github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-04-27 19:46:55 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'master' into eugene/experimenting_with_layout

Browse Source
This commit is contained in:
Eugene Yurtsev 2024-10-07 15:13:45 -04:00
commit a1f6f33d27
76 changed files with 4766 additions and 1258 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
.github/workflows/api_doc_build.yml vendored
View File

@ -101,8 +101,8 @@ jobs:
mv langchain-google/libs/genai langchain/libs/partners/google-genai
mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
mv langchain-google/libs/community langchain/libs/partners/google-community
# mv langchain-datastax/libs/astradb langchain/libs/partners/astradb
# mv langchain-nvidia/libs/ai-endpoints langchain/libs/partners/nvidia-ai-endpoints
mv langchain-datastax/libs/astradb langchain/libs/partners/astradb
mv langchain-nvidia/libs/ai-endpoints langchain/libs/partners/nvidia-ai-endpoints
mv langchain-cohere/libs/cohere langchain/libs/partners/cohere
mv langchain-elastic/libs/elasticsearch langchain/libs/partners/elasticsearch
mv langchain-postgres langchain/libs/partners/postgres

73
MIGRATE.md
View File

@ -1,70 +1,11 @@
# Migrating
## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
Please see the following guides for migratin LangChain code:
In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
This migration has already started, but we are remaining backwards compatible until 7/28.
On that date, we will remove functionality from `langchain`.
Read more about the motivation and the progress [here](https://github.com/langchain-ai/langchain/discussions/8043).
* Migrate to [LangChain v0.3](https://python.langchain.com/docs/versions/v0_3/)
* Migrate to [LangChain v0.2](https://python.langchain.com/docs/versions/v0_2/)
* Migrating from [LangChain 0.0.x Chains](https://python.langchain.com/docs/versions/migrating_chains/)
* Upgrate to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)
### Migrating to `langchain_experimental`
We are moving any experimental components of LangChain, or components with vulnerability issues, into `langchain_experimental`.
This guide covers how to migrate.
### Installation
Previously:
`pip install -U langchain`
Now (only if you want to access things in experimental):
`pip install -U langchain langchain_experimental`
### Things in `langchain.experimental`
Previously:
`from langchain.experimental import ...`
Now:
`from langchain_experimental import ...`
### PALChain
Previously:
`from langchain.chains import PALChain`
Now:
`from langchain_experimental.pal_chain import PALChain`
### SQLDatabaseChain
Previously:
`from langchain.chains import SQLDatabaseChain`
Now:
`from langchain_experimental.sql import SQLDatabaseChain`
Alternatively, if you are just interested in using the query generation part of the SQL chain, you can check out this [`SQL question-answering tutorial`](https://python.langchain.com/v0.2/docs/tutorials/sql_qa/#convert-question-to-sql-query)
`from langchain.chains import create_sql_query_chain`
### `load_prompt` for Python files
Note: this only applies if you want to load Python files as prompts.
If you want to load json/yaml files, no change is needed.
Previously:
`from langchain.prompts import load_prompt`
Now:
`from langchain_experimental.prompts import load_prompt`
The [LangChain CLI](https://python.langchain.com/docs/versions/v0_3/#migrate-using-langchain-cli) can help automatically upgrade your code to use non deprecated imports.
This will be especially helpful if you're still on either version 0.0.x or 0.1.x of LangChain.

2
docs/docs/concepts/index.mdx
View File

@ -618,7 +618,7 @@ Read more about [defining tools that return artifacts here](/docs/how_to/tool_ar
When designing tools to be used by a model, it is important to keep in mind that:
- Chat models that have explicit [tool-calling APIs](/docs/concepts/#functiontool-calling) will be better at tool calling than non-fine-tuned models.
- Models will perform better if the tools have well-chosen names, descriptions, and JSON schemas. This another form of prompt engineering.
- Models will perform better if the tools have well-chosen names, descriptions, and JSON schemas. This is another form of prompt engineering.
- Simple, narrowly scoped tools are easier for models to use than complex tools.
#### Related

