From 777835e1c559b63835f6149160b0fe18817b11d5 Mon Sep 17 00:00:00 2001 From: Claude Code Date: Wed, 20 May 2026 12:46:22 -0400 Subject: [PATCH 1/9] feat: add semantic search marimo notebook Converts docs/semantic-search.ipynb to a marimo notebook with: - Pinecone SDK 9.0.1 API (pc.indexes.*, pc.index(), new search signature) - Refactored dataset preparation into prepare_sentences/to_records functions - Keyword filtering with deduplication - mo.ui.table for dataset inspection - mo.status.progress_bar replacing tqdm - mo.ui.run_button for safe index deletion - Improved prose structure with explanations interspersed between code cells Also pins notebook dependencies in pyproject.toml. Co-Authored-By: Claude Sonnet 4.6 (1M context) --- docs/semantic-search.py | 381 ++++++++++++++ pyproject.toml | 6 + uv.lock | 1058 +++++++++++++++++++++++++++++++++++++-- 3 files changed, 1411 insertions(+), 34 deletions(-) create mode 100644 docs/semantic-search.py diff --git a/docs/semantic-search.py b/docs/semantic-search.py new file mode 100644 index 00000000..2ab1c6f8 --- /dev/null +++ b/docs/semantic-search.py @@ -0,0 +1,381 @@ +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "datasets", +# "marimo>=0.23.6", +# "numpy", +# "pinecone==9.0.1", +# "tqdm", +# ] +# /// + +import marimo + +__generated_with = "0.23.6" +app = marimo.App() + + +@app.cell +def _(): + import marimo as mo + + return (mo,) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + # Semantic Search + + This notebook demonstrates semantic search using Pinecone and a multilingual translation dataset. + + We'll work with a corpus of English sentences and retrieve results by meaning — not by keyword match. + + Semantic search finds documents similar in meaning to a query, regardless of the exact words used. + It works well for use cases where intent matters more than vocabulary, such as question answering + over a document corpus or multilingual search. + """) + return + + +@app.cell +def _(): + import os + + from datasets import load_dataset + from pinecone import Pinecone + + return Pinecone, load_dataset, os + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Setting up + + ### Pinecone API Key + + Set your `PINECONE_API_KEY` environment variable before running this notebook. + You can get a free key at [app.pinecone.io](https://app.pinecone.io). + """) + return + + +@app.cell +def _(Pinecone, os): + # Initialize client + api_key = os.environ.get("PINECONE_API_KEY") + + pc = Pinecone( + api_key=api_key, + source_tag="pinecone_examples:docs:semantic_search", + ) + return (pc,) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### Creating an Index + + Semantic search typically requires three components: records to search over, an embedding model + to encode meaning, and a vector database to store and query embeddings. + + With Pinecone's Integrated Inference, the index is paired with a hosted embedding model. + Pinecone handles embedding automatically when you upsert and query records — no separate + embedding step required. + + We'll use the [llama-text-embed-v2](https://docs.pinecone.io/models/llama-text-embed-v2) model + and map it to the `chunk_text` field in our records. To embed multilingual content instead, + swap in the [multilingual-e5-large](https://docs.pinecone.io/models/multilingual-e5-large) model. + """) + return + + +@app.cell +def _(mo, pc): + index_name = "semantic-search" + + if not pc.indexes.exists(name=index_name): + pc.create_index_for_model( + name=index_name, + cloud="aws", + region="us-east-1", + embed={ + "model": "llama-text-embed-v2", + "field_map": {"text": "chunk_text"}, + }, + ) + + index_desc = pc.describe_index(name=index_name) + + index = pc.index(host=index_desc.host) + + mo.inspect(index.describe_index_stats()) + return index, index_name + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Creating the Dataset + + We're using a subset of [Tatoeba](https://tatoeba.org/), a multilingual sentence translation + dataset with hundreds of thousands of pairs. Here are a few records: + """) + return + + +@app.cell +def _(load_dataset): + tatoeba = load_dataset( + "Helsinki-NLP/tatoeba", + lang1="en", + lang2="es", + split="train", + ) + return (tatoeba,) + + +@app.cell +def _(tatoeba): + tatoeba[0:3] + + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + Before embedding the full dataset, we can use a keyword filter to pre-select a focused + subset of sentences. This keeps the demo small and fast. + """) + return + + +@app.cell +def _(): + def