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📝 WalkthroughWalkthroughA RAG MVP system foundation is introduced with documentation, text processing utilities, embedding models, vector indexing infrastructure, and a Q&A CLI. New modules enable semantic search via sentence embeddings and FAISS indexing, while documentation outlines design goals and project structure. Changes
Sequence DiagramsequenceDiagram
participant User as User (CLI)
participant CLI as qa_cli Module
participant Pipeline as EmbeddingPipeline
participant Embedder as Embedder
participant Chunker as Chunker
participant Index as VectorIndexer
participant FileSystem as File System
User->>CLI: Run script
CLI->>Pipeline: demo_embeddings_pipeline()
Pipeline->>Chunker: chunk_text(sample_text)
Chunker-->>Pipeline: list of chunks
Pipeline->>Embedder: embed(chunks)
Embedder-->>Pipeline: embeddings array
Pipeline->>Index: add(embeddings, chunks)
Index-->>Pipeline: index built
Pipeline->>Index: search(query_embedding)
Index-->>Pipeline: matched chunks
CLI->>FileSystem: load_notes()
FileSystem-->>CLI: markdown notes
User->>CLI: enter query
CLI->>CLI: search_notes(query, notes)
CLI-->>User: matching sentences
User->>CLI: exit
CLI-->>User: done
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Suggested reviewers
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🚥 Pre-merge checks | ✅ 1 | ❌ 2❌ Failed checks (1 warning, 1 inconclusive)
✅ Passed checks (1 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing touches
🧪 Generate unit tests (beta)
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Actionable comments posted: 11
🤖 Fix all issues with AI agents
In `@smart-notes/rag_mvp/embeddings/chunker.py`:
- Around line 9-29: The chunk_text function can infinite-loop when overlap >=
max_length; add an upfront validation in chunk_text (using parameters max_length
and overlap) that either raises a ValueError or adjusts overlap (e.g., require 0
<= overlap < max_length) and return an error if the inputs are invalid; ensure
this guard runs before trimming text or entering the while loop so start always
progresses.
In `@smart-notes/rag_mvp/embeddings/embedder.py`:
- Around line 25-27: The embed method returns a 1-D empty array for empty input;
change it to return a 2-D empty array with zero rows and the embedding
dimensionality so downstream code (e.g., VectorIndexer.add -> self.index.add)
receives shape (0, dim). Update embed to return np.empty((0,
self.embedding_dim)) (or np.empty((0, <detected_dim>)) if the class exposes a
model/embedding size) when texts is empty, or compute the dim from an existing
weight/embedding shape and use that to form the (0, dim) array.
In `@smart-notes/rag_mvp/embeddings/indexer.py`:
- Around line 37-39: FAISS can return -1 for empty neighbor slots which becomes
a valid Python negative index; in the loop in indexer.py that iterates "for idx
in indices[0]:" (inside whatever method populating results), change the guard to
explicitly skip negative indices (e.g., require idx >= 0 and idx <
len(self.texts)) instead of only checking "idx < len(self.texts)"; update the
condition so -1 is not used to index self.texts and only valid non-negative
indices are appended to results.
In `@smart-notes/rag_mvp/pipelines/embedding_pipeline.py`:
- Line 10: The SentenceTransformer instantiation in embedding_pipeline.py
hardcodes a Windows-only cache path ("D:/models_cache"); change the self.model =
SentenceTransformer(...) call to use a platform-agnostic cache location (or no
cache_folder so the library's default is used). Replace the literal with a
cross-platform value obtained from configuration or an environment variable
(e.g., MODEL_CACHE_DIR) or construct one via pathlib/expanduser (e.g.,
Path.home()/".cache"/"models") and pass that variable as cache_folder to
SentenceTransformer to avoid OS-specific paths.
- Around line 8-46: EmbeddingPipeline currently duplicates chunking, embedding,
and indexing logic (see methods chunk_text, build_index, process_notes,
semantic_search) with diverging defaults and missing safeguards; refactor to
compose existing components by injecting/using the shared chunk_text function
(align max_length with chunker.py), the Embedder class for model loading/encode
calls, and the VectorIndexer (or Indexer) for faiss index creation/search, and
remove local model/index implementation; also add input validation (empty
text/query checks) and import guards when instantiating Embedder/VectorIndexer
to avoid reloading models or failing on missing imports.
