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docstrings and hybrid updates
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‎docs/user_guide/release_guide/0_5_0_release.ipynb‎

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@@ -23,26 +23,25 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/Users/robert.shelton/.pyenv/versions/3.11.9/lib/python3.11/site-packages/huggingface_hub/file_download.py:1142: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
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" warnings.warn(\n"
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"\u001b[32m12:44:52\u001b[0m \u001b[34mredisvl.index.index\u001b[0m \u001b[1;30mINFO\u001b[0m Index already exists, overwriting.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"['jobs:01JQYMYZBA6NM6DX9YW35MCHJZ',\n",
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" 'jobs:01JQYMYZBABXYR96H96SQ99ZPS',\n",
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" 'jobs:01JQYMYZBAGEBDS270EZADQ1TM']"
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"['jobs:01JR0V1SA29RVD9AAVSTBV9P5H',\n",
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" 'jobs:01JR0V1SA209KMVHMD7G54P3H5',\n",
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" 'jobs:01JR0V1SA23ZE7BRERXTZWC33Z']"
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]
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},
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"execution_count": 7,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -115,19 +114,101 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Hybrid query and text query classes\n",
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"# HybridQuery class\n",
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"\n",
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"Perform hybrid lexical (BM25) and vector search where results are ranked by: `hybrid_score = (1-alpha)*lexical_Score + alpha*vector_similarity`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'vector_distance': '0.655903100967',\n",
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" 'job_title': 'Software Engineer',\n",
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" 'vector_similarity': '0.672048449516',\n",
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" 'text_score': '0',\n",
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" 'hybrid_score': '0.470433914661'},\n",
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" {'vector_distance': '0.892600417137',\n",
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" 'job_title': 'Data Analyst',\n",
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" 'vector_similarity': '0.553699791431',\n",
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" 'text_score': '0',\n",
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" 'hybrid_score': '0.387589854002'},\n",
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" {'vector_distance': '0.958741784096',\n",
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" 'job_title': 'Marketing Manager',\n",
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" 'vector_similarity': '0.520629107952',\n",
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" 'text_score': '0',\n",
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" 'hybrid_score': '0.364440375566'}]"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from redisvl.query import HybridQuery\n",
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"\n",
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"text = \"Find a job as a software engineer\"\n",
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"vec = emb_model.embed(text, as_buffer=True)\n",
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"\n",
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"query = HybridQuery(\n",
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" text=text,\n",
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" text_field_name=\"job_description\",\n",
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" vector=vec,\n",
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" vector_field_name=\"job_embedding\",\n",
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" alpha=0.7,\n",
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" num_results=10,\n",
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" return_fields=[\"job_title\"],\n",
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")\n",
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"\n",
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"In 0.5.0 we introduced classes to make it easier to perform hybrid lexical (BM25) and vector searches.\n",
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"results = index.query(query)\n",
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"results"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# TextQueries\n",
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"\n",
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"> TODO: update hybrid search notebook to use the class and make sure it works the same"
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"TextQueries make it easy to perform pure lexical search with redisvl."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": []
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[]"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from redisvl.query import TextQuery\n",
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"\n",
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"text = \"Find a job as a software engineer\"\n",
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"\n",
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"query = TextQuery(\n",
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" text=text,\n",
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" text_field_name=\"job_description\",\n",
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" return_fields=[\"job_title\"],\n",
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" num_results=10,\n",
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")\n",
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"\n",
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"results = index.query(query)\n",
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"results"
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]
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},
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{
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"cell_type": "markdown",
@@ -607,7 +688,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "redisvl-56gG2io_-py3.11",
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"language": "python",
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"name": "python3"
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},

‎redisvl/query/aggregate.py‎

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Original file line numberDiff line numberDiff line change
@@ -77,7 +77,7 @@ def __init__(
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from redisvl.query import HybridQuery
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from redisvl.index import SearchIndex
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index = SearchIndex.from_yaml(index.yaml)
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index = SearchIndex.from_yaml("path/to/index.yaml")
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query = HybridQuery(
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text="example text",

