gabykim commited on
Commit
212ff4c
·
1 Parent(s): f4fd6ee

voyage reranking algorithm implemented

Browse files
.env.example CHANGED
@@ -3,6 +3,6 @@ LLM__MODEL_PROVIDER=ollama
3
  LLM__API_KEY=your_api_key
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  DB__PERSIST_DIRECTORY=./my_chroma_db
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  DB__CODEBASE_DIRECTORY=.
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- EMBEDDING__PROVIDER=openai
7
  EMBEDDING__MODEL_NAME=text-embedding-ada-002
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  EMBEDDING__API_KEY=your_api_key
 
3
  LLM__API_KEY=your_api_key
4
  DB__PERSIST_DIRECTORY=./my_chroma_db
5
  DB__CODEBASE_DIRECTORY=.
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+ EMBEDDING__MODEL_PROVIDER=openai
7
  EMBEDDING__MODEL_NAME=text-embedding-ada-002
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  EMBEDDING__API_KEY=your_api_key
poetry.lock CHANGED
@@ -13,6 +13,148 @@ files = [
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16
  [[package]]
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  name = "annotated-types"
18
  version = "0.7.0"
@@ -96,49 +238,39 @@ typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
96
  tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
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98
  [[package]]
99
- name = "audioop-lts"
100
- version = "0.2.1"
101
- description = "LTS Port of Python audioop"
102
  optional = false
103
- python-versions = ">=3.13"
 
 
 
 
 
 
 
 
 
 
 
 
 
104
  groups = ["main"]
105
- markers = "python_version >= \"3.13\""
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- {file = "audioop_lts-0.2.1.tar.gz", hash = "sha256:e81268da0baa880431b68b1308ab7257eb33f356e57a5f9b1f915dfb13dd1387"},
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  ]
141
 
 
 
 
 
 
 
 
 
142
  [[package]]
143
  name = "backoff"
144
  version = "2.2.1"
@@ -713,6 +845,109 @@ files = [
713
  {file = "flatbuffers-25.1.24.tar.gz", hash = "sha256:e0f7b7d806c0abdf166275492663130af40c11f89445045fbef0aa3c9a8643ad"},
714
  ]
715
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
716
  [[package]]
717
  name = "fsspec"
718
  version = "2024.12.0"
@@ -850,7 +1085,6 @@ files = [
850
  [package.dependencies]
851
  aiofiles = ">=22.0,<24.0"
852
  anyio = ">=3.0,<5.0"
853
- audioop-lts = {version = "<1.0", markers = "python_version >= \"3.13\""}
854
  fastapi = ">=0.115.2,<1.0"
855
  ffmpy = "*"
856
  gradio-client = "1.6.0"
@@ -1738,6 +1972,112 @@ docs = ["sphinx"]
1738
  gmpy = ["gmpy2 (>=2.1.0a4)"]
1739
  tests = ["pytest (>=4.6)"]
1740
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1741
  [[package]]
1742
  name = "mypy-extensions"
1743
  version = "1.0.0"
@@ -2675,6 +3015,99 @@ langchain = ["langchain (>=0.2.0)"]
2675
  sentry = ["django", "sentry-sdk"]
2676
  test = ["anthropic", "coverage", "django", "flake8", "freezegun (==0.3.15)", "langchain-anthropic (>=0.2.0)", "langchain-community (>=0.2.0)", "langchain-openai (>=0.2.0)", "langgraph", "mock (>=2.0.0)", "openai", "pylint", "pytest", "pytest-asyncio", "pytest-timeout"]
2677
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2678
  [[package]]
2679
  name = "protobuf"
2680
  version = "5.29.3"
@@ -4122,6 +4555,29 @@ dev = ["Cython (>=3.0,<4.0)", "setuptools (>=60)"]
4122
  docs = ["Sphinx (>=4.1.2,<4.2.0)", "sphinx-rtd-theme (>=0.5.2,<0.6.0)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
4123
  test = ["aiohttp (>=3.10.5)", "flake8 (>=5.0,<6.0)", "mypy (>=0.800)", "psutil", "pyOpenSSL (>=23.0.0,<23.1.0)", "pycodestyle (>=2.9.0,<2.10.0)"]
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4125
  [[package]]
4126
  name = "watchfiles"
4127
  version = "1.0.4"
@@ -4395,6 +4851,104 @@ files = [
4395
  {file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
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  ]
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4398
  [[package]]
4399
  name = "zipp"
4400
  version = "3.21.0"
@@ -4418,5 +4972,5 @@ type = ["pytest-mypy"]
4418
 
