diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index b65da281..3a58d0ed 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -4,7 +4,7 @@ name: Lint & Test
 on:
   pull_request:
     branches:
-      - main
+      - "**"
   workflow_call:
 
 env:
diff --git a/Cargo.lock b/Cargo.lock
index aefa6a32..2d778dde 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -1,6 +1,6 @@
 # This file is automatically @generated by Cargo.
 # It is not intended for manual editing.
-version = 4
+version = 3
 
 [[package]]
 name = "addr2line"
@@ -42,9 +42,9 @@ dependencies = [
 
 [[package]]
 name = "allocator-api2"
-version = "0.2.18"
+version = "0.2.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5c6cb57a04249c6480766f7f7cef5467412af1490f8d1e243141daddada3264f"
+checksum = "45862d1c77f2228b9e10bc609d5bc203d86ebc9b87ad8d5d5167a6c9abf739d9"
 
 [[package]]
 name = "android-tzdata"
@@ -63,15 +63,15 @@ dependencies = [
 
 [[package]]
 name = "anstyle"
-version = "1.0.8"
+version = "1.0.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1bec1de6f59aedf83baf9ff929c98f2ad654b97c9510f4e70cf6f661d49fd5b1"
+checksum = "55cc3b69f167a1ef2e161439aa98aed94e6028e5f9a59be9a6ffb47aef1651f9"
 
 [[package]]
 name = "anyhow"
-version = "1.0.89"
+version = "1.0.93"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "86fdf8605db99b54d3cd748a44c6d04df638eb5dafb219b135d0149bd0db01f6"
+checksum = "4c95c10ba0b00a02636238b814946408b1322d5ac4760326e6fb8ec956d85775"
 
 [[package]]
 name = "arc-swap"
@@ -361,7 +361,7 @@ checksum = "3b43422f69d8ff38f95f1b2bb76517c91589a924d1559a0e935d7c8ce0274c11"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -383,7 +383,7 @@ checksum = "c7c24de15d275a1ecfd47a380fb4d5ec9bfe0933f309ed5e705b775596a3574d"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -394,7 +394,7 @@ checksum = "721cae7de5c34fbb2acd27e21e6d2cf7b886dce0c27388d46c4e6c47ea4318dd"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -426,9 +426,9 @@ checksum = "ace50bade8e6234aa140d9a2f552bbee1db4d353f69b8217bc503490fc1a9f26"
 
 [[package]]
 name = "aws-config"
-version = "1.5.8"
+version = "1.5.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7198e6f03240fdceba36656d8be440297b6b82270325908c7381f37d826a74f6"
+checksum = "9b49afaa341e8dd8577e1a2200468f98956d6eda50bcf4a53246cc00174ba924"
 dependencies = [
  "aws-credential-types",
  "aws-runtime",
@@ -443,7 +443,7 @@ dependencies = [
  "aws-smithy-types",
  "aws-types",
  "bytes",
- "fastrand 2.1.1",
+ "fastrand 2.2.0",
  "hex",
  "http 0.2.12",
  "ring",
@@ -481,7 +481,7 @@ dependencies = [
  "aws-smithy-types",
  "aws-types",
  "bytes",
- "fastrand 2.1.1",
+ "fastrand 2.2.0",
  "http 0.2.12",
  "http-body 0.4.6",
  "once_cell",
@@ -493,9 +493,9 @@ dependencies = [
 
 [[package]]
 name = "aws-sdk-dynamodb"
-version = "1.49.0"
+version = "1.54.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ab0ade000608877169533a54326badd6b5a707d2faf876cfc3976a7f9d7e5329"
+checksum = "8efdda6a491bb4640d35b99b0a4b93f75ce7d6e3a1937c3e902d3cb23d0a179c"
 dependencies = [
  "aws-credential-types",
  "aws-runtime",
@@ -507,7 +507,7 @@ dependencies = [
  "aws-smithy-types",
  "aws-types",
  "bytes",
- "fastrand 2.1.1",
+ "fastrand 2.2.0",
  "http 0.2.12",
  "once_cell",
  "regex-lite",
@@ -516,9 +516,9 @@ dependencies = [
 
 [[package]]
 name = "aws-sdk-sso"
-version = "1.45.0"
+version = "1.49.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e33ae899566f3d395cbf42858e433930682cc9c1889fa89318896082fef45efb"
+checksum = "09677244a9da92172c8dc60109b4a9658597d4d298b188dd0018b6a66b410ca4"
 dependencies = [
  "aws-credential-types",
  "aws-runtime",
@@ -538,9 +538,9 @@ dependencies = [
 
 [[package]]
 name = "aws-sdk-ssooidc"
-version = "1.46.0"
+version = "1.50.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f39c09e199ebd96b9f860b0fce4b6625f211e064ad7c8693b72ecf7ef03881e0"
+checksum = "81fea2f3a8bb3bd10932ae7ad59cc59f65f270fc9183a7e91f501dc5efbef7ee"
 dependencies = [
  "aws-credential-types",
  "aws-runtime",
@@ -560,9 +560,9 @@ dependencies = [
 
 [[package]]
 name = "aws-sdk-sts"
-version = "1.45.0"
+version = "1.50.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3d95f93a98130389eb6233b9d615249e543f6c24a68ca1f109af9ca5164a8765"
+checksum = "6ada54e5f26ac246dc79727def52f7f8ed38915cb47781e2a72213957dc3a7d5"
 dependencies = [
  "aws-credential-types",
  "aws-runtime",
@@ -583,9 +583,9 @@ dependencies = [
 
 [[package]]
 name = "aws-sigv4"
-version = "1.2.4"
+version = "1.2.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "cc8db6904450bafe7473c6ca9123f88cc11089e41a025408f992db4e22d3be68"
+checksum = "5619742a0d8f253be760bfbb8e8e8368c69e3587e4637af5754e488a611499b1"
 dependencies = [
  "aws-credential-types",
  "aws-smithy-http",
@@ -656,22 +656,22 @@ dependencies = [
 
 [[package]]
 name = "aws-smithy-runtime"
-version = "1.7.2"
+version = "1.7.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a065c0fe6fdbdf9f11817eb68582b2ab4aff9e9c39e986ae48f7ec576c6322db"
+checksum = "be28bd063fa91fd871d131fc8b68d7cd4c5fa0869bea68daca50dcb1cbd76be2"
 dependencies = [
  "aws-smithy-async",
  "aws-smithy-http",
  "aws-smithy-runtime-api",
  "aws-smithy-types",
  "bytes",
- "fastrand 2.1.1",
+ "fastrand 2.2.0",
  "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
  "http-body 1.0.1",
  "httparse",
- "hyper 0.14.30",
+ "hyper 0.14.31",
  "hyper-rustls 0.24.2",
  "once_cell",
  "pin-project-lite",
@@ -683,9 +683,9 @@ dependencies = [
 
 [[package]]
 name = "aws-smithy-runtime-api"
-version = "1.7.2"
+version = "1.7.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e086682a53d3aa241192aa110fa8dfce98f2f5ac2ead0de84d41582c7e8fdb96"
+checksum = "92165296a47a812b267b4f41032ff8069ab7ff783696d217f0994a0d7ab585cd"
 dependencies = [
  "aws-smithy-async",
  "aws-smithy-types",
@@ -700,9 +700,9 @@ dependencies = [
 
 [[package]]
 name = "aws-smithy-types"
-version = "1.2.7"
+version = "1.2.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "147100a7bea70fa20ef224a6bad700358305f5dc0f84649c53769761395b355b"
+checksum = "4fbd94a32b3a7d55d3806fe27d98d3ad393050439dd05eb53ece36ec5e3d3510"
 dependencies = [
  "base64-simd",
  "bytes",
@@ -749,9 +749,9 @@ dependencies = [
 
 [[package]]
 name = "axum"
-version = "0.7.7"
+version = "0.7.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "504e3947307ac8326a5437504c517c4b56716c9d98fac0028c2acc7ca47d70ae"
+checksum = "edca88bc138befd0323b20752846e6587272d3b03b0343c8ea28a6f819e6e71f"
 dependencies = [
  "async-trait",
  "axum-core",
@@ -768,7 +768,7 @@ dependencies = [
  "pin-project-lite",
  "rustversion",
  "serde",
- "sync_wrapper 1.0.1",
+ "sync_wrapper 1.0.2",
  "tower 0.5.1",
  "tower-layer",
  "tower-service",
@@ -789,7 +789,7 @@ dependencies = [
  "mime",
  "pin-project-lite",
  "rustversion",
- "sync_wrapper 1.0.1",
+ "sync_wrapper 1.0.2",
  "tower-layer",
  "tower-service",
 ]
@@ -916,9 +916,9 @@ dependencies = [
 
 [[package]]
 name = "bstr"
-version = "1.10.0"
+version = "1.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "40723b8fb387abc38f4f4a37c09073622e41dd12327033091ef8950659e6dc0c"
+checksum = "1a68f1f47cdf0ec8ee4b941b2eee2a80cb796db73118c0dd09ac63fbe405be22"
 dependencies = [
  "memchr",
  "serde",
@@ -938,9 +938,9 @@ checksum = "5ce89b21cab1437276d2650d57e971f9d548a2d9037cc231abdc0562b97498ce"
 
 [[package]]
 name = "bytemuck"
-version = "1.18.0"
+version = "1.20.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "94bbb0ad554ad961ddc5da507a12a29b14e4ae5bda06b19f575a3e6079d2e2ae"
+checksum = "8b37c88a63ffd85d15b406896cc343916d7cf57838a847b3a6f2ca5d39a5695a"
 
 [[package]]
 name = "byteorder"
@@ -950,9 +950,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
 
 [[package]]
 name = "bytes"
-version = "1.7.2"
+version = "1.8.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "428d9aa8fbc0670b7b8d6030a7fadd0f86151cae55e4dbbece15f3780a3dfaf3"
+checksum = "9ac0150caa2ae65ca5bd83f25c7de183dea78d4d366469f148435e2acfbad0da"
 dependencies = [
  "serde",
 ]
@@ -1000,9 +1000,9 @@ dependencies = [
 
 [[package]]
 name = "cc"
-version = "1.1.28"
+version = "1.2.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2e80e3b6a3ab07840e1cae9b0666a63970dc28e8ed5ffbcdacbfc760c281bfc1"
+checksum = "fd9de9f2205d5ef3fd67e685b0df337994ddd4495e2a28d185500d0e1edfea47"
 dependencies = [
  "jobserver",
  "libc",
@@ -1021,6 +1021,12 @@ version = "1.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
 
+[[package]]
+name = "cfg_aliases"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724"
+
 [[package]]
 name = "chrono"
 version = "0.4.38"
@@ -1082,13 +1088,13 @@ dependencies = [
 
 [[package]]
 name = "comfy-table"
-version = "7.1.1"
+version = "7.1.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b34115915337defe99b2aff5c2ce6771e5fbc4079f4b506301f5cf394c8452f7"
+checksum = "24f165e7b643266ea80cb858aed492ad9280e3e05ce24d4a99d7d7b889b6a4d9"
 dependencies = [
  "strum",
  "strum_macros",
- "unicode-width",
+ "unicode-width 0.2.0",
 ]
 
 [[package]]
@@ -1136,6 +1142,16 @@ dependencies = [
  "libc",
 ]
 
+[[package]]
+name = "core-foundation"
+version = "0.10.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b55271e5c8c478ad3f38ad24ef34923091e0548492a266d19b3c0b4d82574c63"
+dependencies = [
+ "core-foundation-sys",
+ "libc",
+]
+
 [[package]]
 name = "core-foundation-sys"
 version = "0.8.7"
@@ -1144,9 +1160,9 @@ checksum = "773648b94d0e5d620f64f280777445740e61fe701025087ec8b57f45c791888b"
 
 [[package]]
 name = "cpufeatures"
-version = "0.2.14"
+version = "0.2.16"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "608697df725056feaccfa42cffdaeeec3fccc4ffc38358ecd19b243e716a78e0"
+checksum = "16b80225097f2e5ae4e7179dd2266824648f3e2f49d9134d584b76389d31c4c3"
 dependencies = [
  "libc",
 ]
@@ -1221,9 +1237,9 @@ dependencies = [
 
 [[package]]
 name = "csv"
-version = "1.3.0"
+version = "1.3.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ac574ff4d437a7b5ad237ef331c17ccca63c46479e5b5453eb8e10bb99a759fe"
+checksum = "acdc4883a9c96732e4733212c01447ebd805833b7275a73ca3ee080fd77afdaf"
 dependencies = [
  "csv-core",
  "itoa",
@@ -1285,7 +1301,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "strsim 0.11.1",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -1307,7 +1323,7 @@ checksum = "d336a2a514f6ccccaa3e09b02d41d35330c07ddf03a62165fcec10bb561c7806"
 dependencies = [
  "darling_core 0.20.10",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -1711,7 +1727,7 @@ dependencies = [
  "darling 0.20.10",
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -1721,7 +1737,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ab63b0e2bf4d5928aff72e83a7dace85d7bba5fe12dcc3c5a572d78caffd3f3c"
 dependencies = [
  "derive_builder_core",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -1734,7 +1750,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustc_version 0.4.1",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -1775,6 +1791,17 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "displaydoc"
+version = "0.2.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "97369cbbc041bc366949bc74d34658d6cda5621039731c6310521892a3a20ae0"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.89",
+]
+
 [[package]]
 name = "doc-comment"
 version = "0.3.3"
@@ -1801,9 +1828,9 @@ checksum = "60b1af1c220855b6ceac025d3f6ecdd2b7c4894bfe9cd9bda4fbb4bc7c0d4cf0"
 
 [[package]]
 name = "encoding_rs"
-version = "0.8.34"
+version = "0.8.35"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b45de904aa0b010bce2ab45264d0631681847fa7b6f2eaa7dab7619943bc4f59"
+checksum = "75030f3c4f45dafd7586dd6780965a8c7e8e285a5ecb86713e63a79c5b2766f3"
 dependencies = [
  "cfg-if",
 ]
@@ -1864,9 +1891,9 @@ dependencies = [
 
 [[package]]
 name = "fastdivide"
-version = "0.4.1"
+version = "0.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "59668941c55e5c186b8b58c391629af56774ec768f73c08bbcd56f09348eb00b"
+checksum = "9afc2bd4d5a73106dd53d10d73d3401c2f32730ba2c0b93ddb888a8983680471"
 
 [[package]]
 name = "fastrand"
@@ -1879,9 +1906,9 @@ dependencies = [
 
 [[package]]
 name = "fastrand"
-version = "2.1.1"
+version = "2.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e8c02a5121d4ea3eb16a80748c74f5549a5665e4c21333c6098f283870fbdea6"
+checksum = "486f806e73c5707928240ddc295403b1b93c96a02038563881c4a2fd84b81ac4"
 
 [[package]]
 name = "fixedbitset"
@@ -1901,9 +1928,9 @@ dependencies = [
 
 [[package]]
 name = "flate2"
-version = "1.0.34"
+version = "1.0.35"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a1b589b4dc103969ad3cf85c950899926ec64300a1a46d76c03a6072957036f0"
+checksum = "c936bfdafb507ebbf50b8074c54fa31c5be9a1e7e5f467dd659697041407d07c"
 dependencies = [
  "crc32fast",
  "miniz_oxide",
@@ -1951,7 +1978,7 @@ version = "0.8.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "f7e180ac76c23b45e767bd7ae9579bc0bb458618c4bc71835926e098e61d15f8"
 dependencies = [
- "rustix 0.38.37",
+ "rustix 0.38.41",
  "windows-sys 0.52.0",
 ]
 
@@ -2041,7 +2068,7 @@ checksum = "162ee34ebcb7c64a8abebc059ce0fee27c2262618d7b60ed8faf72fef13c3650"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -2154,9 +2181,9 @@ dependencies = [
 
 [[package]]
 name = "h2"
-version = "0.4.6"
+version = "0.4.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "524e8ac6999421f49a846c2d4411f337e53497d8ec55d67753beffa43c5d9205"
+checksum = "ccae279728d634d083c00f6099cb58f01cc99c145b84b8be2f6c74618d79922e"
 dependencies = [
  "atomic-waker",
  "bytes",
@@ -2200,9 +2227,9 @@ dependencies = [
 
 [[package]]
 name = "hashbrown"
-version = "0.15.0"
+version = "0.15.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1e087f84d4f86bf4b218b927129862374b72199ae7d8657835f1e89000eea4fb"
+checksum = "3a9bfc1af68b1726ea47d3d5109de126281def866b33970e10fbab11b5dafab3"
 dependencies = [
  "allocator-api2",
  "equivalent",
@@ -2335,9 +2362,9 @@ checksum = "9a3a5bfb195931eeb336b2a7b4d761daec841b97f947d34394601737a7bba5e4"
 
 [[package]]
 name = "hyper"
-version = "0.14.30"
+version = "0.14.31"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a152ddd61dfaec7273fe8419ab357f33aee0d914c5f4efbf0d96fa749eea5ec9"
+checksum = "8c08302e8fa335b151b788c775ff56e7a03ae64ff85c548ee820fecb70356e85"
 dependencies = [
  "bytes",
  "futures-channel",
@@ -2359,14 +2386,14 @@ dependencies = [
 
 [[package]]
 name = "hyper"
-version = "1.4.1"
+version = "1.5.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "50dfd22e0e76d0f662d429a5f80fcaf3855009297eab6a0a9f8543834744ba05"
+checksum = "97818827ef4f364230e16705d4706e2897df2bb60617d6ca15d598025a3c481f"
 dependencies = [
  "bytes",
  "futures-channel",
  "futures-util",
- "h2 0.4.6",
+ "h2 0.4.7",
  "http 1.1.0",
  "http-body 1.0.1",
  "httparse",
@@ -2386,7 +2413,7 @@ checksum = "ec3efd23720e2049821a693cbc7e65ea87c72f1c58ff2f9522ff332b1491e590"
 dependencies = [
  "futures-util",
  "http 0.2.12",
- "hyper 0.14.30",
+ "hyper 0.14.31",
  "log",
  "rustls 0.21.12",
  "rustls-native-certs 0.6.3",
@@ -2402,15 +2429,15 @@ checksum = "08afdbb5c31130e3034af566421053ab03787c640246a446327f550d11bcb333"
 dependencies = [
  "futures-util",
  "http 1.1.0",
- "hyper 1.4.1",
+ "hyper 1.5.1",
  "hyper-util",
- "rustls 0.23.14",
- "rustls-native-certs 0.8.0",
+ "rustls 0.23.18",
+ "rustls-native-certs 0.8.1",
  "rustls-pki-types",
  "tokio",
  "tokio-rustls 0.26.0",
  "tower-service",
- "webpki-roots 0.26.6",
+ "webpki-roots 0.26.7",
 ]
 
 [[package]]
@@ -2419,7 +2446,7 @@ version = "0.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "2b90d566bffbce6a75bd8b09a05aa8c2cb1fabb6cb348f8840c9e4c90a0d83b0"
 dependencies = [
- "hyper 1.4.1",
+ "hyper 1.5.1",
  "hyper-util",
  "pin-project-lite",
  "tokio",
@@ -2433,7 +2460,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d6183ddfa99b85da61a140bea0efc93fdf56ceaa041b37d553518030827f9905"
 dependencies = [
  "bytes",
- "hyper 0.14.30",
+ "hyper 0.14.31",
  "native-tls",
  "tokio",
  "tokio-native-tls",
@@ -2450,7 +2477,7 @@ dependencies = [
  "futures-util",
  "http 1.1.0",
  "http-body 1.0.1",
- "hyper 1.4.1",
+ "hyper 1.5.1",
  "pin-project-lite",
  "socket2 0.5.7",
  "tokio",
@@ -2490,6 +2517,124 @@ dependencies = [
  "cc",
 ]
 
+[[package]]
+name = "icu_collections"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "db2fa452206ebee18c4b5c2274dbf1de17008e874b4dc4f0aea9d01ca79e4526"
+dependencies = [
+ "displaydoc",
+ "yoke",
+ "zerofrom",
+ "zerovec",
+]
+
+[[package]]
+name = "icu_locid"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "13acbb8371917fc971be86fc8057c41a64b521c184808a698c02acc242dbf637"
+dependencies = [
+ "displaydoc",
+ "litemap",
+ "tinystr",
+ "writeable",
+ "zerovec",
+]
+
+[[package]]
+name = "icu_locid_transform"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "01d11ac35de8e40fdeda00d9e1e9d92525f3f9d887cdd7aa81d727596788b54e"
+dependencies = [
+ "displaydoc",
+ "icu_locid",
+ "icu_locid_transform_data",
+ "icu_provider",
+ "tinystr",
+ "zerovec",
+]
+
+[[package]]
+name = "icu_locid_transform_data"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fdc8ff3388f852bede6b579ad4e978ab004f139284d7b28715f773507b946f6e"
+
+[[package]]
+name = "icu_normalizer"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "19ce3e0da2ec68599d193c93d088142efd7f9c5d6fc9b803774855747dc6a84f"
+dependencies = [
+ "displaydoc",
+ "icu_collections",
+ "icu_normalizer_data",
+ "icu_properties",
+ "icu_provider",
+ "smallvec",
+ "utf16_iter",
+ "utf8_iter",
+ "write16",
+ "zerovec",
+]
+
+[[package]]
+name = "icu_normalizer_data"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f8cafbf7aa791e9b22bec55a167906f9e1215fd475cd22adfcf660e03e989516"
+
+[[package]]
+name = "icu_properties"
+version = "1.5.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "93d6020766cfc6302c15dbbc9c8778c37e62c14427cb7f6e601d849e092aeef5"
+dependencies = [
+ "displaydoc",
+ "icu_collections",
+ "icu_locid_transform",
+ "icu_properties_data",
+ "icu_provider",
+ "tinystr",
+ "zerovec",
+]
+
+[[package]]
+name = "icu_properties_data"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "67a8effbc3dd3e4ba1afa8ad918d5684b8868b3b26500753effea8d2eed19569"
+
+[[package]]
+name = "icu_provider"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6ed421c8a8ef78d3e2dbc98a973be2f3770cb42b606e3ab18d6237c4dfde68d9"
+dependencies = [
+ "displaydoc",
+ "icu_locid",
+ "icu_provider_macros",
+ "stable_deref_trait",
+ "tinystr",
+ "writeable",
+ "yoke",
+ "zerofrom",
+ "zerovec",
+]
+
+[[package]]
+name = "icu_provider_macros"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1ec89e9337638ecdc08744df490b221a7399bf8d164eb52a665454e60e075ad6"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.89",
+]
+
 [[package]]
 name = "ident_case"
 version = "1.0.1"
@@ -2509,12 +2654,23 @@ dependencies = [
 
 [[package]]
 name = "idna"
-version = "0.5.0"
+version = "1.0.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "634d9b1461af396cad843f47fdba5597a4f9e6ddd4bfb6ff5d85028c25cb12f6"
+checksum = "686f825264d630750a544639377bae737628043f20d38bbc029e8f29ea968a7e"
 dependencies = [
- "unicode-bidi",
- "unicode-normalization",
+ "idna_adapter",
+ "smallvec",
+ "utf8_iter",
+]
+
+[[package]]
+name = "idna_adapter"
+version = "1.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "daca1df1c957320b2cf139ac61e7bd64fed304c5040df000a745aa1de3b4ef71"
+dependencies = [
+ "icu_normalizer",
+ "icu_properties",
 ]
 
 [[package]]
@@ -2551,10 +2707,16 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "707907fe3c25f5424cce2cb7e1cbcafee6bdbe735ca90ef77c29e84591e5b9da"
 dependencies = [
  "equivalent",
- "hashbrown 0.15.0",
+ "hashbrown 0.15.1",
  "serde",
 ]
 
+[[package]]
+name = "indoc"
+version = "2.0.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b248f5224d1d606005e02c97f5aa4e88eeb230488bcc03bc9ca4d7991399f2b5"
+
 [[package]]
 name = "instant"
 version = "0.1.13"
@@ -2616,9 +2778,9 @@ dependencies = [
 
 [[package]]
 name = "itoa"
-version = "1.0.11"
+version = "1.0.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b"
+checksum = "540654e97a3f4470a492cd30ff187bc95d89557a903a2bbf112e2fae98104ef2"
 
 [[package]]
 name = "jobserver"
@@ -2631,9 +2793,9 @@ dependencies = [
 
 [[package]]
 name = "js-sys"
-version = "0.3.71"
+version = "0.3.72"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0cb94a0ffd3f3ee755c20f7d8752f45cac88605a4dcf808abcff72873296ec7b"
+checksum = "6a88f1bda2bd75b0452a14784937d796722fdebfe50df998aeb3f0b7603019a9"
 dependencies = [
  "wasm-bindgen",
 ]
@@ -3141,15 +3303,15 @@ dependencies = [
 
 [[package]]
 name = "libc"
-version = "0.2.159"
+version = "0.2.164"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "561d97a539a36e26a9a5fad1ea11a3039a67714694aaa379433e580854bc3dc5"
+checksum = "433bfe06b8c75da9b2e3fbea6e5329ff87748f0b144ef75306e674c3f6f7c13f"
 
 [[package]]
 name = "libm"
-version = "0.2.8"
+version = "0.2.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058"
+checksum = "8355be11b20d696c8f18f6cc018c4e372165b1fa8126cef092399c9951984ffa"
 
 [[package]]
 name = "libredox"
@@ -3179,6 +3341,12 @@ version = "0.4.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "78b3ae25bc7c8c38cec158d1f2757ee79e9b3740fbc7ccf0e59e4b08d793fa89"
 
+[[package]]
+name = "litemap"
+version = "0.7.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4ee93343901ab17bd981295f2cf0026d4ad018c7c31ba84549a4ddbb47a45104"
+
 [[package]]
 name = "lock_api"
 version = "0.4.12"
@@ -3221,7 +3389,7 @@ version = "0.12.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "234cf4f4a04dc1f57e24b96cc0cd600cf2af460d4161ac5ecdd0af8e1f3b2a38"
 dependencies = [
- "hashbrown 0.15.0",
+ "hashbrown 0.15.1",
 ]
 
 [[package]]
@@ -3366,7 +3534,7 @@ dependencies = [
  "skeptic",
  "smallvec",
  "tagptr",
- "thiserror",
+ "thiserror 1.0.69",
  "triomphe",
  "uuid",
 ]
@@ -3407,7 +3575,7 @@ dependencies = [
  "stringprep",
  "strsim 0.10.0",
  "take_mut",
- "thiserror",
+ "thiserror 1.0.69",
  "tokio",
  "tokio-rustls 0.24.1",
  "tokio-util",
@@ -3442,7 +3610,7 @@ dependencies = [
  "openssl-probe",
  "openssl-sys",
  "schannel",
- "security-framework",
+ "security-framework 2.11.1",
  "security-framework-sys",
  "tempfile",
 ]
@@ -3468,11 +3636,11 @@ dependencies = [
  "rustls-native-certs 0.7.3",
  "rustls-pemfile 2.2.0",
  "serde",
- "thiserror",
+ "thiserror 1.0.69",
  "tokio",
  "tokio-rustls 0.26.0",
  "url",
- "webpki-roots 0.26.6",
+ "webpki-roots 0.26.7",
 ]
 
