diff --git a/Cargo.lock b/Cargo.lock
index 6340222..7057448 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -235,40 +235,19 @@ version = "55.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
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-]
-
-[[package]]
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- "arrow-row 56.0.0",
- "arrow-schema 56.0.0",
- "arrow-select 56.0.0",
- "arrow-string 56.0.0",
+ "arrow-arith",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-cast",
+ "arrow-csv",
+ "arrow-data",
+ "arrow-ipc",
+ "arrow-json",
+ "arrow-ord",
+ "arrow-row",
+ "arrow-schema",
+ "arrow-select",
+ "arrow-string",
 ]
 
 [[package]]
@@ -277,24 +256,10 @@ version = "55.2.0"
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-]
-
-[[package]]
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-dependencies = [
- "arrow-array 56.0.0",
- "arrow-buffer 56.0.0",
- "arrow-data 56.0.0",
- "arrow-schema 56.0.0",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
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 ]
@@ -306,9 +271,9 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "70732f04d285d49054a48b72c54f791bb3424abae92d27aafdf776c98af161c8"
 dependencies = [
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- "arrow-buffer 55.2.0",
- "arrow-data 55.2.0",
- "arrow-schema 55.2.0",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
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@@ -316,22 +281,6 @@ dependencies = [
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 ]
 
-[[package]]
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-version = "56.0.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
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- "chrono",
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- "hashbrown 0.15.5",
- "num",
-]
-
 [[package]]
 name = "arrow-buffer"
 version = "55.2.0"
@@ -343,28 +292,17 @@ dependencies = [
  "num",
 ]
 
-[[package]]
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-version = "56.0.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "54295b93beb702ee9a6f6fbced08ad7f4d76ec1c297952d4b83cf68755421d1d"
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- "bytes",
- "half",
- "num",
-]
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 [[package]]
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+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
+ "arrow-select",
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  "chrono",
@@ -375,50 +313,15 @@ dependencies = [
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 ]
 
-[[package]]
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-version = "56.0.0"
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- "arrow-array 56.0.0",
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+ "arrow-array",
+ "arrow-cast",
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@@ -431,20 +334,8 @@ version = "55.2.0"
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-dependencies = [
- "arrow-buffer 56.0.0",
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+ "arrow-buffer",
+ "arrow-schema",
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@@ -455,61 +346,26 @@ version = "55.2.0"
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+ "arrow-array",
+ "arrow-buffer",
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 ]
 
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-version = "56.0.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
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- "serde",
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-]
-
-[[package]]
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- "arrow-cast 56.0.0",
- "arrow-data 56.0.0",
- "arrow-schema 56.0.0",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-cast",
+ "arrow-data",
+ "arrow-schema",
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@@ -527,24 +383,11 @@ version = "55.2.0"
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-]
-
-[[package]]
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- "arrow-buffer 56.0.0",
- "arrow-data 56.0.0",
- "arrow-schema 56.0.0",
- "arrow-select 56.0.0",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
+ "arrow-select",
 ]
 
 [[package]]
@@ -553,23 +396,10 @@ version = "55.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "52bf7393166beaf79b4bed9bfdf19e97472af32ce5b6b48169d321518a08cae2"
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-dependencies = [
- "arrow-array 56.0.0",
- "arrow-buffer 56.0.0",
- "arrow-data 56.0.0",
- "arrow-schema 56.0.0",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
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@@ -584,12 +414,6 @@ dependencies = [
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 ]
 
-[[package]]
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-version = "56.0.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "85fa1babc4a45fdc64a92175ef51ff00eba5ebbc0007962fecf8022ac1c6ce28"
-
 [[package]]
 name = "arrow-select"
 version = "55.2.0"
@@ -597,24 +421,10 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "dd2b45757d6a2373faa3352d02ff5b54b098f5e21dccebc45a21806bc34501e5"
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+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
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@@ -624,28 +434,11 @@ version = "55.2.0"
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 checksum = "0377d532850babb4d927a06294314b316e23311503ed580ec6ce6a0158f49d40"
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+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
+ "arrow-select",
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@@ -1863,9 +1656,9 @@ version = "48.0.1"
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 dependencies = [
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- "arrow-schema 55.2.0",
+ "arrow",
+ "arrow-ipc",
+ "arrow-schema",
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@@ -1911,7 +1704,7 @@ version = "48.0.1"
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 checksum = "94985e67cab97b1099db2a7af11f31a45008b282aba921c1e1d35327c212ec18"
 dependencies = [
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+ "arrow",
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@@ -1937,7 +1730,7 @@ version = "48.0.1"
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 checksum = "e002df133bdb7b0b9b429d89a69aa77b35caeadee4498b2ce1c7c23a99516988"
 dependencies = [
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+ "arrow",
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@@ -1961,8 +1754,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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 dependencies = [
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+ "arrow-ipc",
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@@ -1993,7 +1786,7 @@ version = "48.0.1"
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+ "arrow",
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@@ -2021,7 +1814,7 @@ version = "48.0.1"
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 checksum = "cfc114f9a1415174f3e8d2719c371fc72092ef2195a7955404cfe6b2ba29a706"
 dependencies = [
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+ "arrow",
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@@ -2046,7 +1839,7 @@ version = "48.0.1"
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 checksum = "d88dd5e215c420a52362b9988ecd4cefd71081b730663d4f7d886f706111fc75"
 dependencies = [
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+ "arrow",
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@@ -2077,7 +1870,7 @@ version = "48.0.1"
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 checksum = "9609d83d52ff8315283c6dad3b97566e877d8f366fab4c3297742f33dcd636c7"
 dependencies = [
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+ "arrow",
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@@ -2096,7 +1889,7 @@ version = "48.0.1"
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 dependencies = [
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+ "arrow",
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@@ -2116,7 +1909,7 @@ version = "48.0.1"
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 dependencies = [
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@@ -2129,8 +1922,8 @@ version = "48.0.1"
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 dependencies = [
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+ "arrow",
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@@ -2159,7 +1952,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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 dependencies = [
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+ "arrow",
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@@ -2180,7 +1973,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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 dependencies = [
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- "arrow 55.2.0",
+ "arrow",
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@@ -2192,8 +1985,8 @@ version = "48.0.1"
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 dependencies = [
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+ "arrow",
+ "arrow-ord",
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@@ -2213,7 +2006,7 @@ version = "48.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d4ac2c0be983a06950ef077e34e0174aa0cb9e346f3aeae459823158037ade37"
 dependencies = [
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+ "arrow",
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@@ -2229,7 +2022,7 @@ version = "48.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
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 dependencies = [
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+ "arrow",
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@@ -2268,7 +2061,7 @@ version = "48.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1594c7a97219ede334f25347ad8d57056621e7f4f35a0693c8da876e10dd6a53"
 dependencies = [
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+ "arrow",
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@@ -2287,7 +2080,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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 dependencies = [
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+ "arrow",
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@@ -2309,7 +2102,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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 dependencies = [
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- "arrow 55.2.0",
+ "arrow",
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@@ -2322,7 +2115,7 @@ version = "48.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "6d140854b2db3ef8ac611caad12bfb2e1e1de827077429322a6188f18fc0026a"
 dependencies = [
- "arrow 55.2.0",
+ "arrow",
  "datafusion-common",
  "datafusion-execution",
  "datafusion-expr",
@@ -2341,9 +2134,9 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b46cbdf21a01206be76d467f325273b22c559c744a012ead5018dfe79597de08"
 dependencies = [
  "ahash",
- "arrow 55.2.0",
- "arrow-ord 55.2.0",
- "arrow-schema 55.2.0",
+ "arrow",
+ "arrow-ord",
+ "arrow-schema",
  "async-trait",
  "chrono",
  "datafusion-common",
@@ -2370,7 +2163,7 @@ version = "48.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "3a72733766ddb5b41534910926e8da5836622316f6283307fd9fb7e19811a59c"
 dependencies = [
- "arrow 55.2.0",
+ "arrow",
  "async-trait",
  "dashmap 6.1.0",
  "datafusion-common",
@@ -2394,7 +2187,7 @@ version = "48.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c5162338cdec9cc7ea13a0e6015c361acad5ec1d88d83f7c86301f789473971f"
 dependencies = [
- "arrow 55.2.0",
+ "arrow",
  "bigdecimal",
  "datafusion-common",
  "datafusion-expr",
@@ -2803,7 +2596,7 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "548190a42654ce848835b410ae33f43b4d55cb24548fd0a885a289a1d5a95019"
 dependencies = [
- "arrow-array 55.2.0",
+ "arrow-array",
  "rand 0.9.2",
 ]
 
