Repository: langchain-ai/langchain-weaviate (language: python)

Issue:
Improve test worklfow
`make test` now:

1. updates the weaviate image before running the tests
2. more verbose (displays progress and full difference when assertions fail)


Discussion / hints:

Fix patch:
diff --git a/langchain_weaviate/vectorstores.py b/langchain_weaviate/vectorstores.py
index bb401ac..89ced83 100644
--- a/langchain_weaviate/vectorstores.py
+++ b/langchain_weaviate/vectorstores.py
@@ -104,6 +104,37 @@ def __init__(
         if attributes is not None:
             self._query_attrs.extend(attributes)
 
+    def _build_query_obj(self, **kwargs):
+        """
+        Build a weaviate Get query object with the given parameters.
+        The given parameters are mapped to the corresponding methods
+        in the GetBuilder class.
+        """
+        method_map = {
+            "with_where": ["where_filter"],
+            "with_tenant": ["tenant"],
+            "with_additional": ["additional"],
+            "with_limit": ["limit"],
+        }
+
+        hybrid_args = ["query", "alpha", "vector", "properties", "fusion_type"]
+
+        query_obj = self._client.query.get(self._index_name, self._query_attrs)
+
+        for method, kwarg_list in method_map.items():
+            args = [kwargs.get(kwarg) for kwarg in kwarg_list if kwargs.get(kwarg)]
+            if args:
+                func = getattr(query_obj, method)
+                query_obj = func(*args)
+
+        hybrid_args_params_and_values = {
+            arg: kwargs.get(arg) for arg in hybrid_args if kwargs.get(arg)
+        }
+        if any(hybrid_args_params_and_values.values()):
+            query_obj = query_obj.with_hybrid(**hybrid_args_params_and_values)
+
+        return query_obj
+
     @property
     def embeddings(self) -> Optional[Embeddings]:
         return self._embedding
@@ -158,6 +189,19 @@ def add_texts(
                 ids.append(_id)
         return ids
 
+    def _perform_search(self, query: str, k: int, **kwargs: Any) -> dict:
+        """Perform a similarity search and return the raw result."""
+        if self._embedding is None:
+            raise ValueError("_embedding cannot be None for similarity_search")
+        embedding = self._embedding.embed_query(query)
+        query_obj = self._build_query_obj(
+            query=query, limit=k, vector=embedding, **kwargs
+        )
+        result = query_obj.do()
+        if "errors" in result:
+            raise ValueError(f"Error during query: {result['errors']}")
+        return result
+
     def similarity_search(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Document]:
@@ -170,44 +214,20 @@ def similarity_search(
         Returns:
             List of Documents most similar to the query.
         """
-        if self._by_text:
-            return self.similarity_search_by_text(query, k, **kwargs)
-        else:
-            if self._embedding is None:
-                raise ValueError(
-                    "_embedding cannot be None for similarity_search when "
-                    "_by_text=False"
-                )
-            embedding = self._embedding.embed_query(query)
-            return self.similarity_search_by_vector(embedding, k, **kwargs)
 
-    def similarity_search_by_text(
-        self, query: str, k: int = 4, **kwargs: Any
-    ) -> List[Document]:
-        """Return docs most similar to query.
-
-        Args:
-            query: Text to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-
-        Returns:
-            List of Documents most similar to the query.
-        """
-        embedding = self.embeddings.embed_query(query)
-        return self.similarity_search_by_vector(embedding, k, **kwargs)
+        result = self._perform_search(query, k, **kwargs)
+        docs = []
+        for res in result["data"]["Get"][self._index_name]:
+            text = res.pop(self._text_key)
+            docs.append(Document(page_content=text, metadata=res))
+        return docs
 
