Add upsert endpoint

.devcontainer/compose.yaml

@@ -5,4 +5,38 @@ services:
     build: .
     volumes:
       - ..:/workspace:cached
+    ports:
+      - "8000:8000"
+    environment:
+      - QDRANT_URL=qdrant:6334
+      - TEI_BASE_URL=http://text-embeddings-inference
+      - TEI_RERANK_BASE_URL=http://text-embeddings-inference-rerank
     command: sleep infinity
+
+  qdrant:
+    image: qdrant/qdrant:v1.7.4
+    volumes:
+      - ../qdrant_storage:/qdrant/storage:z
+    ports:
+      - "6333:6333"
+      - "6334:6334"
+
+  text-embeddings-inference:
+    image: ghcr.io/huggingface/text-embeddings-inference:cpu-0.6
+    volumes:
+      - "../tei_data:/data"
+    ports:
+      - "8001:80"
+    environment:
+      - MODEL_ID=${TEI_MODEL_ID:-BAAI/bge-large-en-v1.5}
+      - REVISION=${TEI_MODEL_REVISION}
+
+  text-embeddings-inference-rerank:
+    image: ghcr.io/huggingface/text-embeddings-inference:cpu-0.6
+    volumes:
+      - "../tei_data:/data"
+    ports:
+      - "8002:80"
+    environment:
+      - MODEL_ID=${TEI_RERANK_MODEL_ID:-BAAI/bge-reranker-large}
+      - REVISION=${TEI_RERANK_MODEL_REVISION}

.devcontainer/devcontainer.json

@@ -30,7 +30,8 @@
 				"eamodio.gitlens",
 				"yzhang.markdown-all-in-one",
 				"DavidAnson.vscode-markdownlint",
-				"ninoseki.vscode-pylens"
+				"ninoseki.vscode-pylens",
+				"ms-azuretools.vscode-docker"
 			]
 		}
 	},

llm-qa/llm_qa/chains/text_splitters/markdown_header_text_splitter.py

@@ -0,0 +1,36 @@
+from langchain.text_splitter import MarkdownHeaderTextSplitter
+from langchain_core.runnables import Runnable, chain
+
+
+def get_markdown_header_text_splitter_chain(
+    markdown_header_text_splitter: MarkdownHeaderTextSplitter,
+) -> Runnable[str, list[str]]:
+    if not markdown_header_text_splitter.strip_headers:
+        raise ValueError("`strip_headers` must be True")  # noqa: TRY003
+
+    @chain
+    def markdown_header_text_splitter_chain(text: str) -> list[str]:
+        documents = markdown_header_text_splitter.split_text(text)
+        # Add all parent headers to the page content
+        return [
+            "\n...\n".join(
+                f"{header_key} {document.metadata[header_key]}"
+                for _, header_key in markdown_header_text_splitter.headers_to_split_on
+            )
+            + f"\n{document.page_content}"
+            for document in documents
+        ]
+
+    return markdown_header_text_splitter_chain
+
+
+markdown_3_headers_text_splitter_chain = get_markdown_header_text_splitter_chain(
+    MarkdownHeaderTextSplitter(
+        headers_to_split_on=[
+            ("#", "#"),
+            ("##", "##"),
+            ("###", "###"),
+        ],
+        strip_headers=True,
+    )
+)

llm-qa/llm_qa/chains/text_splitters/text_splitter.py

@@ -0,0 +1,17 @@
+from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
+from langchain_core.runnables import Runnable, chain
+
+
+def get_text_splitter_chain(
+    text_splitter: TextSplitter,
+) -> Runnable[str, list[str]]:
+    @chain
+    def text_splitter_chain(text: str) -> list[str]:
+        return text_splitter.split_text(text)
+
+    return text_splitter_chain
+
+
+recursive_character_text_splitter_chain = get_text_splitter_chain(
+    RecursiveCharacterTextSplitter()
+)