4419
  [metadata]
4420
  lock-version = "2.1"
4421
- python-versions = ">=3.10, <4.0"
4422
- content-hash = "ed37bda9891fbefaa9fa3eb73382a1887aec8cfdec3506df79da7bcceaadc63b"
 
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  ]
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16
+ [[package]]
17
+ name = "aiohappyeyeballs"
18
+ version = "2.4.4"
19
+ description = "Happy Eyeballs for asyncio"
20
+ optional = false
21
+ python-versions = ">=3.8"
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+ groups = ["main"]
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+ markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
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+ files = [
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+ ]
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+
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+ [[package]]
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+ name = "aiohttp"
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+ version = "3.11.11"
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+ description = "Async http client/server framework (asyncio)"
33
+ optional = false
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+ python-versions = ">=3.9"
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+ groups = ["main"]
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+ markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
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+ files = [
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+ async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
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128
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129
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131
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146
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149
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155
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156
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157
+
158
  [[package]]
159
  name = "annotated-types"
160
  version = "0.7.0"
 
238
  tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
239
 
240
  [[package]]
241
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242
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243
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244
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245
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246
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255
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257
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258
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259
  groups = ["main"]
260
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264
  ]
265
 
266
+ [package.extras]
267
+ benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
268
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269
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270
+ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
271
+ tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
272
+ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
273
+
274
  [[package]]
275
  name = "backoff"
276
  version = "2.2.1"
 
845
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846
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848
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949
+ ]
950
+
951
  [[package]]
952
  name = "fsspec"
953
  version = "2024.12.0"
 
1085
  [package.dependencies]
1086
  aiofiles = ">=22.0,<24.0"
1087
  anyio = ">=3.0,<5.0"
 
1088
  fastapi = ">=0.115.2,<1.0"
1089
  ffmpy = "*"
1090
  gradio-client = "1.6.0"
 
1972
  gmpy = ["gmpy2 (>=2.1.0a4)"]
1973
  tests = ["pytest (>=4.6)"]
1974
 
1975
+ [[package]]
1976
+ name = "multidict"
1977
+ version = "6.1.0"
1978
+ description = "multidict implementation"
1979
+ optional = false
1980
+ python-versions = ">=3.8"
1981
+ groups = ["main"]
1982
+ markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
1983
+ files = [
1984
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1990
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1991
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1992
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1993
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1994
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1995
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1996
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1997
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1998
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1999
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2000
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2001
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2002
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2003
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2004
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2005
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2006
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2007
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2008
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2009
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2010
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2011
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2012
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2013
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2014
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2015
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2016
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2017
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2018
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2019
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2020
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2021
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2022
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2023
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2024
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2025
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2026
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2027
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2028
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2029
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2030
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2031
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2032
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2033
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2034
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2035
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2036
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2037
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2038
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2039
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2040
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2041
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2042
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2043
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2044
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2045
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2046
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2047
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2048
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2049
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2050
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2051
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2052
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2053
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2054
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2055
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2056
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2057
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2058
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2059
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2060
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2061
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2062
+ {file = "multidict-6.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e617fb6b0b6953fffd762669610c1c4ffd05632c138d61ac7e14ad187870669c"},
2063
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2064
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2065
+ {file = "multidict-6.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:957cf8e4b6e123a9eea554fa7ebc85674674b713551de587eb318a2df3e00255"},
2066
+ {file = "multidict-6.1.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:483a6aea59cb89904e1ceabd2b47368b5600fb7de78a6e4a2c2987b2d256cf30"},
2067
+ {file = "multidict-6.1.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:87701f25a2352e5bf7454caa64757642734da9f6b11384c1f9d1a8e699758057"},
2068
+ {file = "multidict-6.1.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:682b987361e5fd7a139ed565e30d81fd81e9629acc7d925a205366877d8c8657"},
2069
+ {file = "multidict-6.1.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:ce2186a7df133a9c895dea3331ddc5ddad42cdd0d1ea2f0a51e5d161e4762f28"},
2070
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2071
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2072
+ {file = "multidict-6.1.0-cp39-cp39-win32.whl", hash = "sha256:1ca0083e80e791cffc6efce7660ad24af66c8d4079d2a750b29001b53ff59ada"},
2073
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2074
+ {file = "multidict-6.1.0-py3-none-any.whl", hash = "sha256:48e171e52d1c4d33888e529b999e5900356b9ae588c2f09a52dcefb158b27506"},
2075
+ {file = "multidict-6.1.0.tar.gz", hash = "sha256:22ae2ebf9b0c69d206c003e2f6a914ea33f0a932d4aa16f236afc049d9958f4a"},
2076
+ ]
2077
+
2078
+ [package.dependencies]
2079
+ typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
2080
+
2081
  [[package]]
2082
  name = "mypy-extensions"
2083
  version = "1.0.0"
 