     def similarity_search_by_vector(
         self, embedding: List[float], k: int = 4, **kwargs: Any
     ) -> List[Document]:
         """Look up similar documents by embedding vector in Weaviate."""
         vector = {"vector": embedding}
-        query_obj = self._client.query.get(self._index_name, self._query_attrs)
-        if kwargs.get("where_filter"):
-            query_obj = query_obj.with_where(kwargs.get("where_filter"))
-        if kwargs.get("tenant"):
-            query_obj = query_obj.with_tenant(kwargs.get("tenant"))
-        if kwargs.get("additional"):
-            query_obj = query_obj.with_additional(kwargs.get("additional"))
+        query_obj = self._build_query_obj(**kwargs)
         result = query_obj.with_near_vector(vector).with_limit(k).do()
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
@@ -279,11 +299,7 @@ def max_marginal_relevance_search_by_vector(
             List of Documents selected by maximal marginal relevance.
         """
         vector = {"vector": embedding}
-        query_obj = self._client.query.get(self._index_name, self._query_attrs)
-        if kwargs.get("where_filter"):
-            query_obj = query_obj.with_where(kwargs.get("where_filter"))
-        if kwargs.get("tenant"):
-            query_obj = query_obj.with_tenant(kwargs.get("tenant"))
+        query_obj = self._build_query_obj(**kwargs)
         results = (
             query_obj.with_additional("vector")
             .with_near_vector(vector)
@@ -313,37 +329,8 @@ def similarity_search_with_score(
         text and cosine distance in float for each.
         Lower score represents more similarity.
         """
-        if self._embedding is None:
-            raise ValueError(
-                "_embedding cannot be None for similarity_search_with_score"
-            )
-        content: Dict[str, Any] = {"concepts": [query]}
-        if kwargs.get("search_distance"):
-            content["certainty"] = kwargs.get("search_distance")
-        query_obj = self._client.query.get(self._index_name, self._query_attrs)
-        if kwargs.get("where_filter"):
-            query_obj = query_obj.with_where(kwargs.get("where_filter"))
-        if kwargs.get("tenant"):
-            query_obj = query_obj.with_tenant(kwargs.get("tenant"))
-
-        embedded_query = self._embedding.embed_query(query)
-        if not self._by_text:
-            vector = {"vector": embedded_query}
-            result = (
-                query_obj.with_near_vector(vector)
-                .with_limit(k)
-                .with_additional("vector")
-                .do()
-            )
-        else:
-            # TODO: Refactor depending on discussion in issue #12
-            #       see: https://github.com/langchain-ai/langchain-weaviate/issues/12
-            result = (
-                query_obj.with_near_text(content)
-                .with_limit(k)
-                .with_additional("vector")
-                .do()
-            )
+
+        result = self._perform_search(query, k, additional=["score"], **kwargs)
 
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
@@ -351,8 +338,7 @@ def similarity_search_with_score(
         docs_and_scores = []
         for res in result["data"]["Get"][self._index_name]:
             text = res.pop(self._text_key)
-            # TODO: Why not use scores from weaviate?
-            score = np.dot(res["_additional"]["vector"], embedded_query)
+            score = res["_additional"]["score"]
             docs_and_scores.append((Document(page_content=text, metadata=res), score))
         return docs_and_scores
 
diff --git a/poetry.lock b/poetry.lock
index 663cc9b..7e9113b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -185,21 +185,22 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.1.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
-    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
 ]
 
 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]", "pre-commit"]
+dev = ["attrs[tests]", "pre-commit"]
 docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "authlib"
@@ -419,13 +420,13 @@ files = [
 
 [[package]]
 name = "comm"
-version = "0.2.0"
+version = "0.2.1"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "comm-0.2.0-py3-none-any.whl", hash = "sha256:2da8d9ebb8dd7bfc247adaff99f24dce705638a8042b85cb995066793e391001"},
-    {file = "comm-0.2.0.tar.gz", hash = "sha256:a517ea2ca28931c7007a7a99c562a0fa5883cfb48963140cf642c41c948498be"},
+    {file = "comm-0.2.1-py3-none-any.whl", hash = "sha256:87928485c0dfc0e7976fd89fc1e187023cf587e7c353e4a9b417555b44adf021"},
+    {file = "comm-0.2.1.tar.gz", hash = "sha256:0bc91edae1344d39d3661dcbc36937181fdaddb304790458f8b044dbc064b89a"},
 ]
 
