chore: more advancements in rag code

Makefile

@@ -9,6 +9,7 @@ test:
 lint:
 	$(info ****************** linting ******************)
 	uv run pre-commit run -a
+	uv run ruff check --fix
 
 format:
 	$(info ****************** formatting ******************)

data/benchmark/questions_and_answers.json

@@ -0,0 +1,27 @@
+[
+    {
+        "question": "What is the key takeway (and/or term revisiting) for memory and learning from a nested learning perspective?", 
+        "answer": "Key takeaway (Nested Learning perspective): From a nested learning (NL) viewpoint, memory and learning are not separate modules. Instead, memory is any neural parameter update caused by input, and learning is the process of acquiring, encoding, and organizing these memories effectively. All updates—across different levels of the learning hierarchy and at different frequencies—constitute memory, stored on multiple time scales. This distributed, continuum view aligns with neuropsychology and reframes gradient-based optimization itself as a form of (associative) memory, enabling more robust behavior and mitigating catastrophic forgetting.", 
+        "file": "nested_learning.pdf"
+    },
+    {
+        "question": "What is the architecture of the DeepEncoder in DeepSeek OCR?",
+        "answer": "DeepEncoder uses a two-stage architecture: first, a window-attention-based visual perception module (SAM-base) to extract local image features, and second, a global-attention-based visual knowledge module (CLIP-large) to capture global semantics. Between them, a 2-layer convolutional downsampling module compresses vision tokens (from 4096 to 256 for a 1024x1024 image), keeping computation and memory manageable while preserving important information.",
+        "file": "deepseek_ocr.pdf"
+    },
+    {
+        "question": "What related work in the field of Byte-Level LMs do the author of the BOLMO paper individuate?",
+        "answer": "The authors point to byte-level LMs that replace subwords with UTF-8 bytes, especially Latent Tokenizer Language Models (LTLMs) such as Hourglass-style models that use pooling, depooling, and dynamic, learned tokenization, which can match subword models at similar compute.",
+        "file": "bolmo.pdf"
+    },
+    {
+        "question": "What datasets was MUVERA evaluated on?",
+        "answer": "MUVERA's evaluation includes results from six of the well-studied BEIR information retrieval datasets: MS MARCO, HotpotQA, NQ, Quora, SciDocs, and ArguAna. These datasets were selected for varying corpus size (8K-8.8M) and average number of document tokens (18-165).",
+        "file": "muvera.pdf"
+    }, 
+    {
+        "question": "What limitations and future work perspectives are outlined for Recursive Language Models?",
+        "answer": "Limitations and future work for RLMs include exploring asynchronous or sandboxed sub-calls to reduce inference cost, deeper recursion beyond a single sub-call, and training models specifically as RLMs to improve reasoning and context efficiency.",
+        "file": "recursive_language_models.pdf"
+    }
+]

packages/rag-starterkit/pyproject.toml

@@ -10,6 +10,8 @@ readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
     "chonkie>=1.5.2",
+    "diskcache>=5.6.3",
+    "fastembed>=0.7.4",
     "llama-cloud-services>=0.6.88",
     "openai>=2.14.0",
     "qdrant-client>=1.16.2",

packages/rag-starterkit/src/rag_starterkit/chunk.py

@@ -1,3 +1,35 @@
-"""
-Chunking-related logic
-"""
+from chonkie import SentenceChunker, Chunk
+from typing import TypedDict
+from fastembed import SparseEmbedding
+
+
+class ChunkWithMetadata(TypedDict):
+    chunk: Chunk
+    file_path: str
+    embedding: list[float]
+    sparse_embedding: SparseEmbedding | None
+
+
+class Chunker:
+    def __init__(self) -> None:
+        self._chunker = SentenceChunker(
+            chunk_overlap=200,  # allow 10% chunk size overlap
+            chunk_size=2048,
+        )
+
+    def chunk_texts(self, contents: dict[str, str]) -> list[ChunkWithMetadata]:
+        texts = list(contents.values())
+        files = list(contents.keys())
+        batch_chunks = self._chunker.chunk_batch(texts=texts)
+        chunks_w_meta: list[ChunkWithMetadata] = []
+        for i, batch_chunk in enumerate(batch_chunks):
+            for chunk in batch_chunk:
+                chunks_w_meta.append(
+                    ChunkWithMetadata(
+                        chunk=chunk,
+                        file_path=files[i],
+                        embedding=[],
+                        sparse_embedding=None,
+                    )
+                )
+        return chunks_w_meta