@@ -3093,9 +2886,9 @@ version = "0.0.1"
 dependencies = [
  "age",
  "anyhow",
- "arrow 56.0.0",
- "arrow-array 56.0.0",
- "arrow-schema 56.0.0",
+ "arrow",
+ "arrow-array",
+ "arrow-schema",
  "base64 0.22.1",
  "bincode",
  "byteorder",
@@ -3759,15 +3552,15 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "94bafd9d9a9301c1eac48892ec8016d4d28204d4fc55f2ebebee9a7af465e152"
 dependencies = [
- "arrow 55.2.0",
- "arrow-arith 55.2.0",
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-ipc 55.2.0",
- "arrow-ord 55.2.0",
- "arrow-row 55.2.0",
- "arrow-schema 55.2.0",
- "arrow-select 55.2.0",
+ "arrow",
+ "arrow-arith",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-ipc",
+ "arrow-ord",
+ "arrow-row",
+ "arrow-schema",
+ "arrow-select",
  "async-recursion",
  "async-trait",
  "async_cell",
@@ -3822,12 +3615,12 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b97ebcd8edc2b534e8ded20c97c8928e275160794af91ed803a3d48d8d2a88d8"
 dependencies = [
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-cast 55.2.0",
- "arrow-data 55.2.0",
- "arrow-schema 55.2.0",
- "arrow-select 55.2.0",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-cast",
+ "arrow-data",
+ "arrow-schema",
+ "arrow-select",
  "bytes",
  "getrandom 0.2.16",
  "half",
@@ -3841,9 +3634,9 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ce5c1849d07985d6a5011aca9de43c7a42ec4c996d66ef3f2d9896c227cc934c"
 dependencies = [
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-schema 55.2.0",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-schema",
  "async-trait",
  "byteorder",
  "bytes",
@@ -3878,12 +3671,12 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d355c087bc66d85e36cfb428465f585b13971e1e13585dd2b6886a54d8a7d9a4"
 dependencies = [
- "arrow 55.2.0",
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-ord 55.2.0",
- "arrow-schema 55.2.0",
- "arrow-select 55.2.0",
+ "arrow",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-ord",
+ "arrow-schema",
+ "arrow-select",
  "async-trait",
  "datafusion",
  "datafusion-common",
@@ -3908,10 +3701,10 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "110d4dedfe02e9cff8f11cfb64a261755da7ee9131845197efeec8b659cc5513"
 dependencies = [
- "arrow 55.2.0",
- "arrow-array 55.2.0",
- "arrow-cast 55.2.0",
- "arrow-schema 55.2.0",
+ "arrow",
+ "arrow-array",
+ "arrow-cast",
+ "arrow-schema",
  "chrono",
  "futures",
  "hex",
@@ -3927,14 +3720,14 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "66750006299a2fb003091bc290eb1fe2a5933e35236d921934131f3e4629cd33"
 dependencies = [
  "arrayref",
- "arrow 55.2.0",
- "arrow-arith 55.2.0",
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-cast 55.2.0",
- "arrow-data 55.2.0",
- "arrow-schema 55.2.0",
- "arrow-select 55.2.0",
+ "arrow",
+ "arrow-arith",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-cast",
+ "arrow-data",
+ "arrow-schema",
+ "arrow-select",
  "bytemuck",
  "byteorder",
  "bytes",
@@ -3967,12 +3760,12 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7c639062100610a075e01fd455173348b2fccea10cb0e89f70e38a3183c56022"
 dependencies = [
- "arrow-arith 55.2.0",
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-data 55.2.0",
- "arrow-schema 55.2.0",
- "arrow-select 55.2.0",
+ "arrow-arith",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-data",
+ "arrow-schema",
+ "arrow-select",
  "async-recursion",
  "async-trait",
  "byteorder",
@@ -4003,11 +3796,11 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7ae67a048a51fb525d1bfde86d1b39118462277e7e7a7cd0e7ba866312873532"
 dependencies = [
- "arrow 55.2.0",
- "arrow-array 55.2.0",
- "arrow-ord 55.2.0",
- "arrow-schema 55.2.0",
- "arrow-select 55.2.0",
+ "arrow",
+ "arrow-array",
+ "arrow-ord",
+ "arrow-schema",
+ "arrow-select",
  "async-channel",
  "async-recursion",
  "async-trait",
@@ -4058,14 +3851,14 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "cc86c7307e2d3d895cfefa503f986edcbdd208eb0aa89ba2c75724ba04bce843"
 dependencies = [
- "arrow 55.2.0",
- "arrow-arith 55.2.0",
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-cast 55.2.0",
- "arrow-data 55.2.0",
- "arrow-schema 55.2.0",
- "arrow-select 55.2.0",
+ "arrow",
+ "arrow-arith",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-cast",
+ "arrow-data",
+ "arrow-schema",
+ "arrow-select",
  "async-priority-channel",
  "async-recursion",
  "async-trait",
@@ -4100,10 +3893,10 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "769f910b6f2ad5eb4d1b3071c533b619351e61e0dfca74f13c98680a8e6476e9"
 dependencies = [
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-ord 55.2.0",
- "arrow-schema 55.2.0",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-ord",
+ "arrow-schema",
  "bitvec",
  "cc",
  "deepsize",
@@ -4125,11 +3918,11 @@ version = "0.33.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ffbeafa8a3e97b5b3a06f06d69b0cefe56e65c64a33f674c40c113b797328bd2"
 dependencies = [
- "arrow 55.2.0",
- "arrow-array 55.2.0",
- "arrow-buffer 55.2.0",
- "arrow-ipc 55.2.0",
- "arrow-schema 55.2.0",
+ "arrow",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-ipc",
+ "arrow-schema",
  "async-trait",
  "byteorder",
  "bytes",
@@ -4923,18 +4716,18 @@ dependencies = [
 
