results = await collection.rag(
{
CONTEXT: {
vector_search: {
query: {
fields: {
text: {
query: "Is Korvus fast?",
parameters: {
prompt: "Represent this sentence for searching relevant passages: "
},
}
},
},
document: { "keys": ["id"] },
limit: 5,
},
aggregate: { "join": "\n" },
},
chat: {
model: "meta-llama/Meta-Llama-3.1-8B-Instruct",
messages: [
{
role: "system",
content: "You are a friendly and helpful chatbot",
},
{
role: "user",
content: "Given the context\n:{CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
max_tokens: 100,
},
},
pipeline,
)
content_copy
results = await collection.rag(
{
"CONTEXT": {
"vector_search": {
"query": {
"fields": {
"text": {
"query": "Is Korvus fast?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: "
},
}
},
},
"document": {"keys": ["id"]},
"limit": 5,
},
"aggregate": {"join": "\n"},
},
"chat": {
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:{CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
"max_tokens": 100,
},
},
pipeline,
)
content_copy
let results = collection.rag(serde_json::json!(
{
"CONTEXT": {
"vector_search": {
"query": {
"fields": {
"text": {
"query": "Is Korvus fast?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: "
},
}
},
},
"document": {"keys": ["id"]},
"limit": 5,
},
"aggregate": {"join": "\n"},
},
"chat": {
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:{CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
"max_tokens": 100,
},
}
).into(), &mut pipeline).await?;
content_copy
char * results = korvus_collectionc_rag(collection,
"{\
\"CONTEXT\": {\
\"vector_search\": {\
\"query\": {\
\"fields\": {\
\"text\": {\
\"query\": \"Is Korvus fast?\",\
\"parameters\": {\
\"prompt\": \"Represent this sentence for searching relevant passages: \"\
}\
}\
}\
},\
\"document\": {\"keys\": [\"id\"]},\
\"limit\": 5\
},\
\"aggregate\": {\"join\": \"\\n\"}\
},\
\"chat\": {\
\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\",\
\"messages\": [\
{\
\"role\": \"system\",\
\"content\": \"You are a friendly and helpful chatbot\"\
},\
{\
\"role\": \"user\",\
\"content\": \"Given the context:\\n{CONTEXT}\\nAnswer the question: Is Korvus fast?\"\
}\
],\
\"max_tokens\": 100\
}\
}",
pipeline
);
Let's break this down. rag takes in a JSON object and a Pipeline. The JSON object specifies what queries to run and what prompt to pass to the model.
In the example above, we specify one vector search query that we use to build the CONTEXT. We then specify the {CONTEXT} key in the chat.messages which will be replaced by the results from the CONTEXT search.
For example if the results of the CONTEXT search is a list like:
content_copy
[
"Korvus is super fast",
"One of the benefits of Korvus is it's speed"
]
Then the messages being passed to the model would look like:
content_copy
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:\nKorvus is fast\nOne of the benefits of Koruvs is it's speed\nAnswer the question: Is Korvus fast?",
},
]
For more information on performing vector search see the Vector Search guide.
Note that the vector search returns 5 results. The CONTEXT.vector_search.aggregate key specifies how to combine these 5 results. In this situation, they are joined together with new lines seperating them.
Note that mixedbread-ai/mxbai-embed-large-v1 takes in a prompt when creating embeddings for searching against a corpus which we provide in the LLM_CONTEXT.vector_search.query.fields.text.parameters.
content_copy
const results = await collection.rag(
{
LLM_CONTEXT: {
vector_search: {
query: {
fields: {
text: {
query: "Is Korvus fast?",
parameters: {
prompt: "Represent this sentence for searching relevant passages: "
},
full_text_filter: "Korvus"
}
},
},
document: { "keys": ["id"] },
limit: 5,
},
aggregate: { "join": "\n" },
},
chat: {
model: "meta-llama/Meta-Llama-3.1-8B-Instruct",
messages: [
{
role: "system",
content: "You are a friendly and helpful chatbot",
},
{
role: "user",
content: "Given the context\n:{LLM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
max_tokens: 100,
},
},
pipeline,
)
content_copy
results = await collection.rag(
{
"LLM_CONTEXT": {
"vector_search": {
"query": {
"fields": {
"text": {
"query": "Is Korvus fast?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: "
},
"full_text_filter": "Korvus",
}
},
},
"document": {"keys": ["id"]},
"limit": 5,
},
"aggregate": {"join": "\n"},
},
"chat": {
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:{LLM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
"max_tokens": 100,
},
},
pipeline,
)
content_copy
let results = collection.rag(serde_json::json!(
{
"LLM_CONTEXT": {
"vector_search": {
"query": {
"fields": {
"text": {
"query": "Is Korvus fast?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: "
},
"full_text_filter": "Korvus"
}
},
},
"document": {"keys": ["id"]},
"limit": 5,
},
"aggregate": {"join": "\n"},
},
"chat": {
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:{LLM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
"max_tokens": 100,
},
}
).into(), &mut pipeline).await?;
content_copy
char * results = korvus_collectionc_rag(collection,
"{\
\"LLM_CONTEXT\": {\
\"vector_search\": {\
\"query\": {\
\"fields\": {\
\"text\": {\
\"query\": \"Is Korvus fast?\",\
\"parameters\": {\
\"prompt\": \"Represent this sentence for searching relevant passages: \"\
},\
\"full_text_filter\": \"Korvus\"\
}\
}\
},\
\"document\": {\"keys\": [\"id\"]},\
\"limit\": 5\
},\
\"aggregate\": {\"join\": \"\\n\"}\
},\
\"chat\": {\
\"model\": \"meta-llama/Meta-Llama-3-8B-Instruct\",\
\"messages\": [\
{\
\"role\": \"system\",\
\"content\": \"You are a friendly and helpful chatbot\"\
},\
{\
\"role\": \"user\",\
\"content\": \"Given the context:\\n{LLM_CONTEXT}\\nAnswer the question: Is Korvus fast?\"\
}\
],\
\"max_tokens\": 100\
}\
}",
pipeline
);
This is very similar to the example above but note that we renamed CONTEXT to LLM_CONTEXT this changes nothing. We could call it whatever we want.