383
docs/docs/integrations/chat/sambastudio.ipynb Normal file
View File

@ -0,0 +1,383 @@
{
"cells": [
{
"cell_type": "raw",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"---\n",
"sidebar_label: SambaStudio\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ChatSambaStudio\n",
"\n",
"This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n",
"\n",
"**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [ChatSambaStudio](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
"\n",
"### Model features\n",
"\n",
"| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
"| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
"\n",
"## Setup\n",
"\n",
"To access ChatSambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_community` integration package, and install the `SSEClient` Package.\n",
"\n",
"```bash\n",
"pip install langchain-community\n",
"pip install sseclient-py\n",
"```\n",
"\n",
"### Credentials\n",
"\n",
"Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:\n",
"\n",
"``` bash\n",
"export SAMBASTUDIO_URL=\"your-api-key-here\"\n",
"export SAMBASTUDIO_API_KEY=\"your-api-key-here\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"if not os.getenv(\"SAMBASTUDIO_URL\"):\n",
" os.environ[\"SAMBASTUDIO_URL\"] = getpass.getpass(\"Enter your SambaStudio URL: \")\n",
"if not os.getenv(\"SAMBASTUDIO_API_KEY\"):\n",
" os.environ[\"SAMBASTUDIO_API_KEY\"] = getpass.getpass(\n",
" \"Enter your SambaStudio API key: \"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"The LangChain __SambaStudio__ integration lives in the `langchain_community` package:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain-community\n",
"%pip install -qu sseclient-py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instantiation\n",
"\n",
"Now we can instantiate our model object and generate chat completions:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.chat_models.sambanova import ChatSambaStudio\n",
"\n",
"llm = ChatSambaStudio(\n",
" model=\"Meta-Llama-3-70B-Instruct-4096\", # set if using a CoE endpoint\n",
" max_tokens=1024,\n",
" temperature=0.7,\n",
" top_k=1,\n",
" top_p=0.01,\n",
" do_sample=True,\n",
" process_prompt=\"True\", # set if using a CoE endpoint\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Invocation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'adore la programmation.\", response_metadata={'id': 'item0', 'partial': False, 'value': {'completion': \"J'adore la programmation.\", 'logprobs': {'text_offset': [], 'top_logprobs': []}, 'prompt': '<|start_header_id|>system<|end_header_id|>\\n\\nYou are a helpful assistant that translates English to French. Translate the user sentence.<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nI love programming.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n', 'stop_reason': 'end_of_text', 'tokens': ['J', \"'\", 'ad', 'ore', ' la', ' programm', 'ation', '.'], 'total_tokens_count': 43}, 'params': {}, 'status': None}, id='item0')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" (\n",
" \"system\",\n",
" \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
" ),\n",
" (\"human\", \"I love programming.\"),\n",
"]\n",
"ai_msg = llm.invoke(messages)\n",
"ai_msg"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"J'adore la programmation.\n"
]
}
],
"source": [
"print(ai_msg.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chaining\n",
"\n",
"We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Ich liebe das Programmieren.', response_metadata={'id': 'item0', 'partial': False, 'value': {'completion': 'Ich liebe das Programmieren.', 'logprobs': {'text_offset': [], 'top_logprobs': []}, 'prompt': '<|start_header_id|>system<|end_header_id|>\\n\\nYou are a helpful assistant that translates English to German.<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nI love programming.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n', 'stop_reason': 'end_of_text', 'tokens': ['Ich', ' liebe', ' das', ' Programm', 'ieren', '.'], 'total_tokens_count': 36}, 'params': {}, 'status': None}, id='item0')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"prompt = ChatPromptTemplate(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
" ),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"chain = prompt | llm\n",
"chain.invoke(\n",
" {\n",
" \"input_language\": \"English\",\n",
" \"output_language\": \"German\",\n",
" \"input\": \"I love programming.\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Streaming"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Arrr, ye landlubber! Ye be wantin' to learn about owls, eh? Well, matey, settle yerself down with a pint o' grog and listen close, for I be tellin' ye about these fascinatin' creatures o' the night!\n",
"\n",
"Owls be birds, but not just any birds, me hearty! They be nocturnal, meanin' they do their huntin' at night, when the rest o' the world be sleepin'. And they be experts at it, too! Their big, round eyes be designed for seein' in the dark, with a special reflective layer called the tapetum lucidum that helps 'em spot prey in the shadows. It's like havin' a built-in lantern, savvy?\n",
"\n",
"But that be not all, me matey! Owls also have acute hearin', which helps 'em pinpoint the slightest sounds in the dark. And their ears be asymmetrical, meanin' one ear be higher than the other, which gives 'em better depth perception. It's like havin' a built-in sonar system, arrr!\n",
"\n",
"Now, ye might be wonderin' how owls fly so silently, like ghosts in the night. Well, it be because o' their special feathers, me hearty! They have soft, fringed feathers on their wings that help reduce noise and turbulence, makin' 'em the sneakiest flyers on the seven seas... er, skies!\n",
"\n",
"Owls come in all shapes and sizes, from the tiny elf owl to the great grey owl, which be one o' the largest owl species in the world. And they be found on every continent, except Antarctica, o' course. They be solitary creatures, but some species be known to form long-term monogamous relationships, like the barn owl and its mate.\n",
"\n",
"So, there ye have it, me hearty! Owls be amazin' creatures, with their clever adaptations and stealthy ways. Now, go forth and spread the word about these magnificent birds o' the night! And remember, if ye ever encounter an owl in the wild, be sure to show respect and keep a weather eye open, or ye might just find yerself on the receivin' end o' a silent, flyin' tackle! Arrr!"
]
}
],
"source": [
"system = \"You are a helpful assistant with pirate accent.\"\n",
"human = \"I want to learn more about this animal: {animal}\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
"\n",
"chain = prompt | llm\n",
"\n",
"for chunk in chain.stream({\"animal\": \"owl\"}):\n",
" print(chunk.content, end=\"\", flush=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Async"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='The capital of France is Paris.', response_metadata={'id': 'item0', 'partial': False, 'value': {'completion': 'The capital of France is Paris.', 'logprobs': {'text_offset': [], 'top_logprobs': []}, 'prompt': '<|start_header_id|>user<|end_header_id|>\\n\\nwhat is the capital of France?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n', 'stop_reason': 'end_of_text', 'tokens': ['The', ' capital', ' of', ' France', ' is', ' Paris', '.'], 'total_tokens_count': 24}, 'params': {}, 'status': None}, id='item0')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"human\",\n",
" \"what is the capital of {country}?\",\n",
" )\n",
" ]\n",
")\n",
"\n",
"chain = prompt | llm\n",
"await chain.ainvoke({\"country\": \"France\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Async Streaming"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Quantum computers use quantum bits (qubits) to process multiple possibilities simultaneously, exponentially faster than classical computers, enabling breakthroughs in fields like cryptography, optimization, and simulation."
]
}
],
"source": [
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"human\",\n",
" \"in less than {num_words} words explain me {topic} \",\n",
" )\n",
" ]\n",
")\n",
"chain = prompt | llm\n",
"\n",
"async for chunk in chain.astream({\"num_words\": 30, \"topic\": \"quantum computers\"}):\n",
" print(chunk.content, end=\"\", flush=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all ChatSambaStudio features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "langchain",
"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": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

