From 8b9e02da9dad0a8103cf9adcef813f0f8862f523 Mon Sep 17 00:00:00 2001
From: cs0lar <62176855+cs0lar@users.noreply.github.com>
Date: Sun, 16 Apr 2023 21:11:30 +0100
Subject: [PATCH] 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
---
 langchain/vectorstores/weaviate.py            |  57 +-
 ...te.test_max_marginal_relevance_search.yaml | 729 ++++++++++++++++++
 ....test_similarity_search_with_metadata.yaml | 384 +++++++++
 ...st_similarity_search_without_metadata.yaml | 385 +++++++++
 .../vectorstores/docker-compose/weaviate.yml  |   5 +-
 .../vectorstores/test_weaviate.py             |  47 +-
 6 files changed, 1599 insertions(+), 8 deletions(-)
 create mode 100644 tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml
 create mode 100644 tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_with_metadata.yaml
 create mode 100644 tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_without_metadata.yaml

diff --git a/langchain/vectorstores/weaviate.py b/langchain/vectorstores/weaviate.py
index d5d8c134..4211ec7e 100644
--- a/langchain/vectorstores/weaviate.py
+++ b/langchain/vectorstores/weaviate.py
@@ -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)
diff --git a/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml b/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml
new file mode 100644
index 00000000..dc54553a
--- /dev/null
+++ b/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.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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diff --git a/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_with_metadata.yaml b/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_with_metadata.yaml
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diff --git a/tests/integration_tests/vectorstores/docker-compose/weaviate.yml b/tests/integration_tests/vectorstores/docker-compose/weaviate.yml
index a1911480..e270c71c 100644
--- a/tests/integration_tests/vectorstores/docker-compose/weaviate.yml
+++ b/tests/integration_tests/vectorstores/docker-compose/weaviate.yml
@@ -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'
diff --git a/tests/integration_tests/vectorstores/test_weaviate.py b/tests/integration_tests/vectorstores/test_weaviate.py
index 5699ecea..0034cc0d 100644
--- a/tests/integration_tests/vectorstores/test_weaviate.py
+++ b/tests/integration_tests/vectorstores/test_weaviate.py
@@ -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}),
+        ]