couchbase: Add the initial version of Couchbase partner package (#22087)

Co-authored-by: Nithish Raghunandanan <nithishr@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

libs/partners/couchbase/tests/utils.py

@@ -0,0 +1,55 @@
+"""Fake Embedding class for testing purposes."""
+
+from typing import List
+
+from langchain_core.embeddings import Embeddings
+
+fake_texts = ["foo", "bar", "baz"]
+
+
+class FakeEmbeddings(Embeddings):
+    """Fake embeddings functionality for testing."""
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Return simple embeddings.
+        Embeddings encode each text as its index."""
+        return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        return self.embed_documents(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """Return constant query embeddings.
+        Embeddings are identical to embed_documents(texts)[0].
+        Distance to each text will be that text's index,
+        as it was passed to embed_documents."""
+        return [float(1.0)] * 9 + [float(0.0)]
+
+    async def aembed_query(self, text: str) -> List[float]:
+        return self.embed_query(text)
+
+
+class ConsistentFakeEmbeddings(FakeEmbeddings):
+    """Fake embeddings which remember all the texts seen so far to return consistent
+    vectors for the same texts."""
+
+    def __init__(self, dimensionality: int = 10) -> None:
+        self.known_texts: List[str] = []
+        self.dimensionality = dimensionality
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Return consistent embeddings for each text seen so far."""
+        out_vectors = []
+        for text in texts:
+            if text not in self.known_texts:
+                self.known_texts.append(text)
+            vector = [float(1.0)] * (self.dimensionality - 1) + [
+                float(self.known_texts.index(text))
+            ]
+            out_vectors.append(vector)
+        return out_vectors
+
+    def embed_query(self, text: str) -> List[float]:
+        """Return consistent embeddings for the text, if seen before, or a constant
+        one if the text is unknown."""
+        return self.embed_documents([text])[0]