community[minor]: Added GigaChat Embeddings support + updated previous GigaChat integration (#19516)

- **Description:** Added integration with
[GigaChat](https://developers.sber.ru/portal/products/gigachat)
embeddings. Also added support for extra fields in GigaChat LLM and
fixed docs.

docs/docs/integrations/chat/gigachat.ipynb

@@ -13,9 +13,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "pycharm": {
+     "is_executing": true
+    }
    },
    "outputs": [],
    "source": [
@@ -28,13 +31,14 @@
     "collapsed": false
    },
    "source": [
-    "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/api/integration)\n",
+    "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/individuals-quickstart)\n",
+    "\n",
     "## Example"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -48,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -56,12 +60,12 @@
    "source": [
     "from langchain_community.chat_models import GigaChat\n",
     "\n",
-    "chat = GigaChat(verify_ssl_certs=False)"
+    "chat = GigaChat(verify_ssl_certs=False, scope=\"GIGACHAT_API_PERS\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -70,7 +74,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "What do you get when you cross a goat and a skunk? A smelly goat!\n"
+      "The capital of Russia is Moscow.\n"
      ]
     }
    ],
@@ -81,10 +85,10 @@
     "    SystemMessage(\n",
     "        content=\"You are a helpful AI that shares everything you know. Talk in English.\"\n",
     "    ),\n",
-    "    HumanMessage(content=\"Tell me a joke\"),\n",
+    "    HumanMessage(content=\"What is capital of Russia?\"),\n",
     "]\n",
     "\n",
-    "print(chat(messages).content)"
+    "print(chat.invoke(messages).content)"
    ]
   }
  ],

docs/docs/integrations/llms/gigachat.ipynb

@@ -15,7 +15,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "pycharm": {
+     "is_executing": true
+    }
    },
    "outputs": [],
    "source": [
@@ -28,13 +31,14 @@
     "collapsed": false
    },
    "source": [
-    "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/api/integration)\n",
+    "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/individuals-quickstart)\n",
+    "\n",
     "## Example"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -48,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -56,12 +60,12 @@
    "source": [
     "from langchain_community.llms import GigaChat\n",
     "\n",
-    "llm = GigaChat(verify_ssl_certs=False)"
+    "llm = GigaChat(verify_ssl_certs=False, scope=\"GIGACHAT_API_PERS\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -84,8 +88,8 @@
     "\n",
     "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
     "\n",
-    "generated = llm_chain.run(country=\"Russia\")\n",
+    "generated = llm_chain.invoke(input={\"country\": \"Russia\"})\n",
-    "print(generated)"
+    "print(generated[\"text\"])"
    ]
   }
  ],

docs/docs/integrations/providers/salute_devices.mdx

@@ -26,4 +26,12 @@ See a [usage example](/docs/integrations/chat/gigachat).
 
 ```python
 from langchain_community.chat_models import GigaChat
+```
+
+## Embeddings
+
+See a [usage example](/docs/integrations/text_embedding/gigachat).
+
+```python
+from langchain_community.embeddings import GigaChatEmbeddings
 ```

docs/docs/integrations/text_embedding/gigachat.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# GigaChat\n",
+    "This notebook shows how to use LangChain with [GigaChat embeddings](https://developers.sber.ru/portal/products/gigachat).\n",
+    "To use you need to install ```gigachat``` python package."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  gigachat"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/individuals-quickstart)\n",
+    "\n",
+    "## Example"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "from langchain_community.embeddings import GigaChatEmbeddings\n",
+    "\n",
+    "embeddings = GigaChatEmbeddings(verify_ssl_certs=False, scope=\"GIGACHAT_API_PERS\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(\"The quick brown fox jumps over the lazy dog\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[0.8398333191871643,\n -0.14180311560630798,\n -0.6161925792694092,\n -0.17103666067123413,\n 1.2884578704833984]"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_result[:5]"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

libs/community/langchain_community/chat_models/gigachat.py

@@ -1,5 +1,17 @@
+from __future__ import annotations
+
 import logging
-from typing import Any, AsyncIterator, Iterator, List, Optional
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncIterator,
+    Dict,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    Type,
+)
 