- Around line 44-46: FAISS can return -1 for empty neighbor slots so iterating
indices[0] and doing self.chunks[i] may index out-of-bounds or return the wrong
item; in the method where you call self.index.search(query_vec, top_k) and build
results from indices (variables distances, indices), filter or clamp indices[0]
to only non-negative values and within range(len(self.chunks)) before using
them, e.g., map valid_idx = [i for i in indices[0] if 0 <= i < len(self.chunks)]
and then construct results = [self.chunks[i] for i in valid_idx], preserving
distances alignment if needed.
In `@smart-notes/rag_mvp/qa_cli.py`:
- Around line 4-5: Fix the typo in the inline comment above the import: change
"emedding-pipeline-chunking concept" to "embedding-pipeline-chunking concept" so
the comment correctly references the EmbeddingPipeline import
(EmbeddingPipeline) and related embedding pipeline code.
- Around line 63-82: In search_notes, avoid substring matches by replacing the
current "any(word in sentence_lower for word in query_words)" logic with
word-boundary matching: for each sentence in split_sentences(note["content"]),
normalize and either use a regex search with \b{word}\b (case-insensitive) or
tokenize sentence_lower into words and check membership of each query_word in
that set; update the check inside the search_notes function so results only
append when whole words match (refer to search_notes, query_words,
sentence_lower, and split_sentences).
- Around line 85-87: The demo_embeddings_pipeline() call runs unconditionally
and pulls heavy ML deps (sentence-transformers/faiss); make it opt-in or
fail-safe: change the __main__ block to only invoke demo_embeddings_pipeline()
when an explicit flag or env var (e.g., --demo-embeddings or DEMO_EMBEDDINGS) is
present, and/or wrap the call in a try/except ImportError that catches missing
sentence-transformers/faiss, logs a clear warning, and continues so the rest of
the CLI (keyword-based search) can run; refer to demo_embeddings_pipeline() and
the if __name__ == "__main__": block when making the change.
In `@smart-notes/rag_mvp/README.md`:
- Around line 28-45: The README's fenced code block that starts with "```bash"
before the example output is never closed, causing the remainder of the document
to render as a code literal; fix by adding the closing triple-backtick fence
(```) immediately after the shown example output where the qa_cli.py example
ends so subsequent sections (How to run, second project) render normally.
- Around line 75-84: Update the README project tree to match actual filenames
and dirs: replace embed.py with embeddings/embedder.py, index.py with
embeddings/indexer.py, utils.py with embeddings/chunker.py, add the missing
pipelines/ entry, and change the notes bullet to indicate .md files since
qa_cli.py loads Markdown; finally close the unclosed code fence (add the
trailing ```). Reference embeddings/embedder.py, embeddings/indexer.py,
embeddings/chunker.py, pipelines/, and qa_cli.py when making the edits.
🧹 Nitpick comments (3)
.gitignore (1)
1-1: Consider adding standard Python ignore patterns.This
.gitignoreonly ignoresnotes/. A Python project should also ignore__pycache__/,*.pyc,.env,*.egg-info/,dist/,build/, virtual environment directories, and model cache folders. Without these, build artifacts and secrets can accidentally be committed.Proposed .gitignore additions
notes/ +__pycache__/ +*.pyc +*.pyo +.env +*.egg-info/ +dist/ +build/ +.venv/ +models_cache/smart-notes/rag_mvp/embeddings/indexer.py (1)
34-34: Prefix unuseddistanceswith an underscore.Per Ruff RUF059, rename to
_distancesto signal intent.Proposed fix
- distances, indices = self.index.search(query_embedding.reshape(1, -1), k) + _distances, indices = self.index.search(query_embedding.reshape(1, -1), k)smart-notes/rag_mvp/qa_cli.py (1)
39-39:NOTES_DIRis a bare relative path — behavior depends entirely on the working directory.Consider resolving it relative to the script's location so the CLI works regardless of where it's invoked from.
