diff --git a/docs/modules/utils/combine_docs_examples/hyde.ipynb b/docs/modules/utils/combine_docs_examples/hyde.ipynb
index 1efbe5f6537..b105873a820 100644
--- a/docs/modules/utils/combine_docs_examples/hyde.ipynb
+++ b/docs/modules/utils/combine_docs_examples/hyde.ipynb
@@ -21,8 +21,8 @@
    "outputs": [],
    "source": [
     "from langchain.llms import OpenAI\n",
-    "from langchain.embeddings import OpenAIEmbeddings, HypotheticalDocumentEmbedder\n",
-    "from langchain.chains import LLMChain\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.chains import LLMChain, HypotheticalDocumentEmbedder\n",
     "from langchain.prompts import PromptTemplate"
    ]
   },
@@ -220,7 +220,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "llm-env",
    "language": "python",
    "name": "python3"
   },
@@ -234,7 +234,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.0 (default, Nov 15 2020, 06:25:35) \n[Clang 10.0.0 ]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9dd01537e9ab68cf47cb0398488d182358f774f73101197b3bd1b5502c6ec7f9"
+   }
   }
  },
  "nbformat": 4,
diff --git a/langchain/chains/__init__.py b/langchain/chains/__init__.py
index 0079d1e26d0..21bb09d9a4c 100644
--- a/langchain/chains/__init__.py
+++ b/langchain/chains/__init__.py
@@ -1,6 +1,7 @@
 """Chains are easily reusable components which can be linked together."""
 from langchain.chains.api.base import APIChain
 from langchain.chains.conversation.base import ConversationChain
+from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_bash.base import LLMBashChain
 from langchain.chains.llm_checker.base import LLMCheckerChain
@@ -41,4 +42,5 @@ __all__ = [
     "OpenAIModerationChain",
     "SQLDatabaseSequentialChain",
     "load_chain",
+    "HypotheticalDocumentEmbedder",
 ]
diff --git a/langchain/embeddings/hyde/__init__.py b/langchain/chains/hyde/__init__.py
similarity index 100%
rename from langchain/embeddings/hyde/__init__.py
rename to langchain/chains/hyde/__init__.py
diff --git a/langchain/embeddings/hyde/base.py b/langchain/chains/hyde/base.py
similarity index 73%
rename from langchain/embeddings/hyde/base.py
rename to langchain/chains/hyde/base.py
index dbad3535c2d..fd043bbad62 100644
--- a/langchain/embeddings/hyde/base.py
+++ b/langchain/chains/hyde/base.py
@@ -4,18 +4,19 @@ https://arxiv.org/abs/2212.10496
 """
 from __future__ import annotations
 
-from typing import List
+from typing import Dict, List
 
 import numpy as np
 from pydantic import BaseModel, Extra
 
+from langchain.chains.base import Chain
+from langchain.chains.hyde.prompts import PROMPT_MAP
 from langchain.chains.llm import LLMChain
 from langchain.embeddings.base import Embeddings
-from langchain.embeddings.hyde.prompts import PROMPT_MAP
 from langchain.llms.base import BaseLLM
 
 
-class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
+class HypotheticalDocumentEmbedder(Chain, Embeddings, BaseModel):
     """Generate hypothetical document for query, and then embed that.
 
     Based on https://arxiv.org/abs/2212.10496
@@ -30,10 +31,24 @@ class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
+    @property
+    def input_keys(self) -> List[str]:
+        """Input keys for Hyde's LLM chain."""
+        return self.llm_chain.input_keys
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Output keys for Hyde's LLM chain."""
+        return self.llm_chain.output_keys
+
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Call the base embeddings."""
         return self.base_embeddings.embed_documents(texts)
 
+    def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
+        """Combine embeddings into final embeddings."""
+        return list(np.array(embeddings).mean(axis=0))
+
     def embed_query(self, text: str) -> List[float]:
         """Generate a hypothetical document and embedded it."""
         var_name = self.llm_chain.input_keys[0]
@@ -42,9 +57,9 @@ class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
         embeddings = self.embed_documents(documents)
         return self.combine_embeddings(embeddings)
 
-    def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
-        """Combine embeddings into final embeddings."""
-        return list(np.array(embeddings).mean(axis=0))
+    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
+        """Call the internal llm chain."""
+        return self.llm_chain._call(inputs)
 
     @classmethod
     def from_llm(
diff --git a/langchain/embeddings/hyde/prompts.py b/langchain/chains/hyde/prompts.py
similarity index 100%
rename from langchain/embeddings/hyde/prompts.py
rename to langchain/chains/hyde/prompts.py
diff --git a/langchain/embeddings/__init__.py b/langchain/embeddings/__init__.py
index 5a2019e8bf0..6a57deb1364 100644
--- a/langchain/embeddings/__init__.py
+++ b/langchain/embeddings/__init__.py
@@ -2,7 +2,6 @@
 from langchain.embeddings.cohere import CohereEmbeddings
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
 from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
-from langchain.embeddings.hyde.base import HypotheticalDocumentEmbedder
 from langchain.embeddings.openai import OpenAIEmbeddings
 
 __all__ = [
@@ -10,5 +9,4 @@ __all__ = [
     "HuggingFaceEmbeddings",
     "CohereEmbeddings",
     "HuggingFaceHubEmbeddings",
-    "HypotheticalDocumentEmbedder",
 ]
diff --git a/tests/unit_tests/test_hyde.py b/tests/unit_tests/chains/test_hyde.py
similarity index 92%
rename from tests/unit_tests/test_hyde.py
rename to tests/unit_tests/chains/test_hyde.py
index 91b7bb550dc..b609a6295b8 100644
--- a/tests/unit_tests/test_hyde.py
+++ b/tests/unit_tests/chains/test_hyde.py
@@ -4,9 +4,9 @@ from typing import List, Optional
 import numpy as np
 from pydantic import BaseModel
 
+from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
+from langchain.chains.hyde.prompts import PROMPT_MAP
 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
 from langchain.schema import Generation, LLMResult