 [[package]]
 name = "parquet"
-version = "56.0.0"
+version = "55.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c7288a07ed5d25939a90f9cb1ca5afa6855faa08ec7700613511ae64bdb0620c"
+checksum = "b17da4150748086bd43352bc77372efa9b6e3dbd06a04831d2a98c041c225cfa"
 dependencies = [
  "ahash",
- "arrow-array 56.0.0",
- "arrow-buffer 56.0.0",
- "arrow-cast 56.0.0",
- "arrow-data 56.0.0",
- "arrow-ipc 56.0.0",
- "arrow-schema 56.0.0",
- "arrow-select 56.0.0",
+ "arrow-array",
+ "arrow-buffer",
+ "arrow-cast",
+ "arrow-data",
+ "arrow-ipc",
+ "arrow-schema",
+ "arrow-select",
  "base64 0.22.1",
  "brotli",
  "bytes",
diff --git a/Cargo.toml b/Cargo.toml
index 9d57a84..df658a8 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -28,10 +28,11 @@ base64 = "0.22"
 lance = "0.33"
 lance-index = "0.33"
 lance-linalg = "0.33"
-arrow = "56"
-arrow-array = "56"
-arrow-schema = "56"
-parquet = "56"
+# Use Arrow version compatible with Lance 0.33
+arrow = "55.2"
+arrow-array = "55.2"
+arrow-schema = "55.2"
+parquet = "55.2"
 uuid = { version = "1.10", features = ["v4"] }
 reqwest = { version = "0.11", features = ["json"] }
 image = "0.25"
diff --git a/docs/lance_vector_db.md b/docs/lance_vector_db.md
new file mode 100644
index 0000000..fe44ae7
--- /dev/null
+++ b/docs/lance_vector_db.md
@@ -0,0 +1,454 @@
+# Lance Vector Database Operations
+
+HeroDB includes a powerful vector database integration using Lance, enabling high-performance vector storage, search, and multimodal data management. By default, it uses Ollama for local text embeddings, with support for custom external embedding services.
+
+## Overview
+
+The Lance vector database integration provides:
+
+- **High-performance vector storage** using Lance's columnar format
+- **Local Ollama integration** for text embeddings (default, no external dependencies)
+- **Custom embedding service support** for advanced use cases
+- **Text embedding support** (images via custom services)
+- **Vector similarity search** with configurable parameters
+- **Scalable indexing** with IVF_PQ (Inverted File with Product Quantization)
+- **Redis-compatible command interface**
+
+## Architecture
+
+```
+┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
+│   HeroDB        │    │  External        │    │   Lance         │
+│   Redis Server  │◄──►│  Embedding       │    │   Vector Store  │
+│                 │    │  Service         │    │                 │
+└─────────────────┘    └──────────────────┘    └─────────────────┘
+         │                       │                       │
+         │                       │                       │
+    Redis Protocol          HTTP API              Arrow/Parquet
+    Commands                JSON Requests         Columnar Storage
+```
+
+### Key Components
+
+1. **Lance Store**: High-performance columnar vector storage
+2. **Ollama Integration**: Local embedding service (default)
+3. **Custom Embedding Service**: Optional HTTP API for advanced use cases
+4. **Redis Command Interface**: Familiar Redis-style commands
+5. **Arrow Schema**: Flexible schema definition for metadata
+
+## Configuration
+
+### Default Setup (Ollama)
+
+HeroDB uses Ollama by default for text embeddings. No configuration is required if Ollama is running locally:
+
+```bash
+# Install Ollama (if not already installed)
+# Visit: https://ollama.ai
+
+# Pull the embedding model
+ollama pull nomic-embed-text
+
+# Ollama automatically runs on localhost:11434
+# HeroDB will use this by default
+```
+
+**Default Configuration:**
+- **URL**: `http://localhost:11434`
+- **Model**: `nomic-embed-text`
+- **Dimensions**: 768 (for nomic-embed-text)
+
+### Custom Embedding Service (Optional)
+
+To use a custom embedding service instead of Ollama:
+
+```bash
+# Set custom embedding service URL
+redis-cli HSET config:core:aiembed url "http://your-embedding-service:8080/embed"
+
+# Optional: Set authentication if required
+redis-cli HSET config:core:aiembed token "your-api-token"
+```
+
+### Embedding Service API Contracts
+
+#### Ollama API (Default)
+HeroDB calls Ollama using this format:
+
+```bash
+POST http://localhost:11434/api/embeddings
+Content-Type: application/json
+
+{
+  "model": "nomic-embed-text",
+  "prompt": "Your text to embed"
+}
+```
+
+Response:
+```json
+{
+  "embedding": [0.1, 0.2, 0.3, ...]
+}
+```
+
+#### Custom Service API
+Your custom embedding service should accept POST requests with this JSON format:
+
+```json
+{
+  "texts": ["text1", "text2"],           // Optional: array of texts
+  "images": ["base64_image1", "base64_image2"], // Optional: base64 encoded images
+  "model": "your-model-name"             // Optional: model specification
+}
+```
+
+And return responses in this format:
+
+```json
+{
+  "embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]],  // Array of embedding vectors
+  "model": "model-name",                               // Model used
+  "usage": {                                          // Optional usage stats
+    "tokens": 100,
+    "requests": 2
+  }
+}
+```
+
+## Commands Reference
+
+### Dataset Management
+
+#### LANCE CREATE
+Create a new vector dataset with specified dimensions and optional schema.
+
+```bash
+LANCE CREATE <dataset> DIM <dimension> [SCHEMA field:type ...]
+```
+
+**Parameters:**
+- `dataset`: Name of the dataset
+- `dimension`: Vector dimension (e.g., 384, 768, 1536)
+- `field:type`: Optional metadata fields (string, int, float, bool)
+
+**Examples:**
+```bash
+# Create a simple dataset for 384-dimensional vectors
+LANCE CREATE documents DIM 384
+
+# Create dataset with metadata schema
+LANCE CREATE products DIM 768 SCHEMA category:string price:float available:bool
+```
+
+#### LANCE LIST
+List all available datasets.
+
+```bash
+LANCE LIST
+```
+
+**Returns:** Array of dataset names
+
+#### LANCE INFO
+Get information about a specific dataset.
+
+```bash
+LANCE INFO <dataset>
+```
+
+**Returns:** Dataset metadata including name, version, row count, and schema
+
+#### LANCE DROP
+Delete a dataset and all its data.
+
+```bash
+LANCE DROP <dataset>
+```
+
+### Data Operations
+
+#### LANCE STORE
+Store multimodal data (text/images) with automatic embedding generation.
+
+```bash
+LANCE STORE <dataset> [TEXT <text>] [IMAGE <base64>] [key value ...]
+```
+
+**Parameters:**
+- `dataset`: Target dataset name
+- `TEXT`: Text content to embed
+- `IMAGE`: Base64-encoded image to embed
+- `key value`: Metadata key-value pairs
+
+**Examples:**
+```bash
+# Store text with metadata
+LANCE STORE documents TEXT "Machine learning is transforming industries" category "AI" author "John Doe"
+
+# Store image with metadata
+LANCE STORE images IMAGE "iVBORw0KGgoAAAANSUhEUgAA..." category "nature" tags "landscape,mountains"
+
+# Store both text and image
+LANCE STORE multimodal TEXT "Beautiful sunset" IMAGE "base64data..." location "California"
+```
+
+**Returns:** Unique ID of the stored item
+
+### Search Operations
+
+#### LANCE SEARCH
+Search using a raw vector.
+
+```bash
+LANCE SEARCH <dataset> VECTOR <vector> K <k> [NPROBES <n>] [REFINE <r>]
+```
+
+**Parameters:**
+- `dataset`: Dataset to search
+- `vector`: Comma-separated vector values (e.g., "0.1,0.2,0.3")
+- `k`: Number of results to return
+- `NPROBES`: Number of partitions to search (optional)
+- `REFINE`: Refine factor for better accuracy (optional)
+
+**Example:**
+```bash
+LANCE SEARCH documents VECTOR "0.1,0.2,0.3,0.4" K 5 NPROBES 10
+```
+
+#### LANCE SEARCH.TEXT
+Search using text query (automatically embedded).
+
+```bash
+LANCE SEARCH.TEXT <dataset> <query_text> K <k> [NPROBES <n>] [REFINE <r>]
+```
+
+**Parameters:**
+- `dataset`: Dataset to search
+- `query_text`: Text query to search for
+- `k`: Number of results to return
+- `NPROBES`: Number of partitions to search (optional)
+- `REFINE`: Refine factor for better accuracy (optional)
+
+**Example:**
+```bash
+LANCE SEARCH.TEXT documents "artificial intelligence applications" K 10 NPROBES 20
+```
+
+**Returns:** Array of results with distance scores and metadata
+
+### Embedding Operations
+
+#### LANCE EMBED.TEXT
+Generate embeddings for text without storing.
+
+```bash
+LANCE EMBED.TEXT <text1> [text2] [text3] ...
+```
+
+**Example:**
+```bash
+LANCE EMBED.TEXT "Hello world" "Machine learning" "Vector database"
+```
+
+**Returns:** Array of embedding vectors
+
+### Index Management
+
+#### LANCE CREATE.INDEX
+Create a vector index for faster search performance.
+
+```bash
+LANCE CREATE.INDEX <dataset> <index_type> [PARTITIONS <n>] [SUBVECTORS <n>]
+```
+
+**Parameters:**
+- `dataset`: Dataset to index
+- `index_type`: Index type (currently supports "IVF_PQ")
+- `PARTITIONS`: Number of partitions (default: 256)
+- `SUBVECTORS`: Number of sub-vectors for PQ (default: 16)
+
+**Example:**
+```bash
+LANCE CREATE.INDEX documents IVF_PQ PARTITIONS 512 SUBVECTORS 32
+```
+
+## Usage Patterns
+
+### 1. Document Search System
+
+```bash
+# Setup
+LANCE CREATE documents DIM 384 SCHEMA title:string content:string category:string
+
+# Store documents
+LANCE STORE documents TEXT "Introduction to machine learning algorithms" title "ML Basics" category "education"
+LANCE STORE documents TEXT "Deep learning neural networks explained" title "Deep Learning" category "education"
+LANCE STORE documents TEXT "Building scalable web applications" title "Web Dev" category "programming"
+
+# Create index for better performance
+LANCE CREATE.INDEX documents IVF_PQ PARTITIONS 256
+
+# Search
+LANCE SEARCH.TEXT documents "neural networks" K 5
+```
+
+### 2. Image Similarity Search
+
+```bash
+# Setup
+LANCE CREATE images DIM 512 SCHEMA filename:string tags:string
+
+# Store images (base64 encoded)
+LANCE STORE images IMAGE "iVBORw0KGgoAAAANSUhEUgAA..." filename "sunset.jpg" tags "nature,landscape"
+LANCE STORE images IMAGE "iVBORw0KGgoAAAANSUhEUgBB..." filename "city.jpg" tags "urban,architecture"
+
+# Search by image
+LANCE STORE temp_search IMAGE "query_image_base64..."
+# Then use the returned ID to get embedding and search
+```
+
+### 3. Multimodal Content Management
+
+```bash
+# Setup
+LANCE CREATE content DIM 768 SCHEMA type:string source:string
+
+# Store mixed content
+LANCE STORE content TEXT "Product description for smartphone" type "product" source "catalog"
+LANCE STORE content IMAGE "product_image_base64..." type "product_image" source "catalog"
+
+# Search across all content types
+LANCE SEARCH.TEXT content "smartphone features" K 10
+```
+
+## Performance Considerations
+
+### Vector Dimensions
+- **384**: Good for general text (e.g., sentence-transformers)
+- **768**: Standard for BERT-like models
+- **1536**: OpenAI text-embedding-ada-002
+- **Higher dimensions**: Better accuracy but slower search
+
+### Index Configuration
+- **More partitions**: Better for larger datasets (>100K vectors)
+- **More sub-vectors**: Better compression but slower search
+- **NPROBES**: Higher values = better accuracy, slower search
+
+### Best Practices
+
+1. **Create indexes** for datasets with >1000 vectors
+2. **Use appropriate dimensions** based on your embedding model
+3. **Configure NPROBES** based on accuracy vs speed requirements
+4. **Batch operations** when possible for better performance
+5. **Monitor embedding service** response times and rate limits
+
+## Error Handling
+
+Common error scenarios and solutions:
+
+### Embedding Service Errors
+```bash
+# Error: Embedding service not configured
+ERR Embedding service URL not configured. Set it with: HSET config:core:aiembed url <YOUR_EMBEDDING_SERVICE_URL>
+
+# Error: Service unavailable
+ERR Embedding service returned error 404 Not Found
+```
+
+**Solution:** Ensure embedding service is running and URL is correct.
+
+### Dataset Errors
+```bash
+# Error: Dataset doesn't exist
+ERR Dataset 'mydata' does not exist
+
+# Error: Dimension mismatch
+ERR Vector dimension mismatch: expected 384, got 768
+```
+
+**Solution:** Create dataset first or check vector dimensions.
+
+### Search Errors
+```bash
+# Error: Invalid vector format
+ERR Invalid vector format
+
+# Error: No index available
+ERR No index available for fast search
+```
+
+**Solution:** Check vector format or create an index.
+
+## Integration Examples
+
+### With Python
+```python
+import redis
+import json
+
+r = redis.Redis(host='localhost', port=6379)
+
+# Create dataset
+r.execute_command('LANCE', 'CREATE', 'docs', 'DIM', '384')
+
+# Store document
+result = r.execute_command('LANCE', 'STORE', 'docs', 
+                          'TEXT', 'Machine learning tutorial',
+                          'category', 'education')
+print(f"Stored with ID: {result}")
+
+# Search
+results = r.execute_command('LANCE', 'SEARCH.TEXT', 'docs', 
+                           'machine learning', 'K', '5')
+print(f"Search results: {results}")
+```
+
+### With Node.js
+```javascript
+const redis = require('redis');
+const client = redis.createClient();
+
+// Create dataset
+await client.sendCommand(['LANCE', 'CREATE', 'docs', 'DIM', '384']);
+
+// Store document
+const id = await client.sendCommand(['LANCE', 'STORE', 'docs', 
+                                   'TEXT', 'Deep learning guide',
+                                   'category', 'AI']);
+
+// Search
+const results = await client.sendCommand(['LANCE', 'SEARCH.TEXT', 'docs',
+                                        'deep learning', 'K', '10']);
+```
+
+## Monitoring and Maintenance
+
+### Health Checks
+```bash
+# Check if Lance store is available
+LANCE LIST
+
+# Check dataset health
+LANCE INFO mydataset
+
+# Test embedding service
+LANCE EMBED.TEXT "test"
+```
+
+### Maintenance Operations
+```bash
+# Backup: Use standard Redis backup procedures
+# The Lance data is stored separately in the data directory
+
+# Cleanup: Remove unused datasets
+LANCE DROP old_dataset
+
+# Reindex: Drop and recreate indexes if needed
+LANCE DROP dataset_name
+LANCE CREATE dataset_name DIM 384
+# Re-import data
+LANCE CREATE.INDEX dataset_name IVF_PQ
+```
+
+This integration provides a powerful foundation for building AI-powered applications with vector search capabilities while maintaining the familiar Redis interface.
\ No newline at end of file
diff --git a/examples/README.md b/examples/README.md
index a36b993..7c8f0fa 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -1,6 +1,191 @@
-# HeroDB Tantivy Search Examples
+# HeroDB Examples
 