 [[package]]
@@ -3482,7 +3650,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "53a0d57c55d2d1dc62a2b1d16a0a1079eb78d67c36bdf468d582ab4482ec7002"
 dependencies = [
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -3616,14 +3784,14 @@ dependencies = [
  "chrono",
  "futures",
  "humantime",
- "hyper 1.4.1",
+ "hyper 1.5.1",
  "itertools 0.13.0",
  "md-5",
  "parking_lot",
  "percent-encoding",
  "quick-xml",
  "rand",
- "reqwest 0.12.8",
+ "reqwest 0.12.9",
  "ring",
  "rustls-pemfile 2.2.0",
  "serde",
@@ -3649,9 +3817,9 @@ checksum = "e296cf87e61c9cfc1a61c3c63a0f7f286ed4554e0e22be84e8a38e1d264a2a29"
 
 [[package]]
 name = "openssl"
-version = "0.10.66"
+version = "0.10.68"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9529f4786b70a3e8c61e11179af17ab6188ad8d0ded78c5529441ed39d4bd9c1"
+checksum = "6174bc48f102d208783c2c84bf931bb75927a617866870de8a4ea85597f871f5"
 dependencies = [
  "bitflags 2.6.0",
  "cfg-if",
@@ -3670,7 +3838,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -3681,9 +3849,9 @@ checksum = "ff011a302c396a5197692431fc1948019154afc178baf7d8e37367442a4601cf"
 
 [[package]]
 name = "openssl-sys"
-version = "0.9.103"
+version = "0.9.104"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7f9e8deee91df40a943c71b917e5874b951d32a802526c85721ce3b776c929d6"
+checksum = "45abf306cbf99debc8195b66b7346498d7b10c210de50418b5ccd7ceba08c741"
 dependencies = [
  "cc",
  "libc",
@@ -3699,9 +3867,9 @@ checksum = "04744f49eae99ab78e0d5c0b603ab218f515ea8cfe5a456d7629ad883a3b6e7d"
 
 [[package]]
 name = "ordered-float"
-version = "4.3.0"
+version = "4.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "44d501f1a72f71d3c063a6bbc8f7271fa73aa09fe5d6283b6571e2ed176a2537"
+checksum = "c65ee1f9701bf938026630b455d5315f490640234259037edb259798b3bcf85e"
 dependencies = [
  "num-traits",
 ]
@@ -3848,29 +4016,29 @@ dependencies = [
 
 [[package]]
 name = "pin-project"
-version = "1.1.6"
+version = "1.1.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "baf123a161dde1e524adf36f90bc5d8d3462824a9c43553ad07a8183161189ec"
+checksum = "be57f64e946e500c8ee36ef6331845d40a93055567ec57e8fae13efd33759b95"
 dependencies = [
  "pin-project-internal",
 ]
 
 [[package]]
 name = "pin-project-internal"
-version = "1.1.6"
+version = "1.1.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a4502d8515ca9f32f1fb543d987f63d95a14934883db45bdb48060b6b69257f8"
+checksum = "3c0f5fad0874fc7abcd4d750e76917eaebbecaa2c20bde22e1dbeeba8beb758c"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
 name = "pin-project-lite"
-version = "0.2.14"
+version = "0.2.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bda66fc9667c18cb2758a2ac84d1167245054bcf85d5d1aaa6923f45801bdd02"
+checksum = "915a1e146535de9163f3987b8944ed8cf49a18bb0056bcebcdcece385cece4ff"
 
 [[package]]
 name = "pin-utils"
@@ -3944,19 +4112,19 @@ dependencies = [
 
 [[package]]
 name = "prettyplease"
-version = "0.2.22"
+version = "0.2.25"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "479cf940fbbb3426c32c5d5176f62ad57549a0bb84773423ba8be9d089f5faba"
+checksum = "64d1ec885c64d0457d564db4ec299b2dae3f9c02808b8ad9c3a089c591b18033"
 dependencies = [
  "proc-macro2",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
 name = "proc-macro2"
-version = "1.0.87"
+version = "1.0.92"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b3e4daa0dcf6feba26f985457cdf104d4b4256fc5a09547140f3631bb076b19a"
+checksum = "37d3544b3f2748c54e147655edb5025752e2303145b5aefb3c3ea2c78b973bb0"
 dependencies = [
  "unicode-ident",
 ]
@@ -3998,7 +4166,7 @@ dependencies = [
  "prost 0.12.6",
  "prost-types 0.12.6",
  "regex",
- "syn 2.0.79",
+ "syn 2.0.89",
  "tempfile",
 ]
 
@@ -4012,7 +4180,7 @@ dependencies = [
  "itertools 0.12.1",
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -4025,7 +4193,7 @@ dependencies = [
  "itertools 0.13.0",
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -4068,10 +4236,10 @@ dependencies = [
  "futures-util",
  "prost 0.13.3",
  "prost-types 0.13.3",
- "reqwest 0.12.8",
+ "reqwest 0.12.9",
  "serde",
  "serde_json",
- "thiserror",
+ "thiserror 1.0.69",
  "tonic",
 ]
 
@@ -4109,45 +4277,49 @@ dependencies = [
 
 [[package]]
 name = "quinn"
-version = "0.11.5"
+version = "0.11.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8c7c5fdde3cdae7203427dc4f0a68fe0ed09833edc525a03456b153b79828684"
+checksum = "62e96808277ec6f97351a2380e6c25114bc9e67037775464979f3037c92d05ef"
 dependencies = [
  "bytes",
  "pin-project-lite",
  "quinn-proto",
  "quinn-udp",
  "rustc-hash 2.0.0",
- "rustls 0.23.14",
+ "rustls 0.23.18",
  "socket2 0.5.7",
- "thiserror",
+ "thiserror 2.0.3",
  "tokio",
  "tracing",
 ]
 
 [[package]]
 name = "quinn-proto"
-version = "0.11.8"
+version = "0.11.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fadfaed2cd7f389d0161bb73eeb07b7b78f8691047a6f3e73caaeae55310a4a6"
+checksum = "a2fe5ef3495d7d2e377ff17b1a8ce2ee2ec2a18cde8b6ad6619d65d0701c135d"
 dependencies = [
  "bytes",
+ "getrandom",
  "rand",
  "ring",
  "rustc-hash 2.0.0",
- "rustls 0.23.14",
+ "rustls 0.23.18",
+ "rustls-pki-types",
  "slab",
- "thiserror",
+ "thiserror 2.0.3",
  "tinyvec",
  "tracing",
+ "web-time",
 ]
 
 [[package]]
 name = "quinn-udp"
-version = "0.5.5"
+version = "0.5.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4fe68c2e9e1a1234e218683dbdf9f9dfcb094113c5ac2b938dfcb9bab4c4140b"
+checksum = "7d5a626c6807713b15cac82a6acaccd6043c9a5408c24baae07611fec3f243da"
 dependencies = [
+ "cfg_aliases",
  "libc",
  "once_cell",
  "socket2 0.5.7",
@@ -4262,14 +4434,14 @@ checksum = "ba009ff324d1fc1b900bd1fdb31564febe58a8ccc8a6fdbb93b543d33b13ca43"
 dependencies = [
  "getrandom",
  "libredox",
- "thiserror",
+ "thiserror 1.0.69",
 ]
 
 [[package]]
 name = "regex"
-version = "1.11.0"
+version = "1.11.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "38200e5ee88914975b69f657f0801b6f6dccafd44fd9326302a4aaeecfacb1d8"
+checksum = "b544ef1b4eac5dc2db33ea63606ae9ffcfac26c1416a2806ae0bf5f56b201191"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -4279,9 +4451,9 @@ dependencies = [
 
 [[package]]
 name = "regex-automata"
-version = "0.4.8"
+version = "0.4.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "368758f23274712b504848e9d5a6f010445cc8b87a7cdb4d7cbee666c1288da3"
+checksum = "809e8dc61f6de73b46c85f4c96486310fe304c434cfa43669d7b40f711150908"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -4314,7 +4486,7 @@ dependencies = [
  "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.30",
+ "hyper 0.14.31",
  "hyper-tls",
  "ipnet",
  "js-sys",
@@ -4342,19 +4514,19 @@ dependencies = [
 
 [[package]]
 name = "reqwest"
-version = "0.12.8"
+version = "0.12.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f713147fbe92361e52392c73b8c9e48c04c6625bce969ef54dc901e58e042a7b"
+checksum = "a77c62af46e79de0a562e1a9849205ffcb7fc1238876e9bd743357570e04046f"
 dependencies = [
  "base64 0.22.1",
  "bytes",
  "futures-core",
  "futures-util",
- "h2 0.4.6",
+ "h2 0.4.7",
  "http 1.1.0",
  "http-body 1.0.1",
  "http-body-util",
- "hyper 1.4.1",
+ "hyper 1.5.1",
  "hyper-rustls 0.27.3",
  "hyper-util",
  "ipnet",
@@ -4365,14 +4537,14 @@ dependencies = [
  "percent-encoding",
  "pin-project-lite",
  "quinn",
- "rustls 0.23.14",
- "rustls-native-certs 0.8.0",
+ "rustls 0.23.18",
+ "rustls-native-certs 0.8.1",
  "rustls-pemfile 2.2.0",
  "rustls-pki-types",
  "serde",
  "serde_json",
  "serde_urlencoded",
- "sync_wrapper 1.0.1",
+ "sync_wrapper 1.0.2",
  "tokio",
  "tokio-rustls 0.26.0",
  "tokio-util",
@@ -4382,7 +4554,7 @@ dependencies = [
  "wasm-bindgen-futures",
  "wasm-streams",
  "web-sys",
- "webpki-roots 0.26.6",
+ "webpki-roots 0.26.7",
  "windows-registry",
 ]
 
@@ -4413,15 +4585,27 @@ dependencies = [
  "lopdf",
  "ordered-float",
  "reqwest 0.11.27",
+ "rig-derive",
  "schemars",
  "serde",
  "serde_json",
- "thiserror",
+ "thiserror 1.0.69",
  "tokio",
+ "tokio-test",
  "tracing",
  "tracing-subscriber",
 ]
 
+[[package]]
+name = "rig-derive"
+version = "0.1.0"
+dependencies = [
+ "indoc",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.89",
+]
+
 [[package]]
 name = "rig-lancedb"
 version = "0.1.2"
@@ -4575,9 +4759,9 @@ dependencies = [
 
 [[package]]
 name = "rustix"
-version = "0.38.37"
+version = "0.38.41"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8acb788b847c24f28525660c4d7758620a7210875711f79e7f663cc152726811"
+checksum = "d7f649912bc1495e167a6edee79151c84b1bad49748cb4f1f1167f459f6224f6"
 dependencies = [
  "bitflags 2.6.0",
  "errno",
@@ -4600,9 +4784,9 @@ dependencies = [
 
 [[package]]
 name = "rustls"
-version = "0.23.14"
+version = "0.23.18"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "415d9944693cb90382053259f89fbb077ea730ad7273047ec63b19bc9b160ba8"
+checksum = "9c9cc1d47e243d655ace55ed38201c19ae02c148ae56412ab8750e8f0166ab7f"
 dependencies = [
  "log",
  "once_cell",
@@ -4622,7 +4806,7 @@ dependencies = [
  "openssl-probe",
  "rustls-pemfile 1.0.4",
  "schannel",
- "security-framework",
+ "security-framework 2.11.1",
 ]
 
 [[package]]
@@ -4635,20 +4819,19 @@ dependencies = [
  "rustls-pemfile 2.2.0",
  "rustls-pki-types",
  "schannel",
- "security-framework",
+ "security-framework 2.11.1",
 ]
 
 [[package]]
 name = "rustls-native-certs"
-version = "0.8.0"
+version = "0.8.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fcaf18a4f2be7326cd874a5fa579fae794320a0f388d365dca7e480e55f83f8a"
+checksum = "7fcff2dd52b58a8d98a70243663a0d234c4e2b79235637849d15913394a247d3"
 dependencies = [
  "openssl-probe",
- "rustls-pemfile 2.2.0",
  "rustls-pki-types",
  "schannel",
- "security-framework",
+ "security-framework 3.0.1",
 ]
 
 [[package]]
@@ -4671,9 +4854,12 @@ dependencies = [
 
 [[package]]
 name = "rustls-pki-types"
-version = "1.9.0"
+version = "1.10.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0e696e35370c65c9c541198af4543ccd580cf17fc25d8e05c5a242b202488c55"
+checksum = "16f1201b3c9a7ee8039bcadc17b7e605e2945b27eee7631788c1bd2b0643674b"
+dependencies = [
+ "web-time",
+]
 
 [[package]]
 name = "rustls-webpki"
@@ -4698,9 +4884,9 @@ dependencies = [
 
 [[package]]
 name = "rustversion"
-version = "1.0.17"
+version = "1.0.18"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "955d28af4278de8121b7ebeb796b6a45735dc01436d898801014aced2773a3d6"
+checksum = "0e819f2bc632f285be6d7cd36e25940d45b2391dd6d9b939e79de557f7014248"
 
 [[package]]
 name = "ryu"
@@ -4719,9 +4905,9 @@ dependencies = [
 
 [[package]]
 name = "schannel"
-version = "0.1.26"
+version = "0.1.27"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "01227be5826fa0690321a2ba6c5cd57a19cf3f6a09e76973b58e61de6ab9d1c1"
+checksum = "1f29ebaa345f945cec9fbbc532eb307f0fdad8161f281b6369539c8d84876b3d"
 dependencies = [
  "windows-sys 0.59.0",
 ]
@@ -4756,7 +4942,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "serde_derive_internals",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -4782,7 +4968,20 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "897b2245f0b511c87893af39b033e5ca9cce68824c4d7e7630b5a1d339658d02"
 dependencies = [
  "bitflags 2.6.0",
- "core-foundation",
+ "core-foundation 0.9.4",
+ "core-foundation-sys",
+ "libc",
+ "security-framework-sys",
+]
+
+[[package]]
+name = "security-framework"
+version = "3.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e1415a607e92bec364ea2cf9264646dcce0f91e6d65281bd6f2819cca3bf39c8"
+dependencies = [
+ "bitflags 2.6.0",
+ "core-foundation 0.10.0",
  "core-foundation-sys",
  "libc",
  "security-framework-sys",
@@ -4790,9 +4989,9 @@ dependencies = [
 
 [[package]]
 name = "security-framework-sys"
-version = "2.12.0"
+version = "2.12.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ea4a292869320c0272d7bc55a5a6aafaff59b4f63404a003887b679a2e05b4b6"
+checksum = "fa39c7303dc58b5543c94d22c1766b0d31f2ee58306363ea622b10bbc075eaa2"
 dependencies = [
  "core-foundation-sys",
  "libc",
@@ -4824,9 +5023,9 @@ checksum = "388a1df253eca08550bef6c72392cfe7c30914bf41df5269b68cbd6ff8f570a3"
 
 [[package]]
 name = "serde"
-version = "1.0.210"
+version = "1.0.215"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c8e3592472072e6e22e0a54d5904d9febf8508f65fb8552499a1abc7d1078c3a"
+checksum = "6513c1ad0b11a9376da888e3e0baa0077f1aed55c17f50e7b2397136129fb88f"
 dependencies = [
  "serde_derive",
 ]
@@ -4842,13 +5041,13 @@ dependencies = [
 
 [[package]]
 name = "serde_derive"
-version = "1.0.210"
+version = "1.0.215"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "243902eda00fad750862fc144cea25caca5e20d615af0a81bee94ca738f1df1f"
+checksum = "ad1e866f866923f252f05c889987993144fb74e722403468a4ebd70c3cd756c0"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -4859,14 +5058,14 @@ checksum = "18d26a20a969b9e3fdf2fc2d9f21eda6c40e2de84c9408bb5d3b05d499aae711"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
 name = "serde_json"
-version = "1.0.128"
+version = "1.0.133"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6ff5456707a1de34e7e37f2a6fd3d3f808c318259cbd01ab6377795054b483d8"
+checksum = "c7fceb2473b9166b2294ef05efcb65a3db80803f0b03ef86a5fc88a2b85ee377"
 dependencies = [
  "indexmap 2.6.0",
  "itoa",
@@ -4936,7 +5135,7 @@ dependencies = [
  "darling 0.20.10",
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -5111,7 +5310,7 @@ checksum = "01b2e185515564f15375f593fb966b5718bc624ba77fe49fa4616ad619690554"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -5180,7 +5379,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -5202,9 +5401,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.79"
+version = "2.0.89"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "89132cd0bf050864e1d38dc3bbc07a0eb8e7530af26344d3d2bbbef83499f590"
+checksum = "44d46482f1c1c87acd84dea20c1bf5ebff4c757009ed6bf19cfd36fb10e92c4e"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -5219,13 +5418,24 @@ checksum = "2047c6ded9c721764247e62cd3b03c09ffc529b2ba5b10ec482ae507a4a70160"
 
 [[package]]
 name = "sync_wrapper"
-version = "1.0.1"
+version = "1.0.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a7065abeca94b6a8a577f9bd45aa0867a2238b74e8eb67cf10d492bc39351394"
+checksum = "0bf256ce5efdfa370213c1dabab5935a12e49f2c58d15e9eac2870d3b4f27263"
 dependencies = [
  "futures-core",
 ]
 
+[[package]]
+name = "synstructure"
+version = "0.13.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c8af7666ab7b6390ab78131fb5b0fce11d6b7a6951602017c35fa82800708971"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.89",
+]
+
 [[package]]
 name = "system-configuration"
 version = "0.5.1"
@@ -5233,7 +5443,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ba3a3adc5c275d719af8cb4272ea1c4a6d668a777f37e115f6d11ddbc1c8e0e7"
 dependencies = [
  "bitflags 1.3.2",
- "core-foundation",
+ "core-foundation 0.9.4",
  "system-configuration-sys",
 ]
 
@@ -5304,7 +5514,7 @@ dependencies = [
  "tantivy-stacker",
  "tantivy-tokenizer-api",
  "tempfile",
- "thiserror",
+ "thiserror 1.0.69",
  "time",
  "uuid",
  "winapi",
@@ -5408,14 +5618,14 @@ checksum = "55937e1799185b12863d447f42597ed69d9928686b8d88a1df17376a097d8369"
 
 [[package]]
 name = "tempfile"
-version = "3.13.0"
+version = "3.14.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f0f2c9fc62d0beef6951ccffd757e241266a2c833136efbe35af6cd2567dca5b"
+checksum = "28cce251fcbc87fac86a866eeb0d6c2d536fc16d06f184bb61aeae11aa4cee0c"
 dependencies = [
  "cfg-if",
- "fastrand 2.1.1",
+ "fastrand 2.2.0",
  "once_cell",
- "rustix 0.38.37",
+ "rustix 0.38.41",
  "windows-sys 0.59.0",
 ]
 
@@ -5443,27 +5653,47 @@ checksum = "23d434d3f8967a09480fb04132ebe0a3e088c173e6d0ee7897abbdf4eab0f8b9"
 dependencies = [
  "smawk",
  "unicode-linebreak",
- "unicode-width",
+ "unicode-width 0.1.14",
 ]
 
 [[package]]
 name = "thiserror"
-version = "1.0.65"
+version = "1.0.69"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5d11abd9594d9b38965ef50805c5e469ca9cc6f197f883f717e0269a3057b3d5"
+checksum = "b6aaf5339b578ea85b50e080feb250a3e8ae8cfcdff9a461c9ec2904bc923f52"
 dependencies = [
- "thiserror-impl",
+ "thiserror-impl 1.0.69",
+]
+
+[[package]]
+name = "thiserror"
+version = "2.0.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c006c85c7651b3cf2ada4584faa36773bd07bac24acfb39f3c431b36d7e667aa"
+dependencies = [
+ "thiserror-impl 2.0.3",
 ]
 
 [[package]]
 name = "thiserror-impl"
-version = "1.0.65"
+version = "1.0.69"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ae71770322cbd277e69d762a16c444af02aa0575ac0d174f0b9562d3b37f8602"
+checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
+]
+
+[[package]]
+name = "thiserror-impl"
+version = "2.0.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f077553d607adc1caf65430528a576c757a71ed73944b66ebb58ef2bbd243568"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -5516,6 +5746,16 @@ dependencies = [
  "crunchy",
 ]
 
+[[package]]
+name = "tinystr"
+version = "0.7.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9117f5d4db391c1cf6927e7bea3db74b9a1c1add8f7eda9ffd5364f40f57b82f"
+dependencies = [
+ "displaydoc",
+ "zerovec",
+]
+
 [[package]]
 name = "tinyvec"
 version = "1.8.0"
@@ -5533,9 +5773,9 @@ checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
 
 [[package]]
 name = "tokio"
-version = "1.40.0"
+version = "1.41.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e2b070231665d27ad9ec9b8df639893f46727666c6767db40317fbe920a5d998"
+checksum = "22cfb5bee7a6a52939ca9224d6ac897bb669134078daa8735560897f69de4d33"
 dependencies = [
  "backtrace",
  "bytes",
@@ -5557,7 +5797,7 @@ checksum = "693d596312e88961bc67d7f1f97af8a70227d9f90c31bba5806eec004978d752"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -5586,7 +5826,7 @@ version = "0.26.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0c7bc40d0e5a97695bb96e27995cd3a08538541b0a846f65bba7a359f36700d4"
 dependencies = [
- "rustls 0.23.14",
+ "rustls 0.23.18",
  "rustls-pki-types",
  "tokio",
 ]
@@ -5602,6 +5842,19 @@ dependencies = [
  "tokio",
 ]
 
+[[package]]
+name = "tokio-test"
+version = "0.4.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2468baabc3311435b55dd935f702f42cd1b8abb7e754fb7dfb16bd36aa88f9f7"
+dependencies = [
+ "async-stream",
+ "bytes",
+ "futures-core",
+ "tokio",
+ "tokio-stream",
+]
+
 [[package]]
 name = "tokio-util"
 version = "0.7.12"
@@ -5628,17 +5881,17 @@ dependencies = [
  "base64 0.22.1",
  "bytes",
  "flate2",
- "h2 0.4.6",
+ "h2 0.4.7",
  "http 1.1.0",
  "http-body 1.0.1",
  "http-body-util",
- "hyper 1.4.1",
+ "hyper 1.5.1",
  "hyper-timeout",
  "hyper-util",
  "percent-encoding",
  "pin-project",
  "prost 0.13.3",
- "rustls-native-certs 0.8.0",
+ "rustls-native-certs 0.8.1",
  "rustls-pemfile 2.2.0",
  "socket2 0.5.7",
  "tokio",
@@ -5715,7 +5968,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
 ]
 
 [[package]]
@@ -5778,7 +6031,7 @@ dependencies = [
  "log",
  "rand",
  "smallvec",
- "thiserror",
+ "thiserror 1.0.69",
  "tinyvec",
  "tokio",
  "url",
@@ -5799,7 +6052,7 @@ dependencies = [
  "parking_lot",
  "resolv-conf",
  "smallvec",
- "thiserror",
+ "thiserror 1.0.69",
  "tokio",
  "trust-dns-proto",
 ]
@@ -5839,12 +6092,9 @@ checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
 
 [[package]]
 name = "unicase"
-version = "2.7.0"
+version = "2.8.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f7d2d4dafb69621809a81864c9c1b864479e1235c0dd4e199924b9742439ed89"
-dependencies = [
- "version_check",
-]
+checksum = "7e51b68083f157f853b6379db119d1c1be0e6e4dec98101079dec41f6f5cf6df"
 
 [[package]]
 name = "unicode-bidi"
@@ -5854,9 +6104,9 @@ checksum = "5ab17db44d7388991a428b2ee655ce0c212e862eff1768a455c58f9aad6e7893"
 
 [[package]]
 name = "unicode-ident"
-version = "1.0.13"
+version = "1.0.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e91b56cd4cadaeb79bbf1a5645f6b4f8dc5bde8834ad5894a8db35fda9efa1fe"
+checksum = "adb9e6ca4f869e1180728b7950e35922a7fc6397f7b641499e8f3ef06e50dc83"
 
 [[package]]
 name = "unicode-linebreak"
@@ -5891,6 +6141,12 @@ version = "0.1.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7dd6e30e90baa6f72411720665d41d89b9a3d039dc45b8faea1ddd07f617f6af"
 
+[[package]]
+name = "unicode-width"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1fc81956842c57dac11422a97c3b8195a1ff727f06e85c84ed2e8aa277c9a0fd"
+
 [[package]]
 name = "untrusted"
 version = "0.9.0"
@@ -5899,12 +6155,12 @@ checksum = "8ecb6da28b8a351d773b68d5825ac39017e680750f980f3a1a85cd8dd28a47c1"
 
 [[package]]
 name = "url"
-version = "2.5.2"
+version = "2.5.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "22784dbdf76fdde8af1aeda5622b546b422b6fc585325248a2bf9f5e41e94d6c"
+checksum = "32f8b686cadd1473f4bd0117a5d28d36b1ade384ea9b5069a1c40aefed7fda60"
 dependencies = [
  "form_urlencoded",
- "idna 0.5.0",
+ "idna 1.0.3",
  "percent-encoding",
 ]
 
@@ -5914,17 +6170,29 @@ version = "2.1.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "daf8dba3b7eb870caf1ddeed7bc9d2a049f3cfdfae7cb521b087cc33ae4c49da"
 
+[[package]]
+name = "utf16_iter"
+version = "1.0.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c8232dd3cdaed5356e0f716d285e4b40b932ac434100fe9b7e0e8e935b9e6246"
+
 [[package]]
 name = "utf8-ranges"
 version = "1.0.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7fcfc827f90e53a02eaef5e535ee14266c1d569214c6aa70133a624d8a3164ba"
 
+[[package]]
+name = "utf8_iter"
+version = "1.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b6c140620e7ffbb22c2dee59cafe6084a59b5ffc27a8859a5f0d494b5d52b6be"
+
 [[package]]
 name = "uuid"
-version = "1.10.0"
+version = "1.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "81dfa00651efa65069b0b6b651f4aaa31ba9e3c3ce0137aaad053604ee7e0314"
+checksum = "f8c5f0a0af699448548ad1a2fbf920fb4bee257eae39953ba95cb84891a0446a"
 dependencies = [
  "getrandom",
  "serde",
@@ -5987,9 +6255,9 @@ checksum = "9c8d87e72b64a3b4db28d11ce29237c246188f4f51057d65a7eab63b7987e423"
 
 [[package]]
 name = "wasm-bindgen"
-version = "0.2.94"
+version = "0.2.95"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ef073ced962d62984fb38a36e5fdc1a2b23c9e0e1fa0689bb97afa4202ef6887"
+checksum = "128d1e363af62632b8eb57219c8fd7877144af57558fb2ef0368d0087bddeb2e"
 dependencies = [
  "cfg-if",
  "once_cell",
@@ -5998,24 +6266,24 @@ dependencies = [
 
 [[package]]
 name = "wasm-bindgen-backend"
-version = "0.2.94"
+version = "0.2.95"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c4bfab14ef75323f4eb75fa52ee0a3fb59611977fd3240da19b2cf36ff85030e"
+checksum = "cb6dd4d3ca0ddffd1dd1c9c04f94b868c37ff5fac97c30b97cff2d74fce3a358"
 dependencies = [
  "bumpalo",
  "log",
  "once_cell",
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
  "wasm-bindgen-shared",
 ]
 
 [[package]]
 name = "wasm-bindgen-futures"
-version = "0.4.44"
+version = "0.4.45"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "65471f79c1022ffa5291d33520cbbb53b7687b01c2f8e83b57d102eed7ed479d"
+checksum = "cc7ec4f8827a71586374db3e87abdb5a2bb3a15afed140221307c3ec06b1f63b"
 dependencies = [
  "cfg-if",
  "js-sys",
@@ -6025,9 +6293,9 @@ dependencies = [
 
