diff --git a/Cargo.lock b/Cargo.lock
index b51e07de2..6192b80b8 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -123,6 +123,21 @@ version = "0.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "4aa90d7ce82d4be67b64039a3d588d38dbcc6736577de4a847025ce5b0c468d1"
 
+[[package]]
+name = "alloc-no-stdlib"
+version = "2.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cc7bb162ec39d46ab1ca8c77bf72e890535becd1751bb45f64c597edb4c8c6b3"
+
+[[package]]
+name = "alloc-stdlib"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "94fb8275041c72129eb51b7d0322c29b8387a0386127718b096429201a5d6ece"
+dependencies = [
+ "alloc-no-stdlib",
+]
+
 [[package]]
 name = "allocator-api2"
 version = "0.2.18"
@@ -224,6 +239,21 @@ dependencies = [
  "syn 2.0.66",
 ]
 
+[[package]]
+name = "argminmax"
+version = "0.6.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "52424b59d69d69d5056d508b260553afd91c57e21849579cd1f50ee8b8b88eaa"
+dependencies = [
+ "num-traits",
+]
+
+[[package]]
+name = "array-init-cursor"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bf7d0a018de4f6aa429b9d33d69edf69072b1c5b1cb8d3e4a5f7ef898fc3eb76"
+
 [[package]]
 name = "arrayref"
 version = "0.3.7"
@@ -375,7 +405,7 @@ dependencies = [
  "arrow-schema 51.0.0",
  "arrow-select 51.0.0",
  "atoi",
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "comfy-table",
  "half",
@@ -396,7 +426,7 @@ dependencies = [
  "arrow-schema 52.2.0",
  "arrow-select 52.2.0",
  "atoi",
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "comfy-table",
  "half",
@@ -709,6 +739,15 @@ dependencies = [
  "futures-core",
 ]
 
+[[package]]
+name = "async-convert"
+version = "1.0.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6d416feee97712e43152cd42874de162b8f9b77295b1c85e5d92725cc8310bae"
+dependencies = [
+ "async-trait",
+]
+
 [[package]]
 name = "async-executor"
 version = "1.5.1"
@@ -767,6 +806,32 @@ dependencies = [
  "event-listener 2.5.3",
 ]
 
+[[package]]
+name = "async-openai"
+version = "0.23.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "dc0e5ff98f9e7c605df4c88783a0439d1dc667ce86bd79e99d4164f8b0c05ccc"
+dependencies = [
+ "async-convert",
+ "backoff",
+ "base64 0.22.1",
+ "bytes",
+ "derive_builder 0.20.1",
+ "eventsource-stream",
+ "futures",
+ "rand 0.8.5",
+ "reqwest 0.12.5",
+ "reqwest-eventsource",
+ "secrecy",
+ "serde",
+ "serde_json",
+ "thiserror",
+ "tokio",
+ "tokio-stream",
+ "tokio-util 0.7.11",
+ "tracing",
+]
+
 [[package]]
 name = "async-priority-channel"
 version = "0.2.0"
@@ -832,6 +897,28 @@ dependencies = [
  "wasm-bindgen-futures",
 ]
 
+[[package]]
+name = "async-stream"
+version = "0.3.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cd56dd203fef61ac097dd65721a419ddccb106b2d2b70ba60a6b529f03961a51"
+dependencies = [
+ "async-stream-impl",
+ "futures-core",
+ "pin-project-lite",
+]
+
+[[package]]
+name = "async-stream-impl"
+version = "0.3.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.66",
+]
+
 [[package]]
 name = "async-task"
 version = "4.4.0"
@@ -875,6 +962,12 @@ dependencies = [
  "num-traits",
 ]
 
+[[package]]
+name = "atoi_simd"
+version = "0.15.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9ae037714f313c1353189ead58ef9eec30a8e8dc101b2622d461418fd59e28a9"
+
 [[package]]
 name = "atomic-waker"
 version = "1.1.1"
@@ -1384,6 +1477,20 @@ dependencies = [
  "tower-service",
 ]
 
+[[package]]
+name = "backoff"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b62ddb9cb1ec0a098ad4bbf9344d0713fa193ae1a80af55febcff2627b6a00c1"
+dependencies = [
+ "futures-core",
+ "getrandom 0.2.10",
+ "instant",
+ "pin-project-lite",
+ "rand 0.8.5",
+ "tokio",
+]
+
 [[package]]
 name = "backtrace"
 version = "0.3.69"
@@ -1435,9 +1542,9 @@ checksum = "9d297deb1925b89f2ccc13d7635fa0714f12c87adce1c75356b39ca9b7178567"
 
 [[package]]
 name = "base64"
-version = "0.22.0"
+version = "0.22.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9475866fec1451be56a3c2400fd081ff546538961565ccb5b7142cbd22bc7a51"
+checksum = "72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6"
 
 [[package]]
 name = "base64-simd"
@@ -1623,6 +1730,27 @@ dependencies = [
  "syn_derive",
 ]
 
+[[package]]
+name = "brotli"
+version = "5.0.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "19483b140a7ac7174d34b5a581b406c64f84da5409d3e09cf4fff604f9270e67"
+dependencies = [
+ "alloc-no-stdlib",
+ "alloc-stdlib",
+ "brotli-decompressor",
+]
+
+[[package]]
+name = "brotli-decompressor"
+version = "4.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9a45bd2e4095a8b518033b128020dd4a55aab1c0a381ba4404a472630f4bc362"
+dependencies = [
+ "alloc-no-stdlib",
+ "alloc-stdlib",
+]
+
 [[package]]
 name = "bs58"
 version = "0.5.0"
@@ -1633,6 +1761,17 @@ dependencies = [
  "tinyvec",
 ]
 
+[[package]]
+name = "bstr"
+version = "1.10.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "40723b8fb387abc38f4f4a37c09073622e41dd12327033091ef8950659e6dc0c"
+dependencies = [
+ "memchr",
+ "regex-automata 0.4.7",
+ "serde",
+]
+
 [[package]]
 name = "buf_redux"
 version = "0.8.4"
@@ -1700,6 +1839,20 @@ name = "bytemuck"
 version = "1.16.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "102087e286b4677862ea56cf8fc58bb2cdfa8725c40ffb80fe3a008eb7f2fc83"
+dependencies = [
+ "bytemuck_derive",
+]
+
+[[package]]
+name = "bytemuck_derive"
+version = "1.7.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0cc8b54b395f2fcfbb3d90c47b01c7f444d94d05bdeb775811dec868ac3bbc26"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.66",
+]
 
 [[package]]
 name = "byteorder"
@@ -1915,6 +2068,17 @@ dependencies = [
  "parse-zoneinfo",
 ]
 
+[[package]]
+name = "chrono-tz"
+version = "0.8.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d59ae0466b83e838b81a54256c39d5d7c20b9d7daa10510a242d9b75abd5936e"
+dependencies = [
+ "chrono",
+ "chrono-tz-build 0.2.1",
+ "phf 0.11.2",
+]
+
 [[package]]
 name = "chrono-tz"
 version = "0.9.0"
@@ -1922,8 +2086,19 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "93698b29de5e97ad0ae26447b344c482a7284c737d9ddc5f9e52b74a336671bb"
 dependencies = [
  "chrono",
- "chrono-tz-build",
+ "chrono-tz-build 0.3.0",
+ "phf 0.11.2",
+]
+
+[[package]]
+name = "chrono-tz-build"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "433e39f13c9a060046954e0592a8d0a4bcb1040125cbf91cb8ee58964cfb350f"
+dependencies = [
+ "parse-zoneinfo",
  "phf 0.11.2",
+ "phf_codegen 0.11.2",
 ]
 
 [[package]]
@@ -2153,6 +2328,7 @@ version = "7.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b34115915337defe99b2aff5c2ce6771e5fbc4079f4b506301f5cf394c8452f7"
 dependencies = [
+ "crossterm",
  "strum 0.26.3",
  "strum_macros 0.26.4",
  "unicode-width",
@@ -2165,7 +2341,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d0436149c9f6a1935b13306206c739b1ba84fa81f551b5eb87fc2ca7a13700af"
 dependencies = [
  "clap 4.5.4",
- "derive_builder",
+ "derive_builder 0.12.0",
  "entities",
  "memchr",
  "once_cell",
@@ -2451,6 +2627,28 @@ version = "0.8.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
 
+[[package]]
+name = "crossterm"
+version = "0.27.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f476fe445d41c9e991fd07515a6f463074b782242ccf4a5b7b1d1012e70824df"
+dependencies = [
+ "bitflags 2.4.0",
+ "crossterm_winapi",
+ "libc",
+ "parking_lot 0.12.1",
+ "winapi",
+]
+
+[[package]]
+name = "crossterm_winapi"
+version = "0.9.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "acdd7c62a3665c7f6830a51635d9ac9b23ed385797f70a83bb8bafe9c572ab2b"
+dependencies = [
+ "winapi",
+]
+
 [[package]]
 name = "crunchy"
 version = "0.2.2"
@@ -2819,7 +3017,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "a369332afd0ef5bd565f6db2139fb9f1dfdd0afa75a7f70f000b74208d76994f"
 dependencies = [
  "arrow 52.2.0",
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "datafusion-common",
  "datafusion-execution",
@@ -2906,7 +3104,7 @@ dependencies = [
  "arrow-ord 52.2.0",
  "arrow-schema 52.2.0",
  "arrow-string 52.2.0",
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "datafusion-common",
  "datafusion-execution",
@@ -3082,7 +3280,16 @@ version = "0.12.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8d67778784b508018359cbc8696edb3db78160bab2c2a28ba7f56ef6932997f8"
 dependencies = [
- "derive_builder_macro",
+ "derive_builder_macro 0.12.0",
+]
+
+[[package]]
+name = "derive_builder"
+version = "0.20.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cd33f37ee6a119146a1781d3356a7c26028f83d779b2e04ecd45fdc75c76877b"
+dependencies = [
+ "derive_builder_macro 0.20.1",
 ]
 
 [[package]]
@@ -3097,16 +3304,38 @@ dependencies = [
  "syn 1.0.109",
 ]
 
+[[package]]
+name = "derive_builder_core"
+version = "0.20.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7431fa049613920234f22c47fdc33e6cf3ee83067091ea4277a3f8c4587aae38"
+dependencies = [
+ "darling 0.20.10",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.66",
+]
+
 [[package]]
 name = "derive_builder_macro"
 version = "0.12.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ebcda35c7a396850a55ffeac740804b40ffec779b98fffbb1738f4033f0ee79e"
 dependencies = [
- "derive_builder_core",
+ "derive_builder_core 0.12.0",
  "syn 1.0.109",
 ]
 
+[[package]]
+name = "derive_builder_macro"
+version = "0.20.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4abae7035bf79b9877b779505d8cf3749285b80c43941eda66604841889451dc"
+dependencies = [
+ "derive_builder_core 0.20.1",
+ "syn 2.0.66",
+]
+
 [[package]]
 name = "derive_more"
 version = "0.99.18"
@@ -3278,6 +3507,12 @@ version = "1.0.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "56ce8c6da7551ec6c462cbaf3bfbc75131ebbfa1c944aeaa9dab51ca1c5f0c3b"
 
+[[package]]
+name = "dyn-clone"
+version = "1.0.17"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0d6ef0072f8a535281e4876be788938b528e9a1d43900b82c2569af7da799125"
+
 [[package]]
 name = "ecdsa"
 version = "0.14.8"
@@ -3337,9 +3572,9 @@ checksum = "3a68a4904193147e0a8dec3314640e6db742afd5f6e634f428a6af230d9b3591"
 
 [[package]]
 name = "either"
-version = "1.9.0"
+version = "1.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a26ae43d7bcc3b814de94796a5e736d4029efb0ee900c12e2d54c993ad1a1e07"
+checksum = "60b1af1c220855b6ceac025d3f6ecdd2b7c4894bfe9cd9bda4fbb4bc7c0d4cf0"
 
 [[package]]
 name = "elliptic-curve"
@@ -3422,6 +3657,18 @@ version = "1.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b5320ae4c3782150d900b79807611a59a99fc9a1d61d686faafc24b93fc8d7ca"
 
+[[package]]
+name = "enum_dispatch"
+version = "0.3.13"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "aa18ce2bc66555b3218614519ac839ddb759a7d6720732f979ef8d13be147ecd"
+dependencies = [
+ "once_cell",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.66",
+]
+
 [[package]]
 name = "env_logger"
 version = "0.9.3"
@@ -3778,6 +4025,12 @@ dependencies = [
  "yansi",
 ]
 
+[[package]]
+name = "ethnum"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b90ca2580b73ab6a1f724b76ca11ab632df820fd6040c336200d2c1df7b3c82c"
+
 [[package]]
 name = "event-listener"
 version = "2.5.3"
@@ -3795,6 +4048,17 @@ dependencies = [
  "pin-project-lite",
 ]
 
+[[package]]
+name = "eventsource-stream"
+version = "0.2.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "74fef4569247a5f429d9156b9d0a2599914385dd189c539334c625d8099d90ab"
+dependencies = [
+ "futures-core",
+ "nom",
+ "pin-project-lite",
+]
+
 [[package]]
 name = "exr"
 version = "1.72.0"
@@ -3821,6 +4085,12 @@ dependencies = [
  "once_cell",
 ]
 
+[[package]]
+name = "fallible-streaming-iterator"
+version = "0.1.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7360491ce676a36bf9bb3c56c1aa791658183a54d2744120f27285738d90465a"
+
 [[package]]
 name = "fancy-regex"
 version = "0.11.0"
@@ -3831,6 +4101,22 @@ dependencies = [
  "regex",
 ]
 