3015
  sentry = ["django", "sentry-sdk"]
3016
  test = ["anthropic", "coverage", "django", "flake8", "freezegun (==0.3.15)", "langchain-anthropic (>=0.2.0)", "langchain-community (>=0.2.0)", "langchain-openai (>=0.2.0)", "langgraph", "mock (>=2.0.0)", "openai", "pylint", "pytest", "pytest-asyncio", "pytest-timeout"]
3017
 
3018
+ [[package]]
3019
+ name = "propcache"
3020
+ version = "0.2.1"
3021
+ description = "Accelerated property cache"
3022
+ optional = false
3023
+ python-versions = ">=3.9"
3024
+ groups = ["main"]
3025
+ markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
3026
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3089
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3090
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3095
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3096
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3097
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3098
+ {file = "propcache-0.2.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:781e65134efaf88feb447e8c97a51772aa75e48b794352f94cb7ea717dedda0d"},
3099
+ {file = "propcache-0.2.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:31f5af773530fd3c658b32b6bdc2d0838543de70eb9a2156c03e410f7b0d3aae"},
3100
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3101
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3102
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3103
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3104
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3105
+ {file = "propcache-0.2.1-cp39-cp39-win32.whl", hash = "sha256:9caac6b54914bdf41bcc91e7eb9147d331d29235a7c967c150ef5df6464fd1bb"},
3106
+ {file = "propcache-0.2.1-cp39-cp39-win_amd64.whl", hash = "sha256:92fc4500fcb33899b05ba73276dfb684a20d31caa567b7cb5252d48f896a91b1"},
3107
+ {file = "propcache-0.2.1-py3-none-any.whl", hash = "sha256:52277518d6aae65536e9cea52d4e7fd2f7a66f4aa2d30ed3f2fcea620ace3c54"},
3108
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3109
+ ]
3110
+
3111
  [[package]]
3112
  name = "protobuf"
3113
  version = "5.29.3"
 
4555
  docs = ["Sphinx (>=4.1.2,<4.2.0)", "sphinx-rtd-theme (>=0.5.2,<0.6.0)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
4556
  test = ["aiohttp (>=3.10.5)", "flake8 (>=5.0,<6.0)", "mypy (>=0.800)", "psutil", "pyOpenSSL (>=23.0.0,<23.1.0)", "pycodestyle (>=2.9.0,<2.10.0)"]
4557
 