Proposed fix
-NOTES_DIR = "notes" +NOTES_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "notes")
Comment on lines
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| def chunk_text(text: str, max_length: int = 500, overlap: int = 50) -> List[str]: | ||
| if not text: | ||
| return [] | ||
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| chunks = [] | ||
| start = 0 | ||
| text = text.strip() | ||
|
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| while start < len(text): | ||
| end = start + max_length | ||
| chunk = text[start:end].strip() | ||
|
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| if chunk: | ||
| chunks.append(chunk) | ||
|
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| if end >= len(text): | ||
| break | ||
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| start = end - overlap | ||
| if start < 0: | ||
| start = 0 |
Contributor
There was a problem hiding this comment.
Infinite loop when overlap >= max_length.
If overlap >= max_length, then start = end - overlap never advances past the current position (and the start < 0 guard resets it to 0), causing an infinite loop on any text longer than max_length. Add a validation guard at the top.
Proposed fix
def chunk_text(text: str, max_length: int = 500, overlap: int = 50) -> List[str]:
if not text:
return []
+ if overlap >= max_length:
+ raise ValueError("overlap must be less than max_length")
chunks = []🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/embeddings/chunker.py` around lines 9 - 29, The
chunk_text function can infinite-loop when overlap >= max_length; add an upfront
validation in chunk_text (using parameters max_length and overlap) that either
raises a ValueError or adjusts overlap (e.g., require 0 <= overlap < max_length)
and return an error if the inputs are invalid; ensure this guard runs before
trimming text or entering the while loop so start always progresses.
Comment on lines
+25
to
+27
| def embed(self, texts: List[str]) -> np.ndarray: | ||
| if not texts: | ||
| return np.array([]) |
Contributor
There was a problem hiding this comment.
Empty-input return shape is 1-D, but callers likely expect 2-D.
np.array([]) returns shape (0,), while successful calls return shape (n, dim). Downstream code (e.g., VectorIndexer.add which calls self.index.add(embeddings)) may fail or behave unexpectedly with a 1-D array. Consider returning a properly shaped empty array.
Proposed fix
def embed(self, texts: List[str]) -> np.ndarray:
if not texts:
- return np.array([])
+ return np.empty((0, self.model.get_sentence_embedding_dimension()), dtype=np.float32)
embeddings = self.model.encode(texts, convert_to_numpy=True)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| def embed(self, texts: List[str]) -> np.ndarray: | |
| if not texts: | |
| return np.array([]) | |
| def embed(self, texts: List[str]) -> np.ndarray: | |
| if not texts: | |
| return np.empty((0, self.model.get_sentence_embedding_dimension()), dtype=np.float32) | |
| embeddings = self.model.encode(texts, convert_to_numpy=True) |
🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/embeddings/embedder.py` around lines 25 - 27, The embed
method returns a 1-D empty array for empty input; change it to return a 2-D
empty array with zero rows and the embedding dimensionality so downstream code
(e.g., VectorIndexer.add -> self.index.add) receives shape (0, dim). Update
embed to return np.empty((0, self.embedding_dim)) (or np.empty((0,
<detected_dim>)) if the class exposes a model/embedding size) when texts is
empty, or compute the dim from an existing weight/embedding shape and use that
to form the (0, dim) array.
Comment on lines
+37
to
+39
| for idx in indices[0]: | ||
| if idx < len(self.texts): | ||
| results.append(self.texts[idx]) |
Contributor
There was a problem hiding this comment.
Bug: FAISS returns -1 for unfilled neighbor slots, which is a valid Python negative index.
When fewer than k vectors are in the index, FAISS sets missing indices to -1. Since -1 < len(self.texts) is always True in Python, self.texts[-1] silently returns the last stored chunk instead of being skipped.
Proposed fix
for idx in indices[0]:
- if idx < len(self.texts):
+ if 0 <= idx < len(self.texts):
results.append(self.texts[idx])🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/embeddings/indexer.py` around lines 37 - 39, FAISS can
return -1 for empty neighbor slots which becomes a valid Python negative index;
in the loop in indexer.py that iterates "for idx in indices[0]:" (inside
whatever method populating results), change the guard to explicitly skip
negative indices (e.g., require idx >= 0 and idx < len(self.texts)) instead of
only checking "idx < len(self.texts)"; update the condition so -1 is not used to
index self.texts and only valid non-negative indices are appended to results.
