voyage reranking algorithm implemented
Browse files- .env.example +1 -1
- poetry.lock +596 -42
- pyproject.toml +3 -2
- src/know_lang_bot/chat_bot/__main__.py +1 -1
- src/know_lang_bot/chat_bot/chat_graph.py +117 -24
- src/know_lang_bot/config.py +45 -15
- src/know_lang_bot/core/types.py +1 -0
- src/know_lang_bot/models/embeddings.py +3 -3
.env.example
CHANGED
@@ -3,6 +3,6 @@ LLM__MODEL_PROVIDER=ollama
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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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-
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EMBEDDING__MODEL_NAME=text-embedding-ada-002
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EMBEDDING__API_KEY=your_api_key
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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__MODEL_PROVIDER=openai
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EMBEDDING__MODEL_NAME=text-embedding-ada-002
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EMBEDDING__API_KEY=your_api_key
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poetry.lock
CHANGED
@@ -13,6 +13,148 @@ files = [
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{file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"},
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]
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[[package]]
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name = "annotated-types"
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version = "0.7.0"
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@@ -96,49 +238,39 @@ typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
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tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
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[[package]]
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name = "
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version = "0.
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description = "
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optional = false
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-
python-versions = ">=3.
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groups = ["main"]
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markers = "python_version >= \"3.
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files = [
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{file = "
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{file = "
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-
{file = "audioop_lts-0.2.1-cp313-abi3-macosx_11_0_arm64.whl", hash = "sha256:4a8dd6a81770f6ecf019c4b6d659e000dc26571b273953cef7cd1d5ce2ff3ae6"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d1cd3c0b6f2ca25c7d2b1c3adeecbe23e65689839ba73331ebc7d893fcda7ffe"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ff3f97b3372c97782e9c6d3d7fdbe83bce8f70de719605bd7ee1839cd1ab360a"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a351af79edefc2a1bd2234bfd8b339935f389209943043913a919df4b0f13300"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2aeb6f96f7f6da80354330470b9134d81b4cf544cdd1c549f2f45fe964d28059"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c589f06407e8340e81962575fcffbba1e92671879a221186c3d4662de9fe804e"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:fbae5d6925d7c26e712f0beda5ed69ebb40e14212c185d129b8dfbfcc335eb48"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_i686.whl", hash = "sha256:d2d5434717f33117f29b5691fbdf142d36573d751716249a288fbb96ba26a281"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_ppc64le.whl", hash = "sha256:f626a01c0a186b08f7ff61431c01c055961ee28769591efa8800beadd27a2959"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_s390x.whl", hash = "sha256:05da64e73837f88ee5c6217d732d2584cf638003ac72df124740460531e95e47"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:56b7a0a4dba8e353436f31a932f3045d108a67b5943b30f85a5563f4d8488d77"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-win32.whl", hash = "sha256:6e899eb8874dc2413b11926b5fb3857ec0ab55222840e38016a6ba2ea9b7d5e3"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-win_amd64.whl", hash = "sha256:64562c5c771fb0a8b6262829b9b4f37a7b886c01b4d3ecdbae1d629717db08b4"},
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-
{file = "audioop_lts-0.2.1-cp313-abi3-win_arm64.whl", hash = "sha256:c45317debeb64002e980077642afbd977773a25fa3dfd7ed0c84dccfc1fafcb0"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:3827e3fce6fee4d69d96a3d00cd2ab07f3c0d844cb1e44e26f719b34a5b15455"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:161249db9343b3c9780ca92c0be0d1ccbfecdbccac6844f3d0d44b9c4a00a17f"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5b7b4ff9de7a44e0ad2618afdc2ac920b91f4a6d3509520ee65339d4acde5abf"},
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{file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:72e37f416adb43b0ced93419de0122b42753ee74e87070777b53c5d2241e7fab"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:534ce808e6bab6adb65548723c8cbe189a3379245db89b9d555c4210b4aaa9b6"},
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{file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d2de9b6fb8b1cf9f03990b299a9112bfdf8b86b6987003ca9e8a6c4f56d39543"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f24865991b5ed4b038add5edbf424639d1358144f4e2a3e7a84bc6ba23e35074"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2bdb3b7912ccd57ea53197943f1bbc67262dcf29802c4a6df79ec1c715d45a78"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:120678b208cca1158f0a12d667af592e067f7a50df9adc4dc8f6ad8d065a93fb"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:54cd4520fc830b23c7d223693ed3e1b4d464997dd3abc7c15dce9a1f9bd76ab2"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:d6bd20c7a10abcb0fb3d8aaa7508c0bf3d40dfad7515c572014da4b979d3310a"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:f0ed1ad9bd862539ea875fb339ecb18fcc4148f8d9908f4502df28f94d23491a"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e1af3ff32b8c38a7d900382646e91f2fc515fd19dea37e9392275a5cbfdbff63"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-win32.whl", hash = "sha256:f51bb55122a89f7a0817d7ac2319744b4640b5b446c4c3efcea5764ea99ae509"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-win_amd64.whl", hash = "sha256:f0f2f336aa2aee2bce0b0dcc32bbba9178995454c7b979cf6ce086a8801e14c7"},
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-