4558
+ [[package]]
4559
+ name = "voyageai"
4560
+ version = "0.3.2"
4561
+ description = ""
4562
+ optional = false
4563
+ python-versions = "<3.13,>=3.9"
4564
+ groups = ["main"]
4565
+ markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
4566
+ files = [
4567
+ {file = "voyageai-0.3.2-py3-none-any.whl", hash = "sha256:1398d6c6bfb1dd3b484f400713e538f00ce8a335250442b0902c21116d9705a8"},
4568
+ {file = "voyageai-0.3.2.tar.gz", hash = "sha256:bd1b52d26179d91853cbd2a0e52dc95cb0d526760c6c830959e01eb5ff9eaa12"},
4569
+ ]
4570
+
4571
+ [package.dependencies]
4572
+ aiohttp = "*"
4573
+ aiolimiter = "*"
4574
+ numpy = "*"
4575
+ pillow = "*"
4576
+ pydantic = ">=1.10.8"
4577
+ requests = "*"
4578
+ tenacity = "*"
4579
+ tokenizers = ">=0.14.0"
4580
+
4581
  [[package]]
4582
  name = "watchfiles"
4583
  version = "1.0.4"
 
4851
  {file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
4852
  ]
4853
 
4854
+ [[package]]
4855
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4938
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4942
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+ {file = "yarl-1.18.3-py3-none-any.whl", hash = "sha256:b57f4f58099328dfb26c6a771d09fb20dbbae81d20cfb66141251ea063bd101b"},
4944
+ {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
4945
+ ]
4946
+
4947
+ [package.dependencies]
4948
+ idna = ">=2.0"
4949
+ multidict = ">=4.0"
4950
+ propcache = ">=0.2.0"
4951
+
4952
  [[package]]
4953
  name = "zipp"
4954
  version = "3.21.0"
 
4972
 
4973
  [metadata]
4974
  lock-version = "2.1"
4975
+ python-versions = ">=3.10, <3.13"
4976
+ content-hash = "1fadad8dff111d7324b75ce2fe892dd2321c743d771a1d8754e6d8f1bb1b181a"
pyproject.toml CHANGED
@@ -10,7 +10,7 @@ authors = [
10
  {name = "gabhyun kim",email = "[email protected]"}
11
  ]
12
  readme = "README.md"
13
- requires-python = ">=3.10, <4.0"
14
  dependencies = [
15
  "pydantic-ai[logfire] (>=0.0.20,<0.0.21)",
16
  "gitpython (>=3.1.44,<4.0.0)",
@@ -19,7 +19,8 @@ dependencies = [
19
  "pydantic-settings (>=2.7.1,<3.0.0)",
20
  "chromadb (>=0.6.3,<0.7.0)",
21
  "ollama (>=0.4.7,<0.5.0)",
22
- "gradio (>=5.13.1,<6.0.0)"
 
23
  ]
24
 
25
  [tool.poetry]
 
10
  {name = "gabhyun kim",email = "[email protected]"}
11
  ]
12
  readme = "README.md"
13
+ requires-python = ">=3.10, <3.13"
14
  dependencies = [
15
  "pydantic-ai[logfire] (>=0.0.20,<0.0.21)",
16
  "gitpython (>=3.1.44,<4.0.0)",
 
19
  "pydantic-settings (>=2.7.1,<3.0.0)",
20
  "chromadb (>=0.6.3,<0.7.0)",
21
  "ollama (>=0.4.7,<0.5.0)",
22
+ "gradio (>=5.13.1,<6.0.0)",
23
+ "voyageai (>=0.3.2,<0.4.0)"
24
  ]
25
 
26
  [tool.poetry]
src/know_lang_bot/chat_bot/__main__.py CHANGED
@@ -13,7 +13,7 @@ async def test_chat_processing():
13
  )
14
 
15
  result = await process_chat(
16
- "How does the parser handle nested classes?",
17
  collection,
18
  config
19
  )
 
13
  )
14
 
15
  result = await process_chat(
16
+ "How are different quantization methods implemented in the transformers library, and what are the key components required to implement a new quantization method?",
17
  collection,
18
  config
19
  )
src/know_lang_bot/chat_bot/chat_graph.py CHANGED
@@ -5,7 +5,7 @@ from typing import AsyncGenerator, List, Dict, Any, Optional
5
  import chromadb
6
  from pydantic import BaseModel
7
  from pydantic_graph import BaseNode, EndStep, Graph, GraphRunContext, End, HistoryStep
8
- from know_lang_bot.config import AppConfig
9
  from know_lang_bot.utils.fancy_log import FancyLogger
10
  from pydantic_ai import Agent
11
  import logfire
@@ -14,6 +14,8 @@ from enum import Enum
14
  from rich.console import Console
15
  from know_lang_bot.utils.model_provider import create_pydantic_model
16
  from know_lang_bot.models.embeddings import generate_embedding
 