+[[package]]
+name = "fancy-regex"
+version = "0.12.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7493d4c459da9f84325ad297371a6b2b8a162800873a22e3b6b6512e61d18c05"
+dependencies = [
+ "bit-set",
+ "regex",
+]
+
+[[package]]
+name = "fast-float"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "95765f67b4b18863968b4a1bd5bb576f732b29a4a28c7cd84c09fa3e2875f33c"
+
 [[package]]
 name = "fastdivide"
 version = "0.4.1"
@@ -3993,6 +4279,12 @@ version = "0.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "00b0228411908ca8685dba7fc2cdd70ec9990a6e753e89b6ac91a84c40fbaf4b"
 
+[[package]]
+name = "foreign_vec"
+version = "0.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ee1b05cbd864bcaecbd3455d6d967862d446e4ebfc3c2e5e5b9841e53cba6673"
+
 [[package]]
 name = "form_urlencoded"
 version = "1.2.1"
@@ -4441,6 +4733,8 @@ checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1"
 dependencies = [
  "ahash 0.8.11",
  "allocator-api2",
+ "rayon",
+ "serde",
 ]
 
 [[package]]
@@ -4521,9 +4815,9 @@ dependencies = [
 
 [[package]]
 name = "hermit-abi"
-version = "0.3.2"
+version = "0.3.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "443144c8cdadd93ebf52ddb4056d257f5b52c04d3c804e657d19eb73fc33668b"
+checksum = "d231dfb89cfffdbc30e7fc41579ed6066ad03abda9e567ccafae602b97ec5024"
 
 [[package]]
 name = "hex"
@@ -4854,7 +5148,7 @@ dependencies = [
  "iana-time-zone-haiku",
  "js-sys",
  "wasm-bindgen",
- "windows",
+ "windows 0.48.0",
 ]
 
 [[package]]
@@ -5055,7 +5349,7 @@ version = "1.0.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "eae7b9aee968036d54dce06cebaefd919e4472e753296daccd6d344e3e2df0c2"
 dependencies = [
- "hermit-abi 0.3.2",
+ "hermit-abi 0.3.9",
  "libc",
  "windows-sys 0.48.0",
 ]
@@ -5088,7 +5382,7 @@ version = "0.4.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "cb0889898416213fab133e1d33a0e5858a48177452750691bde3666d0fdbaf8b"
 dependencies = [
- "hermit-abi 0.3.2",
+ "hermit-abi 0.3.9",
  "rustix 0.38.32",
  "windows-sys 0.48.0",
 ]
@@ -5141,6 +5435,12 @@ version = "1.0.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "af150ab688ff2122fcef229be89cb50dd66af9e01a4ff320cc137eecc9bacc38"
 
+[[package]]
+name = "itoap"
+version = "1.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9028f49264629065d057f340a86acb84867925865f73bbf8d47b4d149a7e88b8"
+
 [[package]]
 name = "jetscii"
 version = "0.5.3"
@@ -6028,11 +6328,21 @@ version = "0.11.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9106e1d747ffd48e6be5bb2d97fa706ed25b144fbee4d5c02eae110cd8d6badd"
 
+[[package]]
+name = "lz4"
+version = "1.26.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "958b4caa893816eea05507c20cfe47574a43d9a697138a7872990bba8a0ece68"
+dependencies = [
+ "libc",
+ "lz4-sys",
+]
+
 [[package]]
 name = "lz4-sys"
-version = "1.9.4"
+version = "1.10.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
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+checksum = "f49388d20533534cd19360ad3d6a7dadc885944aa802ba3995040c5ec11288c6"
+dependencies = [
+ "libc",
+]
+
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@@ -6297,13 +6616,14 @@ dependencies = [
 
 [[package]]
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-version = "0.8.10"
+version = "1.0.2"
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-checksum = "8f3d0b296e374a4e6f3c7b0a1f5a51d748a0d34c85e7dc48fc3fa9a87657fe09"
+checksum = "80e04d1dcff3aae0704555fe5fee3bcfaf3d1fdf8a7e521d5b9d2b42acb52cec"
 dependencies = [
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  "libc",
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- "windows-sys 0.48.0",
+ "windows-sys 0.52.0",
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 [[package]]
@@ -6410,19 +6730,41 @@ dependencies = [
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-
-[[package]]
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+name = "multiversion"
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+checksum = "c4851161a11d3ad0bf9402d90ffc3967bf231768bfd7aeb61755ad06dbf1a142"
 dependencies = [
- "log 0.3.9",
- "serde",
+ "multiversion-macros",
+ "target-features",
+]
+
+[[package]]
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+checksum = "79a74ddee9e0c27d2578323c13905793e91622148f138ba29738f9dddb835e90"
+dependencies = [
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+ "quote",
+ "syn 1.0.109",
+ "target-features",
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+
+[[package]]
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+checksum = "2195bf6aa996a481483b29d62a7663eed3fe39600c460e323f8ff41e90bdd89b"
+
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+dependencies = [
+ "log 0.3.9",
+ "serde",
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 checksum = "0676bb32a98c1a483ce53e500a81ad9c3d5b3f7c920c28c24e9cb0980d0b5bc8"
 
+[[package]]
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+checksum = "6d89e9874397a1f0a52fc1f197a8effd9735223cb2390e9dcc83ac6cd02923d0"
+dependencies = [
+ "chrono",
+]
+
+[[package]]
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+checksum = "e8a3895c6391c39d7fe7ebc444a87eb2991b2a0bc718fdabd071eec617fc68e4"
+dependencies = [
+ "winapi",
+]
+
 [[package]]
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@@ -6642,7 +7002,7 @@ version = "1.16.0"
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 dependencies = [
- "hermit-abi 0.3.2",
+ "hermit-abi 0.3.9",
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@@ -6683,7 +7043,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e6da452820c715ce78221e8202ccc599b4a52f3e1eb3eedb487b680c81a8e3f3"
 dependencies = [
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- "base64 0.22.0",
+ "base64 0.22.1",
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  "futures",
@@ -7088,6 +7448,16 @@ dependencies = [
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 ]
 
+[[package]]
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+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1131c54b167dd4e4799ce762e1ab01549ebb94d5bdd13e6ec1b467491c378e1f"
+dependencies = [
+ "async-trait",
+ "futures",
+]
+
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@@ -7554,6 +7924,15 @@ version = "0.3.27"
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 checksum = "26072860ba924cbfa98ea39c8c19b4dd6a4a25423dbdf219c1eca91aa0cf6964"
 
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+checksum = "fc1691dd09e82f428ce8d6310bd6d5da2557c82ff17694d2a32cad7242aea89f"
+dependencies = [
+ "array-init-cursor",
+]
+
 [[package]]
 name = "platforms"
 version = "3.2.0"
@@ -7608,6 +7987,416 @@ dependencies = [
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 ]
 
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+ "polars-arrow",
+ "polars-core",
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+ "polars-lazy",
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+ "polars-parquet",
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+ "version_check 0.9.4",
+]
+
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+ "foreign_vec",
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+ "hashbrown 0.14.5",
+ "itoa 1.0.9",
+ "itoap",
+ "lz4",
+ "multiversion",
+ "num-traits",
+ "polars-arrow-format",
+ "polars-error",
+ "polars-utils",
+ "ryu",
+ "simdutf8",
+ "streaming-iterator",
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+ "zstd 0.13.2",
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+ "once_cell",
+ "polars-arrow",
+ "polars-core",
+ "polars-error",
+ "polars-lazy",
+ "polars-ops",
+ "polars-plan",
+ "polars-time",
+ "rand 0.8.5",
+ "serde",
+ "serde_json",
+ "sqlparser",
+]
+
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+dependencies = [
+ "atoi",
+ "bytemuck",
+ "chrono",
+ "chrono-tz 0.8.6",
+ "now",
+ "once_cell",
+ "polars-arrow",
+ "polars-core",
+ "polars-error",
+ "polars-ops",
+ "polars-utils",
+ "regex",
+ "smartstring",
+]
+
+[[package]]
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+source = "registry+https://github.com/rust-lang/crates.io-index"
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+dependencies = [
+ "ahash 0.8.11",
+ "bytemuck",
+ "hashbrown 0.14.5",
+ "indexmap 2.1.0",
+ "num-traits",
+ "once_cell",
+ "polars-error",
+ "raw-cpuid 11.0.1",
+ "rayon",
+ "smartstring",
+ "stacker",
+ "sysinfo",
+ "version_check 0.9.4",
+]
+
 [[package]]
 name = "polling"
 version = "2.8.0"
@@ -7923,6 +8712,15 @@ dependencies = [
  "prost 0.12.6",
 ]
 
+[[package]]
+name = "psm"
+version = "0.1.21"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5787f7cda34e3033a72192c018bc5883100330f362ef279a8cbccfce8bb4e874"
+dependencies = [
+ "cc",
+]
+
 [[package]]
 name = "ptr_meta"
 version = "0.1.4"
@@ -8449,6 +9247,26 @@ dependencies = [
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 ]
 
+[[package]]
+name = "recursive"
+version = "0.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0786a43debb760f491b1bc0269fe5e84155353c67482b9e60d0cfb596054b43e"
+dependencies = [
+ "recursive-proc-macro-impl",
+ "stacker",
+]
+
+[[package]]
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+source = "registry+https://github.com/rust-lang/crates.io-index"
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+dependencies = [
+ "quote",
+ "syn 2.0.66",
+]
+
 [[package]]
 name = "redox_syscall"
 version = "0.3.5"
@@ -8596,7 +9414,7 @@ version = "0.12.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c7d6d2a27d57148378eb5e111173f4276ad26340ecc5c49a4a2152167a2d6a37"
 dependencies = [
- "base64 0.22.0",
+ "base64 0.22.1",
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  "futures-channel",
  "futures-core",
@@ -8612,6 +9430,7 @@ dependencies = [
  "js-sys",
  "log 0.4.21",
  "mime 0.3.17",
+ "mime_guess 2.0.4",
  "once_cell",
  "percent-encoding 2.3.1",
  "pin-project-lite",
@@ -8637,6 +9456,22 @@ dependencies = [
  "winreg 0.52.0",
 ]
 
+[[package]]
+name = "reqwest-eventsource"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "632c55746dbb44275691640e7b40c907c16a2dc1a5842aa98aaec90da6ec6bde"
+dependencies = [
+ "eventsource-stream",
+ "futures-core",
+ "futures-timer",
+ "mime 0.3.17",
+ "nom",
+ "pin-project-lite",
+ "reqwest 0.12.5",
+ "thiserror",
+]
+
 [[package]]
 name = "rfc6979"
 version = "0.3.1"
@@ -9014,7 +9849,7 @@ version = "2.1.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "29993a25686778eb88d4189742cd713c9bce943bc54251a33509dc63cbacf73d"
 dependencies = [
- "base64 0.22.0",
+ "base64 0.22.1",
  "rustls-pki-types",
 ]
 
@@ -9229,6 +10064,16 @@ dependencies = [
  "zeroize",
 ]
 
+[[package]]
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+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9bd1c54ea06cfd2f6b63219704de0b9b4f72dcc2b8fdef820be6cd799780e91e"
+dependencies = [
+ "serde",
+ "zeroize",
+]
+
 [[package]]
 name = "security-framework"
 version = "2.9.2"
@@ -9370,7 +10215,7 @@ version = "3.8.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0ad483d2ab0149d5a5ebcd9972a3852711e0153d863bf5a5d0391d28883c4a20"
 dependencies = [
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "hex",
  "indexmap 1.9.3",
@@ -9495,6 +10340,24 @@ dependencies = [
  "dirs",
 ]
 
+[[package]]
+name = "shinkai-graphrag"
+version = "0.1.0"
+dependencies = [
+ "anyhow",
+ "async-openai",
+ "async-trait",
+ "futures",
+ "polars",
+ "polars-lazy",
+ "rand 0.8.5",
+ "reqwest 0.11.27",
+ "serde",
+ "serde_json",
+ "tiktoken-rs",
+ "tokio",
+]
+
 [[package]]
 name = "shinkai_crypto_identities"
 version = "0.1.1"
@@ -9537,7 +10400,7 @@ dependencies = [
  "aes-gcm",
  "anyhow",
  "async-channel",
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "chrono-tz 0.5.3",
  "clap 3.2.25",
@@ -9717,7 +10580,7 @@ dependencies = [
  "aes-gcm",
  "anyhow",
  "async-channel",
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "chrono-tz 0.5.3",
  "clap 3.2.25",
@@ -9973,6 +10836,17 @@ version = "1.13.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "3c5e1a9a646d36c3599cd173a41282daf47c44583ad367b8e6837255952e5c67"
 
+[[package]]
+name = "smartstring"
+version = "1.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3fb72c633efbaa2dd666986505016c32c3044395ceaf881518399d2f4127ee29"
+dependencies = [
+ "autocfg 1.1.0",
+ "static_assertions",
+ "version_check 0.9.4",
+]
+
 [[package]]
 name = "snafu"
 version = "0.7.5"
@@ -9995,6 +10869,12 @@ dependencies = [
  "syn 1.0.109",
 ]
 
+[[package]]
+name = "snap"
+version = "1.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1b6b67fb9a61334225b5b790716f609cd58395f895b3fe8b328786812a40bc3b"
+
 [[package]]
 name = "socket2"
 version = "0.4.9"
@@ -10100,6 +10980,19 @@ version = "1.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "a8f112729512f8e442d81f95a8a7ddf2b7c6b8a1a6f509a95864142b30cab2d3"
 
