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

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- **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.
This commit is contained in:
Mikelarg 2024-03-26 02:08:37 +03:00 committed by GitHub
parent e5bdb26f76
commit dac2e0165a
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9 changed files with 548 additions and 62 deletions
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24
docs/docs/integrations/chat/gigachat.ipynb
View File

@ -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)"
]
}
],

20
docs/docs/integrations/llms/gigachat.ipynb
View File

@ -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",
"print(generated)"
"generated = llm_chain.invoke(input={\"country\": \"Russia\"})\n",
"print(generated[\"text\"])"
]
}
],

8
docs/docs/integrations/providers/salute_devices.mdx
View File

@ -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
```

116
docs/docs/integrations/text_embedding/gigachat.ipynb Normal file
View File

@ -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
}

151
libs/community/langchain_community/chat_models/gigachat.py
View File

@ -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:
from gigachat.models import MessagesRole
def _convert_dict_to_message(message: gm.Messages) -> BaseMessage:
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:
content = chunk.choices[0].delta.content
cg_chunk = ChatGenerationChunk(message=AIMessageChunk(content=content))
if run_manager:
run_manager.on_llm_new_token(content, chunk=cg_chunk)
yield cg_chunk
if not isinstance(chunk, dict):
chunk = chunk.dict()
if len(chunk["choices"]) == 0:
continue
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:
content = chunk.choices[0].delta.content
cg_chunk = ChatGenerationChunk(message=AIMessageChunk(content=content))
if run_manager:
await run_manager.on_llm_new_token(content, chunk=cg_chunk)
yield cg_chunk
if not isinstance(chunk, dict):
chunk = chunk.dict()
if len(chunk["choices"]) == 0:
continue
def get_num_tokens(self, text: str) -> int:
"""Count approximate number of tokens"""
return round(len(text) / 4.6)
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
)
yield ChatGenerationChunk(message=chunk, generation_info=generation_info)
if run_manager:
await run_manager.on_llm_new_token(content)

1
libs/community/langchain_community/embeddings/__init__.py
View File

@ -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",

187
libs/community/langchain_community/embeddings/gigachat.py Normal file
View File

@ -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]

102
libs/community/langchain_community/llms/gigachat.py
View File

@ -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],
"profanity_check": self.profanity,
"messages": [{"role": self.payload_role, "content": m} for m in messages],
}
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:
"""Count approximate number of tokens"""
return round(len(text) / 4.6)
class Config:
extra = "allow"

1
libs/community/tests/unit_tests/embeddings/test_imports.py
View File

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