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community[minor]: Prem AI langchain integration (#19113)

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### Prem SDK integration in LangChain

This PR adds the integration with [PremAI's](https://www.premai.io/)
prem-sdk with langchain. User can now access to deployed models
(llms/embeddings) and use it with langchain's ecosystem. This PR adds
the following:

### This PR adds the following:

- [x]  Add chat support
- [X]  Adding embedding support
- [X]  writing integration tests
    - [X]  writing tests for chat 
    - [X]  writing tests for embedding
- [X]  writing unit tests
    - [X]  writing tests for chat 
    - [X]  writing tests for embedding
- [X]  Adding documentation
    - [X]  writing documentation for chat
    - [X]  writing documentation for embedding
- [X] run `make test`
- [X] run `make lint`, `make lint_diff` 
- [X]  Final checks (spell check, lint, format and overall testing)

---------

Co-authored-by: Anindyadeep Sannigrahi <anindyadeepsannigrahi@Anindyadeeps-MacBook-Pro.local>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
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docs/docs/integrations/chat/premai.ipynb Normal file
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{
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"---\n",
"sidebar_label: PremAI\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ChatPremAI\n",
"\n",
">[PremAI](https://app.premai.io) is a unified platform that lets you build powerful production-ready GenAI-powered applications with the least effort so that you can focus more on user experience and overall growth. \n",
"\n",
"\n",
"This example goes over how to use LangChain to interact with `ChatPremAI`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation and setup\n",
"\n",
"We start by installing langchain and premai-sdk. You can type the following command to install:\n",
"\n",
"```bash\n",
"pip install premai langchain\n",
"```\n",
"\n",
"Before proceeding further, please make sure that you have made an account on PremAI and already started a project. If not, then here's how you can start for free:\n",
"\n",
"1. Sign in to [PremAI](https://app.premai.io/accounts/login/), if you are coming for the first time and create your API key [here](https://app.premai.io/api_keys/).\n",
"\n",
"2. Go to [app.premai.io](https://app.premai.io) and this will take you to the project's dashboard. \n",
"\n",
"3. Create a project and this will generate a project-id (written as ID). This ID will help you to interact with your deployed application. \n",
"\n",
"4. Head over to LaunchPad (the one with 🚀 icon). And there deploy your model of choice. Your default model will be `gpt-4`. You can also set and fix different generation parameters (like max-tokens, temperature, etc) and also pre-set your system prompt. \n",
"\n",
"Congratulations on creating your first deployed application on PremAI 🎉 Now we can use langchain to interact with our application. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.chat_models import ChatPremAI\n",
"from langchain_core.messages import HumanMessage, SystemMessage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup ChatPremAI instance in LangChain \n",
"\n",
"Once we import our required modules, let's set up our client. For now, let's assume that our `project_id` is 8. But make sure you use your project-id, otherwise, it will throw an error.\n",
"\n",
"To use langchain with prem, you do not need to pass any model name or set any parameters with our chat client. All of those will use the default model name and parameters of the LaunchPad model. \n",
"\n",
"`NOTE:` If you change the `model_name` or any other parameter like `temperature` while setting the client, it will override existing default configurations. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"# First step is to set up the env variable.\n",
"# you can also pass the API key while instantiating the model but this\n",
"# comes under a best practices to set it as env variable.\n",
"\n",
"if os.environ.get(\"PREMAI_API_KEY\") is None:\n",
" os.environ[\"PREMAI_API_KEY\"] = getpass.getpass(\"PremAI API Key:\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# By default it will use the model which was deployed through the platform\n",
"# in my case it will is \"claude-3-haiku\"\n",
"\n",
"chat = ChatPremAI(project_id=8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calling the Model\n",
"\n",
"Now you are all set. We can now start by interacting with our application. `ChatPremAI` supports two methods `invoke` (which is the same as `generate`) and `stream`. \n",
"\n",
"The first one will give us a static result. Whereas the second one will stream tokens one by one. Here's how you can generate chat-like completions. \n",
"\n",
"### Generation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I am an artificial intelligence created by Anthropic. I'm here to help with a wide variety of tasks, from research and analysis to creative projects and open-ended conversation. I have general knowledge and capabilities, but I'm not a real person - I'm an AI assistant. Please let me know if you have any other questions!\n"
]
}
],
"source": [
"human_message = HumanMessage(content=\"Who are you?\")\n",
"\n",
"response = chat.invoke([human_message])\n",
"print(response.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above looks interesting right? I set my default lanchpad system-prompt as: `Always sound like a pirate` You can also, override the default system prompt if you need to. Here's how you can do it. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"I am an artificial intelligence created by Anthropic. My purpose is to assist and converse with humans in a friendly and helpful way. I have a broad knowledge base that I can use to provide information, answer questions, and engage in discussions on a wide range of topics. Please let me know if you have any other questions - I'm here to help!\")"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"system_message = SystemMessage(content=\"You are a friendly assistant.\")\n",
"human_message = HumanMessage(content=\"Who are you?\")\n",
"\n",
"chat.invoke([system_message, human_message])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also change generation parameters while calling the model. Here's how you can do that"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='I am an artificial intelligence created by Anthropic')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat.invoke([system_message, human_message], temperature=0.7, max_tokens=10, top_p=0.95)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Important notes:\n",
"\n",
"Before proceeding further, please note that the current version of ChatPrem does not support parameters: [n](https://platform.openai.com/docs/api-reference/chat/create#chat-create-n) and [stop](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stop) are not supported. \n",
"\n",
"We will provide support for those two above parameters in sooner versions. \n",
"\n",
"### Streaming\n",
"\n",
"And finally, here's how you do token streaming for dynamic chat like applications. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello! As an AI language model, I don't have feelings or a physical state, but I'm functioning properly and ready to assist you with any questions or tasks you might have. How can I help you today?"
