(WIP) add HyDE (#393)

Co-authored-by: cameronccohen <cameron.c.cohen@gmail.com>
Co-authored-by: Cameron Cohen <cameron.cohen@quantco.com>

tests/unit_tests/test_hyde.py

@@ -0,0 +1,57 @@
+"""Test HyDE."""
+from typing import List, Optional
+
+import numpy as np
+from pydantic import BaseModel
+
+from langchain.embeddings.base import Embeddings
+from langchain.embeddings.hyde.base import HypotheticalDocumentEmbedder
+from langchain.embeddings.hyde.prompts import PROMPT_MAP
+from langchain.llms.base import BaseLLM, LLMResult
+from langchain.schema import Generation
+
+
+class FakeEmbeddings(Embeddings):
+    """Fake embedding class for tests."""
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Return random floats."""
+        return [list(np.random.uniform(0, 1, 10)) for _ in range(10)]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Return random floats."""
+        return list(np.random.uniform(0, 1, 10))
+
+
+class FakeLLM(BaseLLM, BaseModel):
+    """Fake LLM wrapper for testing purposes."""
+
+    n: int = 1
+
+    def _generate(
+        self, prompts: List[str], stop: Optional[List[str]] = None
+    ) -> LLMResult:
+        return LLMResult(generations=[[Generation(text="foo") for _ in range(self.n)]])
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "fake"
+
+
+def test_hyde_from_llm() -> None:
+    """Test loading HyDE from all prompts."""
+    for key in PROMPT_MAP:
+        embedding = HypotheticalDocumentEmbedder.from_llm(
+            FakeLLM(), FakeEmbeddings(), key
+        )
+        embedding.embed_query("foo")
+
+
+def test_hyde_from_llm_with_multiple_n() -> None:
+    """Test loading HyDE from all prompts."""
+    for key in PROMPT_MAP:
+        embedding = HypotheticalDocumentEmbedder.from_llm(
+            FakeLLM(n=8), FakeEmbeddings(), key
+        )
+        embedding.embed_query("foo")