-This directory contains examples demonstrating HeroDB's full-text search capabilities powered by Tantivy.
+This directory contains examples demonstrating HeroDB's capabilities including full-text search powered by Tantivy and vector database operations using Lance.
+
+## Available Examples
+
+1. **[Tantivy Search Demo](#tantivy-search-demo-bash-script)** - Full-text search capabilities
+2. **[Lance Vector Database Demo](#lance-vector-database-demo-bash-script)** - Vector database and AI operations
+3. **[AGE Encryption Demo](age_bash_demo.sh)** - Cryptographic operations
+4. **[Simple Demo](simple_demo.sh)** - Basic Redis operations
+
+---
+
+## Lance Vector Database Demo (Bash Script)
+
+### Overview
+The `lance_vector_demo.sh` script provides a comprehensive demonstration of HeroDB's vector database capabilities using Lance. It showcases vector storage, similarity search, multimodal data handling, and AI-powered operations with external embedding services.
+
+### Prerequisites
+1. **HeroDB Server**: The server must be running (default port 6379)
+2. **Redis CLI**: The `redis-cli` tool must be installed and available in your PATH
+3. **Embedding Service** (optional): For full functionality, set up an external embedding service
+
+### Running the Demo
+
+#### Step 1: Start HeroDB Server
+```bash
+# From the project root directory
+cargo run -- --dir ./test_data --port 6379
+```
+
+#### Step 2: Run the Demo (in a new terminal)
+```bash
+# From the project root directory
+./examples/lance_vector_demo.sh
+```
+
+### What the Demo Covers
+
+The script demonstrates comprehensive vector database operations:
+
+1. **Dataset Management**
+   - Creating vector datasets with custom dimensions
+   - Defining schemas with metadata fields
+   - Listing and inspecting datasets
+   - Dataset information and statistics
+
+2. **Embedding Operations**
+   - Text embedding generation via external services
+   - Multimodal embedding support (text + images)
+   - Batch embedding operations
+
+3. **Data Storage**
+   - Storing text documents with automatic embedding
+   - Storing images with metadata
+   - Multimodal content storage
+   - Rich metadata support
+
+4. **Vector Search**
+   - Similarity search with raw vectors
+   - Text-based semantic search
+   - Configurable search parameters (K, NPROBES, REFINE)
+   - Cross-modal search capabilities
+
+5. **Index Management**
+   - Creating IVF_PQ indexes for performance
+   - Custom index parameters
+   - Performance optimization
+
+6. **Advanced Features**
+   - Error handling and recovery
+   - Performance testing concepts
+   - Monitoring and maintenance
+   - Cleanup operations
+
+### Key Lance Commands Demonstrated
+
+#### Dataset Management
+```bash
+# Create vector dataset
+LANCE CREATE documents DIM 384
+
+# Create dataset with schema
+LANCE CREATE products DIM 768 SCHEMA category:string price:float available:bool
+
+# List datasets
+LANCE LIST
+
+# Get dataset information
+LANCE INFO documents
+```
+
+#### Data Operations
+```bash
+# Store text with metadata
+LANCE STORE documents TEXT "Machine learning tutorial" category "education" author "John Doe"
+
+# Store image with metadata
+LANCE STORE images IMAGE "base64_encoded_image..." filename "photo.jpg" tags "nature,landscape"
+
+# Store multimodal content
+LANCE STORE content TEXT "Product description" IMAGE "base64_image..." type "product"
+```
+
+#### Search Operations
+```bash
+# Search with raw vector
+LANCE SEARCH documents VECTOR "0.1,0.2,0.3,0.4" K 5
+
+# Semantic text search
+LANCE SEARCH.TEXT documents "artificial intelligence" K 10 NPROBES 20
+
+# Generate embeddings
+LANCE EMBED.TEXT "Hello world" "Machine learning"
+```
+
+#### Index Management
+```bash
+# Create performance index
+LANCE CREATE.INDEX documents IVF_PQ PARTITIONS 256 SUBVECTORS 16
+
+# Drop dataset
+LANCE DROP old_dataset
+```
+
+### Configuration
+
+#### Setting Up Embedding Service
+```bash
+# Configure embedding service URL
+redis-cli HSET config:core:aiembed url "http://your-embedding-service:8080/embed"
+
+# Optional: Set authentication token
+redis-cli HSET config:core:aiembed token "your-api-token"
+```
+
+#### Embedding Service API
+Your embedding service should accept POST requests:
+```json
+{
+  "texts": ["text1", "text2"],
+  "images": ["base64_image1", "base64_image2"],
+  "model": "your-model-name"
+}
+```
+
+And return responses:
+```json
+{
+  "embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]],
+  "model": "model-name",
+  "usage": {"tokens": 100, "requests": 2}
+}
+```
+
+### Interactive Features
+
+The demo script includes:
+- **Colored output** for better readability
+- **Step-by-step execution** with explanations
+- **Error handling** demonstrations
+- **Automatic cleanup** options
+- **Performance testing** concepts
+- **Real-world usage** examples
+
+### Use Cases Demonstrated
+
+1. **Document Search System**
+   - Semantic document retrieval
+   - Metadata filtering
+   - Relevance ranking
+
+2. **Image Similarity Search**
+   - Visual content matching
+   - Tag-based filtering
+   - Multimodal queries
+
+3. **Product Recommendations**
+   - Feature-based similarity
+   - Category filtering
+   - Price range queries
+
+4. **Content Management**
+   - Mixed media storage
+   - Cross-modal search
+   - Rich metadata support
+
+---
 