 [[package]]
 name = "wasm-bindgen-macro"
-version = "0.2.94"
+version = "0.2.95"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a7bec9830f60924d9ceb3ef99d55c155be8afa76954edffbb5936ff4509474e7"
+checksum = "e79384be7f8f5a9dd5d7167216f022090cf1f9ec128e6e6a482a2cb5c5422c56"
 dependencies = [
  "quote",
  "wasm-bindgen-macro-support",
@@ -6035,28 +6303,28 @@ dependencies = [
 
 [[package]]
 name = "wasm-bindgen-macro-support"
-version = "0.2.94"
+version = "0.2.95"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4c74f6e152a76a2ad448e223b0fc0b6b5747649c3d769cc6bf45737bf97d0ed6"
+checksum = "26c6ab57572f7a24a4985830b120de1594465e5d500f24afe89e16b4e833ef68"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
  "wasm-bindgen-backend",
  "wasm-bindgen-shared",
 ]
 
 [[package]]
 name = "wasm-bindgen-shared"
-version = "0.2.94"
+version = "0.2.95"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a42f6c679374623f295a8623adfe63d9284091245c3504bde47c17a3ce2777d9"
+checksum = "65fc09f10666a9f147042251e0dda9c18f166ff7de300607007e96bdebc1068d"
 
 [[package]]
 name = "wasm-streams"
-version = "0.4.1"
+version = "0.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4e072d4e72f700fb3443d8fe94a39315df013eef1104903cdb0a2abd322bbecd"
+checksum = "15053d8d85c7eccdbefef60f06769760a563c7f0a9d6902a13d35c7800b0ad65"
 dependencies = [
  "futures-util",
  "js-sys",
@@ -6067,9 +6335,19 @@ dependencies = [
 
 [[package]]
 name = "web-sys"
-version = "0.3.71"
+version = "0.3.72"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "44188d185b5bdcae1052d08bcbcf9091a5524038d4572cc4f4f2bb9d5554ddd9"
+checksum = "f6488b90108c040df0fe62fa815cbdee25124641df01814dd7282749234c6112"
+dependencies = [
+ "js-sys",
+ "wasm-bindgen",
+]
+
+[[package]]
+name = "web-time"
+version = "1.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5a6580f308b1fad9207618087a65c04e7a10bc77e02c8e84e9b00dd4b12fa0bb"
 dependencies = [
  "js-sys",
  "wasm-bindgen",
@@ -6083,9 +6361,9 @@ checksum = "5f20c57d8d7db6d3b86154206ae5d8fba62dd39573114de97c2cb0578251f8e1"
 
 [[package]]
 name = "webpki-roots"
-version = "0.26.6"
+version = "0.26.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "841c67bff177718f1d4dfefde8d8f0e78f9b6589319ba88312f567fc5841a958"
+checksum = "5d642ff16b7e79272ae451b7322067cdc17cadf68c23264be9d94a32319efe7e"
 dependencies = [
  "rustls-pki-types",
 ]
@@ -6330,6 +6608,18 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "write16"
+version = "1.0.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d1890f4022759daae28ed4fe62859b1236caebfc61ede2f63ed4e695f3f6d936"
+
+[[package]]
+name = "writeable"
+version = "0.5.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1e9df38ee2d2c3c5948ea468a8406ff0db0b29ae1ffde1bcf20ef305bcc95c51"
+
 [[package]]
 name = "wyz"
 version = "0.5.1"
@@ -6345,6 +6635,30 @@ version = "0.13.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "66fee0b777b0f5ac1c69bb06d361268faafa61cd4682ae064a171c16c433e9e4"
 
+[[package]]
+name = "yoke"
+version = "0.7.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "120e6aef9aa629e3d4f52dc8cc43a015c7724194c97dfaf45180d2daf2b77f40"
+dependencies = [
+ "serde",
+ "stable_deref_trait",
+ "yoke-derive",
+ "zerofrom",
+]
+
+[[package]]
+name = "yoke-derive"
+version = "0.7.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2380878cad4ac9aac1e2435f3eb4020e8374b5f13c296cb75b4620ff8e229154"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.89",
+ "synstructure",
+]
+
 [[package]]
 name = "zerocopy"
 version = "0.7.35"
@@ -6363,7 +6677,28 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.79",
+ "syn 2.0.89",
+]
+
+[[package]]
+name = "zerofrom"
+version = "0.1.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cff3ee08c995dee1859d998dea82f7374f2826091dd9cd47def953cae446cd2e"
+dependencies = [
+ "zerofrom-derive",
+]
+
+[[package]]
+name = "zerofrom-derive"
+version = "0.1.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "595eed982f7d355beb85837f651fa22e90b3c044842dc7f2c2842c086f295808"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.89",
+ "synstructure",
 ]
 
 [[package]]
@@ -6372,6 +6707,28 @@ version = "1.8.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ced3678a2879b30306d323f4542626697a464a97c0a07c9aebf7ebca65cd4dde"
 
+[[package]]
+name = "zerovec"
+version = "0.10.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "aa2b893d79df23bfb12d5461018d408ea19dfafe76c2c7ef6d4eba614f8ff079"
+dependencies = [
+ "yoke",
+ "zerofrom",
+ "zerovec-derive",
+]
+
+[[package]]
+name = "zerovec-derive"
+version = "0.10.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6eafa6dfb17584ea3e2bd6e76e0cc15ad7af12b09abdd1ca55961bed9b1063c6"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.89",
+]
+
 [[package]]
 name = "zstd"
 version = "0.13.2"
diff --git a/Cargo.toml b/Cargo.toml
index c8d75273..2f6d642c 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -3,5 +3,5 @@ resolver = "2"
 members = [
     "rig-core", "rig-lancedb",
     "rig-mongodb", "rig-neo4j",
-    "rig-qdrant",
+    "rig-qdrant", "rig-core/rig-core-derive"
 ]
diff --git a/rig-core/Cargo.toml b/rig-core/Cargo.toml
index 63cd23e5..f8c44e8f 100644
--- a/rig-core/Cargo.toml
+++ b/rig-core/Cargo.toml
@@ -23,6 +23,7 @@ futures = "0.3.29"
 ordered-float = "4.2.0"
 schemars = "0.8.16"
 thiserror = "1.0.61"
+rig-derive = { path = "./rig-core-derive", optional = true }
 glob = "0.3.1"
 lopdf = { version = "0.34.0", optional = true }
 
@@ -31,7 +32,33 @@ anyhow = "1.0.75"
 assert_fs = "1.1.2"
 tokio = { version = "1.34.0", features = ["full"] }
 tracing-subscriber = "0.3.18"
+tokio-test = "0.4.4"
 
 [features]
-all = ["pdf"]
+all = ["derive", "pdf"]
+derive = ["dep:rig-derive"]
 pdf = ["dep:lopdf"]
+
+[[test]]
+name = "embed_macro"
+required-features = ["derive"]
+
+[[example]]
+name = "rag"
+required-features = ["derive"] 
+
+[[example]]
+name = "vector_search"
+required-features = ["derive"] 
+
+[[example]]
+name = "vector_search_cohere"
+required-features = ["derive"] 
+
+[[example]]
+name = "gemini_embeddings"
+required-features = ["derive"] 
+
+[[example]]
+name = "xai_embeddings"
+required-features = ["derive"] 
diff --git a/rig-core/examples/calculator_chatbot.rs b/rig-core/examples/calculator_chatbot.rs
index 04d26dc3..149b1ce4 100644
--- a/rig-core/examples/calculator_chatbot.rs
+++ b/rig-core/examples/calculator_chatbot.rs
@@ -5,7 +5,7 @@ use rig::{
     embeddings::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;
@@ -247,13 +247,13 @@ async fn main() -> Result<(), anyhow::Error> {
 
     let embedding_model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
     let embeddings = EmbeddingsBuilder::new(embedding_model.clone())
-        .tools(&toolset)?
+        .documents(toolset.schemas()?)?
         .build()
         .await?;
 
-    let mut store = InMemoryVectorStore::default();
-    store.add_documents(embeddings).await?;
-    let index = store.index(embedding_model);
+    let index = InMemoryVectorStore::default()
+        .add_documents_with_id(embeddings, |tool| tool.name.clone())?
+        .index(embedding_model);
 
     // Create RAG agent with a single context prompt and a dynamic tool source
     let calculator_rag = openai_client
diff --git a/rig-core/examples/gemini_embeddings.rs b/rig-core/examples/gemini_embeddings.rs
index 4ce24636..6f8badbe 100644
--- a/rig-core/examples/gemini_embeddings.rs
+++ b/rig-core/examples/gemini_embeddings.rs
@@ -1,4 +1,11 @@
 use rig::providers::gemini;
+use rig::Embed;
+
+#[derive(Embed, Debug)]
+struct Greetings {
+    #[embed]
+    message: String,
+}
 
 #[tokio::main]
 async fn main() -> Result<(), anyhow::Error> {
@@ -8,8 +15,12 @@ async fn main() -> Result<(), anyhow::Error> {
 
     let embeddings = client
         .embeddings(gemini::embedding::EMBEDDING_001)
-        .simple_document("doc0", "Hello, world!")
-        .simple_document("doc1", "Goodbye, world!")
+        .document(Greetings {
+            message: "Hello, world!".to_string(),
+        })?
+        .document(Greetings {
+            message: "Goodbye, world!".to_string(),
+        })?
         .build()
         .await
         .expect("Failed to embed documents");
diff --git a/rig-core/examples/rag.rs b/rig-core/examples/rag.rs
index 3abd8ee9..376c37db 100644
--- a/rig-core/examples/rag.rs
+++ b/rig-core/examples/rag.rs
@@ -1,34 +1,73 @@
-use std::env;
+use std::{env, vec};
 
 use rig::{
     completion::Prompt,
     embeddings::EmbeddingsBuilder,
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
-    vector_store::{in_memory_store::InMemoryVectorStore, VectorStore},
+    vector_store::in_memory_store::InMemoryVectorStore,
+    Embed,
 };
+use serde::Serialize;
+
+// Data to be RAGged.
+// A vector search needs to be performed on the `definitions` field, so we derive the `Embed` trait for `WordDefinition`
+// and tag that field with `#[embed]`.
+#[derive(Embed, Serialize, Clone, Debug, Eq, PartialEq, Default)]
+struct WordDefinition {
+    id: String,
+    word: String,
+    #[embed]
+    definitions: Vec<String>,
+}
 
 #[tokio::main]
 async fn main() -> Result<(), anyhow::Error> {
+    // Initialize tracing
+    tracing_subscriber::fmt()
+        .with_max_level(tracing::Level::INFO)
+        .with_target(false)
+        .init();
+
     // Create OpenAI client
     let openai_api_key = env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY not set");
     let openai_client = Client::new(&openai_api_key);
 
     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();
-
+    // Generate embeddings for the definitions of all the documents using the specified embedding model.
     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.")
+        .documents(vec![
+            WordDefinition {
+                id: "doc0".to_string(),
+                word: "flurbo".to_string(),
+                definitions: vec![
+                    "1. *flurbo* (name): A flurbo is a green alien that lives on cold planets.".to_string(),
+                    "2. *flurbo* (name): A fictional digital currency that originated in the animated series Rick and Morty.".to_string()
+                ]
+            },
+            WordDefinition {
+                id: "doc1".to_string(),
+                word: "glarb-glarb".to_string(),
+                definitions: vec![
+                    "1. *glarb-glarb* (noun): A glarb-glarb is a ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
+                    "2. *glarb-glarb* (noun): A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
+                ]
+            },
+            WordDefinition {
+                id: "doc2".to_string(),
+                word: "linglingdong".to_string(),
+                definitions: vec![
+                    "1. *linglingdong* (noun): A term used by inhabitants of the far side of the moon to describe humans.".to_string(),
+                    "2. *linglingdong* (noun): A rare, mystical instrument crafted by the ancient monks of the Nebulon Mountain Ranges on the planet Quarm.".to_string()
+                ]
+            },
+        ])?
         .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_with_id(embeddings, |definition| definition.id.clone())?
+        .index(embedding_model);
 
     let rag_agent = openai_client.agent("gpt-4")
         .preamble("
diff --git a/rig-core/examples/rag_dynamic_tools.rs b/rig-core/examples/rag_dynamic_tools.rs
index 6e45730b..bc92f7c5 100644
--- a/rig-core/examples/rag_dynamic_tools.rs
+++ b/rig-core/examples/rag_dynamic_tools.rs
@@ -4,7 +4,7 @@ use rig::{
     embeddings::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;
@@ -137,11 +137,6 @@ async fn main() -> Result<(), anyhow::Error> {
         .with_max_level(tracing::Level::INFO)
         // disable printing the name of the module in every log line.
         .with_target(false)
-        // this needs to be set to false, otherwise ANSI color codes will
-        // show up in a confusing manner in CloudWatch logs.
-        .with_ansi(false)
-        // disabling time is handy because CloudWatch will add the ingestion time.
-        .without_time()
         .init();
 
     // Create OpenAI client
@@ -150,23 +145,19 @@ 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)
         .build();
 
     let embeddings = EmbeddingsBuilder::new(embedding_model.clone())
-        .tools(&toolset)?
+        .documents(toolset.schemas()?)?
         .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_with_id(embeddings, |tool| tool.name.clone())?
+        .index(embedding_model);
 
     // Create RAG agent with a single context prompt and a dynamic tool source
     let calculator_rag = openai_client
diff --git a/rig-core/examples/vector_search.rs b/rig-core/examples/vector_search.rs
index 1328a3a5..e8cbf894 100644
--- a/rig-core/examples/vector_search.rs
+++ b/rig-core/examples/vector_search.rs
@@ -3,8 +3,20 @@ use std::env;
 use rig::{
     embeddings::EmbeddingsBuilder,
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
-    vector_store::{in_memory_store::InMemoryVectorIndex, VectorStoreIndex},
+    vector_store::{in_memory_store::InMemoryVectorStore, VectorStoreIndex},
+    Embed,
 };
+use serde::{Deserialize, Serialize};
+
+// Shape of data that needs to be RAG'ed.
+// The definition field will be used to generate embeddings.
+#[derive(Embed, Clone, Deserialize, Debug, Serialize, Eq, PartialEq, Default)]
+struct WordDefinition {
+    id: String,
+    word: String,
+    #[embed]
+    definitions: Vec<String>,
+}
 
 #[tokio::main]
 async fn main() -> Result<(), anyhow::Error> {
@@ -15,25 +27,50 @@ async fn main() -> Result<(), anyhow::Error> {
     let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
 
     let embeddings = EmbeddingsBuilder::new(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.")
+        .documents(vec![
+            WordDefinition {
+                id: "doc0".to_string(),
+                word: "flurbo".to_string(),
+                definitions: vec![
+                    "A green alien that lives on cold planets.".to_string(),
+                    "A fictional digital currency that originated in the animated series Rick and Morty.".to_string()
+                ]
+            },
+            WordDefinition {
+                id: "doc1".to_string(),
+                word: "glarb-glarb".to_string(),
+                definitions: vec![
+                    "An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
+                    "A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
+                ]
+            },
+            WordDefinition {
+                id: "doc2".to_string(),
+                word: "linglingdong".to_string(),
+                definitions: vec![
+                    "A term used by inhabitants of the sombrero galaxy to describe humans.".to_string(),
+                    "A rare, mystical instrument crafted by the ancient monks of the Nebulon Mountain Ranges on the planet Quarm.".to_string()
+                ]
+            },
+        ])?
         .build()
         .await?;
 
-    let index = InMemoryVectorIndex::from_embeddings(model, embeddings).await?;
+    let index = InMemoryVectorStore::default()
+        .add_documents_with_id(embeddings, |definition| definition.id.clone())?
+        .index(model);
 
     let results = index
-        .top_n::<String>("What is a linglingdong?", 1)
+        .top_n::<WordDefinition>("I need to buy something in a fictional universe. What type of money can I use for this?", 1)
         .await?
         .into_iter()
-        .map(|(score, id, doc)| (score, id, doc))
+        .map(|(score, id, doc)| (score, id, doc.word))
         .collect::<Vec<_>>();
 
     println!("Results: {:?}", results);
 
     let id_results = index
-        .top_n_ids("What is a linglingdong?", 1)
+        .top_n_ids("I need to buy something in a fictional universe. What type of money can I use for this?", 1)
         .await?
         .into_iter()
         .collect::<Vec<_>>();
diff --git a/rig-core/examples/vector_search_cohere.rs b/rig-core/examples/vector_search_cohere.rs
index a49ac231..aace89fa 100644
--- a/rig-core/examples/vector_search_cohere.rs
+++ b/rig-core/examples/vector_search_cohere.rs
@@ -1,10 +1,22 @@
 use std::env;
 
 use rig::{
-    embeddings::{DocumentEmbeddings, EmbeddingsBuilder},
+    embeddings::EmbeddingsBuilder,
     providers::cohere::{Client, EMBED_ENGLISH_V3},
-    vector_store::{in_memory_store::InMemoryVectorStore, VectorStore, VectorStoreIndex},
+    vector_store::{in_memory_store::InMemoryVectorStore, VectorStoreIndex},
+    Embed,
 };
+use serde::{Deserialize, Serialize};
+
+// Shape of data that needs to be RAG'ed.
+// The definition field will be used to generate embeddings.
+#[derive(Embed, Clone, Deserialize, Debug, Serialize, Eq, PartialEq, Default)]
+struct WordDefinition {
+    id: String,
+    word: String,
+    #[embed]
+    definitions: Vec<String>,
+}
 
 #[tokio::main]
 async fn main() -> Result<(), anyhow::Error> {
@@ -15,24 +27,48 @@ 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.")
+    let embeddings = EmbeddingsBuilder::new(document_model.clone())
+        .documents(vec![
+            WordDefinition {
+                id: "doc0".to_string(),
+                word: "flurbo".to_string(),
+                definitions: vec![
+                    "A green alien that lives on cold planets.".to_string(),
+                    "A fictional digital currency that originated in the animated series Rick and Morty.".to_string()
+                ]
+            },
+            WordDefinition {
+                id: "doc1".to_string(),
+                word: "glarb-glarb".to_string(),
+                definitions: vec![
+                    "An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
+                    "A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
+                ]
+            },
+            WordDefinition {
+                id: "doc2".to_string(),
+                word: "linglingdong".to_string(),
+                definitions: vec![
+                    "A term used by inhabitants of the sombrero galaxy to describe humans.".to_string(),
+                    "A rare, mystical instrument crafted by the ancient monks of the Nebulon Mountain Ranges on the planet Quarm.".to_string()
+                ]
+            },
+        ])?
         .build()
         .await?;
 
-    vector_store.add_documents(embeddings).await?;
-
-    let index = vector_store.index(search_model);
+    let index = InMemoryVectorStore::default()
+        .add_documents_with_id(embeddings, |definition| definition.id.clone())?
+        .index(search_model);
 
     let results = index
-        .top_n::<DocumentEmbeddings>("What is a linglingdong?", 1)
+        .top_n::<WordDefinition>(
+            "Which instrument is found in the Nebulon Mountain Ranges?",
+            1,
+        )
         .await?
         .into_iter()
-        .map(|(score, id, doc)| (score, id, doc.document))
+        .map(|(score, id, doc)| (score, id, doc.word))
         .collect::<Vec<_>>();
 
     println!("Results: {:?}", results);
diff --git a/rig-core/examples/xai_embeddings.rs b/rig-core/examples/xai_embeddings.rs
index ba24a9b0..a127c389 100644
--- a/rig-core/examples/xai_embeddings.rs
+++ b/rig-core/examples/xai_embeddings.rs
@@ -1,4 +1,11 @@
 use rig::providers::xai;
+use rig::Embed;
+
+#[derive(Embed, Debug)]
+struct Greetings {
+    #[embed]
+    message: String,
+}
 
 #[tokio::main]
 async fn main() -> Result<(), anyhow::Error> {
@@ -7,8 +14,12 @@ async fn main() -> Result<(), anyhow::Error> {
 
     let embeddings = client
         .embeddings(xai::embedding::EMBEDDING_V1)
-        .simple_document("doc0", "Hello, world!")
-        .simple_document("doc1", "Goodbye, world!")
+        .document(Greetings {
+            message: "Hello, world!".to_string(),
+        })?
+        .document(Greetings {
+            message: "Goodbye, world!".to_string(),
+        })?
         .build()
         .await
         .expect("Failed to embed documents");
diff --git a/rig-core/rig-core-derive/Cargo.toml b/rig-core/rig-core-derive/Cargo.toml
new file mode 100644
index 00000000..1ab5e5ac
--- /dev/null
+++ b/rig-core/rig-core-derive/Cargo.toml
@@ -0,0 +1,13 @@
+[package]
+name = "rig-derive"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+indoc = "2.0.5"
+proc-macro2 = { version = "1.0.87", features = ["proc-macro"] }
+quote = "1.0.37"
+syn = { version = "2.0.79", features = ["full"]}
+
+[lib]
+proc-macro = true
diff --git a/rig-core/rig-core-derive/src/basic.rs b/rig-core/rig-core-derive/src/basic.rs
new file mode 100644
index 00000000..b9c1e5c4
--- /dev/null
+++ b/rig-core/rig-core-derive/src/basic.rs
@@ -0,0 +1,25 @@
+use syn::{parse_quote, Attribute, DataStruct, Meta};
+
+use crate::EMBED;
+
+/// Finds and returns fields with simple `#[embed]` attribute tags only.
+pub(crate) fn basic_embed_fields(data_struct: &DataStruct) -> impl Iterator<Item = &syn::Field> {
+    data_struct.fields.iter().filter(|field| {
+        field.attrs.iter().any(|attribute| match attribute {
+            Attribute {
+                meta: Meta::Path(path),
+                ..
+            } => path.is_ident(EMBED),
+            _ => false,
+        })
+    })
+}
+
+/// Adds bounds to where clause that force all fields tagged with `#[embed]` to implement the `Embed` trait.
+pub(crate) fn add_struct_bounds(generics: &mut syn::Generics, field_type: &syn::Type) {
+    let where_clause = generics.make_where_clause();
+
+    where_clause.predicates.push(parse_quote! {
+        #field_type: Embed
+    });
+}
diff --git a/rig-core/rig-core-derive/src/custom.rs b/rig-core/rig-core-derive/src/custom.rs
new file mode 100644
index 00000000..f29ed20e
--- /dev/null
+++ b/rig-core/rig-core-derive/src/custom.rs
@@ -0,0 +1,123 @@
+use quote::ToTokens;
+use syn::{meta::ParseNestedMeta, ExprPath};
+
+use crate::EMBED;
+
+const EMBED_WITH: &str = "embed_with";
+
+/// Finds and returns fields with #[embed(embed_with = "...")] attribute tags only.
+/// Also returns the "..." part of the tag (ie. the custom function).
+pub(crate) fn custom_embed_fields(
+    data_struct: &syn::DataStruct,
+) -> syn::Result<Vec<(&syn::Field, syn::ExprPath)>> {
+    data_struct
+        .fields
+        .iter()
+        .filter_map(|field| {
+            field
+                .attrs
+                .iter()
+                .filter_map(|attribute| match attribute.is_custom() {
+                    Ok(true) => match attribute.expand_tag() {
+                        Ok(path) => Some(Ok((field, path))),
+                        Err(e) => Some(Err(e)),
+                    },
+                    Ok(false) => None,
+                    Err(e) => Some(Err(e)),
+                })
+                .next()
+        })
+        .collect::<Result<Vec<_>, _>>()
+}
+
+trait CustomAttributeParser {
+    // Determine if field is tagged with an #[embed(embed_with = "...")] attribute.
+    fn is_custom(&self) -> syn::Result<bool>;
+
+    // Get the "..." part of the #[embed(embed_with = "...")] attribute.
+    // Ex: If attribute is tagged with #[embed(embed_with = "my_embed")], returns "my_embed".
+    fn expand_tag(&self) -> syn::Result<syn::ExprPath>;
+}
+
+impl CustomAttributeParser for syn::Attribute {
+    fn is_custom(&self) -> syn::Result<bool> {
+        // Check that the attribute is a list.
+        match &self.meta {
+            syn::Meta::List(meta) => {
+                if meta.tokens.is_empty() {
+                    return Ok(false);
+                }
+            }
+            _ => return Ok(false),
+        };
+
+        // Check the first attribute tag (the first "embed")
+        if !self.path().is_ident(EMBED) {
+            return Ok(false);
+        }
+
+        self.parse_nested_meta(|meta| {
+            // Parse the meta attribute as an expression. Need this to compile.
+            meta.value()?.parse::<syn::Expr>()?;
+
+            if meta.path.is_ident(EMBED_WITH) {
+                Ok(())
+            } else {
+                let path = meta.path.to_token_stream().to_string().replace(' ', "");
+                Err(syn::Error::new_spanned(
+                    meta.path,
+                    format_args!("unknown embedding field attribute `{}`", path),
+                ))
+            }
+        })?;
+
+        Ok(true)
+    }
+
+    fn expand_tag(&self) -> syn::Result<syn::ExprPath> {
+        fn function_path(meta: &ParseNestedMeta<'_>) -> syn::Result<ExprPath> {
+            // #[embed(embed_with = "...")]
+            let expr = meta.value()?.parse::<syn::Expr>().unwrap();
+            let mut value = &expr;
+            while let syn::Expr::Group(e) = value {
+                value = &e.expr;
+            }
+            let string = if let syn::Expr::Lit(syn::ExprLit {
+                lit: syn::Lit::Str(lit_str),
+                ..
+            }) = value
+            {
+                let suffix = lit_str.suffix();
+                if !suffix.is_empty() {
+                    return Err(syn::Error::new_spanned(
+                        lit_str,
+                        format!("unexpected suffix `{}` on string literal", suffix),
+                    ));
+                }
+                lit_str.clone()
+            } else {
+                return Err(syn::Error::new_spanned(
+                    value,
+                    format!(
+                        "expected {} attribute to be a string: `{} = \"...\"`",
+                        EMBED_WITH, EMBED_WITH
+                    ),
+                ));
+            };
+
+            string.parse()
+        }
+
+        let mut custom_func_path = None;
+
+        self.parse_nested_meta(|meta| match function_path(&meta) {
+            Ok(path) => {
+                custom_func_path = Some(path);
+                Ok(())
+            }
+            Err(e) => Err(e),
+        })?;
+
+        Ok(custom_func_path.unwrap())
+    }
+}
diff --git a/rig-core/rig-core-derive/src/embed.rs b/rig-core/rig-core-derive/src/embed.rs
new file mode 100644
index 00000000..73b89205
--- /dev/null
+++ b/rig-core/rig-core-derive/src/embed.rs
@@ -0,0 +1,110 @@
+use proc_macro2::TokenStream;
+use quote::quote;
+use syn::DataStruct;
+
+use crate::{
+    basic::{add_struct_bounds, basic_embed_fields},
+    custom::custom_embed_fields,
+};
+
+pub(crate) fn expand_derive_embedding(input: &mut syn::DeriveInput) -> syn::Result<TokenStream> {
+    let name = &input.ident;
+    let data = &input.data;
+    let generics = &mut input.generics;
+
+    let target_stream = match data {
+        syn::Data::Struct(data_struct) => {
+            let (basic_targets, basic_target_size) = data_struct.basic(generics);
+            let (custom_targets, custom_target_size) = data_struct.custom()?;
+
+            // If there are no fields tagged with `#[embed]` or `#[embed(embed_with = "...")]`, return an empty TokenStream.
+            // ie. do not implement `Embed` trait for the struct.
+            if basic_target_size + custom_target_size == 0 {
+                return Err(syn::Error::new_spanned(
+                    name,
+                    "Add at least one field tagged with #[embed] or #[embed(embed_with = \"...\")].",
+                ));
+            }
+
+            quote! {
+                #basic_targets;
+                #custom_targets;
+            }
+        }
+        _ => {
+            return Err(syn::Error::new_spanned(
+                input,
+                "Embed derive macro should only be used on structs",
+            ))
+        }
+    };
+
+    let (impl_generics, ty_generics, where_clause) = input.generics.split_for_impl();
+
+    let gen = quote! {
+        // Note: `Embed` trait is imported with the macro.
+
+        impl #impl_generics Embed for #name #ty_generics #where_clause {
+            fn embed(&self, embedder: &mut rig::embeddings::embed::TextEmbedder) -> Result<(), rig::embeddings::embed::EmbedError> {
+                #target_stream;
+
+                Ok(())
+            }
+        }
+    };
+
+    Ok(gen)
+}
+
+trait StructParser {
+    // Handles fields tagged with `#[embed]`
+    fn basic(&self, generics: &mut syn::Generics) -> (TokenStream, usize);
+
+    // Handles fields tagged with `#[embed(embed_with = "...")]`
+    fn custom(&self) -> syn::Result<(TokenStream, usize)>;
+}
+
+impl StructParser for DataStruct {
+    fn basic(&self, generics: &mut syn::Generics) -> (TokenStream, usize) {
+        let embed_targets = basic_embed_fields(self)
+            // Iterate over every field tagged with `#[embed]`
+            .map(|field| {
+                add_struct_bounds(generics, &field.ty);
+
+                let field_name = &field.ident;
+
+                quote! {
+                    self.#field_name
+                }
+            })
+            .collect::<Vec<_>>();
+
+        (
+            quote! {
+                #(#embed_targets.embed(embedder)?;)*
+            },
+            embed_targets.len(),
+        )
+    }
+
+    fn custom(&self) -> syn::Result<(TokenStream, usize)> {
+        let embed_targets = custom_embed_fields(self)?
+            // Iterate over every field tagged with `#[embed(embed_with = "...")]`
+            .into_iter()
+            .map(|(field, custom_func_path)| {
+                let field_name = &field.ident;
+
+                quote! {
+                    #custom_func_path(embedder, self.#field_name.clone())?;
+                }
+            })
+            .collect::<Vec<_>>();
+
+        Ok((
+            quote! {
+                #(#embed_targets)*
+            },
+            embed_targets.len(),
+        ))
+    }
+}
diff --git a/rig-core/rig-core-derive/src/lib.rs b/rig-core/rig-core-derive/src/lib.rs
new file mode 100644
index 00000000..4ce20cfa
--- /dev/null
+++ b/rig-core/rig-core-derive/src/lib.rs
@@ -0,0 +1,21 @@
+extern crate proc_macro;
+use proc_macro::TokenStream;
+use syn::{parse_macro_input, DeriveInput};
+
+mod basic;
+mod custom;
+mod embed;
+
+pub(crate) const EMBED: &str = "embed";
+
+/// References:
+/// <https://doc.rust-lang.org/book/ch19-06-macros.html#how-to-write-a-custom-derive-macro>
+/// <https://doc.rust-lang.org/reference/procedural-macros.html>
+#[proc_macro_derive(Embed, attributes(embed))]
+pub fn derive_embedding_trait(item: TokenStream) -> TokenStream {
+    let mut input = parse_macro_input!(item as DeriveInput);
+
+    embed::expand_derive_embedding(&mut input)
+        .unwrap_or_else(syn::Error::into_compile_error)
+        .into()
+}
diff --git a/rig-core/src/completion.rs b/rig-core/src/completion.rs
index e766fb27..f13f316b 100644
--- a/rig-core/src/completion.rs
+++ b/rig-core/src/completion.rs
@@ -82,7 +82,7 @@ pub enum CompletionError {
 