 
17
 
18
  LOG = FancyLogger(__name__)
19
  console = Console()
@@ -148,41 +150,132 @@ Focus on making the question more searchable while preserving its original inten
148
 
149
  @dataclass
150
  class RetrieveContextNode(BaseNode[ChatGraphState, ChatGraphDeps, ChatResult]):
151
- """Node that retrieves relevant code context"""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
152
 
153
  async def run(self, ctx: GraphRunContext[ChatGraphState, ChatGraphDeps]) -> AnswerQuestionNode:
154
  try:
155
- question_embedding = generate_embedding(
156
- input=ctx.state.polished_question or ctx.state.original_question,
157
- config=ctx.deps.config.embedding
158
- )
159
-
160
- results = ctx.deps.collection.query(
161
- query_embeddings=question_embedding,
162
- n_results=ctx.deps.config.chat.max_context_chunks,
163
- include=['metadatas', 'documents', 'distances']
164
- )
165
- logfire.debug('query result: {result}', result=pformat(results))
166
 
167
- relevant_chunks = []
168
- relevant_metadatas = []
 
 
 
 
 
 
 
 
 
 
 
 
169
 
170
- for doc, meta, dist in zip(
171
- results['documents'][0],
172
- results['metadatas'][0],
173
- results['distances'][0]
174
- ):
175
- if dist <= ctx.deps.config.chat.similarity_threshold:
176
- relevant_chunks.append(doc)
177
- relevant_metadatas.append(meta)
178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
179
  ctx.state.retrieved_context = RetrievedContext(
180
  chunks=relevant_chunks,
181
  metadatas=relevant_metadatas,
182
  )
183
 
184
  except Exception as e:
185
- LOG.error(f"Error retrieving context: {e}")
 
 
186
  finally:
187
  return AnswerQuestionNode()
188
 
 
5
  import chromadb
6
  from pydantic import BaseModel
7
  from pydantic_graph import BaseNode, EndStep, Graph, GraphRunContext, End, HistoryStep
8
+ from know_lang_bot.config import AppConfig, RerankerConfig, EmbeddingConfig
9
  from know_lang_bot.utils.fancy_log import FancyLogger
10
  from pydantic_ai import Agent
11
  import logfire
 
14
  from rich.console import Console
15
  from know_lang_bot.utils.model_provider import create_pydantic_model
16
  from know_lang_bot.models.embeddings import generate_embedding
17
+ import voyageai
18
+ from voyageai.object.reranking import RerankingObject
19
 
20
  LOG = FancyLogger(__name__)
21
  console = Console()
 
150
 
151
  @dataclass
152
  class RetrieveContextNode(BaseNode[ChatGraphState, ChatGraphDeps, ChatResult]):
153
+ """Node that retrieves relevant code context using hybrid search: embeddings + reranking"""
154
+ async def _get_initial_chunks(
155
+ self,
156
+ query: str,
157
+ embedding_config: EmbeddingConfig,
158
+ collection: chromadb.Collection,
159
+ n_results: int
160
+ ) -> tuple[List[str], List[Dict], List[float]]:
161
+ """Get initial chunks using embedding search"""
162
+ question_embedding = generate_embedding(
163
+ input=query,
164
+ config=embedding_config
165
+ )
166
+
167
+ results = collection.query(
168
+ query_embeddings=question_embedding,
169
+ n_results=n_results,
170
+ include=['metadatas', 'documents', 'distances']
171
+ )
172
+
173
+ return (
174
+ results['documents'][0],
175
+ results['metadatas'][0],
176
+ results['distances'][0]
177
+ )
178
+
179
+ async def _rerank_chunks(
180
+ self,
181
+ query: str,
182
+ chunks: List[str],
183
+ reranker_config: RerankerConfig,
184
+ ) -> RerankingObject:
185
+ """Rerank chunks using Voyage AI"""
186
+ voyage_client = voyageai.Client()
187
+ return voyage_client.rerank(
188
+ query=query,
189
+ documents=chunks,
190
+ model=reranker_config.model_name,
191
+ top_k=reranker_config.top_k,
192
+ truncation=True
193
+ )
194
+
195
+ def _filter_by_distance(
196
+ self,
197
+ chunks: List[str],
198
+ metadatas: List[Dict],
199
+ distances: List[float],
200
+ threshold: float
201
+ ) -> tuple[List[str], List[Dict]]:
202
+ """Filter chunks by distance threshold"""
203
+ filtered_chunks = []
204
+ filtered_metadatas = []
205
+
206
+ for chunk, meta, dist in zip(chunks, metadatas, distances):
207
+ if dist <= threshold:
208
+ filtered_chunks.append(chunk)
209
+ filtered_metadatas.append(meta)
210
+
211
+ return filtered_chunks, filtered_metadatas
212
 