packages/rag-starterkit/src/rag_starterkit/embed.py

@@ -1,3 +1,56 @@
-"""
-Embedding-related logic
-"""
+from openai import AsyncOpenAI
+from fastembed import SparseTextEmbedding, SparseEmbedding
+
+from .chunk import ChunkWithMetadata
+
+DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"
+DEFAULT_FASTEMBED_MODEL = "Qdrant/bm25"
+
+
+class Embedder:
+    def __init__(
+        self,
+        api_key: str,
+        openai_model: str | None = None,
+        fastembed_model: str | None = None,
+    ):
+        self._client = AsyncOpenAI(api_key=api_key)
+        self.model = openai_model or DEFAULT_EMBEDDING_MODEL
+        self._sparse_embedder = SparseTextEmbedding(
+            model_name=(fastembed_model or DEFAULT_FASTEMBED_MODEL),
+            cache_dir="tmp/fastembed",
+        )
+
+    async def embed_chunks(
+        self, chunks: list[ChunkWithMetadata]
+    ) -> list[ChunkWithMetadata]:
+        texts = [chunk["chunk"].text for chunk in chunks]
+        embeddings = await self._client.embeddings.create(
+            input=texts,
+            model=self.model,
+            dimensions=768,
+        )
+        for i, embedding in enumerate(embeddings.data):
+            chunks[i]["embedding"] = embedding.embedding
+        return chunks
+
+    def sparse_embed_chunks(
+        self, chunks: list[ChunkWithMetadata]
+    ) -> list[ChunkWithMetadata]:
+        texts = [chunk["chunk"].text for chunk in chunks]
+        embeddings = list(self._sparse_embedder.embed(texts))
+        for i, embedding in enumerate(embeddings):
+            chunks[i]["sparse_embedding"] = embedding
+        return chunks
+
+    async def embed_query(self, query: str) -> list[float]:
+        embeddings = await self._client.embeddings.create(
+            input=query,
+            model=self.model,
+            dimensions=768,
+        )
+        return embeddings.data[0].embedding
+
+    def sparse_embed_query(self, query: str) -> SparseEmbedding:
+        embeddings = list(self._sparse_embedder.query_embed(query=query))
+        return embeddings[0]

packages/rag-starterkit/src/rag_starterkit/llm_filter.py

@@ -0,0 +1,47 @@
+from openai import AsyncOpenAI
+from openai.types.responses.easy_input_message_param import EasyInputMessageParam
+from typing import Any
+from pydantic import BaseModel, Field
+
+DEFAULT_OPENAI_MODEL = "gpt-4.1"
+SYSTEM_PROMPT = """
+Your task is to individuate, among the files that the user provides, the one that is most likely to have the answer to the user's query. Provide the file, and the confidence you have in your response (as an integer between 0 and 100).
+"""
+
+
+class FileFilter(BaseModel):
+    file_path: str = Field(
+        description="File path to filter for when searching an answer for the query"
+    )
+    confidence: int = Field(
+        description="Confidence in your choice for the file_path field. Must be between 0 and 100",
+        ge=0,
+        le=100,
+    )
+
+
+class LLMFilter:
+    def __init__(self, api_key: str, model: str | None = None):
+        self._client = AsyncOpenAI(api_key=api_key)
+        self.model = model or DEFAULT_OPENAI_MODEL
+
+    def _build_user_message(
+        self, query: str, file_paths: list[str]
+    ) -> EasyInputMessageParam:
+        fls = "\n- ".join(file_paths)
+        content = f"Find, among these files:\n\n- {fls}\n\nThe one that would be the most likely to contain the answer to this query: '{query}'"
+        return EasyInputMessageParam(role="user", content=content, type="message")
+
+    async def generate_filter(
+        self, query: str, file_paths: list[str]
+    ) -> FileFilter | None:
+        messages: list[Any] = [
+            EasyInputMessageParam(content=SYSTEM_PROMPT, role="system", type="message")
+        ]
+        messages.append(self._build_user_message(query, file_paths))
+        response = await self._client.responses.parse(
+            text_format=FileFilter,
+            input=messages,
+            model=self.model,
+        )
+        return response.output_parsed