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
@@ -14,31 +26,47 @@ from langchain_core.messages import (
     AIMessage,
     AIMessageChunk,
     BaseMessage,
+    BaseMessageChunk,
     ChatMessage,
+    ChatMessageChunk,
+    FunctionMessage,
+    FunctionMessageChunk,
     HumanMessage,
+    HumanMessageChunk,
     SystemMessage,
+    SystemMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 
 from langchain_community.llms.gigachat import _BaseGigaChat
 
+if TYPE_CHECKING:
+    import gigachat.models as gm
+
 logger = logging.getLogger(__name__)
 
 
-def _convert_dict_to_message(message: Any) -> BaseMessage:
+def _convert_dict_to_message(message: gm.Messages) -> BaseMessage:
-    from gigachat.models import MessagesRole
+    from gigachat.models import FunctionCall, MessagesRole
+
+    additional_kwargs: Dict = {}
+    if function_call := message.function_call:
+        if isinstance(function_call, FunctionCall):
+            additional_kwargs["function_call"] = dict(function_call)
+        elif isinstance(function_call, dict):
+            additional_kwargs["function_call"] = function_call
 
     if message.role == MessagesRole.SYSTEM:
         return SystemMessage(content=message.content)
     elif message.role == MessagesRole.USER:
         return HumanMessage(content=message.content)
     elif message.role == MessagesRole.ASSISTANT:
-        return AIMessage(content=message.content)
+        return AIMessage(content=message.content, additional_kwargs=additional_kwargs)
     else:
         raise TypeError(f"Got unknown role {message.role} {message}")
 
 
-def _convert_message_to_dict(message: BaseMessage) -> Any:
+def _convert_message_to_dict(message: gm.BaseMessage) -> gm.Messages:
     from gigachat.models import Messages, MessagesRole
 
     if isinstance(message, SystemMessage):
@@ -46,13 +74,45 @@ def _convert_message_to_dict(message: BaseMessage) -> Any:
     elif isinstance(message, HumanMessage):
         return Messages(role=MessagesRole.USER, content=message.content)
     elif isinstance(message, AIMessage):
-        return Messages(role=MessagesRole.ASSISTANT, content=message.content)
+        return Messages(
+            role=MessagesRole.ASSISTANT,
+            content=message.content,
+            function_call=message.additional_kwargs.get("function_call", None),
+        )
     elif isinstance(message, ChatMessage):
         return Messages(role=MessagesRole(message.role), content=message.content)
+    elif isinstance(message, FunctionMessage):
+        return Messages(role=MessagesRole.FUNCTION, content=message.content)
     else:
         raise TypeError(f"Got unknown type {message}")
 
 
+def _convert_delta_to_message_chunk(
+    _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
+) -> BaseMessageChunk:
+    role = _dict.get("role")
+    content = _dict.get("content") or ""
+    additional_kwargs: Dict = {}
+    if _dict.get("function_call"):
+        function_call = dict(_dict["function_call"])
+        if "name" in function_call and function_call["name"] is None:
+            function_call["name"] = ""
+        additional_kwargs["function_call"] = function_call
+
+    if role == "user" or default_class == HumanMessageChunk:
+        return HumanMessageChunk(content=content)
+    elif role == "assistant" or default_class == AIMessageChunk:
+        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
+    elif role == "system" or default_class == SystemMessageChunk:
+        return SystemMessageChunk(content=content)
+    elif role == "function" or default_class == FunctionMessageChunk:
+        return FunctionMessageChunk(content=content, name=_dict["name"])
+    elif role or default_class == ChatMessageChunk:
+        return ChatMessageChunk(content=content, role=role)
+    else:
+        return default_class(content=content)
+
+
 class GigaChat(_BaseGigaChat, BaseChatModel):
     """`GigaChat` large language models API.
 
@@ -62,23 +122,33 @@ class GigaChat(_BaseGigaChat, BaseChatModel):
         .. code-block:: python
 
             from langchain_community.chat_models import GigaChat
-            giga = GigaChat(credentials=..., verify_ssl_certs=False)
+            giga = GigaChat(credentials=..., scope=..., verify_ssl_certs=False)
     """
 