     /// Error building the completion request
     #[error("RequestError: {0}")]
-    RequestError(#[from] Box<dyn std::error::Error + Send + Sync>),
+    RequestError(#[from] Box<dyn std::error::Error + Send + Sync + 'static>),
 
     /// Error parsing the completion response
     #[error("ResponseError: {0}")]
diff --git a/rig-core/src/embeddings.rs b/rig-core/src/embeddings.rs
deleted file mode 100644
index eaced08b..00000000
--- a/rig-core/src/embeddings.rs
+++ /dev/null
@@ -1,335 +0,0 @@
-//! This module provides functionality for working with embeddings and embedding models.
-//! Embeddings are numerical representations of documents or other objects, typically used in
-//! natural language processing (NLP) tasks such as text classification, information retrieval,
-//! and document similarity.
-//!
-//! The module defines the [EmbeddingModel] trait, which represents an embedding model that can
-//! generate embeddings for documents. It also provides an implementation of the [EmbeddingsBuilder]
-//! struct, which allows users to build collections of document embeddings using different embedding
-//! models and document sources.
-//!
-//! The module also defines the [Embedding] struct, which represents a single document embedding,
-//! and the [DocumentEmbeddings] struct, which represents a document along with its associated
-//! embeddings. These structs are used to store and manipulate collections of document embeddings.
-//!
-//! Finally, the module defines the [EmbeddingError] enum, which represents various errors that
-//! can occur during embedding generation or processing.
-//!
-//! # Example
-//! ```rust
-//! use rig::providers::openai::{Client, self};
-//! use rig::embeddings::{EmbeddingModel, EmbeddingsBuilder};
-//!
-//! // Initialize the OpenAI client
-//! let openai = Client::new("your-openai-api-key");
-//!
-//! // Create an instance of the `text-embedding-ada-002` model
-//! let embedding_model = openai.embedding_model(openai::TEXT_EMBEDDING_ADA_002);
-//!
-//! // Create an embeddings builder and add documents
-//! let embeddings = EmbeddingsBuilder::new(embedding_model)
-//!     .simple_document("doc1", "This is the first document.")
-//!     .simple_document("doc2", "This is the second document.")
-//!     .build()
-//!     .await
-//!     .expect("Failed to build embeddings.");
-//!
-//! // Use the generated embeddings
-//! // ...
-//! ```
-
-use std::{cmp::max, collections::HashMap};
-
-use futures::{stream, StreamExt, TryStreamExt};
-use serde::{Deserialize, Serialize};
-
-use crate::tool::{ToolEmbedding, ToolSet, ToolType};
-
-#[derive(Debug, thiserror::Error)]
-pub enum EmbeddingError {
-    /// Http error (e.g.: connection error, timeout, etc.)
-    #[error("HttpError: {0}")]
-    HttpError(#[from] reqwest::Error),
-
-    /// Json error (e.g.: serialization, deserialization)
-    #[error("JsonError: {0}")]
-    JsonError(#[from] serde_json::Error),
-
-    /// Error processing the document for embedding
-    #[error("DocumentError: {0}")]
-    DocumentError(String),
-
-    /// Error parsing the completion response
-    #[error("ResponseError: {0}")]
-    ResponseError(String),
-
-    /// Error returned by the embedding model provider
-    #[error("ProviderError: {0}")]
-    ProviderError(String),
-}
-
-/// Trait for embedding models that can generate embeddings for documents.
-pub trait EmbeddingModel: Clone + Sync + Send {
-    /// The maximum number of documents that can be embedded in a single request.
-    const MAX_DOCUMENTS: usize;
-
-    /// The number of dimensions in the embedding vector.
-    fn ndims(&self) -> usize;
-
-    /// Embed a single document
-    fn embed_document(
-        &self,
-        document: &str,
-    ) -> impl std::future::Future<Output = Result<Embedding, EmbeddingError>> + Send
-    where
-        Self: Sync,
-    {
-        async {
-            Ok(self
-                .embed_documents(vec![document.to_string()])
-                .await?
-                .first()
-                .cloned()
-                .expect("One embedding should be present"))
-        }
-    }
-
-    /// Embed multiple documents in a single request
-    fn embed_documents(
-        &self,
-        documents: impl IntoIterator<Item = String> + Send,
-    ) -> impl std::future::Future<Output = Result<Vec<Embedding>, EmbeddingError>> + Send;
-}
-
-/// Struct that holds a single document and its embedding.
-#[derive(Clone, Default, Deserialize, Serialize, Debug)]
-pub struct Embedding {
-    /// The document that was embedded
-    pub document: String,
-    /// The embedding vector
-    pub vec: Vec<f64>,
-}
-
-impl PartialEq for Embedding {
-    fn eq(&self, other: &Self) -> bool {
-        self.document == other.document
-    }
-}
-
-impl Eq for Embedding {}
-
-impl Embedding {
-    pub fn distance(&self, other: &Self) -> f64 {
-        let dot_product: f64 = self
-            .vec
-            .iter()
-            .zip(other.vec.iter())
-            .map(|(x, y)| x * y)
-            .sum();
-
-        let product_of_lengths = (self.vec.len() * other.vec.len()) as f64;
-
-        dot_product / product_of_lengths
-    }
-}
-
-/// Struct that holds a document and its embeddings.
-///
-/// The struct is designed to model any kind of documents that can be serialized to JSON
-/// (including a simple string).
-///
-/// Moreover, it can hold multiple embeddings for the same document, thus allowing a
-/// large document to be retrieved from a query that matches multiple smaller and
-/// distinct text documents. For example, if the document is a textbook, a summary of
-/// each chapter could serve as the book's embeddings.
-#[derive(Clone, Eq, PartialEq, Serialize, Deserialize, Debug)]
-pub struct DocumentEmbeddings {
-    #[serde(rename = "_id")]
-    pub id: String,
-    pub document: serde_json::Value,
-    pub embeddings: Vec<Embedding>,
-}
-
-type Embeddings = Vec<DocumentEmbeddings>;
-
-/// Builder for creating a collection of embeddings
-pub struct EmbeddingsBuilder<M: EmbeddingModel> {
-    model: M,
-    documents: Vec<(String, serde_json::Value, Vec<String>)>,
-}
-
-impl<M: EmbeddingModel> EmbeddingsBuilder<M> {
-    /// Create a new embedding builder with the given embedding model
-    pub fn new(model: M) -> Self {
-        Self {
-            model,
-            documents: vec![],
-        }
-    }
-
-    /// Add a simple document to the embedding collection.
-    /// The provided document string will be used for the embedding.
-    pub fn simple_document(mut self, id: &str, document: &str) -> Self {
-        self.documents.push((
-            id.to_string(),
-            serde_json::Value::String(document.to_string()),
-            vec![document.to_string()],
-        ));
-        self
-    }
-
-    /// Add multiple documents to the embedding collection.
-    /// Each element of the vector is a tuple of the form (id, document).
-    pub fn simple_documents(mut self, documents: Vec<(String, String)>) -> Self {
-        self.documents
-            .extend(documents.into_iter().map(|(id, document)| {
-                (
-                    id,
-                    serde_json::Value::String(document.clone()),
-                    vec![document],
-                )
-            }));
-        self
-    }
-
-    /// Add a tool to the embedding collection.
-    /// The `tool.context()` corresponds to the document being stored while
-    /// `tool.embedding_docs()` corresponds to the documents that will be used to generate the embeddings.
-    pub fn tool(mut self, tool: impl ToolEmbedding + 'static) -> Result<Self, EmbeddingError> {
-        self.documents.push((
-            tool.name(),
-            serde_json::to_value(tool.context())?,
-            tool.embedding_docs(),
-        ));
-        Ok(self)
-    }
-
-    /// Add the tools from the given toolset to the embedding collection.
-    pub fn tools(mut self, toolset: &ToolSet) -> Result<Self, EmbeddingError> {
-        for (name, tool) in toolset.tools.iter() {
-            if let ToolType::Embedding(tool) = tool {
-                self.documents.push((
-                    name.clone(),
-                    tool.context().map_err(|e| {
-                        EmbeddingError::DocumentError(format!(
-                            "Failed to generate context for tool {}: {}",
-                            name, e
-                        ))
-                    })?,
-                    tool.embedding_docs(),
-                ));
-            }
-        }
-        Ok(self)
-    }
-
-    /// Add a document to the embedding collection.
-    /// `embed_documents` are the documents that will be used to generate the embeddings
-    /// for `document`.
-    pub fn document<T: Serialize>(
-        mut self,
-        id: &str,
-        document: T,
-        embed_documents: Vec<String>,
-    ) -> Self {
-        self.documents.push((
-            id.to_string(),
-            serde_json::to_value(document).expect("Document should serialize"),
-            embed_documents,
-        ));
-        self
-    }
-
-    /// Add multiple documents to the embedding collection.
-    /// Each element of the vector is a tuple of the form (id, document, embed_documents).
-    pub fn documents<T: Serialize>(mut self, documents: Vec<(String, T, Vec<String>)>) -> Self {
-        self.documents.extend(
-            documents
-                .into_iter()
-                .map(|(id, document, embed_documents)| {
-                    (
-                        id,
-                        serde_json::to_value(document).expect("Document should serialize"),
-                        embed_documents,
-                    )
-                }),
-        );
-        self
-    }
-
-    /// Add a json document to the embedding collection.
-    pub fn json_document(
-        mut self,
-        id: &str,
-        document: serde_json::Value,
-        embed_documents: Vec<String>,
-    ) -> Self {
-        self.documents
-            .push((id.to_string(), document, embed_documents));
-        self
-    }
-
-    /// Add multiple json documents to the embedding collection.
-    pub fn json_documents(
-        mut self,
-        documents: Vec<(String, serde_json::Value, Vec<String>)>,
-    ) -> Self {
-        self.documents.extend(documents);
-        self
-    }
-
-    /// Generate the embeddings for the given documents
-    pub async fn build(self) -> Result<Embeddings, EmbeddingError> {
-        // Create a temporary store for the documents
-        let documents_map = self
-            .documents
-            .into_iter()
-            .map(|(id, document, docs)| (id, (document, docs)))
-            .collect::<HashMap<_, _>>();
-
-        let embeddings = stream::iter(documents_map.iter())
-            // Flatten the documents
-            .flat_map(|(id, (_, docs))| {
-                stream::iter(docs.iter().map(|doc| (id.clone(), doc.clone())))
-            })
-            // Chunk them into N (the emebdding API limit per request)
-            .chunks(M::MAX_DOCUMENTS)
-            // Generate the embeddings
-            .map(|docs| async {
-                let (ids, docs): (Vec<_>, Vec<_>) = docs.into_iter().unzip();
-                Ok::<_, EmbeddingError>(
-                    ids.into_iter()
-                        .zip(self.model.embed_documents(docs).await?.into_iter())
-                        .collect::<Vec<_>>(),
-                )
-            })
-            .boxed()
-            // Parallelize the embeddings generation over 10 concurrent requests
-            .buffer_unordered(max(1, 1024 / M::MAX_DOCUMENTS))
-            .try_fold(vec![], |mut acc, mut embeddings| async move {
-                Ok({
-                    acc.append(&mut embeddings);
-                    acc
-                })
-            })
-            .await?;
-
-        // Assemble the DocumentEmbeddings
-        let mut document_embeddings: HashMap<String, DocumentEmbeddings> = HashMap::new();
-        embeddings.into_iter().for_each(|(id, embedding)| {
-            let (document, _) = documents_map.get(&id).expect("Document not found");
-            let document_embedding =
-                document_embeddings
-                    .entry(id.clone())
-                    .or_insert_with(|| DocumentEmbeddings {
-                        id: id.clone(),
-                        document: document.clone(),
-                        embeddings: vec![],
-                    });
-
-            document_embedding.embeddings.push(embedding);
-        });
-
-        Ok(document_embeddings.into_values().collect())
-    }
-}
diff --git a/rig-core/src/embeddings/builder.rs b/rig-core/src/embeddings/builder.rs
new file mode 100644
index 00000000..f9e80779
--- /dev/null
+++ b/rig-core/src/embeddings/builder.rs
@@ -0,0 +1,387 @@
+//! The module defines the [EmbeddingsBuilder] struct which accumulates objects to be embedded
+//! and batch generates the embeddings for each object when built.
+//! Only types that implement the [Embed] trait can be added to the [EmbeddingsBuilder].
+
+use std::{cmp::max, collections::HashMap};
+
+use futures::{stream, StreamExt};
+
+use crate::{
+    embeddings::{
+        embed::TextEmbedder, Embed, EmbedError, Embedding, EmbeddingError, EmbeddingModel,
+    },
+    OneOrMany,
+};
+
+/// Builder for creating embeddings from one or more documents of type `T`.
+/// Note: `T` can be any type that implements the [Embed] trait.
+///
+/// Using the builder is preferred over using [EmbeddingModel::embed_text] directly as
+/// it will batch the documents in a single request to the model provider.
+///
+/// # Example
+/// ```rust
+/// use std::env;
+///
+/// use rig::{
+///     embeddings::EmbeddingsBuilder,
+///     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
+/// };
+/// use serde::{Deserialize, Serialize};
+///
+/// // Create OpenAI client
+/// let openai_api_key = env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY not set");
+/// let openai_client = Client::new(&openai_api_key);
+///
+/// let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
+///
+/// let embeddings = EmbeddingsBuilder::new(model.clone())
+///     .documents(vec![
+///         "1. *flurbo* (noun): A green alien that lives on cold planets.".to_string(),
+///         "2. *flurbo* (noun): A fictional digital currency that originated in the animated series Rick and Morty.".to_string()
+///         "1. *glarb-glarb* (noun): An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
+///         "2. *glarb-glarb* (noun): A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
+///         "1. *linlingdong* (noun): A term used by inhabitants of the sombrero galaxy to describe humans.".to_string(),
+///         "2. *linlingdong* (noun): A rare, mystical instrument crafted by the ancient monks of the Nebulon Mountain Ranges on the planet Quarm.".to_string()
+///     ])?
+///     .build()
+///     .await?;
+/// ```
+pub struct EmbeddingsBuilder<M: EmbeddingModel, T: Embed> {
+    model: M,
+    documents: Vec<(T, Vec<String>)>,
+}
+
+impl<M: EmbeddingModel, T: Embed> EmbeddingsBuilder<M, T> {
+    /// Create a new embedding builder with the given embedding model
+    pub fn new(model: M) -> Self {
+        Self {
+            model,
+            documents: vec![],
+        }
+    }
+
+    /// Add a document to be embedded to the builder. `document` must implement the [Embed] trait.
+    pub fn document(mut self, document: T) -> Result<Self, EmbedError> {
+        let mut embedder = TextEmbedder::default();
+        document.embed(&mut embedder)?;
+
+        self.documents.push((document, embedder.texts));
+
+        Ok(self)
+    }
+
+    /// Add multiple documents to be embedded to the builder. `documents` must be iteratable
+    /// with items that implement the [Embed] trait.
+    pub fn documents(self, documents: impl IntoIterator<Item = T>) -> Result<Self, EmbedError> {
+        let builder = documents
+            .into_iter()
+            .try_fold(self, |builder, doc| builder.document(doc))?;
+
+        Ok(builder)
+    }
+}
+
+impl<M: EmbeddingModel, T: Embed + Send> EmbeddingsBuilder<M, T> {
+    /// Generate embeddings for all documents in the builder.
+    /// Returns a vector of tuples, where the first element is the document and the second element is the embeddings (either one embedding or many).
+    pub async fn build(self) -> Result<Vec<(T, OneOrMany<Embedding>)>, EmbeddingError> {
+        use stream::TryStreamExt;
+
+        // Store the documents and their texts in a HashMap for easy access.
+        let mut docs = HashMap::new();
+        let mut texts = HashMap::new();
+
+        // Iterate over all documents in the builder and insert their docs and texts into the lookup stores.
+        for (i, (doc, doc_texts)) in self.documents.into_iter().enumerate() {
+            docs.insert(i, doc);
+            texts.insert(i, doc_texts);
+        }
+
+        // Compute the embeddings.
+        let mut embeddings = stream::iter(texts.into_iter())
+            // Merge the texts of each document into a single list of texts.
+            .flat_map(|(i, texts)| stream::iter(texts.into_iter().map(move |text| (i, text))))
+            // Chunk them into batches. Each batch size is at most the embedding API limit per request.
+            .chunks(M::MAX_DOCUMENTS)
+            // Generate the embeddings for each batch.
+            .map(|text| async {
+                let (ids, docs): (Vec<_>, Vec<_>) = text.into_iter().unzip();
+
+                let embeddings = self.model.embed_texts(docs).await?;
+                Ok::<_, EmbeddingError>(ids.into_iter().zip(embeddings).collect::<Vec<_>>())
+            })
+            // Parallelize the embeddings generation over 10 concurrent requests
+            .buffer_unordered(max(1, 1024 / M::MAX_DOCUMENTS))
+            // Collect the embeddings into a HashMap.
+            .try_fold(
+                HashMap::new(),
+                |mut acc: HashMap<_, OneOrMany<Embedding>>, embeddings| async move {
+                    embeddings.into_iter().for_each(|(i, embedding)| {
+                        acc.entry(i)
+                            .and_modify(|embeddings| embeddings.push(embedding.clone()))
+                            .or_insert(OneOrMany::one(embedding.clone()));
+                    });
+
+                    Ok(acc)
+                },
+            )
+            .await?;
+
+        // Merge the embeddings with their respective documents
+        Ok(docs
+            .into_iter()
+            .map(|(i, doc)| {
+                (
+                    doc,
+                    embeddings.remove(&i).expect("Document should be present"),
+                )
+            })
+            .collect())
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::{
+        embeddings::{embed::EmbedError, embed::TextEmbedder, Embedding, EmbeddingModel},
+        Embed,
+    };
+
+    use super::EmbeddingsBuilder;
+
+    #[derive(Clone)]
+    struct Model;
+
+    impl EmbeddingModel for Model {
+        const MAX_DOCUMENTS: usize = 5;
+
+        fn ndims(&self) -> usize {
+            10
+        }
+
+        async fn embed_texts(
+            &self,
+            documents: impl IntoIterator<Item = String> + Send,
+        ) -> Result<Vec<crate::embeddings::Embedding>, crate::embeddings::EmbeddingError> {
+            Ok(documents
+                .into_iter()
+                .map(|doc| Embedding {
+                    document: doc.to_string(),
+                    vec: vec![0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+                })
+                .collect())
+        }
+    }
+
+    #[derive(Clone, Debug)]
+    struct WordDefinition {
+        id: String,
+        definitions: Vec<String>,
+    }
+
+    impl Embed for WordDefinition {
+        fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+            for definition in &self.definitions {
+                embedder.embed(definition.clone());
+            }
+            Ok(())
+        }
+    }
+
+    fn definitions_multiple_text() -> Vec<WordDefinition> {
+        vec![
+            WordDefinition {
+                id: "doc0".to_string(),
+                definitions: vec![
+                    "A green alien that lives on cold planets.".to_string(),
+                    "A fictional digital currency that originated in the animated series Rick and Morty.".to_string()
+                ]
+            },
+            WordDefinition {
+                id: "doc1".to_string(),
+                definitions: vec![
+                    "An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
+                    "A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
+                ]
+            }
+        ]
+    }
+
+    fn definitions_multiple_text_2() -> Vec<WordDefinition> {
+        vec![
+            WordDefinition {
+                id: "doc2".to_string(),
+                definitions: vec!["Another fake definitions".to_string()],
+            },
+            WordDefinition {
+                id: "doc3".to_string(),
+                definitions: vec!["Some fake definition".to_string()],
+            },
+        ]
+    }
+
+    #[derive(Clone, Debug)]
+    struct WordDefinitionSingle {
+        id: String,
+        definition: String,
+    }
+
+    impl Embed for WordDefinitionSingle {
+        fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+            embedder.embed(self.definition.clone());
+            Ok(())
+        }
+    }
+
+    fn definitions_single_text() -> Vec<WordDefinitionSingle> {
+        vec![
+            WordDefinitionSingle {
+                id: "doc0".to_string(),
+                definition: "A green alien that lives on cold planets.".to_string(),
+            },
+            WordDefinitionSingle {
+                id: "doc1".to_string(),
+                definition: "An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
+            }
+        ]
+    }
+
+    #[tokio::test]
+    async fn test_build_multiple_text() {
+        let fake_definitions = definitions_multiple_text();
+
+        let fake_model = Model;
+        let mut result = EmbeddingsBuilder::new(fake_model)
+            .documents(fake_definitions)
+            .unwrap()
+            .build()
+            .await
+            .unwrap();
+
+        result.sort_by(|(fake_definition_1, _), (fake_definition_2, _)| {
+            fake_definition_1.id.cmp(&fake_definition_2.id)
+        });
+
+        assert_eq!(result.len(), 2);
+
+        let first_definition = &result[0];
+        assert_eq!(first_definition.0.id, "doc0");
+        assert_eq!(first_definition.1.len(), 2);
+        assert_eq!(
+            first_definition.1.first().document,
+            "A green alien that lives on cold planets.".to_string()
+        );
+
+        let second_definition = &result[1];
+        assert_eq!(second_definition.0.id, "doc1");
+        assert_eq!(second_definition.1.len(), 2);
+        assert_eq!(
+            second_definition.1.rest()[0].document, "A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
+        )
+    }
+
+    #[tokio::test]
+    async fn test_build_single_text() {
+        let fake_definitions = definitions_single_text();
+
+        let fake_model = Model;
+        let mut result = EmbeddingsBuilder::new(fake_model)
+            .documents(fake_definitions)
+            .unwrap()
+            .build()
+            .await
+            .unwrap();
+
+        result.sort_by(|(fake_definition_1, _), (fake_definition_2, _)| {
+            fake_definition_1.id.cmp(&fake_definition_2.id)
+        });
+
+        assert_eq!(result.len(), 2);
+
+        let first_definition = &result[0];
+        assert_eq!(first_definition.0.id, "doc0");
+        assert_eq!(first_definition.1.len(), 1);
+        assert_eq!(
+            first_definition.1.first().document,
+            "A green alien that lives on cold planets.".to_string()
+        );
+
+        let second_definition = &result[1];
+        assert_eq!(second_definition.0.id, "doc1");
+        assert_eq!(second_definition.1.len(), 1);
+        assert_eq!(
+            second_definition.1.first().document, "An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string()
+        )
+    }
+
+    #[tokio::test]
+    async fn test_build_multiple_and_single_text() {
+        let fake_definitions = definitions_multiple_text();
+        let fake_definitions_single = definitions_multiple_text_2();
+
+        let fake_model = Model;
+        let mut result = EmbeddingsBuilder::new(fake_model)
+            .documents(fake_definitions)
+            .unwrap()
+            .documents(fake_definitions_single)
+            .unwrap()
+            .build()
+            .await
+            .unwrap();
+
+        result.sort_by(|(fake_definition_1, _), (fake_definition_2, _)| {
+            fake_definition_1.id.cmp(&fake_definition_2.id)
+        });
+
+        assert_eq!(result.len(), 4);
+
+        let second_definition = &result[1];
+        assert_eq!(second_definition.0.id, "doc1");
+        assert_eq!(second_definition.1.len(), 2);
+        assert_eq!(
+            second_definition.1.first().document, "An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string()
+        );
+
+        let third_definition = &result[2];
+        assert_eq!(third_definition.0.id, "doc2");
+        assert_eq!(third_definition.1.len(), 1);
+        assert_eq!(
+            third_definition.1.first().document,
+            "Another fake definitions".to_string()
+        )
+    }
+
+    #[tokio::test]
+    async fn test_build_string() {
+        let bindings = definitions_multiple_text();
+        let fake_definitions = bindings.iter().map(|def| def.definitions.clone());
+
+        let fake_model = Model;
+        let mut result = EmbeddingsBuilder::new(fake_model)
+            .documents(fake_definitions)
+            .unwrap()
+            .build()
+            .await
+            .unwrap();
+
+        result.sort_by(|(fake_definition_1, _), (fake_definition_2, _)| {
+            fake_definition_1.cmp(&fake_definition_2)
+        });
+
+        assert_eq!(result.len(), 2);
+
+        let first_definition = &result[0];
+        assert_eq!(first_definition.1.len(), 2);
+        assert_eq!(
+            first_definition.1.first().document,
+            "A green alien that lives on cold planets.".to_string()
+        );
+
+        let second_definition = &result[1];
+        assert_eq!(second_definition.1.len(), 2);
+        assert_eq!(
+            second_definition.1.rest()[0].document, "A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
+        )
+    }
+}
diff --git a/rig-core/src/embeddings/embed.rs b/rig-core/src/embeddings/embed.rs
new file mode 100644
index 00000000..e8acc613
--- /dev/null
+++ b/rig-core/src/embeddings/embed.rs
@@ -0,0 +1,190 @@
+//! The module defines the [Embed] trait, which must be implemented for types
+//! that can be embedded by the [crate::embeddings::EmbeddingsBuilder].
+//!
+//! The module also defines the [EmbedError] struct which is used for when the [Embed::embed]
+//! method of the [Embed] trait fails.
+//!
+//! The module also defines the [TextEmbedder] struct which accumulates string values that need to be embedded.
+//! It is used directly with the [Embed] trait.
+//!
+//! Finally, the module implements [Embed] for many common primitive types.
+
+/// Error type used for when the [Embed::embed] method fo the [Embed] trait fails.
+/// Used by default implementations of [Embed] for common types.
+#[derive(Debug, thiserror::Error)]
+#[error("{0}")]
+pub struct EmbedError(#[from] Box<dyn std::error::Error + Send + Sync>);
+
+impl EmbedError {
+    pub fn new<E: std::error::Error + Send + Sync + 'static>(error: E) -> Self {
+        EmbedError(Box::new(error))
+    }
+}
+
+/// Derive this trait for objects that need to be converted to vector embeddings.
+/// The [Embed::embed] method accumulates string values that need to be embedded by adding them to the [TextEmbedder].
+/// If an error occurs, the method should return [EmbedError].
+/// # Example
+/// ```rust
+/// use std::env;
+///
+/// use serde::{Deserialize, Serialize};
+/// use rig::{Embed, embeddings::{TextEmbedder, EmbedError}};
+///
+/// struct WordDefinition {
+///     id: String,
+///     word: String,
+///     definitions: String,
+/// }
+///
+/// impl Embed for WordDefinition {
+///     fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+///        // Embeddings only need to be generated for `definition` field.
+///        // Split the definitions by comma and collect them into a vector of strings.
+///        // That way, different embeddings can be generated for each definition in the `definitions` string.
+///        self.definitions
+///            .split(",")
+///            .for_each(|s| {
+///                embedder.embed(s.to_string());
+///            });
+///
+///        Ok(())
+///     }
+/// }
+///
+/// let fake_definition = WordDefinition {
+///    id: "1".to_string(),
+///    word: "apple".to_string(),
+///    definitions: "a fruit, a tech company".to_string(),
+/// };
+///
+/// assert_eq!(embeddings::to_texts(fake_definition).unwrap(), vec!["a fruit", " a tech company"]);
+/// ```
+pub trait Embed {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError>;
+}
+
+/// Accumulates string values that need to be embedded.
+/// Used by the [Embed] trait.
+#[derive(Default)]
+pub struct TextEmbedder {
+    pub(crate) texts: Vec<String>,
+}
+
+impl TextEmbedder {
+    /// Adds input `text` string to the list of texts in the [TextEmbedder] that need to be embedded.
+    pub fn embed(&mut self, text: String) {
+        self.texts.push(text);
+    }
+}
+
+/// Utility function that returns a vector of strings that need to be embedded for a
+/// given object that implements the [Embed] trait.
+pub fn to_texts(item: impl Embed) -> Result<Vec<String>, EmbedError> {
+    let mut embedder = TextEmbedder::default();
+    item.embed(&mut embedder)?;
+    Ok(embedder.texts)
+}
+
+// ================================================================
+// Implementations of Embed for common types
+// ================================================================
+
+impl Embed for String {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.clone());
+        Ok(())
+    }
+}
+
+impl Embed for &str {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for i8 {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for i16 {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for i32 {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for i64 {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for i128 {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for f32 {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for f64 {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for bool {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for char {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(self.to_string());
+        Ok(())
+    }
+}
+
+impl Embed for serde_json::Value {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        embedder.embed(serde_json::to_string(self).map_err(EmbedError::new)?);
+        Ok(())
+    }
+}
+
+impl<T: Embed> Embed for &T {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        (*self).embed(embedder)
+    }
+}
+
+impl<T: Embed> Embed for Vec<T> {
+    fn embed(&self, embedder: &mut TextEmbedder) -> Result<(), EmbedError> {
+        for item in self {
+            item.embed(embedder).map_err(EmbedError::new)?;
+        }
+        Ok(())
+    }
+}
diff --git a/rig-core/src/embeddings/embedding.rs b/rig-core/src/embeddings/embedding.rs
new file mode 100644
index 00000000..7c8877d9
--- /dev/null
+++ b/rig-core/src/embeddings/embedding.rs
@@ -0,0 +1,93 @@
+//! The module defines the [EmbeddingModel] trait, which represents an embedding model that can
+//! generate embeddings for documents.
+//!
+//! The module also defines the [Embedding] struct, which represents a single document embedding.
+//!
+//! Finally, the module defines the [EmbeddingError] enum, which represents various errors that
+//! can occur during embedding generation or processing.
+
+use serde::{Deserialize, Serialize};
+
+#[derive(Debug, thiserror::Error)]
+pub enum EmbeddingError {
+    /// Http error (e.g.: connection error, timeout, etc.)
+    #[error("HttpError: {0}")]
+    HttpError(#[from] reqwest::Error),
+
+    /// Json error (e.g.: serialization, deserialization)
+    #[error("JsonError: {0}")]
+    JsonError(#[from] serde_json::Error),
+
+    /// Error processing the document for embedding
+    #[error("DocumentError: {0}")]
+    DocumentError(Box<dyn std::error::Error + Send + Sync + 'static>),
+
+    /// Error parsing the completion response
+    #[error("ResponseError: {0}")]
+    ResponseError(String),
+
+    /// Error returned by the embedding model provider
+    #[error("ProviderError: {0}")]
+    ProviderError(String),
+}
+
+/// Trait for embedding models that can generate embeddings for documents.
+pub trait EmbeddingModel: Clone + Sync + Send {
+    /// The maximum number of documents that can be embedded in a single request.
+    const MAX_DOCUMENTS: usize;
+
+    /// The number of dimensions in the embedding vector.
+    fn ndims(&self) -> usize;
+
+    /// Embed multiple text documents in a single request
+    fn embed_texts(
+        &self,
+        texts: impl IntoIterator<Item = String> + Send,
+    ) -> impl std::future::Future<Output = Result<Vec<Embedding>, EmbeddingError>> + Send;
+
+    /// Embed a single text document.
+    fn embed_text(
+        &self,
+        text: &str,
+    ) -> impl std::future::Future<Output = Result<Embedding, EmbeddingError>> + Send {
+        async {
+            Ok(self
+                .embed_texts(vec![text.to_string()])
+                .await?
+                .pop()
+                .expect("There should be at least one embedding"))
+        }
+    }
+}
+
+/// Struct that holds a single document and its embedding.
+#[derive(Clone, Default, Deserialize, Serialize, Debug)]
+pub struct Embedding {
+    /// The document that was embedded. Used for debugging.
+    pub document: String,
+    /// The embedding vector
+    pub vec: Vec<f64>,
+}
+
+impl PartialEq for Embedding {
+    fn eq(&self, other: &Self) -> bool {
+        self.document == other.document
+    }
+}
+
+impl Eq for Embedding {}
+
+impl Embedding {
+    pub fn distance(&self, other: &Self) -> f64 {
+        let dot_product: f64 = self
+            .vec
+            .iter()
+            .zip(other.vec.iter())
+            .map(|(x, y)| x * y)
+            .sum();
+
+        let product_of_lengths = (self.vec.len() * other.vec.len()) as f64;
+
+        dot_product / product_of_lengths
+    }
+}
diff --git a/rig-core/src/embeddings/mod.rs b/rig-core/src/embeddings/mod.rs
new file mode 100644
index 00000000..1ae16436
--- /dev/null
+++ b/rig-core/src/embeddings/mod.rs
@@ -0,0 +1,14 @@
+//! This module provides functionality for working with embeddings.
+//! Embeddings are numerical representations of documents or other objects, typically used in
+//! natural language processing (NLP) tasks such as text classification, information retrieval,
+//! and document similarity.
+
+pub mod builder;
+pub mod embed;
+pub mod embedding;
+pub mod tool;
+
+pub use builder::EmbeddingsBuilder;
+pub use embed::{to_texts, Embed, EmbedError, TextEmbedder};
+pub use embedding::{Embedding, EmbeddingError, EmbeddingModel};
+pub use tool::ToolSchema;
diff --git a/rig-core/src/embeddings/tool.rs b/rig-core/src/embeddings/tool.rs
new file mode 100644
index 00000000..a8441a23
--- /dev/null
+++ b/rig-core/src/embeddings/tool.rs
@@ -0,0 +1,96 @@
+//! The module defines the [ToolSchema] struct, which is used to embed an object that implements [crate::tool::ToolEmbedding]
+
+use crate::{tool::ToolEmbeddingDyn, Embed};
+use serde::Serialize;
+
+use super::embed::EmbedError;
+
+/// Embeddable document that is used as an intermediate representation of a tool when
+/// RAGging tools.
+#[derive(Clone, Serialize, Default, Eq, PartialEq)]
+pub struct ToolSchema {
+    pub name: String,
+    pub context: serde_json::Value,
+    pub embedding_docs: Vec<String>,
+}
+
+impl Embed for ToolSchema {
+    fn embed(&self, embedder: &mut super::embed::TextEmbedder) -> Result<(), EmbedError> {
+        for doc in &self.embedding_docs {
+            embedder.embed(doc.clone());
+        }
+        Ok(())
+    }
+}
+
+impl ToolSchema {
+    /// Convert item that implements [ToolEmbeddingDyn] to an [ToolSchema].
+    ///
+    /// # Example
+    /// ```rust
+    /// use rig::{
+    ///     completion::ToolDefinition,
+    ///     embeddings::ToolSchema,
+    ///     tool::{Tool, ToolEmbedding, ToolEmbeddingDyn},
+    /// };
+    /// use serde_json::json;
+    ///
+    /// #[derive(Debug, thiserror::Error)]
+    /// #[error("Math error")]
+    /// struct NothingError;
+    ///
+    /// #[derive(Debug, thiserror::Error)]
+    /// #[error("Init error")]
+    /// struct InitError;
+    ///
+    /// struct Nothing;
+    /// impl Tool for Nothing {
+    ///     const NAME: &'static str = "nothing";
+    ///
+    ///     type Error = NothingError;
+    ///     type Args = ();
+    ///     type Output = ();
+    ///
+    ///     async fn definition(&self, _prompt: String) -> ToolDefinition {
+    ///         serde_json::from_value(json!({
+    ///             "name": "nothing",
+    ///             "description": "nothing",
+    ///             "parameters": {}
+    ///         }))
+    ///         .expect("Tool Definition")
+    ///     }
+    ///
+    ///     async fn call(&self, args: Self::Args) -> Result<Self::Output, Self::Error> {
+    ///         Ok(())
+    ///     }
+    /// }
+    ///
+    /// impl ToolEmbedding for Nothing {
+    ///     type InitError = InitError;
+    ///     type Context = ();
+    ///     type State = ();
+    ///
+    ///     fn init(_state: Self::State, _context: Self::Context) -> Result<Self, Self::InitError> {
+    ///         Ok(Nothing)
+    ///     }
+    ///
+    ///     fn embedding_docs(&self) -> Vec<String> {
+    ///         vec!["Do nothing.".into()]
+    ///     }
+    ///
+    ///     fn context(&self) -> Self::Context {}
+    /// }
+    ///
+    /// let tool = ToolSchema::try_from(&Nothing).unwrap();
+    ///
+    /// assert_eq!(tool.name, "nothing".to_string());
+    /// assert_eq!(tool.embedding_docs, vec!["Do nothing.".to_string()]);
+    /// ```
+    pub fn try_from(tool: &dyn ToolEmbeddingDyn) -> Result<Self, EmbedError> {
+        Ok(ToolSchema {
+            name: tool.name(),
+            context: tool.context().map_err(EmbedError::new)?,
+            embedding_docs: tool.embedding_docs(),
+        })
+    }
+}
diff --git a/rig-core/src/lib.rs b/rig-core/src/lib.rs
index 9da3abfc..f1b5427b 100644
--- a/rig-core/src/lib.rs
+++ b/rig-core/src/lib.rs
@@ -50,9 +50,9 @@
 //! RAG systems that can be used to answer questions using a knowledge base.
 //!
 //! ## Vector stores and indexes
-//! Rig defines a common interface for working with vector stores and indexes. Specifically, the library
-//! provides the [VectorStore](crate::vector_store::VectorStore) and [VectorStoreIndex](crate::vector_store::VectorStoreIndex)
-//! traits, which can be implemented on a given type to define vector stores and indices respectively.
+//! Rig provides a common interface for working with vector stores and indexes. Specifically, the library
+//! provides the [VectorStoreIndex](crate::vector_store::VectorStoreIndex)
+//! trait, which can be implemented to define vector stores and indices respectively.
 //! Those can then be used as the knowledge base for a RAG enabled [Agent](crate::agent::Agent), or
 //! as a source of context documents in a custom architecture that use multiple LLMs or agents.
 //!
@@ -85,6 +85,14 @@ pub mod embeddings;
 pub mod extractor;
 pub(crate) mod json_utils;
 pub mod loaders;
+pub mod one_or_many;
 pub mod providers;
 pub mod tool;
 pub mod vector_store;
+
+// Re-export commonly used types and traits
+pub use embeddings::Embed;
+pub use one_or_many::{EmptyListError, OneOrMany};
+
+#[cfg(feature = "derive")]
+pub use rig_derive::Embed;
diff --git a/rig-core/src/loaders/file.rs b/rig-core/src/loaders/file.rs
index 17c2f1f3..84eb1864 100644
--- a/rig-core/src/loaders/file.rs
+++ b/rig-core/src/loaders/file.rs
@@ -162,7 +162,7 @@ impl<'a, T: 'a> FileLoader<'a, Result<T, FileLoaderError>> {
     }
 }
 