+[[package]]
+name = "stacker"
+version = "0.1.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c886bd4480155fd3ef527d45e9ac8dd7118a898a46530b7b94c3e21866259fce"
+dependencies = [
+ "cc",
+ "cfg-if",
+ "libc",
+ "psm",
+ "winapi",
+]
+
 [[package]]
 name = "static_assertions"
 version = "1.1.0"
@@ -10118,6 +11011,27 @@ version = "0.2.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e51f1e89f093f99e7432c491c382b88a6860a5adbe6bf02574bf0a08efff1978"
 
+[[package]]
+name = "streaming-decompression"
+version = "0.1.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bf6cc3b19bfb128a8ad11026086e31d3ce9ad23f8ea37354b31383a187c44cf3"
+dependencies = [
+ "fallible-streaming-iterator",
+]
+
+[[package]]
+name = "streaming-iterator"
+version = "0.1.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2b2231b7c3057d5e4ad0156fb3dc807d900806020c5ffa3ee6ff2c8c76fb8520"
+
+[[package]]
+name = "strength_reduce"
+version = "0.2.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fe895eb47f22e2ddd4dabc02bce419d2e643c8e3b585c78158b349195bc24d82"
+
 [[package]]
 name = "string_cache"
 version = "0.8.7"
@@ -10280,7 +11194,7 @@ checksum = "874dcfa363995604333cf947ae9f751ca3af4522c60886774c4963943b4746b1"
 dependencies = [
  "bincode",
  "bitflags 1.3.2",
- "fancy-regex",
+ "fancy-regex 0.11.0",
  "flate2",
  "fnv",
  "once_cell",
@@ -10295,6 +11209,20 @@ dependencies = [
  "yaml-rust",
 ]
 
+[[package]]
+name = "sysinfo"
+version = "0.30.13"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0a5b4ddaee55fb2bea2bf0e5000747e5f5c0de765e5a5ff87f4cd106439f4bb3"
+dependencies = [
+ "cfg-if",
+ "core-foundation-sys",
+ "libc",
+ "ntapi",
+ "once_cell",
+ "windows 0.52.0",
+]
+
 [[package]]
 name = "system-configuration"
 version = "0.5.1"
@@ -10343,7 +11271,7 @@ checksum = "f8d0582f186c0a6d55655d24543f15e43607299425c5ad8352c242b914b31856"
 dependencies = [
  "aho-corasick",
  "arc-swap",
- "base64 0.22.0",
+ "base64 0.22.1",
  "bitpacking",
  "byteorder",
  "census",
@@ -10360,7 +11288,7 @@ dependencies = [
  "lru 0.12.3",
  "lz4_flex",
  "measure_time",
- "memmap2",
+ "memmap2 0.9.4",
  "num_cpus",
  "once_cell",
  "oneshot",
@@ -10493,6 +11421,12 @@ dependencies = [
  "xattr",
 ]
 
+[[package]]
+name = "target-features"
+version = "0.1.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c1bbb9f3c5c463a01705937a24fdabc5047929ac764b2d5b9cf681c1f5041ed5"
+
 [[package]]
 name = "target-lexicon"
 version = "0.12.14"
@@ -10622,6 +11556,21 @@ dependencies = [
  "weezl",
 ]
 
+[[package]]
+name = "tiktoken-rs"
+version = "0.5.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c314e7ce51440f9e8f5a497394682a57b7c323d0f4d0a6b1b13c429056e0e234"
+dependencies = [
+ "anyhow",
+ "base64 0.21.7",
+ "bstr",
+ "fancy-regex 0.12.0",
+ "lazy_static",
+ "parking_lot 0.12.1",
+ "rustc-hash",
+]
+
 [[package]]
 name = "time"
 version = "0.1.45"
@@ -10722,22 +11671,21 @@ checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
 
 [[package]]
 name = "tokio"
-version = "1.36.0"
+version = "1.40.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "61285f6515fa018fb2d1e46eb21223fff441ee8db5d0f1435e8ab4f5cdb80931"
+checksum = "e2b070231665d27ad9ec9b8df639893f46727666c6767db40317fbe920a5d998"
 dependencies = [
  "backtrace",
  "bytes",
  "libc",
  "mio",
- "num_cpus",
  "parking_lot 0.12.1",
  "pin-project-lite",
  "signal-hook-registry",
  "socket2 0.5.6",
  "tokio-macros",
  "tracing",
- "windows-sys 0.48.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -10752,9 +11700,9 @@ dependencies = [
 
 [[package]]
 name = "tokio-macros"
-version = "2.2.0"
+version = "2.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5b8a1e28f2deaa14e508979454cb3a223b10b938b45af148bc0986de36f1923b"
+checksum = "693d596312e88961bc67d7f1f97af8a70227d9f90c31bba5806eec004978d752"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -10794,9 +11742,9 @@ dependencies = [
 
 [[package]]
 name = "tokio-stream"
-version = "0.1.14"
+version = "0.1.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "397c988d37662c7dda6d2208364a706264bf3d6138b11d436cbac0ad38832842"
+checksum = "267ac89e0bec6e691e5813911606935d77c476ff49024f98abcea3e7b15e37af"
 dependencies = [
  "futures-core",
  "pin-project-lite",
@@ -11250,6 +12198,15 @@ dependencies = [
  "tinyvec",
 ]
 
+[[package]]
+name = "unicode-reverse"
+version = "1.0.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4b6f4888ebc23094adfb574fdca9fdc891826287a6397d2cd28802ffd6f20c76"
+dependencies = [
+ "unicode-segmentation",
+]
+
 [[package]]
 name = "unicode-segmentation"
 version = "1.10.1"
@@ -11758,6 +12715,25 @@ dependencies = [
  "windows-targets 0.48.5",
 ]
 
+[[package]]
+name = "windows"
+version = "0.52.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e48a53791691ab099e5e2ad123536d0fff50652600abaf43bbf952894110d0be"
+dependencies = [
+ "windows-core",
+ "windows-targets 0.52.3",
+]
+
+[[package]]
+name = "windows-core"
+version = "0.52.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "33ab640c8d7e35bf8ba19b884ba838ceb4fba93a4e8c65a9059d08afcfc683d9"
+dependencies = [
+ "windows-targets 0.52.3",
+]
+
 [[package]]
 name = "windows-sys"
 version = "0.48.0"
@@ -11993,6 +12969,12 @@ version = "0.13.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "66fee0b777b0f5ac1c69bb06d361268faafa61cd4682ae064a171c16c433e9e4"
 
+[[package]]
+name = "xxhash-rust"
+version = "0.8.12"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6a5cbf750400958819fb6178eaa83bee5cd9c29a26a40cc241df8c70fdd46984"
+
 [[package]]
 name = "yaml-rust"
 version = "0.4.5"
diff --git a/Cargo.toml b/Cargo.toml
index b4fdec92c..7e4bb91c6 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -4,13 +4,14 @@ members = [
   "shinkai-libs/shinkai-dsl",
   "shinkai-libs/shinkai-sheet",
   "shinkai-libs/shinkai-fs-mirror",
+  "shinkai-libs/shinkai-graphrag",
   "shinkai-libs/shinkai-message-primitives",
   "shinkai-libs/shinkai-ocr",
   "shinkai-libs/shinkai-tcp-relayer",
   "shinkai-libs/shinkai-vector-resources",
   "shinkai-bin/*",
   "shinkai-cli-tools/*"
-]
+, "shinkai-libs/shinkai-graphrag"]
 resolver = "2"
 