packages/rag-starterkit/src/rag_starterkit/parse.py

@@ -1,3 +1,96 @@
-"""
-Parsing-related logic
-"""
+import logging
+import asyncio
+import os
+
+from pathlib import Path
+from typing import cast
+from diskcache import Cache
+from llama_cloud_services.parse.utils import ResultType
+from llama_cloud_services.parse.types import JobResult
+from llama_cloud_services import LlamaParse
+
+
+async def parse_directory(
+    directory: str, recursive: bool, to_skip: list[str]
+) -> dict[str, str]:
+    logging.basicConfig(
+        filename="rag-starterkit.log",
+        filemode="w",
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
+    dir_path = Path(directory)
+    to_skip_resolved = [str((dir_path / path).resolve()) for path in to_skip]
+    if not recursive:
+        files = []
+        fls = os.listdir(dir_path)
+        for fl in fls:
+            resolved = str(Path(dir_path / fl).resolve())
+            if resolved not in to_skip_resolved:
+                files.append(resolved)
+    else:
+        files = []
+        for root, dirs, fls in os.walk(dir_path):
+            dirs[:] = [
+                str((Path(root) / d).resolve())
+                for d in dirs
+                if d not in to_skip_resolved
+            ]
+            fls[:] = [
+                str((Path(root) / f).resolve())
+                for f in fls
+                if f not in to_skip_resolved
+            ]
+            for fl in fls:
+                files.append(str((Path(root) / fl).resolve()))
+    semaphore = asyncio.Semaphore(5)
+    parser = LlamaParse(
+        api_key=cast(str, os.getenv("LLAMA_CLOUD_API_KEY")),
+        result_type=ResultType.TXT,
+        fast_mode=True,
+    )
+
+    async def parse_job(file_path: str) -> tuple[str, str] | None:
+        async with semaphore:
+            result = cast(JobResult, await parser.aparse(file_path=file_path))
+            if result.error is None:
+                text = await result.aget_text()
+                return file_path, text
+            else:
+                logging.info(
+                    f"Could not parse file {file_path} because of {result.error}"
+                )
+
+    files_contents = await asyncio.gather(*(parse_job(file) for file in files))
+    data: dict[str, str] = {}
+    for el in files_contents:
+        if el is not None:
+            data[el[0]] = el[1]
+    return data
+
+
+def contents_from_cache(cache_directory: str = "tmp/cache") -> dict[str, str]:
+    logging.basicConfig(
+        filename="rag-starterkit.log",
+        filemode="w",
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
+    cache = Cache(directory=cache_directory)
+    data: dict[str, str] = {}
+    cache_keys = list(cache.iterkeys())
+    for key in cache_keys:
+        if isinstance(key, str):
+            data_key = key
+        elif isinstance(key, bytes):
+            data_key = str(key, encoding="utf-8")
+        else:
+            raise ValueError(
+                "Cache keys shold be either strings or byte-encoded strings"
+            )
+        value = cache.get(data_key)
+        if value is not None:
+            data[data_key] = cast(str, value)
+        else:
+            logging.info(f"Skipping file {data_key} as it stored a null content")
+    return data

packages/rag-starterkit/src/rag_starterkit/pipeline.py

@@ -0,0 +1,78 @@
+import os
+import inspect
+
+from qdrant_client import AsyncQdrantClient
+from typing import Any, cast
+from .parse import parse_directory, contents_from_cache
+from .chunk import Chunker
+from .embed import Embedder
+from .vectordb import VectorDB, SearchResult
+from .llm_filter import LLMFilter
+
+
+class Pipeline:
+    def __init__(
+        self,
+        qdrant_client: AsyncQdrantClient,
+        qdrant_collection_name: str,
+        parsing_kwargs: dict[str, Any] | None = None,
+        cache_directory: str | None = None,
+        openai_api_key: str | None = None,
+        openai_emebdding_model: str | None = None,
+        fastembed_model: str | None = None,
+        openai_llm_model: str | None = None,
+    ):
+        if cache_directory is None and parsing_kwargs is None:
+            raise ValueError(
+                "At least one between parsing_kwargs and cache_directory has to be provided"
+            )
+        self.parsing_strategy = (
+            parse_directory if cache_directory is None else contents_from_cache
+        )
+        self.parsing_kwargs = (
+            parsing_kwargs
+            if cache_directory is None
+            else {"cache_directory": cache_directory}
+        )
+        if openai_api_key is None:
+            openai_api_key = os.getenv("OPENAI_API_KEY")
+        if openai_api_key is None:
+            raise ValueError(
+                "OPENAI_API_KEY must be set within the environment if openai_api_key is not provided as argument"
+            )
+        self.chunker = Chunker()
+        self.embedder = Embedder(
+            api_key=openai_api_key,
+            openai_model=openai_emebdding_model,
+            fastembed_model=fastembed_model,
+        )
+        self.vector_db = VectorDB(
+            qdrant_client=qdrant_client,
+            collection_name=qdrant_collection_name,
+            embedder=self.embedder,
+        )
+        self.filter_llm = LLMFilter(api_key=openai_api_key, model=openai_llm_model)
+        self.file_paths: list[str] = []
+
+    async def prepare(self) -> None:
+        if inspect.iscoroutinefunction(self.parsing_strategy):
+            assert self.parsing_kwargs is not None, "parsing_kwargs cannot be null"
+            contents = await self.parsing_strategy(**self.parsing_kwargs)
+        else:
+            contents = self.parsing_strategy(**self.parsing_kwargs)  # type: ignore
+        contents = cast(dict[str, str], contents)
+        self.file_paths = [key for key in contents]
+        chunks = self.chunker.chunk_texts(contents)
+        chunks = await self.embedder.embed_chunks(chunks)
+        chunks = self.embedder.sparse_embed_chunks(chunks)
+        await self.vector_db.configure_collection()
+        await self.vector_db.upload(chunks)
+
+    async def run(self, query: str, limit: int = 1) -> list[SearchResult]:
+        filter_file = await self.filter_llm.generate_filter(query, self.file_paths)
+        file_path = (
+            filter_file.file_path
+            if filter_file is not None and filter_file.confidence > 50
+            else None
+        )
+        return await self.vector_db.search(query, file_path=file_path, limit=limit)