]
}
],
"source": [
"import sys\n",
"\n",
"for chunk in chat.stream(\"hello how are you\"):\n",
" sys.stdout.write(chunk.content)\n",
" sys.stdout.flush()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to above, if you want to override the system-prompt and the generation parameters, here's how you can do it. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello! As an AI language model, I don't have feelings or a physical form, but I'm functioning properly and ready to assist you. How can I help you today?"
]
}
],
"source": [
"import sys\n",
"\n",
"# For some experimental reasons if you want to override the system prompt then you\n",
"# can pass that here too. However it is not recommended to override system prompt\n",
"# of an already deployed model.\n",
"\n",
"for chunk in chat.stream(\n",
" \"hello how are you\",\n",
" system_prompt=\"act like a dog\",\n",
" temperature=0.7,\n",
" max_tokens=200,\n",
"):\n",
" sys.stdout.write(chunk.content)\n",
" sys.stdout.flush()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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# PremAI
>[PremAI](https://app.premai.io) is a unified platform that lets you build powerful production-ready GenAI-powered applications with the least effort so that you can focus more on user experience and overall growth.
## ChatPremAI
This example goes over how to use LangChain to interact with different chat models with `ChatPremAI`
### Installation and setup
We start by installing langchain and premai-sdk. You can type the following command to install:
```bash
pip install premai langchain
```
Before proceeding further, please make sure that you have made an account on PremAI and already started a project. If not, then here's how you can start for free:
1. Sign in to [PremAI](https://app.premai.io/accounts/login/), if you are coming for the first time and create your API key [here](https://app.premai.io/api_keys/).
2. Go to [app.premai.io](https://app.premai.io) and this will take you to the project's dashboard.
3. Create a project and this will generate a project-id (written as ID). This ID will help you to interact with your deployed application.
4. Head over to LaunchPad (the one with 🚀 icon). And there deploy your model of choice. Your default model will be `gpt-4`. You can also set and fix different generation parameters (like max-tokens, temperature, etc) and also pre-set your system prompt.
Congratulations on creating your first deployed application on PremAI 🎉 Now we can use langchain to interact with our application.
```python
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_community.chat_models import ChatPremAI
```
### Setup ChatPrem instance in LangChain
Once we import our required modules, let's set up our client. For now, let's assume that our `project_id` is 8. But make sure you use your project-id, otherwise, it will throw an error.
To use langchain with prem, you do not need to pass any model name or set any parameters with our chat client. All of those will use the default model name and parameters of the LaunchPad model.
`NOTE:` If you change the `model_name` or any other parameter like `temperature` while setting the client, it will override existing default configurations.
```python
import os
import getpass
if "PREMAI_API_KEY" not in os.environ:
os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")
chat = ChatPremAI(project_id=8)
```
### Calling the Model
Now you are all set. We can now start by interacting with our application. `ChatPremAI` supports two methods `invoke` (which is the same as `generate`) and `stream`.
The first one will give us a static result. Whereas the second one will stream tokens one by one. Here's how you can generate chat-like completions.
### Generation
```python
human_message = HumanMessage(content="Who are you?")
chat.invoke([human_message])
```
The above looks interesting, right? I set my default launchpad system-prompt as: `Always sound like a pirate` You can also, override the default system prompt if you need to. Here's how you can do it.
```python
system_message = SystemMessage(content="You are a friendly assistant.")
human_message = HumanMessage(content="Who are you?")
chat.invoke([system_message, human_message])
```
You can also change generation parameters while calling the model. Here's how you can do that:
```python
chat.invoke(
[system_message, human_message],
temperature = 0.7, max_tokens = 20, top_p = 0.95
)
```
### Important notes:
Before proceeding further, please note that the current version of ChatPrem does not support parameters: [n](https://platform.openai.com/docs/api-reference/chat/create#chat-create-n) and [stop](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stop) are not supported.
We will provide support for those two above parameters in later versions.
### Streaming
And finally, here's how you do token streaming for dynamic chat-like applications.
```python
import sys
for chunk in chat.stream("hello how are you"):
sys.stdout.write(chunk.content)
sys.stdout.flush()
```
Similar to above, if you want to override the system-prompt and the generation parameters, here's how you can do it.
```python
import sys
for chunk in chat.stream(
"hello how are you",
system_prompt = "You are an helpful assistant", temperature = 0.7, max_tokens = 20
):
sys.stdout.write(chunk.content)
sys.stdout.flush()
```
## Embedding
In this section, we are going to discuss how we can get access to different embedding models using `PremEmbeddings`. Let's start by doing some imports and defining our embedding object
```python
from langchain_community.embeddings import PremEmbeddings
```
Once we import our required modules, let's set up our client. For now, let's assume that our `project_id` is 8. But make sure you use your project-id, otherwise, it will throw an error.
```python
import os
import getpass
if os.environ.get("PREMAI_API_KEY") is None:
os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")
# Define a model as a required parameter here since there is no default embedding model
model = "text-embedding-3-large"
embedder = PremEmbeddings(project_id=8, model=model)
```
We have defined our embedding model. We support a lot of embedding models. Here is a table that shows the number of embedding models we support.
| Provider | Slug | Context Tokens |
|-------------|------------------------------------------|----------------|
| cohere | embed-english-v3.0 | N/A |
| openai | text-embedding-3-small | 8191 |
| openai | text-embedding-3-large | 8191 |
| openai | text-embedding-ada-002 | 8191 |
| replicate | replicate/all-mpnet-base-v2 | N/A |
| together | togethercomputer/Llama-2-7B-32K-Instruct | N/A |
| mistralai | mistral-embed | 4096 |
To change the model, you simply need to copy the `slug` and access your embedding model. Now let's start using our embedding model with a single query followed by multiple queries (which is also called as a document)
```python
query = "Hello, this is a test query"
query_result = embedder.embed_query(query)
# Let's print the first five elements of the query embedding vector
print(query_result[:5])
```
Finally, let's embed a document
```python
documents = [
"This is document1",
"This is document2",
"This is document3"
]
doc_result = embedder.embed_documents(documents)
# Similar to the previous result, let's print the first five element
# of the first document vector
print(doc_result[0][:5])
```