-    def _build_payload(self, messages: List[BaseMessage]) -> Any:
+    def _build_payload(self, messages: List[BaseMessage], **kwargs: Any) -> gm.Chat:
         from gigachat.models import Chat
 
         payload = Chat(
             messages=[_convert_message_to_dict(m) for m in messages],
-            profanity_check=self.profanity,
         )
+
+        payload.functions = kwargs.get("functions", None)
+
+        if self.profanity_check is not None:
+            payload.profanity_check = self.profanity_check
         if self.temperature is not None:
             payload.temperature = self.temperature
+        if self.top_p is not None:
+            payload.top_p = self.top_p
         if self.max_tokens is not None:
             payload.max_tokens = self.max_tokens
+        if self.repetition_penalty is not None:
+            payload.repetition_penalty = self.repetition_penalty
+        if self.update_interval is not None:
+            payload.update_interval = self.update_interval
 
         if self.verbose:
-            logger.info("Giga request: %s", payload.dict())
+            logger.warning("Giga request: %s", payload.dict())
 
         return payload
 
@@ -98,7 +168,7 @@ class GigaChat(_BaseGigaChat, BaseChatModel):
                     finish_reason,
                 )
             if self.verbose:
-                logger.info("Giga response: %s", message.content)
+                logger.warning("Giga response: %s", message.content)
         llm_output = {"token_usage": response.usage, "model_name": response.model}
         return ChatResult(generations=generations, llm_output=llm_output)
 
@@ -117,7 +187,7 @@ class GigaChat(_BaseGigaChat, BaseChatModel):
             )
             return generate_from_stream(stream_iter)
 
-        payload = self._build_payload(messages)
+        payload = self._build_payload(messages, **kwargs)
         response = self._client.chat(payload)
 
         return self._create_chat_result(response)
@@ -137,7 +207,7 @@ class GigaChat(_BaseGigaChat, BaseChatModel):
             )
             return await agenerate_from_stream(stream_iter)
 
-        payload = self._build_payload(messages)
+        payload = self._build_payload(messages, **kwargs)
         response = await self._client.achat(payload)
 
         return self._create_chat_result(response)
@@ -149,15 +219,28 @@ class GigaChat(_BaseGigaChat, BaseChatModel):
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
-        payload = self._build_payload(messages)
+        payload = self._build_payload(messages, **kwargs)
 
         for chunk in self._client.stream(payload):
-            if chunk.choices:
+            if not isinstance(chunk, dict):
-                content = chunk.choices[0].delta.content
+                chunk = chunk.dict()
-                cg_chunk = ChatGenerationChunk(message=AIMessageChunk(content=content))
+            if len(chunk["choices"]) == 0:
-                if run_manager:
+                continue
-                    run_manager.on_llm_new_token(content, chunk=cg_chunk)
+
-                yield cg_chunk
+            choice = chunk["choices"][0]
+            content = choice.get("delta", {}).get("content", {})
+            chunk = _convert_delta_to_message_chunk(choice["delta"], AIMessageChunk)
+
+            finish_reason = choice.get("finish_reason")
+
+            generation_info = (
+                dict(finish_reason=finish_reason) if finish_reason is not None else None
+            )
+
+            if run_manager:
+                run_manager.on_llm_new_token(content)
+
+            yield ChatGenerationChunk(message=chunk, generation_info=generation_info)
 
     async def _astream(
         self,
@@ -166,16 +249,24 @@ class GigaChat(_BaseGigaChat, BaseChatModel):
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> AsyncIterator[ChatGenerationChunk]:
-        payload = self._build_payload(messages)
+        payload = self._build_payload(messages, **kwargs)
 
         async for chunk in self._client.astream(payload):
-            if chunk.choices:
+            if not isinstance(chunk, dict):
-                content = chunk.choices[0].delta.content
+                chunk = chunk.dict()
-                cg_chunk = ChatGenerationChunk(message=AIMessageChunk(content=content))
+            if len(chunk["choices"]) == 0:
-                if run_manager:
+                continue
-                    await run_manager.on_llm_new_token(content, chunk=cg_chunk)
-                yield cg_chunk
 
