Added hybrid search example [skip ci]

Author: ankane

README.md

@@ -37,6 +37,7 @@ Or check out some examples:
 
 - [Embeddings](examples/openai/example.cpp) with OpenAI
 - [Binary embeddings](examples/cohere/example.cpp) with Cohere
+- [Hybrid search](examples/hybrid/example.cpp) with llama.cpp (Reciprocal Rank Fusion)
 - [Sparse search](examples/sparse/example.cpp) with Text Embeddings Inference
 - [Morgan fingerprints](examples/rdkit/example.cpp) with RDKit
 - [Recommendations](examples/disco/example.cpp) with Disco

examples/hybrid/CMakeLists.txt

@@ -0,0 +1,20 @@
+cmake_minimum_required(VERSION 3.18)
+
+project(example)
+
+set(CMAKE_CXX_STANDARD 17)
+
+# for libpqxx
+set(CMAKE_CXX_FLAGS "-Wno-unknown-attributes")
+set(SKIP_BUILD_TEST ON)
+
+include(FetchContent)
+
+FetchContent_Declare(cpr GIT_REPOSITORY https://github.com/libcpr/cpr.git GIT_TAG 1.11.1)
+FetchContent_Declare(json GIT_REPOSITORY https://github.com/nlohmann/json.git GIT_TAG v3.11.3)
+FetchContent_Declare(libpqxx GIT_REPOSITORY https://github.com/jtv/libpqxx.git GIT_TAG 7.10.0)
+FetchContent_MakeAvailable(cpr json libpqxx)
+
+add_executable(example example.cpp)
+target_include_directories(example PRIVATE ${CMAKE_SOURCE_DIR}/../../include)
+target_link_libraries(example PRIVATE cpr::cpr libpqxx::pqxx nlohmann_json::nlohmann_json)

examples/hybrid/example.cpp

@@ -0,0 +1,88 @@
+// run with
+// llama-server -hf nomic-ai/nomic-embed-text-v1.5-GGUF --embedding --pooling mean
+
+#include <cstdint>
+#include <iostream>
+
+#include <cpr/cpr.h>
+#include <nlohmann/json.hpp>
+#include <pgvector/pqxx.hpp>
+#include <pqxx/pqxx>
+
+using json = nlohmann::json;
+
+std::vector<std::vector<float>> fetch_embeddings(const std::vector<std::string>& input) {
+    std::string url = "http://localhost:8080/v1/embeddings";
+    json data = {
+        {"input", input}
+    };
+
+    cpr::Response r = cpr::Post(
+        cpr::Url{url},
+        cpr::Body{data.dump()},
+        cpr::Header{{"Content-Type", "application/json"}}
+    );
+    json response = json::parse(r.text);
+
+    std::vector<std::vector<float>> embeddings;
+    for (auto& v: response["data"]) {
+        embeddings.emplace_back(v["embedding"]);
+    }
+    return embeddings;
+}
+
+int main() {
+    pqxx::connection conn("dbname=pgvector_example");
+
+    pqxx::work tx(conn);
+    tx.exec("CREATE EXTENSION IF NOT EXISTS vector");
+    tx.exec("DROP TABLE IF EXISTS documents");
+    tx.exec("CREATE TABLE documents (id bigserial PRIMARY KEY, content text, embedding vector(768))");
+    tx.exec("CREATE INDEX ON documents USING GIN (to_tsvector('english', content))");
+    tx.commit();
+
+    std::vector<std::string> input = {
+        "The dog is barking",
+        "The cat is purring",
+        "The bear is growling"
+    };
+    auto embeddings = fetch_embeddings(input);
+
+    for (size_t i = 0; i < input.size(); i++) {
+        tx.exec("INSERT INTO documents (content, embedding) VALUES ($1, $2)", pqxx::params{input[i], pgvector::Vector(embeddings[i])});
+    }
+    tx.commit();
+
+    std::string sql = R"(
+    WITH semantic_search AS (
+        SELECT id, RANK () OVER (ORDER BY embedding <=> $2) AS rank
+        FROM documents
+        ORDER BY embedding <=> $2
+        LIMIT 20
+    ),
+    keyword_search AS (
+        SELECT id, RANK () OVER (ORDER BY ts_rank_cd(to_tsvector('english', content), query) DESC)
+        FROM documents, plainto_tsquery('english', $1) query
+        WHERE to_tsvector('english', content) @@ query
+        ORDER BY ts_rank_cd(to_tsvector('english', content), query) DESC
+        LIMIT 20
+    )
+    SELECT
+        COALESCE(semantic_search.id, keyword_search.id) AS id,
+        COALESCE(1.0 / ($3 + semantic_search.rank), 0.0) +
+        COALESCE(1.0 / ($3 + keyword_search.rank), 0.0) AS score
+    FROM semantic_search
+    FULL OUTER JOIN keyword_search ON semantic_search.id = keyword_search.id
+    ORDER BY score DESC
+    LIMIT 5
+    )";
+    std::string query = "growling bear";
+    auto query_embedding = fetch_embeddings({query})[0];
+    double k = 60;
+    pqxx::result result = tx.exec(sql, pqxx::params{query, pgvector::Vector(query_embedding), k});
+    for (const auto& row : result) {
+        std::cout << "document: " << row[0].as<std::string>() << ", RRF score: " << row[1].as<double>() << std::endl;
+    }
+
+    return 0;
+}