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@ -0,0 +1,166 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PremAI\n",
"\n",
">[PremAI](https://app.premai.io) is an unified platform that let's you build powerful production-ready GenAI powered applications with least effort, so that you can focus more on user experience and overall growth. In this section we are going to dicuss how we can get access to different embedding model using `PremAIEmbeddings`\n",
"\n",
"## Installation and Setup\n",
"\n",
"We start by installing langchain and premai-sdk. You can type the following command to install:\n",
"\n",
"```bash\n",
"pip install premai langchain\n",
"```\n",
"\n",
"Before proceeding further, please make sure that you have made an account on Prem and already started a project. If not, then here's how you can start for free:\n",
"\n",
"1. Sign in to [PremAI](https://app.premai.io/accounts/login/), if you are coming for the first time and create your API key [here](https://app.premai.io/api_keys/).\n",
"\n",
"2. Go to [app.premai.io](https://app.premai.io) and this will take you to the project's dashboard. \n",
"\n",
"3. Create a project and this will generate a project-id (written as ID). This ID will help you to interact with your deployed application. \n",
"\n",
"Congratulations on creating your first deployed application on Prem 🎉 Now we can use langchain to interact with our application. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Let's start by doing some imports and define our embedding object\n",
"\n",
"from langchain_community.embeddings import PremAIEmbeddings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we imported our required modules, let's setup our client. For now let's assume that our `project_id` is 8. But make sure you use your project-id, otherwise it will throw error.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"if os.environ.get(\"PREMAI_API_KEY\") is None:\n",
" os.environ[\"PREMAI_API_KEY\"] = getpass.getpass(\"PremAI API Key:\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"model = \"text-embedding-3-large\"\n",
"embedder = PremAIEmbeddings(project_id=8, model=model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have defined our embedding model. We support a lot of embedding models. Here is a table that shows the number of embedding models we support. \n",
"\n",
"\n",
"| Provider | Slug | Context Tokens |\n",
"|-------------|------------------------------------------|----------------|\n",
"| cohere | embed-english-v3.0 | N/A |\n",
"| openai | text-embedding-3-small | 8191 |\n",
"| openai | text-embedding-3-large | 8191 |\n",
"| openai | text-embedding-ada-002 | 8191 |\n",
"| replicate | replicate/all-mpnet-base-v2 | N/A |\n",
"| together | togethercomputer/Llama-2-7B-32K-Instruct | N/A |\n",
"| mistralai | mistral-embed | 4096 |\n",
"\n",
"To change the model, you simply need to copy the `slug` and access your embedding model. Now let's start using our embedding model with a single query followed by multiple queries (which is also called as a document)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.02129288576543331, 0.0008162345038726926, -0.004556538071483374, 0.02918623760342598, -0.02547479420900345]\n"
]
}
],
"source": [
"query = \"Hello, this is a test query\"\n",
"query_result = embedder.embed_query(query)\n",
"\n",
"# Let's print the first five elements of the query embedding vector\n",
"\n",
"print(query_result[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally let's embed a document"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.0030691148713231087, -0.045334383845329285, -0.0161729846149683, 0.04348714277148247, -0.0036920777056366205]\n"
]
}
],
"source": [
"documents = [\"This is document1\", \"This is document2\", \"This is document3\"]\n",
"\n",
"doc_result = embedder.embed_documents(documents)\n",
"\n",
"# Similar to previous result, let's print the first five element\n",
"# of the first document vector\n",
"\n",
"print(doc_result[0][:5])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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

@ -64,6 +64,7 @@ _module_lookup = {
"PromptLayerChatOpenAI": "langchain_community.chat_models.promptlayer_openai",
"QianfanChatEndpoint": "langchain_community.chat_models.baidu_qianfan_endpoint",
"VolcEngineMaasChat": "langchain_community.chat_models.volcengine_maas",
"ChatPremAI": "langchain_community.chat_models.premai",
}