Comment on lines
+8
to
+46
| class EmbeddingPipeline: | ||
| def __init__(self, model_name="all-MiniLM-L6-v2"): | ||
| self.model = SentenceTransformer(model_name, cache_folder="D:/models_cache") | ||
| self.index = None | ||
| self.chunks = [] | ||
|
|
||
| def chunk_text(self, text, max_length=300, overlap=50): | ||
| chunks = [] | ||
| start = 0 | ||
|
|
||
| while start < len(text): | ||
| end = start + max_length | ||
| chunk = text[start:end] | ||
| chunks.append(chunk) | ||
| start = end - overlap | ||
|
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||
| return chunks | ||
|
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| def build_index(self, chunks): | ||
| embeddings = self.model.encode(chunks) | ||
| embeddings = np.array(embeddings).astype("float32") | ||
|
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| dim = embeddings.shape[1] | ||
| self.index = faiss.IndexFlatL2(dim) | ||
| self.index.add(embeddings) | ||
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| return embeddings | ||
|
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| def process_notes(self, text): | ||
| self.chunks = self.chunk_text(text) | ||
| embeddings = self.build_index(self.chunks) | ||
| return self.chunks, embeddings | ||
|
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| def semantic_search(self, query, top_k=3): | ||
| query_vec = self.model.encode([query]) | ||
| query_vec = np.array(query_vec).astype("float32") | ||
|
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| distances, indices = self.index.search(query_vec, top_k) | ||
| results = [self.chunks[i] for i in indices[0]] |
Contributor
There was a problem hiding this comment.
🛠️ Refactor suggestion | 🟠 Major
EmbeddingPipeline duplicates the modular components instead of composing them.
This class re-implements chunking (vs chunker.py), embedding (vs embedder.py), and indexing (vs indexer.py) with diverging defaults (max_length=300 here vs 500 in chunker.py) and missing safeguards (no empty-input checks, no import guards). Consider composing Embedder, VectorIndexer, and chunk_text instead of duplicating their logic.
Sketch of a composed pipeline
-from sentence_transformers import SentenceTransformer
-import faiss
-import numpy as np
+from rag_mvp.embeddings.chunker import chunk_text
+from rag_mvp.embeddings.embedder import Embedder
+from rag_mvp.embeddings.indexer import VectorIndexer
class EmbeddingPipeline:
def __init__(self, model_name="all-MiniLM-L6-v2"):
- self.model = SentenceTransformer(model_name, cache_folder="D:/models_cache")
- self.index = None
+ self.embedder = Embedder(model_name)
+ self.indexer = None
self.chunks = []
- def chunk_text(self, text, max_length=300, overlap=50):
- ...
-
def build_index(self, chunks):
- embeddings = self.model.encode(chunks)
- ...
+ embeddings = self.embedder.embed(chunks)
+ self.indexer = VectorIndexer(embeddings.shape[1])
+ self.indexer.add(embeddings, chunks)
+ return embeddings
def process_notes(self, text):
- self.chunks = self.chunk_text(text)
+ self.chunks = chunk_text(text)
embeddings = self.build_index(self.chunks)
return self.chunks, embeddings
def semantic_search(self, query, top_k=3):
- query_vec = self.model.encode([query])
- ...
+ query_vec = self.embedder.embed([query])
+ return self.indexer.search(query_vec[0], k=top_k)🧰 Tools
🪛 Ruff (0.15.0)
[warning] 45-45: Unpacked variable distances is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/pipelines/embedding_pipeline.py` around lines 8 - 46,
EmbeddingPipeline currently duplicates chunking, embedding, and indexing logic
(see methods chunk_text, build_index, process_notes, semantic_search) with
diverging defaults and missing safeguards; refactor to compose existing
components by injecting/using the shared chunk_text function (align max_length
with chunker.py), the Embedder class for model loading/encode calls, and the
VectorIndexer (or Indexer) for faiss index creation/search, and remove local
model/index implementation; also add input validation (empty text/query checks)
and import guards when instantiating Embedder/VectorIndexer to avoid reloading
models or failing on missing imports.
|
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||
| class EmbeddingPipeline: | ||
| def __init__(self, model_name="all-MiniLM-L6-v2"): | ||
| self.model = SentenceTransformer(model_name, cache_folder="D:/models_cache") |
Contributor
There was a problem hiding this comment.