The main difference is that we have included the full_text_filter key in the LLM_CONTEXT.vector_search.query.fields.text object. This restricts us from retrieving chunks that do not contain the string Korvus. This utilizes Postgre's full text filter mechanics. For more information see the guide on performing vector search.
Before we pass the results of our LLM_CONTEXT to the LLM, we can rerank them:
content_copy
const results = await collection.rag(
{
LLM_CONTEXT: {
vector_search: {
query: {
fields: {
text: {
query: "Is Korvus fast?",
parameters: {
prompt: "Represent this sentence for searching relevant passages: "
},
full_text_filter: "Korvus"
}
},
},
document: { "keys": ["id"] },
rerank: {
model: "mixedbread-ai/mxbai-rerank-base-v1",
query: "Is Korvus fast?",
num_documents_to_rerank: 100
},
limit: 5,
},
aggregate: { "join": "\n" },
},
chat: {
model: "meta-llama/Meta-Llama-3-8B-Instruct",
messages: [
{
role: "system",
content: "You are a friendly and helpful chatbot",
},
{
role: "user",
content: "Given the context\n:{LLM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
max_tokens: 100,
},
},
pipeline,
)
content_copy
results = await collection.rag(
{
"LLM_CONTEXT": {
"vector_search": {
"query": {
"fields": {
"text": {
"query": "Is Korvus fast?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: "
},
"full_text_filter": "Korvus",
}
},
},
"document": {"keys": ["id"]},
"rerank": {
"model": "mixedbread-ai/mxbai-rerank-base-v1",
"query": "Is Korvus fast?",
"num_documents_to_rerank": 100,
},
"limit": 5,
},
"aggregate": {"join": "\n"},
},
"chat": {
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:{LLM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
"max_tokens": 100,
},
},
pipeline,
)
content_copy
let results = collection.rag(serde_json::json!(
{
"LLM_CONTEXT": {
"vector_search": {
"query": {
"fields": {
"text": {
"query": "Is Korvus fast?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: "
},
"full_text_filter": "Korvus"
}
},
},
"document": {"keys": ["id"]},
"rerank": {
"model": "mixedbread-ai/mxbai-rerank-base-v1",
"query": "Is Korvus fast?",
"num_documents_to_rerank": 100
},
"limit": 5,
},
"aggregate": {"join": "\n"},
},
"chat": {
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:{LLM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
"max_tokens": 100,
},
}
).into(), &mut pipeline).await?;
content_copy
char * results = korvus_collectionc_rag(collection,
"{\
\"LLM_CONTEXT\": {\
\"vector_search\": {\
\"query\": {\
\"fields\": {\
\"text\": {\
\"query\": \"Is Korvus fast?\",\
\"parameters\": {\
\"prompt\": \"Represent this sentence for searching relevant passages: \"\
},\
\"full_text_filter\": \"Korvus\"\
}\
}\
},\
\"document\": {\"keys\": [\"id\"]},\
\"rerank\": {\
\"model\": \"mixedbread-ai/mxbai-rerank-base-v1\",\
\"query\": \"Is Korvus fast?\",\
\"num_documents_to_rerank\": 100\
},\
\"limit\": 5\
},\
\"aggregate\": {\"join\": \"\\n\"}\
},\
\"chat\": {\
\"model\": \"meta-llama/Meta-Llama-3-8B-Instruct\",\
\"messages\": [\
{\
\"role\": \"system\",\
\"content\": \"You are a friendly and helpful chatbot\"\
},\
{\
\"role\": \"user\",\
\"content\": \"Given the context:\\n{LLM_CONTEXT}\\nAnswer the question: Is Korvus fast?\"\
}\
],\
\"max_tokens\": 100\
}\
}",
pipeline
);
This utilizes the re-ranking capabilities found in the vector_search method. For more information check out our guides on Re-ranking and Vector Search.