{file = "audioop_lts-0.2.1-cp313-cp313t-win_arm64.whl", hash = "sha256:78bfb3703388c780edf900be66e07de5a3d4105ca8e8720c5c4d67927e0b15d0"},
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-
{file = "audioop_lts-0.2.1.tar.gz", hash = "sha256:e81268da0baa880431b68b1308ab7257eb33f356e57a5f9b1f915dfb13dd1387"},
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]
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[[package]]
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name = "backoff"
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version = "2.2.1"
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{file = "flatbuffers-25.1.24.tar.gz", hash = "sha256:e0f7b7d806c0abdf166275492663130af40c11f89445045fbef0aa3c9a8643ad"},
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[[package]]
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name = "fsspec"
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version = "2024.12.0"
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@@ -850,7 +1085,6 @@ files = [
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[package.dependencies]
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aiofiles = ">=22.0,<24.0"
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anyio = ">=3.0,<5.0"
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-
audioop-lts = {version = "<1.0", markers = "python_version >= \"3.13\""}
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fastapi = ">=0.115.2,<1.0"
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ffmpy = "*"
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gradio-client = "1.6.0"
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gmpy = ["gmpy2 (>=2.1.0a4)"]
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tests = ["pytest (>=4.6)"]
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[[package]]
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name = "mypy-extensions"
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version = "1.0.0"
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sentry = ["django", "sentry-sdk"]
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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"]
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[[package]]
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name = "protobuf"
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version = "5.29.3"
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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)"]
|
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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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[[package]]
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name = "watchfiles"
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version = "1.0.4"
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@@ -4395,6 +4851,104 @@ files = [
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{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, <
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4422 |
-
content-hash = "
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|
13 |
{file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"},
|
14 |
]
|
15 |
|
16 |
+
[[package]]
|
17 |
+
name = "aiohappyeyeballs"
|
18 |
+
version = "2.4.4"
|
19 |
+
description = "Happy Eyeballs for asyncio"
|
20 |
+
optional = false
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21 |
+
python-versions = ">=3.8"
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22 |
+
groups = ["main"]
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23 |
+
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
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+
files = [
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{file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
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27 |
+
]
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28 |
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29 |
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[[package]]
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30 |
+
name = "aiohttp"
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31 |
+
version = "3.11.11"
|
32 |
+
description = "Async http client/server framework (asyncio)"
|
33 |
+
optional = false
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34 |
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python-versions = ">=3.9"
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35 |
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groups = ["main"]
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36 |
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markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
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939 |
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945 |
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946 |
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947 |
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948 |
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949 |
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]
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950 |
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951 |
[[package]]
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952 |
name = "fsspec"
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953 |
version = "2024.12.0"
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|
|
1085 |
[package.dependencies]
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1086 |
aiofiles = ">=22.0,<24.0"
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1087 |
anyio = ">=3.0,<5.0"
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|
|
1088 |
fastapi = ">=0.115.2,<1.0"
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1089 |
ffmpy = "*"
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1090 |
gradio-client = "1.6.0"
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|
|
1972 |
gmpy = ["gmpy2 (>=2.1.0a4)"]
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1973 |
tests = ["pytest (>=4.6)"]
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1974 |
|
1975 |
+
[[package]]
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1976 |
+
name = "multidict"
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1977 |
+
version = "6.1.0"
|
1978 |
+
description = "multidict implementation"
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1979 |
+
optional = false
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1980 |