416
libs/community/langchain_community/chat_models/premai.py Normal file
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@ -0,0 +1,416 @@
"""Wrapper around Prem's Chat API."""
from __future__ import annotations
import logging
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterator,
List,
Optional,
Tuple,
Type,
Union,
)
from langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.language_models.llms import create_base_retry_decorator
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
from langchain_core.utils import get_from_dict_or_env
if TYPE_CHECKING:
from premai.api.chat_completions.v1_chat_completions_create import (
ChatCompletionResponseStream,
)
from premai.models.chat_completion_response import ChatCompletionResponse
logger = logging.getLogger(__name__)
class ChatPremAPIError(Exception):
"""Error with the `PremAI` API."""
def _truncate_at_stop_tokens(
text: str,
stop: Optional[List[str]],
) -> str:
"""Truncates text at the earliest stop token found."""
if stop is None:
return text
for stop_token in stop:
stop_token_idx = text.find(stop_token)
if stop_token_idx != -1:
text = text[:stop_token_idx]
return text
def _response_to_result(
response: ChatCompletionResponse,
stop: Optional[List[str]],
) -> ChatResult:
"""Converts a Prem API response into a LangChain result"""
if not response.choices:
raise ChatPremAPIError("ChatResponse must have at least one candidate")
generations: List[ChatGeneration] = []
for choice in response.choices:
role = choice.message.role
if role is None:
raise ChatPremAPIError(f"ChatResponse {choice} must have a role.")
# If content is None then it will be replaced by ""
content = _truncate_at_stop_tokens(text=choice.message.content or "", stop=stop)
if content is None:
raise ChatPremAPIError(f"ChatResponse must have a content: {content}")
if role == "assistant":
generations.append(
ChatGeneration(text=content, message=AIMessage(content=content))
)
elif role == "user":
generations.append(
ChatGeneration(text=content, message=HumanMessage(content=content))
)
else:
generations.append(
ChatGeneration(
text=content, message=ChatMessage(role=role, content=content)
)
)
return ChatResult(generations=generations)
def _convert_delta_response_to_message_chunk(
response: ChatCompletionResponseStream, default_class: Type[BaseMessageChunk]
) -> Tuple[
Union[BaseMessageChunk, HumanMessageChunk, AIMessageChunk, SystemMessageChunk],
Optional[str],
]:
"""Converts delta response to message chunk"""
_delta = response.choices[0].delta # type: ignore
role = _delta.get("role", "") # type: ignore
content = _delta.get("content", "") # type: ignore
additional_kwargs: Dict = {}
if role is None or role == "":
raise ChatPremAPIError("Role can not be None. Please check the response")
finish_reasons: Optional[str] = response.choices[0].finish_reason
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content), finish_reasons
elif role == "assistant" or default_class == AIMessageChunk:
return (
AIMessageChunk(content=content, additional_kwargs=additional_kwargs),
finish_reasons,
)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content), finish_reasons
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role), finish_reasons
else:
return default_class(content=content), finish_reasons
def _messages_to_prompt_dict(
input_messages: List[BaseMessage],
) -> Tuple[Optional[str], List[Dict[str, str]]]:
"""Converts a list of LangChain Messages into a simple dict
which is the message structure in Prem"""
system_prompt: Optional[str] = None
examples_and_messages: List[Dict[str, str]] = []
for input_msg in input_messages:
if isinstance(input_msg, SystemMessage):
system_prompt = str(input_msg.content)
elif isinstance(input_msg, HumanMessage):
examples_and_messages.append(
{"role": "user", "content": str(input_msg.content)}
)
elif isinstance(input_msg, AIMessage):
examples_and_messages.append(
{"role": "assistant", "content": str(input_msg.content)}
)
else:
raise ChatPremAPIError("No such role explicitly exists")
return system_prompt, examples_and_messages
class ChatPremAI(BaseChatModel, BaseModel):
"""Use any LLM provider with Prem and Langchain.
To use, you will need to have an API key. You can find your existing API Key
or generate a new one here: https://app.premai.io/api_keys/
"""
# TODO: Need to add the default parameters through prem-sdk here
project_id: int
"""The project ID in which the experiments or deployments are carried out.
You can find all your projects here: https://app.premai.io/projects/"""
premai_api_key: Optional[SecretStr] = None
"""Prem AI API Key. Get it here: https://app.premai.io/api_keys/"""
model: Optional[str] = None
"""Name of the model. This is an optional parameter.
The default model is the one deployed from Prem's LaunchPad: https://app.premai.io/projects/8/launchpad
If model name is other than default model then it will override the calls
from the model deployed from launchpad."""
session_id: Optional[str] = None
"""The ID of the session to use. It helps to track the chat history."""
temperature: Optional[float] = None
"""Model temperature. Value should be >= 0 and <= 1.0"""
top_p: Optional[float] = None
"""top_p adjusts the number of choices for each predicted tokens based on
cumulative probabilities. Value should be ranging between 0.0 and 1.0.
"""
max_tokens: Optional[int] = None
"""The maximum number of tokens to generate"""
max_retries: int = 1
"""Max number of retries to call the API"""
system_prompt: Optional[str] = ""
"""Acts like a default instruction that helps the LLM act or generate