So far we have only used the CONTEXT or LLM_CONTEXT variables individually for vector search, but we can combine them together or specify a RAW sql query.
content_copy
const results = await collection.rag(
{
LLM_CONTEXT: {
vector_search: {
query: {
fields: {
text: {
query: "Is Korvus fast?",
parameters: {
prompt: "Represent this sentence for searching relevant passages: "
},
full_text_filter: "Korvus"
}
},
},
document: { "keys": ["id"] },
rerank: {
model: "mixedbread-ai/mxbai-rerank-base-v1",
query: "Is Korvus fast?",
num_documents_to_rerank: 100
},
limit: 5,
},
aggregate: { "join": "\n" },
},
CUSTOM_CONTEXT: {sql: "SELECT 'Korvus is super fast!!!'"},
chat: {
model: "meta-llama/Meta-Llama-3-8B-Instruct",
messages: [
{
role: "system",
content: "You are a friendly and helpful chatbot",
},
{
role: "user",
content: "Given the context\n:{LLM_CONTEXT}\n{CUSTOM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
max_tokens: 100,
},
},
pipeline,
)
content_copy
results = await collection.rag(
{
"LLM_CONTEXT": {
"vector_search": {
"query": {
"fields": {
"text": {
"query": "Is Korvus fast?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: "
},
"full_text_filter": "Korvus",
}
},
},
"document": {"keys": ["id"]},
"rerank": {
"model": "mixedbread-ai/mxbai-rerank-base-v1",
"query": "Is Korvus fast?",
"num_documents_to_rerank": 100,
},
"limit": 5,
},
"aggregate": {"join": "\n"},
},
"CUSTOM_CONTEXT": {"sql": "SELECT 'Korvus is super fast!!!'"},
"chat": {
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:{LLM_CONTEXT}\n{CUSTOM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
"max_tokens": 100,
},
},
pipeline,
)
content_copy
let results = collection.rag(serde_json::json!(
{
"LLM_CONTEXT": {
"vector_search": {
"query": {
"fields": {
"text": {
"query": "Is Korvus fast?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: "
},
"full_text_filter": "Korvus"
}
},
},
"document": {"keys": ["id"]},
"rerank": {
"model": "mixedbread-ai/mxbai-rerank-base-v1",
"query": "Is Korvus fast?",
"num_documents_to_rerank": 100,
},
"limit": 1,
},
"aggregate": {"join": "\n"},
},
"CUSTOM_CONTEXT": {"sql": "SELECT 'Korvus is super fast!!!'"},
"chat": {
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a friendly and helpful chatbot",
},
{
"role": "user",
"content": "Given the context\n:{LLM_CONTEXT}\n{CUSTOM_CONTEXT}\nAnswer the question: Is Korvus fast?",
},
],
"max_tokens": 100,
},
}
).into(), &mut pipeline).await?;
content_copy
char * results = korvus_collectionc_rag(collection,
"{\
\"LLM_CONTEXT\": {\
\"vector_search\": {\
\"query\": {\
\"fields\": {\
\"text\": {\
\"query\": \"Is Korvus fast?\",\
\"parameters\": {\
\"prompt\": \"Represent this sentence for searching relevant passages: \"\
},\
\"full_text_filter\": \"Korvus\"\
}\
}\
},\
\"document\": {\"keys\": [\"id\"]},\
\"rerank\": {\
\"model\": \"mixedbread-ai/mxbai-rerank-base-v1\",\
\"query\": \"Is Korvus fast?\",\
\"num_documents_to_rerank\": 100\
},\
\"limit\": 1\
},\
\"aggregate\": {\"join\": \"\\n\"}\
},\
\"CUSTOM_CONTEXT\": {\"sql\": \"SELECT 'Korvus is super fast!!!'\"},\
\"chat\": {\
\"model\": \"meta-llama/Meta-Llama-3-8B-Instruct\",\
\"messages\": [\
{\
\"role\": \"system\",\
\"content\": \"You are a friendly and helpful chatbot\"\
},\
{\
\"role\": \"user\",\
\"content\": \"Given the context:\\n{LLM_CONTEXT}\\n\\n{CUSTOM_CONTEXT}\\nAnswer the question: Is Korvus fast?\"\
}\
],\
\"max_tokens\": 100\
}\
}",
pipeline
);
By specifying the sql key instead of vector_search in CUSTOM_CONTEXT we are performing a raw SQL query. In this case we are selecting the text Korvus is super fast!!! but you can perform any sql query that returns a string.
Just like the LLM_CONTEXT key, the result of the CUSTOM_CONTEXTquery will replace the {CUSTOM_CONTEXT} placeholder in the messages.
PostgresML