This commit is contained in:
parent
7e7036c490
commit
f63dc7148a
324
poetry.lock
generated
324
poetry.lock
generated
@ -617,14 +617,14 @@ numpy = "*"
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[[package]]
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name = "chromadb"
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version = "0.6.3"
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version = "0.5.23"
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description = "Chroma."
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optional = false
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python-versions = ">=3.9"
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python-versions = ">=3.8"
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groups = ["main"]
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files = [
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{file = "chromadb-0.6.3-py3-none-any.whl", hash = "sha256:4851258489a3612b558488d98d09ae0fe0a28d5cad6bd1ba64b96fdc419dc0e5"},
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{file = "chromadb-0.6.3.tar.gz", hash = "sha256:c8f34c0b704b9108b04491480a36d42e894a960429f87c6516027b5481d59ed3"},
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{file = "chromadb-0.5.23-py3-none-any.whl", hash = "sha256:ffe5bdd7276d12cb682df0d38a13aa37573e6a3678e71889ac45f539ae05ad7e"},
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{file = "chromadb-0.5.23.tar.gz", hash = "sha256:360a12b9795c5a33cb1f839d14410ccbde662ef1accd36153b0ae22312edabd1"},
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]
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[package.dependencies]
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@ -651,7 +651,7 @@ pypika = ">=0.48.9"
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PyYAML = ">=6.0.0"
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rich = ">=10.11.0"
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tenacity = ">=8.2.3"
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tokenizers = ">=0.13.2"
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tokenizers = ">=0.13.2,<=0.20.3"
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tqdm = ">=4.65.0"
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typer = ">=0.9.0"
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typing_extensions = ">=4.5.0"
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@ -710,7 +710,7 @@ description = "cryptography is a package which provides cryptographic recipes an
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optional = false
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python-versions = ">=3.7"
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groups = ["main"]
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markers = "python_full_version < \"3.12.4\""
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markers = "python_version < \"3.12\""
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files = [
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@ -761,7 +761,7 @@ description = "cryptography is a package which provides cryptographic recipes an
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optional = false
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python-versions = "!=3.9.0,!=3.9.1,>=3.7"
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groups = ["main"]
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markers = "python_full_version >= \"3.12.4\""
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markers = "python_version >= \"3.12\""
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@ -1171,7 +1171,7 @@ description = "Lightweight in-process concurrent programming"
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optional = false
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python-versions = ">=3.7"
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groups = ["main"]
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markers = "python_version <= \"3.13\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"
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markers = "python_version < \"3.14\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"
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files = [
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{file = "greenlet-3.1.1-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:0bbae94a29c9e5c7e4a2b7f0aae5c17e8e90acbfd3bf6270eeba60c39fce3563"},