-    def get_num_tokens(self, text: str) -> int:
+            choice = chunk["choices"][0]
-        """Count approximate number of tokens"""
+            content = choice.get("delta", {}).get("content", {})
-        return round(len(text) / 4.6)
+            chunk = _convert_delta_to_message_chunk(choice["delta"], AIMessageChunk)
+
+            finish_reason = choice.get("finish_reason")
+
+            generation_info = (
+                dict(finish_reason=finish_reason) if finish_reason is not None else None
+            )
+
+            yield ChatGenerationChunk(message=chunk, generation_info=generation_info)
+            if run_manager:
+                await run_manager.on_llm_new_token(content)

libs/community/langchain_community/embeddings/__init__.py

@@ -38,6 +38,7 @@ _module_lookup = {
     "GPT4AllEmbeddings": "langchain_community.embeddings.gpt4all",
     "GooglePalmEmbeddings": "langchain_community.embeddings.google_palm",
     "GradientEmbeddings": "langchain_community.embeddings.gradient_ai",
+    "GigaChatEmbeddings": "langchain_community.embeddings.gigachat",
     "HuggingFaceBgeEmbeddings": "langchain_community.embeddings.huggingface",
     "HuggingFaceEmbeddings": "langchain_community.embeddings.huggingface",
     "HuggingFaceHubEmbeddings": "langchain_community.embeddings.huggingface_hub",

libs/community/langchain_community/embeddings/gigachat.py

@@ -0,0 +1,187 @@
+from __future__ import annotations
+
+import logging
+from functools import cached_property
+from typing import Any, Dict, List, Optional
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, root_validator
+
+logger = logging.getLogger(__name__)
+
+MAX_BATCH_SIZE_CHARS = 1000000
+MAX_BATCH_SIZE_PARTS = 90
+
+
+class GigaChatEmbeddings(BaseModel, Embeddings):
+    """GigaChat Embeddings models.
+
+    Example:
+        .. code-block:: python
+            from langchain_community.embeddings.gigachat import GigaChatEmbeddings
+
+            embeddings = GigaChatEmbeddings(
+                credentials=..., scope=..., verify_ssl_certs=False
+            )
+    """
+
+    base_url: Optional[str] = None
+    """ Base API URL """
+    auth_url: Optional[str] = None
+    """ Auth URL """
+    credentials: Optional[str] = None
+    """ Auth Token """
+    scope: Optional[str] = None
+    """ Permission scope for access token """
+
+    access_token: Optional[str] = None
+    """ Access token for GigaChat """
+
+    model: Optional[str] = None
+    """Model name to use."""
+    user: Optional[str] = None
+    """ Username for authenticate """
+    password: Optional[str] = None
+    """ Password for authenticate """
+
+    timeout: Optional[float] = 600
+    """ Timeout for request. By default it works for long requests. """
+    verify_ssl_certs: Optional[bool] = None
+    """ Check certificates for all requests """
+
+    ca_bundle_file: Optional[str] = None
+    cert_file: Optional[str] = None
+    key_file: Optional[str] = None
+    key_file_password: Optional[str] = None
+    # Support for connection to GigaChat through SSL certificates
+
+    @cached_property
+    def _client(self) -> Any:
+        """Returns GigaChat API client"""
+        import gigachat
+
+        return gigachat.GigaChat(
+            base_url=self.base_url,
+            auth_url=self.auth_url,
+            credentials=self.credentials,
+            scope=self.scope,
+            access_token=self.access_token,
+            model=self.model,
+            user=self.user,
+            password=self.password,
+            timeout=self.timeout,
+            verify_ssl_certs=self.verify_ssl_certs,
+            ca_bundle_file=self.ca_bundle_file,
+            cert_file=self.cert_file,
+            key_file=self.key_file,
+            key_file_password=self.key_file_password,
+        )
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate authenticate data in environment and python package is installed."""
+        try:
+            import gigachat  # noqa: F401
+        except ImportError:
+            raise ImportError(
+                "Could not import gigachat python package. "
+                "Please install it with `pip install gigachat`."
+            )
+        fields = set(cls.__fields__.keys())
+        diff = set(values.keys()) - fields
+        if diff:
+            logger.warning(f"Extra fields {diff} in GigaChat class")
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed documents using a GigaChat embeddings models.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        result: List[List[float]] = []
+        size = 0
+        local_texts = []
+        embed_kwargs = {}
+        if self.model is not None:
+            embed_kwargs["model"] = self.model
+        for text in texts:
+            local_texts.append(text)
+            size += len(text)
+            if size > MAX_BATCH_SIZE_CHARS or len(local_texts) > MAX_BATCH_SIZE_PARTS:
+                for embedding in self._client.embeddings(
+                    texts=local_texts, **embed_kwargs
+                ).data:
+                    result.append(embedding.embedding)
+                size = 0
+                local_texts = []
+        # Call for last iteration
+        if local_texts:
+            for embedding in self._client.embeddings(
+                texts=local_texts, **embed_kwargs
+            ).data:
+                result.append(embedding.embedding)
+
+        return result
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed documents using a GigaChat embeddings models.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        result: List[List[float]] = []
+        size = 0
+        local_texts = []
+        embed_kwargs = {}
+        if self.model is not None:
+            embed_kwargs["model"] = self.model
+        for text in texts:
+            local_texts.append(text)
+            size += len(text)
+            if size > MAX_BATCH_SIZE_CHARS or len(local_texts) > MAX_BATCH_SIZE_PARTS:
+                embeddings = await self._client.aembeddings(
+                    texts=local_texts, **embed_kwargs
+                )
+                for embedding in embeddings.data:
+                    result.append(embedding.embedding)
+                size = 0
+                local_texts = []
+        # Call for last iteration
+        if local_texts:
+            embeddings = await self._client.aembeddings(
+                texts=local_texts, **embed_kwargs
+            )
+            for embedding in embeddings.data:
+                result.append(embedding.embedding)
+
+        return result
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query using a GigaChat embeddings models.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self.embed_documents(texts=[text])[0]
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Embed a query using a GigaChat embeddings models.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        docs = await self.aembed_documents(texts=[text])
+        return docs[0]

