community[patch]: Fix YandexGPT embeddings (#19720)

Fix of YandexGPT embeddings. 

The current version uses a single `model_name` for queries and
documents, essentially making the `embed_documents` and `embed_query`
methods the same. Yandex has a different endpoint (`model_uri`) for
encoding documents, see
[this](https://yandex.cloud/en/docs/yandexgpt/concepts/embeddings). The
bug may impact retrievers built with `YandexGPTEmbeddings` (for instance
FAISS database as retriever) since they use both `embed_documents` and
`embed_query`.

A simple snippet to test the behaviour:
```python
from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings()
q_emb = embeddings.embed_query('hello world')
doc_emb = embeddings.embed_documents(['hello world', 'hello world'])
q_emb == doc_emb[0]
```
The response is `True` with the current version and `False` with the
changes I made.


Twitter: @egor_krash

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/community/langchain_community/embeddings/yandex.py

@@ -6,7 +6,7 @@ import time
 from typing import Any, Callable, Dict, List
 
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
+from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 from tenacity import (
     before_sleep_log,
@@ -33,14 +33,13 @@ class YandexGPTEmbeddings(BaseModel, Embeddings):
 
     To use the default model specify the folder ID in a parameter `folder_id`
     or in an environment variable `YC_FOLDER_ID`.
-    Or specify the model URI in a constructor parameter `model_uri`
 
     Example:
         .. code-block:: python
 
             from langchain_community.embeddings.yandex import YandexGPTEmbeddings
-            embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", model_uri="emb://<folder-id>/text-search-query/latest")
+            embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", folder_id=<folder-id>)
-    """
+    """  # noqa: E501
 
     iam_token: SecretStr = ""  # type: ignore[assignment]
     """Yandex Cloud IAM token for service account
@@ -48,12 +47,16 @@ class YandexGPTEmbeddings(BaseModel, Embeddings):
     api_key: SecretStr = ""  # type: ignore[assignment]
     """Yandex Cloud Api Key for service account
     with the `ai.languageModels.user` role"""
-    model_uri: str = ""
+    model_uri: str = Field(default="", alias="query_model_uri")
-    """Model uri to use."""
+    """Query model uri to use."""
+    doc_model_uri: str = ""
+    """Doc model uri to use."""
     folder_id: str = ""
     """Yandex Cloud folder ID"""
-    model_name: str = "text-search-query"
+    doc_model_name: str = "text-search-doc"
-    """Model name to use."""
+    """Doc model name to use."""
+    model_name: str = Field(default="text-search-query", alias="query_model_name")
+    """Query model name to use."""
     model_version: str = "latest"
     """Model version to use."""
     url: str = "llm.api.cloud.yandex.net:443"
@@ -63,6 +66,11 @@ class YandexGPTEmbeddings(BaseModel, Embeddings):
     sleep_interval: float = 0.0
     """Delay between API requests"""
 
+    class Config:
+        """Configuration for this pydantic object."""
+
+        allow_population_by_field_name = True
+
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that iam token exists in environment."""
@@ -89,12 +97,19 @@ class YandexGPTEmbeddings(BaseModel, Embeddings):
             values["_grpc_metadata"] = (
                 ("authorization", f"Api-Key {values['api_key'].get_secret_value()}"),
             )
-        if values["model_uri"] == "" and values["folder_id"] == "":
+
-            raise ValueError("Either 'model_uri' or 'folder_id' must be provided.")
+        if not values.get("doc_model_uri"):
-        if not values["model_uri"]:
+            if values["folder_id"] == "":
+                raise ValueError("'doc_model_uri' or 'folder_id' must be provided.")
+            values[
+                "doc_model_uri"
+            ] = f"emb://{values['folder_id']}/{values['doc_model_name']}/{values['model_version']}"  # noqa: E501
+        if not values.get("model_uri"):
+            if values["folder_id"] == "":
+                raise ValueError("'model_uri' or 'folder_id' must be provided.")
             values[
                 "model_uri"
-            ] = f"emb://{values['folder_id']}/{values['model_name']}/{values['model_version']}"
+            ] = f"emb://{values['folder_id']}/{values['model_name']}/{values['model_version']}"  # noqa: E501
         return values
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
@@ -118,7 +133,7 @@ class YandexGPTEmbeddings(BaseModel, Embeddings):
         Returns:
             Embeddings for the text.
         """
-        return _embed_with_retry(self, texts=[text])[0]
+        return _embed_with_retry(self, texts=[text], embed_query=True)[0]
 
 
 def _create_retry_decorator(llm: YandexGPTEmbeddings) -> Callable[[Any], Any]:
@@ -146,7 +161,7 @@ def _embed_with_retry(llm: YandexGPTEmbeddings, **kwargs: Any) -> Any:
     return _completion_with_retry(**kwargs)
 
 
-def _make_request(self: YandexGPTEmbeddings, texts: List[str]):  # type: ignore[no-untyped-def]
+def _make_request(self: YandexGPTEmbeddings, texts: List[str], **kwargs):  # type: ignore[no-untyped-def]
     try:
         import grpc
 
@@ -172,9 +187,14 @@ def _make_request(self: YandexGPTEmbeddings, texts: List[str]):  # type: ignore[
     result = []
     channel_credentials = grpc.ssl_channel_credentials()
     channel = grpc.secure_channel(self.url, channel_credentials)
+    # Use the query model if embed_query is True
+    if kwargs.get("embed_query"):
+        model_uri = self.model_uri
+    else:
+        model_uri = self.doc_model_uri
 
     for text in texts:
-        request = TextEmbeddingRequest(model_uri=self.model_uri, text=text)
+        request = TextEmbeddingRequest(model_uri=model_uri, text=text)
         stub = EmbeddingsServiceStub(channel)
         res = stub.TextEmbedding(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]
         result.append(list(res.embedding))

libs/community/tests/unit_tests/embeddings/test_yandex.py

@@ -0,0 +1,24 @@
+import os
+
+from langchain_community.embeddings import YandexGPTEmbeddings
+
+
+def test_init() -> None:
+    os.environ["YC_API_KEY"] = "foo"
+    models = [
+        YandexGPTEmbeddings(folder_id="bar"),
+        YandexGPTEmbeddings(
+            query_model_uri="emb://bar/text-search-query/latest",
+            doc_model_uri="emb://bar/text-search-doc/latest",
+        ),
+        YandexGPTEmbeddings(
+            folder_id="bar",
+            query_model_name="text-search-query",
+            doc_model_name="text-search-doc",
+        ),
+    ]
+    for embeddings in models:
+        assert embeddings.model_uri == "emb://bar/text-search-query/latest"
+        assert embeddings.doc_model_uri == "emb://bar/text-search-doc/latest"
+        assert embeddings.model_name == "text-search-query"
+        assert embeddings.doc_model_name == "text-search-doc"