2
docs/docs/integrations/document_loaders/airbyte.ipynb
View File

@ -29,7 +29,7 @@
"metadata": {},
"outputs": [],
"source": [
"% pip install -qU langchain-airbyte"
"%pip install -qU langchain-airbyte"
]
},
{

2
docs/docs/integrations/document_loaders/browserbase.ipynb
View File

@ -26,7 +26,7 @@
"metadata": {},
"outputs": [],
"source": [
"% pip install browserbase"
"%pip install browserbase"
]
},
{

8
docs/docs/integrations/document_loaders/upstage.ipynb
View File

@ -25,9 +25,9 @@
}
},
"source": [
"# UpstageLayoutAnalysisLoader\n",
"# UpstageDocumentParseLoader\n",
"\n",
"This notebook covers how to get started with `UpstageLayoutAnalysisLoader`.\n",
"This notebook covers how to get started with `UpstageDocumentParseLoader`.\n",
"\n",
"## Installation\n",
"\n",
@ -89,10 +89,10 @@
}
],
"source": [
"from langchain_upstage import UpstageLayoutAnalysisLoader\n",
"from langchain_upstage import UpstageDocumentParseLoader\n",
"\n",
"file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
"layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
"layzer = UpstageDocumentParseLoader(file_path, split=\"page\")\n",
"\n",
"# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
"docs = layzer.load() # or layzer.lazy_load()\n",

309
docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb Normal file
View File