213
  async def run(self, ctx: GraphRunContext[ChatGraphState, ChatGraphDeps]) -> AnswerQuestionNode:
214
  try:
215
+ # Get query
216
+ query = ctx.state.polished_question or ctx.state.original_question
 
 
 
 
 
 
 
 
 
217
 
218
+ # First pass: Get more candidates using embedding search
219
+ initial_chunks, initial_metadatas, distances = await self._get_initial_chunks(
220
+ query=query,
221
+ embedding_config=ctx.deps.config.embedding,
222
+ collection=ctx.deps.collection,
223
+ n_results=min(ctx.deps.config.chat.max_context_chunks * 2, 20)
224
+ )
225
+
226
+ # Log top k initial results by distance
227
+ top_k_initial = sorted(
228
+ zip(initial_chunks, distances),
229
+ key=lambda x: x[1]
230
+ )[:ctx.deps.config.reranker.top_k]
231
+ logfire.info('top k initial results: {results}', results=top_k_initial)
232
 
233
+ # Only proceed to reranking if we have initial results
234
+ if not initial_chunks:
235
+ LOG.warning("No initial chunks found through embedding search")
236
+ raise Exception("No chunks found through embedding search")
 
 
 
 
237
 
238
+ # Second pass: Rerank the candidates
239
+ try:
240
+ # Second pass: Rerank candidates
241
+ reranking = await self._rerank_chunks(
242
+ query=query,
243
+ chunks=initial_chunks,
244
+ reranker_config=ctx.deps.config.reranker
245
+ )
246
+ logfire.info('reranked results: {results}', results=reranking.results)
247
+
248
+ # Build final context from reranked results
249
+ relevant_chunks = []
250
+ relevant_metadatas = []
251
+
252
+ for result in reranking.results:
253
+ # Only include if score is good enough
254
+ if result.relevance_score >= ctx.deps.config.chat.similarity_threshold:
255
+ relevant_chunks.append(result.document)
256
+ # Get corresponding metadata using original index
257
+ relevant_metadatas.append(initial_metadatas[result.index])
258
+
259
+
260
+ except Exception as e:
261
+ # Fallback to distance-based filtering if reranking fails
262
+ LOG.error(f"Reranking failed, falling back to distance-based filtering: {e}")
263
+ relevant_chunks, relevant_metadatas = self._filter_by_distance(
264
+ chunks=initial_chunks,
265
+ metadatas=initial_metadatas,
266
+ distances=distances,
267
+ threshold=ctx.deps.config.chat.similarity_threshold
268
+ )
269
+
270
  ctx.state.retrieved_context = RetrievedContext(
271
  chunks=relevant_chunks,
272
  metadatas=relevant_metadatas,
273
  )
274
 