Hardcoded Windows-specific cache path will break on all other environments.
"D:/models_cache" is a local developer path. This will fail on Linux/macOS and on any other developer's machine. Remove it or use a platform-agnostic default.
Proposed fix
- self.model = SentenceTransformer(model_name, cache_folder="D:/models_cache")
+ self.model = SentenceTransformer(model_name)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| self.model = SentenceTransformer(model_name, cache_folder="D:/models_cache") | |
| self.model = SentenceTransformer(model_name) |
🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/pipelines/embedding_pipeline.py` at line 10, The
SentenceTransformer instantiation in embedding_pipeline.py hardcodes a
Windows-only cache path ("D:/models_cache"); change the self.model =
SentenceTransformer(...) call to use a platform-agnostic cache location (or no
cache_folder so the library's default is used). Replace the literal with a
cross-platform value obtained from configuration or an environment variable
(e.g., MODEL_CACHE_DIR) or construct one via pathlib/expanduser (e.g.,
Path.home()/".cache"/"models") and pass that variable as cache_folder to
SentenceTransformer to avoid OS-specific paths.
Comment on lines
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+5
| #-------------------emedding-pipeline-chunking concept | ||
| from rag_mvp.pipelines.embedding_pipeline import EmbeddingPipeline |
Contributor
There was a problem hiding this comment.
Typo: "emedding" → "embedding".
-#-------------------emedding-pipeline-chunking concept
+#-------------------embedding-pipeline-chunking concept🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/qa_cli.py` around lines 4 - 5, Fix the typo in the inline
comment above the import: change "emedding-pipeline-chunking concept" to
"embedding-pipeline-chunking concept" so the comment correctly references the
EmbeddingPipeline import (EmbeddingPipeline) and related embedding pipeline
code.
Comment on lines
+63
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+82
| def search_notes(query, notes): | ||
| results = [] | ||
|
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| query_words = [ | ||
| word.lower() | ||
| for word in query.split() | ||
| if word.lower() not in QUESTION_WORDS | ||
| ] | ||
|
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| for note in notes: | ||
| sentences = split_sentences(note["content"]) | ||
| for sentence in sentences: | ||
| sentence_lower = sentence.lower() | ||
| if any(word in sentence_lower for word in query_words): | ||
| results.append({ | ||
| "filename": note["filename"], | ||
| "sentence": sentence.strip() | ||
| }) | ||
|
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| return results |
Contributor
There was a problem hiding this comment.
Substring matching produces false positives on partial words.
word in sentence_lower (line 76) matches substrings, so a query word like "art" matches "start", "smart", etc. Use word-boundary matching for better precision.
Proposed fix using word boundaries
+import re
+
def search_notes(query, notes):
results = []
query_words = [
word.lower()
for word in query.split()
if word.lower() not in QUESTION_WORDS
]
for note in notes:
sentences = split_sentences(note["content"])
for sentence in sentences:
sentence_lower = sentence.lower()
- if any(word in sentence_lower for word in query_words):
+ if any(re.search(r'\b' + re.escape(word) + r'\b', sentence_lower) for word in query_words):
results.append({
"filename": note["filename"],
"sentence": sentence.strip()
})
return results🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/qa_cli.py` around lines 63 - 82, In search_notes, avoid
substring matches by replacing the current "any(word in sentence_lower for word
in query_words)" logic with word-boundary matching: for each sentence in
split_sentences(note["content"]), normalize and either use a regex search with
\b{word}\b (case-insensitive) or tokenize sentence_lower into words and check
membership of each query_word in that set; update the check inside the
search_notes function so results only append when whole words match (refer to
search_notes, query_words, sentence_lower, and split_sentences).
Comment on lines
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| if __name__ == "__main__": | ||
|
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| demo_embeddings_pipeline() # Temporary demo for embeddings pipeline |
Contributor
There was a problem hiding this comment.
demo_embeddings_pipeline() runs unconditionally and requires heavy ML dependencies.
If sentence-transformers or faiss aren't installed, this crashes the entire CLI before the keyword-based search (which has no such dependencies) can be used. Guard it or make it opt-in.