 [workspace.package]
diff --git a/shinkai-libs/shinkai-graphrag/.gitignore b/shinkai-libs/shinkai-graphrag/.gitignore
new file mode 100644
index 000000000..74deb7343
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/.gitignore
@@ -0,0 +1,2 @@
+.vscode
+dataset
\ No newline at end of file
diff --git a/shinkai-libs/shinkai-graphrag/Cargo.toml b/shinkai-libs/shinkai-graphrag/Cargo.toml
new file mode 100644
index 000000000..7975fa77c
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/Cargo.toml
@@ -0,0 +1,20 @@
+[package]
+name = "shinkai-graphrag"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+anyhow = "1.0.86"
+async-trait = "0.1.74"
+futures = "0.3.30"
+polars = { version = "0.41.3", features = ["dtype-struct", "lazy", "parquet"] }
+polars-lazy = "0.41.3"
+rand = "0.8.5"
+serde = { version = "1.0.188", features = ["derive"] }
+serde_json = "1.0.117"
+tokio = { version = "1.36", features = ["full"] }
+
+[dev-dependencies]
+async-openai = "0.23.4"
+reqwest = { version = "0.11.26", features = ["json"] }
+tiktoken-rs = "0.5.9"
\ No newline at end of file
diff --git a/shinkai-libs/shinkai-graphrag/src/context_builder/community_context.rs b/shinkai-libs/shinkai-graphrag/src/context_builder/community_context.rs
new file mode 100644
index 000000000..a6a1a7485
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/context_builder/community_context.rs
@@ -0,0 +1,473 @@
+use std::{
+    collections::{HashMap, HashSet},
+    io::{Cursor, Read},
+};
+
+use polars::{
+    frame::DataFrame,
+    io::SerWriter,
+    prelude::{col, concat, CsvWriter, DataType, IntoLazy, LazyFrame, NamedFrom, SortMultipleOptions, UnionArgs},
+    series::Series,
+};
+use rand::prelude::SliceRandom;
+
+use super::{context_builder::ContextBuilderParams, indexer_entities::Entity, indexer_reports::CommunityReport};
+
+pub struct GlobalCommunityContext {
+    community_reports: Vec<CommunityReport>,
+    entities: Option<Vec<Entity>>,
+    num_tokens_fn: fn(&str) -> usize,
+}
+
+impl GlobalCommunityContext {
+    pub fn new(
+        community_reports: Vec<CommunityReport>,
+        entities: Option<Vec<Entity>>,
+        num_tokens_fn: fn(&str) -> usize,
+    ) -> Self {
+        Self {
+            community_reports,
+            entities,
+            num_tokens_fn,
+        }
+    }
+
+    pub async fn build_context(
+        &self,
+        context_builder_params: ContextBuilderParams,
+    ) -> anyhow::Result<(Vec<String>, HashMap<String, DataFrame>)> {
+        let ContextBuilderParams {
+            use_community_summary,
+            column_delimiter,
+            shuffle_data,
+            include_community_rank,
+            min_community_rank,
+            community_rank_name,
+            include_community_weight,
+            community_weight_name,
+            normalize_community_weight,
+            max_tokens,
+            context_name,
+        } = context_builder_params;
+
+        let (community_context, community_context_data) = CommunityContext::build_community_context(
+            self.community_reports.clone(),
+            self.entities.clone(),
+            self.num_tokens_fn,
+            use_community_summary,
+            &column_delimiter,
+            shuffle_data,
+            include_community_rank,
+            min_community_rank,
+            &community_rank_name,
+            include_community_weight,
+            &community_weight_name,
+            normalize_community_weight,
+            max_tokens,
+            false,
+            &context_name,
+        )?;
+
+        let final_context = community_context;
+        let final_context_data = community_context_data;
+
+        Ok((final_context, final_context_data))
+    }
+}
+
+pub struct CommunityContext {}
+
+impl CommunityContext {
+    pub fn build_community_context(
+        community_reports: Vec<CommunityReport>,
+        entities: Option<Vec<Entity>>,
+        num_tokens_fn: fn(&str) -> usize,
+        use_community_summary: bool,
+        column_delimiter: &str,
+        shuffle_data: bool,
+        include_community_rank: bool,
+        min_community_rank: u32,
+        community_rank_name: &str,
+        include_community_weight: bool,
+        community_weight_name: &str,
+        normalize_community_weight: bool,
+        max_tokens: usize,
+        single_batch: bool,
+        context_name: &str,
+    ) -> anyhow::Result<(Vec<String>, HashMap<String, DataFrame>)> {
+        let _is_included = |report: &CommunityReport| -> bool {
+            report.rank.is_some() && report.rank.unwrap() >= min_community_rank.into()
+        };
+
+        let _get_header = |attributes: Vec<String>| -> Vec<String> {
+            let mut header = vec!["id".to_string(), "title".to_string()];
+            let mut filtered_attributes: Vec<String> = attributes
+                .iter()
+                .filter(|&col| !header.contains(&col.to_string()))
+                .cloned()
+                .collect();
+
+            if !include_community_weight {
+                filtered_attributes.retain(|col| col != community_weight_name);
+            }
+
+            header.extend(filtered_attributes.into_iter().map(|s| s.to_string()));
+            header.push(if use_community_summary {
+                "summary".to_string()
+            } else {
+                "content".to_string()
+            });
+
+            if include_community_rank {
+                header.push(community_rank_name.to_string());
+            }
+
+            header
+        };
+
+        let _report_context_text = |report: &CommunityReport, attributes: &[String]| -> (String, Vec<String>) {
+            let mut context: Vec<String> = vec![report.short_id.clone().unwrap_or_default(), report.title.clone()];
+
+            for field in attributes {
+                let value = report
+                    .attributes
+                    .as_ref()
+                    .and_then(|attrs| attrs.get(field))
+                    .cloned()
+                    .unwrap_or_default();
+                context.push(value);
+            }
+
+            context.push(if use_community_summary {
+                report.summary.clone()
+            } else {
+                report.full_content.clone()
+            });
+
+            if include_community_rank {
+                context.push(report.rank.unwrap_or_default().to_string());
+            }
+
+            let result = context.join(column_delimiter) + "\n";
+            (result, context)
+        };
+
+        let compute_community_weights = entities.as_ref().is_some_and(|e| !e.is_empty())
+            && !community_reports.is_empty()
+            && include_community_weight
+            && (community_reports[0].attributes.is_none()
+                || !community_reports[0]
+                    .attributes
+                    .as_ref()
+                    .unwrap()
+                    .contains_key(community_weight_name));
+
+        let mut community_reports = community_reports;
+        if compute_community_weights {
+            community_reports = Self::_compute_community_weights(
+                community_reports,
+                entities.clone(),
+                community_weight_name,
+                normalize_community_weight,
+            );
+        }
+
+        let mut selected_reports: Vec<CommunityReport> = community_reports
+            .iter()
+            .filter(|&report| _is_included(report))
+            .cloned()
+            .collect();
+
+        if selected_reports.is_empty() {
+            return Ok((Vec::new(), HashMap::new()));
+        }
+
+        if shuffle_data {
+            let mut rng = rand::thread_rng();
+            selected_reports.shuffle(&mut rng);
+        }
+
+        let attributes = if let Some(attributes) = &community_reports[0].attributes {
+            attributes.keys().cloned().collect::<Vec<_>>()
+        } else {
+            Vec::new()
+        };
+
+        let header = _get_header(attributes.clone());
+        let mut all_context_text: Vec<String> = Vec::new();
+        let mut all_context_records: Vec<DataFrame> = Vec::new();
+
+        let mut batch = Batch::new();
+
+        batch.init_batch(context_name, &header, column_delimiter, num_tokens_fn);
+
+        for report in selected_reports {
+            let (new_context_text, new_context) = _report_context_text(&report, &attributes);
+            let new_tokens = num_tokens_fn(&new_context_text);
+
+            // add the current batch to the context data and start a new batch if we are in multi-batch mode
+            if batch.batch_tokens + new_tokens > max_tokens {
+                batch.cut_batch(
+                    &mut all_context_text,
+                    &mut all_context_records,
+                    entities.clone(),
+                    &header,
+                    community_weight_name,
+                    community_rank_name,
+                    include_community_weight,
+                    include_community_rank,
+                    column_delimiter,
+                )?;
+
+                if single_batch {
+                    break;
+                }
+
+                batch.init_batch(context_name, &header, column_delimiter, num_tokens_fn);
+            }
+
+            batch.batch_text.push_str(&new_context_text);
+            batch.batch_tokens += new_tokens;
+            batch.batch_records.push(new_context);
+        }
+
+        if !all_context_text.contains(&batch.batch_text) {
+            batch.cut_batch(
+                &mut all_context_text,
+                &mut all_context_records,
+                entities.clone(),
+                &header,
+                community_weight_name,
+                community_rank_name,
+                include_community_weight,
+                include_community_rank,
+                column_delimiter,
+            )?;
+        }
+
+        if all_context_records.is_empty() {
+            eprintln!("Warning: No community records added when building community context.");
+            return Ok((Vec::new(), HashMap::new()));
+        }
+
+        let records_concat = concat(
+            all_context_records
+                .into_iter()
+                .map(|df| df.lazy())
+                .collect::<Vec<LazyFrame>>(),
+            UnionArgs::default(),
+        )?
+        .collect()?;
+
+        Ok((
+            all_context_text,
+            HashMap::from([(context_name.to_lowercase(), records_concat)]),
+        ))
+    }
+
+    fn _compute_community_weights(
+        community_reports: Vec<CommunityReport>,
+        entities: Option<Vec<Entity>>,
+        weight_attribute: &str,
+        normalize: bool,
+    ) -> Vec<CommunityReport> {
+        // Calculate a community's weight as the count of text units associated with entities within the community.
+        if let Some(entities) = entities {
+            let mut community_reports = community_reports;
+            let mut community_text_units = std::collections::HashMap::new();
+            for entity in entities {
+                if let Some(community_ids) = entity.community_ids.clone() {
+                    for community_id in community_ids {
+                        community_text_units
+                            .entry(community_id)
+                            .or_insert_with(Vec::new)
+                            .extend(entity.text_unit_ids.clone());
+                    }
+                }
+            }
+            for report in &mut community_reports {
+                if report.attributes.is_none() {
+                    report.attributes = Some(std::collections::HashMap::new());
+                }
+                if let Some(attributes) = &mut report.attributes {
+                    attributes.insert(
+                        weight_attribute.to_string(),
+                        community_text_units
+                            .get(&report.community_id)
+                            .map(|text_units| text_units.iter().flatten().cloned().collect::<HashSet<String>>().len())
+                            .unwrap_or(0)
+                            .to_string(),
+                    );
+                }
+            }
+            if normalize {
+                // Normalize by max weight
+                let all_weights: Vec<f64> = community_reports
+                    .iter()
+                    .filter_map(|report| {
+                        report
+                            .attributes
+                            .as_ref()
+                            .and_then(|attributes| attributes.get(weight_attribute))
+                            .map(|weight| weight.parse::<f64>().unwrap_or(0.0))
+                    })
+                    .collect();
+                if let Some(max_weight) = all_weights.iter().cloned().max_by(|a, b| a.partial_cmp(b).unwrap()) {
+                    for report in &mut community_reports {
+                        if let Some(attributes) = &mut report.attributes {
+                            if let Some(weight) = attributes.get_mut(weight_attribute) {
+                                *weight = (weight.parse::<f64>().unwrap_or(0.0) / max_weight).to_string();
+                            }
+                        }
+                    }
+                }
+            }
+
+            return community_reports;
+        }
+        community_reports
+    }
+}
+
+struct Batch {
+    batch_text: String,
+    batch_tokens: usize,
+    batch_records: Vec<Vec<String>>,
+}
+
+impl Batch {
+    fn new() -> Self {
+        Batch {
+            batch_text: String::new(),
+            batch_tokens: 0,
+            batch_records: Vec::new(),
+        }
+    }
+
+    fn init_batch(
+        &mut self,
+        context_name: &str,
+        header: &Vec<String>,
+        column_delimiter: &str,
+        num_tokens_fn: fn(&str) -> usize,
+    ) {
+        self.batch_text = format!("-----{}-----\n{}\n", context_name, header.join(column_delimiter));
+        self.batch_tokens = num_tokens_fn(&self.batch_text);
+        self.batch_records.clear();
+    }
+
+    fn cut_batch(
+        &mut self,
+        all_context_text: &mut Vec<String>,
+        all_context_records: &mut Vec<DataFrame>,
+        entities: Option<Vec<Entity>>,
+        header: &Vec<String>,
+        community_weight_name: &str,
+        community_rank_name: &str,
+        include_community_weight: bool,
+        include_community_rank: bool,
+        column_delimiter: &str,
+    ) -> anyhow::Result<()> {
+        let weight_column = if include_community_weight && entities.as_ref().is_some_and(|e| !e.is_empty()) {
+            Some(community_weight_name)
+        } else {
+            None
+        };
+        let rank_column = if include_community_rank {
+            Some(community_rank_name)
+        } else {
+            None
+        };
+
+        let mut record_df = Self::_convert_report_context_to_df(
+            self.batch_records.clone(),
+            header.clone(),
+            weight_column,
+            rank_column,
+        )?;
+        if record_df.is_empty() {
+            return Ok(());
+        }
+
+        let column_delimiter = if column_delimiter.is_empty() {
+            b'|'
+        } else {
+            column_delimiter.as_bytes()[0]
+        };
+
+        let mut buffer = Cursor::new(Vec::new());
+        CsvWriter::new(&mut buffer)
+            .include_header(true)
+            .with_separator(column_delimiter)
+            .finish(&mut record_df)?;
+
+        let mut current_context_text = String::new();
+        buffer.set_position(0);
+        buffer.read_to_string(&mut current_context_text)?;
+
+        all_context_text.push(current_context_text);
+        all_context_records.push(record_df);
+
+        Ok(())
+    }
+
+    fn _convert_report_context_to_df(
+        context_records: Vec<Vec<String>>,
+        header: Vec<String>,
+        weight_column: Option<&str>,
+        rank_column: Option<&str>,
+    ) -> anyhow::Result<DataFrame> {