-impl<'a> FileLoader<'a, Result<PathBuf, FileLoaderError>> {
+impl FileLoader<'_, Result<PathBuf, FileLoaderError>> {
     /// Creates a new [FileLoader] using a glob pattern to match files.
     ///
     /// # Example
@@ -227,7 +227,7 @@ impl<'a, T> IntoIterator for FileLoader<'a, T> {
     }
 }
 
-impl<'a, T> Iterator for IntoIter<'a, T> {
+impl<T> Iterator for IntoIter<'_, T> {
     type Item = T;
 
     fn next(&mut self) -> Option<Self::Item> {
diff --git a/rig-core/src/loaders/pdf.rs b/rig-core/src/loaders/pdf.rs
index ea18e4e6..410643f8 100644
--- a/rig-core/src/loaders/pdf.rs
+++ b/rig-core/src/loaders/pdf.rs
@@ -335,7 +335,7 @@ impl<'a, T: 'a> PdfFileLoader<'a, Result<T, PdfLoaderError>> {
     }
 }
 
-impl<'a> PdfFileLoader<'a, Result<PathBuf, FileLoaderError>> {
+impl PdfFileLoader<'_, Result<PathBuf, FileLoaderError>> {
     /// Creates a new [PdfFileLoader] using a glob pattern to match files.
     ///
     /// # Example
@@ -396,7 +396,7 @@ impl<'a, T> IntoIterator for PdfFileLoader<'a, T> {
     }
 }
 
-impl<'a, T> Iterator for IntoIter<'a, T> {
+impl<T> Iterator for IntoIter<'_, T> {
     type Item = T;
 
     fn next(&mut self) -> Option<Self::Item> {
diff --git a/rig-core/src/one_or_many.rs b/rig-core/src/one_or_many.rs
new file mode 100644
index 00000000..64584603
--- /dev/null
+++ b/rig-core/src/one_or_many.rs
@@ -0,0 +1,302 @@
+/// Struct containing either a single item or a list of items of type T.
+/// If a single item is present, `first` will contain it and `rest` will be empty.
+/// If multiple items are present, `first` will contain the first item and `rest` will contain the rest.
+/// IMPORTANT: this struct cannot be created with an empty vector.
+/// OneOrMany objects can only be created using OneOrMany::from() or OneOrMany::try_from().
+#[derive(PartialEq, Eq, Debug, Clone)]
+pub struct OneOrMany<T> {
+    /// First item in the list.
+    first: T,
+    /// Rest of the items in the list.
+    rest: Vec<T>,
+}
+
+/// Error type for when trying to create a OneOrMany object with an empty vector.
+#[derive(Debug, thiserror::Error)]
+#[error("Cannot create OneOrMany with an empty vector.")]
+pub struct EmptyListError;
+
+impl<T: Clone> OneOrMany<T> {
+    /// Get the first item in the list.
+    pub fn first(&self) -> T {
+        self.first.clone()
+    }
+
+    /// Get the rest of the items in the list (excluding the first one).
+    pub fn rest(&self) -> Vec<T> {
+        self.rest.clone()
+    }
+
+    /// After `OneOrMany<T>` is created, add an item of type T to the `rest`.
+    pub fn push(&mut self, item: T) {
+        self.rest.push(item);
+    }
+
+    /// Length of all items in `OneOrMany<T>`.
+    pub fn len(&self) -> usize {
+        1 + self.rest.len()
+    }
+
+    /// If `OneOrMany<T>` is empty. This will always be false because you cannot create an empty `OneOrMany<T>`.
+    /// This method is required when the method `len` exists.
+    pub fn is_empty(&self) -> bool {
+        false
+    }
+
+    /// Create a OneOrMany object with a single item of any type.
+    pub fn one(item: T) -> Self {
+        OneOrMany {
+            first: item,
+            rest: vec![],
+        }
+    }
+
+    /// Create a OneOrMany object with a vector of items of any type.
+    pub fn many(items: Vec<T>) -> Result<Self, EmptyListError> {
+        let mut iter = items.into_iter();
+        Ok(OneOrMany {
+            first: match iter.next() {
+                Some(item) => item,
+                None => return Err(EmptyListError),
+            },
+            rest: iter.collect(),
+        })
+    }
+
+    /// Merge a list of OneOrMany items into a single OneOrMany item.
+    pub fn merge(one_or_many_items: Vec<OneOrMany<T>>) -> Result<Self, EmptyListError> {
+        let items = one_or_many_items
+            .into_iter()
+            .flat_map(|one_or_many| one_or_many.into_iter())
+            .collect::<Vec<_>>();
+
+        OneOrMany::many(items)
+    }
+
+    pub fn iter(&self) -> Iter<T> {
+        Iter {
+            first: Some(&self.first),
+            rest: self.rest.iter(),
+        }
+    }
+
+    pub fn iter_mut(&mut self) -> IterMut<'_, T> {
+        IterMut {
+            first: Some(&mut self.first),
+            rest: self.rest.iter_mut(),
+        }
+    }
+}
+
+// ================================================================
+// Implementations of Iterator for OneOrMany
+//   - OneOrMany<T>::iter() -> iterate over references of T objects
+//   - OneOrMany<T>::into_iter() -> iterate over owned T objects
+//   - OneOrMany<T>::iter_mut() -> iterate over mutable references of T objects
+// ================================================================
+
+/// Struct returned by call to `OneOrMany::iter()`.
+pub struct Iter<'a, T> {
+    // References.
+    first: Option<&'a T>,
+    rest: std::slice::Iter<'a, T>,
+}
+
+/// Implement `Iterator` for `Iter<T>`.
+/// The Item type of the `Iterator` trait is a reference of `T`.
+impl<'a, T> Iterator for Iter<'a, T> {
+    type Item = &'a T;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        if let Some(first) = self.first.take() {
+            Some(first)
+        } else {
+            self.rest.next()
+        }
+    }
+}
+
+/// Struct returned by call to `OneOrMany::into_iter()`.
+pub struct IntoIter<T> {
+    // Owned.
+    first: Option<T>,
+    rest: std::vec::IntoIter<T>,
+}
+
+/// Implement `Iterator` for `IntoIter<T>`.
+impl<T: Clone> IntoIterator for OneOrMany<T> {
+    type Item = T;
+    type IntoIter = IntoIter<T>;
+
+    fn into_iter(self) -> Self::IntoIter {
+        IntoIter {
+            first: Some(self.first),
+            rest: self.rest.into_iter(),
+        }
+    }
+}
+
+/// Implement `Iterator` for `IntoIter<T>`.
+/// The Item type of the `Iterator` trait is an owned `T`.
+impl<T: Clone> Iterator for IntoIter<T> {
+    type Item = T;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        if let Some(first) = self.first.take() {
+            Some(first)
+        } else {
+            self.rest.next()
+        }
+    }
+}
+
+/// Struct returned by call to `OneOrMany::iter_mut()`.
+pub struct IterMut<'a, T> {
+    // Mutable references.
+    first: Option<&'a mut T>,
+    rest: std::slice::IterMut<'a, T>,
+}
+
+// Implement `Iterator` for `IterMut<T>`.
+// The Item type of the `Iterator` trait is a mutable reference of `OneOrMany<T>`.
+impl<'a, T> Iterator for IterMut<'a, T> {
+    type Item = &'a mut T;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        if let Some(first) = self.first.take() {
+            Some(first)
+        } else {
+            self.rest.next()
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::OneOrMany;
+
+    #[test]
+    fn test_single() {
+        let one_or_many = OneOrMany::one("hello".to_string());
+
+        assert_eq!(one_or_many.iter().count(), 1);
+
+        one_or_many.iter().for_each(|i| {
+            assert_eq!(i, "hello");
+        });
+    }
+
+    #[test]
+    fn test() {
+        let one_or_many = OneOrMany::many(vec!["hello".to_string(), "word".to_string()]).unwrap();
+
+        assert_eq!(one_or_many.iter().count(), 2);
+
+        one_or_many.iter().enumerate().for_each(|(i, item)| {
+            if i == 0 {
+                assert_eq!(item, "hello");
+            }
+            if i == 1 {
+                assert_eq!(item, "word");
+            }
+        });
+    }
+
+    #[test]
+    fn test_one_or_many_into_iter_single() {
+        let one_or_many = OneOrMany::one("hello".to_string());
+
+        assert_eq!(one_or_many.clone().into_iter().count(), 1);
+
+        one_or_many.into_iter().for_each(|i| {
+            assert_eq!(i, "hello".to_string());
+        });
+    }
+
+    #[test]
+    fn test_one_or_many_into_iter() {
+        let one_or_many = OneOrMany::many(vec!["hello".to_string(), "word".to_string()]).unwrap();
+
+        assert_eq!(one_or_many.clone().into_iter().count(), 2);
+
+        one_or_many.into_iter().enumerate().for_each(|(i, item)| {
+            if i == 0 {
+                assert_eq!(item, "hello".to_string());
+            }
+            if i == 1 {
+                assert_eq!(item, "word".to_string());
+            }
+        });
+    }
+
+    #[test]
+    fn test_one_or_many_merge() {
+        let one_or_many_1 = OneOrMany::many(vec!["hello".to_string(), "word".to_string()]).unwrap();
+
+        let one_or_many_2 = OneOrMany::one("sup".to_string());
+
+        let merged = OneOrMany::merge(vec![one_or_many_1, one_or_many_2]).unwrap();
+
+        assert_eq!(merged.iter().count(), 3);
+
+        merged.iter().enumerate().for_each(|(i, item)| {
+            if i == 0 {
+                assert_eq!(item, "hello");
+            }
+            if i == 1 {
+                assert_eq!(item, "word");
+            }
+            if i == 2 {
+                assert_eq!(item, "sup");
+            }
+        });
+    }
+
+    #[test]
+    fn test_mut_single() {
+        let mut one_or_many = OneOrMany::one("hello".to_string());
+
+        assert_eq!(one_or_many.iter_mut().count(), 1);
+
+        one_or_many.iter_mut().for_each(|i| {
+            assert_eq!(i, "hello");
+        });
+    }
+
+    #[test]
+    fn test_mut() {
+        let mut one_or_many =
+            OneOrMany::many(vec!["hello".to_string(), "word".to_string()]).unwrap();
+
+        assert_eq!(one_or_many.iter_mut().count(), 2);
+
+        one_or_many.iter_mut().enumerate().for_each(|(i, item)| {
+            if i == 0 {
+                item.push_str(" world");
+                assert_eq!(item, "hello world");
+            }
+            if i == 1 {
+                assert_eq!(item, "word");
+            }
+        });
+    }
+
+    #[test]
+    fn test_one_or_many_error() {
+        assert!(OneOrMany::<String>::many(vec![]).is_err())
+    }
+
+    #[test]
+    fn test_len_single() {
+        let one_or_many = OneOrMany::one("hello".to_string());
+
+        assert_eq!(one_or_many.len(), 1);
+    }
+
+    #[test]
+    fn test_len_many() {
+        let one_or_many = OneOrMany::many(vec!["hello".to_string(), "word".to_string()]).unwrap();
+
+        assert_eq!(one_or_many.len(), 2);
+    }
+}
diff --git a/rig-core/src/providers/cohere.rs b/rig-core/src/providers/cohere.rs
index baa6f1d5..883204d4 100644
--- a/rig-core/src/providers/cohere.rs
+++ b/rig-core/src/providers/cohere.rs
@@ -15,7 +15,7 @@ use crate::{
     completion::{self, CompletionError},
     embeddings::{self, EmbeddingError, EmbeddingsBuilder},
     extractor::ExtractorBuilder,
-    json_utils,
+    json_utils, Embed,
 };
 
 use schemars::JsonSchema;
@@ -92,7 +92,11 @@ impl Client {
         EmbeddingModel::new(self.clone(), model, input_type, ndims)
     }
 
-    pub fn embeddings(&self, model: &str, input_type: &str) -> EmbeddingsBuilder<EmbeddingModel> {
+    pub fn embeddings<D: Embed>(
+        &self,
+        model: &str,
+        input_type: &str,
+    ) -> EmbeddingsBuilder<EmbeddingModel, D> {
         EmbeddingsBuilder::new(self.embedding_model(model, input_type))
     }
 
@@ -207,7 +211,7 @@ impl embeddings::EmbeddingModel for EmbeddingModel {
         self.ndims
     }
 
-    async fn embed_documents(
+    async fn embed_texts(
         &self,
         documents: impl IntoIterator<Item = String>,
     ) -> Result<Vec<embeddings::Embedding>, EmbeddingError> {
@@ -238,11 +242,14 @@ impl embeddings::EmbeddingModel for EmbeddingModel {
                     };
 
                     if response.embeddings.len() != documents.len() {
-                        return Err(EmbeddingError::DocumentError(format!(
-                            "Expected {} embeddings, got {}",
-                            documents.len(),
-                            response.embeddings.len()
-                        )));
+                        return Err(EmbeddingError::DocumentError(
+                            format!(
+                                "Expected {} embeddings, got {}",
+                                documents.len(),
+                                response.embeddings.len()
+                            )
+                            .into(),
+                        ));
                     }
 
