Fix/issue 1213 (#2932)

### Background

Continuing to implement all the interface methods defined by the
`VectorStore` class. This PR pertains to implementation of the
`max_marginal_relevance_search` method.

### Changes

- a `max_marginal_relevance_search` method implementation has been added
in `weaviate.py`
- tests have been added to the the new method
- vcr cassettes have been added for the weaviate tests

### Test Plan

Added tests for the `max_marginal_relevance_search` implementation

### Change Safety

- [x] I have added tests to cover my changes
fix_agent_callbacks
cs0lar 1 year ago committed by GitHub
parent 4c02f4bc30
commit 8b9e02da9d
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23

@ -4,10 +4,13 @@ from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional, Type
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
def _default_schema(index_name: str) -> Dict:
@ -42,6 +45,7 @@ class Weaviate(VectorStore):
client: Any,
index_name: str,
text_key: str,
embedding: Optional[Embeddings] = None,
attributes: Optional[List[str]] = None,
):
"""Initialize with Weaviate client."""
@ -58,6 +62,7 @@ class Weaviate(VectorStore):
)
self._client = client
self._index_name = index_name
self._embedding = embedding
self._text_key = text_key
self._query_attrs = [self._text_key]
if attributes is not None:
@ -129,6 +134,54 @@ class Weaviate(VectorStore):
docs.append(Document(page_content=text, metadata=res))
return docs
def max_marginal_relevance_search(
self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Returns:
List of Documents selected by maximal marginal relevance.
"""
lambda_mult = kwargs.get("lambda_mult", 0.5)
if self._embedding is not None:
embedding = self._embedding.embed_query(query)
else:
raise ValueError(
"max_marginal_relevance_search requires a suitable Embeddings object"
)
vector = {"vector": embedding}
query_obj = self._client.query.get(self._index_name, self._query_attrs)
results = (
query_obj.with_additional("vector")
.with_near_vector(vector)
.with_limit(fetch_k)
.do()
)
payload = results["data"]["Get"][self._index_name]
embeddings = [result["_additional"]["vector"] for result in payload]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
docs = []
for idx in mmr_selected:
text = payload[idx].pop(self._text_key)
payload[idx].pop("_additional")
meta = payload[idx]
docs.append(Document(page_content=text, metadata=meta))
return docs
@classmethod
def from_texts(
cls: Type[Weaviate],
@ -201,10 +254,10 @@ class Weaviate(VectorStore):
"class_name": index_name,
}
if embeddings is not None:
params["vector"] = (embeddings[i],)
params["vector"] = embeddings[i]
batch.add_data_object(**params)
batch.flush()
return cls(client, index_name, text_key, attributes)
return cls(client, index_name, text_key, embedding, attributes)

@ -0,0 +1,729 @@
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@ -17,6 +17,7 @@ services:
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'none'
ENABLE_MODULES: ''
DEFAULT_VECTORIZER_MODULE: 'text2vec-openai'
ENABLE_MODULES: 'text2vec-openai'
OPENAI_APIKEY: '${OPENAI_API_KEY}'
CLUSTER_HOSTNAME: 'node1'

@ -1,5 +1,6 @@
"""Test Weaviate functionality."""
import logging
import os
from typing import Generator, Union
import pytest
@ -18,6 +19,11 @@ docker compose -f weaviate.yml up
class TestWeaviate:
@classmethod
def setup_class(cls) -> None:
if not os.getenv("OPENAI_API_KEY"):
raise ValueError("OPENAI_API_KEY environment variable is not set")
@pytest.fixture(scope="class", autouse=True)
def weaviate_url(self) -> Union[str, Generator[str, None, None]]:
"""Return the weaviate url."""
@ -28,24 +34,57 @@ class TestWeaviate:
client = Client(url)
client.schema.delete_all()
def test_similarity_search_without_metadata(self, weaviate_url: str) -> None:
@pytest.mark.vcr(ignore_localhost=True)
def test_similarity_search_without_metadata(
self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search without metadata."""
texts = ["foo", "bar", "baz"]
docsearch = Weaviate.from_texts(
texts,
OpenAIEmbeddings(),
embedding_openai,
weaviate_url=weaviate_url,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_similarity_search_with_metadata(self, weaviate_url: str) -> None:
@pytest.mark.vcr(ignore_localhost=True)
def test_similarity_search_with_metadata(
self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search with metadata."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Weaviate.from_texts(
texts, OpenAIEmbeddings(), metadatas=metadatas, weaviate_url=weaviate_url
texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": 0})]
@pytest.mark.vcr(ignore_localhost=True)
def test_max_marginal_relevance_search(
self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and MRR search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Weaviate.from_texts(
texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
)
# if lambda=1 the algorithm should be equivalent to standard ranking
standard_ranking = docsearch.similarity_search("foo", k=2)
output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=1.0
)
assert output == standard_ranking
# if lambda=0 the algorithm should favour maximal diversity
output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0
)
assert output == [
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="bar", metadata={"page": 1}),
]

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