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@ -1696,6 +1696,27 @@ openai = ["langchain-openai"]
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together = ["langchain-together"]
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xai = ["langchain-xai"]
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[[package]]
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name = "langchain-chroma"
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version = "0.1.4"
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description = "An integration package connecting Chroma and LangChain"
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optional = false
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python-versions = "<4,>=3.8.1"
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groups = ["main"]
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files = [
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{file = "langchain_chroma-0.1.4-py3-none-any.whl", hash = "sha256:2877b284fc736bfd31628aa542ed0f5410c3cdc63ad2c670cb67fc360b4a236a"},
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{file = "langchain_chroma-0.1.4.tar.gz", hash = "sha256:5963a79bf72af0f72019084bbc1e610d03ba33ec2df9a4b47b27c0132aa533fb"},
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]
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[package.dependencies]
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chromadb = ">=0.4.0,<0.5.4 || >0.5.4,<0.5.5 || >0.5.5,<0.6.0"
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fastapi = ">=0.95.2,<1"
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langchain-core = {version = ">=0.1.40,<0.4", markers = "python_version >= \"3.9\""}
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numpy = [
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{version = ">=1,<2", markers = "python_version < \"3.12\""},
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{version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
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]
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[[package]]
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name = "langchain-community"
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version = "0.3.20"
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@ -2320,124 +2341,48 @@ twitter = ["twython"]
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[[package]]
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name = "numpy"
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version = "2.0.2"
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version = "1.26.4"
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description = "Fundamental package for array computing in Python"
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optional = false
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python-versions = ">=3.9"
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groups = ["main"]
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{file = "tokenizers-0.20.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bee8f53b2594749f4460d53253bae55d718f04e9b633efa0f5df8938bd98e4f0"},
|
||||
{file = "tokenizers-0.20.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:938441babf3e5720e4459e306ef2809fb267680df9d1ff2873458b22aef60248"},
|
||||
{file = "tokenizers-0.20.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:7310ab23d7b0caebecc0e8be11a1146f320f5f07284000f6ea54793e83de1b75"},
|
||||
{file = "tokenizers-0.20.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:16121eb030a2b13094cfec936b0c12e8b4063c5f839591ea7d0212336d8f9921"},
|
||||
{file = "tokenizers-0.20.3-cp39-none-win32.whl", hash = "sha256:401cc21ef642ee235985d747f65e18f639464d377c70836c9003df208d582064"},
|
||||
{file = "tokenizers-0.20.3-cp39-none-win_amd64.whl", hash = "sha256:7498f3ea7746133335a6adb67a77cf77227a8b82c8483f644a2e5f86fea42b8d"},
|
||||
{file = "tokenizers-0.20.3-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:e919f2e3e68bb51dc31de4fcbbeff3bdf9c1cad489044c75e2b982a91059bd3c"},
|
||||
{file = "tokenizers-0.20.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:b8e9608f2773996cc272156e305bd79066163a66b0390fe21750aff62df1ac07"},
|
||||
{file = "tokenizers-0.20.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39270a7050deaf50f7caff4c532c01b3c48f6608d42b3eacdebdc6795478c8df"},
|
||||
{file = "tokenizers-0.20.3-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e005466632b1c5d2d2120f6de8aa768cc9d36cd1ab7d51d0c27a114c91a1e6ee"},