simple_keyword_filter(sentence, keywords): + for keyword in keywords: + if keyword in sentence: + return True + return False + + + def prepare_sentences(dataset, keywords=None): + if keywords: + dataset = dataset.filter( + lambda x: simple_keyword_filter( + sentence=x["translation"]["en"], keywords=keywords + ) + ) + + dataset = dataset.flatten() + dataset = dataset.remove_columns("translation.es") + dataset = dataset.rename_column("translation.en", "sentence") + + # The dataset has some english sentences multiple times + # with different spanish translations, but we're not interested + # in that for this demo so we remove the dupes. + seen = set() + def is_unique(example): + if example["sentence"] in seen: + return False + seen.add(example["sentence"]) + return True + + dataset = dataset.filter(is_unique) + + return dataset.add_column("lang", ["en"] * len(dataset)) + + return (prepare_sentences,) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + The filtered dataset contains mostly what we expect, but also some sentences where "park" + appears as a substring but not at a word boundary — for example, "A glass of sparkling water, please." + + This isn't a problem. The embedding model encodes meaning, so these sentences will land in + a different region of the vector space and won't surface as relevant results for queries + about parks or parking. + """) + return + + +@app.cell +def _(mo, prepare_sentences, tatoeba): + keywords = ["park"] + sentences = prepare_sentences(tatoeba, keywords=keywords) + + mo.ui.table(sentences, page_size=10) + return (sentences,) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + `to_records` converts the prepared sentences into the format Pinecone's `upsert_records` + expects: a list of dicts, each with a unique `id`, a `chunk_text` field, and any additional + metadata — here, `lang`. + + The `chunk_text` field name comes from the `field_map` we set when creating the index. + Pinecone uses that mapping to know which field to embed automatically on upsert. + """) + return + + +@app.function +def to_records(sentences, column): + return [ + { + "id": sentence["id"], + "chunk_text": sentence[column], + "lang": sentence["lang"], + } + for sentence in sentences ] + + +@app.cell +def _(mo, sentences): + records = to_records(sentences, column="sentence") + + mo.ui.table(records) + return (records,) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Upserting Records + + Each record flows through the embedding model specified at index creation time, producing a vector. + We store these vectors in Pinecone alongside any metadata fields. + + We use a namespace (`english-sentences`) to group records. + [Namespaces](https://docs.pinecone.io/guides/index-data/indexing-overview#namespaces) partition + an index and scope queries to a subset of records. + + Records also include a `lang` metadata field. + [Metadata filtering](https://docs.pinecone.io/guides/index-data/indexing-overview#metadata) + lets you narrow results by field value — useful if you later add sentences in other languages + to the same index. + """) + return + + +@app.cell +def _(index, mo, records): + batch_size = 96 + namespace = "english-sentences" + + # Batching avoids hitting the embedding model's rate limit + for start in mo.status.progress_bar( + range(0, len(records), batch_size), + title="Upserting records", + show_rate=True, + show_eta=True, + ): + index.upsert_records( + records=records[start : start + batch_size], namespace=namespace + ) + return (namespace,) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Querying + + With Integrated Inference, querying works the same way as upserting: pass text directly and + Pinecone embeds it using the same model. The query vector is compared against all stored vectors + and the closest matches are returned. + + We'll run two queries using different meanings of "park" and observe how the results differ. + """) + return + + +@app.cell(hide_code=True) +def _(index, namespace): + def print_results(query, results): + print(f"Query: '{query}'") + for hit in results.result.hits: + print(f" {hit.fields['chunk_text']} (score: {hit.score:.4f})") + print() + + + def search(query, top_k=10): + results = index.search( + namespace=namespace, + top_k=top_k, + inputs={"text": query}, + ) + print_results(query, results) + + return (search,) + + +@app.cell +def _(search): + search("I want to go to the park and relax") + return + + +@app.cell(hide_code=True) +def _(search): + search("I need a place to park") + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## How It Works + + When you call `index.search` with a text string, Pinecone first embeds it using the same model + configured at index creation — in this case, `llama-text-embed-v2`. This produces a query vector + in the same embedding space as the stored sentence vectors. + + Pinecone then compares the query vector against all stored vectors using cosine similarity: the + cosine of the angle between two vectors. A score of 1.0 means the vectors point in the same + direction (identical meaning); a score near 0 means they are unrelated. The `top_k` results + with the highest scores are returned. + + Because the query string and the stored sentences are encoded by the same model, the proximity + in vector space reflects proximity in meaning — which is what makes semantic search work. + + At scale, comparing a query vector against every stored vector would be slow. Pinecone uses + approximate nearest neighbor (ANN) algorithms to find the closest matches in sub-linear time, + maintaining low latency even across billions of vectors. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Cleanup + + Delete the index when you're done to free up resources. + """) + return + + +@app.cell +def _(mo): + delete_button = mo.ui.run_button(label="Delete index") + delete_button + return (delete_button,) + + +@app.cell(hide_code=True) +def _(delete_button, index_name, mo, pc): + mo.stop(not delete_button.value) + pc.indexes.delete(index_name) + return + + +if __name__ == "__main__": + app.run() diff --git a/pyproject.toml b/pyproject.toml index 9b00b703..8cccdaa5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,6 +4,12 @@ version = "0.1.0" description = "Example notebooks and documentation for Pinecone" readme = "README.md" requires-python = ">=3.10,<3.14" +dependencies = [ + "datasets==3.5.1", + "numpy>=2.2.6", + "pinecone==9.0.1", 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Co-Authored-By: Claude Sonnet 4.6 (1M context) --- docs/semantic-search.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/docs/semantic-search.py b/docs/semantic-search.py index 2ab1c6f8..ab5910af 100644 --- a/docs/semantic-search.py +++ b/docs/semantic-search.py @@ -140,7 +140,6 @@ def _(load_dataset): @app.cell def _(tatoeba): tatoeba[0:3] - return @@ -297,13 +296,17 @@ def _(mo): return -@app.cell(hide_code=True) -def _(index, namespace): +@app.cell +def _(index, mo, namespace): def print_results(query, results): - print(f"Query: '{query}'") - for hit in results.result.hits: - print(f" {hit.fields['chunk_text']} (score: {hit.score:.4f})") - print() + data = [ + {"sentence": hit.fields["chunk_text"], "score": round(hit.score, 4)} + for hit in results.result.hits + ] + return mo.vstack([ + mo.md(f"**Query:** {query}"), + mo.ui.table(data, show_column_summaries=False), + ]) def search(query, top_k=10): @@ -312,7 +315,7 @@ def search(query, top_k=10): top_k=top_k, inputs={"text": query}, ) - print_results(query, results) + return print_results(query, results) return (search,) @@ -323,7 +326,7 @@ def _(search): return -@app.cell(hide_code=True) +@app.cell def _(search): search("I need a place to park") return @@ -370,7 +373,7 @@ def _(mo): return (delete_button,) -@app.cell(hide_code=True) +@app.cell def _(delete_button, index_name, mo, pc): mo.stop(not delete_button.value) pc.indexes.delete(index_name) From c6fd489669bd412ccf2d45f71f433b6215bce65a Mon Sep 17 00:00:00 2001 From: Claude Code Date: Wed, 20 May 2026 14:43:50 -0400 Subject: [PATCH 3/9] feat: expand semantic search notebook with multilingual support - Switch embedding model to multilingual-e5-large - Refactor data prep: filter_pairs + extract_sentences(lang) to embed both English and Spanish sentences with prefixed IDs - Upsert both languages to a single namespace - Add lang column to search results table - Add cross-lingual queries (English + Spanish) and a no-keyword query to demonstrate meaning-over-keywords retrieval - Add language filtering section with lang= parameter on search() - Update How It Works to explain model selection's role in vector space - Improve prose throughout querying and filtering sections Co-Authored-By: Claude Sonnet 4.6 (1M context) --- docs/semantic-search.py | 200 ++++++++++++++++++++++++++++------------ 1 file changed, 140 insertions(+), 60 deletions(-) diff --git a/docs/semantic-search.py b/docs/semantic-search.py index ab5910af..6dd6bcd8 100644 --- a/docs/semantic-search.py +++ b/docs/semantic-search.py @@ -85,33 +85,33 @@ def _(mo): Pinecone