 [package.dependencies]
@@ -436,63 +437,63 @@ test = ["pytest"]
 
 [[package]]
 name = "coverage"
-version = "7.3.4"
+version = "7.4.0"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "coverage-7.3.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e4353923f38d752ecfbd3f1f20bf7a3546993ae5ecd7c07fd2f25d40b4e54571"},
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-    {file = "coverage-7.3.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5214362abf26e254d749fc0c18af4c57b532a4bfde1a057565616dd3b8d7cc94"},
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-docs = ["Sphinx"]
+docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
 [[package]]
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 ]
 
 [package.dependencies]
@@ -942,7 +943,7 @@ matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
 psutil = "*"
-pyzmq = ">=20"
+pyzmq = ">=24"
 tornado = ">=6.1"
 traitlets = ">=5.4.0"
 
@@ -1059,13 +1060,13 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
 
 [[package]]
 name = "jupyter-core"
-version = "5.5.1"
+version = "5.7.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.5.1-py3-none-any.whl", hash = "sha256:220dfb00c45f0d780ce132bb7976b58263f81a3ada6e90a9b6823785a424f739"},
-    {file = "jupyter_core-5.5.1.tar.gz", hash = "sha256:1553311a97ccd12936037f36b9ab4d6ae8ceea6ad2d5c90d94a909e752178e40"},
+    {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
+    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
 ]
 
 [package.dependencies]
@@ -1119,19 +1120,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.5"
+version = "0.0.11"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_community-0.0.5-py3-none-any.whl", hash = "sha256:7579ff28b3bbaa73dd17ee5e88b84d09c785691d2af2f00f0bb98e7478072af6"},
-    {file = "langchain_community-0.0.5.tar.gz", hash = "sha256:425953df8035b6d278fa724a5d1a33b95ced3787ffff5b9128b3c16f0474335e"},
+    {file = "langchain_community-0.0.11-py3-none-any.whl", hash = "sha256:30ab1d7dbf35d0ebe684d8a1e8964e8dedd3d31a3703790436b39674cfa06f41"},
+    {file = "langchain_community-0.0.11.tar.gz", hash = "sha256:eaeaa8d63427ecf0cb32fe2f1ba4d05ad6d5ef9f7019baf21dc2dde5b1403002"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1,<0.2"
+langchain-core = ">=0.1.8,<0.2"
 langsmith = ">=0.0.63,<0.1.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@@ -1141,17 +1142,17 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
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 [[package]]
 name = "langchain-core"
-version = "0.1.1"
+version = "0.1.10"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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 [package.dependencies]
@@ -1169,13 +1170,13 @@ extended-testing = ["jinja2 (>=3,<4)"]
 
 [[package]]
 name = "langsmith"
-version = "0.0.72"
+version = "0.0.80"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1184,22 +1185,22 @@ requests = ">=2,<3"
 
 [[package]]
 name = "marshmallow"
-version = "3.20.1"
+version = "3.20.2"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
 packaging = ">=17.0"
 
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-lint = ["flake8 (==6.0.0)", "flake8-bugbear (==23.7.10)", "mypy (==1.4.1)", "pre-commit (>=2.4,<4.0)"]
+dev = ["pre-commit (>=2.4,<4.0)", "pytest", "pytz", "simplejson", "tox"]
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+lint = ["pre-commit (>=2.4,<4.0)"]
 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
@@ -1373,58 +1374,58 @@ files = [
 
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 [package.dependencies]
@@ -1740,13 +1741,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
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 ]
 