in a specific way.This is an Optional Parameter. By default the
system prompt would be using Prem's Launchpad models system prompt.
Changing the system prompt would override the default system prompt.
"""
streaming: Optional[bool] = False
"""Whether to stream the responses or not."""
tools: Optional[Dict[str, Any]] = None
"""A list of tools the model may call. Currently, only functions are
supported as a tool"""
frequency_penalty: Optional[float] = None
"""Number between -2.0 and 2.0. Positive values penalize new tokens based"""
presence_penalty: Optional[float] = None
"""Number between -2.0 and 2.0. Positive values penalize new tokens based
on whether they appear in the text so far."""
logit_bias: Optional[dict] = None
"""JSON object that maps tokens to an associated bias value from -100 to 100."""
stop: Optional[Union[str, List[str]]] = None
"""Up to 4 sequences where the API will stop generating further tokens."""
seed: Optional[int] = None
"""This feature is in Beta. If specified, our system will make a best effort
to sample deterministically."""
client: Any
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environments(cls, values: Dict) -> Dict:
"""Validate that the package is installed and that the API token is valid"""
try:
from premai import Prem
except ImportError as error:
raise ImportError(
"Could not import Prem Python package."
"Please install it with: `pip install premai`"
) from error
try:
premai_api_key = get_from_dict_or_env(
values, "premai_api_key", "PREMAI_API_KEY"
)
values["client"] = Prem(api_key=premai_api_key)
except Exception as error:
raise ValueError("Your API Key is incorrect. Please try again.") from error
return values
@property
def _llm_type(self) -> str:
return "premai"
@property
def _default_params(self) -> Dict[str, Any]:
# FIXME: n and stop is not supported, so hardcoding to current default value
return {
"model": self.model,
"system_prompt": self.system_prompt,
"top_p": self.top_p,
"temperature": self.temperature,
"logit_bias": self.logit_bias,
"max_tokens": self.max_tokens,
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"seed": self.seed,
"stop": None,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
all_kwargs = {**self._default_params, **kwargs}
for key in list(self._default_params.keys()):
if all_kwargs.get(key) is None or all_kwargs.get(key) == "":
all_kwargs.pop(key, None)
return all_kwargs
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
system_prompt, messages_to_pass = _messages_to_prompt_dict(messages) # type: ignore
kwargs["stop"] = stop
if system_prompt is not None and system_prompt != "":
kwargs["system_prompt"] = system_prompt
all_kwargs = self._get_all_kwargs(**kwargs)
response = chat_with_retry(
self,
project_id=self.project_id,
messages=messages_to_pass,
stream=False,
run_manager=run_manager,
**all_kwargs,
)
return _response_to_result(response=response, stop=stop)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
system_prompt, messages_to_pass = _messages_to_prompt_dict(messages)
kwargs["stop"] = stop
if "system_prompt" not in kwargs:
if system_prompt is not None and system_prompt != "":
kwargs["system_prompt"] = system_prompt
all_kwargs = self._get_all_kwargs(**kwargs)
default_chunk_class = AIMessageChunk
for streamed_response in chat_with_retry(
self,
project_id=self.project_id,
messages=messages_to_pass,
stream=True,
run_manager=run_manager,
**all_kwargs,
):
try:
chunk, finish_reason = _convert_delta_response_to_message_chunk(
response=streamed_response, default_class=default_chunk_class
)
generation_info = (
dict(finish_reason=finish_reason)
if finish_reason is not None
else None
)
cg_chunk = ChatGenerationChunk(
message=chunk, generation_info=generation_info
)
if run_manager:
run_manager.on_llm_new_token(cg_chunk.text, chunk=cg_chunk)
yield cg_chunk
except Exception as _:
continue
def create_prem_retry_decorator(
llm: ChatPremAI,
*,
max_retries: int = 1,
run_manager: Optional[Union[CallbackManagerForLLMRun]] = None,
) -> Callable[[Any], Any]:
import premai.models
errors = [
premai.models.api_response_validation_error.APIResponseValidationError,
premai.models.conflict_error.ConflictError,
premai.models.model_not_found_error.ModelNotFoundError,
premai.models.permission_denied_error.PermissionDeniedError,
premai.models.provider_api_connection_error.ProviderAPIConnectionError,
premai.models.provider_api_status_error.ProviderAPIStatusError,
premai.models.provider_api_timeout_error.ProviderAPITimeoutError,
premai.models.provider_internal_server_error.ProviderInternalServerError,
premai.models.provider_not_found_error.ProviderNotFoundError,
premai.models.rate_limit_error.RateLimitError,
premai.models.unprocessable_entity_error.UnprocessableEntityError,
premai.models.validation_error.ValidationError,
]
decorator = create_base_retry_decorator(
error_types=errors, max_retries=max_retries, run_manager=run_manager
)
return decorator
def chat_with_retry(
llm: ChatPremAI,
project_id: int,
messages: List[dict],
stream: bool = False,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Using tenacity for retry in completion call"""
retry_decorator = create_prem_retry_decorator(
llm, max_retries=llm.max_retries, run_manager=run_manager
)
@retry_decorator
def _completion_with_retry(
project_id: int,
messages: List[dict],
stream: Optional[bool] = False,
**kwargs: Any,
) -> Any:
response = llm.client.chat.completions.create(
project_id=project_id,
messages=messages,
stream=stream,
**kwargs,
)
return response
return _completion_with_retry(
project_id=project_id,
messages=messages,
stream=stream,
**kwargs,
)