@ -0,0 +1,309 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NVIDIA\n",
"\n",
"This will help you getting started with NVIDIA [models](/docs/concepts/#llms). For detailed documentation of all `NVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.NVIDIA.html).\n",
"\n",
"## Overview\n",
"The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",
"NVIDIA NIM inference microservice. These models are optimized by NVIDIA to deliver the best performance on NVIDIA \n",
"accelerated infrastructure and deployed as a NIM, an easy-to-use, prebuilt containers that deploy anywhere using a single \n",
"command on NVIDIA accelerated infrastructure.\n",
"\n",
"NVIDIA hosted deployments of NIMs are available to test on the [NVIDIA API catalog](https://build.nvidia.com/). After testing, \n",
"NIMs can be exported from NVIDIA’s API catalog using the NVIDIA AI Enterprise license and run on-premises or in the cloud, \n",
"giving enterprises ownership and full control of their IP and AI application.\n",
"\n",
"NIMs are packaged as container images on a per model basis and are distributed as NGC container images through the NVIDIA NGC Catalog. \n",
"At their core, NIMs provide easy, consistent, and familiar APIs for running inference on an AI model.\n",
"\n",
"This example goes over how to use LangChain to interact with NVIDIA supported via the `NVIDIA` class.\n",
"\n",
"For more information on accessing the llm models through this api, check out the [NVIDIA](https://python.langchain.com/docs/integrations/llms/nvidia_ai_endpoints/) documentation.\n",
"\n",
"### Integration details\n",
"\n",
"| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [NVIDIA](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html) | [langchain_nvidia_ai_endpoints](https://python.langchain.com/api_reference/nvidia_ai_endpoints/index.html) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) |\n",
"\n",
"### Model features\n",
"| JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
"| ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | \n",
"\n",
"## Setup\n",
"\n",
"**To get started:**\n",
"\n",
"1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models.\n",
"\n",
"2. Click on your model of choice.\n",
"\n",
"3. Under `Input` select the `Python` tab, and click `Get API Key`. Then click `Generate Key`.\n",
"\n",
"4. Copy and save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints.\n",
"\n",
"### Credentials\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"if not os.getenv(\"NVIDIA_API_KEY\"):\n",
" # Note: the API key should start with \"nvapi-\"\n",
" os.environ[\"NVIDIA_API_KEY\"] = getpass.getpass(\"Enter your NVIDIA API key: \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"The LangChain NVIDIA AI Endpoints integration lives in the `langchain_nvidia_ai_endpoints` package:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-nvidia-ai-endpoints"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instantiation\n",
"\n",
"See [LLM](/docs/how_to#llms) for full functionality."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from langchain_nvidia_ai_endpoints import NVIDIA"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = NVIDIA().bind(max_tokens=256)\n",
"llm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Invocation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"prompt = \"# Function that does quicksort written in Rust without comments:\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(llm.invoke(prompt))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stream, Batch, and Async\n",
"\n",
"These models natively support streaming, and as is the case with all LangChain LLMs they expose a batch method to handle concurrent requests, as well as async methods for invoke, stream, and batch. Below are a few examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for chunk in llm.stream(prompt):\n",
" print(chunk, end=\"\", flush=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm.batch([prompt])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"await llm.ainvoke(prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"async for chunk in llm.astream(prompt):\n",
" print(chunk, end=\"\", flush=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"await llm.abatch([prompt])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"async for chunk in llm.astream_log(prompt):\n",
" print(chunk)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = llm.invoke(\n",
" \"X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1) #Train a logistic regression model, predict the labels on the test set and compute the accuracy score\"\n",
")\n",
"print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Supported models\n",
"\n",
"Querying `available_models` will still give you all of the other models offered by your API credentials."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"NVIDIA.get_available_models()\n",
"# llm.get_available_models()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chaining\n",
"\n",
"We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"prompt = ChatPromptTemplate(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
" ),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"chain = prompt | llm\n",
"chain.invoke(\n",
" {\n",
" \"input_language\": \"English\",\n",
" \"output_language\": \"German\",\n",
" \"input\": \"I love programming.\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all `NVIDIA` features and configurations head to the API reference: https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.llms.NVIDIA.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "langchain-nvidia-ai-endpoints-m0-Y4aGr-py3.10",
"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
}