llm-qa/llm_qa/dependencies.py

@@ -0,0 +1,24 @@
+from typing import Annotated
+
+from fastapi import Depends
+
+from llm_qa.embeddings.tei import TeiEmbeddings
+from llm_qa.settings import Settings
+
+
+def settings() -> Settings:
+    return Settings()
+
+
+def tei_embeddings(settings: Annotated[Settings, Depends(settings)]) -> TeiEmbeddings:
+    return TeiEmbeddings(
+        base_url=settings.tei_base_url,
+    )
+
+
+def tei_rerank_embeddings(
+    settings: Annotated[Settings, Depends(settings)],
+) -> TeiEmbeddings:
+    return TeiEmbeddings(
+        base_url=settings.tei_rerank_base_url,
+    )

llm-qa/llm_qa/embeddings/base.py

@@ -0,0 +1,8 @@
+from abc import ABC
+
+from langchain.embeddings.base import Embeddings
+from pydantic import BaseModel
+
+
+class PydanticEmbeddings(Embeddings, BaseModel, ABC):
+    pass

llm-qa/llm_qa/embeddings/tei.py

@@ -0,0 +1,105 @@
+from typing import override
+from urllib.parse import urljoin
+
+import httpx
+
+from llm_qa.embeddings.base import PydanticEmbeddings
+from llm_qa.models.tei import (
+    EmbedRequest,
+    EmbedResponseAdapter,
+    ErrorResponse,
+    RerankRequest,
+    RerankResponse,
+    RerankResponseAdapter,
+)
+
+
+class TeiEmbeddings(PydanticEmbeddings):
+    base_url: str
+    embed_endpoint: str = "/embed"
+    rerank_endpoint: str = "/rerank"
+    document_prefix: str = "passage: "
+    query_prefix: str = "query: "
+    _client: httpx.Client = httpx.Client()
+    _async_client: httpx.AsyncClient = httpx.AsyncClient()
+
+    @property
+    def embed_url(self) -> str:
+        return urljoin(self.base_url, self.embed_endpoint)
+
+    @property
+    def rerank_url(self) -> str:
+        return urljoin(self.base_url, self.rerank_endpoint)
+
+    @staticmethod
+    def _handle_status(response: httpx.Response) -> None:
+        try:
+            response.raise_for_status()
+        except httpx.HTTPStatusError as e:
+            match e.response.status_code:
+                case 413 | 422 | 424 | 429:
+                    try:
+                        error_response = ErrorResponse.model_validate_json(
+                            e.response.content
+                        )
+                        note = f"Error: {error_response.error}, Error Type: {error_response.error_type}"
+                    except ValueError:
+                        note = e.response.text
+                    e.add_note(note)
+            raise
+
+    def _embed(self, text: str | list[str]) -> list[list[float]]:
+        """Embed text."""
+        response = self._client.post(
+            url=self.embed_url,
+            json=EmbedRequest(inputs=text).model_dump(),
+        )
+        self._handle_status(response)
+        return EmbedResponseAdapter.validate_json(response.content)
+
+    async def _aembed(self, text: str | list[str]) -> list[list[float]]:
+        """Asynchronously embed text."""
+        response = await self._async_client.post(
+            url=self.embed_url,
+            json=EmbedRequest(inputs=text).model_dump(),
+        )
+        self._handle_status(response)
+        return EmbedResponseAdapter.validate_json(response.content)
+
+    @override
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Embed search docs."""
+        return self._embed([self.document_prefix + text for text in texts])
+
+    @override
+    def embed_query(self, text: str) -> list[float]:
+        """Embed query text."""
+        return self._embed(self.document_prefix + text)[0]
+
+    @override
+    async def aembed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Asynchronous Embed search docs."""
+        return await self._aembed([self.document_prefix + text for text in texts])
+
+    @override
+    async def aembed_query(self, text: str) -> list[float]:
+        """Asynchronous Embed query text."""
+        return (await self._aembed(self.document_prefix + text))[0]
+
+    def rerank(self, query: str, texts: list[str]) -> list[RerankResponse]:
+        """Rerank texts."""
+        response = self._client.post(
+            url=self.rerank_url,
+            json=RerankRequest(query=query, texts=texts).model_dump(),
+        )
+        self._handle_status(response)
+        return RerankResponseAdapter.validate_json(response.content)
+
+    async def arerank(self, query: str, texts: list[str]) -> list[RerankResponse]:
+        """Asynchronously rerank texts."""
+        response = await self._async_client.post(
+            url=self.rerank_url,
+            json=RerankRequest(query=query, texts=texts).model_dump(),
+        )
+        self._handle_status(response)
+        return RerankResponseAdapter.validate_json(response.content)