275
  except Exception as e:
276
+ LOG.error(f"Error in context retrieval: {e}")
277
+ ctx.state.retrieved_context = RetrievedContext(chunks=[], metadatas=[])
278
+
279
  finally:
280
  return AnswerQuestionNode()
281
 
src/know_lang_bot/config.py CHANGED
@@ -6,6 +6,24 @@ import fnmatch
6
  from know_lang_bot.core.types import ModelProvider
7
  import os
8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  class PathPatterns(BaseSettings):
10
  include: List[str] = Field(
11
  default=["**/*"],
@@ -66,7 +84,7 @@ class EmbeddingConfig(BaseSettings):
66
  default="mxbai-embed-large",
67
  description="Name of the embedding model"
68
  )
69
- provider: ModelProvider = Field(
70
  default=ModelProvider.OLLAMA,
71
  description="Provider for embeddings"
72
  )
@@ -86,10 +104,7 @@ class EmbeddingConfig(BaseSettings):
86
  @field_validator('api_key', mode='after')
87
  @classmethod
88
  def validate_api_key(cls, v: Optional[str], info: ValidationInfo) -> Optional[str]:
89
- """Validate API key is present when required"""
90
- if info.data['provider'] in [ModelProvider.OPENAI] and not v:
91
- raise ValueError(f"API key required for {info.data['provider']}")
92
- return v
93
 
94
  class LLMConfig(BaseSettings):
95
  model_name: str = Field(
@@ -112,16 +127,7 @@ class LLMConfig(BaseSettings):
112
  @field_validator('api_key', mode='after')
113
  @classmethod
114
  def validate_api_key(cls, v: Optional[str], info: ValidationInfo) -> Optional[str]:
115
- """Validate API key is present when required"""
116
- if info.data['model_provider'] in [ModelProvider.OPENAI, ModelProvider.ANTHROPIC]:
117
- if not v:
118
- raise ValueError(f"API key required for {info.data['model_provider']}")
119
- elif info.data['model_provider'] == ModelProvider.ANTHROPIC:
120
- os.environ["ANTHROPIC_API_KEY"] = v
121
- elif info.data['model_provider'] == ModelProvider.OPENAI:
122
- os.environ["OPENAI_API_KEY"] = v
123
-
124
- return v
125
 
126
  class DBConfig(BaseSettings):
127
  persist_directory: Path = Field(
@@ -137,6 +143,29 @@ class DBConfig(BaseSettings):
137
  description="Root directory of the codebase to analyze"
138
  )
139
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
140
  class ChatConfig(BaseSettings):
141
  max_context_chunks: int = Field(
142
  default=5,
@@ -164,6 +193,7 @@ class AppConfig(BaseSettings):
164
 
165
  llm: LLMConfig = Field(default_factory=LLMConfig)
166
  evaluator: LLMConfig = Field(default_factory=LLMConfig)
 
167
  db: DBConfig = Field(default_factory=DBConfig)
168
  parser: ParserConfig = Field(default_factory=ParserConfig)
169
  chat: ChatConfig = Field(default_factory=ChatConfig)
 
6
  from know_lang_bot.core.types import ModelProvider
7
  import os
8
 
9
+ def _validate_api_key(v: Optional[str], info: ValidationInfo) -> Optional[str]:
10
+ """Validate API key is present when required"""
11
+ if info.data['model_provider'] in [
12
+ ModelProvider.OPENAI,
13
+ ModelProvider.ANTHROPIC,
14
+ ModelProvider.VOYAGE
15
+ ]:
16
+ if not v:
17
+ raise ValueError(f"API key required for {info.data['model_provider']}")
18
+ elif info.data['model_provider'] == ModelProvider.ANTHROPIC:
19
+ os.environ["ANTHROPIC_API_KEY"] = v
20
+ elif info.data['model_provider'] == ModelProvider.OPENAI:
21
+ os.environ["OPENAI_API_KEY"] = v
22
+ elif info.data['model_provider'] == ModelProvider.VOYAGE:
23
+ os.environ["VOYAGE_API_KEY"] = v
24
+
25
+ return v
26
+
27
  class PathPatterns(BaseSettings):
28
  include: List[str] = Field(
29
  default=["**/*"],
 
84
  default="mxbai-embed-large",
85
  description="Name of the embedding model"
86
  )
87
+ model_provider: ModelProvider = Field(
88
  default=ModelProvider.OLLAMA,
89
  description="Provider for embeddings"
90
  )
 