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@@ -2070,99 +2071,104 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "regex"
-version = "2023.10.3"
+version = "2023.12.25"
 description = "Alternative regular expression module, to replace re."
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [[package]]
@@ -2188,64 +2194,64 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "ruff"
-version = "0.1.8"
+version = "0.1.12"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
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 asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
 mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
@@ -2345,7 +2351,7 @@ mssql-pyodbc = ["pyodbc"]
 mypy = ["mypy (>=0.910)"]
 mysql = ["mysqlclient (>=1.4.0)"]
 mysql-connector = ["mysql-connector-python"]
-oracle = ["cx-oracle (>=8)"]
+oracle = ["cx_oracle (>=8)"]
 oracle-oracledb = ["oracledb (>=1.0.1)"]
 postgresql = ["psycopg2 (>=2.7)"]
 postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
@@ -2355,7 +2361,7 @@ postgresql-psycopg2binary = ["psycopg2-binary"]
 postgresql-psycopg2cffi = ["psycopg2cffi"]
 postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
-sqlcipher = ["sqlcipher3-binary"]
+sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "stack-data"
@@ -2509,13 +2515,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.14.0"
+version = "5.14.1"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.0-py3-none-any.whl", hash = "sha256:f14949d23829023013c47df20b4a76ccd1a85effb786dc060f34de7948361b33"},
-    {file = "traitlets-5.14.0.tar.gz", hash = "sha256:fcdaa8ac49c04dfa0ed3ee3384ef6dfdb5d6f3741502be247279407679296772"},
+    {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
+    {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"},
 ]
 
 [package.extras]
@@ -2627,24 +2633,24 @@ watchmedo = ["PyYAML (>=3.10)"]
 
 [[package]]
 name = "wcwidth"
-version = "0.2.12"
+version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
 files = [
-    {file = "wcwidth-0.2.12-py2.py3-none-any.whl", hash = "sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c"},
-    {file = "wcwidth-0.2.12.tar.gz", hash = "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02"},
+    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
+    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
 ]
 
 [[package]]
 name = "weaviate-client"
-version = "3.25.3"
+version = "3.26.0"
 description = "A python native Weaviate client"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "weaviate-client-3.25.3.tar.gz", hash = "sha256:894df700fc1f0a932fa370029f644af4062f8718026fda5ad07647d357d95167"},
-    {file = "weaviate_client-3.25.3-py3-none-any.whl", hash = "sha256:cb049ed7b710088ff9038d27b97f28c80206ce9e4d12d622c3597da5790b2be0"},
+    {file = "weaviate-client-3.26.0.tar.gz", hash = "sha256:ee809bf2d1f5a500c9a68c5edc2eb174ab5142a36802e274ab24afe675ffb28b"},
+    {file = "weaviate_client-3.26.0-py3-none-any.whl", hash = "sha256:0816861d0d6c893d4c457209d5299b265f54f3cda7ee0a37e66ac50ddf16a33d"},
 ]
 
 [package.dependencies]


Test patch:
diff --git a/tests/integration_tests/test_vectorstores.py b/tests/integration_tests/test_vectorstores.py
index c30c28d..044e34c 100644
--- a/tests/integration_tests/test_vectorstores.py
+++ b/tests/integration_tests/test_vectorstores.py
@@ -116,12 +116,12 @@ def test_similarity_search_with_metadata_and_additional(
     output = docsearch.similarity_search(
         "foo",
         k=1,
-        additional=["certainty"],
+        additional=["score"],
     )
     assert output == [
         Document(
             page_content="foo",
-            metadata={"page": 0, "_additional": {"certainty": 1.0000003576278687}},
+            metadata={"_additional": {"score": "0.016393442"}, "page": 0},
         )
     ]
 
@@ -353,6 +353,8 @@ def test_similarity_search_with_score(
 
     doc, score = results[0]
 
+    # TODO: figure out why score is a string
+    score = float(score)
     assert isinstance(score, float)
     assert score > 0
     assert doc.page_content == "cat"