llm-qa/llm_qa/models/tei.py

@@ -0,0 +1,42 @@
+from enum import StrEnum
+
+from pydantic import BaseModel, Field, TypeAdapter
+
+
+class EmbedRequest(BaseModel):
+    inputs: str | list[str]
+    normalize: bool = True
+    truncate: bool = False
+
+
+EmbedResponseAdapter = TypeAdapter(list[list[float]])
+
+
+class RerankRequest(BaseModel):
+    query: str
+    texts: list[str]
+    raw_scores: bool = False
+    return_text: bool = False
+    truncate: bool = False
+
+
+class RerankResponse(BaseModel):
+    index: int = Field(..., ge=0)
+    score: float
+    text: str | None = None
+
+
+RerankResponseAdapter = TypeAdapter(list[RerankResponse])
+
+
+class ErrorType(StrEnum):
+    Unhealthy = "Unhealthy"
+    Backend = "Backend"
+    Overloaded = "Overloaded"
+    Validation = "Validation"
+    Tokenizer = "Tokenizer"
+
+
+class ErrorResponse(BaseModel):
+    error: str
+    error_type: ErrorType

llm-qa/llm_qa/models/upsert.py

@@ -0,0 +1,18 @@
+from enum import StrEnum
+
+from pydantic import BaseModel
+
+
+class TextType(StrEnum):
+    PLAIN_TEXT = "PLAIN_TEXT"
+    MARKDOWN = "MARKDOWN"
+
+
+class UpsertTextRequest(BaseModel):
+    text: str
+    type: TextType
+    collection: str
+
+
+class UpsertTextResponse(BaseModel):
+    num_documents: int

llm-qa/llm_qa/routers/api_v1.py

@@ -0,0 +1,6 @@
+from fastapi import APIRouter
+
+from llm_qa.routers import upsert
+
+router = APIRouter(prefix="/api/v1", tags=["v1"])
+router.include_router(upsert.router)

llm-qa/llm_qa/routers/upsert.py

@@ -0,0 +1,32 @@
+from typing import Annotated
+
+from fastapi import APIRouter, Depends
+
+from llm_qa.dependencies import settings, tei_embeddings
+from llm_qa.embeddings.base import PydanticEmbeddings
+from llm_qa.models.upsert import UpsertTextRequest, UpsertTextResponse
+from llm_qa.services.upsert import upsert_text as upsert_text_service
+from llm_qa.settings import Settings
+
+router = APIRouter()
+
+
+@router.post("/upsert-text")
+async def upsert_text(
+    upsert_request: UpsertTextRequest,
+    settings: Annotated[Settings, Depends(settings)],
+    embeddings: Annotated[PydanticEmbeddings, Depends(tei_embeddings)],
+) -> UpsertTextResponse:
+    num_documents = await upsert_text_service(
+        text=upsert_request.text,
+        text_type=upsert_request.type,
+        collection=upsert_request.collection,
+        embeddings=embeddings,
+        qdrant_url=settings.qdrant_url,
+    )
+    return UpsertTextResponse(num_documents=num_documents)
+
+
+@router.post("/upsert-file")
+async def upsert_file() -> None:
+    raise NotImplementedError