+        if context_records.is_empty() {
+            return Ok(DataFrame::empty());
+        }
+
+        let mut data_series = Vec::new();
+        for (index, header) in header.iter().enumerate() {
+            let records = context_records
+                .iter()
+                .map(|r| r.get(index).unwrap_or(&String::new()).to_owned())
+                .collect::<Vec<_>>();
+            let series = Series::new(header, records);
+            data_series.push(series);
+        }
+
+        let record_df = DataFrame::new(data_series)?;
+
+        return Self::_rank_report_context(record_df, weight_column, rank_column);
+    }
+
+    fn _rank_report_context(
+        report_df: DataFrame,
+        weight_column: Option<&str>,
+        rank_column: Option<&str>,
+    ) -> anyhow::Result<DataFrame> {
+        let mut rank_attributes = Vec::new();
+
+        let mut report_df = report_df;
+
+        if let Some(weight_column) = weight_column {
+            rank_attributes.push(weight_column);
+            report_df = report_df
+                .lazy()
+                .with_column(col(weight_column).cast(DataType::Float64))
+                .collect()?;
+        }
+
+        if let Some(rank_column) = rank_column {
+            rank_attributes.push(rank_column);
+            report_df = report_df
+                .lazy()
+                .with_column(col(rank_column).cast(DataType::Float64))
+                .collect()?;
+        }
+
+        if !rank_attributes.is_empty() {
+            report_df = report_df
+                .lazy()
+                .sort(rank_attributes, SortMultipleOptions::new().with_order_descending(true))
+                .collect()?;
+        }
+
+        Ok(report_df)
+    }
+}
diff --git a/shinkai-libs/shinkai-graphrag/src/context_builder/context_builder.rs b/shinkai-libs/shinkai-graphrag/src/context_builder/context_builder.rs
new file mode 100644
index 000000000..87db20231
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/context_builder/context_builder.rs
@@ -0,0 +1,19 @@
+#[derive(Debug, Clone)]
+pub struct ContextBuilderParams {
+    //conversation_history: Option<ConversationHistory>,
+    pub use_community_summary: bool,
+    pub column_delimiter: String,
+    pub shuffle_data: bool,
+    pub include_community_rank: bool,
+    pub min_community_rank: u32,
+    pub community_rank_name: String,
+    pub include_community_weight: bool,
+    pub community_weight_name: String,
+    pub normalize_community_weight: bool,
+    pub max_tokens: usize,
+    pub context_name: String,
+    // conversation_history_user_turns_only: bool,
+    // conversation_history_max_turns: Option<i32>,
+}
+
+pub struct ConversationHistory {}
diff --git a/shinkai-libs/shinkai-graphrag/src/context_builder/indexer_entities.rs b/shinkai-libs/shinkai-graphrag/src/context_builder/indexer_entities.rs
new file mode 100644
index 000000000..26d8566fd
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/context_builder/indexer_entities.rs
@@ -0,0 +1,262 @@
+use std::collections::{HashMap, HashSet};
+
+use polars::prelude::*;
+use polars_lazy::dsl::col;
+use serde::{Deserialize, Serialize};
+
+use super::indexer_reports::filter_under_community_level;
+
+pub fn read_indexer_entities(
+    final_nodes: &DataFrame,
+    final_entities: &DataFrame,
+    community_level: u32,
+) -> anyhow::Result<Vec<Entity>> {
+    let entity_df = final_nodes.clone();
+    let entity_df = filter_under_community_level(&entity_df, community_level)?;
+
+    let entity_embedding_df = final_entities.clone();
+
+    let entity_df = entity_df
+        .lazy()
+        .rename(["title", "degree"], ["name", "rank"])
+        .with_column(col("community").fill_null(lit(-1)))
+        .with_column(col("community").cast(DataType::Int32))
+        .with_column(col("rank").cast(DataType::Int32))
+        .group_by([col("name"), col("rank")])
+        .agg([col("community").max()])
+        .with_column(col("community").cast(DataType::String))
+        .join(
+            entity_embedding_df.lazy(),
+            [col("name")],
+            [col("name")],
+            JoinArgs::new(JoinType::Inner),
+        )
+        .collect()?;
+
+    let entities = read_entities(
+        entity_df,
+        "id",
+        Some("human_readable_id"),
+        "name",
+        Some("type"),
+        Some("description"),
+        None,
+        Some("description_embedding"),
+        None,
+        Some("community"),
+        Some("text_unit_ids"),
+        None,
+        Some("rank"),
+    )?;
+
+    Ok(entities)
+}
+
+#[derive(Debug, Clone, Deserialize, Serialize)]
+pub struct Entity {
+    pub id: String,
+    pub short_id: Option<String>,
+    pub title: String,
+    pub entity_type: Option<String>,
+    pub description: Option<String>,
+    pub description_embedding: Option<Vec<f64>>,
+    pub name_embedding: Option<Vec<f64>>,
+    pub graph_embedding: Option<Vec<f64>>,
+    pub community_ids: Option<Vec<String>>,
+    pub text_unit_ids: Option<Vec<String>>,
+    pub document_ids: Option<Vec<String>>,
+    pub rank: Option<i32>,
+    pub attributes: Option<HashMap<String, String>>,
+}
+
+pub fn read_entities(
+    df: DataFrame,
+    id_col: &str,
+    short_id_col: Option<&str>,
+    title_col: &str,
+    type_col: Option<&str>,
+    description_col: Option<&str>,
+    name_embedding_col: Option<&str>,
+    description_embedding_col: Option<&str>,
+    graph_embedding_col: Option<&str>,
+    community_col: Option<&str>,
+    text_unit_ids_col: Option<&str>,
+    document_ids_col: Option<&str>,
+    rank_col: Option<&str>,
+    // attributes_cols: Option<Vec<&str>>,
+) -> anyhow::Result<Vec<Entity>> {
+    let column_names = [
+        Some(id_col),
+        short_id_col,
+        Some(title_col),
+        type_col,
+        description_col,
+        name_embedding_col,
+        description_embedding_col,
+        graph_embedding_col,
+        community_col,
+        text_unit_ids_col,
+        document_ids_col,
+        rank_col,
+    ]
+    .iter()
+    .filter_map(|&v| v.map(|v| v.to_string()))
+    .collect::<Vec<_>>();
+
+    let column_names = column_names.into_iter().collect::<HashSet<String>>().into_vec();
+
+    let mut df = df;
+    df.as_single_chunk_par();
+    let mut iters = df
+        .columns(column_names.clone())?
+        .iter()
+        .map(|s| s.iter())
+        .collect::<Vec<_>>();
+
+    let mut rows = Vec::new();
+    for _row in 0..df.height() {
+        let mut row_values = Vec::new();
+        for iter in &mut iters {
+            let value = iter.next();
+            if let Some(value) = value {
+                row_values.push(value);
+            }
+        }
+        rows.push(row_values);
+    }
+
+    let mut entities = Vec::new();
+    for (idx, row) in rows.iter().enumerate() {
+        let report = Entity {
+            id: get_field(&row, id_col, &column_names)
+                .map(|id| id.to_string())
+                .unwrap_or(String::new()),
+            short_id: Some(
+                short_id_col
+                    .map(|short_id| get_field(&row, short_id, &column_names))
+                    .flatten()
+                    .map(|short_id| short_id.to_string())
+                    .unwrap_or(idx.to_string()),
+            ),
+            title: get_field(&row, title_col, &column_names)
+                .map(|title| title.to_string())
+                .unwrap_or(String::new()),
+            entity_type: type_col
+                .map(|type_col| get_field(&row, type_col, &column_names))
+                .flatten()
+                .map(|entity_type| entity_type.to_string()),
+            description: description_col
+                .map(|description_col| get_field(&row, description_col, &column_names))
+                .flatten()
+                .map(|description| description.to_string()),
+            name_embedding: name_embedding_col.map(|name_embedding_col| {
+                get_field(&row, name_embedding_col, &column_names)
+                    .map(|name_embedding| match name_embedding {
+                        AnyValue::List(series) => series
+                            .f64()
+                            .unwrap_or(&ChunkedArray::from_vec(name_embedding_col, vec![]))
+                            .iter()
+                            .map(|v| v.unwrap_or(0.0))
+                            .collect::<Vec<f64>>(),
+                        value => vec![value.to_string().parse::<f64>().unwrap_or(0.0)],
+                    })
+                    .unwrap_or_else(|| Vec::new())
+            }),
+            description_embedding: description_embedding_col.map(|description_embedding_col| {
+                get_field(&row, description_embedding_col, &column_names)
+                    .map(|description_embedding| match description_embedding {
+                        AnyValue::List(series) => series
+                            .f64()
+                            .unwrap_or(&ChunkedArray::from_vec(description_embedding_col, vec![]))
+                            .iter()
+                            .map(|v| v.unwrap_or(0.0))
+                            .collect::<Vec<f64>>(),
+                        value => vec![value.to_string().parse::<f64>().unwrap_or(0.0)],
+                    })
+                    .unwrap_or_else(|| Vec::new())
+            }),
+            graph_embedding: graph_embedding_col.map(|graph_embedding_col| {
+                get_field(&row, graph_embedding_col, &column_names)
+                    .map(|graph_embedding| match graph_embedding {
+                        AnyValue::List(series) => series
+                            .f64()
+                            .unwrap_or(&ChunkedArray::from_vec(graph_embedding_col, vec![]))
+                            .iter()
+                            .map(|v| v.unwrap_or(0.0))
+                            .collect::<Vec<f64>>(),
+                        value => vec![value.to_string().parse::<f64>().unwrap_or(0.0)],
+                    })
+                    .unwrap_or_else(|| Vec::new())
+            }),
+            community_ids: community_col.map(|community_col| {
+                get_field(&row, community_col, &column_names)
+                    .map(|community_ids| match community_ids {
+                        AnyValue::List(series) => series
+                            .str()
+                            .unwrap_or(&StringChunked::default())
+                            .iter()
+                            .map(|v| v.unwrap_or("").to_string())
+                            .collect::<Vec<String>>(),
+                        value => vec![value.to_string()],
+                    })
+                    .unwrap_or_else(|| Vec::new())
+            }),
+            text_unit_ids: text_unit_ids_col.map(|text_unit_ids_col| {
+                get_field(&row, text_unit_ids_col, &column_names)
+                    .map(|text_unit_ids| match text_unit_ids {
+                        AnyValue::List(series) => series
+                            .str()
+                            .unwrap_or(&StringChunked::default())
+                            .iter()
+                            .map(|v| v.unwrap_or("").to_string())
+                            .collect::<Vec<String>>(),
+                        value => vec![value.to_string()],
+                    })
+                    .unwrap_or_else(|| Vec::new())
+            }),
+            document_ids: document_ids_col.map(|document_ids_col| {
+                get_field(&row, document_ids_col, &column_names)
+                    .map(|document_ids| match document_ids {
+                        AnyValue::List(series) => series
+                            .str()
+                            .unwrap_or(&StringChunked::default())
+                            .iter()
+                            .map(|v| v.unwrap_or("").to_string())
+                            .collect::<Vec<String>>(),
+                        value => vec![value.to_string()],
+                    })
+                    .unwrap_or_else(|| Vec::new())
+            }),
+            rank: rank_col
+                .map(|rank_col| {
+                    get_field(&row, rank_col, &column_names).map(|v| v.to_string().parse::<i32>().unwrap_or(0))
+                })
+                .flatten(),
+            attributes: None,
+        };
+        entities.push(report);
+    }
+
+    let mut unique_entities: Vec<Entity> = Vec::new();
+    let mut entity_ids: HashSet<String> = HashSet::new();
+
+    for entity in entities {
+        if !entity_ids.contains(&entity.id) {
+            unique_entities.push(entity.clone());
+            entity_ids.insert(entity.id);
+        }
+    }
+
+    Ok(unique_entities)
+}
+
+pub fn get_field<'a>(
+    row: &'a Vec<AnyValue<'a>>,
+    column_name: &'a str,
+    column_names: &'a Vec<String>,
+) -> Option<AnyValue<'a>> {
+    match column_names.iter().position(|x| x == column_name) {
+        Some(index) => row.get(index).cloned(),
+        None => None,
+    }
+}
diff --git a/shinkai-libs/shinkai-graphrag/src/context_builder/indexer_reports.rs b/shinkai-libs/shinkai-graphrag/src/context_builder/indexer_reports.rs
new file mode 100644
index 000000000..1b59b9c59
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/context_builder/indexer_reports.rs
@@ -0,0 +1,172 @@
+use std::collections::{HashMap, HashSet};
+
+use polars::prelude::*;
+use polars_lazy::dsl::col;
+use serde::{Deserialize, Serialize};
+
+use super::indexer_entities::get_field;
+
+pub fn read_indexer_reports(
+    final_community_reports: &DataFrame,
+    final_nodes: &DataFrame,
+    community_level: u32,
+) -> anyhow::Result<Vec<CommunityReport>> {
+    let entity_df = final_nodes.clone();
+    let entity_df = filter_under_community_level(&entity_df, community_level)?;
+
+    let filtered_community_df = entity_df
+        .lazy()
+        .with_column(col("community").fill_null(lit(-1)))
+        .with_column(col("community").cast(DataType::Int32))
+        .group_by([col("title")])
+        .agg([col("community").max()])
+        .with_column(col("community").cast(DataType::String))
+        .filter(len().over([col("community")]).gt(lit(1)))
+        .collect()?;
+
+    let report_df = final_community_reports.clone();
+    let report_df = filter_under_community_level(&report_df, community_level)?;
+
+    let report_df = report_df
+        .lazy()
+        .join(
+            filtered_community_df.lazy(),
+            [col("community")],
+            [col("community")],
+            JoinArgs::new(JoinType::Inner),
+        )
+        .collect()?;
+
+    let reports = read_community_reports(
+        report_df,
+        "community",
+        Some("community"),
+        "title",
+        "community",
+        "summary",
+        "full_content",
+        Some("rank"),
+        None,
+        None,
+    )?;
+    Ok(reports)
+}
+
+pub fn filter_under_community_level(df: &DataFrame, community_level: u32) -> anyhow::Result<DataFrame> {
+    let mask = df.column("level")?.i64()?.lt_eq(community_level);
+    let result = df.filter(&mask)?;
+
+    Ok(result)
+}
+
+#[derive(Debug, Clone, Deserialize, Serialize)]
+pub struct CommunityReport {
+    pub id: String,
+    pub short_id: Option<String>,
+    pub title: String,
+    pub community_id: String,
+    pub summary: String,
+    pub full_content: String,
+    pub rank: Option<f64>,
+    pub summary_embedding: Option<Vec<f64>>,
+    pub full_content_embedding: Option<Vec<f64>>,
+    pub attributes: Option<HashMap<String, String>>,
+}
+
+pub fn read_community_reports(
+    df: DataFrame,
+    id_col: &str,
+    short_id_col: Option<&str>,
+    title_col: &str,
+    community_col: &str,
+    summary_col: &str,
+    content_col: &str,
+    rank_col: Option<&str>,
+    _summary_embedding_col: Option<&str>,
+    _content_embedding_col: Option<&str>,
+    // attributes_cols: Option<&[&str]>,
+) -> anyhow::Result<Vec<CommunityReport>> {
+    let column_names = [