handles embedding automatically when you upsert and query records — no separate embedding step required. - We'll use the [llama-text-embed-v2](https://docs.pinecone.io/models/llama-text-embed-v2) model - and map it to the `chunk_text` field in our records. To embed multilingual content instead, - swap in the [multilingual-e5-large](https://docs.pinecone.io/models/multilingual-e5-large) model. + We'll use the [multilingual-e5-large](https://docs.pinecone.io/models/multilingual-e5-large) + model, which encodes text from many languages into the same vector space. This means a query + in English can return results in Spanish (and vice versa) without any translation step. """) return @app.cell -def _(mo, pc): +def _(pc): index_name = "semantic-search" - if not pc.indexes.exists(name=index_name): - pc.create_index_for_model( - name=index_name, - cloud="aws", - region="us-east-1", - embed={ - "model": "llama-text-embed-v2", - "field_map": {"text": "chunk_text"}, - }, - ) - - index_desc = pc.describe_index(name=index_name) + if pc.indexes.exists(name=index_name): + pc.indexes.delete(name=index_name) + + pc.create_index_for_model( + name=index_name, + cloud="aws", + region="us-east-1", + embed={ + "model": "multilingual-e5-large", + "field_map": {"text": "chunk_text"}, + }, + ) - index = pc.index(host=index_desc.host) + index = pc.index(index_name) - mo.inspect(index.describe_index_stats()) + index.describe_index_stats() return index, index_name @@ -146,8 +146,9 @@ def _(tatoeba): @app.cell(hide_code=True) def _(mo): mo.md(r""" - Before embedding the full dataset, we can use a keyword filter to pre-select a focused - subset of sentences. This keeps the demo small and fast. + Before embedding, we filter the dataset to a focused subset. We filter on the English side + of each translation pair, then extract sentences for both languages separately — so the + Spanish sentences are the actual translations of the matched English sentences. """) return @@ -161,21 +162,19 @@ def simple_keyword_filter(sentence, keywords): return False - def prepare_sentences(dataset, keywords=None): - if keywords: - dataset = dataset.filter( - lambda x: simple_keyword_filter( - sentence=x["translation"]["en"], keywords=keywords - ) - ) + def filter_pairs(dataset, keywords): + """Filter translation pairs where the English sentence contains any keyword.""" + return dataset.filter( + lambda x: simple_keyword_filter(x["translation"]["en"], keywords) + ).flatten() - dataset = dataset.flatten() - dataset = dataset.remove_columns("translation.es") - dataset = dataset.rename_column("translation.en", "sentence") - # The dataset has some english sentences multiple times - # with different spanish translations, but we're not interested - # in that for this demo so we remove the dupes. + def extract_sentences(pairs, lang): + """Extract and deduplicate sentences for one language from filtered pairs.""" + other = "es" if lang == "en" else "en" + dataset = pairs.remove_columns(f"translation.{other}") + dataset = dataset.rename_column(f"translation.{lang}", "sentence") + seen = set() def is_unique(example): if example["sentence"] in seen: @@ -184,32 +183,36 @@ def is_unique(example): return True dataset = dataset.filter(is_unique) + return dataset.add_column("lang", [lang] * len(dataset)) - return dataset.add_column("lang", ["en"] * len(dataset)) - - return (prepare_sentences,) + return extract_sentences, filter_pairs @app.cell(hide_code=True) def _(mo): mo.md(r""" - The filtered dataset contains mostly what we expect, but also some sentences where "park" - appears as a substring but not at a word boundary — for example, "A glass of sparkling water, please." - - This isn't a problem. The embedding model encodes meaning, so these sentences will land in - a different region of the vector space and won't surface as relevant results for queries - about parks or parking. + The English sentences contain mostly what we expect, but also some where "park" appears as a + substring — for example, "A glass of sparkling water, please." This isn't a problem: the + embedding model encodes meaning, so these sentences will land in a different region of the + vector space and won't surface as relevant results. """) return @app.cell -def _(mo, prepare_sentences, tatoeba): +def _(extract_sentences, filter_pairs, mo, tatoeba): keywords = ["park"] - sentences = prepare_sentences(tatoeba, keywords=keywords) + filtered_pairs = filter_pairs(tatoeba, keywords=keywords) + english = extract_sentences(filtered_pairs, lang="en") + spanish = extract_sentences(filtered_pairs, lang="es") - mo.ui.table(sentences, page_size=10) - return (sentences,) + mo.vstack([ + mo.md(f"**English** — {len(english)} sentences"), + mo.ui.table(english, page_size=5), + mo.md(f"**Spanish** — {len(spanish)} sentences"), + mo.ui.table(spanish, page_size=5), + ]) + return english, spanish @app.cell(hide_code=True) @@ -221,26 +224,33 @@ def _(mo): The `chunk_text` field name comes from the `field_map` we set when creating the index. Pinecone uses that mapping to know which field to embed automatically on upsert. + + We prefix IDs with the language code (`en-`, `es-`) to avoid collisions when combining + records from multiple languages. """) return @app.function -def to_records(sentences, column): +def to_records(sentences, column, id_prefix=""): return [ { - "id": sentence["id"], + "id": f"{id_prefix}{idx}", "chunk_text": sentence[column], "lang": sentence["lang"], } - for sentence in sentences ] + for idx, sentence in enumerate(sentences) + ] @app.cell -def _(mo, sentences): - records = to_records(sentences, column="sentence") +def _(english, mo, spanish): + records = ( + to_records(english, column="sentence", id_prefix="en-") + + to_records(spanish, column="sentence", id_prefix="es-") + ) - mo.ui.table(records) + mo.ui.table(records, page_size=10) return (records,) @@ -267,7 +277,7 @@ def _(mo): @app.cell def _(index, mo, records): batch_size = 96 - namespace = "english-sentences" + namespace = "sentences" # Batching avoids hitting the embedding model's rate limit for start in mo.status.progress_bar( @@ -291,7 +301,11 @@ def _(mo): Pinecone embeds it using the same model. The query vector is compared against all stored vectors and the closest matches are returned. - We'll run two queries using different meanings of "park" and observe how the results differ. + Because both English and Spanish sentences share the same vector space, an English query can + surface Spanish results — and vice versa. The `lang` column in the results shows where each + match came from. + + We'll run the same query in English and Spanish to show they retrieve semantically similar results. """) return @@ -300,7 +314,11 @@ def _(mo): def _(index, mo, namespace): def print_results(query, results): data = [ - {"sentence": hit.fields["chunk_text"], "score": round(hit.score, 4)} + { + "lang": hit.fields.get("lang", ""), + "sentence": hit.fields["chunk_text"], + "score": round(hit.score, 4), + } for hit in results.result.hits ] return mo.vstack([ @@ -309,11 +327,12 @@ def print_results(query, results): ]) - def search(query, top_k=10): + def search(query, top_k=10, lang=None): results = index.search( namespace=namespace, top_k=top_k, inputs={"text": query}, + filter={"lang": {"$eq": lang}} if lang else None, ) return print_results(query, results) @@ -328,7 +347,30 @@ def _(search): @app.cell def _(search): - search("I need a place to park") + search("Quiero ir al parque a relajarme") + return + + +@app.cell +def _(): + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### Meaning Over Keywords + + The next query contains no form of the word "park" — yet it will still retrieve sentences + about parking a car. This is the key distinction between semantic search and keyword search: + results are **ranked by meaning**, not by word overlap. + """) + return + + +@app.cell +def _(search): + search("where can I leave my car downtown") return @@ -338,7 +380,7 @@ def _(mo): ## How It Works When you call `index.search` with a text string, Pinecone first embeds it using the same model - configured at index creation — in this case, `llama-text-embed-v2`. This produces a query vector + configured at index creation — in this case, `multilingual-e5-large`. This produces a query vector in the same embedding space as the stored sentence vectors. Pinecone then compares the query vector against all stored vectors using cosine similarity: the @@ -346,8 +388,17 @@ def _(mo): direction (identical meaning); a score near 0 means they are unrelated. The `top_k` results with the highest scores are returned. - Because the query string and the stored sentences are encoded by the same model, the proximity - in vector space reflects proximity in meaning — which is what makes semantic search work. + Because `multilingual-e5-large` encodes text from many languages into the same vector space, + a query in English can retrieve Spanish results — and vice versa — without any translation step. + Proximity in vector space reflects proximity in meaning, regardless of which language the text + is in. + + **Model selection determines what the vector space looks like.