llm-qa/llm_qa/services/upsert.py

@@ -0,0 +1,43 @@
+import logging
+
+from langchain.schema.document import Document
+from langchain_core.embeddings import Embeddings
+
+from llm_qa.chains.text_splitters.markdown_header_text_splitter import (
+    markdown_3_headers_text_splitter_chain,
+)
+from llm_qa.chains.text_splitters.text_splitter import (
+    recursive_character_text_splitter_chain,
+)
+from llm_qa.models.upsert import TextType
+from llm_qa.vectorstores.qdrant import upsert_documents
+
+logger = logging.getLogger(__name__)
+
+
+async def upsert_text(
+    text: str,
+    text_type: TextType,
+    collection: str,
+    embeddings: Embeddings,
+    qdrant_url: str,
+) -> int:
+    match text_type:
+        case TextType.PLAIN_TEXT:
+            text_splitter_chain = recursive_character_text_splitter_chain
+        case TextType.MARKDOWN:
+            text_splitter_chain = markdown_3_headers_text_splitter_chain
+        case _:
+            raise ValueError(f"Unknown text type: `{text_type}`")  # noqa: TRY003
+
+    text_chunks = await text_splitter_chain.ainvoke(text)
+
+    documents = [Document(page_content=chunk) for chunk in text_chunks]
+
+    await upsert_documents(
+        documents=documents,
+        embeddings=embeddings,
+        qdrant_url=qdrant_url,
+        collection=collection,
+    )
+    return len(documents)

llm-qa/llm_qa/settings.py

@@ -0,0 +1,9 @@
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+
+    qdrant_url: str
+    tei_base_url: str
+    tei_rerank_base_url: str

llm-qa/llm_qa/vectorstores/qdrant.py

@@ -0,0 +1,22 @@
+import logging
+
+from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.qdrant import Qdrant
+
+logger = logging.getLogger(__name__)
+
+
+async def upsert_documents(
+    documents: list[Document], embeddings: Embeddings, qdrant_url: str, collection: str
+) -> None:
+    logger.info(
+        "Upserting %d documents to Qdrant collection `%s`", len(documents), collection
+    )
+    await Qdrant.afrom_documents(
+        documents=documents,
+        embedding=embeddings,
+        url=qdrant_url,
+        prefer_grpc=True,
+        collection_name=collection,
+    )

llm-qa/llm_qa/web.py

@@ -0,0 +1,12 @@
+from fastapi import FastAPI
+
+from llm_qa.routers import api_v1
+
+app = FastAPI(title="LLM QA")
+app.include_router(api_v1.router)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run("llm_qa.web:app", host="0.0.0.0", port=8000, reload=True)  # noqa: S104

llm-qa/poetry.lock

@@ -287,6 +287,20 @@ files = [
     {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
 ]
 
+[[package]]
+name = "click"
+version = "8.1.7"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
+    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -338,6 +352,25 @@ files = [
 [package.extras]
 tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
 