packages/rag-starterkit/src/rag_starterkit/vectordb.py

@@ -1,3 +1,169 @@
-"""
-Vector Database and Retrieval-related logic
-"""
+from qdrant_client import AsyncQdrantClient
+from qdrant_client.models import (
+    VectorParams,
+    SparseVectorParams,
+    Distance,
+    SparseIndexParams,
+    SparseVector,
+    Filter,
+    FieldCondition,
+    MatchValue,
+)
+from statistics import mean
+from typing import TypedDict, Literal, cast
+
+
+from .chunk import ChunkWithMetadata
+from .embed import Embedder
+
+
+class SearchResult(TypedDict):
+    id: int
+    content: str
+    file_path: str
+    score: float
+    type: Literal["sparse", "dense"]
+
+
+class SimpleReranker:
+    def __init__(self) -> None:
+        pass
+
+    def _dedupe(
+        self, dense_results: list[SearchResult], sparse_results: list[SearchResult]
+    ) -> list[SearchResult]:
+        dense_results_ranked = dense_results.copy()
+        sparse_results_ranked = sparse_results.copy()
+        dense_results_ranked.sort(key=lambda x: x["content"], reverse=False)
+        sparse_results_ranked.sort(key=lambda x: x["content"], reverse=False)
+        for i, r in enumerate(dense_results_ranked):
+            r["score"] = i + 1
+        for i, r in enumerate(sparse_results_ranked):
+            r["score"] = i + 1
+        for result in sparse_results_ranked:
+            for i, r in enumerate(dense_results_ranked):
+                if r["content"] == result["content"]:
+                    r["score"] = mean([r["score"], result["score"]])
+                    dense_results_ranked[i] = r
+                    break
+            else:
+                dense_results_ranked.append(result)
+        return dense_results_ranked
+
+    def rerank(
+        self,
+        dense_results: list[SearchResult],
+        sparse_results: list[SearchResult],
+        limit: int = 1,
+    ) -> list[SearchResult]:
+        results = self._dedupe(dense_results, sparse_results)
+        results.sort(key=lambda x: x["score"], reverse=True)
+        return results[:limit]
+
+
+class VectorDB:
+    def __init__(
+        self, qdrant_client: AsyncQdrantClient, collection_name: str, embedder: Embedder
+    ) -> None:
+        self._client = qdrant_client
+        self.collection_name = collection_name
+        self.embedder = embedder
+        self._reranker = SimpleReranker()
+
+    async def configure_collection(self) -> None:
+        if await self._client.collection_exists(self.collection_name):
+            return None
+        else:
+            await self._client.create_collection(
+                collection_name=self.collection_name,
+                vectors_config={
+                    "dense-text": VectorParams(size=768, distance=Distance.COSINE)
+                },
+                sparse_vectors_config={
+                    "sparse-text": SparseVectorParams(
+                        index=SparseIndexParams(on_disk=False)
+                    )
+                },
+            )
+
+    async def upload(self, data: list[ChunkWithMetadata]) -> None:
+        sparse_embeddings: list[dict[str, SparseVector]] = []
+        dense_embeddings: list[dict[str, list[float]]] = []
+        payloads: list[dict[str, str]] = []
+        for d in data:
+            assert d["sparse_embedding"] is not None
+            sparse_embedding = {
+                "sparse-text": SparseVector(
+                    indices=d["sparse_embedding"].indices.tolist(),
+                    values=d["sparse_embedding"].values.tolist(),
+                )
+            }
+            dense_embedding = {"dense-text": d["embedding"]}
+            payload = {"content": d["chunk"].text, "file_path": d["file_path"]}
+            sparse_embeddings.append(sparse_embedding)
+            dense_embeddings.append(dense_embedding)
+            payloads.append(payload)
+        self._client.upload_collection(
+            self.collection_name,
+            vectors=dense_embeddings,
+            payload=payloads,
+            ids=range(len(dense_embeddings)),
+        )
+        self._client.upload_collection(
+            self.collection_name,
+            vectors=sparse_embeddings,
+            payload=payloads,
+            ids=range(
+                len(dense_embeddings), len(dense_embeddings) + len(sparse_embeddings)
+            ),
+        )
+
+    async def search(
+        self, query: str, file_path: str | None = None, limit: int = 1
+    ) -> list[SearchResult]:
+        dense_embedding = await self.embedder.embed_query(query)
+        sparse_embedding = self.embedder.sparse_embed_query(query)
+        if file_path:
+            filt = Filter(
+                must=FieldCondition(key="file_path", match=MatchValue(value=file_path))
+            )
+        else:
+            filt = None
+        result_dense = await self._client.query_points(
+            collection_name=self.collection_name,
+            query=dense_embedding,
+            using="dense-text",
+            query_filter=filt,
+        )
+        result_sparse = await self._client.query_points(
+            collection_name=self.collection_name,
+            query=SparseVector(
+                indices=sparse_embedding.indices.tolist(),
+                values=sparse_embedding.values.tolist(),
+            ),
+            using="sparse-text",
+            query_filter=filt,
+        )
+        dense_results: list[SearchResult] = []
+        sparse_results: list[SearchResult] = []
+        for point in result_dense.points:
+            if point.payload is not None:
+                result = SearchResult(
+                    id=cast(int, point.id),
+                    content=point.payload.get("content", ""),
+                    file_path=point.payload.get("file_path", ""),
+                    score=point.score,
+                    type="dense",
+                )
+                dense_results.append(result)
+        for point in result_sparse.points:
+            if point.payload is not None:
+                result = SearchResult(
+                    id=cast(int, point.id),
+                    content=point.payload.get("content", ""),
+                    file_path=point.payload.get("file_path", ""),
+                    score=point.score,
+                    type="sparse",
+                )
+                sparse_results.append(result)
+        return self._reranker.rerank(dense_results, sparse_results, limit)