+
python-versions = ">=3.8"
|
1981 |
+
groups = ["main"]
|
1982 |
+
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
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1983 |
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files = [
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1984 |
+
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1985 |
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1986 |
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1987 |
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1988 |
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1989 |
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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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{file = "yarl-1.18.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88a19f62ff30117e706ebc9090b8ecc79aeb77d0b1f5ec10d2d27a12bc9f66d0"},
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{file = "yarl-1.18.3-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1a74a13a4c857a84a845505fd2d68e54826a2cd01935a96efb1e9d86c728e186"},
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{file = "yarl-1.18.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41f7ce59d6ee7741af71d82020346af364949314ed3d87553763a2df1829cc58"},
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{file = "yarl-1.18.3-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f52a265001d830bc425f82ca9eabda94a64a4d753b07d623a9f2863fde532b53"},
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{file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:82123d0c954dc58db301f5021a01854a85bf1f3bb7d12ae0c01afc414a882ca2"},
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{file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:2ec9bbba33b2d00999af4631a3397d1fd78290c48e2a3e52d8dd72db3a067ac8"},
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{file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:fbd6748e8ab9b41171bb95c6142faf068f5ef1511935a0aa07025438dd9a9bc1"},
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4922 |
+
{file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:877d209b6aebeb5b16c42cbb377f5f94d9e556626b1bfff66d7b0d115be88d0a"},
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{file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:b464c4ab4bfcb41e3bfd3f1c26600d038376c2de3297760dfe064d2cb7ea8e10"},
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4924 |
+
{file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8d39d351e7faf01483cc7ff7c0213c412e38e5a340238826be7e0e4da450fdc8"},
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+
{file = "yarl-1.18.3-cp313-cp313-win32.whl", hash = "sha256:61ee62ead9b68b9123ec24bc866cbef297dd266175d53296e2db5e7f797f902d"},
|
4926 |
+
{file = "yarl-1.18.3-cp313-cp313-win_amd64.whl", hash = "sha256:578e281c393af575879990861823ef19d66e2b1d0098414855dd367e234f5b3c"},
|
4927 |
+
{file = "yarl-1.18.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:61e5e68cb65ac8f547f6b5ef933f510134a6bf31bb178be428994b0cb46c2a04"},
|
4928 |
+
{file = "yarl-1.18.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fe57328fbc1bfd0bd0514470ac692630f3901c0ee39052ae47acd1d90a436719"},
|
4929 |
+
{file = "yarl-1.18.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a440a2a624683108a1b454705ecd7afc1c3438a08e890a1513d468671d90a04e"},
|
4930 |
+
{file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09c7907c8548bcd6ab860e5f513e727c53b4a714f459b084f6580b49fa1b9cee"},
|
4931 |
+
{file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b4f6450109834af88cb4cc5ecddfc5380ebb9c228695afc11915a0bf82116789"},
|
4932 |
+
{file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a9ca04806f3be0ac6d558fffc2fdf8fcef767e0489d2684a21912cc4ed0cd1b8"},
|
4933 |
+
{file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77a6e85b90a7641d2e07184df5557132a337f136250caafc9ccaa4a2a998ca2c"},
|
4934 |
+
{file = "yarl-1.18.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6333c5a377c8e2f5fae35e7b8f145c617b02c939d04110c76f29ee3676b5f9a5"},
|
4935 |
+
{file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:0b3c92fa08759dbf12b3a59579a4096ba9af8dd344d9a813fc7f5070d86bbab1"},
|
4936 |
+
{file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:4ac515b860c36becb81bb84b667466885096b5fc85596948548b667da3bf9f24"},
|
4937 |
+
{file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:045b8482ce9483ada4f3f23b3774f4e1bf4f23a2d5c912ed5170f68efb053318"},
|
4938 |
+
{file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:a4bb030cf46a434ec0225bddbebd4b89e6471814ca851abb8696170adb163985"},
|
4939 |
+
{file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:54d6921f07555713b9300bee9c50fb46e57e2e639027089b1d795ecd9f7fa910"},
|
4940 |
+
{file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:1d407181cfa6e70077df3377938c08012d18893f9f20e92f7d2f314a437c30b1"},
|
4941 |
+
{file = "yarl-1.18.3-cp39-cp39-win32.whl", hash = "sha256:ac36703a585e0929b032fbaab0707b75dc12703766d0b53486eabd5139ebadd5"},
|
4942 |
+
{file = "yarl-1.18.3-cp39-cp39-win_amd64.whl", hash = "sha256:ba87babd629f8af77f557b61e49e7c7cac36f22f871156b91e10a6e9d4f829e9"},
|
4943 |
+
{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, <
|
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
|
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"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
152 |
|
153 |
async def run(self, ctx: GraphRunContext[ChatGraphState, ChatGraphDeps]) -> AnswerQuestionNode:
|
154 |
try:
|
155 |
-
|
156 |
-
|
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 |
-
|
168 |
-
|
|
|
|
|
|
|
|
|
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|
169 |
|
170 |
-
|
171 |
-
|
172 |
-
|
173 |
-
|
174 |
-
):
|
175 |
-
if dist <= ctx.deps.config.chat.similarity_threshold:
|
176 |
-
relevant_chunks.append(doc)
|
177 |
-
relevant_metadatas.append(meta)
|
178 |
|
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|
|
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|
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
|
|
|
|
|
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 |
-
|
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 |
-
|
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 |
-
|
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.
|
56 |
embeddings = _process_ollama_batch(inputs, config.model_name)
|
57 |
-
elif config.
|
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.
|
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
|