+[[package]]
+name = "fastapi"
+version = "0.109.2"
+description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fastapi-0.109.2-py3-none-any.whl", hash = "sha256:2c9bab24667293b501cad8dd388c05240c850b58ec5876ee3283c47d6e1e3a4d"},
+    {file = "fastapi-0.109.2.tar.gz", hash = "sha256:f3817eac96fe4f65a2ebb4baa000f394e55f5fccdaf7f75250804bc58f354f73"},
+]
+
+[package.dependencies]
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
+starlette = ">=0.36.3,<0.37.0"
+typing-extensions = ">=4.8.0"
+
+[package.extras]
+all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+
 [[package]]
 name = "frozenlist"
 version = "1.4.1"
@@ -495,6 +528,62 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.2"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpcore-1.0.2-py3-none-any.whl", hash = "sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7"},
+    {file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<0.23.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.26.0"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpx-0.26.0-py3-none-any.whl", hash = "sha256:8915f5a3627c4d47b73e8202457cb28f1266982d1159bd5779d86a80c0eab1cd"},
+    {file = "httpx-0.26.0.tar.gz", hash = "sha256:451b55c30d5185ea6b23c2c793abf9bb237d2a7dfb901ced6ff69ad37ec1dfaf"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+sniffio = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+
 [[package]]
 name = "idna"
 version = "3.6"
@@ -1113,6 +1202,21 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
+[[package]]
+name = "pydantic-settings"
+version = "2.1.0"
+description = "Settings management using Pydantic"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pydantic_settings-2.1.0-py3-none-any.whl", hash = "sha256:7621c0cb5d90d1140d2f0ef557bdf03573aac7035948109adf2574770b77605a"},
+    {file = "pydantic_settings-2.1.0.tar.gz", hash = "sha256:26b1492e0a24755626ac5e6d715e9077ab7ad4fb5f19a8b7ed7011d52f36141c"},
+]
+
+[package.dependencies]
+pydantic = ">=2.3.0"
+python-dotenv = ">=0.21.0"
+
 [[package]]
 name = "pygments"
 version = "2.17.2"
@@ -1128,6 +1232,20 @@ files = [
 plugins = ["importlib-metadata"]
 windows-terminal = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "python-dotenv"
+version = "1.0.1"
+description = "Read key-value pairs from a .env file and set them as environment variables"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
+    {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
+]
+
+[package.extras]
+cli = ["click (>=5.0)"]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.1"
@@ -1363,6 +1481,23 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "starlette"
+version = "0.36.3"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "starlette-0.36.3-py3-none-any.whl", hash = "sha256:13d429aa93a61dc40bf503e8c801db1f1bca3dc706b10ef2434a36123568f044"},
+    {file = "starlette-0.36.3.tar.gz", hash = "sha256:90a671733cfb35771d8cc605e0b679d23b992f8dcfad48cc60b38cb29aeb7080"},
+]
+
+[package.dependencies]
+anyio = ">=3.4.0,<5"
+
+[package.extras]
+full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
+
 [[package]]
 name = "tenacity"
 version = "8.2.3"
@@ -1435,6 +1570,24 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "uvicorn"
+version = "0.27.1"
+description = "The lightning-fast ASGI server."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "uvicorn-0.27.1-py3-none-any.whl", hash = "sha256:5c89da2f3895767472a35556e539fd59f7edbe9b1e9c0e1c99eebeadc61838e4"},
+    {file = "uvicorn-0.27.1.tar.gz", hash = "sha256:3d9a267296243532db80c83a959a3400502165ade2c1338dea4e67915fd4745a"},
+]
+
+[package.dependencies]
+click = ">=7.0"
+h11 = ">=0.8"
+
+[package.extras]
+standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
+
 [[package]]
 name = "wcwidth"
 version = "0.2.13"
@@ -1552,4 +1705,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "97e8b5f3e3e62c576dc50012212e077c00fd9fe18b6273a8929bf95d24ba168f"
+content-hash = "7ffd6635973f31fbcbc5e962102b8cf12920f83e383dc0bb73732522db897a6f"

llm-qa/pyproject.toml

@@ -9,6 +9,10 @@ readme = "README.md"
 python = "^3.12"
 langchain-community = "^0.0.19"
 langchain = "^0.1.6"
+fastapi = "^0.109.2"
+uvicorn = "^0.27.1"
+httpx = "^0.26.0"
+pydantic-settings = "^2.1.0"
 
 [tool.poetry.group.dev.dependencies]
 ruff = "0.2.1"
@@ -18,3 +22,52 @@ ipython = "^8.21.0"
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
+
+
+[tool.mypy]
+python_version = "3.12"
+plugins = ["pydantic.mypy"]
+modules = ["llm_qa"]
+strict = true
+
+[tool.ruff]
+target-version = "py312"
+preview = true
+include = ["llm_qa/**/*.py", "pyproject.toml"]
+lint.select = [
+    "F",
+    "I",
+    "N",
+    "UP",
+    "YTT",
+    "ANN",
+    "ASYNC",
+    "S",
+    "B",
+    "C4",
+    "DTZ",
+    "FA",
+    "ISC",
+    "ICN",
+    "G",
+    "INP",
+    "PIE",
+    "PT",
+    "RSE",
+    "RET",
+    "SLF",
+    "SIM",
+    "TID",
+    "TCH",
+    "PTH",
+    "ERA",
+    "PGH",
+    "PL",
+    "TRY",
+    "FLY",
+    "PERF",
+    "FURB",
+    "LOG",
+    "RUF",
+]
+lint.ignore = ["E501", "ANN101", "PLR0913", "PLR0917", "ISC001"]