|
||||
{file = "tokenizers-0.20.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a07962340b36189b6c8feda552ea1bfeee6cf067ff922a1d7760662c2ee229e5"},
|
||||
{file = "tokenizers-0.20.3-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:55046ad3dd5f2b3c67501fcc8c9cbe3e901d8355f08a3b745e9b57894855f85b"},
|
||||
{file = "tokenizers-0.20.3-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:efcf0eb939988b627558aaf2b9dc3e56d759cad2e0cfa04fcab378e4b48fc4fd"},
|
||||
{file = "tokenizers-0.20.3-pp37-pypy37_pp73-macosx_10_12_x86_64.whl", hash = "sha256:f3558a7ae6a6d38a77dfce12172a1e2e1bf3e8871e744a1861cd7591ea9ebe24"},
|
||||
{file = "tokenizers-0.20.3-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d53029fe44bc70c3ff14ef512460a0cf583495a0f8e2f4b70e26eb9438e38a9"},
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||||
{file = "tokenizers-0.20.3-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:57a2a56397b2bec5a629b516b23f0f8a3e4f978c7488d4a299980f8375954b85"},
|
||||
{file = "tokenizers-0.20.3-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1e5bfaae740ef9ece000f8a07e78ac0e2b085c5ce9648f8593ddf0243c9f76d"},
|
||||
{file = "tokenizers-0.20.3-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:fbaf3ea28fedfb2283da60e710aff25492e795a7397cad8a50f1e079b65a5a70"},
|
||||
{file = "tokenizers-0.20.3-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:c47c037116310dc976eb96b008e41b9cfaba002ed8005848d4d632ee0b7ba9ae"},
|
||||
{file = "tokenizers-0.20.3-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:c31751f0721f58f5e19bb27c1acc259aeff860d8629c4e1a900b26a1979ada8e"},
|
||||
{file = "tokenizers-0.20.3-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:c697cbd3be7a79ea250ea5f380d6f12e534c543cfb137d5c734966b3ee4f34cc"},
|
||||
{file = "tokenizers-0.20.3-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b48971b88ef9130bf35b41b35fd857c3c4dae4a9cd7990ebc7fc03e59cc92438"},
|
||||
{file = "tokenizers-0.20.3-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4e615de179bbe060ab33773f0d98a8a8572b5883dd7dac66c1de8c056c7e748c"},
|
||||
{file = "tokenizers-0.20.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da1ec842035ed9999c62e45fbe0ff14b7e8a7e02bb97688cc6313cf65e5cd755"},
|
||||
{file = "tokenizers-0.20.3-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:6ee4954c1dd23aadc27958dad759006e71659d497dcb0ef0c7c87ea992c16ebd"},
|
||||
{file = "tokenizers-0.20.3-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:3eda46ca402751ec82553a321bf35a617b76bbed7586e768c02ccacbdda94d6d"},
|
||||
{file = "tokenizers-0.20.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:de082392a85eb0055cc055c535bff2f0cc15d7a000bdc36fbf601a0f3cf8507a"},
|
||||
{file = "tokenizers-0.20.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:c3db46cc0647bfd88263afdb739b92017a02a87ee30945cb3e86c7e25c7c9917"},
|
||||
{file = "tokenizers-0.20.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a292392f24ab9abac5cfa8197e5a6208f2e43723420217e1ceba0b4ec77816ac"},
|
||||
{file = "tokenizers-0.20.3-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8dcd91f4e60f62b20d83a87a84fe062035a1e3ff49a8c2bbdeb2d441c8e311f4"},
|
||||
{file = "tokenizers-0.20.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:900991a2b8ee35961b1095db7e265342e0e42a84c1a594823d5ee9f8fb791958"},
|
||||
{file = "tokenizers-0.20.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:5a8d8261ca2133d4f98aa9627c748189502b3787537ba3d7e2beb4f7cfc5d627"},
|
||||
{file = "tokenizers-0.20.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:c4fd4d71e6deb6ddf99d8d0eab87d1d16f635898906e631914a9bae8ae9f2cfb"},
|
||||
{file = "tokenizers-0.20.3.tar.gz", hash = "sha256:2278b34c5d0dd78e087e1ca7f9b1dcbf129d80211afa645f214bd6e051037539"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -4831,4 +4873,4 @@ cffi = ["cffi (>=1.11)"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.9,<4.0"
|
||||
content-hash = "80cdaf7087c769b58f57aaded008d64788774e8e6a7fe49db9540831c8546649"
|
||||
content-hash = "859e55e8e7ac53bafd53b793453da20ed3803fea94dc70d2452e72130b3f1248"
|
||||
|
||||
@ -5,7 +5,15 @@ description = "Simple RAG implementation for use with neovim"
|
||||
authors = [{ name = "Alex Selimov", email = "alex@alexselimov.com" }]
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.9,<4.0"
|
||||
dependencies = ["langchain (>=0.3.21,<0.4.0)", "ollama (>=0.4.7,<0.5.0)", "langchain-community (>=0.3.20,<0.4.0)", "langchain-ollama (>=0.2.3,<0.3.0)", "chromadb (>=0.6.3,<0.7.0)", "unstructured (>=0.17.2,<0.18.0)"]