+        Some(id_col),
+        short_id_col,
+        Some(title_col),
+        Some(community_col),
+        Some(summary_col),
+        Some(content_col),
+        rank_col,
+    ]
+    .iter()
+    .filter_map(|&v| v.map(|v| v.to_string()))
+    .collect::<Vec<_>>();
+
+    let column_names: Vec<String> = column_names.into_iter().collect::<HashSet<String>>().into_vec();
+
+    let mut df = df;
+    df.as_single_chunk_par();
+    let mut iters = df
+        .columns(column_names.clone())?
+        .iter()
+        .map(|s| s.iter())
+        .collect::<Vec<_>>();
+
+    let mut rows = Vec::new();
+    for _row in 0..df.height() {
+        let mut row_values = Vec::new();
+        for iter in &mut iters {
+            let value = iter.next();
+            if let Some(value) = value {
+                row_values.push(value);
+            }
+        }
+        rows.push(row_values);
+    }
+
+    let mut reports = Vec::new();
+    for (idx, row) in rows.iter().enumerate() {
+        let report = CommunityReport {
+            id: get_field(&row, id_col, &column_names)
+                .map(|id| id.to_string())
+                .unwrap_or(String::new()),
+            short_id: Some(
+                short_id_col
+                    .map(|short_id| get_field(&row, short_id, &column_names))
+                    .flatten()
+                    .map(|short_id| short_id.to_string())
+                    .unwrap_or(idx.to_string()),
+            ),
+            title: get_field(&row, title_col, &column_names)
+                .map(|title| title.to_string())
+                .unwrap_or(String::new()),
+            community_id: get_field(&row, community_col, &column_names)
+                .map(|community| community.to_string())
+                .unwrap_or(String::new()),
+            summary: get_field(&row, summary_col, &column_names)
+                .map(|summary| summary.to_string())
+                .unwrap_or(String::new()),
+            full_content: get_field(&row, content_col, &column_names)
+                .map(|content| content.to_string())
+                .unwrap_or(String::new()),
+            rank: rank_col
+                .map(|rank_col| {
+                    get_field(&row, rank_col, &column_names).map(|v| v.to_string().parse::<f64>().unwrap_or(0.0))
+                })
+                .flatten(),
+            summary_embedding: None,
+            full_content_embedding: None,
+            attributes: None,
+        };
+        reports.push(report);
+    }
+
+    let mut unique_reports: Vec<CommunityReport> = Vec::new();
+    let mut report_ids: HashSet<String> = HashSet::new();
+
+    for report in reports {
+        if !report_ids.contains(&report.id) {
+            unique_reports.push(report.clone());
+            report_ids.insert(report.id);
+        }
+    }
+
+    Ok(unique_reports)
+}
diff --git a/shinkai-libs/shinkai-graphrag/src/context_builder/mod.rs b/shinkai-libs/shinkai-graphrag/src/context_builder/mod.rs
new file mode 100644
index 000000000..0abed5320
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/context_builder/mod.rs
@@ -0,0 +1,4 @@
+pub mod community_context;
+pub mod context_builder;
+pub mod indexer_entities;
+pub mod indexer_reports;
diff --git a/shinkai-libs/shinkai-graphrag/src/lib.rs b/shinkai-libs/shinkai-graphrag/src/lib.rs
new file mode 100644
index 000000000..08bc3d655
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/lib.rs
@@ -0,0 +1,3 @@
+pub mod context_builder;
+pub mod llm;
+pub mod search;
diff --git a/shinkai-libs/shinkai-graphrag/src/llm/llm.rs b/shinkai-libs/shinkai-graphrag/src/llm/llm.rs
new file mode 100644
index 000000000..5fa5cf633
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/llm/llm.rs
@@ -0,0 +1,55 @@
+use std::collections::HashMap;
+
+use async_trait::async_trait;
+use serde::{Deserialize, Serialize};
+
+#[derive(Debug, Clone)]
+pub struct BaseLLMCallback {
+    pub response: Vec<String>,
+}
+
+impl BaseLLMCallback {
+    pub fn new() -> Self {
+        BaseLLMCallback { response: Vec::new() }
+    }
+
+    pub fn on_llm_new_token(&mut self, token: &str) {
+        self.response.push(token.to_string());
+    }
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub enum MessageType {
+    String(String),
+    Strings(Vec<String>),
+    Dictionary(Vec<HashMap<String, String>>),
+}
+
+#[derive(Debug, Clone)]
+pub struct LLMParams {
+    pub max_tokens: u32,
+    pub temperature: f32,
+    pub response_format: HashMap<String, String>,
+}
+
+#[async_trait]
+pub trait BaseLLM {
+    async fn agenerate(
+        &self,
+        messages: MessageType,
+        streaming: bool,
+        callbacks: Option<Vec<BaseLLMCallback>>,
+        llm_params: LLMParams,
+        search_phase: Option<GlobalSearchPhase>,
+    ) -> anyhow::Result<String>;
+}
+
+#[async_trait]
+pub trait BaseTextEmbedding {
+    async fn aembed(&self, text: &str) -> Vec<f64>;
+}
+
+pub enum GlobalSearchPhase {
+    Map,
+    Reduce,
+}
diff --git a/shinkai-libs/shinkai-graphrag/src/llm/mod.rs b/shinkai-libs/shinkai-graphrag/src/llm/mod.rs
new file mode 100644
index 000000000..214bbef7c
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/llm/mod.rs
@@ -0,0 +1 @@
+pub mod llm;
diff --git a/shinkai-libs/shinkai-graphrag/src/search/global_search/global_search.rs b/shinkai-libs/shinkai-graphrag/src/search/global_search/global_search.rs
new file mode 100644
index 000000000..8bb60b955
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/search/global_search/global_search.rs
@@ -0,0 +1,434 @@
+use futures::future::join_all;
+use polars::frame::DataFrame;
+use serde_json::Value;
+use std::collections::HashMap;
+use std::time::Instant;
+
+use crate::context_builder::community_context::GlobalCommunityContext;
+use crate::context_builder::context_builder::{ContextBuilderParams, ConversationHistory};
+use crate::llm::llm::{BaseLLM, BaseLLMCallback, GlobalSearchPhase, LLMParams, MessageType};
+use crate::search::global_search::prompts::NO_DATA_ANSWER;
+
+use super::prompts::{GENERAL_KNOWLEDGE_INSTRUCTION, MAP_SYSTEM_PROMPT, REDUCE_SYSTEM_PROMPT};
+
+#[derive(Debug, Clone)]
+pub struct SearchResult {
+    pub response: ResponseType,
+    pub context_data: ContextData,
+    pub context_text: ContextText,
+    pub completion_time: f64,
+    pub llm_calls: usize,
+    pub prompt_tokens: usize,
+}
+
+#[derive(Debug, Clone)]
+pub enum ResponseType {
+    String(String),
+    KeyPoints(Vec<KeyPoint>),
+}
+
+#[derive(Debug, Clone)]
+pub enum ContextData {
+    String(String),
+    DataFrames(Vec<DataFrame>),
+    Dictionary(HashMap<String, DataFrame>),
+}
+
+#[derive(Debug, Clone)]
+pub enum ContextText {
+    String(String),
+    Strings(Vec<String>),
+    Dictionary(HashMap<String, String>),
+}
+
+#[derive(Debug, Clone)]
+pub struct KeyPoint {
+    pub answer: String,
+    pub score: i32,
+}
+
+pub struct GlobalSearchResult {
+    pub response: ResponseType,
+    pub context_data: ContextData,
+    pub context_text: ContextText,
+    pub completion_time: f64,
+    pub llm_calls: usize,
+    pub prompt_tokens: usize,
+    pub map_responses: Vec<SearchResult>,
+    pub reduce_context_data: ContextData,
+    pub reduce_context_text: ContextText,
+}
+
+#[derive(Debug, Clone)]
+pub struct GlobalSearchLLMCallback {
+    response: Vec<String>,
+    map_response_contexts: Vec<String>,
+    map_response_outputs: Vec<SearchResult>,
+}
+
+impl GlobalSearchLLMCallback {
+    pub fn new() -> Self {
+        GlobalSearchLLMCallback {
+            response: Vec::new(),
+            map_response_contexts: Vec::new(),
+            map_response_outputs: Vec::new(),
+        }
+    }
+
+    pub fn on_map_response_start(&mut self, map_response_contexts: Vec<String>) {
+        self.map_response_contexts = map_response_contexts;
+    }
+
+    pub fn on_map_response_end(&mut self, map_response_outputs: Vec<SearchResult>) {
+        self.map_response_outputs = map_response_outputs;
+    }
+}
+
+pub struct GlobalSearch {
+    llm: Box<dyn BaseLLM>,
+    context_builder: GlobalCommunityContext,
+    num_tokens_fn: fn(&str) -> usize,
+    context_builder_params: ContextBuilderParams,
+    map_system_prompt: String,
+    reduce_system_prompt: String,
+    response_type: String,
+    allow_general_knowledge: bool,
+    general_knowledge_inclusion_prompt: String,
+    callbacks: Option<Vec<GlobalSearchLLMCallback>>,
+    max_data_tokens: usize,
+    map_llm_params: LLMParams,
+    reduce_llm_params: LLMParams,
+}
+
+pub struct GlobalSearchParams {
+    pub llm: Box<dyn BaseLLM>,
+    pub context_builder: GlobalCommunityContext,
+    pub num_tokens_fn: fn(&str) -> usize,
+    pub map_system_prompt: Option<String>,
+    pub reduce_system_prompt: Option<String>,
+    pub response_type: String,
+    pub allow_general_knowledge: bool,
+    pub general_knowledge_inclusion_prompt: Option<String>,
+    pub json_mode: bool,
+    pub callbacks: Option<Vec<GlobalSearchLLMCallback>>,
+    pub max_data_tokens: usize,
+    pub map_llm_params: LLMParams,
+    pub reduce_llm_params: LLMParams,
+    pub context_builder_params: ContextBuilderParams,
+}
+
+impl GlobalSearch {
+    pub fn new(global_search_params: GlobalSearchParams) -> Self {
+        let GlobalSearchParams {
+            llm,
+            context_builder,
+            num_tokens_fn,
+            map_system_prompt,
+            reduce_system_prompt,
+            response_type,
+            allow_general_knowledge,
+            general_knowledge_inclusion_prompt,
+            json_mode,
+            callbacks,
+            max_data_tokens,
+            map_llm_params,
+            reduce_llm_params,
+            context_builder_params,
+        } = global_search_params;
+
+        let mut map_llm_params = map_llm_params;
+
+        if json_mode {
+            map_llm_params
+                .response_format
+                .insert("type".to_string(), "json_object".to_string());
+        } else {
+            map_llm_params.response_format.remove("response_format");
+        }
+
+        let map_system_prompt = map_system_prompt.unwrap_or(MAP_SYSTEM_PROMPT.to_string());
+        let reduce_system_prompt = reduce_system_prompt.unwrap_or(REDUCE_SYSTEM_PROMPT.to_string());
+        let general_knowledge_inclusion_prompt =
+            general_knowledge_inclusion_prompt.unwrap_or(GENERAL_KNOWLEDGE_INSTRUCTION.to_string());
+
+        GlobalSearch {
+            llm,
+            context_builder,
+            num_tokens_fn,
+            context_builder_params,
+            map_system_prompt,
+            reduce_system_prompt,
+            response_type,
+            allow_general_knowledge,
+            general_knowledge_inclusion_prompt,
+            callbacks,
+            max_data_tokens,
+            map_llm_params,
+            reduce_llm_params,
+        }
+    }
+
+    pub async fn asearch(
+        &self,
+        query: String,
+        _conversation_history: Option<ConversationHistory>,
+    ) -> anyhow::Result<GlobalSearchResult> {
+        // Step 1: Generate answers for each batch of community short summaries
+        let start_time = Instant::now();
+        let (context_chunks, context_records) = self
+            .context_builder
+            .build_context(self.context_builder_params.clone())
+            .await?;
+
+        let mut callbacks = match &self.callbacks {
+            Some(callbacks) => {
+                let mut llm_callbacks = Vec::new();
+                for callback in callbacks {
+                    let mut callback = callback.clone();
+                    callback.on_map_response_start(context_chunks.clone());
+                    llm_callbacks.push(callback);
+                }
+                Some(llm_callbacks)
+            }
+            None => None,
+        };
+
+        let map_responses: Vec<_> = join_all(
+            context_chunks
+                .iter()
+                .map(|data| self._map_response_single_batch(data, &query, self.map_llm_params.clone())),
+        )
+        .await;
+
+        let map_responses: Result<Vec<_>, _> = map_responses.into_iter().collect();
+        let map_responses = map_responses?;
+
+        callbacks = match &callbacks {
+            Some(callbacks) => {
+                let mut llm_callbacks = Vec::new();
+                for callback in callbacks {
+                    let mut callback = callback.clone();
+                    callback.on_map_response_end(map_responses.clone());
+                    llm_callbacks.push(callback);
+                }
+                Some(llm_callbacks)
+            }
+            None => None,
+        };
+
+        let map_llm_calls: usize = map_responses.iter().map(|response| response.llm_calls).sum();
+        let map_prompt_tokens: usize = map_responses.iter().map(|response| response.prompt_tokens).sum();
+
+        // Step 2: Combine the intermediate answers from step 2 to generate the final answer
+        let reduce_response = self
+            ._reduce_response(map_responses.clone(), &query, callbacks, self.reduce_llm_params.clone())
+            .await?;
+
+        Ok(GlobalSearchResult {
+            response: reduce_response.response,
+            context_data: ContextData::Dictionary(context_records),
+            context_text: ContextText::Strings(context_chunks),
+            completion_time: start_time.elapsed().as_secs_f64(),
+            llm_calls: map_llm_calls + reduce_response.llm_calls,
+            prompt_tokens: map_prompt_tokens + reduce_response.prompt_tokens,
+            map_responses,
+            reduce_context_data: reduce_response.context_data,
+            reduce_context_text: reduce_response.context_text,
+        })
+    }
+
+    async fn _map_response_single_batch(
+        &self,
+        context_data: &str,
+        query: &str,
+        llm_params: LLMParams,
+    ) -> anyhow::Result<SearchResult> {
+        let start_time = Instant::now();
+        let search_prompt = self.map_system_prompt.replace("{context_data}", context_data);
+
+        let mut search_messages = Vec::new();
+        search_messages.push(HashMap::from([
+            ("role".to_string(), "system".to_string()),
+            ("content".to_string(), search_prompt.clone()),
+        ]));
+        search_messages.push(HashMap::from([
+            ("role".to_string(), "user".to_string()),
+            ("content".to_string(), query.to_string()),
+        ]));
+
+        let search_response = self
+            .llm
+            .agenerate(
+                MessageType::Dictionary(search_messages),
+                false,
+                None,
+                llm_params,
+                Some(GlobalSearchPhase::Map),
+            )
+            .await?;
+
+        let processed_response = self.parse_search_response(&search_response);
+
+        Ok(SearchResult {
+            response: ResponseType::KeyPoints(processed_response),
+            context_data: ContextData::String(context_data.to_string()),
+            context_text: ContextText::String(context_data.to_string()),
+            completion_time: start_time.elapsed().as_secs_f64(),
+            llm_calls: 1,
+            prompt_tokens: (self.num_tokens_fn)(&search_prompt),
+        })
+    }
+
+    fn parse_search_response(&self, search_response: &str) -> Vec<KeyPoint> {
+        let parsed_elements: Value = serde_json::from_str(search_response).unwrap_or_default();
+
+        if let Some(points) = parsed_elements.get("points") {
+            if let Some(points) = points.as_array() {
+                return points
+                    .iter()
+                    .filter(|element| element.get("description").is_some() && element.get("score").is_some())
+                    .map(|element| KeyPoint {
+                        answer: element
+                            .get("description")
+                            .unwrap_or(&Value::String("".to_string()))
+                            .to_string(),
+                        score: element
