From aa44bfdf1fbb3849f541c4adad2859ea3a54829a Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Wed, 16 Oct 2024 22:46:50 -0400 Subject: [PATCH] feat(agents-api): Tweak queries for search (#685) - **feat(agents-api): Tweak the proximity indices** - **feat(agents-api): Tweak queries for search** ---- > [!IMPORTANT] > Enhances search functionality by adding NLP-based query generation, adjusting search parameters, and updating database indices in the `agents-api`. > > - **NLP Module**: > - Added `nlp.py` for keyword extraction and query building using spaCy. > - Functions include `extract_keywords()`, `find_proximity_groups()`, and `text_to_custom_query()`. > - **Search Functionality**: > - Updated `search_docs_by_embedding()` in `search_docs_by_embedding.py` to adjust `confidence` to 0.5 and `ef` to 32. > - Modified `search_docs_by_text()` in `search_docs_by_text.py` to use `paragraph_to_custom_queries()` for query generation. > - **Database Indices**: > - Migration `migrate_1729114011_tweak_proximity_indices.py` updates LSH and FTS indices for better proximity handling. > - **Dependencies**: > - Added `spacy`, `en-core-web-sm`, and `msgpack` to `pyproject.toml`. > - Adjusted `numpy` version constraint in `pyproject.toml`. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 3018c6e47c052c9f9c1c0a740a887a48fac41144. It will automatically update as commits are pushed. --------- Signed-off-by: Diwank Singh Tomer Co-authored-by: creatorrr --- agents-api/agents_api/common/nlp.py | 216 ++++ .../models/docs/search_docs_by_embedding.py | 4 +- .../models/docs/search_docs_by_text.py | 9 +- ...rate_1729114011_tweak_proximity_indices.py | 92 ++ agents-api/poetry.lock | 1068 ++++++++++++++--- agents-api/pyproject.toml | 5 +- 6 files changed, 1241 insertions(+), 153 deletions(-) create mode 100644 agents-api/agents_api/common/nlp.py create mode 100644 agents-api/migrations/migrate_1729114011_tweak_proximity_indices.py diff --git a/agents-api/agents_api/common/nlp.py b/agents-api/agents_api/common/nlp.py new file mode 100644 index 000000000..2fec52421 --- /dev/null +++ b/agents-api/agents_api/common/nlp.py @@ -0,0 +1,216 @@ +import re +from collections import Counter, defaultdict + +import spacy + +# Load spaCy English model +spacy.prefer_gpu() +nlp = spacy.load("en_core_web_sm") + + +def extract_keywords(text: str, top_n: int = 10) -> list[str]: + """ + Extracts significant keywords and phrases from the text. + + Args: + text (str): The input text to process. + top_n (int): Number of top keywords to extract based on frequency. + + Returns: + List[str]: A list of extracted keywords/phrases. + """ + doc = nlp(text) + + # Extract named entities + entities = [ + ent.text.strip() + for ent in doc.ents + if ent.label_ + not in ["DATE", "TIME", "PERCENT", "MONEY", "QUANTITY", "ORDINAL", "CARDINAL"] + ] + + # Extract nouns and proper nouns + nouns = [ + chunk.text.strip().lower() + for chunk in doc.noun_chunks + if not chunk.root.is_stop + ] + + # Combine entities and nouns + combined = entities + nouns + + # Normalize and count frequency + normalized = [re.sub(r"\s+", " ", kw).strip().lower() for kw in combined] + freq = Counter(normalized) + + # Get top_n keywords + keywords = [item for item, count in freq.most_common(top_n)] + + return keywords + + +def find_keyword_positions(doc, keyword: str) -> list[int]: + """ + Finds all start indices of the keyword in the tokenized doc. + + Args: + doc (spacy.tokens.Doc): The tokenized document. + keyword (str): The keyword or phrase to search for. + + Returns: + List[int]: List of starting token indices where the keyword appears. + """ + keyword_tokens = keyword.split() + n = len(keyword_tokens) + positions = [] + for i in range(len(doc) - n + 1): + window = doc[i : i + n] + window_text = " ".join([token.text.lower() for token in window]) + if window_text == keyword: + positions.append(i) + return positions + + +def find_proximity_groups( + text: str, keywords: list[str], n: int = 10 +) -> list[set[str]]: + """ + Groups keywords that appear within n words of each other. + + Args: + text (str): The input text. + keywords (List[str]): List of keywords to consider. + n (int): The proximity window in words. + + Returns: + List[Set[str]]: List of sets, each containing keywords that are proximate. + """ + doc = nlp(text.lower()) + keyword_positions = defaultdict(list) + + for kw in keywords: + positions = find_keyword_positions(doc, kw) + keyword_positions[kw].extend(positions) + + # Initialize Union-Find structure + parent = {} + + def find(u): + while parent[u] != u: + parent[u] = parent[parent[u]] + u = parent[u] + return u + + def union(u, v): + u_root = find(u) + v_root = find(v) + if u_root == v_root: + return + parent[v_root] = u_root + + # Initialize each keyword as its own parent + for kw in keywords: + parent[kw] = kw + + # Compare all pairs of keywords + for i in range(len(keywords)): + for j in range(i + 1, len(keywords)): + kw1 = keywords[i] + kw2 = keywords[j] + positions1 = keyword_positions[kw1] + positions2 = keyword_positions[kw2] + # Check if any positions are within n words + for pos1 in positions1: + for pos2 in positions2: + distance = abs(pos1 - pos2) + if distance <= n: + union(kw1, kw2) + break + else: + continue + break + + # Group keywords by their root parent + groups = defaultdict(set) + for kw in keywords: + root = find(kw) + groups[root].add(kw) + + # Convert to list of sets + group_list = list(groups.values()) + + return group_list + + +def build_query(groups: list[set[str]], keywords: list[str], n: int = 10) -> str: + """ + Builds a query string using the custom query language. + + Args: + groups (List[Set[str]]): List of keyword groups. + keywords (List[str]): Original list of keywords. + n (int): The proximity window for NEAR. + + Returns: + str: The constructed query string. + """ + grouped_keywords = set() + clauses = [] + + for group in groups: + if len(group) == 1: + clauses.append(f'"{list(group)[0]}"') + else: + sorted_group = sorted( + group, key=lambda x: -len(x) + ) # Sort by length to prioritize phrases + escaped_keywords = [f'"{kw}"' for kw in sorted_group] + near_clause = f"NEAR/{n}(" + " ".join(escaped_keywords) + ")" + clauses.append(near_clause) + grouped_keywords.update(group) + + # Identify keywords not in any group (if any) + remaining = set(keywords) - grouped_keywords + for kw in remaining: + clauses.append(f'"{kw}"') + + # Combine all clauses with OR + query = " OR ".join(clauses) + + return query + + +def text_to_custom_query(text: str, top_n: int = 10, proximity_n: int = 10) -> str: + """ + Converts arbitrary text to the custom query language. + + Args: + text (str): The input text to convert. + top_n (int): Number of top keywords to extract. + proximity_n (int): The proximity window for NEAR/n. + + Returns: + str: The custom query string. + """ + keywords = extract_keywords(text, top_n) + if not keywords: + return "" + groups = find_proximity_groups(text, keywords, proximity_n) + query = build_query(groups, keywords, proximity_n) + return query + + +def paragraph_to_custom_queries(paragraph: str) -> list[str]: + """ + Converts a paragraph to a list of custom query strings. + + Args: + paragraph (str): The input paragraph to convert. + + Returns: + List[str]: The list of custom query strings. + """ + + queries = [text_to_custom_query(sentence.text) for sentence in nlp(paragraph).sents] + + return queries diff --git a/agents-api/agents_api/models/docs/search_docs_by_embedding.py b/agents-api/agents_api/models/docs/search_docs_by_embedding.py index 90db8ac26..7d3bbbd2f 100644 --- a/agents-api/agents_api/models/docs/search_docs_by_embedding.py +++ b/agents-api/agents_api/models/docs/search_docs_by_embedding.py @@ -47,8 +47,8 @@ def search_docs_by_embedding( owners: list[tuple[Literal["user", "agent"], UUID]], query_embedding: list[float], k: int = 3, - confidence: float = 0.7, - ef: int = 128, + confidence: float = 0.5, + ef: int = 32, mmr_lambda: float = 0.25, embedding_size: int = 1024, ) -> tuple[list[str], dict]: diff --git a/agents-api/agents_api/models/docs/search_docs_by_text.py b/agents-api/agents_api/models/docs/search_docs_by_text.py