forked from Archives/langchain
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
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cs0lar
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@ -4,10 +4,13 @@ from __future__ import annotations
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from typing import Any, Dict, Iterable, List, Optional, Type
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from typing import Any, Dict, Iterable, List, Optional, Type
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from uuid import uuid4
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from uuid import uuid4
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import numpy as np
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from langchain.docstore.document import Document
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.embeddings.base import Embeddings
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from langchain.utils import get_from_dict_or_env
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from langchain.utils import get_from_dict_or_env
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from langchain.vectorstores.base import VectorStore
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from langchain.vectorstores.base import VectorStore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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def _default_schema(index_name: str) -> Dict:
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def _default_schema(index_name: str) -> Dict:
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@ -42,6 +45,7 @@ class Weaviate(VectorStore):
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client: Any,
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client: Any,
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index_name: str,
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index_name: str,
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text_key: str,
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text_key: str,
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embedding: Optional[Embeddings] = None,
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attributes: Optional[List[str]] = None,
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attributes: Optional[List[str]] = None,
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):
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):
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"""Initialize with Weaviate client."""
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"""Initialize with Weaviate client."""
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@ -58,6 +62,7 @@ class Weaviate(VectorStore):
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)
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)
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self._client = client
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self._client = client
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self._index_name = index_name
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self._index_name = index_name
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self._embedding = embedding
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self._text_key = text_key
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self._text_key = text_key
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self._query_attrs = [self._text_key]
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self._query_attrs = [self._text_key]
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if attributes is not None:
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if attributes is not None:
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@ -129,6 +134,54 @@ class Weaviate(VectorStore):
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docs.append(Document(page_content=text, metadata=res))
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docs.append(Document(page_content=text, metadata=res))
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return docs
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return docs
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def max_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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lambda_mult = kwargs.get("lambda_mult", 0.5)
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if self._embedding is not None:
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embedding = self._embedding.embed_query(query)
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else:
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raise ValueError(
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"max_marginal_relevance_search requires a suitable Embeddings object"
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)
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vector = {"vector": embedding}
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query_obj = self._client.query.get(self._index_name, self._query_attrs)
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results = (
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query_obj.with_additional("vector")
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.with_near_vector(vector)
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.with_limit(fetch_k)
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.do()
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)
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payload = results["data"]["Get"][self._index_name]
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embeddings = [result["_additional"]["vector"] for result in payload]
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mmr_selected = maximal_marginal_relevance(
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np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
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)
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docs = []
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for idx in mmr_selected:
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text = payload[idx].pop(self._text_key)
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payload[idx].pop("_additional")
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meta = payload[idx]
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docs.append(Document(page_content=text, metadata=meta))
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return docs
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@classmethod
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@classmethod
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def from_texts(
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def from_texts(
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cls: Type[Weaviate],
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cls: Type[Weaviate],
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@ -201,10 +254,10 @@ class Weaviate(VectorStore):
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"class_name": index_name,
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"class_name": index_name,
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}
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}
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if embeddings is not None:
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if embeddings is not None:
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params["vector"] = (embeddings[i],)
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params["vector"] = embeddings[i]
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batch.add_data_object(**params)
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batch.add_data_object(**params)
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batch.flush()
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batch.flush()
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return cls(client, index_name, text_key, attributes)
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return cls(client, index_name, text_key, embedding, attributes)
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@ -0,0 +1,729 @@
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interactions:
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Accept-Encoding:
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Connection:
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Content-Length:
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- '65'
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Content-Type:
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User-Agent:
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X-OpenAI-Client-User-Agent:
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authorization:
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method: POST
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uri: https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings
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headers:
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CF-Cache-Status:
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- DYNAMIC
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CF-RAY:
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- 7b8693c3fe087743-LHR
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Connection:
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- keep-alive
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Content-Encoding:
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- gzip
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Content-Type:
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- application/json
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Date:
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- Sat, 15 Apr 2023 19:25:55 GMT
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Server:
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- cloudflare
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Transfer-Encoding:
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- chunked
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access-control-allow-origin:
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alt-svc:
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- h3=":443"; ma=86400, h3-29=":443"; ma=86400
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openai-organization:
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- user-iy0qn7phyookv8vra62ulvxe
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openai-processing-ms:
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- '110'
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openai-version:
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- '2020-10-01'
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strict-transport-security:
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- max-age=15724800; includeSubDomains
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x-ratelimit-limit-requests:
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- '60'
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x-ratelimit-remaining-requests:
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- '59'
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x-ratelimit-reset-requests:
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status:
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code: 200
|
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message: OK
|
||||||
|
version: 1
|
||||||
@ -17,6 +17,7 @@ services:
|
|||||||
QUERY_DEFAULTS_LIMIT: 25
|
QUERY_DEFAULTS_LIMIT: 25
|
||||||
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
|
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
|
||||||
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
|
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
|
||||||
DEFAULT_VECTORIZER_MODULE: 'none'
|
DEFAULT_VECTORIZER_MODULE: 'text2vec-openai'
|
||||||
ENABLE_MODULES: ''
|
ENABLE_MODULES: 'text2vec-openai'
|
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|
OPENAI_APIKEY: '${OPENAI_API_KEY}'
|
||||||
CLUSTER_HOSTNAME: 'node1'
|
CLUSTER_HOSTNAME: 'node1'
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||||||
|
|||||||
@ -1,5 +1,6 @@
|
|||||||
"""Test Weaviate functionality."""
|
"""Test Weaviate functionality."""
|
||||||
import logging
|
import logging
|
||||||
|
import os
|
||||||
from typing import Generator, Union
|
from typing import Generator, Union
|
||||||
|
|
||||||
import pytest
|
import pytest
|
||||||
@ -18,6 +19,11 @@ docker compose -f weaviate.yml up
|
|||||||
|
|
||||||
|
|
||||||
class TestWeaviate:
|
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)
|
@pytest.fixture(scope="class", autouse=True)
|
||||||
def weaviate_url(self) -> Union[str, Generator[str, None, None]]:
|
def weaviate_url(self) -> Union[str, Generator[str, None, None]]:
|
||||||
"""Return the weaviate url."""
|
"""Return the weaviate url."""
|
||||||
@ -28,24 +34,57 @@ class TestWeaviate:
|
|||||||
client = Client(url)
|
client = Client(url)
|
||||||
client.schema.delete_all()
|
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."""
|
"""Test end to end construction and search without metadata."""
|
||||||
texts = ["foo", "bar", "baz"]
|
texts = ["foo", "bar", "baz"]
|
||||||
docsearch = Weaviate.from_texts(
|
docsearch = Weaviate.from_texts(
|
||||||
texts,
|
texts,
|
||||||
OpenAIEmbeddings(),
|
embedding_openai,
|
||||||
weaviate_url=weaviate_url,
|
weaviate_url=weaviate_url,
|
||||||
)
|
)
|
||||||
|
|
||||||
output = docsearch.similarity_search("foo", k=1)
|
output = docsearch.similarity_search("foo", k=1)
|
||||||
assert output == [Document(page_content="foo")]
|
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."""
|
"""Test end to end construction and search with metadata."""
|
||||||
texts = ["foo", "bar", "baz"]
|
texts = ["foo", "bar", "baz"]
|
||||||
metadatas = [{"page": i} for i in range(len(texts))]
|
metadatas = [{"page": i} for i in range(len(texts))]
|
||||||
docsearch = Weaviate.from_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)
|
output = docsearch.similarity_search("foo", k=1)
|
||||||
assert output == [Document(page_content="foo", metadata={"page": 0})]
|
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}),
|
||||||
|
]
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user