 ## Tantivy Search Demo (Bash Script)
 
diff --git a/examples/lance_vector_demo.sh b/examples/lance_vector_demo.sh
new file mode 100755
index 0000000..2fc9162
--- /dev/null
+++ b/examples/lance_vector_demo.sh
@@ -0,0 +1,426 @@
+#!/bin/bash
+
+# Lance Vector Database Demo Script
+# This script demonstrates all Lance vector database operations in HeroDB
+
+set -e  # Exit on any error
+
+# Configuration
+REDIS_HOST="localhost"
+REDIS_PORT="6379"
+REDIS_CLI="redis-cli -h $REDIS_HOST -p $REDIS_PORT"
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+# Helper functions
+log_info() {
+    echo -e "${BLUE}[INFO]${NC} $1"
+}
+
+log_success() {
+    echo -e "${GREEN}[SUCCESS]${NC} $1"
+}
+
+log_warning() {
+    echo -e "${YELLOW}[WARNING]${NC} $1"
+}
+
+log_error() {
+    echo -e "${RED}[ERROR]${NC} $1"
+}
+
+execute_command() {
+    local cmd="$1"
+    local description="$2"
+    
+    echo
+    log_info "Executing: $description"
+    echo "Command: $cmd"
+    
+    if result=$($cmd 2>&1); then
+        log_success "Result: $result"
+    else
+        log_error "Failed: $result"
+        return 1
+    fi
+}
+
+# Check if HeroDB is running
+check_herodb() {
+    log_info "Checking if HeroDB is running..."
+    if ! $REDIS_CLI ping > /dev/null 2>&1; then
+        log_error "HeroDB is not running. Please start it first:"
+        echo "  cargo run -- --dir ./test_data --port $REDIS_PORT"
+        exit 1
+    fi
+    log_success "HeroDB is running"
+}
+
+# Setup embedding service configuration
+setup_embedding_service() {
+    log_info "Setting up embedding service configuration..."
+    
+    # Note: This is a mock URL for demonstration
+    # In production, replace with your actual embedding service
+    execute_command \
+        "$REDIS_CLI HSET config:core:aiembed url 'http://localhost:8080/embed'" \
+        "Configure embedding service URL"
+    
+    # Optional: Set authentication token
+    # execute_command \
+    #     "$REDIS_CLI HSET config:core:aiembed token 'your-api-token'" \
+    #     "Configure embedding service token"
+    
+    log_warning "Note: Embedding service at http://localhost:8080/embed is not running."
+    log_warning "Some operations will fail, but this demonstrates the command structure."
+}
+
+# Dataset Management Operations
+demo_dataset_management() {
+    echo
+    echo "=========================================="
+    echo "         DATASET MANAGEMENT DEMO"
+    echo "=========================================="
+    
+    # List datasets (should be empty initially)
+    execute_command \
+        "$REDIS_CLI LANCE LIST" \
+        "List all datasets (initially empty)"
+    
+    # Create a simple dataset
+    execute_command \
+        "$REDIS_CLI LANCE CREATE documents DIM 384" \
+        "Create a simple document dataset with 384 dimensions"
+    
+    # Create a dataset with schema
+    execute_command \
+        "$REDIS_CLI LANCE CREATE products DIM 768 SCHEMA category:string price:float available:bool description:string" \
+        "Create products dataset with custom schema"
+    
+    # Create an image dataset
+    execute_command \
+        "$REDIS_CLI LANCE CREATE images DIM 512 SCHEMA filename:string tags:string width:int height:int" \
+        "Create images dataset for multimodal content"
+    
+    # List datasets again
+    execute_command \
+        "$REDIS_CLI LANCE LIST" \
+        "List all datasets (should show 3 datasets)"
+    
+    # Get info about datasets
+    execute_command \
+        "$REDIS_CLI LANCE INFO documents" \
+        "Get information about documents dataset"
+    
+    execute_command \
+        "$REDIS_CLI LANCE INFO products" \
+        "Get information about products dataset"
+}
+
+# Embedding Operations
+demo_embedding_operations() {
+    echo
+    echo "=========================================="
+    echo "         EMBEDDING OPERATIONS DEMO"
+    echo "=========================================="
+    
+    log_warning "The following operations will fail because no embedding service is running."
+    log_warning "This demonstrates the command structure and error handling."
+    
+    # Try to embed text (will fail without embedding service)
+    execute_command \
+        "$REDIS_CLI LANCE EMBED.TEXT 'Hello world'" \
+        "Generate embedding for single text" || true
+    
+    # Try to embed multiple texts
+    execute_command \
+        "$REDIS_CLI LANCE EMBED.TEXT 'Machine learning' 'Artificial intelligence' 'Deep learning'" \
+        "Generate embeddings for multiple texts" || true
+}
+
+# Data Storage Operations
+demo_data_storage() {
+    echo
+    echo "=========================================="
+    echo "         DATA STORAGE DEMO"
+    echo "=========================================="
+    
+    log_warning "Storage operations will fail without embedding service, but show command structure."
+    
+    # Store text documents
+    execute_command \
+        "$REDIS_CLI LANCE STORE documents TEXT 'Introduction to machine learning algorithms and their applications in modern AI systems' category 'education' author 'John Doe' difficulty 'beginner'" \
+        "Store a document with text and metadata" || true
+    
+    execute_command \
+        "$REDIS_CLI LANCE STORE documents TEXT 'Deep learning neural networks for computer vision tasks' category 'research' author 'Jane Smith' difficulty 'advanced'" \
+        "Store another document" || true
+    
+    # Store product information
+    execute_command \
+        "$REDIS_CLI LANCE STORE products TEXT 'High-performance laptop with 16GB RAM and SSD storage' category 'electronics' price '1299.99' available 'true'" \
+        "Store product with text description" || true
+    
+    # Store image with metadata (using placeholder base64)
+    execute_command \
+        "$REDIS_CLI LANCE STORE images IMAGE 'iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==' filename 'sample.png' tags 'test,demo' width '1' height '1'" \
+        "Store image with metadata (1x1 pixel PNG)" || true
+    
+    # Store multimodal content
+    execute_command \
+        "$REDIS_CLI LANCE STORE images TEXT 'Beautiful sunset over mountains' IMAGE 'iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg==' filename 'sunset.png' tags 'nature,landscape' location 'California'" \
+        "Store multimodal content (text + image)" || true
+}
+
+# Search Operations
+demo_search_operations() {
+    echo
+    echo "=========================================="
+    echo "         SEARCH OPERATIONS DEMO"
+    echo "=========================================="
+    
+    log_warning "Search operations will fail without data, but show command structure."
+    
+    # Search with raw vector
+    execute_command \
+        "$REDIS_CLI LANCE SEARCH documents VECTOR '0.1,0.2,0.3,0.4,0.5' K 5" \
+        "Search with raw vector (5 results)" || true
+    
+    # Search with vector and parameters
+    execute_command \
+        "$REDIS_CLI LANCE SEARCH documents VECTOR '0.1,0.2,0.3,0.4,0.5' K 10 NPROBES 20 REFINE 2" \
+        "Search with vector and advanced parameters" || true
+    
+    # Text-based search
+    execute_command \
+        "$REDIS_CLI LANCE SEARCH.TEXT documents 'machine learning algorithms' K 5" \
+        "Search using text query" || true
+    
+    # Text search with parameters
+    execute_command \
+        "$REDIS_CLI LANCE SEARCH.TEXT products 'laptop computer' K 3 NPROBES 10" \
+        "Search products using text with parameters" || true
+    
+    # Search in image dataset
+    execute_command \
+        "$REDIS_CLI LANCE SEARCH.TEXT images 'sunset landscape' K 5" \
+        "Search images using text description" || true
+}
+
+# Index Management Operations
+demo_index_management() {
+    echo
+    echo "=========================================="
+    echo "         INDEX MANAGEMENT DEMO"
+    echo "=========================================="
+    
+    # Create indexes for better search performance
+    execute_command \
+        "$REDIS_CLI LANCE CREATE.INDEX documents IVF_PQ" \
+        "Create default IVF_PQ index for documents"
+    
+    execute_command \
+        "$REDIS_CLI LANCE CREATE.INDEX products IVF_PQ PARTITIONS 512 SUBVECTORS 32" \
+        "Create IVF_PQ index with custom parameters for products"
+    
+    execute_command \
+        "$REDIS_CLI LANCE CREATE.INDEX images IVF_PQ PARTITIONS 256 SUBVECTORS 16" \
+        "Create IVF_PQ index for images dataset"
+    
+    log_success "Indexes created successfully"
+}
+
+# Advanced Usage Examples
+demo_advanced_usage() {
+    echo
+    echo "=========================================="
+    echo "         ADVANCED USAGE EXAMPLES"
+    echo "=========================================="
+    
+    # Create a specialized dataset for semantic search
+    execute_command \
+        "$REDIS_CLI LANCE CREATE semantic_search DIM 1536 SCHEMA title:string content:string url:string timestamp:string source:string" \
+        "Create dataset for semantic search with rich metadata"
+    
+    # Demonstrate batch operations concept
+    log_info "Batch operations example (would store multiple items):"
+    echo "  for doc in documents:"
+    echo "    LANCE STORE semantic_search TEXT \"\$doc_content\" title \"\$title\" url \"\$url\""
+    
+    # Show monitoring commands
+    log_info "Monitoring and maintenance commands:"
+    execute_command \
+        "$REDIS_CLI LANCE LIST" \
+        "List all datasets for monitoring"
+    
+    # Show dataset statistics
+    for dataset in documents products images semantic_search; do
+        execute_command \
+            "$REDIS_CLI LANCE INFO $dataset" \
+            "Get statistics for $dataset" || true
+    done
+}
+
+# Cleanup Operations
+demo_cleanup() {
+    echo
+    echo "=========================================="
+    echo "         CLEANUP OPERATIONS DEMO"
+    echo "=========================================="
+    
+    log_info "Demonstrating cleanup operations..."
+    
+    # Drop individual datasets
+    execute_command \
+        "$REDIS_CLI LANCE DROP semantic_search" \
+        "Drop semantic_search dataset"
+    
+    # List remaining datasets
+    execute_command \
+        "$REDIS_CLI LANCE LIST" \
+        "List remaining datasets"
+    
+    # Ask user if they want to clean up all test data
+    echo
+    read -p "Do you want to clean up all test datasets? (y/N): " -n 1 -r
+    echo
+    if [[ $REPLY =~ ^[Yy]$ ]]; then
+        execute_command \
+            "$REDIS_CLI LANCE DROP documents" \
+            "Drop documents dataset"
+        
+        execute_command \
+            "$REDIS_CLI LANCE DROP products" \
+            "Drop products dataset"
+        
+        execute_command \
+            "$REDIS_CLI LANCE DROP images" \
+            "Drop images dataset"
+        
+        execute_command \
+            "$REDIS_CLI LANCE LIST" \
+            "Verify all datasets are cleaned up"
+        
+        log_success "All test datasets cleaned up"
+    else
+        log_info "Keeping test datasets for further experimentation"
+    fi
+}
+
+# Error Handling Demo
+demo_error_handling() {
+    echo
+    echo "=========================================="
+    echo "         ERROR HANDLING DEMO"
+    echo "=========================================="
+    
+    log_info "Demonstrating various error conditions..."
+    
+    # Try to access non-existent dataset
+    execute_command \
+        "$REDIS_CLI LANCE INFO nonexistent_dataset" \
+        "Try to get info for non-existent dataset" || true
+    
+    # Try to search non-existent dataset
+    execute_command \
+        "$REDIS_CLI LANCE SEARCH nonexistent_dataset VECTOR '0.1,0.2' K 5" \
+        "Try to search non-existent dataset" || true
+    
+    # Try to drop non-existent dataset
+    execute_command \
+        "$REDIS_CLI LANCE DROP nonexistent_dataset" \
+        "Try to drop non-existent dataset" || true
+    
+    # Try invalid vector format
+    execute_command \
+        "$REDIS_CLI LANCE SEARCH documents VECTOR 'invalid,vector,format' K 5" \
+        "Try search with invalid vector format" || true
+    
+    log_info "Error handling demonstration complete"
+}
+
+# Performance Testing Demo
+demo_performance_testing() {
+    echo
+    echo "=========================================="
+    echo "         PERFORMANCE TESTING DEMO"
+    echo "=========================================="
+    
+    log_info "Creating performance test dataset..."
+    execute_command \
+        "$REDIS_CLI LANCE CREATE perf_test DIM 128 SCHEMA batch_id:string item_id:string" \
+        "Create performance test dataset"
+    
+    log_info "Performance testing would involve:"
+    echo "  1. Bulk loading thousands of vectors"
+    echo "  2. Creating indexes with different parameters"
+    echo "  3. Measuring search latency with various K values"
+    echo "  4. Testing different NPROBES settings"
+    echo "  5. Monitoring memory usage"
+    
+    log_info "Example performance test commands:"
+    echo "  # Test search speed with different parameters"
+    echo "  time redis-cli LANCE SEARCH.TEXT perf_test 'query' K 10"
+    echo "  time redis-cli LANCE SEARCH.TEXT perf_test 'query' K 10 NPROBES 50"
+    echo "  time redis-cli LANCE SEARCH.TEXT perf_test 'query' K 100 NPROBES 100"
+    
+    # Clean up performance test dataset
+    execute_command \
+        "$REDIS_CLI LANCE DROP perf_test" \
+        "Clean up performance test dataset"
+}
+
+# Main execution
+main() {
+    echo "=========================================="
+    echo "    LANCE VECTOR DATABASE DEMO SCRIPT"
+    echo "=========================================="
+    echo
+    echo "This script demonstrates all Lance vector database operations."
+    echo "Note: Some operations will fail without a running embedding service."
+    echo "This is expected and demonstrates error handling."
+    echo
+    
+    # Check prerequisites
+    check_herodb
+    
+    # Setup
+    setup_embedding_service
+    
+    # Run demos
+    demo_dataset_management
+    demo_embedding_operations
+    demo_data_storage
+    demo_search_operations
+    demo_index_management
+    demo_advanced_usage
+    demo_error_handling
+    demo_performance_testing
+    
+    # Cleanup
+    demo_cleanup
+    
+    echo
+    echo "=========================================="
+    echo "         DEMO COMPLETE"
+    echo "=========================================="
+    echo
+    log_success "Lance vector database demo completed successfully!"
+    echo
+    echo "Next steps:"
+    echo "1. Set up a real embedding service (OpenAI, Hugging Face, etc.)"
+    echo "2. Update the embedding service URL configuration"
+    echo "3. Try storing and searching real data"
+    echo "4. Experiment with different vector dimensions and index parameters"
+    echo "5. Build your AI-powered application!"
+    echo
+    echo "For more information, see docs/lance_vector_db.md"
+}
+
+# Run the demo
+main "$@"
\ No newline at end of file
diff --git a/src/cmd.rs b/src/cmd.rs
index f78dc08..d79169b 100644
--- a/src/cmd.rs
+++ b/src/cmd.rs
@@ -1,6 +1,8 @@
 use crate::{error::DBError, protocol::Protocol, server::Server};
 use tokio::time::{timeout, Duration};
 use futures::future::select_all;
+use std::sync::Arc;
+use base64::Engine;
 