index bc7063e87..1df34a5e2 100644 --- a/agents-api/agents_api/models/docs/search_docs_by_text.py +++ b/agents-api/agents_api/models/docs/search_docs_by_text.py @@ -1,6 +1,5 @@ """This module contains functions for searching documents in the CozoDB based on embedding queries.""" -import json from typing import Any, Literal, TypeVar from uuid import UUID @@ -10,6 +9,7 @@ from pydantic import ValidationError from ...autogen.openapi_model import DocReference +from ...common.nlp import paragraph_to_custom_queries from ..utils import ( cozo_query, partialclass, @@ -64,7 +64,7 @@ def search_docs_by_text( # Need to use NEAR/3($query) to search for arbitrary text within 3 words of each other # See: https://docs.cozodb.org/en/latest/vector.html#full-text-search-fts - query = f"NEAR/3({json.dumps(query)})" + fts_queries = paragraph_to_custom_queries(query) # Construct the datalog query for searching document snippets search_query = f""" @@ -112,11 +112,12 @@ def search_docs_by_text( index, content | - query: $query, + query: query, k: {k}, score_kind: 'tf_idf', bind_score: score, }}, + query in $fts_queries, distance = -score, snippet_data = [index, content] @@ -183,5 +184,5 @@ def search_docs_by_text( return ( queries, - {"owners": owners, "query": query}, + {"owners": owners, "query": query, "fts_queries": fts_queries}, ) diff --git a/agents-api/migrations/migrate_1729114011_tweak_proximity_indices.py b/agents-api/migrations/migrate_1729114011_tweak_proximity_indices.py new file mode 100644 index 000000000..e8fbbaa58 --- /dev/null +++ b/agents-api/migrations/migrate_1729114011_tweak_proximity_indices.py @@ -0,0 +1,92 @@ +# /usr/bin/env python3 + +MIGRATION_ID = "tweak_proximity_indices" +CREATED_AT = 1729114011.022733 + + +def run(client, *queries): + joiner = "}\n\n{" + + query = joiner.join(queries) + query = f"{{\n{query}\n}}" + client.run(query) + + +drop_snippets_lsh_index = dict( + up=""" + ::lsh drop snippets:lsh + """, + down=""" + ::lsh create snippets:lsh { + extractor: content, + tokenizer: Simple, + filters: [Stopwords('en')], + n_perm: 200, + target_threshold: 0.9, + n_gram: 3, + false_positive_weight: 1.0, + false_negative_weight: 1.0, + } + """, +) + +snippets_lsh_index = dict( + up=""" + ::lsh create snippets:lsh { + extractor: content, + tokenizer: Simple, + filters: [Lowercase, AsciiFolding, Stemmer('english'), Stopwords('en')], + n_perm: 200, + target_threshold: 0.5, + n_gram: 2, + false_positive_weight: 1.0, + false_negative_weight: 1.0, + } + """, + down=""" + ::lsh drop snippets:lsh + """, +) + +# See: https://docs.cozodb.org/en/latest/vector.html#full-text-search-fts +drop_snippets_fts_index = dict( + down=""" + ::fts create snippets:fts { + extractor: content, + tokenizer: Simple, + filters: [Lowercase, Stemmer('english'), Stopwords('en')], + } + """, + up=""" + ::fts drop snippets:fts + """, +) + +# See: https://docs.cozodb.org/en/latest/vector.html#full-text-search-fts +snippets_fts_index = dict( + up=""" + ::fts create snippets:fts { + extractor: content, + tokenizer: Simple, + filters: [Lowercase, AsciiFolding, Stemmer('english'), Stopwords('en')], + } + """, + down=""" + ::fts drop snippets:fts + """, +) + +queries_to_run = [ + drop_snippets_lsh_index, + drop_snippets_fts_index, + snippets_lsh_index, + snippets_fts_index, +] + + +def up(client): + run(client, *[q["up"] for q in queries_to_run]) + + +def down(client): + run(client, *[q["down"] for q in reversed(queries_to_run)]) diff --git a/agents-api/poetry.lock b/agents-api/poetry.lock index b4814e3ab..d2bb2fb2d 100644 --- a/agents-api/poetry.lock +++ b/agents-api/poetry.lock @@ -431,19 +431,60 @@ webencodings = "*" [package.extras] css = ["tinycss2 (>=1.1.0,<1.3)"] +[[package]] +name = "blis" +version = "1.0.1" +description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension." +optional = false +python-versions = "*" +files = [ + {file = "blis-1.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:232f68f78fd9ab2f8bf01424cd6a265edd97e14e26186ef48eca1b6f212e928f"}, + {file = 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