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

@ -80,6 +80,7 @@ _module_lookup = {
"VolcanoEmbeddings": "langchain_community.embeddings.volcengine",
"VoyageEmbeddings": "langchain_community.embeddings.voyageai",
"XinferenceEmbeddings": "langchain_community.embeddings.xinference",
"PremAIEmbeddings": "langchain_community.embeddings.premai",
"YandexGPTEmbeddings": "langchain_community.embeddings.yandex",
}

121
libs/community/langchain_community/embeddings/premai.py Normal file
View File

@ -0,0 +1,121 @@
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional, Union
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.llms import create_base_retry_decorator
from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
from langchain_core.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class PremAIEmbeddings(BaseModel, Embeddings):
"""Prem's Embedding APIs"""
project_id: int
"""The project ID in which the experiments or deployments are carried out.
You can find all your projects here: https://app.premai.io/projects/"""
premai_api_key: Optional[SecretStr] = None
"""Prem AI API Key. Get it here: https://app.premai.io/api_keys/"""
model: str
"""The Embedding model to choose from"""
show_progress_bar: bool = False
"""Whether to show a tqdm progress bar. Must have `tqdm` installed."""
max_retries: int = 1
"""Max number of retries for tenacity"""
client: Any
@root_validator()
def validate_environments(cls, values: Dict) -> Dict:
"""Validate that the package is installed and that the API token is valid"""
try:
from premai import Prem
except ImportError as error:
raise ImportError(
"Could not import Prem Python package."
"Please install it with: `pip install premai`"
) from error
try:
premai_api_key = get_from_dict_or_env(
values, "premai_api_key", "PREMAI_API_KEY"
)
values["client"] = Prem(api_key=premai_api_key)
except Exception as error:
raise ValueError("Your API Key is incorrect. Please try again.") from error
return values
def embed_query(self, text: str) -> List[float]:
"""Embed query text"""
embeddings = embed_with_retry(
self, model=self.model, project_id=self.project_id, input=text
)
return embeddings.data[0].embedding
def embed_documents(self, texts: List[str]) -> List[List[float]]:
embeddings = embed_with_retry(
self, model=self.model, project_id=self.project_id, input=texts
).data
return [embedding.embedding for embedding in embeddings]
def create_prem_retry_decorator(
embedder: PremAIEmbeddings,
*,
max_retries: int = 1,
) -> Callable[[Any], Any]:
import premai.models
errors = [
premai.models.api_response_validation_error.APIResponseValidationError,
premai.models.conflict_error.ConflictError,
premai.models.model_not_found_error.ModelNotFoundError,
premai.models.permission_denied_error.PermissionDeniedError,
premai.models.provider_api_connection_error.ProviderAPIConnectionError,
premai.models.provider_api_status_error.ProviderAPIStatusError,
premai.models.provider_api_timeout_error.ProviderAPITimeoutError,
premai.models.provider_internal_server_error.ProviderInternalServerError,
premai.models.provider_not_found_error.ProviderNotFoundError,
premai.models.rate_limit_error.RateLimitError,
premai.models.unprocessable_entity_error.UnprocessableEntityError,
premai.models.validation_error.ValidationError,
]
decorator = create_base_retry_decorator(
error_types=errors, max_retries=max_retries, run_manager=None
)
return decorator
def embed_with_retry(
embedder: PremAIEmbeddings,
model: str,
project_id: int,
input: Union[str, List[str]],
) -> Any:
"""Using tenacity for retry in embedding calls"""
retry_decorator = create_prem_retry_decorator(
embedder, max_retries=embedder.max_retries
)
@retry_decorator
def _embed_with_retry(
embedder: PremAIEmbeddings,
project_id: int,
model: str,
input: Union[str, List[str]],
) -> Any:
embedding_response = embedder.client.embeddings.create(
project_id=project_id, model=model, input=input
)
return embedding_response
return _embed_with_retry(embedder, project_id=project_id, model=model, input=input)