** A model trained only on English + text would not place Spanish and English sentences near each other. A model trained on code would + cluster programs by functionality rather than natural language meaning. Choosing the right model + for your data and use case is the most consequential decision in a semantic search system — + Pinecone's [model catalog](https://docs.pinecone.io/models/overview) lists available options + with guidance on when to use each. At scale, comparing a query vector against every stored vector would be slow. Pinecone uses approximate nearest neighbor (ANN) algorithms to find the closest matches in sub-linear time, @@ -356,6 +407,35 @@ def _(mo): return +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Filtering by Language + + Querying in one language to find semantically similar results in another is a basic form of + translation — without any explicit translation step. But sometimes you want to search within + a single language instead. + + The `lang` metadata field on each record lets you scope results using Pinecone's + [metadata filtering](https://docs.pinecone.io/guides/search/filter-by-metadata). + The embedding model still encodes the query the same way — the filter simply restricts which + records are eligible to be returned. + """) + return + + +@app.cell +def _(search): + search("I am meeting a friend at the park", lang="en") + return + + +@app.cell +def _(search): + search("I am meeting a friend at the park", lang="en") + return + + @app.cell(hide_code=True) def _(mo): mo.md(r""" From 72b422c1f8e205a4aa1cd24eedd46fbab4695aec Mon Sep 17 00:00:00 2001 From: Claude Code Date: Wed, 20 May 2026 15:11:12 -0400 Subject: [PATCH 4/9] feat: add interactive query input to semantic search notebook - Add Try It Yourself section with mo.ui.text and mo.ui.radio for language filter, results update reactively on input change - Fix empty cell and duplicate filter query cells - Correct second language filter query to Spanish Co-Authored-By: Claude Sonnet 4.6 (1M context) --- docs/semantic-search.py | 40 ++++++++++++++++++++++++++++++++++------ 1 file changed, 34 insertions(+), 6 deletions(-) diff --git a/docs/semantic-search.py b/docs/semantic-search.py index 6dd6bcd8..473c99ae 100644 --- a/docs/semantic-search.py +++ b/docs/semantic-search.py @@ -351,11 +351,6 @@ def _(search): return -@app.cell -def _(): - return - - @app.cell(hide_code=True) def _(mo): mo.md(r""" @@ -432,7 +427,40 @@ def _(search): @app.cell def _(search): - search("I am meeting a friend at the park", lang="en") + search("Quiero reunirme con un amigo en el parque", lang="es") + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Try It Yourself + + Enter a query and select a language filter. Results update when you finish typing. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + query_input = mo.ui.text( + placeholder="Enter a search query...", + value="The park is crowded today", + full_width=True, + ) + + lang_select = mo.ui.radio( + options={"All languages": None, "English only": "en", "Spanish only": "es"}, + value="All languages", + ) + + mo.vstack([query_input, lang_select]) + return lang_select, query_input + + +@app.cell(hide_code=True) +def _(lang_select, query_input, search): + search(query_input.value, lang=lang_select.value) return From ca9b8dcdfc9a937d92c91d71fabfdbb5e0a7f8c5 Mon Sep 17 00:00:00 2001 From: Claude Code Date: Wed, 20 May 2026 15:30:58 -0400 Subject: [PATCH 5/9] chore: remove unused numpy and tqdm dependencies MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Both were added during development but are no longer used in the notebook — tqdm was replaced by mo.status.progress_bar. Co-Authored-By: Claude Sonnet 4.6 (1M context) --- docs/semantic-search.py | 2 -- pyproject.toml | 2 -- uv.lock | 5 ----- 3 files changed, 9 deletions(-) diff --git a/docs/semantic-search.py b/docs/semantic-search.py index 473c99ae..cd20f09b 100644 --- a/docs/semantic-search.py +++ b/docs/semantic-search.py @@ -3,9 +3,7 @@ # dependencies = [ # "datasets", # "marimo>=0.23.6", -# "numpy", # "pinecone==9.0.1", -# "tqdm", # ] # /// diff --git a/pyproject.toml b/pyproject.toml index 8cccdaa5..24718975 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,9 +6,7 @@ readme = "README.md" requires-python = ">=3.10,<3.14" dependencies = [ "datasets==3.5.1", - "numpy>=2.2.6", "pinecone==9.0.1", - "tqdm>=4.67.1", ] [project.optional-dependencies] diff --git a/uv.lock b/uv.lock index 30638c2d..e3fcf9bc 100644 --- a/uv.lock +++ b/uv.lock @@ -410,10 +410,7 @@ version = "0.1.0" source = { virtual = "." } dependencies = [ { name = "datasets" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, - { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pinecone" }, - { name = "tqdm" }, ] [package.optional-dependencies] @@ -438,7 +435,6 @@ requires-dist = [ { name = "langchain", marker = "extra == 'dev'", specifier = ">=0.3.21" }, { name = "marimo", marker = "extra == 'dev'", specifier = ">=0.23.6" }, { name = "nbformat", marker = "extra == 'dev'", specifier = ">=5.10.4" }, - { name = "numpy", specifier = ">=2.2.6" }, { name = "openai", marker = "extra == 'dev'", specifier = ">=1.68.0" }, { name = "pinecone", specifier = "==9.0.1" }, { name = "pinecone", marker = "extra == 'dev'", specifier = ">=6.0.2" }, @@ -447,7 +443,6 @@ requires-dist = [ { name = "python-dotenv", marker = "extra == 'dev'", specifier = ">=1.0.1" }, { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.9" }, { name = "tiktoken", marker = "extra == 'dev'", specifier = ">=0.9.0" }, - { name = "tqdm", specifier = ">=4.67.1" }, ] provides-extras = ["dev"] From bf59a63d86eb0feb80622a4782f0439afc732a46 Mon Sep 17 00:00:00 2001 From: Claude Code Date: Wed, 20 May 2026 15:34:14 -0400 Subject: [PATCH 6/9] chore: remove notebook deps from root pyproject.toml datasets and pinecone were added to [project.dependencies] by marimo's package manager during development. Notebook-specific deps belong in the notebook's inline PEP 723 metadata (# /// script block), not the root project config. Run with --sandbox to use the inline deps. 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def filter_pairs(dataset, keywords): """Filter translation pairs where the English sentence contains any keyword.""" return dataset.filter( lambda x: simple_keyword_filter(x["translation"]["en"], keywords) ).flatten() - def extract_sentences(pairs, lang): """Extract and deduplicate sentences for one language from filtered pairs.""" other = "es" if lang == "en" else "en" @@ -174,6 +172,7 @@ def extract_sentences(pairs, lang): dataset = dataset.rename_column(f"translation.{lang}", "sentence") seen = set() + def is_unique(example): if example["sentence"] in seen: return False @@ -204,12 +203,14 @@ def _(extract_sentences, filter_pairs, mo, tatoeba): english = extract_sentences(filtered_pairs, lang="en") spanish = extract_sentences(filtered_pairs, lang="es") - mo.vstack([ - mo.md(f"**English** — {len(english)} sentences"), - mo.ui.table(english, page_size=5), - mo.md(f"**Spanish** — {len(spanish)} sentences"), - mo.ui.table(spanish, page_size=5), - ]) + mo.vstack( + [ + mo.md(f"**English** — {len(english)} sentences"), + mo.ui.table(english, page_size=5), + mo.md(f"**Spanish** — {len(spanish)} sentences"), + mo.ui.table(spanish, page_size=5), + ] + ) return english, spanish @@ -243,9 +244,8 @@ def to_records(sentences, column, id_prefix=""): @app.cell def _(english, mo, spanish): - records = ( - to_records(english, column="sentence", id_prefix="en-") + - to_records(spanish, column="sentence", id_prefix="es-") + records = to_records(english, column="sentence", id_prefix="en-") + to_records( + spanish, column="sentence", id_prefix="es-" ) mo.ui.table(records, page_size=10) @@ -319,11 +319,12 @@ def print_results(query, results): } for hit in results.result.hits ] - return mo.vstack([ - mo.md(f"**Query:** {query}"), - mo.ui.table(data, show_column_summaries=False), - ]) - + return mo.vstack( + [ + mo.md(f"**Query:** {query}"), + mo.ui.table(data, show_column_summaries=False), + ] + ) def search(query, top_k=10, lang=None): results = index.search( From a233d4bcf49e81f5c86b42c6f1e9a119c5a7ee0e Mon Sep 17 00:00:00 2001 From: Claude Code Date: Thu, 21 May 2026 09:47:29 -0400 Subject: [PATCH 9/9] chore: use keyword argument names in all Pinecone calls Co-Authored-By: Claude Sonnet 4.6 (1M context) --- docs/semantic-search.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/semantic-search.py b/docs/semantic-search.py index 4a1fe653..92572732 100644 --- a/docs/semantic-search.py +++ b/docs/semantic-search.py @@ -107,7 +107,7 @@ def _(pc): }, ) - index = pc.index(index_name) + index = pc.index(name=index_name) index.describe_index_stats() return index, index_name @@ -483,7 +483,7 @@ def _(mo): @app.cell def _(delete_button, index_name, mo, pc): mo.stop(not delete_button.value) - pc.indexes.delete(index_name) + pc.indexes.delete(name=index_name) return