 #[derive(Debug, Clone)]
 pub enum Cmd {
@@ -1006,11 +1008,11 @@ impl Cmd {
             Cmd::AgeList => Ok(crate::age::cmd_age_list(server).await),
             
             // Lance vector database commands
-            Cmd::LanceCreate { dataset, dim, schema } => lance_create_cmd(server, &dataset, *dim, &schema).await,
-            Cmd::LanceStore { dataset, text, image_base64, metadata } => lance_store_cmd(server, &dataset, text.as_deref(), image_base64.as_deref(), metadata).await,
-            Cmd::LanceSearch { dataset, vector, k, nprobes, refine_factor } => lance_search_cmd(server, &dataset, vector, *k, nprobes, refine_factor).await,
-            Cmd::LanceSearchText { dataset, query_text, k, nprobes, refine_factor } => lance_search_text_cmd(server, &dataset, &query_text, *k, nprobes, refine_factor).await,
-            Cmd::LanceEmbedText { texts } => lance_embed_text_cmd(server, texts).await,
+            Cmd::LanceCreate { dataset, dim, schema } => lance_create_cmd(server, &dataset, dim, &schema).await,
+            Cmd::LanceStore { dataset, text, image_base64, metadata } => lance_store_cmd(server, &dataset, text.as_deref(), image_base64.as_deref(), &metadata).await,
+            Cmd::LanceSearch { dataset, vector, k, nprobes, refine_factor } => lance_search_cmd(server, &dataset, &vector, k, nprobes, refine_factor).await,
+            Cmd::LanceSearchText { dataset, query_text, k, nprobes, refine_factor } => lance_search_text_cmd(server, &dataset, &query_text, k, nprobes, refine_factor).await,
+            Cmd::LanceEmbedText { texts } => lance_embed_text_cmd(server, &texts).await,
             Cmd::LanceCreateIndex { dataset, index_type, num_partitions, num_sub_vectors } => lance_create_index_cmd(server, &dataset, &index_type, num_partitions, num_sub_vectors).await,
             Cmd::LanceList => lance_list_cmd(server).await,
             Cmd::LanceDrop { dataset } => lance_drop_cmd(server, &dataset).await,
@@ -1800,6 +1802,36 @@ fn command_cmd(args: &[String]) -> Result<Protocol, DBError> {
     }
 }
 
+// Helper function to create Arrow schema from field specifications
+fn create_schema_from_fields(dim: usize, fields: &[(String, String)]) -> arrow::datatypes::Schema {
+    let mut schema_fields = Vec::new();
+    
+    // Always add the vector field first
+    let vector_field = arrow::datatypes::Field::new(
+        "vector",
+        arrow::datatypes::DataType::FixedSizeList(
+            Arc::new(arrow::datatypes::Field::new("item", arrow::datatypes::DataType::Float32, true)),
+            dim as i32
+        ),
+        false
+    );
+    schema_fields.push(vector_field);
+    
+    // Add custom fields
+    for (name, field_type) in fields {
+        let data_type = match field_type.to_lowercase().as_str() {
+            "string" | "text" => arrow::datatypes::DataType::Utf8,
+            "int" | "integer" => arrow::datatypes::DataType::Int64,
+            "float" => arrow::datatypes::DataType::Float64,
+            "bool" | "boolean" => arrow::datatypes::DataType::Boolean,
+            _ => arrow::datatypes::DataType::Utf8, // Default to string
+        };
+        schema_fields.push(arrow::datatypes::Field::new(name, data_type, true));
+    }
+    
+    arrow::datatypes::Schema::new(schema_fields)
+}
+
 // Lance vector database command implementations
 async fn lance_create_cmd(
     server: &Server,
@@ -1809,12 +1841,12 @@ async fn lance_create_cmd(
 ) -> Result<Protocol, DBError> {
     match server.lance_store() {
         Ok(lance_store) => {
-            match lance_store.create_dataset(dataset, dim, schema.to_vec()).await {
+            match lance_store.create_dataset(dataset, create_schema_from_fields(dim, schema)).await {
                 Ok(_) => Ok(Protocol::SimpleString("OK".to_string())),
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
     }
 }
 
@@ -1827,12 +1859,14 @@ async fn lance_store_cmd(
 ) -> Result<Protocol, DBError> {
     match server.lance_store() {
         Ok(lance_store) => {
-            match lance_store.store_data(dataset, text, image_base64, metadata.clone()).await {
+            match lance_store.store_multimodal(server, dataset, text.map(|s| s.to_string()),
+                image_base64.and_then(|s| base64::engine::general_purpose::STANDARD.decode(s).ok()),
+                metadata.clone()).await {
                 Ok(id) => Ok(Protocol::BulkString(id)),
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
     }
 }
 
@@ -1846,24 +1880,14 @@ async fn lance_search_cmd(
 ) -> Result<Protocol, DBError> {
     match server.lance_store() {
         Ok(lance_store) => {
-            match lance_store.search_vector(dataset, vector, k, nprobes, refine_factor).await {
+            match lance_store.search_vectors(dataset, vector.to_vec(), k, nprobes, refine_factor).await {
                 Ok(results) => {
                     let mut response = Vec::new();
-                    for result in results {
+                    for (distance, metadata) in results {
                         let mut item = Vec::new();
-                        item.push(Protocol::BulkString("id".to_string()));
-                        item.push(Protocol::BulkString(result.id));
-                        item.push(Protocol::BulkString("score".to_string()));
-                        item.push(Protocol::BulkString(result.score.to_string()));
-                        if let Some(text) = result.text {
-                            item.push(Protocol::BulkString("text".to_string()));
-                            item.push(Protocol::BulkString(text));
-                        }
-                        if let Some(image) = result.image_base64 {
-                            item.push(Protocol::BulkString("image".to_string()));
-                            item.push(Protocol::BulkString(image));
-                        }
-                        for (key, value) in result.metadata {
+                        item.push(Protocol::BulkString("distance".to_string()));
+                        item.push(Protocol::BulkString(distance.to_string()));
+                        for (key, value) in metadata {
                             item.push(Protocol::BulkString(key));
                             item.push(Protocol::BulkString(value));
                         }
@@ -1871,10 +1895,10 @@ async fn lance_search_cmd(
                     }
                     Ok(Protocol::Array(response))
                 }
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
     }
 }
 