libs/community/langchain_community/llms/gigachat.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import logging
 from functools import cached_property
-from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
+from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, Iterator, List, Optional
 
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
@@ -13,6 +13,10 @@ from langchain_core.load.serializable import Serializable
 from langchain_core.outputs import Generation, GenerationChunk, LLMResult
 from langchain_core.pydantic_v1 import root_validator
 
+if TYPE_CHECKING:
+    import gigachat
+    import gigachat.models as gm
+
 logger = logging.getLogger(__name__)
 
 
@@ -48,13 +52,25 @@ class _BaseGigaChat(Serializable):
     # Support for connection to GigaChat through SSL certificates
 
     profanity: bool = True
+    """ DEPRECATED: Check for profanity """
+    profanity_check: Optional[bool] = None
     """ Check for profanity """
     streaming: bool = False
     """ Whether to stream the results or not. """
     temperature: Optional[float] = None
-    """What sampling temperature to use."""
+    """ What sampling temperature to use. """
     max_tokens: Optional[int] = None
     """ Maximum number of tokens to generate """
+    use_api_for_tokens: bool = False
+    """ Use GigaChat API for tokens count """
+    verbose: bool = False
+    """ Verbose logging """
+    top_p: Optional[float] = None
+    """ top_p value to use for nucleus sampling. Must be between 0.0 and 1.0 """
+    repetition_penalty: Optional[float] = None
+    """ The penalty applied to repeated tokens """
+    update_interval: Optional[float] = None
+    """ Minimum interval in seconds that elapses between sending tokens """
 
     @property
     def _llm_type(self) -> str:
@@ -74,7 +90,7 @@ class _BaseGigaChat(Serializable):
         return True
 
     @cached_property
-    def _client(self) -> Any:
+    def _client(self) -> gigachat.GigaChat:
         """Returns GigaChat API client"""
         import gigachat
 
@@ -85,6 +101,7 @@ class _BaseGigaChat(Serializable):
             scope=self.scope,
             access_token=self.access_token,
             model=self.model,
+            profanity_check=self.profanity_check,
             user=self.user,
             password=self.password,
             timeout=self.timeout,
@@ -93,6 +110,7 @@ class _BaseGigaChat(Serializable):
             cert_file=self.cert_file,
             key_file=self.key_file,
             key_file_password=self.key_file_password,
+            verbose=self.verbose,
         )
 
     @root_validator()
@@ -105,6 +123,16 @@ class _BaseGigaChat(Serializable):
                 "Could not import gigachat python package. "
                 "Please install it with `pip install gigachat`."
             )
+        fields = set(cls.__fields__.keys())
+        diff = set(values.keys()) - fields
+        if diff:
+            logger.warning(f"Extra fields {diff} in GigaChat class")
+        if "profanity" in fields and values.get("profanity") is False:
+            logger.warning(
+                "'profanity' field is deprecated. Use 'profanity_check' instead."
+            )
+            if values.get("profanity_check") is None:
+                values["profanity_check"] = values.get("profanity")
         return values
 