Proposed fix
if __name__ == "__main__":
-
- demo_embeddings_pipeline() # Temporary demo for embeddings pipeline
+ try:
+ demo_embeddings_pipeline() # Temporary demo for embeddings pipeline
+ except (ImportError, Exception) as e:
+ print(f"Embedding demo skipped: {e}")
notes = load_notes()📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| if __name__ == "__main__": | |
| demo_embeddings_pipeline() # Temporary demo for embeddings pipeline | |
| if __name__ == "__main__": | |
| try: | |
| demo_embeddings_pipeline() # Temporary demo for embeddings pipeline | |
| except (ImportError, Exception) as e: | |
| print(f"Embedding demo skipped: {e}") | |
| notes = load_notes() |
🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/qa_cli.py` around lines 85 - 87, The
demo_embeddings_pipeline() call runs unconditionally and pulls heavy ML deps
(sentence-transformers/faiss); make it opt-in or fail-safe: change the __main__
block to only invoke demo_embeddings_pipeline() when an explicit flag or env var
(e.g., --demo-embeddings or DEMO_EMBEDDINGS) is present, and/or wrap the call in
a try/except ImportError that catches missing sentence-transformers/faiss, logs
a clear warning, and continues so the rest of the CLI (keyword-based search) can
run; refer to demo_embeddings_pipeline() and the if __name__ == "__main__":
block when making the change.
Comment on lines
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+45
| ```bash | ||
| python smart-notes/rag_mvp/qa_cli.py | ||
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| >> what is AI | ||
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| [1] From test.md: | ||
| Artificial Intelligence (AI) is the simulation of human intelligence in machines. | ||
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| >> what is machine learning | ||
| how is machine learning used | ||
| difference between AI and ML | ||
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Contributor
There was a problem hiding this comment.
Unclosed code block causes the rest of the README to render as a code literal.
The fenced code block opened at line 28 is never closed. Everything after line 29 (including the "How to run" examples and the second project section) will render as preformatted text. Add the closing ``` after the example output.
🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/README.md` around lines 28 - 45, The README's fenced code
block that starts with "```bash" before the example output is never closed,
causing the remainder of the document to render as a code literal; fix by adding
the closing triple-backtick fence (```) immediately after the shown example
output where the qa_cli.py example ends so subsequent sections (How to run,
second project) render normally.
Comment on lines
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| ```bash | ||
| smart-notes/ | ||
| ├── rag_mvp/ | ||
| │ ├── embed.py # Embedding logic | ||
| │ ├── index.py # FAISS index creation | ||
| │ ├── qa_cli.py # CLI for asking questions | ||
| │ └── utils.py # Helper functions | ||
| ├── notes/ # Put your .txt notes here | ||
| ├── requirements.txt | ||
| └── README.md |
Contributor
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Project structure doesn't match actual file names and the code block is unclosed.
embed.py→ actual:embeddings/embedder.pyindex.py→ actual:embeddings/indexer.pyutils.py→ not present; actual utilities are inembeddings/chunker.py- The
pipelines/directory is missing from the structure - Line 82 says
.txtnotes butqa_cli.pyloads.mdfiles - The code fence is never closed (file ends without
```)
🤖 Prompt for AI Agents
In `@smart-notes/rag_mvp/README.md` around lines 75 - 84, Update the README
project tree to match actual filenames and dirs: replace embed.py with
embeddings/embedder.py, index.py with embeddings/indexer.py, utils.py with
embeddings/chunker.py, add the missing pipelines/ entry, and change the notes
bullet to indicate .md files since qa_cli.py loads Markdown; finally close the
unclosed code fence (add the trailing ```). Reference embeddings/embedder.py,
embeddings/indexer.py, embeddings/chunker.py, pipelines/, and qa_cli.py when
making the edits.
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Overview
This PR adds a design-only contribution for the Smart Notes landing page.
The goal is to visually communicate the app’s privacy-first and offline-by-default
philosophy through a clean and focused interface.
Scope
Figma Design
The complete UI design and layout exploration is available on Figma:
https://www.figma.com/design/BE02AKFWjPlCOpULm8zy5x/Untitled?node-id=0-1&t=IArt6JFAfD2xXQ9t-1
What’s Included
Design Goals
Notes
This design is kept separate from existing Smart Notes contributions
to maintain clear scope and improve review clarity.
Summary by CodeRabbit
Release Notes
New Features
Documentation