                     Ok(response
diff --git a/rig-core/src/providers/gemini/client.rs b/rig-core/src/providers/gemini/client.rs
index c22e6996..04316dfe 100644
--- a/rig-core/src/providers/gemini/client.rs
+++ b/rig-core/src/providers/gemini/client.rs
@@ -2,6 +2,7 @@ use crate::{
     agent::AgentBuilder,
     embeddings::{self},
     extractor::ExtractorBuilder,
+    Embed,
 };
 use schemars::JsonSchema;
 use serde::{Deserialize, Serialize};
@@ -104,7 +105,10 @@ impl Client {
     ///     .await
     ///     .expect("Failed to embed documents");
     /// ```
-    pub fn embeddings(&self, model: &str) -> embeddings::EmbeddingsBuilder<EmbeddingModel> {
+    pub fn embeddings<D: Embed>(
+        &self,
+        model: &str,
+    ) -> embeddings::EmbeddingsBuilder<EmbeddingModel, D> {
         embeddings::EmbeddingsBuilder::new(self.embedding_model(model))
     }
 
diff --git a/rig-core/src/providers/gemini/embedding.rs b/rig-core/src/providers/gemini/embedding.rs
index 1249387a..c2b76e02 100644
--- a/rig-core/src/providers/gemini/embedding.rs
+++ b/rig-core/src/providers/gemini/embedding.rs
@@ -41,7 +41,7 @@ impl embeddings::EmbeddingModel for EmbeddingModel {
         }
     }
 
-    async fn embed_documents(
+    async fn embed_texts(
         &self,
         documents: impl IntoIterator<Item = String> + Send,
     ) -> Result<Vec<embeddings::Embedding>, EmbeddingError> {
diff --git a/rig-core/src/providers/openai.rs b/rig-core/src/providers/openai.rs
index 33aa7b79..6b5a3079 100644
--- a/rig-core/src/providers/openai.rs
+++ b/rig-core/src/providers/openai.rs
@@ -11,9 +11,9 @@
 use crate::{
     agent::AgentBuilder,
     completion::{self, CompletionError, CompletionRequest},
-    embeddings::{self, EmbeddingError},
+    embeddings::{self, EmbeddingError, EmbeddingsBuilder},
     extractor::ExtractorBuilder,
-    json_utils,
+    json_utils, Embed,
 };
 use schemars::JsonSchema;
 use serde::{Deserialize, Serialize};
@@ -121,8 +121,8 @@ impl Client {
     ///     .await
     ///     .expect("Failed to embed documents");
     /// ```
-    pub fn embeddings(&self, model: &str) -> embeddings::EmbeddingsBuilder<EmbeddingModel> {
-        embeddings::EmbeddingsBuilder::new(self.embedding_model(model))
+    pub fn embeddings<D: Embed>(&self, model: &str) -> EmbeddingsBuilder<EmbeddingModel, D> {
+        EmbeddingsBuilder::new(self.embedding_model(model))
     }
 
     /// Create a completion model with the given name.
@@ -219,7 +219,7 @@ pub struct EmbeddingData {
     pub index: usize,
 }
 
-#[derive(Debug, Deserialize)]
+#[derive(Clone, Debug, Deserialize)]
 pub struct Usage {
     pub prompt_tokens: usize,
     pub total_tokens: usize,
@@ -229,7 +229,7 @@ impl std::fmt::Display for Usage {
     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
         write!(
             f,
-            "Prompt tokens: {}\nTotal tokens: {}",
+            "Prompt tokens: {} Total tokens: {}",
             self.prompt_tokens, self.total_tokens
         )
     }
@@ -249,7 +249,7 @@ impl embeddings::EmbeddingModel for EmbeddingModel {
         self.ndims
     }
 
-    async fn embed_documents(
+    async fn embed_texts(
         &self,
         documents: impl IntoIterator<Item = String>,
     ) -> Result<Vec<embeddings::Embedding>, EmbeddingError> {
@@ -535,7 +535,7 @@ impl completion::CompletionModel for CompletionModel {
                 ApiResponse::Ok(response) => {
                     tracing::info!(target: "rig",
                         "OpenAI completion token usage: {:?}",
-                        response.usage
+                        response.usage.clone().map(|usage| format!("{usage}")).unwrap_or("N/A".to_string())
                     );
                     response.try_into()
                 }
diff --git a/rig-core/src/providers/perplexity.rs b/rig-core/src/providers/perplexity.rs
index 41be57f7..fa1e34fb 100644
--- a/rig-core/src/providers/perplexity.rs
+++ b/rig-core/src/providers/perplexity.rs
@@ -155,7 +155,7 @@ impl std::fmt::Display for Usage {
     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
         write!(
             f,
-            "Prompt tokens: {}\nCompletion tokens: {}\nTotal tokens: {}",
+            "Prompt tokens: {}\nCompletion tokens: {} Total tokens: {}",
             self.prompt_tokens, self.completion_tokens, self.total_tokens
         )
     }
diff --git a/rig-core/src/providers/xai/client.rs b/rig-core/src/providers/xai/client.rs
index e03c6978..6af7cd31 100644
--- a/rig-core/src/providers/xai/client.rs
+++ b/rig-core/src/providers/xai/client.rs
@@ -2,6 +2,7 @@ use crate::{
     agent::AgentBuilder,
     embeddings::{self},
     extractor::ExtractorBuilder,
+    Embed,
 };
 use schemars::JsonSchema;
 use serde::{Deserialize, Serialize};
@@ -113,7 +114,10 @@ impl Client {
     ///     .await
     ///     .expect("Failed to embed documents");
     /// ```
-    pub fn embeddings(&self, model: &str) -> embeddings::EmbeddingsBuilder<EmbeddingModel> {
+    pub fn embeddings<D: Embed>(
+        &self,
+        model: &str,
+    ) -> embeddings::EmbeddingsBuilder<EmbeddingModel, D> {
         embeddings::EmbeddingsBuilder::new(self.embedding_model(model))
     }
 
diff --git a/rig-core/src/providers/xai/embedding.rs b/rig-core/src/providers/xai/embedding.rs
index 1c588071..f0ad1c92 100644
--- a/rig-core/src/providers/xai/embedding.rs
+++ b/rig-core/src/providers/xai/embedding.rs
@@ -69,7 +69,7 @@ impl embeddings::EmbeddingModel for EmbeddingModel {
         self.ndims
     }
 
-    async fn embed_documents(
+    async fn embed_texts(
         &self,
         documents: impl IntoIterator<Item = String>,
     ) -> Result<Vec<embeddings::Embedding>, EmbeddingError> {
diff --git a/rig-core/src/tool.rs b/rig-core/src/tool.rs
index 83a27640..e540c918 100644
--- a/rig-core/src/tool.rs
+++ b/rig-core/src/tool.rs
@@ -14,7 +14,10 @@ use std::{collections::HashMap, pin::Pin};
 use futures::Future;
 use serde::{Deserialize, Serialize};
 
-use crate::completion::{self, ToolDefinition};
+use crate::{
+    completion::{self, ToolDefinition},
+    embeddings::{embed::EmbedError, tool::ToolSchema},
+};
 
 #[derive(Debug, thiserror::Error)]
 pub enum ToolError {
@@ -334,6 +337,22 @@ impl ToolSet {
         }
         Ok(docs)
     }
+
+    /// Convert tools in self to objects of type ToolSchema.
+    /// This is necessary because when adding tools to the EmbeddingBuilder because all
+    /// documents added to the builder must all be of the same type.
+    pub fn schemas(&self) -> Result<Vec<ToolSchema>, EmbedError> {
+        self.tools
+            .values()
+            .filter_map(|tool_type| {
+                if let ToolType::Embedding(tool) = tool_type {
+                    Some(ToolSchema::try_from(&**tool))
+                } else {
+                    None
+                }
+            })
+            .collect::<Result<Vec<_>, _>>()
+    }
 }
 
 #[derive(Default)]
diff --git a/rig-core/src/vector_store/in_memory_store.rs b/rig-core/src/vector_store/in_memory_store.rs
index eda13ba4..931946eb 100644
--- a/rig-core/src/vector_store/in_memory_store.rs
+++ b/rig-core/src/vector_store/in_memory_store.rs
@@ -7,28 +7,32 @@ use std::{
 use ordered_float::OrderedFloat;
 use serde::{Deserialize, Serialize};
 
-use super::{VectorStore, VectorStoreError, VectorStoreIndex};
-use crate::embeddings::{DocumentEmbeddings, Embedding, EmbeddingModel, EmbeddingsBuilder};
+use super::{VectorStoreError, VectorStoreIndex};
+use crate::{
+    embeddings::{Embedding, EmbeddingModel},
+    OneOrMany,
+};
 
 /// InMemoryVectorStore is a simple in-memory vector store that stores embeddings
 /// in-memory using a HashMap.
-#[derive(Clone, Default, Deserialize, Serialize)]
-pub struct InMemoryVectorStore {
-    /// The embeddings are stored in a HashMap with the document ID as the key.
-    embeddings: HashMap<String, DocumentEmbeddings>,
+#[derive(Clone, Default)]
+pub struct InMemoryVectorStore<D: Serialize> {
+    /// The embeddings are stored in a HashMap.
+    /// Hashmap key is the document id.
+    /// Hashmap value is a tuple of the serializable document and its corresponding embeddings.
+    embeddings: HashMap<String, (D, OneOrMany<Embedding>)>,
 }
 
-impl InMemoryVectorStore {
+impl<D: Serialize + Eq> InMemoryVectorStore<D> {
     /// Implement vector search on InMemoryVectorStore.
     /// To be used by implementations of top_n and top_n_ids methods on VectorStoreIndex trait for InMemoryVectorStore.
-    fn vector_search(&self, prompt_embedding: &Embedding, n: usize) -> EmbeddingRanking {
+    fn vector_search(&self, prompt_embedding: &Embedding, n: usize) -> EmbeddingRanking<D> {
         // Sort documents by best embedding distance
-        let mut docs: EmbeddingRanking = BinaryHeap::new();
+        let mut docs = BinaryHeap::new();
 
-        for (id, doc_embeddings) in self.embeddings.iter() {
+        for (id, (doc, embeddings)) in self.embeddings.iter() {
             // Get the best context for the document given the prompt
-            if let Some((distance, embed_doc)) = doc_embeddings
-                .embeddings
+            if let Some((distance, embed_doc)) = embeddings
                 .iter()
                 .map(|embedding| {
                     (
@@ -38,12 +42,7 @@ impl InMemoryVectorStore {
                 })
                 .max_by(|a, b| a.0.cmp(&b.0))
             {
-                docs.push(Reverse(RankingItem(
-                    distance,
-                    id,
-                    doc_embeddings,
-                    embed_doc,
-                )));
+                docs.push(Reverse(RankingItem(distance, id, doc, embed_doc)));
             };
 
             // If the heap size exceeds n, pop the least old element.
@@ -63,77 +62,72 @@ impl InMemoryVectorStore {
 
         docs
     }
-}
 
-/// RankingItem(distance, document_id, document, embed_doc)
-#[derive(Eq, PartialEq)]
-struct RankingItem<'a>(
-    OrderedFloat<f64>,
-    &'a String,
-    &'a DocumentEmbeddings,
-    &'a String,
-);
-
-impl Ord for RankingItem<'_> {
-    fn cmp(&self, other: &Self) -> std::cmp::Ordering {
-        self.0.cmp(&other.0)
-    }
-}
+    /// Add documents to the store.
+    /// Returns the store with the added documents.
+    pub fn add_documents(
+        mut self,
+        documents: Vec<(String, D, OneOrMany<Embedding>)>,
+    ) -> Result<Self, VectorStoreError> {
+        for (id, doc, embeddings) in documents {
+            self.embeddings.insert(id, (doc, embeddings));
+        }
 
-impl PartialOrd for RankingItem<'_> {
-    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
-        Some(self.cmp(other))
+        Ok(self)
     }
-}
 
-type EmbeddingRanking<'a> = BinaryHeap<Reverse<RankingItem<'a>>>;
-
-impl VectorStore for InMemoryVectorStore {
-    type Q = ();
-
-    async fn add_documents(
-        &mut self,
-        documents: Vec<DocumentEmbeddings>,
-    ) -> Result<(), VectorStoreError> {
-        for doc in documents {
-            self.embeddings.insert(doc.id.clone(), doc);
+    /// Add documents to the store. Define a function that takes as input the reference of the document and returns its id.
+    /// Returns the store with the added documents.
+    pub fn add_documents_with_id(
+        mut self,
+        documents: Vec<(D, OneOrMany<Embedding>)>,
+        id_f: fn(&D) -> String,
+    ) -> Result<Self, VectorStoreError> {
+        for (doc, embeddings) in documents {
+            let id = id_f(&doc);
+            self.embeddings.insert(id, (doc, embeddings));
         }
 
-        Ok(())
+        Ok(self)
     }
 
-    async fn get_document<T: for<'a> Deserialize<'a>>(
+    /// Get the document by its id and deserialize it into the given type.
+    pub fn get_document<T: for<'a> Deserialize<'a>>(
         &self,
         id: &str,
     ) -> Result<Option<T>, VectorStoreError> {
         Ok(self
             .embeddings
             .get(id)
-            .map(|document| serde_json::from_value(document.document.clone()))
+            .map(|(doc, _)| serde_json::from_str(&serde_json::to_string(doc)?))
             .transpose()?)
     }
+}
 
-    async fn get_document_embeddings(
-        &self,
-        id: &str,
-    ) -> Result<Option<DocumentEmbeddings>, VectorStoreError> {
-        Ok(self.embeddings.get(id).cloned())
+/// RankingItem(distance, document_id, serializable document, embeddings document)
+#[derive(Eq, PartialEq)]
+struct RankingItem<'a, D: Serialize>(OrderedFloat<f64>, &'a String, &'a D, &'a String);
+
+impl<D: Serialize + Eq> Ord for RankingItem<'_, D> {
+    fn cmp(&self, other: &Self) -> std::cmp::Ordering {
+        self.0.cmp(&other.0)
     }
+}
 
-    async fn get_document_by_query(
-        &self,
-        _query: Self::Q,
-    ) -> Result<Option<DocumentEmbeddings>, VectorStoreError> {
-        Ok(None)
+impl<D: Serialize + Eq> PartialOrd for RankingItem<'_, D> {
+    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
+        Some(self.cmp(other))
     }
 }
 
-impl InMemoryVectorStore {
-    pub fn index<M: EmbeddingModel>(self, model: M) -> InMemoryVectorIndex<M> {
+type EmbeddingRanking<'a, D> = BinaryHeap<Reverse<RankingItem<'a, D>>>;
+
+impl<D: Serialize> InMemoryVectorStore<D> {
+    pub fn index<M: EmbeddingModel>(self, model: M) -> InMemoryVectorIndex<M, D> {
         InMemoryVectorIndex::new(model, self)
     }
 
-    pub fn iter(&self) -> impl Iterator<Item = (&String, &DocumentEmbeddings)> {
+    pub fn iter(&self) -> impl Iterator<Item = (&String, &(D, OneOrMany<Embedding>))> {
         self.embeddings.iter()
     }
 
@@ -144,54 +138,19 @@ impl InMemoryVectorStore {
     pub fn is_empty(&self) -> bool {
         self.embeddings.is_empty()
     }
-
-    /// Uitilty method to create an InMemoryVectorStore from a list of embeddings.
-    pub async fn from_embeddings(
-        embeddings: Vec<DocumentEmbeddings>,
-    ) -> Result<Self, VectorStoreError> {
-        let mut store = Self::default();
-        store.add_documents(embeddings).await?;
-        Ok(store)
-    }
-
-    /// Create an InMemoryVectorStore from a list of documents.
-    /// The documents are serialized to JSON and embedded using the provided embedding model.
-    /// The resulting embeddings are stored in an InMemoryVectorStore created by the method.
-    pub async fn from_documents<M: EmbeddingModel, T: Serialize>(
-        embedding_model: M,
-        documents: &[(String, T)],
-    ) -> Result<Self, VectorStoreError> {
-        let embeddings = documents
-            .iter()
-            .fold(
-                EmbeddingsBuilder::new(embedding_model),
-                |builder, (id, doc)| {
-                    builder.json_document(
-                        id,
-                        serde_json::to_value(doc).expect("Document should be serializable"),
-                        vec![serde_json::to_string(doc).expect("Document should be serializable")],
-                    )
-                },
-            )
-            .build()
-            .await?;
-
-        let store = Self::from_embeddings(embeddings).await?;
-        Ok(store)
-    }
 }
 
-pub struct InMemoryVectorIndex<M: EmbeddingModel> {
+pub struct InMemoryVectorIndex<M: EmbeddingModel, D: Serialize> {
     model: M,
-    pub store: InMemoryVectorStore,
+    pub store: InMemoryVectorStore<D>,
 }
 
-impl<M: EmbeddingModel> InMemoryVectorIndex<M> {
-    pub fn new(model: M, store: InMemoryVectorStore) -> Self {
+impl<M: EmbeddingModel, D: Serialize> InMemoryVectorIndex<M, D> {
+    pub fn new(model: M, store: InMemoryVectorStore<D>) -> Self {
         Self { model, store }
     }
 
-    pub fn iter(&self) -> impl Iterator<Item = (&String, &DocumentEmbeddings)> {
+    pub fn iter(&self) -> impl Iterator<Item = (&String, &(D, OneOrMany<Embedding>))> {
         self.store.iter()
     }
 
@@ -202,66 +161,30 @@ impl<M: EmbeddingModel> InMemoryVectorIndex<M> {
     pub fn is_empty(&self) -> bool {
         self.store.is_empty()
     }
-
-    /// Create an InMemoryVectorIndex from a list of documents.
-    /// The documents are serialized to JSON and embedded using the provided embedding model.
-    /// The resulting embeddings are stored in an InMemoryVectorStore created by the method.
-    /// The InMemoryVectorIndex is then created from the store and the provided query model.
-    pub async fn from_documents<T: Serialize>(
-        embedding_model: M,
-        query_model: M,
-        documents: &[(String, T)],
-    ) -> Result<Self, VectorStoreError> {
-        let mut store = InMemoryVectorStore::default();
-
-        let embeddings = documents
-            .iter()
-            .fold(
-                EmbeddingsBuilder::new(embedding_model),
-                |builder, (id, doc)| {
-                    builder.json_document(
-                        id,
-                        serde_json::to_value(doc).expect("Document should be serializable"),
-                        vec![serde_json::to_string(doc).expect("Document should be serializable")],
-                    )
-                },
-            )
-            .build()
-            .await?;
-
-        store.add_documents(embeddings).await?;
-        Ok(store.index(query_model))
-    }
-
-    /// Utility method to create an InMemoryVectorIndex from a list of embeddings
-    /// and an embedding model.
-    pub async fn from_embeddings(
-        query_model: M,
-        embeddings: Vec<DocumentEmbeddings>,
-    ) -> Result<Self, VectorStoreError> {
-        let store = InMemoryVectorStore::from_embeddings(embeddings).await?;
-        Ok(store.index(query_model))
-    }
 }
 
-impl<M: EmbeddingModel + std::marker::Sync> VectorStoreIndex for InMemoryVectorIndex<M> {
+impl<M: EmbeddingModel + Sync, D: Serialize + Sync + Send + Eq> VectorStoreIndex
+    for InMemoryVectorIndex<M, D>
+{
     async fn top_n<T: for<'a> Deserialize<'a>>(
         &self,
         query: &str,
         n: usize,
     ) -> Result<Vec<(f64, String, T)>, VectorStoreError> {
-        let prompt_embedding = &self.model.embed_document(query).await?;
+        let prompt_embedding = &self.model.embed_text(query).await?;
 
         let docs = self.store.vector_search(prompt_embedding, n);
 
         // Return n best
         docs.into_iter()
-            .map(|Reverse(RankingItem(distance, _, doc, _))| {
+            .map(|Reverse(RankingItem(distance, id, doc, _))| {
                 Ok((
                     distance.0,
-                    doc.id.clone(),
-                    serde_json::from_value(doc.document.clone())
-                        .map_err(VectorStoreError::JsonError)?,
+                    id.clone(),
+                    serde_json::from_str(
+                        &serde_json::to_string(doc).map_err(VectorStoreError::JsonError)?,
+                    )
+                    .map_err(VectorStoreError::JsonError)?,
                 ))
             })
             .collect::<Result<Vec<_>, _>>()
@@ -272,13 +195,159 @@ impl<M: EmbeddingModel + std::marker::Sync> VectorStoreIndex for InMemoryVectorI
         query: &str,
         n: usize,
     ) -> Result<Vec<(f64, String)>, VectorStoreError> {
-        let prompt_embedding = &self.model.embed_document(query).await?;
+        let prompt_embedding = &self.model.embed_text(query).await?;
 
         let docs = self.store.vector_search(prompt_embedding, n);
 
         // Return n best
         docs.into_iter()
-            .map(|Reverse(RankingItem(distance, _, doc, _))| Ok((distance.0, doc.id.clone())))
+            .map(|Reverse(RankingItem(distance, id, _, _))| Ok((distance.0, id.clone())))
             .collect::<Result<Vec<_>, _>>()
     }
 }
+
+#[cfg(test)]
+mod tests {
+    use std::cmp::Reverse;
+
+    use crate::{embeddings::embedding::Embedding, OneOrMany};
+
+    use super::{InMemoryVectorStore, RankingItem};
+
+    #[test]
+    fn test_single_embedding() {
+        let index = InMemoryVectorStore::default()
+            .add_documents(vec![
+                (
+                    "doc1".to_string(),
+                    "glarb-garb",
+                    OneOrMany::one(Embedding {
+                        document: "glarb-garb".to_string(),
+                        vec: vec![0.1, 0.1, 0.5],
+                    }),
+                ),
+                (
+                    "doc2".to_string(),
+                    "marble-marble",
+                    OneOrMany::one(Embedding {
+                        document: "marble-marble".to_string(),
+                        vec: vec![0.7, -0.3, 0.0],
+                    }),
+                ),
+                (
+                    "doc3".to_string(),
+                    "flumb-flumb",
+                    OneOrMany::one(Embedding {
+                        document: "flumb-flumb".to_string(),
+                        vec: vec![0.3, 0.7, 0.1],
+                    }),
+                ),
+            ])
+            .unwrap();
+
+        let ranking = index.vector_search(
+            &Embedding {
+                document: "glarby-glarble".to_string(),
+                vec: vec![0.0, 0.1, 0.6],
+            },
+            1,
+        );
+
+        assert_eq!(
+            ranking
+                .into_iter()
+                .map(|Reverse(RankingItem(distance, id, doc, _))| {
+                    (
+                        distance.0,
+                        id.clone(),
+                        serde_json::from_str(&serde_json::to_string(doc).unwrap()).unwrap(),
+                    )
+                })
+                .collect::<Vec<(_, _, String)>>(),
+            vec![(
+                0.034444444444444444,
+                "doc1".to_string(),
+                "glarb-garb".to_string()
+            )]
+        )
+    }
+
+    #[test]
+    fn test_multiple_embeddings() {
+        let index = InMemoryVectorStore::default()
+            .add_documents(vec![
+                (
+                    "doc1".to_string(),
+                    "glarb-garb",
+                    OneOrMany::many(vec![
+                        Embedding {
+                            document: "glarb-garb".to_string(),
+                            vec: vec![0.1, 0.1, 0.5],
+                        },
+                        Embedding {
+                            document: "don't-choose-me".to_string(),
+                            vec: vec![-0.5, 0.9, 0.1],
+                        },
+                    ])
+                    .unwrap(),
+                ),
+                (
+                    "doc2".to_string(),
+                    "marble-marble",
+                    OneOrMany::many(vec![
+                        Embedding {
+                            document: "marble-marble".to_string(),
+                            vec: vec![0.7, -0.3, 0.0],
+                        },
+                        Embedding {
+                            document: "sandwich".to_string(),
+                            vec: vec![0.5, 0.5, -0.7],
+                        },
+                    ])
+                    .unwrap(),
+                ),
+                (
+                    "doc3".to_string(),
+                    "flumb-flumb",
+                    OneOrMany::many(vec![
+                        Embedding {
+                            document: "flumb-flumb".to_string(),
+                            vec: vec![0.3, 0.7, 0.1],
+                        },
+                        Embedding {
+                            document: "banana".to_string(),
+                            vec: vec![0.1, -0.5, -0.5],
+                        },
+                    ])
+                    .unwrap(),
+                ),
+            ])
+            .unwrap();
+
+        let ranking = index.vector_search(
+            &Embedding {
+                document: "glarby-glarble".to_string(),
+                vec: vec![0.0, 0.1, 0.6],
+            },
+            1,
+        );
+
+        assert_eq!(
+            ranking
+                .into_iter()
+                .map(|Reverse(RankingItem(distance, id, doc, _))| {
+                    (
+                        distance.0,
+                        id.clone(),
+                        serde_json::from_str(&serde_json::to_string(doc).unwrap()).unwrap(),
+                    )
+                })
+                .collect::<Vec<(_, _, String)>>(),
+            vec![(
+                0.034444444444444444,
+                "doc1".to_string(),
+                "glarb-garb".to_string()
+            )]
+        )
+    }
+}
diff --git a/rig-core/src/vector_store/mod.rs b/rig-core/src/vector_store/mod.rs
index 405ef83b..3d6e8369 100644
--- a/rig-core/src/vector_store/mod.rs
+++ b/rig-core/src/vector_store/mod.rs
@@ -2,7 +2,7 @@ use futures::future::BoxFuture;
 use serde::Deserialize;
 use serde_json::Value;
 
-use crate::embeddings::{DocumentEmbeddings, EmbeddingError};
+use crate::embeddings::EmbeddingError;
 
 pub mod in_memory_store;
 
@@ -16,44 +16,17 @@ pub enum VectorStoreError {
     JsonError(#[from] serde_json::Error),
 
     #[error("Datastore error: {0}")]
-    DatastoreError(#[from] Box<dyn std::error::Error + Send + Sync>),
-}
-
-/// Trait for vector stores
-pub trait VectorStore: Send + Sync {
-    /// Query type for the vector store
-    type Q;
-
-    /// Add a list of documents to the vector store
-    fn add_documents(
-        &mut self,
-        documents: Vec<DocumentEmbeddings>,
-    ) -> impl std::future::Future<Output = Result<(), VectorStoreError>> + Send;
-
-    /// Get the embeddings of a document by its id
-    fn get_document_embeddings(
-        &self,
-        id: &str,
-    ) -> impl std::future::Future<Output = Result<Option<DocumentEmbeddings>, VectorStoreError>> + Send;
-
-    /// Get the document by its id and deserialize it into the given type
-    fn get_document<T: for<'a> Deserialize<'a>>(
-        &self,
-        id: &str,
-    ) -> impl std::future::Future<Output = Result<Option<T>, VectorStoreError>> + Send;
+    DatastoreError(#[from] Box<dyn std::error::Error + Send + Sync + 'static>),
 
-    /// Get the document by a query and deserialize it into the given type
-    fn get_document_by_query(
-        &self,
-        query: Self::Q,
-    ) -> impl std::future::Future<Output = Result<Option<DocumentEmbeddings>, VectorStoreError>> + Send;
+    #[error("Missing Id: {0}")]
+    MissingIdError(String),
 }
 