101
libs/community/poetry.lock generated
View File

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
[[package]]
name = "aenum"
@ -1167,10 +1167,7 @@ files = [
[package.dependencies]
jmespath = ">=0.7.1,<2.0.0"
python-dateutil = ">=2.1,<3.0.0"
urllib3 = [
{version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""},
{version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""},
]
urllib3 = {version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""}
[package.extras]
crt = ["awscrt (==0.19.19)"]
@ -2689,6 +2686,37 @@ uvicorn = ">=0.23.2,<0.24.0"
[package.extras]
mllib = ["accelerate (==0.21.0)", "datasets (==2.16.0)", "einops (>=0.6.1,<0.7.0)", "h5py (>=3.9.0,<4.0.0)", "peft (==0.6.0)", "transformers (==4.36.2)"]
[[package]]
name = "friendli-client"
version = "1.3.1"
description = "Client of Friendli Suite."
optional = true
python-versions = "<4.0.0,>=3.8.1"
files = [
{file = "friendli_client-1.3.1-py3-none-any.whl", hash = "sha256:1a77b046c57b0d70bac8d13ac6ecc861f8fc84d3c63e39b34543f862373a670b"},
{file = "friendli_client-1.3.1.tar.gz", hash = "sha256:85f87976f7bb75eb424f384e3e73ac3256b7aad477361b51341e520c2aed3a0e"},
]
[package.dependencies]
fastapi = ">=0.104.0,<0.105.0"
gql = ">=3.4.1,<4.0.0"
httpx = ">=0.24.1,<0.25.0"
injector = ">=0.21.0,<0.22.0"
jsonschema = ">=4.17.3,<5.0.0"
pathspec = ">=0.9.0,<0.10.0"
protobuf = ">=4.24.2,<5.0.0"
pydantic = {version = ">=1.9.0,<3", extras = ["email"]}
PyYaml = ">=6.0.1,<7.0.0"
requests = ">=2,<3"
rich = ">=12.2.0,<13.0.0"
tqdm = ">=4.48.0,<5.0.0"
typer = ">=0.9.0,<0.10.0"
types-protobuf = ">=4.24.0.1,<5.0.0.0"
uvicorn = ">=0.23.2,<0.24.0"
[package.extras]
mllib = ["accelerate (==0.21.0)", "datasets (==2.16.0)", "einops (>=0.6.1,<0.7.0)", "h5py (>=3.9.0,<4.0.0)", "peft (==0.6.0)", "transformers (==4.36.2)"]
[[package]]
name = "frozenlist"
version = "1.4.1"
@ -5965,6 +5993,23 @@ dev = ["packaging", "prawcore[lint]", "prawcore[test]"]
lint = ["pre-commit", "ruff (>=0.0.291)"]
test = ["betamax (>=0.8,<0.9)", "pytest (>=2.7.3)", "urllib3 (==1.26.*)"]
[[package]]
name = "premai"
version = "0.3.25"
description = "A client library for accessing Prem APIs"
optional = true
python-versions = ">=3.8,<4.0"
files = [
{file = "premai-0.3.25-py3-none-any.whl", hash = "sha256:bddace7340e1827f048b410748d365e8663e4bbeb6bf7e8b8657f3cc267f7f28"},
{file = "premai-0.3.25.tar.gz", hash = "sha256:c387980ecf3bdcb07886dd4f7a1c0f0701df67e772e62f444394cea97d5970a0"},
]
[package.dependencies]
attrs = ">=21.3.0"
httpx = ">=0.20.0,<0.27.0"
python-dateutil = ">=2.8.0,<3.0.0"
typing_extensions = ">=4.9.0"
[[package]]
name = "prometheus-client"
version = "0.20.0"
@ -9071,20 +9116,6 @@ files = [
cryptography = ">=35.0.0"
types-pyOpenSSL = "*"
[[package]]
name = "types-requests"
version = "2.31.0.6"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.7"
files = [
{file = "types-requests-2.31.0.6.tar.gz", hash = "sha256:cd74ce3b53c461f1228a9b783929ac73a666658f223e28ed29753771477b3bd0"},
{file = "types_requests-2.31.0.6-py3-none-any.whl", hash = "sha256:a2db9cb228a81da8348b49ad6db3f5519452dd20a9c1e1a868c83c5fe88fd1a9"},
]
[package.dependencies]
types-urllib3 = "*"
[[package]]
name = "types-requests"
version = "2.31.0.20240311"
@ -9121,17 +9152,6 @@ files = [
{file = "types_toml-0.10.8.20240310-py3-none-any.whl", hash = "sha256:627b47775d25fa29977d9c70dc0cbab3f314f32c8d8d0c012f2ef5de7aaec05d"},
]
[[package]]
name = "types-urllib3"
version = "1.26.25.14"
description = "Typing stubs for urllib3"
optional = false
python-versions = "*"
files = [
{file = "types-urllib3-1.26.25.14.tar.gz", hash = "sha256:229b7f577c951b8c1b92c1bc2b2fdb0b49847bd2af6d1cc2a2e3dd340f3bda8f"},
{file = "types_urllib3-1.26.25.14-py3-none-any.whl", hash = "sha256:9683bbb7fb72e32bfe9d2be6e04875fbe1b3eeec3cbb4ea231435aa7fd6b4f0e"},
]
[[package]]
name = "typing"
version = "3.7.4.3"
@ -9228,22 +9248,6 @@ files = [
[package.extras]
dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
[[package]]
name = "urllib3"
version = "1.26.18"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
files = [
{file = "urllib3-1.26.18-py2.py3-none-any.whl", hash = "sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07"},
{file = "urllib3-1.26.18.tar.gz", hash = "sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0"},
]
[package.extras]
brotli = ["brotli (==1.0.9)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
[[package]]
name = "urllib3"
version = "2.0.7"
@ -9319,7 +9323,6 @@ files = [
[package.dependencies]
PyYAML = "*"
urllib3 = {version = "<2", markers = "platform_python_implementation == \"PyPy\" or python_version < \"3.10\""}
wrapt = "*"
yarl = "*"
@ -9873,9 +9876,9 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[extras]
cli = ["typer"]
extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cloudpickle", "cloudpickle", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "friendli-client", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "tidb-vector", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "upstash-redis", "xata", "xmltodict", "zhipuai"]
extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cloudpickle", "cloudpickle", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "friendli-client", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "premai", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "tidb-vector", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "upstash-redis", "xata", "xmltodict", "zhipuai"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "5b2a17ed079fa4cc1776f0474a9e73a428c10bf30a22b7185d2f7a77b2d146e5"
content-hash = "dcaae2110a70843fa3cb375618bebbe16b3da9bfdbc1e471e57f144d0906f58b"

4
libs/community/pyproject.toml
View File

@ -97,6 +97,7 @@ rdflib = {version = "7.0.0", optional = true}
nvidia-riva-client = {version = "^2.14.0", optional = true}
tidb-vector = {version = ">=0.0.3,<1.0.0", optional = true}
friendli-client = {version = "^1.2.4", optional = true}
premai = {version = "^0.3.25", optional = true}
[tool.poetry.group.test]
optional = true
@ -267,7 +268,8 @@ extended_testing = [
"rdflib",
"tidb-vector",
"cloudpickle",
"friendli-client"
"friendli-client",
"premai"
]
[tool.ruff]