‎redisvl/utils/optimize/cache.py‎

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Original file line numberDiff line numberDiff line change
@@ -80,7 +80,7 @@ def __init__(
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opt_fn: Callable = _grid_search_opt_cache,
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eval_metric: str = "f1",
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):
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"""Initialize the optimizer.
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"""Initialize the cache optimizer.
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Args:
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cache (SemanticCache): The RedisVL SemanticCache instance to optimize.
@@ -91,18 +91,45 @@ def __init__(
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Defaults to "f1" score.
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.. code-block:: python
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# TODO
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from redisvl.extensions.llmcache import SemanticCache
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from redisvl.utils.optimize import CacheThresholdOptimizer
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97+
sem_cache = SemanticCache(
98+
name="sem_cache", # underlying search index name
99+
redis_url="redis://localhost:6379", # redis connection url string
100+
distance_threshold=0.5 # semantic cache distance threshold
101+
)
102+
103+
paris_key = sem_cache.store(prompt="what is the capital of france?", response="paris")
104+
rabat_key = sem_cache.store(prompt="what is the capital of morocco?", response="rabat")
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106+
test_data = [
107+
{
108+
"query": "What's the capital of Britain?",
109+
"query_match": ""
110+
},
111+
{
112+
"query": "What's the capital of France??",
113+
"query_match": paris_key
114+
},
115+
{
116+
"query": "What's the capital city of Morocco?",
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"query_match": rabat_key
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},
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]
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optimizer = CacheThresholdOptimizer(sem_cache, test_data)
122+
optimizer.optimize()
97123
"""
98124
super().__init__(cache, test_dict, opt_fn, eval_metric)
99125

100126
def optimize(self, **kwargs: Any):
101127
"""Optimize thresholds using the provided optimization function for cache case.
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.. code-block:: python
130+
from redisvl.utils.optimize import CacheThresholdOptimizer
104131
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# TODO
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optimizer = CacheThresholdOptimizer(semantic_cache, test_data)
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optimizer.optimize(*kwargs)
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"""
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self.opt_fn(self.optimizable, self.test_data, self.eval_metric, **kwargs)

‎redisvl/utils/optimize/router.py‎

Lines changed: 46 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -99,21 +99,63 @@ def __init__(
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opt_fn: Callable = _random_search_opt_router,
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eval_metric: str = "f1",
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):
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"""
103-
# TODO
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"""Initialize the router optimizer.
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Args:
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router (SemanticRouter): The RedisVL SemanticRouter instance to optimize.
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test_dict (List[Dict[str, Any]]): List of test cases.
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opt_fn (Callable): Function to perform optimization. Defaults to
108+
grid search.
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eval_metric (str): Evaluation metric for threshold optimization.
110+
Defaults to "f1" score.
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.. code-block:: python
113+
from redisvl.extensions.router import Route, SemanticRouter
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from redisvl.utils.vectorize import HFTextVectorizer
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from redisvl.utils.optimize import RouterThresholdOptimizer
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routes = [
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Route(
119+
name="greeting",
120+
references=["hello", "hi"],
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metadata={"type": "greeting"},
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distance_threshold=0.5,
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),
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Route(
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name="farewell",
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references=["bye", "goodbye"],
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metadata={"type": "farewell"},
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distance_threshold=0.5,
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),
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]
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router = SemanticRouter(
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name="greeting-router",
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vectorizer=HFTextVectorizer(),
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routes=routes,
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redis_url="redis://localhost:6379",
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overwrite=True # Blow away any other routing index with this name
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)
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test_data = [
141+
{"query": "hello", "query_match": "greeting"},
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{"query": "goodbye", "query_match": "farewell"},
143+
...
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]
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# TODO
146+
optimizer = RouterThresholdOptimizer(router, test_data)
147+
optimizer.optimize()
108148
"""
109149
super().__init__(router, test_dict, opt_fn, eval_metric)
110150

111151
def optimize(self, **kwargs: Any):
112152
"""Optimize thresholds using the provided optimization function for router case.
113153
114154
.. code-block:: python
155+
from redisvl.utils.optimize import RouterThresholdOptimizer
115156
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# TODO
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optimizer = RouterThresholdOptimizer(router, test_data)
158+
optimizer.optimize(search_step=0.05, max_iterations=50)
117159
"""
118160
qrels = _format_qrels(self.test_data)
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self.opt_fn(self.optimizable, self.test_data, qrels, self.eval_metric, **kwargs)

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