+                            .get("score")
+                            .unwrap_or(&Value::Number(serde_json::Number::from(0)))
+                            .as_i64()
+                            .unwrap_or(0) as i32,
+                    })
+                    .collect::<Vec<KeyPoint>>();
+            }
+        }
+
+        vec![KeyPoint {
+            answer: "".to_string(),
+            score: 0,
+        }]
+    }
+
+    async fn _reduce_response(
+        &self,
+        map_responses: Vec<SearchResult>,
+        query: &str,
+        callbacks: Option<Vec<GlobalSearchLLMCallback>>,
+        llm_params: LLMParams,
+    ) -> anyhow::Result<SearchResult> {
+        let start_time = Instant::now();
+        let mut key_points: Vec<HashMap<String, String>> = Vec::new();
+
+        for (index, response) in map_responses.iter().enumerate() {
+            if let ResponseType::KeyPoints(response_list) = &response.response {
+                for key_point in response_list {
+                    let mut point = HashMap::new();
+                    point.insert("analyst".to_string(), (index + 1).to_string());
+                    point.insert("answer".to_string(), key_point.answer.clone());
+                    point.insert("score".to_string(), key_point.score.to_string());
+                    key_points.push(point);
+                }
+            }
+        }
+
+        let filtered_key_points: Vec<HashMap<String, String>> = key_points
+            .into_iter()
+            .filter(|point| point.get("score").unwrap().parse::<i32>().unwrap() > 0)
+            .collect();
+
+        if filtered_key_points.is_empty() && !self.allow_general_knowledge {
+            eprintln!("Warning: All map responses have score 0 (i.e., no relevant information found from the dataset), returning a canned 'I do not know' answer. You can try enabling `allow_general_knowledge` to encourage the LLM to incorporate relevant general knowledge, at the risk of increasing hallucinations.");
+
+            return Ok(SearchResult {
+                response: ResponseType::String(NO_DATA_ANSWER.to_string()),
+                context_data: ContextData::String("".to_string()),
+                context_text: ContextText::String("".to_string()),
+                completion_time: start_time.elapsed().as_secs_f64(),
+                llm_calls: 0,
+                prompt_tokens: 0,
+            });
+        }
+
+        let mut sorted_key_points = filtered_key_points;
+        sorted_key_points.sort_by(|a, b| {
+            b.get("score")
+                .unwrap()
+                .parse::<i32>()
+                .unwrap()
+                .cmp(&a.get("score").unwrap().parse::<i32>().unwrap())
+        });
+
+        let mut data: Vec<String> = Vec::new();
+        let mut total_tokens = 0;
+        for point in sorted_key_points {
+            let mut formatted_response_data: Vec<String> = Vec::new();
+            formatted_response_data.push(format!("----Analyst {}----", point.get("analyst").unwrap()));
+            formatted_response_data.push(format!("Importance Score: {}", point.get("score").unwrap()));
+            formatted_response_data.push(point.get("answer").unwrap().to_string());
+            let formatted_response_text = formatted_response_data.join("\n");
+
+            if total_tokens + (self.num_tokens_fn)(&formatted_response_text) > self.max_data_tokens {
+                break;
+            }
+
+            data.push(formatted_response_text.clone());
+            total_tokens += (self.num_tokens_fn)(&formatted_response_text);
+        }
+        let text_data = data.join("\n\n");
+
+        let search_prompt = format!(
+            "{}\n{}",
+            self.reduce_system_prompt
+                .replace("{report_data}", &text_data)
+                .replace("{response_type}", &self.response_type),
+            if self.allow_general_knowledge {
+                self.general_knowledge_inclusion_prompt.clone()
+            } else {
+                String::new()
+            }
+        );
+
+        let search_messages = vec![
+            HashMap::from([
+                ("role".to_string(), "system".to_string()),
+                ("content".to_string(), search_prompt.clone()),
+            ]),
+            HashMap::from([
+                ("role".to_string(), "user".to_string()),
+                ("content".to_string(), query.to_string()),
+            ]),
+        ];
+
+        let llm_callbacks = match callbacks {
+            Some(callbacks) => {
+                let mut llm_callbacks = Vec::new();
+                for callback in callbacks {
+                    llm_callbacks.push(BaseLLMCallback {
+                        response: callback.response.clone(),
+                    });
+                }
+                Some(llm_callbacks)
+            }
+            None => None,
+        };
+
+        let search_response = self
+            .llm
+            .agenerate(
+                MessageType::Dictionary(search_messages),
+                true,
+                llm_callbacks,
+                llm_params,
+                Some(GlobalSearchPhase::Reduce),
+            )
+            .await?;
+
+        Ok(SearchResult {
+            response: ResponseType::String(search_response),
+            context_data: ContextData::String(text_data.clone()),
+            context_text: ContextText::String(text_data),
+            completion_time: start_time.elapsed().as_secs_f64(),
+            llm_calls: 1,
+            prompt_tokens: (self.num_tokens_fn)(&search_prompt),
+        })
+    }
+}
diff --git a/shinkai-libs/shinkai-graphrag/src/search/global_search/mod.rs b/shinkai-libs/shinkai-graphrag/src/search/global_search/mod.rs
new file mode 100644
index 000000000..79f16f1e0
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/search/global_search/mod.rs
@@ -0,0 +1,2 @@
+pub mod global_search;
+pub mod prompts;
diff --git a/shinkai-libs/shinkai-graphrag/src/search/global_search/prompts.rs b/shinkai-libs/shinkai-graphrag/src/search/global_search/prompts.rs
new file mode 100644
index 000000000..7c9fef5cb
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/search/global_search/prompts.rs
@@ -0,0 +1,164 @@
+// Copyright (c) 2024 Microsoft Corporation.
+// Licensed under the MIT License
+
+// System prompts for global search.
+
+pub const MAP_SYSTEM_PROMPT: &str = r#"
+---Role---
+
+You are a helpful assistant responding to questions about data in the tables provided.
+
+
+---Goal---
+
+Generate a response consisting of a list of key points that responds to the user's question, summarizing all relevant information in the input data tables.
+
+You should use the data provided in the data tables below as the primary context for generating the response.
+If you don't know the answer or if the input data tables do not contain sufficient information to provide an answer, just say so. Do not make anything up.
+
+Each key point in the response should have the following element:
+- Description: A comprehensive description of the point.
+- Importance Score: An integer score between 0-100 that indicates how important the point is in answering the user's question. An 'I don't know' type of response should have a score of 0.
+
+The response should be JSON formatted as follows:
+{{
+    "points": [
+        {{"description": "Description of point 1 [Data: Reports (report ids)]", "score": score_value}},
+        {{"description": "Description of point 2 [Data: Reports (report ids)]", "score": score_value}}
+    ]
+}}
+
+The response shall preserve the original meaning and use of modal verbs such as "shall", "may" or "will".
+
+Points supported by data should list the relevant reports as references as follows:
+"This is an example sentence supported by data references [Data: Reports (report ids)]"
+
+**Do not list more than 5 record ids in a single reference**. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more.
+
+For example:
+"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (2, 7, 64, 46, 34, +more)]. He is also CEO of company X [Data: Reports (1, 3)]"
+
+where 1, 2, 3, 7, 34, 46, and 64 represent the id (not the index) of the relevant data report in the provided tables.
+
+Do not include information where the supporting evidence for it is not provided.
+
+
+---Data tables---
+
+{context_data}
+
+---Goal---
+
+Generate a response consisting of a list of key points that responds to the user's question, summarizing all relevant information in the input data tables.
+
+You should use the data provided in the data tables below as the primary context for generating the response.
+If you don't know the answer or if the input data tables do not contain sufficient information to provide an answer, just say so. Do not make anything up.
+
+Each key point in the response should have the following element:
+- Description: A comprehensive description of the point.
+- Importance Score: An integer score between 0-100 that indicates how important the point is in answering the user's question. An 'I don't know' type of response should have a score of 0.
+
+The response shall preserve the original meaning and use of modal verbs such as "shall", "may" or "will".
+
+Points supported by data should list the relevant reports as references as follows:
+"This is an example sentence supported by data references [Data: Reports (report ids)]"
+
+**Do not list more than 5 record ids in a single reference**. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more.
+
+For example:
+"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (2, 7, 64, 46, 34, +more)]. He is also CEO of company X [Data: Reports (1, 3)]"
+
+where 1, 2, 3, 7, 34, 46, and 64 represent the id (not the index) of the relevant data report in the provided tables.
+
+Do not include information where the supporting evidence for it is not provided.
+
+The response should be JSON formatted as follows:
+{{
+    "points": [
+        {{"description": "Description of point 1 [Data: Reports (report ids)]", "score": score_value}},
+        {{"description": "Description of point 2 [Data: Reports (report ids)]", "score": score_value}}
+    ]
+}}
+"#;
+
+pub const REDUCE_SYSTEM_PROMPT: &str = r#"
+---Role---
+
+You are a helpful assistant responding to questions about a dataset by synthesizing perspectives from multiple analysts.
+
+
+---Goal---
+
+Generate a response of the target length and format that responds to the user's question, summarize all the reports from multiple analysts who focused on different parts of the dataset.
+
+Note that the analysts' reports provided below are ranked in the **descending order of importance**.
+
+If you don't know the answer or if the provided reports do not contain sufficient information to provide an answer, just say so. Do not make anything up.
+
+The final response should remove all irrelevant information from the analysts' reports and merge the cleaned information into a comprehensive answer that provides explanations of all the key points and implications appropriate for the response length and format.
+
+Add sections and commentary to the response as appropriate for the length and format. Style the response in markdown.
+
+The response shall preserve the original meaning and use of modal verbs such as "shall", "may" or "will".
+
+The response should also preserve all the data references previously included in the analysts' reports, but do not mention the roles of multiple analysts in the analysis process.
+
+**Do not list more than 5 record ids in a single reference**. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more.
+
+For example:
+
+"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (2, 7, 34, 46, 64, +more)]. He is also CEO of company X [Data: Reports (1, 3)]"
+
+where 1, 2, 3, 7, 34, 46, and 64 represent the id (not the index) of the relevant data record.
+
+Do not include information where the supporting evidence for it is not provided.
+
+
+---Target response length and format---
+
+{response_type}
+
+
+---Analyst Reports---
+
+{report_data}
+
+
+---Goal---
+
+Generate a response of the target length and format that responds to the user's question, summarize all the reports from multiple analysts who focused on different parts of the dataset.
+
+Note that the analysts' reports provided below are ranked in the **descending order of importance**.
+
+If you don't know the answer or if the provided reports do not contain sufficient information to provide an answer, just say so. Do not make anything up.
+
+The final response should remove all irrelevant information from the analysts' reports and merge the cleaned information into a comprehensive answer that provides explanations of all the key points and implications appropriate for the response length and format.
+
+The response shall preserve the original meaning and use of modal verbs such as "shall", "may" or "will".
+
+The response should also preserve all the data references previously included in the analysts' reports, but do not mention the roles of multiple analysts in the analysis process.
+
+**Do not list more than 5 record ids in a single reference**. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more.
+
+For example:
+
+"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (2, 7, 34, 46, 64, +more)]. He is also CEO of company X [Data: Reports (1, 3)]"
+
+where 1, 2, 3, 7, 34, 46, and 64 represent the id (not the index) of the relevant data record.
+
+Do not include information where the supporting evidence for it is not provided.
+
+
+---Target response length and format---
+
+{response_type}
+
+Add sections and commentary to the response as appropriate for the length and format. Style the response in markdown.
+"#;
+
+pub const NO_DATA_ANSWER: &str = "I am sorry but I am unable to answer this question given the provided data.";
+
+pub const GENERAL_KNOWLEDGE_INSTRUCTION: &str = r#"
+The response may also include relevant real-world knowledge outside the dataset, but it must be explicitly annotated with a verification tag [LLM: verify]. For example:
+"This is an example sentence supported by real-world knowledge [LLM: verify]."
+"#;
diff --git a/shinkai-libs/shinkai-graphrag/src/search/mod.rs b/shinkai-libs/shinkai-graphrag/src/search/mod.rs
new file mode 100644
index 000000000..a12441830
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/src/search/mod.rs
@@ -0,0 +1 @@
+pub mod global_search;
diff --git a/shinkai-libs/shinkai-graphrag/tests/global_search_tests.rs b/shinkai-libs/shinkai-graphrag/tests/global_search_tests.rs
new file mode 100644
index 000000000..5c888a8df
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/tests/global_search_tests.rs
@@ -0,0 +1,211 @@
+use polars::{io::SerReader, prelude::ParquetReader};
+use shinkai_graphrag::{
+    context_builder::{
+        community_context::GlobalCommunityContext, context_builder::ContextBuilderParams,
+        indexer_entities::read_indexer_entities, indexer_reports::read_indexer_reports,
+    },
+    llm::llm::LLMParams,
+    search::global_search::global_search::{GlobalSearch, GlobalSearchParams},
+};
+use utils::{
+    ollama::Ollama,
+    openai::{num_tokens, ChatOpenAI},
+};
+
+mod utils;
+
+// #[tokio::test]
+async fn ollama_global_search_test() -> Result<(), Box<dyn std::error::Error>> {
+    let base_url = "http://localhost:11434";
+    let model_type = "llama3.1";
+
+    let llm = Ollama::new(base_url.to_string(), model_type.to_string());
+
+    // Load community reports
+    // Download dataset: https://microsoft.github.io/graphrag/data/operation_dulce/dataset.zip
+
+    let input_dir = "./dataset";
+    let community_report_table = "create_final_community_reports";
+    let entity_table = "create_final_nodes";
+    let entity_embedding_table = "create_final_entities";
+
+    let community_level = 2;
+
+    let mut entity_file = std::fs::File::open(format!("{}/{}.parquet", input_dir, entity_table)).unwrap();
+    let entity_df = ParquetReader::new(&mut entity_file).finish().unwrap();