70
libs/community/tests/integration_tests/chat_models/test_premai.py Normal file
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@ -0,0 +1,70 @@
"""Test ChatPremAI from PremAI API wrapper.
Note: This test must be run with the PREMAI_API_KEY environment variable set to a valid
API key and a valid project_id.
For this we need to have a project setup in PremAI's platform: https://app.premai.io
"""
import pytest
from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_community.chat_models import ChatPremAI
@pytest.fixture
def chat() -> ChatPremAI:
return ChatPremAI(project_id=8)
def test_chat_premai() -> None:
"""Test ChatPremAI wrapper."""
chat = ChatPremAI(project_id=8)
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, BaseMessage)
assert isinstance(response.content, str)
def test_chat_prem_system_message() -> None:
"""Test ChatPremAI wrapper for system message"""
chat = ChatPremAI(project_id=8)
system_message = SystemMessage(content="You are to chat with the user.")
human_message = HumanMessage(content="Hello")
response = chat([system_message, human_message])
assert isinstance(response, BaseMessage)
assert isinstance(response.content, str)
def test_chat_prem_model() -> None:
"""Test ChatPremAI wrapper handles model_name."""
chat = ChatPremAI(model="foo", project_id=8)
assert chat.model == "foo"
def test_chat_prem_generate() -> None:
"""Test ChatPremAI wrapper with generate."""
chat = ChatPremAI(project_id=8)
message = HumanMessage(content="Hello")
response = chat.generate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
for generations in response.generations:
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
async def test_prem_invoke(chat: ChatPremAI) -> None:
"""Tests chat completion with invoke"""
result = chat.invoke("How is the weather in New York today?")
assert isinstance(result.content, str)
def test_prem_streaming() -> None:
"""Test streaming tokens from Prem."""
chat = ChatPremAI(project_id=8, streaming=True)
for token in chat.stream("I'm Pickle Rick"):
assert isinstance(token.content, str)

40
libs/community/tests/integration_tests/embeddings/test_premai.py Normal file
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@ -0,0 +1,40 @@
"""Test PremAIEmbeddings from PremAI API wrapper.
Note: This test must be run with the PREMAI_API_KEY environment variable set to a valid
API key and a valid project_id. This needs to setup a project in PremAI's platform.
You can check it out here: https://app.premai.io
"""
import pytest
from langchain_community.embeddings.premai import PremAIEmbeddings
@pytest.fixture
def embedder() -> PremAIEmbeddings:
return PremAIEmbeddings(project_id=8, model="text-embedding-3-small")
def test_prem_embedding_documents(embedder: PremAIEmbeddings) -> None:
"""Test Prem embeddings."""
documents = ["foo bar"]
output = embedder.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 1536
def test_prem_embedding_documents_multiple(embedder: PremAIEmbeddings) -> None:
"""Test prem embeddings for multiple queries or documents."""
documents = ["foo bar", "bar foo", "foo"]
output = embedder.embed_documents(documents)
assert len(output) == 3
assert len(output[0]) == 1536
assert len(output[1]) == 1536
assert len(output[2]) == 1536
def test_prem_embedding_query(embedder: PremAIEmbeddings) -> None:
"""Test Prem embeddings for single query"""
document = "foo bar"
output = embedder.embed_query(document)
assert len(output) == 1536

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

@ -44,6 +44,7 @@ EXPECTED_ALL = [
"ChatPerplexity",
"ChatKinetica",
"ChatFriendli",
"ChatPremAI",
]

47
libs/community/tests/unit_tests/chat_models/test_premai.py Normal file
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@ -0,0 +1,47 @@
"""Test PremChat model"""
import pytest
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langchain_core.pydantic_v1 import SecretStr
from pytest import CaptureFixture
from langchain_community.chat_models import ChatPremAI
from langchain_community.chat_models.premai import _messages_to_prompt_dict
@pytest.mark.requires("premai")
def test_api_key_is_string() -> None:
llm = ChatPremAI(premai_api_key="secret-api-key", project_id=8)
assert isinstance(llm.premai_api_key, SecretStr)
@pytest.mark.requires("premai")
def test_api_key_masked_when_passed_via_constructor(
capsys: CaptureFixture,
) -> None:
llm = ChatPremAI(premai_api_key="secret-api-key", project_id=8)
print(llm.premai_api_key, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
def test_messages_to_prompt_dict_with_valid_messages() -> None:
system_message, result = _messages_to_prompt_dict(
[
SystemMessage(content="System Prompt"),
HumanMessage(content="User message #1"),
AIMessage(content="AI message #1"),
HumanMessage(content="User message #2"),
AIMessage(content="AI message #2"),
]
)
expected = [
{"role": "user", "content": "User message #1"},
{"role": "assistant", "content": "AI message #1"},
{"role": "user", "content": "User message #2"},
{"role": "assistant", "content": "AI message #2"},
]
assert system_message == "System Prompt"
assert result == expected

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

@ -65,6 +65,7 @@ EXPECTED_ALL = [
"QuantizedBiEncoderEmbeddings",
"NeMoEmbeddings",
"SparkLLMTextEmbeddings",
"PremAIEmbeddings",
"YandexGPTEmbeddings",
]

28
libs/community/tests/unit_tests/embeddings/test_premai.py Normal file
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@ -0,0 +1,28 @@
"""Test EmbaasEmbeddings embeddings"""
import pytest
from langchain_core.pydantic_v1 import SecretStr
from pytest import CaptureFixture
from langchain_community.embeddings import PremAIEmbeddings
@pytest.mark.requires("premai")
def test_api_key_is_string() -> None:
llm = PremAIEmbeddings(
premai_api_key="secret-api-key", project_id=8, model="fake-model"
)
assert isinstance(llm.premai_api_key, SecretStr)
@pytest.mark.requires("premai")
def test_api_key_masked_when_passed_via_constructor(
capsys: CaptureFixture,
) -> None:
llm = PremAIEmbeddings(
premai_api_key="secret-api-key", project_id=8, model="fake-model"
)
print(llm.premai_api_key, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"