src/fs_explorer/caching.py

@@ -66,12 +66,12 @@ async def parse_and_cache(directory: str, recursive: bool, to_skip: list[str]) -
                 str((Path(root) / d).resolve())
                 for d in dirs
                 if d not in to_skip_resolved
-            ]
+            ] # type: ignore[invalid-assignment]
             fls[:] = [
                 str((Path(root) / f).resolve())
                 for f in fls
                 if f not in to_skip_resolved
-            ]
+            ] # type: ignore[invalid-assignment]
             for fl in fls:
                 files.append(str((Path(root) / fl).resolve()))
     semaphore = asyncio.Semaphore(5)

tests/test_fs.py

@@ -1,7 +1,6 @@
 import pytest
 import os
 
-from unittest.mock import patch
 from fs_explorer.fs import (
     describe_dir_content,
     read_file,
@@ -18,7 +17,7 @@ def test_describe_dir_content() -> None:
         == "Content of tests/testfiles\nFILES:\n- tests/testfiles/file1.txt\n- tests/testfiles/file2.md\nSUBFOLDERS:\n- tests/testfiles/last"
     )
     description = describe_dir_content("tests/testfile")
-    assert description == f"No such directory: tests/testfile"
+    assert description == "No such directory: tests/testfile"
     description = describe_dir_content("tests/testfiles/last")
     assert (
         description

uv.lock

@@ -407,6 +407,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "coloredlogs"
+version = "15.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "humanfriendly" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/cc/c7/eed8f27100517e8c0e6b923d5f0845d0cb99763da6fdee00478f91db7325/coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0", size = 278520, upload-time = "2021-06-11T10:22:45.202Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934", size = 46018, upload-time = "2021-06-11T10:22:42.561Z" },
+]
+
 [[package]]
 name = "dataclasses-json"
 version = "0.6.7"
@@ -480,6 +492,28 @@ wheels = [
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