|
||||
dependencies = [
|
||||
"langchain (>=0.3.21,<0.4.0)",
|
||||
"ollama (>=0.4.7,<0.5.0)",
|
||||
"langchain-community (>=0.3.20,<0.4.0)",
|
||||
"langchain-ollama (>=0.2.3,<0.3.0)",
|
||||
"chromadb (>=0.4.0,<0.6.0)",
|
||||
"unstructured (>=0.17.2,<0.18.0)",
|
||||
"langchain-chroma (>=0.1.0,<0.2.0)"
|
||||
]
|
||||
|
||||
[tool.poetry]
|
||||
packages = [{ include = "code_rag", from = "src" }]
|
||||
|
||||
39
src/code_rag/ollama_wrapper.py
Normal file
39
src/code_rag/ollama_wrapper.py
Normal file
@ -0,0 +1,39 @@
|
||||
from langchain_ollama import OllamaEmbeddings, OllamaLLM
|
||||
|
||||
|
||||
class OllamaWrapper:
|
||||
def __init__(
|
||||
self,
|
||||
ollama_url="http://127.0.0.1:11434",
|
||||
embedding_model="nomic-embed-text",
|
||||
llm_model="llama3.2",
|
||||
):
|
||||
self.embedding_model = embedding_model
|
||||
self.llm_model = llm_model
|
||||
self._embeddings = None
|
||||
self._llm = None
|
||||
self._ollama_url = ollama_url
|
||||
self._chat = []
|
||||
|
||||
@property
|
||||
def embeddings(self):
|
||||
if self._embeddings is None:
|
||||
self._embeddings = OllamaEmbeddings(
|
||||
model=self.embedding_model, base_url=self._ollama_url
|
||||
)
|
||||
return self._embeddings
|
||||
|
||||
@property
|
||||
def llm(self):
|
||||
if self._llm is None:
|
||||
self._llm = OllamaLLM(model=self.llm_model, base_url=self._ollama_url)
|
||||
return self._llm
|
||||
|
||||
def update_models(self, embedding_model=None, llm_model=None):
|
||||
"""Update model names and reset clients"""
|
||||
if embedding_model:
|
||||
self.embedding_model = embedding_model
|
||||
self._embeddings = None
|
||||
if llm_model:
|
||||
self.llm_model = llm_model
|
||||
self._llm = None
|
||||
@ -2,20 +2,30 @@ import os
|
||||
import uuid
|
||||
from langchain_community.document_loaders import DirectoryLoader, TextLoader
|
||||
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_community.llms import Ollama
|
||||
from langchain.chains import RetrievalQA
|
||||
from langchain.prompts import PromptTemplate
|
||||
|
||||
from code_rag.doc_tracker import DocumentTracker
|
||||
from code_rag.ollama_wrapper import OllamaWrapper
|
||||
|
||||
|
||||
class RAG:
|
||||
def __init__(self, docs_dir, db_dir, tracker_file):
|
||||
def __init__(
|
||||
self,
|
||||
docs_dir,
|
||||
db_dir,
|
||||
tracker_file,
|
||||
ollama_url="127.0.0.1",
|
||||
embedding_model="nomic-embed-text",
|
||||
llm_model="llama3.2",
|
||||
):
|
||||
self.docs_dir = docs_dir
|
||||
self.db_dir = db_dir
|
||||
self.tracker = DocumentTracker(tracker_file)
|
||||
self.ollama = OllamaWrapper(
|
||||
ollama_url, embedding_model=embedding_model, llm_model=llm_model
|
||||
)
|
||||
|
||||
def process_documents(self, files, text_splitter):
|
||||
"""Process document files into chunks with tracking metadata"""
|
||||
@ -61,7 +71,9 @@ class RAG:
|
||||
)
|
||||
|
||||
# Create embeddings
|
||||
embeddings = OllamaEmbeddings(model="nomic-embed-text")
|
||||
print("Before embedding")
|
||||
embeddings = self.ollama.embeddings
|
||||
print("after embedding")
|
||||
|
||||
if force_refresh:
|
||||
print("Force refresh: Processing all documents")
|
||||
@ -69,6 +81,14 @@ class RAG:
|
||||
loader = DirectoryLoader(self.docs_dir, glob=f"**/*{extension}")
|
||||
all_documents = loader.load()
|
||||
|
||||
if not all_documents:
|
||||
print("No documents found to process")
|
||||
# Create an empty vector store
|
||||
vectorstore = Chroma(
|
||||
persist_directory=self.db_dir, embedding_function=embeddings
|
||||
)
|
||||
return vectorstore
|
||||
|
||||
# Add unique IDs to each document
|
||||
for doc in all_documents:
|
||||
doc.metadata["source"] = os.path.abspath(doc.metadata["source"])
|
||||
@ -101,9 +121,6 @@ class RAG:
|
||||
vectorstore = Chroma.from_documents(
|
||||
documents=chunks, embedding=embeddings, persist_directory=self.db_dir
|
||||
)
|
||||
vectorstore.persist()
|
||||
print(f"Created new vector database at {self.db_dir}")
|
||||
|
||||
return vectorstore
|
||||
|
||||
# Get changes since last update
|
||||
@ -114,7 +131,8 @@ class RAG:
|
||||
# Load existing vector store if available
|
||||
if os.path.exists(self.db_dir):
|
||||
return Chroma(
|
||||
persist_directory=self.db_dir, embedding_function=embeddings
|
||||
persist_directory=self.db_dir,
|
||||
embedding_function=self.ollama.embeddings,
|
||||
)
|
||||
else:
|
||||
print("No vector database exists. Creating from all documents...")