 /// Trait for vector store indexes
 pub trait VectorStoreIndex: Send + Sync {
     /// Get the top n documents based on the distance to the given query.
     /// The result is a list of tuples of the form (score, id, document)
-    fn top_n<T: for<'a> Deserialize<'a> + std::marker::Send>(
+    fn top_n<T: for<'a> Deserialize<'a> + Send>(
         &self,
         query: &str,
         n: usize,
diff --git a/rig-core/tests/embed_macro.rs b/rig-core/tests/embed_macro.rs
new file mode 100644
index 00000000..daf6dcf8
--- /dev/null
+++ b/rig-core/tests/embed_macro.rs
@@ -0,0 +1,217 @@
+use rig::{
+    embeddings::{self, embed::EmbedError, TextEmbedder},
+    Embed,
+};
+use serde::Serialize;
+
+#[test]
+fn test_custom_embed() {
+    #[derive(Embed)]
+    struct WordDefinition {
+        #[allow(dead_code)]
+        id: String,
+        #[allow(dead_code)]
+        word: String,
+        #[embed(embed_with = "custom_embedding_function")]
+        definition: Definition,
+    }
+
+    #[derive(Serialize, Clone)]
+    struct Definition {
+        word: String,
+        link: String,
+        speech: String,
+    }
+
+    fn custom_embedding_function(
+        embedder: &mut TextEmbedder,
+        definition: Definition,
+    ) -> Result<(), EmbedError> {
+        embedder.embed(serde_json::to_string(&definition).map_err(EmbedError::new)?);
+
+        Ok(())
+    }
+
+    let definition = WordDefinition {
+        id: "doc1".to_string(),
+        word: "house".to_string(),
+        definition: Definition {
+            speech: "noun".to_string(),
+            word: "a building in which people live; residence for human beings.".to_string(),
+            link: "https://www.dictionary.com/browse/house".to_string(),
+        },
+    };
+
+    assert_eq!(
+        embeddings::to_texts(definition).unwrap(),
+            vec!["{\"word\":\"a building in which people live; residence for human beings.\",\"link\":\"https://www.dictionary.com/browse/house\",\"speech\":\"noun\"}".to_string()]
+        )
+}
+
+#[test]
+fn test_custom_and_basic_embed() {
+    #[derive(Embed)]
+    struct WordDefinition {
+        #[allow(dead_code)]
+        id: String,
+        #[embed]
+        word: String,
+        #[embed(embed_with = "custom_embedding_function")]
+        definition: Definition,
+    }
+
+    #[derive(Serialize, Clone)]
+    struct Definition {
+        word: String,
+        link: String,
+        speech: String,
+    }
+
+    fn custom_embedding_function(
+        embedder: &mut TextEmbedder,
+        definition: Definition,
+    ) -> Result<(), EmbedError> {
+        embedder.embed(serde_json::to_string(&definition).map_err(EmbedError::new)?);
+
+        Ok(())
+    }
+
+    let definition = WordDefinition {
+        id: "doc1".to_string(),
+        word: "house".to_string(),
+        definition: Definition {
+            speech: "noun".to_string(),
+            word: "a building in which people live; residence for human beings.".to_string(),
+            link: "https://www.dictionary.com/browse/house".to_string(),
+        },
+    };
+
+    let texts = embeddings::to_texts(definition).unwrap();
+
+    assert_eq!(
+        texts,
+        vec![
+            "house".to_string(),
+            "{\"word\":\"a building in which people live; residence for human beings.\",\"link\":\"https://www.dictionary.com/browse/house\",\"speech\":\"noun\"}".to_string()
+        ]
+    );
+}
+
+#[test]
+fn test_single_embed() {
+    #[derive(Embed)]
+    struct WordDefinition {
+        #[allow(dead_code)]
+        id: String,
+        #[allow(dead_code)]
+        word: String,
+        #[embed]
+        definition: String,
+    }
+
+    let definition = "a building in which people live; residence for human beings.".to_string();
+
+    let word_definition = WordDefinition {
+        id: "doc1".to_string(),
+        word: "house".to_string(),
+        definition: definition.clone(),
+    };
+
+    assert_eq!(
+        embeddings::to_texts(word_definition).unwrap(),
+        vec![definition]
+    )
+}
+
+#[test]
+fn test_embed_vec_non_string() {
+    #[derive(Embed)]
+    struct Company {
+        #[allow(dead_code)]
+        id: String,
+        #[allow(dead_code)]
+        company: String,
+        #[embed]
+        employee_ages: Vec<i32>,
+    }
+
+    let company = Company {
+        id: "doc1".to_string(),
+        company: "Google".to_string(),
+        employee_ages: vec![25, 30, 35, 40],
+    };
+
+    assert_eq!(
+        embeddings::to_texts(company).unwrap(),
+        vec![
+            "25".to_string(),
+            "30".to_string(),
+            "35".to_string(),
+            "40".to_string()
+        ]
+    );
+}
+
+#[test]
+fn test_embed_vec_string() {
+    #[derive(Embed)]
+    struct Company {
+        #[allow(dead_code)]
+        id: String,
+        #[allow(dead_code)]
+        company: String,
+        #[embed]
+        employee_names: Vec<String>,
+    }
+
+    let company = Company {
+        id: "doc1".to_string(),
+        company: "Google".to_string(),
+        employee_names: vec![
+            "Alice".to_string(),
+            "Bob".to_string(),
+            "Charlie".to_string(),
+            "David".to_string(),
+        ],
+    };
+
+    assert_eq!(
+        embeddings::to_texts(company).unwrap(),
+        vec![
+            "Alice".to_string(),
+            "Bob".to_string(),
+            "Charlie".to_string(),
+            "David".to_string()
+        ]
+    );
+}
+
+#[test]
+fn test_multiple_embed_tags() {
+    #[derive(Embed)]
+    struct Company {
+        #[allow(dead_code)]
+        id: String,
+        #[embed]
+        company: String,
+        #[embed]
+        employee_ages: Vec<i32>,
+    }
+
+    let company = Company {
+        id: "doc1".to_string(),
+        company: "Google".to_string(),
+        employee_ages: vec![25, 30, 35, 40],
+    };
+
+    assert_eq!(
+        embeddings::to_texts(company).unwrap(),
+        vec![
+            "Google".to_string(),
+            "25".to_string(),
+            "30".to_string(),
+            "35".to_string(),
+            "40".to_string()
+        ]
+    );
+}
diff --git a/rig-lancedb/Cargo.toml b/rig-lancedb/Cargo.toml
index 0eb5fdbe..12d51b08 100644
--- a/rig-lancedb/Cargo.toml
+++ b/rig-lancedb/Cargo.toml
@@ -18,3 +18,15 @@ futures = "0.3.30"
 [dev-dependencies]
 tokio = "1.40.0"
 anyhow = "1.0.89"
+
+[[example]]
+name = "vector_search_local_ann"
+required-features = ["rig-core/derive"]
+
+[[example]]
+name = "vector_search_local_enn"
+required-features = ["rig-core/derive"]
+
+[[example]]
+name = "vector_search_s3_ann"
+required-features = ["rig-core/derive"]
diff --git a/rig-lancedb/examples/fixtures/lib.rs b/rig-lancedb/examples/fixtures/lib.rs
index 9a91432e..94704822 100644
--- a/rig-lancedb/examples/fixtures/lib.rs
+++ b/rig-lancedb/examples/fixtures/lib.rs
@@ -2,13 +2,39 @@ use std::sync::Arc;
 
 use arrow_array::{types::Float64Type, ArrayRef, FixedSizeListArray, RecordBatch, StringArray};
 use lancedb::arrow::arrow_schema::{DataType, Field, Fields, Schema};
-use rig::embeddings::DocumentEmbeddings;
+use rig::embeddings::Embedding;
+use rig::{Embed, OneOrMany};
+use serde::Deserialize;
+
+#[derive(Embed, Clone, Deserialize, Debug)]
+pub struct WordDefinition {
+    pub id: String,
+    #[embed]
+    pub definition: String,
+}
+
+pub fn word_definitions() -> Vec<WordDefinition> {
+    vec![
+        WordDefinition {
+            id: "doc0".to_string(),
+            definition: "Definition of *flumbrel (noun)*: a small, seemingly insignificant item that you constantly lose or misplace, such as a pen, hair tie, or remote control.".to_string()
+        },
+        WordDefinition {
+            id: "doc1".to_string(),
+            definition: "Definition of *zindle (verb)*: to pretend to be working on something important while actually doing something completely unrelated or unproductive.".to_string()
+        },
+        WordDefinition {
+            id: "doc2".to_string(),
+            definition: "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.".to_string()
+        }
+    ]
+}
 
 // Schema of table in LanceDB.
 pub fn schema(dims: usize) -> Schema {
     Schema::new(Fields::from(vec![
         Field::new("id", DataType::Utf8, false),
-        Field::new("content", DataType::Utf8, false),
+        Field::new("definition", DataType::Utf8, false),
         Field::new(
             "embedding",
             DataType::FixedSizeList(
@@ -20,40 +46,37 @@ pub fn schema(dims: usize) -> Schema {
     ]))
 }
 
-// Convert DocumentEmbeddings objects to a RecordBatch.
+// Convert WordDefinition objects and their embedding to a RecordBatch.
 pub fn as_record_batch(
-    records: Vec<DocumentEmbeddings>,
+    records: Vec<(WordDefinition, OneOrMany<Embedding>)>,
     dims: usize,
 ) -> Result<RecordBatch, lancedb::arrow::arrow_schema::ArrowError> {
     let id = StringArray::from_iter_values(
         records
             .iter()
-            .flat_map(|record| (0..record.embeddings.len()).map(|i| format!("{}-{i}", record.id)))
+            .map(|(WordDefinition { id, .. }, _)| id)
             .collect::<Vec<_>>(),
     );
 
-    let content = StringArray::from_iter_values(
+    let definition = StringArray::from_iter_values(
         records
             .iter()
-            .flat_map(|record| {
-                record
-                    .embeddings
-                    .iter()
-                    .map(|embedding| embedding.document.clone())
-            })
+            .map(|(WordDefinition { definition, .. }, _)| definition)
             .collect::<Vec<_>>(),
     );
 
     let embedding = FixedSizeListArray::from_iter_primitive::<Float64Type, _, _>(
         records
             .into_iter()
-            .flat_map(|record| {
-                record
-                    .embeddings
-                    .into_iter()
-                    .map(|embedding| embedding.vec.into_iter().map(Some).collect::<Vec<_>>())
-                    .map(Some)
-                    .collect::<Vec<_>>()
+            .map(|(_, embeddings)| {
+                Some(
+                    embeddings
+                        .first()
+                        .vec
+                        .into_iter()
+                        .map(Some)
+                        .collect::<Vec<_>>(),
+                )
             })
             .collect::<Vec<_>>(),
         dims as i32,
@@ -61,7 +84,7 @@ pub fn as_record_batch(
 
     RecordBatch::try_from_iter(vec![
         ("id", Arc::new(id) as ArrayRef),
-        ("content", Arc::new(content) as ArrayRef),
+        ("definition", Arc::new(definition) as ArrayRef),
         ("embedding", Arc::new(embedding) as ArrayRef),
     ])
 }
diff --git a/rig-lancedb/examples/vector_search_local_ann.rs b/rig-lancedb/examples/vector_search_local_ann.rs
index 3ecd6b23..03636089 100644
--- a/rig-lancedb/examples/vector_search_local_ann.rs
+++ b/rig-lancedb/examples/vector_search_local_ann.rs
@@ -1,25 +1,18 @@
 use std::{env, sync::Arc};
 
 use arrow_array::RecordBatchIterator;
-use fixture::{as_record_batch, schema};
+use fixture::{as_record_batch, schema, word_definitions, WordDefinition};
 use lancedb::index::vector::IvfPqIndexBuilder;
-use rig::vector_store::VectorStoreIndex;
 use rig::{
     embeddings::{EmbeddingModel, EmbeddingsBuilder},
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
+    vector_store::VectorStoreIndex,
 };
-use rig_lancedb::{LanceDbVectorStore, SearchParams};
-use serde::Deserialize;
+use rig_lancedb::{LanceDbVectorIndex, SearchParams};
 
 #[path = "./fixtures/lib.rs"]
 mod fixture;
 
-#[derive(Deserialize, Debug)]
-pub struct VectorSearchResult {
-    pub id: String,
-    pub content: String,
-}
-
 #[tokio::main]
 async fn main() -> Result<(), anyhow::Error> {
     // Initialize OpenAI client. Use this to generate embeddings (and generate test data for RAG demo).
@@ -32,27 +25,27 @@ async fn main() -> Result<(), anyhow::Error> {
     // Initialize LanceDB locally.
     let db = lancedb::connect("data/lancedb-store").execute().await?;
 
-    // Set up test data for RAG demo
-    let definition = "Definition of *flumbuzzle (verb)*: to bewilder or confuse someone completely, often by using nonsensical or overly complex explanations or instructions.".to_string();
-
-    // Note: need at least 256 rows in order to create an index so copy the definition 256 times for testing purposes.
-    let definitions = vec![definition; 256];
-
     // Generate embeddings for the test data.
     let embeddings = EmbeddingsBuilder::new(model.clone())
-        .simple_document("doc0", "Definition of *flumbrel (noun)*: a small, seemingly insignificant item that you constantly lose or misplace, such as a pen, hair tie, or remote control.")
-        .simple_document("doc1", "Definition of *zindle (verb)*: to pretend to be working on something important while actually doing something completely unrelated or unproductive")
-        .simple_document("doc2", "Definition of *glimber (adjective)*: describing a state of excitement mixed with nervousness, often experienced before an important event or decision.")
-        .simple_documents(definitions.clone().into_iter().enumerate().map(|(i, def)| (format!("doc{}", i+3), def)).collect())
+        .documents(word_definitions())?
+        // Note: need at least 256 rows in order to create an index so copy the definition 256 times for testing purposes.
+        .documents(
+            (0..256)
+                .map(|i| WordDefinition {
+                    id: format!("doc{}", i),
+                    definition: "Definition of *flumbuzzle (noun)*: A sudden, inexplicable urge to rearrange or reorganize small objects, such as desk items or books, for no apparent reason.".to_string()
+                })
+        )?
         .build()
         .await?;
 
-    // Create table with embeddings.
-    let record_batch = as_record_batch(embeddings, model.ndims());
     let table = db
         .create_table(
             "definitions",
-            RecordBatchIterator::new(vec![record_batch], Arc::new(schema(model.ndims()))),
+            RecordBatchIterator::new(
+                vec![as_record_batch(embeddings, model.ndims())],
+                Arc::new(schema(model.ndims())),
+            ),
         )
         .execute()
         .await?;
@@ -68,11 +61,11 @@ async fn main() -> Result<(), anyhow::Error> {
 
     // Define search_params params that will be used by the vector store to perform the vector search.
     let search_params = SearchParams::default();
-    let vector_store = LanceDbVectorStore::new(table, model, "id", search_params).await?;
+    let vector_store_index = LanceDbVectorIndex::new(table, model, "id", search_params).await?;
 
     // Query the index
-    let results = vector_store
-        .top_n::<VectorSearchResult>("My boss says I zindle too much, what does that mean?", 1)
+    let results = vector_store_index
+        .top_n::<WordDefinition>("My boss says I zindle too much, what does that mean?", 1)
         .await?;
 
     println!("Results: {:?}", results);
diff --git a/rig-lancedb/examples/vector_search_local_enn.rs b/rig-lancedb/examples/vector_search_local_enn.rs
index 5932dcd0..0244d33e 100644
--- a/rig-lancedb/examples/vector_search_local_enn.rs
+++ b/rig-lancedb/examples/vector_search_local_enn.rs
@@ -1,13 +1,13 @@
 use std::{env, sync::Arc};
 
 use arrow_array::RecordBatchIterator;
-use fixture::{as_record_batch, schema};
+use fixture::{as_record_batch, schema, word_definitions};
 use rig::{
     embeddings::{EmbeddingModel, EmbeddingsBuilder},
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
     vector_store::VectorStoreIndexDyn,
 };
-use rig_lancedb::{LanceDbVectorStore, SearchParams};
+use rig_lancedb::{LanceDbVectorIndex, SearchParams};
 
 #[path = "./fixtures/lib.rs"]
 mod fixture;
@@ -21,10 +21,9 @@ async fn main() -> Result<(), anyhow::Error> {
     // Select the embedding model and generate our embeddings
     let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
 
+    // Generate embeddings for the test data.
     let embeddings = EmbeddingsBuilder::new(model.clone())
-        .simple_document("doc0", "Definition of *flumbrel (noun)*: a small, seemingly insignificant item that you constantly lose or misplace, such as a pen, hair tie, or remote control.")
-        .simple_document("doc1", "Definition of *zindle (verb)*: to pretend to be working on something important while actually doing something completely unrelated or unproductive")
-        .simple_document("doc2", "Definition of *glimber (adjective)*: describing a state of excitement mixed with nervousness, often experienced before an important event or decision.")
+        .documents(word_definitions())?
         .build()
         .await?;
 
@@ -34,17 +33,18 @@ async fn main() -> Result<(), anyhow::Error> {
     // Initialize LanceDB locally.
     let db = lancedb::connect("data/lancedb-store").execute().await?;
 
-    // Create table with embeddings.
-    let record_batch = as_record_batch(embeddings, model.ndims());
     let table = db
         .create_table(
             "definitions",
-            RecordBatchIterator::new(vec![record_batch], Arc::new(schema(model.ndims()))),
+            RecordBatchIterator::new(
+                vec![as_record_batch(embeddings, model.ndims())],
+                Arc::new(schema(model.ndims())),
+            ),
         )
         .execute()
         .await?;
 
-    let vector_store = LanceDbVectorStore::new(table, model, "id", search_params).await?;
+    let vector_store = LanceDbVectorIndex::new(table, model, "id", search_params).await?;
 
     // Query the index
     let results = vector_store
diff --git a/rig-lancedb/examples/vector_search_s3_ann.rs b/rig-lancedb/examples/vector_search_s3_ann.rs
index 70f0c8c5..f296d1d7 100644
--- a/rig-lancedb/examples/vector_search_s3_ann.rs
+++ b/rig-lancedb/examples/vector_search_s3_ann.rs
@@ -1,25 +1,18 @@
 use std::{env, sync::Arc};
 
 use arrow_array::RecordBatchIterator;
-use fixture::{as_record_batch, schema};
+use fixture::{as_record_batch, schema, word_definitions, WordDefinition};
 use lancedb::{index::vector::IvfPqIndexBuilder, DistanceType};
 use rig::{
     embeddings::{EmbeddingModel, EmbeddingsBuilder},
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
     vector_store::VectorStoreIndex,
 };
-use rig_lancedb::{LanceDbVectorStore, SearchParams};
-use serde::Deserialize;
+use rig_lancedb::{LanceDbVectorIndex, SearchParams};
 
 #[path = "./fixtures/lib.rs"]
 mod fixture;
 
-#[derive(Deserialize, Debug)]
-pub struct VectorSearchResult {
-    pub id: String,
-    pub content: String,
-}
-
 // Note: see docs to deploy LanceDB on other cloud providers such as google and azure.
 // https://lancedb.github.io/lancedb/guides/storage/
 #[tokio::main]
@@ -38,27 +31,27 @@ async fn main() -> Result<(), anyhow::Error> {
         .execute()
         .await?;
 
-    // Set up test data for RAG demo
-    let definition = "Definition of *flumbuzzle (verb)*: to bewilder or confuse someone completely, often by using nonsensical or overly complex explanations or instructions.".to_string();
-
-    // Note: need at least 256 rows in order to create an index so copy the definition 256 times for testing purposes.
-    let definitions = vec![definition; 256];
-
     // Generate embeddings for the test data.
     let embeddings = EmbeddingsBuilder::new(model.clone())
-        .simple_document("doc0", "Definition of *flumbrel (noun)*: a small, seemingly insignificant item that you constantly lose or misplace, such as a pen, hair tie, or remote control.")
-        .simple_document("doc1", "Definition of *zindle (verb)*: to pretend to be working on something important while actually doing something completely unrelated or unproductive")
-        .simple_document("doc2", "Definition of *glimber (adjective)*: describing a state of excitement mixed with nervousness, often experienced before an important event or decision.")
-        .simple_documents(definitions.clone().into_iter().enumerate().map(|(i, def)| (format!("doc{}", i+3), def)).collect())
+        .documents(word_definitions())?
+        // Note: need at least 256 rows in order to create an index so copy the definition 256 times for testing purposes.
+        .documents(
+            (0..256)
+                .map(|i| WordDefinition {
+                    id: format!("doc{}", i),
+                    definition: "Definition of *flumbuzzle (noun)*: A sudden, inexplicable urge to rearrange or reorganize small objects, such as desk items or books, for no apparent reason.".to_string()
+                })
+        )?
         .build()
         .await?;
 
-    // Create table with embeddings.
-    let record_batch = as_record_batch(embeddings, model.ndims());
     let table = db
         .create_table(
             "definitions",
-            RecordBatchIterator::new(vec![record_batch], Arc::new(schema(model.ndims()))),
+            RecordBatchIterator::new(
+                vec![as_record_batch(embeddings, model.ndims())],
+                Arc::new(schema(model.ndims())),
+            ),
         )
         .execute()
         .await?;
@@ -80,11 +73,11 @@ async fn main() -> Result<(), anyhow::Error> {
     // Define search_params params that will be used by the vector store to perform the vector search.
     let search_params = SearchParams::default().distance_type(DistanceType::Cosine);
 
-    let vector_store = LanceDbVectorStore::new(table, model, "id", search_params).await?;
+    let vector_store = LanceDbVectorIndex::new(table, model, "id", search_params).await?;
 
     // Query the index
     let results = vector_store
-        .top_n::<VectorSearchResult>("I'm always looking for my phone, I always seem to forget it in the most counterintuitive places. What's the word for this feeling?", 1)
+        .top_n::<WordDefinition>("I'm always looking for my phone, I always seem to forget it in the most counterintuitive places. What's the word for this feeling?", 1)
         .await?;
 
     println!("Results: {:?}", results);
diff --git a/rig-lancedb/src/lib.rs b/rig-lancedb/src/lib.rs
index 13009bf9..7567bc60 100644
--- a/rig-lancedb/src/lib.rs
+++ b/rig-lancedb/src/lib.rs
@@ -3,7 +3,7 @@ use lancedb::{
     DistanceType,
 };
 use rig::{
-    embeddings::EmbeddingModel,
+    embeddings::embedding::EmbeddingModel,
     vector_store::{VectorStoreError, VectorStoreIndex},
 };
 use serde::Deserialize;
@@ -20,75 +20,17 @@ fn serde_to_rig_error(e: serde_json::Error) -> VectorStoreError {
     VectorStoreError::JsonError(e)
 }
 
+/// Type on which vector searches can be performed for a lanceDb table.
 /// # Example
 /// ```
-/// use std::{env, sync::Arc};
+/// use rig_lancedb::{LanceDbVectorIndex, SearchParams};
+/// use rig::embeddings::EmbeddingModel;
 ///
-/// use arrow_array::RecordBatchIterator;
-/// use fixture::{as_record_batch, schema};
-/// use rig::{
-///     embeddings::{EmbeddingModel, EmbeddingsBuilder},
-///     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
-///     vector_store::VectorStoreIndexDyn,
-/// };
-/// use rig_lancedb::{LanceDbVectorStore, SearchParams};
-/// use serde::Deserialize;
-///
-/// #[derive(Deserialize, Debug)]
-/// pub struct VectorSearchResult {
-///     pub id: String,
-///     pub content: String,
-/// }
-///
-/// // Initialize OpenAI client. Use this to generate embeddings (and generate test data for RAG demo).
-/// let openai_api_key = env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY not set");
-/// let openai_client = Client::new(&openai_api_key);
-///
-/// // Select the embedding model and generate our embeddings
-/// let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
-///
-/// let embeddings = EmbeddingsBuilder::new(model.clone())
-///     .simple_document("doc0", "Definition of *flumbrel (noun)*: a small, seemingly insignificant item that you constantly lose or misplace, such as a pen, hair tie, or remote control.")
-///     .simple_document("doc1", "Definition of *zindle (verb)*: to pretend to be working on something important while actually doing something completely unrelated or unproductive")
-///     .simple_document("doc2", "Definition of *glimber (adjective)*: describing a state of excitement mixed with nervousness, often experienced before an important event or decision.")
-///     .build()
-///     .await?;
-///
-/// // Define search_params params that will be used by the vector store to perform the vector search.
-/// let search_params = SearchParams::default();
-///
-/// // Initialize LanceDB locally.
-/// let db = lancedb::connect("data/lancedb-store").execute().await?;
-///
-/// // Create table with embeddings.
-/// let record_batch = as_record_batch(embeddings, model.ndims());
-/// let table = db
-///     .create_table(
-///         "definitions",
-///         RecordBatchIterator::new(vec![record_batch], Arc::new(schema(model.ndims()))),
-///     )
-///     .execute()
-///     .await?;
-///
-/// let vector_store = LanceDbVectorStore::new(table, model, "id", search_params).await?;
-///
-/// // Query the index
-/// let results = vector_store
-/// .top_n("My boss says I zindle too much, what does that mean?", 1)
-/// .await?
-/// .into_iter()
-/// .map(|(score, id, doc)| {
-///     anyhow::Ok((
-///         score,
-///         id,
-///         serde_json::from_value::<VectorSearchResult>(doc)?,
-///     ))
-/// })
-/// .collect::<Result<Vec<_>, _>>()?;
-///
-/// println!("Results: {:?}", results);
+/// let table: table: lancedb::Table = db.create_table(""); // <-- Replace with your lancedb table here.
+/// let model: model: EmbeddingModel = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002); // <-- Replace with your embedding model here.
+/// let vector_store_index = LanceDbVectorIndex::new(table, model, "id", SearchParams::default()).await?;
 /// ```
-pub struct LanceDbVectorStore<M: EmbeddingModel> {
+pub struct LanceDbVectorIndex<M: EmbeddingModel> {
     /// Defines which model is used to generate embeddings for the vector store.
     model: M,
     /// LanceDB table containing embeddings.
@@ -99,7 +41,24 @@ pub struct LanceDbVectorStore<M: EmbeddingModel> {
     search_params: SearchParams,
 }
 
-impl<M: EmbeddingModel> LanceDbVectorStore<M> {
+impl<M: EmbeddingModel> LanceDbVectorIndex<M> {
+    /// Create an instance of `LanceDbVectorIndex` with an existing table and model.
+    /// Define the id field name of the table.
+    /// Define search parameters that will be used to perform vector searches on the table.
+    pub async fn new(
+        table: lancedb::Table,
+        model: M,
+        id_field: &str,
+        search_params: SearchParams,
+    ) -> Result<Self, lancedb::Error> {
+        Ok(Self {
+            table,
+            model,
+            id_field: id_field.to_string(),
+            search_params,
+        })
+    }
+
     /// Apply the search_params to the vector query.
     /// This is a helper function used by the methods `top_n` and `top_n_ids` of the `VectorStoreIndex` trait.
     fn build_query(&self, mut query: VectorQuery) -> VectorQuery {
@@ -151,6 +110,10 @@ pub enum SearchType {
 }
 
 /// Parameters used to perform a vector search on a LanceDb table.
+/// # Example
+/// ```
+/// let search_params = SearchParams::default().distance_type(DistanceType::Cosine);
+/// ```
 #[derive(Debug, Clone, Default)]
 pub struct SearchParams {
     distance_type: Option<DistanceType>,
@@ -211,32 +174,28 @@ impl SearchParams {
     }
 }
 