+
+    let mut report_file = std::fs::File::open(format!("{}/{}.parquet", input_dir, community_report_table)).unwrap();
+    let report_df = ParquetReader::new(&mut report_file).finish().unwrap();
+
+    let mut entity_embedding_file =
+        std::fs::File::open(format!("{}/{}.parquet", input_dir, entity_embedding_table)).unwrap();
+    let entity_embedding_df = ParquetReader::new(&mut entity_embedding_file).finish().unwrap();
+
+    let reports = read_indexer_reports(&report_df, &entity_df, community_level)?;
+    let entities = read_indexer_entities(&entity_df, &entity_embedding_df, community_level)?;
+
+    println!("Reports: {:?}", report_df.head(Some(5)));
+
+    // Build global context based on community reports
+
+    // Using tiktoken for token count estimation
+    let context_builder = GlobalCommunityContext::new(reports, Some(entities), num_tokens);
+
+    let context_builder_params = ContextBuilderParams {
+        use_community_summary: false, // False means using full community reports. True means using community short summaries.
+        shuffle_data: true,
+        include_community_rank: true,
+        min_community_rank: 0,
+        community_rank_name: String::from("rank"),
+        include_community_weight: true,
+        community_weight_name: String::from("occurrence weight"),
+        normalize_community_weight: true,
+        max_tokens: 5000, // change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000)
+        context_name: String::from("Reports"),
+        column_delimiter: String::from("|"),
+    };
+
+    // LLM params are ignored for Ollama
+    let map_llm_params = LLMParams {
+        max_tokens: 1000,
+        temperature: 0.0,
+        response_format: std::collections::HashMap::from([("type".to_string(), "json_object".to_string())]),
+    };
+
+    let reduce_llm_params = LLMParams {
+        max_tokens: 2000,
+        temperature: 0.0,
+        response_format: std::collections::HashMap::new(),
+    };
+
+    // Perform global search
+
+    let search_engine = GlobalSearch::new(GlobalSearchParams {
+        llm: Box::new(llm),
+        context_builder,
+        num_tokens_fn: num_tokens,
+        map_system_prompt: None,
+        reduce_system_prompt: None,
+        response_type: String::from("multiple paragraphs"),
+        allow_general_knowledge: false,
+        general_knowledge_inclusion_prompt: None,
+        json_mode: true,
+        callbacks: None,
+        max_data_tokens: 5000,
+        map_llm_params,
+        reduce_llm_params,
+        context_builder_params,
+    });
+
+    let result = search_engine
+        .asearch(
+            "What is the major conflict in this story and who are the protagonist and antagonist?".to_string(),
+            None,
+        )
+        .await?;
+
+    println!("Response: {:?}", result.response);
+
+    println!("Context: {:?}", result.context_data);
+
+    println!("LLM calls: {}. LLM tokens: {}", result.llm_calls, result.prompt_tokens);
+
+    Ok(())
+}
+
+// #[tokio::test]
+async fn openai_global_search_test() -> Result<(), Box<dyn std::error::Error>> {
+    let api_key = std::env::var("GRAPHRAG_API_KEY").unwrap();
+    let llm_model = std::env::var("GRAPHRAG_LLM_MODEL").unwrap();
+
+    let llm = ChatOpenAI::new(Some(api_key), llm_model, 5);
+
+    // Load community reports
+    // Download dataset: https://microsoft.github.io/graphrag/data/operation_dulce/dataset.zip
+
+    let input_dir = "./dataset";
+    let community_report_table = "create_final_community_reports";
+    let entity_table = "create_final_nodes";
+    let entity_embedding_table = "create_final_entities";
+
+    let community_level = 2;
+
+    let mut entity_file = std::fs::File::open(format!("{}/{}.parquet", input_dir, entity_table)).unwrap();
+    let entity_df = ParquetReader::new(&mut entity_file).finish().unwrap();
+
+    let mut report_file = std::fs::File::open(format!("{}/{}.parquet", input_dir, community_report_table)).unwrap();
+    let report_df = ParquetReader::new(&mut report_file).finish().unwrap();
+
+    let mut entity_embedding_file =
+        std::fs::File::open(format!("{}/{}.parquet", input_dir, entity_embedding_table)).unwrap();
+    let entity_embedding_df = ParquetReader::new(&mut entity_embedding_file).finish().unwrap();
+
+    let reports = read_indexer_reports(&report_df, &entity_df, community_level)?;
+    let entities = read_indexer_entities(&entity_df, &entity_embedding_df, community_level)?;
+
+    println!("Reports: {:?}", report_df.head(Some(5)));
+
+    // Build global context based on community reports
+
+    let context_builder = GlobalCommunityContext::new(reports, Some(entities), num_tokens);
+
+    let context_builder_params = ContextBuilderParams {
+        use_community_summary: false, // False means using full community reports. True means using community short summaries.
+        shuffle_data: true,
+        include_community_rank: true,
+        min_community_rank: 0,
+        community_rank_name: String::from("rank"),
+        include_community_weight: true,
+        community_weight_name: String::from("occurrence weight"),
+        normalize_community_weight: true,
+        max_tokens: 12_000, // change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000)
+        context_name: String::from("Reports"),
+        column_delimiter: String::from("|"),
+    };
+
+    let map_llm_params = LLMParams {
+        max_tokens: 1000,
+        temperature: 0.0,
+        response_format: std::collections::HashMap::from([("type".to_string(), "json_object".to_string())]),
+    };
+
+    let reduce_llm_params = LLMParams {
+        max_tokens: 2000,
+        temperature: 0.0,
+        response_format: std::collections::HashMap::new(),
+    };
+
+    // Perform global search
+
+    let search_engine = GlobalSearch::new(GlobalSearchParams {
+        llm: Box::new(llm),
+        context_builder,
+        num_tokens_fn: num_tokens,
+        map_system_prompt: None,
+        reduce_system_prompt: None,
+        response_type: String::from("multiple paragraphs"),
+        allow_general_knowledge: false,
+        general_knowledge_inclusion_prompt: None,
+        json_mode: true,
+        callbacks: None,
+        max_data_tokens: 12_000,
+        map_llm_params,
+        reduce_llm_params,
+        context_builder_params,
+    });
+
+    let result = search_engine
+        .asearch(
+            "What is the major conflict in this story and who are the protagonist and antagonist?".to_string(),
+            None,
+        )
+        .await?;
+
+    println!("Response: {:?}", result.response);
+
+    println!("Context: {:?}", result.context_data);
+
+    println!("LLM calls: {}. LLM tokens: {}", result.llm_calls, result.prompt_tokens);
+
+    Ok(())
+}
diff --git a/shinkai-libs/shinkai-graphrag/tests/utils/mod.rs b/shinkai-libs/shinkai-graphrag/tests/utils/mod.rs
new file mode 100644
index 000000000..3ef32f620
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/tests/utils/mod.rs
@@ -0,0 +1,2 @@
+pub mod ollama;
+pub mod openai;
diff --git a/shinkai-libs/shinkai-graphrag/tests/utils/ollama.rs b/shinkai-libs/shinkai-graphrag/tests/utils/ollama.rs
new file mode 100644
index 000000000..41d3619b8
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/tests/utils/ollama.rs
@@ -0,0 +1,100 @@
+use async_trait::async_trait;
+use reqwest::Client;
+use serde::{Deserialize, Serialize};
+use serde_json::json;
+use shinkai_graphrag::llm::llm::{BaseLLM, BaseLLMCallback, GlobalSearchPhase, LLMParams, MessageType};
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct OllamaResponse {
+    pub model: String,
+    pub created_at: String,
+    pub message: OllamaMessage,
+}
+
+#[derive(Serialize, Deserialize, Debug, PartialEq, Clone)]
+pub struct OllamaMessage {
+    pub role: String,
+    pub content: String,
+}
+
+pub struct Ollama {
+    base_url: String,
+    model_type: String,
+}
+
+impl Ollama {
+    pub fn new(base_url: String, model_type: String) -> Self {
+        Ollama { base_url, model_type }
+    }
+}
+
+#[async_trait]
+impl BaseLLM for Ollama {
+    async fn agenerate(
+        &self,
+        messages: MessageType,
+        _streaming: bool,
+        _callbacks: Option<Vec<BaseLLMCallback>>,
+        _llm_params: LLMParams,
+        search_phase: Option<GlobalSearchPhase>,
+    ) -> anyhow::Result<String> {
+        let client = Client::new();
+        let chat_url = format!("{}{}", &self.base_url, "/api/chat");
+
+        let messages_json = match messages {
+            MessageType::String(message) => json![message],
+            MessageType::Strings(messages) => json!(messages),
+            MessageType::Dictionary(messages) => {
+                let messages = match search_phase {
+                    Some(GlobalSearchPhase::Map) => {
+                        // Filter out system messages and convert them to user messages
+                        messages
+                            .into_iter()
+                            .filter(|map| map.get_key_value("role").is_some_and(|(_, v)| v == "system"))
+                            .map(|map| {
+                                map.into_iter()
+                                    .map(|(key, value)| {
+                                        if key == "role" {
+                                            return (key, "user".to_string());
+                                        }
+                                        (key, value)
+                                    })
+                                    .collect()
+                            })
+                            .collect()
+                    }
+                    Some(GlobalSearchPhase::Reduce) => {
+                        // Convert roles to user
+                        messages
+                            .into_iter()
+                            .map(|map| {
+                                map.into_iter()
+                                    .map(|(key, value)| {
+                                        if key == "role" {
+                                            return (key, "user".to_string());
+                                        }
+                                        (key, value)
+                                    })
+                                    .collect()
+                            })
+                            .collect()
+                    }
+                    _ => messages,
+                };
+
+                json!(messages)
+            }
+        };
+
+        let payload = json!({
+            "model": self.model_type,
+            "messages": messages_json,
+            "stream": false,
+        });
+
+        let response = client.post(chat_url).json(&payload).send().await?;
+        let response = response.json::<OllamaResponse>().await?;
+
+        Ok(response.message.content)
+    }
+}
diff --git a/shinkai-libs/shinkai-graphrag/tests/utils/openai.rs b/shinkai-libs/shinkai-graphrag/tests/utils/openai.rs
new file mode 100644
index 000000000..255d5b4e5
--- /dev/null
+++ b/shinkai-libs/shinkai-graphrag/tests/utils/openai.rs
@@ -0,0 +1,146 @@
+use async_openai::{
+    config::OpenAIConfig,
+    types::{
+        ChatCompletionRequestMessage, ChatCompletionRequestSystemMessageArgs, ChatCompletionResponseFormat,
+        ChatCompletionResponseFormatType, CreateChatCompletionRequestArgs,
+    },
+    Client,
+};
+use async_trait::async_trait;
+use shinkai_graphrag::llm::llm::{BaseLLM, BaseLLMCallback, GlobalSearchPhase, LLMParams, MessageType};
+use tiktoken_rs::{get_bpe_from_tokenizer, tokenizer::Tokenizer};
+
+pub struct ChatOpenAI {
+    pub api_key: Option<String>,
+    pub model: String,
+    pub max_retries: usize,
+}
+
+impl ChatOpenAI {
+    pub fn new(api_key: Option<String>, model: String, max_retries: usize) -> Self {
+        ChatOpenAI {
+            api_key,
+            model,
+            max_retries,
+        }
+    }
+
+    pub async fn agenerate(
+        &self,
+        messages: MessageType,
+        streaming: bool,
+        callbacks: Option<Vec<BaseLLMCallback>>,
+        llm_params: LLMParams,
+    ) -> anyhow::Result<String> {
+        let mut retry_count = 0;
+
+        loop {
+            match self
+                ._agenerate(messages.clone(), streaming, callbacks.clone(), llm_params.clone())
+                .await
+            {
+                Ok(response) => return Ok(response),
+                Err(e) => {
+                    if retry_count < self.max_retries {
+                        retry_count += 1;
+                        continue;
+                    }
+                    return Err(e);
+                }
+            }
+        }
+    }
+
+    async fn _agenerate(
+        &self,
+        messages: MessageType,
+        _streaming: bool,
+        _callbacks: Option<Vec<BaseLLMCallback>>,
+        llm_params: LLMParams,
+    ) -> anyhow::Result<String> {
+        let client = match &self.api_key {
+            Some(api_key) => Client::with_config(OpenAIConfig::new().with_api_key(api_key)),
+            None => Client::new(),
+        };
+
+        let messages = match messages {
+            MessageType::String(message) => vec![message],
+            MessageType::Strings(messages) => messages,
+            MessageType::Dictionary(messages) => {
+                let messages = messages
+                    .iter()
+                    .map(|message_map| {
+                        message_map
+                            .iter()
+                            .map(|(key, value)| format!("{}: {}", key, value))
+                            .collect::<Vec<_>>()
+                            .join("\n")
+                    })
+                    .collect();
+                messages
+            }
+        };
+
+        let request_messages = messages
+            .into_iter()
+            .map(|m| ChatCompletionRequestSystemMessageArgs::default().content(m).build())
+            .collect::<Vec<_>>();
+
+        let request_messages: Result<Vec<_>, _> = request_messages.into_iter().collect();
+        let request_messages = request_messages?;
+        let request_messages = request_messages
+            .into_iter()
+            .map(|m| Into::<ChatCompletionRequestMessage>::into(m.clone()))
+            .collect::<Vec<ChatCompletionRequestMessage>>();
+
+        let _response_format = if llm_params
+            .response_format
+            .get_key_value("type")
+            .is_some_and(|(_k, v)| v == "json_object")
+        {
+            ChatCompletionResponseFormat {
+                r#type: ChatCompletionResponseFormatType::JsonObject,
+            }
+        } else {
+            ChatCompletionResponseFormat {
+                r#type: ChatCompletionResponseFormatType::Text,
+            }
+        };
+
+        let request = CreateChatCompletionRequestArgs::default()
+            .max_tokens(llm_params.max_tokens)
+            .temperature(llm_params.temperature)
+            //.response_format(response_format)
+            .model(self.model.clone())
+            .messages(request_messages)
+            .build()?;
+
+        let response = client.chat().create(request).await?;
+
+        if let Some(choice) = response.choices.get(0) {
+            return Ok(choice.message.content.clone().unwrap_or_default());
+        }
+
+        return Ok(String::new());
+    }
+}
+
+#[async_trait]
+impl BaseLLM for ChatOpenAI {
+    async fn agenerate(
+        &self,
+        messages: MessageType,
+        streaming: bool,
+        callbacks: Option<Vec<BaseLLMCallback>>,
+        llm_params: LLMParams,
+        _search_phase: Option<GlobalSearchPhase>,
+    ) -> anyhow::Result<String> {
+        self.agenerate(messages, streaming, callbacks, llm_params).await
+    }
+}
+
+pub fn num_tokens(text: &str) -> usize {
+    let token_encoder = Tokenizer::Cl100kBase;
+    let bpe = get_bpe_from_tokenizer(token_encoder).unwrap();
+    bpe.encode_with_special_tokens(text).len()
+}