Harrison/weaviate (#3494)

Co-authored-by: Nick Rubell <nick@rubell.com>

langchain/retrievers/weaviate_hybrid_search.py

@@ -54,22 +54,22 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
         with self._client.batch as batch:
             ids = []
             for i, doc in enumerate(docs):
-                data_properties = {
-                    self._text_key: doc.page_content,
-                }
+                metadata = doc.metadata or {}
+                data_properties = {self._text_key: doc.page_content, **metadata}
                 _id = get_valid_uuid(uuid4())
                 batch.add_data_object(data_properties, self._index_name, _id)
                 ids.append(_id)
         return ids
 
-    def get_relevant_documents(self, query: str) -> List[Document]:
+    def get_relevant_documents(
+        self, query: str, where_filter: Optional[Dict[str, object]] = None
+    ) -> List[Document]:
         """Look up similar documents in Weaviate."""
-        content: Dict[str, Any] = {"concepts": [query]}
         query_obj = self._client.query.get(self._index_name, self._query_attrs)
+        if where_filter:
+            query_obj = query_obj.with_where(where_filter)
 
-        result = (
-            query_obj.with_hybrid(content, alpha=self.alpha).with_limit(self.k).do()
-        )
+        result = query_obj.with_hybrid(query, alpha=self.alpha).with_limit(self.k).do()
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
 
@@ -80,5 +80,7 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
             docs.append(Document(page_content=text, metadata=res))
         return docs
 
-    async def aget_relevant_documents(self, query: str) -> List[Document]:
+    async def aget_relevant_documents(
+        self, query: str, where_filter: Optional[Dict[str, object]] = None
+    ) -> List[Document]:
         raise NotImplementedError

langchain/vectorstores/weaviate.py

@@ -139,6 +139,8 @@ class Weaviate(VectorStore):
         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"))
         result = query_obj.with_near_text(content).with_limit(k).do()
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
@@ -154,6 +156,8 @@ class Weaviate(VectorStore):
         """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"))
         result = query_obj.with_near_vector(vector).with_limit(k).do()
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
@@ -226,6 +230,8 @@ class Weaviate(VectorStore):
         """
         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"))
         results = (
             query_obj.with_additional("vector")
             .with_near_vector(vector)

tests/integration_tests/retrievers/test_weaviate_hybrid_search.py

@@ -0,0 +1,87 @@
+"""Test Weaviate functionality."""
+import logging
+import os
+from typing import Generator, Union
+from uuid import uuid4
+
+import pytest
+from weaviate import Client
+
+from langchain.docstore.document import Document
+from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
+
+logging.basicConfig(level=logging.DEBUG)
+
+"""
+cd tests/integration_tests/vectorstores/docker-compose
+docker compose -f weaviate.yml up
+"""
+
+
+class TestWeaviateHybridSearchRetriever:
+    @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."""
+        url = "http://localhost:8080"
+        yield url
+
+        # Clear the test index
+        client = Client(url)
+        client.schema.delete_all()
+
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_get_relevant_documents(self, weaviate_url: str) -> None:
+        """Test end to end construction and MRR search."""
+        texts = ["foo", "bar", "baz"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+
+        client = Client(weaviate_url)
+
+        retriever = WeaviateHybridSearchRetriever(
+            client=client,
+            index_name=f"LangChain_{uuid4().hex}",
+            text_key="text",
+            attributes=["page"],
+        )
+        for i, text in enumerate(texts):
+            retriever.add_documents(
+                [Document(page_content=text, metadata=metadatas[i])]
+            )
+
+        output = retriever.get_relevant_documents("foo")
+        assert output == [
+            Document(page_content="foo", metadata={"page": 0}),
+            Document(page_content="baz", metadata={"page": 2}),
+            Document(page_content="bar", metadata={"page": 1}),
+        ]
+
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_get_relevant_documents_with_filter(self, weaviate_url: str) -> None:
+        """Test end to end construction and MRR search."""
+        texts = ["foo", "bar", "baz"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+
+        client = Client(weaviate_url)
+
+        retriever = WeaviateHybridSearchRetriever(
+            client=client,
+            index_name=f"LangChain_{uuid4().hex}",
+            text_key="text",
+            attributes=["page"],
+        )
+        for i, text in enumerate(texts):
+            retriever.add_documents(
+                [Document(page_content=text, metadata=metadatas[i])]
+            )
+
+        where_filter = {"path": ["page"], "operator": "Equal", "valueNumber": 0}
+
+        output = retriever.get_relevant_documents("foo", where_filter=where_filter)
+        assert output == [
+            Document(page_content="foo", metadata={"page": 0}),
+        ]

tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search_with_filter.yaml

@@ -0,0 +1,729 @@
+interactions:
+- request:
+    body: '{"input": [[8134], [2308], [43673]], "encoding_format": "base64"}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '65'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - User-Agent-DUMMY
+      X-OpenAI-Client-User-Agent:
+      - X-OpenAI-Client-User-Agent-DUMMY
+      authorization:
+      - authorization-DUMMY
+    method: POST
+    uri: https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings
+  response:
+    body:
+      string: !!binary |
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tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_with_metadata_and_filter.yaml

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tests/integration_tests/vectorstores/test_weaviate.py

@@ -62,6 +62,23 @@ class TestWeaviate:
         output = docsearch.similarity_search("foo", k=1)
         assert output == [Document(page_content="foo", metadata={"page": 0})]
 
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_similarity_search_with_metadata_and_filter(
+        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, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
+        )
+        output = docsearch.similarity_search(
+            "foo",
+            k=2,
+            where_filter={"path": ["page"], "operator": "Equal", "valueNumber": 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
@@ -117,3 +134,32 @@ class TestWeaviate:
             Document(page_content="foo", metadata={"page": 0}),
             Document(page_content="bar", metadata={"page": 1}),
         ]
+
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_max_marginal_relevance_search_with_filter(
+        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
+        )
+        where_filter = {"path": ["page"], "operator": "Equal", "valueNumber": 0}
+        # if lambda=1 the algorithm should be equivalent to standard ranking
+        standard_ranking = docsearch.similarity_search(
+            "foo", k=2, where_filter=where_filter
+        )
+        output = docsearch.max_marginal_relevance_search(
+            "foo", k=2, fetch_k=3, lambda_mult=1.0, where_filter=where_filter
+        )
+        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, where_filter=where_filter
+        )
+        assert output == [
+            Document(page_content="foo", metadata={"page": 0}),
+        ]