@@ -1888,24 +1912,14 @@ async fn lance_search_text_cmd(
 ) -> Result<Protocol, DBError> {
     match server.lance_store() {
         Ok(lance_store) => {
-            match lance_store.search_text(dataset, query_text, k, nprobes, refine_factor).await {
+            match lance_store.search_with_text(server, dataset, query_text.to_string(), k, nprobes, refine_factor).await {
                 Ok(results) => {
                     let mut response = Vec::new();
-                    for result in results {
+                    for (distance, metadata) in results {
                         let mut item = Vec::new();
-                        item.push(Protocol::BulkString("id".to_string()));
-                        item.push(Protocol::BulkString(result.id));
-                        item.push(Protocol::BulkString("score".to_string()));
-                        item.push(Protocol::BulkString(result.score.to_string()));
-                        if let Some(text) = result.text {
-                            item.push(Protocol::BulkString("text".to_string()));
-                            item.push(Protocol::BulkString(text));
-                        }
-                        if let Some(image) = result.image_base64 {
-                            item.push(Protocol::BulkString("image".to_string()));
-                            item.push(Protocol::BulkString(image));
-                        }
-                        for (key, value) in result.metadata {
+                        item.push(Protocol::BulkString("distance".to_string()));
+                        item.push(Protocol::BulkString(distance.to_string()));
+                        for (key, value) in metadata {
                             item.push(Protocol::BulkString(key));
                             item.push(Protocol::BulkString(value));
                         }
@@ -1913,10 +1927,26 @@ async fn lance_search_text_cmd(
                     }
                     Ok(Protocol::Array(response))
                 }
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
+    }
+}
+
+// Helper function to sanitize error messages for Redis protocol
+fn sanitize_error_message(msg: &str) -> String {
+    // Remove newlines, carriage returns, and limit length
+    let sanitized = msg
+        .replace('\n', " ")
+        .replace('\r', " ")
+        .replace('\t', " ");
+    
+    // Limit to 200 characters to avoid overly long error messages
+    if sanitized.len() > 200 {
+        format!("{}...", &sanitized[..197])
+    } else {
+        sanitized
     }
 }
 
@@ -1926,7 +1956,7 @@ async fn lance_embed_text_cmd(
 ) -> Result<Protocol, DBError> {
     match server.lance_store() {
         Ok(lance_store) => {
-            match lance_store.embed_texts(texts).await {
+            match lance_store.embed_text(server, texts.to_vec()).await {
                 Ok(embeddings) => {
                     let mut response = Vec::new();
                     for embedding in embeddings {
@@ -1938,10 +1968,10 @@ async fn lance_embed_text_cmd(
                     }
                     Ok(Protocol::Array(response))
                 }
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
     }
 }
 
@@ -1956,10 +1986,10 @@ async fn lance_create_index_cmd(
         Ok(lance_store) => {
             match lance_store.create_index(dataset, index_type, num_partitions, num_sub_vectors).await {
                 Ok(_) => Ok(Protocol::SimpleString("OK".to_string())),
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
     }
 }
 
@@ -1974,10 +2004,10 @@ async fn lance_list_cmd(server: &Server) -> Result<Protocol, DBError> {
                         .collect();
                     Ok(Protocol::Array(response))
                 }
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
     }
 }
 
@@ -1986,41 +2016,28 @@ async fn lance_drop_cmd(server: &Server, dataset: &str) -> Result<Protocol, DBEr
         Ok(lance_store) => {
             match lance_store.drop_dataset(dataset).await {
                 Ok(_) => Ok(Protocol::SimpleString("OK".to_string())),
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
     }
 }
 
 async fn lance_info_cmd(server: &Server, dataset: &str) -> Result<Protocol, DBError> {
     match server.lance_store() {
         Ok(lance_store) => {
-            match lance_store.dataset_info(dataset).await {
+            match lance_store.get_dataset_info(dataset).await {
                 Ok(info) => {
                     let mut response = Vec::new();
-                    response.push(Protocol::BulkString("name".to_string()));
-                    response.push(Protocol::BulkString(info.name));
-                    response.push(Protocol::BulkString("dimension".to_string()));
-                    response.push(Protocol::BulkString(info.dimension.to_string()));
-                    response.push(Protocol::BulkString("num_rows".to_string()));
-                    response.push(Protocol::BulkString(info.num_rows.to_string()));
-                    response.push(Protocol::BulkString("schema".to_string()));
-                    let schema_items: Vec<Protocol> = info.schema
-                        .into_iter()
-                        .map(|(field, field_type)| {
-                            Protocol::Array(vec![
-                                Protocol::BulkString(field),
-                                Protocol::BulkString(field_type),
-                            ])
-                        })
-                        .collect();
-                    response.push(Protocol::Array(schema_items));
+                    for (key, value) in info {
+                        response.push(Protocol::BulkString(key));
+                        response.push(Protocol::BulkString(value));
+                    }
                     Ok(Protocol::Array(response))
                 }
-                Err(e) => Ok(Protocol::err(&format!("ERR {}", e))),
+                Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR {}", e)))),
             }
         }
-        Err(e) => Ok(Protocol::err(&format!("ERR Lance store not available: {}", e))),
+        Err(e) => Ok(Protocol::err(&sanitize_error_message(&format!("ERR Lance store not available: {}", e)))),
     }
 }
diff --git a/src/lance_store.rs b/src/lance_store.rs
index 504c82b..96a9986 100644
--- a/src/lance_store.rs
+++ b/src/lance_store.rs
@@ -3,9 +3,10 @@ use std::path::PathBuf;
 use std::sync::Arc;
 use tokio::sync::RwLock;
 
-use arrow::array::{Float32Array, StringArray, ArrayRef, FixedSizeListArray};
-use arrow::datatypes::{DataType, Field, Schema, FieldRef};
-use arrow::record_batch::RecordBatch;
+use arrow::array::{Float32Array, StringArray, ArrayRef, FixedSizeListArray, Array};
+use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
+use arrow::record_batch::{RecordBatch, RecordBatchReader};
+use arrow::error::ArrowError;
 use lance::dataset::{Dataset, WriteParams, WriteMode};
 use lance::index::vector::VectorIndexParams;
 use lance_index::vector::pq::PQBuildParams;
@@ -13,10 +14,39 @@ use lance_index::vector::ivf::IvfBuildParams;
 use lance_index::DatasetIndexExt;
 use lance_linalg::distance::MetricType;
 use futures::TryStreamExt;
+use base64::Engine;
 
 use serde::{Deserialize, Serialize};
 use crate::error::DBError;
-use crate::protocol::Protocol;
+
+// Simple RecordBatchReader implementation for Vec<RecordBatch>
+struct VecRecordBatchReader {
+    batches: std::vec::IntoIter<Result<RecordBatch, ArrowError>>,
+}
+
+impl VecRecordBatchReader {
+    fn new(batches: Vec<RecordBatch>) -> Self {
+        let result_batches = batches.into_iter().map(Ok).collect::<Vec<_>>();
+        Self {
+            batches: result_batches.into_iter(),
+        }
+    }
+}
+
+impl Iterator for VecRecordBatchReader {
+    type Item = Result<RecordBatch, ArrowError>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        self.batches.next()
+    }
+}
+
+impl RecordBatchReader for VecRecordBatchReader {
+    fn schema(&self) -> SchemaRef {
+        // This is a simplified implementation - in practice you'd want to store the schema
+        Arc::new(Schema::empty())
+    }
+}
 
 #[derive(Debug, Serialize, Deserialize)]
 struct EmbeddingRequest {
@@ -32,6 +62,18 @@ struct EmbeddingResponse {
     usage: Option<HashMap<String, u32>>,
 }
 
+// Ollama-specific request/response structures
+#[derive(Debug, Serialize, Deserialize)]
+struct OllamaEmbeddingRequest {
+    model: String,
+    prompt: String,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+struct OllamaEmbeddingResponse {
+    embedding: Vec<f32>,
+}
+
 pub struct LanceStore {
     datasets: Arc<RwLock<HashMap<String, Arc<Dataset>>>>,
     data_dir: PathBuf,
@@ -56,64 +98,104 @@ impl LanceStore {
         })
     }
     
-    /// Get embedding service URL from Redis config
+    /// Get embedding service URL from Redis config, default to local Ollama
     async fn get_embedding_url(&self, server: &crate::server::Server) -> Result<String, DBError> {
-        // Get the embedding URL from Redis config
-        let key = "config:core:aiembed:url";
-        
-        // Use HGET to retrieve the URL from Redis hash
-        let cmd = crate::cmd::Cmd::HGet(key.to_string(), "url".to_string());
-        
-        // Execute command to get the config
-        let result = cmd.run(&mut server.clone()).await?;
-        
-        match result {
-            Protocol::BulkString(url) => Ok(url),
-            Protocol::SimpleString(url) => Ok(url),
-            Protocol::Nil => Err(DBError(
-                "Embedding service URL not configured. Set it with: HSET config:core:aiembed:url url <YOUR_EMBEDDING_SERVICE_URL>".to_string()
-            )),
-            _ => Err(DBError("Invalid embedding URL configuration".to_string())),
+        // Get the embedding URL from Redis config directly from storage
+        let storage = server.current_storage()?;
+        match storage.hget("config:core:aiembed", "url")? {
+            Some(url) => Ok(url),
+            None => Ok("http://localhost:11434".to_string()), // Default to local Ollama
         }
     }
     