|
||||
@ -128,7 +146,7 @@ class RAG:
|
||||
# Load existing vector store if it exists
|
||||
if os.path.exists(self.db_dir):
|
||||
vectorstore = Chroma(
|
||||
persist_directory=self.db_dir, embedding_function=embeddings
|
||||
persist_directory=self.db_dir, embedding_function=self.ollama.embeddings
|
||||
)
|
||||
|
||||
# 1. Handle deleted documents
|
||||
@ -174,12 +192,12 @@ class RAG:
|
||||
|
||||
return vectorstore
|
||||
|
||||
def setup_rag(self, model_name="llama3"):
|
||||
def setup_rag(self):
|
||||
"""
|
||||
Set up the RAG system with an existing vector database
|
||||
"""
|
||||
# Load the embeddings
|
||||
embeddings = OllamaEmbeddings(model="nomic-embed-text")
|
||||
embeddings = self.ollama.embeddings
|
||||
|
||||
# Load the vector store
|
||||
vectorstore = Chroma(
|
||||
@ -190,7 +208,7 @@ class RAG:
|
||||
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
|
||||
|
||||
# Set up the LLM
|
||||
llm = Ollama(model=model_name)
|
||||
llm = self.ollama.llm
|
||||
|
||||
# Create a custom prompt template
|
||||
template = """
|
||||
|
||||
@ -1,13 +1,14 @@
|
||||
import os
|
||||
import pytest
|
||||
import shutil
|
||||
|
||||
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_chroma import Chroma
|
||||
from .fixtures import *
|
||||
from .utility import *
|
||||
from code_rag.rag import RAG
|
||||
from code_rag.doc_tracker import DocumentTracker
|
||||
from code_rag.ollama_wrapper import OllamaWrapper
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
@ -58,9 +59,9 @@ def test_create_vector_db(docs_dir, db_dir, tracker_file, sample_docs):
|
||||
assert os.path.exists(rag_pipeline.db_dir)
|
||||
assert vectorstore is not None
|
||||
# Check the database has content
|
||||
embeddings = OllamaEmbeddings(model="nomic-embed-text")
|
||||
loaded_db = Chroma(
|
||||
persist_directory=rag_pipeline.db_dir, embedding_function=embeddings
|
||||
persist_directory=rag_pipeline.db_dir,
|
||||
embedding_function=rag_pipeline.ollama.embeddings,
|
||||
)
|
||||
assert loaded_db._collection.count() > 0
|
||||
|
||||
@ -75,9 +76,9 @@ def test_update_vector_db_with_changes(docs_dir, db_dir, tracker_file, sample_do
|
||||
rag_pipeline.create_vector_db(force_refresh=True)
|
||||
|
||||
# Get initial count
|
||||
embeddings = OllamaEmbeddings(model="nomic-embed-text")
|
||||
initial_db = Chroma(
|
||||
persist_directory=rag_pipeline.db_dir, embedding_function=embeddings
|
||||
persist_directory=rag_pipeline.db_dir,
|
||||
embedding_function=rag_pipeline.ollama.embeddings,
|
||||
)
|
||||
initial_count = initial_db._collection.count()
|
||||
|
||||
@ -92,7 +93,8 @@ def test_update_vector_db_with_changes(docs_dir, db_dir, tracker_file, sample_do
|
||||
|
||||
# Check the database has been updated
|
||||
updated_db = Chroma(
|
||||
persist_directory=rag_pipeline.db_dir, embedding_function=embeddings
|
||||
persist_directory=rag_pipeline.db_dir,
|
||||
embedding_function=rag_pipeline.ollama.embeddings,
|
||||
)
|
||||
assert updated_db._collection.count() > initial_count
|
||||
|
||||
@ -112,7 +114,7 @@ def test_full_rag_pipeline(docs_dir, db_dir, tracker_file, sample_docs):
|
||||
rag_pipeline.create_vector_db(force_refresh=True)
|
||||
|
||||
# Set up RAG
|
||||
rag_chain = rag_pipeline.setup_rag(model_name="llama3.2")
|
||||
rag_chain = rag_pipeline.setup_rag()
|
||||
|
||||
# Query the system
|
||||
query = "What is Python?"
|
||||
@ -122,3 +124,4 @@ def test_full_rag_pipeline(docs_dir, db_dir, tracker_file, sample_docs):
|
||||
# This is a soft test since the exact response will depend on the LLM
|
||||
assert response.strip() != ""
|
||||
assert "programming" in response.lower() or "language" in response.lower()
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user