104
  @field_validator('api_key', mode='after')
105
  @classmethod
106
  def validate_api_key(cls, v: Optional[str], info: ValidationInfo) -> Optional[str]:
107
+ return _validate_api_key(v, info)
 
 
 
108
 
109
  class LLMConfig(BaseSettings):
110
  model_name: str = Field(
 
127
  @field_validator('api_key', mode='after')
128
  @classmethod
129
  def validate_api_key(cls, v: Optional[str], info: ValidationInfo) -> Optional[str]:
130
+ return _validate_api_key(v, info)
 
 
 
 
 
 
 
 
 
131
 
132
  class DBConfig(BaseSettings):
133
  persist_directory: Path = Field(
 
143
  description="Root directory of the codebase to analyze"
144
  )
145
 
146
+ class RerankerConfig(BaseSettings):
147
+ model_name: str = Field(
148
+ default="reranker",
149
+ description="Name of the reranker model to use"
150
+ )
151
+ model_provider: str = Field(
152
+ default=ModelProvider.OLLAMA,
153
+ description="Model provider (anthropic, openai, ollama, etc)"
154
+ )
155
+ api_key: Optional[str] = Field(
156
+ default=None,
157
+ description="API key for the model provider"
158
+ )
159
+ top_k: int = Field(
160
+ default=4,
161
+ description="Number of most relevant documents to return from reranking"
162
+ )
163
+
164
+ @field_validator('api_key', mode='after')
165
+ @classmethod
166
+ def validate_api_key(cls, v: Optional[str], info: ValidationInfo) -> Optional[str]:
167
+ return _validate_api_key(v, info)
168
+
169
  class ChatConfig(BaseSettings):
170
  max_context_chunks: int = Field(
171
  default=5,
 
193
 
194
  llm: LLMConfig = Field(default_factory=LLMConfig)
195
  evaluator: LLMConfig = Field(default_factory=LLMConfig)
196
+ reranker: RerankerConfig = Field(default_factory=RerankerConfig)
197
  db: DBConfig = Field(default_factory=DBConfig)
198
  parser: ParserConfig = Field(default_factory=ParserConfig)
199
  chat: ChatConfig = Field(default_factory=ChatConfig)
src/know_lang_bot/core/types.py CHANGED
@@ -24,4 +24,5 @@ class ModelProvider(str, Enum):
24
  ANTHROPIC = "anthropic"
25
  OLLAMA = "ollama"
26
  HUGGINGFACE = "huggingface"
 
27
  TESTING = "testing"
 
24
  ANTHROPIC = "anthropic"
25
  OLLAMA = "ollama"
26
  HUGGINGFACE = "huggingface"
27
+ VOYAGE = "voyage"
28
  TESTING = "testing"
src/know_lang_bot/models/embeddings.py CHANGED
@@ -52,13 +52,13 @@ def generate_embedding(
52
  inputs = [input] if is_single_input else input
53
 
54
  try:
55
- if config.provider == ModelProvider.OLLAMA:
56
  embeddings = _process_ollama_batch(inputs, config.model_name)
57
- elif config.provider == ModelProvider.OPENAI:
58
  openai.api_key = config.api_key
59
  embeddings = _process_openai_batch(inputs, config.model_name)
60
  else:
61
- raise ValueError(f"Unsupported provider: {config.provider}")
62
 
63
  # Return single embedding for single input
64
  return embeddings[0] if is_single_input else embeddings
 
52
  inputs = [input] if is_single_input else input
53
 
54
  try:
55
+ if config.model_provider == ModelProvider.OLLAMA:
56
  embeddings = _process_ollama_batch(inputs, config.model_name)
57
+ elif config.model_provider == ModelProvider.OPENAI:
58
  openai.api_key = config.api_key
59
  embeddings = _process_openai_batch(inputs, config.model_name)
60
  else:
61
+ raise ValueError(f"Unsupported provider: {config.model_provider}")
62
 
63
  # Return single embedding for single input
64
  return embeddings[0] if is_single_input else embeddings