     @property
@@ -113,11 +141,48 @@ class _BaseGigaChat(Serializable):
         return {
             "temperature": self.temperature,
             "model": self.model,
-            "profanity": self.profanity,
+            "profanity": self.profanity_check,
             "streaming": self.streaming,
             "max_tokens": self.max_tokens,
+            "top_p": self.top_p,
+            "repetition_penalty": self.repetition_penalty,
         }
 
+    def tokens_count(
+        self, input_: List[str], model: Optional[str] = None
+    ) -> List[gm.TokensCount]:
+        """Get tokens of string list"""
+        return self._client.tokens_count(input_, model)
+
+    async def atokens_count(
+        self, input_: List[str], model: Optional[str] = None
+    ) -> List[gm.TokensCount]:
+        """Get tokens of strings list (async)"""
+        return await self._client.atokens_count(input_, model)
+
+    def get_models(self) -> gm.Models:
+        """Get available models of Gigachat"""
+        return self._client.get_models()
+
+    async def aget_models(self) -> gm.Models:
+        """Get available models of Gigachat (async)"""
+        return await self._client.aget_models()
+
+    def get_model(self, model: str) -> gm.Model:
+        """Get info about model"""
+        return self._client.get_model(model)
+
+    async def aget_model(self, model: str) -> gm.Model:
+        """Get info about model (async)"""
+        return await self._client.aget_model(model)
+
+    def get_num_tokens(self, text: str) -> int:
+        """Count approximate number of tokens"""
+        if self.use_api_for_tokens:
+            return self.tokens_count([text])[0].tokens  # type: ignore
+        else:
+            return round(len(text) / 4.6)
+
 
 class GigaChat(_BaseGigaChat, BaseLLM):
     """`GigaChat` large language models API.
@@ -128,20 +193,29 @@ class GigaChat(_BaseGigaChat, BaseLLM):
         .. code-block:: python
 
             from langchain_community.llms import GigaChat
-            giga = GigaChat(credentials=..., verify_ssl_certs=False)
+            giga = GigaChat(credentials=..., scope=..., verify_ssl_certs=False)
     """
 
+    payload_role: str = "user"
+
     def _build_payload(self, messages: List[str]) -> Dict[str, Any]:
         payload: Dict[str, Any] = {
-            "messages": [{"role": "user", "content": m} for m in messages],
+            "messages": [{"role": self.payload_role, "content": m} for m in messages],
-            "profanity_check": self.profanity,
         }
-        if self.temperature is not None:
-            payload["temperature"] = self.temperature
-        if self.max_tokens is not None:
-            payload["max_tokens"] = self.max_tokens
         if self.model:
             payload["model"] = self.model
+        if self.profanity_check is not None:
+            payload["profanity_check"] = self.profanity_check
+        if self.temperature is not None:
+            payload["temperature"] = self.temperature
+        if self.top_p is not None:
+            payload["top_p"] = self.top_p
+        if self.max_tokens is not None:
+            payload["max_tokens"] = self.max_tokens
+        if self.repetition_penalty is not None:
+            payload["repetition_penalty"] = self.repetition_penalty
+        if self.update_interval is not None:
+            payload["update_interval"] = self.update_interval
 
         if self.verbose:
             logger.info("Giga request: %s", payload)
@@ -164,6 +238,7 @@ class GigaChat(_BaseGigaChat, BaseLLM):
                 )
             if self.verbose:
                 logger.info("Giga response: %s", res.message.content)
+
         token_usage = response.usage
         llm_output = {"token_usage": token_usage, "model_name": response.model}
         return LLMResult(generations=generations, llm_output=llm_output)
@@ -254,6 +329,5 @@ class GigaChat(_BaseGigaChat, BaseLLM):
                 if run_manager:
                     await run_manager.on_llm_new_token(content)
 
-    def get_num_tokens(self, text: str) -> int:
+    class Config:
-        """Count approximate number of tokens"""
+        extra = "allow"
-        return round(len(text) / 4.6)

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

@@ -48,6 +48,7 @@ EXPECTED_ALL = [
     "SpacyEmbeddings",
     "NLPCloudEmbeddings",
     "GPT4AllEmbeddings",
+    "GigaChatEmbeddings",
     "XinferenceEmbeddings",
     "LocalAIEmbeddings",
     "AwaEmbeddings",