-impl<M: EmbeddingModel> LanceDbVectorStore<M> {
-    /// Create an instance of `LanceDbVectorStore` with an existing table and model.
-    /// Define the id field name of the table.
-    /// Define search parameters that will be used to perform vector searches on the table.
-    pub async fn new(
-        table: lancedb::Table,
-        model: M,
-        id_field: &str,
-        search_params: SearchParams,
-    ) -> Result<Self, lancedb::Error> {
-        Ok(Self {
-            table,
-            model,
-            id_field: id_field.to_string(),
-            search_params,
-        })
-    }
-}
-
-impl<M: EmbeddingModel + std::marker::Sync + Send> VectorStoreIndex for LanceDbVectorStore<M> {
-    async fn top_n<T: for<'a> Deserialize<'a> + std::marker::Send>(
+impl<M: EmbeddingModel + Sync + Send> VectorStoreIndex for LanceDbVectorIndex<M> {
+    /// Implement the `top_n` method of the `VectorStoreIndex` trait for `LanceDbVectorIndex`.
+    /// # Example
+    /// ```
+    /// use rig_lancedb::{LanceDbVectorIndex, SearchParams};
+    /// use rig::embeddings::EmbeddingModel;
+    ///
+    /// let table: lancedb::Table = db.create_table("fake_definitions"); // <-- Replace with your lancedb table here.
+    /// let model: EmbeddingModel = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002); // <-- Replace with your embedding model here.
+    /// let vector_store_index = LanceDbVectorIndex::new(table, model, "id", SearchParams::default()).await?;
+    ///
+    /// // Query the index
+    /// let result = vector_store_index
+    ///     .top_n::<String>("My boss says I zindle too much, what does that mean?", 1)
+    ///     .await?;
+    /// ```
+    async fn top_n<T: for<'a> Deserialize<'a> + Send>(
         &self,
         query: &str,
         n: usize,
     ) -> Result<Vec<(f64, String, T)>, VectorStoreError> {
-        let prompt_embedding = self.model.embed_document(query).await?;
+        let prompt_embedding = self.model.embed_text(query).await?;
 
         let query = self
             .table
@@ -268,12 +227,24 @@ impl<M: EmbeddingModel + std::marker::Sync + Send> VectorStoreIndex for LanceDbV
             .collect()
     }
 
+    /// Implement the `top_n_ids` method of the `VectorStoreIndex` trait for `LanceDbVectorIndex`.
+    /// # Example
+    /// ```
+    /// let table: table: lancedb::Table = db.create_table(""); // <-- Replace with your lancedb table here.
+    /// let model: model: EmbeddingModel = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002); // <-- Replace with your embedding model here.
+    /// let vector_store_index = LanceDbVectorIndex::new(table, model, "id", SearchParams::default()).await?;
+    ///
+    /// // Query the index
+    /// let result = vector_store_index
+    ///     .top_n_ids("My boss says I zindle too much, what does that mean?", 1)
+    ///     .await?;
+    /// ```
     async fn top_n_ids(
         &self,
         query: &str,
         n: usize,
     ) -> Result<Vec<(f64, String)>, VectorStoreError> {
-        let prompt_embedding = self.model.embed_document(query).await?;
+        let prompt_embedding = self.model.embed_text(query).await?;
 
         let query = self
             .table
diff --git a/rig-lancedb/src/utils/deserializer.rs b/rig-lancedb/src/utils/deserializer.rs
index fd890280..7f9d1d7d 100644
--- a/rig-lancedb/src/utils/deserializer.rs
+++ b/rig-lancedb/src/utils/deserializer.rs
@@ -356,9 +356,9 @@ fn type_matcher(column: &Arc<dyn Array>) -> Result<Vec<Value>, VectorStoreError>
     }
 }
 
-///////////////////////////////////////////////////////////////////////////////////
-/// Everything below includes helpers for the recursive function `type_matcher`.///
-///////////////////////////////////////////////////////////////////////////////////
+// ================================================================
+// Everything below includes helpers for the recursive function `type_matcher`
+// ================================================================
 
 /// Trait used to "deserialize" an arrow_array::Array as as list of primitive objects.
 trait DeserializePrimitiveArray {
diff --git a/rig-mongodb/Cargo.toml b/rig-mongodb/Cargo.toml
index edb755e0..f52f8630 100644
--- a/rig-mongodb/Cargo.toml
+++ b/rig-mongodb/Cargo.toml
@@ -20,3 +20,7 @@ tracing = "0.1.40"
 [dev-dependencies]
 anyhow = "1.0.86"
 tokio = { version = "1.38.0", features = ["macros"] }
+
+[[example]]
+name = "vector_search_mongodb"
+required-features = ["rig-core/derive"]
\ No newline at end of file
diff --git a/rig-mongodb/examples/vector_search_mongodb.rs b/rig-mongodb/examples/vector_search_mongodb.rs
index 5943ac3f..5d0ed81b 100644
--- a/rig-mongodb/examples/vector_search_mongodb.rs
+++ b/rig-mongodb/examples/vector_search_mongodb.rs
@@ -1,20 +1,36 @@
-use mongodb::bson;
-use mongodb::{options::ClientOptions, Client as MongoClient, Collection};
-use rig::vector_store::VectorStore;
-use rig::{
-    embeddings::EmbeddingsBuilder,
-    providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
-    vector_store::VectorStoreIndex,
-};
-use rig_mongodb::{MongoDbVectorStore, SearchParams};
+use mongodb::{bson::doc, options::ClientOptions, Client as MongoClient, Collection};
+use rig::providers::openai::TEXT_EMBEDDING_ADA_002;
 use serde::{Deserialize, Serialize};
 use std::env;
 
-#[derive(Clone, Eq, PartialEq, Serialize, Deserialize, Debug)]
-pub struct DocumentResponse {
+use rig::{
+    embeddings::EmbeddingsBuilder, providers::openai::Client, vector_store::VectorStoreIndex, Embed,
+};
+use rig_mongodb::{MongoDbVectorIndex, SearchParams};
+
+// Shape of data that needs to be RAG'ed.
+// The definition field will be used to generate embeddings.
+#[derive(Embed, Clone, Deserialize, Debug)]
+struct WordDefinition {
+    #[serde(rename = "_id")]
+    id: String,
+    #[embed]
+    definition: String,
+}
+
+#[derive(Clone, Deserialize, Debug, Serialize)]
+struct Link {
+    word: String,
+    link: String,
+}
+
+// Shape of the document to be stored in MongoDB, with embeddings.
+#[derive(Serialize, Debug)]
+struct Document {
     #[serde(rename = "_id")]
-    pub id: String,
-    pub document: serde_json::Value,
+    id: String,
+    definition: String,
+    embedding: Vec<f64>,
 }
 
 #[tokio::main]
@@ -34,41 +50,59 @@ async fn main() -> Result<(), anyhow::Error> {
         MongoClient::with_options(options).expect("MongoDB client options should be valid");
 
     // Initialize MongoDB vector store
-    let collection: Collection<bson::Document> = mongodb_client
+    let collection: Collection<Document> = mongodb_client
         .database("knowledgebase")
         .collection("context");
 
-    let mut vector_store = MongoDbVectorStore::new(collection);
-
     // Select the embedding model and generate our embeddings
     let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
 
+    let fake_definitions = vec![
+        WordDefinition {
+            id: "doc0".to_string(),
+            definition: "Definition of a *flurbo*: A flurbo is a green alien that lives on cold planets".to_string(),
+        },
+        WordDefinition {
+            id: "doc1".to_string(),
+            definition: "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.".to_string(),
+        },
+        WordDefinition {
+            id: "doc2".to_string(),
+            definition: "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.".to_string(),
+        }
+    ];
+
     let embeddings = EmbeddingsBuilder::new(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.")
+        .documents(fake_definitions)?
         .build()
         .await?;
 
-    // Add embeddings to vector store
-    match vector_store.add_documents(embeddings).await {
+    let mongo_documents = embeddings
+        .iter()
+        .map(
+            |(WordDefinition { id, definition, .. }, embedding)| Document {
+                id: id.clone(),
+                definition: definition.clone(),
+                embedding: embedding.first().vec.clone(),
+            },
+        )
+        .collect::<Vec<_>>();
+
+    match collection.insert_many(mongo_documents, None).await {
         Ok(_) => println!("Documents added successfully"),
         Err(e) => println!("Error adding documents: {:?}", e),
-    }
+    };
 
-    // Create a vector index on our vector store
+    // Create a vector index on our vector store.
+    // Note: a vector index called "vector_index" must exist on the MongoDB collection you are querying.
     // IMPORTANT: Reuse the same model that was used to generate the embeddings
-    let index = vector_store
-        .index(model, "vector_index", SearchParams::default())
-        .await?;
+    let index =
+        MongoDbVectorIndex::new(collection, model, "vector_index", SearchParams::new()).await?;
 
     // Query the index
     let results = index
-        .top_n::<DocumentResponse>("What is a linglingdong?", 1)
-        .await?
-        .into_iter()
-        .map(|(score, id, doc)| (score, id, doc.document))
-        .collect::<Vec<_>>();
+        .top_n::<WordDefinition>("What is a linglingdong?", 1)
+        .await?;
 
     println!("Results: {:?}", results);
 
diff --git a/rig-mongodb/src/lib.rs b/rig-mongodb/src/lib.rs
index 56c0009d..813818e9 100644
--- a/rig-mongodb/src/lib.rs
+++ b/rig-mongodb/src/lib.rs
@@ -2,16 +2,11 @@ use futures::StreamExt;
 use mongodb::bson::{self, doc};
 
 use rig::{
-    embeddings::{DocumentEmbeddings, Embedding, EmbeddingModel},
-    vector_store::{VectorStore, VectorStoreError, VectorStoreIndex},
+    embeddings::embedding::{Embedding, EmbeddingModel},
+    vector_store::{VectorStoreError, VectorStoreIndex},
 };
 use serde::{Deserialize, Serialize};
 
-/// A MongoDB vector store.
-pub struct MongoDbVectorStore {
-    collection: mongodb::Collection<bson::Document>,
-}
-
 #[derive(Debug, Serialize, Deserialize)]
 #[serde(rename_all = "camelCase")]
 struct SearchIndex {
@@ -25,8 +20,8 @@ struct SearchIndex {
 }
 
 impl SearchIndex {
-    async fn get_search_index(
-        collection: mongodb::Collection<bson::Document>,
+    async fn get_search_index<C>(
+        collection: mongodb::Collection<C>,
         index_name: &str,
     ) -> Result<SearchIndex, VectorStoreError> {
         collection
@@ -61,100 +56,38 @@ fn mongodb_to_rig_error(e: mongodb::error::Error) -> VectorStoreError {
     VectorStoreError::DatastoreError(Box::new(e))
 }
 
-impl VectorStore for MongoDbVectorStore {
-    type Q = mongodb::bson::Document;
-
-    async fn add_documents(
-        &mut self,
-        documents: Vec<DocumentEmbeddings>,
-    ) -> Result<(), VectorStoreError> {
-        self.collection
-            .clone_with_type::<DocumentEmbeddings>()
-            .insert_many(documents, None)
-            .await
-            .map_err(mongodb_to_rig_error)?;
-        Ok(())
-    }
-
-    async fn get_document_embeddings(
-        &self,
-        id: &str,
-    ) -> Result<Option<DocumentEmbeddings>, VectorStoreError> {
-        self.collection
-            .clone_with_type::<DocumentEmbeddings>()
-            .find_one(doc! { "_id": id }, None)
-            .await
-            .map_err(mongodb_to_rig_error)
-    }
-
-    async fn get_document<T: for<'a> serde::Deserialize<'a>>(
-        &self,
-        id: &str,
-    ) -> Result<Option<T>, VectorStoreError> {
-        Ok(self
-            .collection
-            .clone_with_type::<String>()
-            .aggregate(
-                [
-                    doc! {"$match": { "_id": id}},
-                    doc! {"$project": { "document": 1 }},
-                    doc! {"$replaceRoot": { "newRoot": "$document" }},
-                ],
-                None,
-            )
-            .await
-            .map_err(mongodb_to_rig_error)?
-            .with_type::<String>()
-            .next()
-            .await
-            .transpose()
-            .map_err(mongodb_to_rig_error)?
-            .map(|doc| serde_json::from_str(&doc))
-            .transpose()?)
-    }
-
-    async fn get_document_by_query(
-        &self,
-        query: Self::Q,
-    ) -> Result<Option<DocumentEmbeddings>, VectorStoreError> {
-        self.collection
-            .clone_with_type::<DocumentEmbeddings>()
-            .find_one(query, None)
-            .await
-            .map_err(mongodb_to_rig_error)
-    }
-}
-
-impl MongoDbVectorStore {
-    /// Create a new `MongoDbVectorStore` from a MongoDB collection.
-    pub fn new(collection: mongodb::Collection<bson::document::Document>) -> Self {
-        Self { collection }
-    }
-
-    /// Create a new `MongoDbVectorIndex` from an existing `MongoDbVectorStore`.
-    ///
-    /// The index (of type "vector") must already exist for the MongoDB collection.
-    /// See the MongoDB [documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/) for more information on creating indexes.
-    pub async fn index<M: EmbeddingModel>(
-        &self,
-        model: M,
-        index_name: &str,
-        search_params: SearchParams,
-    ) -> Result<MongoDbVectorIndex<M>, VectorStoreError> {
-        MongoDbVectorIndex::new(self.collection.clone(), model, index_name, search_params).await
-    }
-}
-
 /// A vector index for a MongoDB collection.
-pub struct MongoDbVectorIndex<M: EmbeddingModel> {
-    collection: mongodb::Collection<bson::Document>,
+/// # Example
+/// ```
+/// use rig_mongodb::{MongoDbVectorIndex, SearchParams};
+/// use rig::embeddings::EmbeddingModel;
+///
+/// #[derive(serde::Serialize, Debug)]
+/// struct Document {
+///     #[serde(rename = "_id")]
+///     id: String,
+///     definition: String,
+///     embedding: Vec<f64>,
+/// }
+///
+/// let collection: collection: mongodb::Collection<Document> = mongodb_client.collection(""); // <-- replace with your mongodb collection.
+/// let model: model: EmbeddingModel = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002); // <-- replace with your embedding model.
+/// let index = MongoDbVectorIndex::new(
+///     collection,
+///     model,
+///     "vector_index", // <-- replace with the name of the index in your mongodb collection.
+///     SearchParams::new("embedding"), // <-- field name in `Document` that contains the embeddings.
+/// );
+/// ```
+pub struct MongoDbVectorIndex<M: EmbeddingModel, C> {
+    collection: mongodb::Collection<C>,
     model: M,
     index_name: String,
     embedded_field: String,
     search_params: SearchParams,
 }
 
-impl<M: EmbeddingModel> MongoDbVectorIndex<M> {
+impl<M: EmbeddingModel, C> MongoDbVectorIndex<M, C> {
     /// Vector search stage of aggregation pipeline of mongoDB collection.
     /// To be used by implementations of top_n and top_n_ids methods on VectorStoreIndex trait for MongoDbVectorIndex.
     fn pipeline_search_stage(&self, prompt_embedding: &Embedding, n: usize) -> bson::Document {
@@ -188,9 +121,13 @@ impl<M: EmbeddingModel> MongoDbVectorIndex<M> {
     }
 }
 
-impl<M: EmbeddingModel> MongoDbVectorIndex<M> {
+impl<M: EmbeddingModel, C> MongoDbVectorIndex<M, C> {
+    /// Create a new `MongoDbVectorIndex`.
+    ///
+    /// The index (of type "vector") must already exist for the MongoDB collection.
+    /// See the MongoDB [documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/) for more information on creating indexes.
     pub async fn new(
-        collection: mongodb::Collection<bson::Document>,
+        collection: mongodb::Collection<C>,
         model: M,
         index_name: &str,
         search_params: SearchParams,
@@ -226,6 +163,7 @@ impl<M: EmbeddingModel> MongoDbVectorIndex<M> {
 
 /// See [MongoDB Vector Search](`https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/`) for more information
 /// on each of the fields
+#[derive(Default)]
 pub struct SearchParams {
     filter: mongodb::bson::Document,
     exact: Option<bool>,
@@ -268,19 +206,51 @@ impl SearchParams {
     }
 }
 
-impl Default for SearchParams {
-    fn default() -> Self {
-        Self::new()
-    }
-}
-
-impl<M: EmbeddingModel + std::marker::Sync + Send> VectorStoreIndex for MongoDbVectorIndex<M> {
-    async fn top_n<T: for<'a> Deserialize<'a> + std::marker::Send>(
+impl<M: EmbeddingModel + Sync + Send, C: Sync + Send> VectorStoreIndex
+    for MongoDbVectorIndex<M, C>
+{
+    /// Implement the `top_n` method of the `VectorStoreIndex` trait for `MongoDbVectorIndex`.
+    /// # Example
+    /// ```
+    /// use rig_mongodb::{MongoDbVectorIndex, SearchParams};
+    /// use rig::embeddings::EmbeddingModel;
+    ///
+    /// #[derive(serde::Serialize, Debug)]
+    /// struct Document {
+    ///     #[serde(rename = "_id")]
+    ///     id: String,
+    ///     definition: String,
+    ///     embedding: Vec<f64>,
+    /// }
+    ///
+    /// #[derive(serde::Deserialize, Debug)]
+    /// struct Definition {
+    ///     #[serde(rename = "_id")]
+    ///     id: String,
+    ///     definition: String,
+    /// }
+    ///
+    /// let collection: collection: mongodb::Collection<Document> = mongodb_client.collection(""); // <-- replace with your mongodb collection.
+    /// let model: model: EmbeddingModel = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002); // <-- replace with your embedding model.
+    ///
+    /// let vector_store_index = MongoDbVectorIndex::new(
+    ///     collection,
+    ///     model,
+    ///     "vector_index", // <-- replace with the name of the index in your mongodb collection.
+    ///     SearchParams::new("embedding"), // <-- field name in `Document` that contains the embeddings.
+    /// );
+    ///
+    /// // Query the index
+    /// vector_store_index
+    ///     .top_n::<Definition>("My boss says I zindle too much, what does that mean?", 1)
+    ///     .await?;
+    /// ```
+    async fn top_n<T: for<'a> Deserialize<'a> + Send>(
         &self,
         query: &str,
         n: usize,
     ) -> Result<Vec<(f64, String, T)>, VectorStoreError> {
-        let prompt_embedding = self.model.embed_document(query).await?;
+        let prompt_embedding = self.model.embed_text(query).await?;
 
         let mut cursor = self
             .collection
@@ -322,12 +292,40 @@ impl<M: EmbeddingModel + std::marker::Sync + Send> VectorStoreIndex for MongoDbV
         Ok(results)
     }
 
+    /// Implement the `top_n_ids` method of the `VectorStoreIndex` trait for `MongoDbVectorIndex`.
+    /// # Example
+    /// ```
+    /// use rig_mongodb::{MongoDbVectorIndex, SearchParams};
+    /// use rig::embeddings::EmbeddingModel;
+    ///
+    /// #[derive(serde::Serialize, Debug)]
+    /// struct Document {
+    ///     #[serde(rename = "_id")]
+    ///     id: String,
+    ///     definition: String,
+    ///     embedding: Vec<f64>,
+    /// }
+    ///
+    /// let collection: collection: mongodb::Collection<Document> = mongodb_client.collection(""); // <-- replace with your mongodb collection.
+    /// let model: model: EmbeddingModel = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002); // <-- replace with your embedding model.
+    /// let vector_store_index = MongoDbVectorIndex::new(
+    ///     collection,
+    ///     model,
+    ///     "vector_index", // <-- replace with the name of the index in your mongodb collection.
+    ///     SearchParams::new("embedding"), // <-- field name in `Document` that contains the embeddings.
+    /// );
+    ///
+    /// // Query the index
+    /// vector_store_index
+    ///     .top_n_ids("My boss says I zindle too much, what does that mean?", 1)
+    ///     .await?;
+    /// ```
     async fn top_n_ids(
         &self,
         query: &str,
         n: usize,
     ) -> Result<Vec<(f64, String)>, VectorStoreError> {
-        let prompt_embedding = self.model.embed_document(query).await?;
+        let prompt_embedding = self.model.embed_text(query).await?;
 
         let mut cursor = self
             .collection
diff --git a/rig-neo4j/Cargo.toml b/rig-neo4j/Cargo.toml
index a0a94633..aae275c7 100644
--- a/rig-neo4j/Cargo.toml
+++ b/rig-neo4j/Cargo.toml
@@ -22,3 +22,7 @@ anyhow = "1.0.86"
 tokio = { version = "1.38.0", features = ["macros"] }
 textwrap = { version = "0.16.1"}
 term_size = { version = "0.3.2"}
+
+[[example]]
+name = "vector_search_simple"
+required-features = ["rig-core/derive"]
\ No newline at end of file
diff --git a/rig-neo4j/examples/vector_search_simple.rs b/rig-neo4j/examples/vector_search_simple.rs
index 2cb0030d..fca43d27 100644
--- a/rig-neo4j/examples/vector_search_simple.rs
+++ b/rig-neo4j/examples/vector_search_simple.rs
@@ -13,12 +13,20 @@ use rig::{
     embeddings::EmbeddingsBuilder,
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
     vector_store::VectorStoreIndex as _,
+    Embed,
 };
 use rig_neo4j::{
     vector_index::{IndexConfig, SearchParams},
     Neo4jClient, ToBoltType,
 };
 
+#[derive(Embed, Clone, Debug)]
+pub struct WordDefinition {
+    pub id: String,
+    #[embed]
+    pub definition: String,
+}
+
 #[tokio::main]
 async fn main() -> Result<(), anyhow::Error> {
     // Initialize OpenAI client
@@ -36,9 +44,18 @@ async fn main() -> Result<(), anyhow::Error> {
     let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
 
     let embeddings = EmbeddingsBuilder::new(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.")
+        .document(WordDefinition {
+            id: "doc0".to_string(),
+            definition: "Definition of a *flurbo*: A flurbo is a green alien that lives on cold planets".to_string(),
+        })?
+        .document(WordDefinition {
+            id: "doc1".to_string(),
+            definition: "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.".to_string(),
+        })?
+        .document(WordDefinition {
+            id: "doc2".to_string(),
+            definition: "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.".to_string(),
+        })?
         .build()
         .await?;
 
@@ -54,7 +71,7 @@ async fn main() -> Result<(), anyhow::Error> {
     }
 
     let create_nodes = futures::stream::iter(embeddings)
-        .map(|doc| {
+        .map(|(doc, embeddings)| {
             neo4j_client.graph.run(
                 neo4rs::query(
                     "
@@ -68,8 +85,8 @@ async fn main() -> Result<(), anyhow::Error> {
                 .param("id", doc.id)
                 // Here we use the first embedding but we could use any of them.
                 // Neo4j only takes primitive types or arrays as properties.
-                .param("embedding", doc.embeddings[0].vec.clone())
-                .param("document", doc.document.to_bolt_type()),
+                .param("embedding", embeddings.first().vec.clone())
+                .param("document", doc.definition.to_bolt_type()),
             )
         })
         .buffer_unordered(3)
diff --git a/rig-neo4j/src/vector_index.rs b/rig-neo4j/src/vector_index.rs
index db6fd3df..bf39644d 100644
--- a/rig-neo4j/src/vector_index.rs
+++ b/rig-neo4j/src/vector_index.rs
@@ -259,7 +259,7 @@ impl<M: EmbeddingModel + std::marker::Sync + Send> VectorStoreIndex for Neo4jVec
         query: &str,
         n: usize,
     ) -> Result<Vec<(f64, String, T)>, VectorStoreError> {
-        let prompt_embedding = self.embedding_model.embed_document(query).await?;
+        let prompt_embedding = self.embedding_model.embed_text(query).await?;
         let query = self.build_vector_search_query(prompt_embedding, true, n);
 
         let rows = self.execute_and_collect::<RowResultNode<T>>(query).await?;
@@ -279,7 +279,7 @@ impl<M: EmbeddingModel + std::marker::Sync + Send> VectorStoreIndex for Neo4jVec
         query: &str,
         n: usize,
     ) -> Result<Vec<(f64, String)>, VectorStoreError> {
-        let prompt_embedding = self.embedding_model.embed_document(query).await?;
+        let prompt_embedding = self.embedding_model.embed_text(query).await?;
 
         let query = self.build_vector_search_query(prompt_embedding, false, n);
 
diff --git a/rig-qdrant/Cargo.toml b/rig-qdrant/Cargo.toml
index 4a7360a9..35c4b96e 100644
--- a/rig-qdrant/Cargo.toml
+++ b/rig-qdrant/Cargo.toml
@@ -16,3 +16,7 @@ qdrant-client = "1.12.1"
 [dev-dependencies]
 tokio = { version = "1.40.0", features = ["rt-multi-thread"] }
 anyhow = "1.0.89"
+
+[[example]]
+name = "qdrant_vector_search"
+required-features = ["rig-core/derive"]
\ No newline at end of file
diff --git a/rig-qdrant/examples/qdrant_vector_search.rs b/rig-qdrant/examples/qdrant_vector_search.rs
index c9148d69..b1a91349 100644
--- a/rig-qdrant/examples/qdrant_vector_search.rs
+++ b/rig-qdrant/examples/qdrant_vector_search.rs
@@ -19,10 +19,18 @@ use rig::{
     embeddings::EmbeddingsBuilder,
     providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
     vector_store::VectorStoreIndex,
+    Embed,
 };
 use rig_qdrant::QdrantVectorStore;
 use serde_json::json;
 
+#[derive(Embed)]
+struct WordDefinition {
+    id: String,
+    #[embed]
+    definition: String,
+}
+
 #[tokio::main]
 async fn main() -> Result<(), anyhow::Error> {
     const COLLECTION_NAME: &str = "rig-collection";
@@ -49,21 +57,30 @@ async fn main() -> Result<(), anyhow::Error> {
     let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);
 
     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.")
+        .document(WordDefinition {
+            id: "0981d983-a5f8-49eb-89ea-f7d3b2196d2e".to_string(),
+            definition: "Definition of a *flurbo*: A flurbo is a green alien that lives on cold planets".to_string(),
+        })?
+        .document(WordDefinition {
+            id: "62a36d43-80b6-4fd6-990c-f75bb02287d1".to_string(),
+            definition: "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.".to_string(),
+        })?
+        .document(WordDefinition {
+            id: "f9e17d59-32e5-440c-be02-b2759a654824".to_string(),
+            definition: "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.".to_string(),
+        })?
         .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();
+        .map(|(d, embeddings)| {
+            let vec: Vec<f32> = embeddings.first().vec.iter().map(|&x| x as f32).collect();
             PointStruct::new(
                 d.id,
                 vec,
                 Payload::try_from(json!({
-                    "document": d.document,
+                    "document": d.definition,
                 }))
                 .unwrap(),
             )
diff --git a/rig-qdrant/src/lib.rs b/rig-qdrant/src/lib.rs
index e88878cc..666d0a4f 100644
--- a/rig-qdrant/src/lib.rs
+++ b/rig-qdrant/src/lib.rs
@@ -36,7 +36,7 @@ impl<M: EmbeddingModel> QdrantVectorStore<M> {
 
     /// Embed query based on `QdrantVectorStore` model and modify the vector in the required format.
     async fn generate_query_vector(&self, query: &str) -> Result<Vec<f32>, VectorStoreError> {
-        let embedding = self.model.embed_document(query).await?;
+        let embedding = self.model.embed_text(query).await?;
         Ok(embedding.vec.iter().map(|&x| x as f32).collect())
     }