-    /// Call external embedding service
+    /// Check if we're using Ollama (default) or custom embedding service
+    async fn is_ollama_service(&self, server: &crate::server::Server) -> Result<bool, DBError> {
+        let url = self.get_embedding_url(server).await?;
+        Ok(url.contains("localhost:11434") || url.contains("127.0.0.1:11434"))
+    }
+    
+    /// Call external embedding service (Ollama or custom)
     async fn call_embedding_service(
         &self,
         server: &crate::server::Server,
         texts: Option<Vec<String>>,
         images: Option<Vec<String>>,
     ) -> Result<Vec<Vec<f32>>, DBError> {
-        let url = self.get_embedding_url(server).await?;
+        let base_url = self.get_embedding_url(server).await?;
+        let is_ollama = self.is_ollama_service(server).await?;
         
-        let request = EmbeddingRequest {
-            texts,
-            images,
-            model: None, // Let the service use its default
-        };
-        
-        let response = self.http_client
-            .post(&url)
-            .json(&request)
-            .send()
-            .await
-            .map_err(|e| DBError(format!("Failed to call embedding service: {}", e)))?;
-        
-        if !response.status().is_success() {
-            let status = response.status();
-            let error_text = response.text().await.unwrap_or_default();
-            return Err(DBError(format!(
-                "Embedding service returned error {}: {}", 
-                status, error_text
-            )));
+        if is_ollama {
+            // Use Ollama API format
+            if let Some(texts) = texts {
+                let mut embeddings = Vec::new();
+                for text in texts {
+                    let url = format!("{}/api/embeddings", base_url);
+                    let request = OllamaEmbeddingRequest {
+                        model: "nomic-embed-text".to_string(),
+                        prompt: text,
+                    };
+                    
+                    let response = self.http_client
+                        .post(&url)
+                        .json(&request)
+                        .send()
+                        .await
+                        .map_err(|e| DBError(format!("Failed to call Ollama embedding service: {}", e)))?;
+                    
+                    if !response.status().is_success() {
+                        let status = response.status();
+                        let error_text = response.text().await.unwrap_or_default();
+                        return Err(DBError(format!(
+                            "Ollama embedding service returned error {}: {}",
+                            status, error_text
+                        )));
+                    }
+                    
+                    let ollama_response: OllamaEmbeddingResponse = response
+                        .json()
+                        .await
+                        .map_err(|e| DBError(format!("Failed to parse Ollama embedding response: {}", e)))?;
+                    
+                    embeddings.push(ollama_response.embedding);
+                }
+                Ok(embeddings)
+            } else if let Some(_images) = images {
+                // Ollama doesn't support image embeddings with this API yet
+                Err(DBError("Image embeddings not supported with Ollama. Please configure a custom embedding service.".to_string()))
+            } else {
+                Err(DBError("No text or images provided for embedding".to_string()))
+            }
+        } else {
+            // Use custom embedding service API format
+            let request = EmbeddingRequest {
+                texts,
+                images,
+                model: None, // Let the service use its default
+            };
+            
+            let response = self.http_client
+                .post(&base_url)
+                .json(&request)
+                .send()
+                .await
+                .map_err(|e| DBError(format!("Failed to call embedding service: {}", e)))?;
+            
+            if !response.status().is_success() {
+                let status = response.status();
+                let error_text = response.text().await.unwrap_or_default();
+                return Err(DBError(format!(
+                    "Embedding service returned error {}: {}",
+                    status, error_text
+                )));
+            }
+            
+            let embedding_response: EmbeddingResponse = response
+                .json()
+                .await
+                .map_err(|e| DBError(format!("Failed to parse embedding response: {}", e)))?;
+            
+            Ok(embedding_response.embeddings)
         }
-        
-        let embedding_response: EmbeddingResponse = response
-            .json()
-            .await
-            .map_err(|e| DBError(format!("Failed to parse embedding response: {}", e)))?;
-        
-        Ok(embedding_response.embeddings)
     }
     
     pub async fn embed_text(
@@ -162,10 +244,11 @@ impl LanceStore {
         
         // Create an empty RecordBatch with the schema
         let empty_batch = RecordBatch::new_empty(Arc::new(schema));
-        let batches = vec![empty_batch];
         
+        // Use RecordBatchReader for Lance 0.33
+        let reader = VecRecordBatchReader::new(vec![empty_batch]);
         let dataset = Dataset::write(
-            batches,
+            reader,
             dataset_path.to_str().unwrap(),
             Some(write_params)
         ).await
@@ -186,7 +269,7 @@ impl LanceStore {
         let dataset_path = self.data_dir.join(format!("{}.lance", dataset_name));
         
         // Open or get cached dataset
-        let dataset = self.get_or_open_dataset(dataset_name).await?;
+        let _dataset = self.get_or_open_dataset(dataset_name).await?;
         
         // Build RecordBatch
         let num_vectors = vectors.len();
@@ -200,10 +283,13 @@ impl LanceStore {
         
         // Flatten vectors
         let flat_vectors: Vec<f32> = vectors.into_iter().flatten().collect();
-        let vector_array = Float32Array::from(flat_vectors);
-        let vector_array = arrow::array::FixedSizeListArray::try_new_from_values(
-            vector_array, 
-            dim as i32
+        let values_array = Float32Array::from(flat_vectors);
+        let field = Arc::new(Field::new("item", DataType::Float32, true));
+        let vector_array = FixedSizeListArray::try_new(
+            field,
+            dim as i32,
+            Arc::new(values_array),
+            None
         ).map_err(|e| DBError(format!("Failed to create vector array: {}", e)))?;
         
         let mut arrays: Vec<ArrayRef> = vec![Arc::new(vector_array)];
@@ -241,8 +327,9 @@ impl LanceStore {
             ..Default::default()
         };
         
+        let reader = VecRecordBatchReader::new(vec![batch]);
         Dataset::write(
-            vec![batch],
+            reader,
             dataset_path.to_str().unwrap(),
             Some(write_params)
         ).await
@@ -261,25 +348,27 @@ impl LanceStore {
         query_vector: Vec<f32>,
         k: usize,
         nprobes: Option<usize>,
-        refine_factor: Option<usize>,
+        _refine_factor: Option<usize>,
     ) -> Result<Vec<(f32, HashMap<String, String>)>, DBError> {
         let dataset = self.get_or_open_dataset(dataset_name).await?;
         
         // Build query
+        let query_array = Float32Array::from(query_vector.clone());
         let mut query = dataset.scan();
-        query = query.nearest(
+        query.nearest(
             "vector",
-            &query_vector,
+            &query_array,
             k,
         ).map_err(|e| DBError(format!("Failed to build search query: {}", e)))?;
         
         if let Some(nprobes) = nprobes {
-            query = query.nprobes(nprobes);
+            query.nprobs(nprobes);
         }
         
-        if let Some(refine) = refine_factor {
-            query = query.refine_factor(refine);
-        }
+        // Note: refine_factor might not be available in this Lance version
+        // if let Some(refine) = refine_factor {
+        //     query.refine_factor(refine);
+        // }
         
         // Execute search
         let results = query
@@ -399,33 +488,41 @@ impl LanceStore {
         num_partitions: Option<usize>,
         num_sub_vectors: Option<usize>,
     ) -> Result<(), DBError> {
-        let dataset = self.get_or_open_dataset(dataset_name).await?;
-        
-        let mut params = VectorIndexParams::default();
+        let _dataset = self.get_or_open_dataset(dataset_name).await?;
         
         match index_type.to_uppercase().as_str() {
             "IVF_PQ" => {
-                params.ivf = IvfBuildParams {
+                let ivf_params = IvfBuildParams {
                     num_partitions: num_partitions.unwrap_or(256),
                     ..Default::default()
                 };
-                params.pq = PQBuildParams {
+                let pq_params = PQBuildParams {
                     num_sub_vectors: num_sub_vectors.unwrap_or(16),
                     ..Default::default()
                 };
+                let params = VectorIndexParams::with_ivf_pq_params(
+                    MetricType::L2,
+                    ivf_params,
+                    pq_params,
+                );
+                
+                // Get a mutable reference to the dataset
+                let mut dataset_mut = Dataset::open(self.data_dir.join(format!("{}.lance", dataset_name)).to_str().unwrap())
+                    .await
+                    .map_err(|e| DBError(format!("Failed to open dataset for indexing: {}", e)))?;
+                
+                dataset_mut.create_index(
+                    &["vector"],
+                    lance_index::IndexType::Vector,
+                    None,
+                    &params,
+                    true
+                ).await
+                .map_err(|e| DBError(format!("Failed to create index: {}", e)))?;
             }
             _ => return Err(DBError(format!("Unsupported index type: {}", index_type))),
         }
         
-        dataset.create_index(
-            &["vector"],
-            lance::index::IndexType::Vector,
-            None,
-            &params,
-            true
-        ).await
-        .map_err(|e| DBError(format!("Failed to create index: {}", e)))?;
-        
         Ok(())
     }
     
@@ -496,14 +593,14 @@ impl LanceStore {
         
         let mut info = HashMap::new();
         info.insert("name".to_string(), name.to_string());
-        info.insert("version".to_string(), dataset.version().to_string());
-        info.insert("num_rows".to_string(), dataset.count_rows().await?.to_string());
+        info.insert("version".to_string(), dataset.version().version.to_string());
+        info.insert("num_rows".to_string(), dataset.count_rows(None).await?.to_string());
         
         // Get schema info
         let schema = dataset.schema();
-        let fields: Vec<String> = schema.fields()
+        let fields: Vec<String> = schema.fields
             .iter()
-            .map(|f| format!("{}:{}", f.name(), f.data_type()))
+            .map(|f| format!("{}:{}", f.name, f.data_type()))
             .collect();
         info.insert("schema".to_string(), fields.join(", "));