10
docs/docs/integrations/providers/upstage.ipynb
View File

@ -10,7 +10,7 @@
">\n",
">**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
"\n",
">Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. \n"
">Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Document Parse** and **Groundedness Check**. \n"
]
},
{
@ -24,7 +24,7 @@
"| Chat | Build assistants using Solar Mini Chat | `from langchain_upstage import ChatUpstage` | [Go](../../chat/upstage) |\n",
"| Text Embedding | Embed strings to vectors | `from langchain_upstage import UpstageEmbeddings` | [Go](../../text_embedding/upstage) |\n",
"| Groundedness Check | Verify groundedness of assistant's response | `from langchain_upstage import UpstageGroundednessCheck` | [Go](../../tools/upstage_groundedness_check) |\n",
"| Layout Analysis | Serialize documents with tables and figures | `from langchain_upstage import UpstageLayoutAnalysisLoader` | [Go](../../document_loaders/upstage) |\n",
"| Document Parse | Serialize documents with tables and figures | `from langchain_upstage import UpstageDocumentParseLoader` | [Go](../../document_loaders/upstage) |\n",
"\n",
"See [documentations](https://developers.upstage.ai/) for more details about the features."
]
@ -122,7 +122,7 @@
"source": [
"## Document loader\n",
"\n",
"### Layout Analysis\n",
"### Document Parse\n",
"\n",
"See [a usage example](/docs/integrations/document_loaders/upstage)."
]
@ -133,10 +133,10 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_upstage import UpstageLayoutAnalysisLoader\n",
"from langchain_upstage import UpstageDocumentParseLoader\n",
"\n",
"file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
"layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
"layzer = UpstageDocumentParseLoader(file_path, split=\"page\")\n",
"\n",
"# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
"docs = layzer.load() # or layzer.lazy_load()\n",

116
docs/docs/integrations/retrievers/box.ipynb
View File

@ -52,18 +52,10 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"id": "b87a8e8b-9b5a-4e78-97e4-274b6b0dd29f",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter your Box Developer Token: ········\n"
]
}
],
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
@ -81,7 +73,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
"metadata": {},
"outputs": [],
@ -102,10 +94,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"id": "652d6238-1f87-422a-b135-f5abbb8652fc",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install -qU langchain-box"
]
@ -124,7 +124,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 5,
"id": "70cc8e65-2a02-408a-bbc6-8ef649057d82",
"metadata": {},
"outputs": [],
@ -146,7 +146,7 @@
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 6,
"id": "97f3ae67",
"metadata": {},
"outputs": [
@ -156,7 +156,7 @@
"[Document(metadata={'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n - Gravitational Wave Detector Kit: $800\\n - Exoplanet Terrarium: $120\\nTotal: $920')]"
]
},
"execution_count": 33,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@ -192,7 +192,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 7,
"id": "ee0e726d-9974-4aa0-9ce1-0057ec3e540a",
"metadata": {},
"outputs": [],
@ -216,17 +216,17 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 8,
"id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(metadata={'source': 'Box AI', 'title': 'Box AI What was the most expensive item purchased'}, page_content='The most expensive item purchased was the **Gravitational Wave Detector Kit** from AstroTech Solutions, which cost $800.')]"
"[Document(metadata={'source': 'Box AI', 'title': 'Box AI What was the most expensive item purchased'}, page_content='The most expensive item purchased is the **Gravitational Wave Detector Kit** from AstroTech Solutions, which costs **$800**.')]"
]
},
"execution_count": 5,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@ -237,6 +237,80 @@
"retriever.invoke(query)"
]
},
{
"cell_type": "markdown",
"id": "31a59a51",
"metadata": {},
"source": [
"## Citations\n",
"\n",
"With Box AI and the `BoxRetriever`, you can return the answer to your prompt, return the citations used by Box to get that answer, or both. No matter how you choose to use Box AI, the retriever returns a `List[Document]` object. We offer this flexibility with two `bool` arguments, `answer` and `citations`. Answer defaults to `True` and citations defaults to `False`, do you can omit both if you just want the answer. If you want both, you can just include `citations=True` and if you only want citations, you would include `answer=False` and `citations=True`\n",
"\n",
"### Get both"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2eddc8c1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(metadata={'source': 'Box AI', 'title': 'Box AI What was the most expensive item purchased'}, page_content='The most expensive item purchased is the **Gravitational Wave Detector Kit** from AstroTech Solutions, which costs **$800**.'),\n",
" Document(metadata={'source': 'Box AI What was the most expensive item purchased', 'file_name': 'Invoice-A5555.txt', 'file_id': '1514555423624', 'file_type': 'file'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n - Gravitational Wave Detector Kit: $800\\n - Exoplanet Terrarium: $120\\nTotal: $920')]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever = BoxRetriever(\n",
" box_developer_token=box_developer_token, box_file_ids=box_file_ids, citations=True\n",
")\n",
"\n",
"retriever.invoke(query)"
]
},
{
"cell_type": "markdown",
"id": "d2e93a2e",
"metadata": {},
"source": [
"### Citations only"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c1892b07",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(metadata={'source': 'Box AI What was the most expensive item purchased', 'file_name': 'Invoice-A5555.txt', 'file_id': '1514555423624', 'file_type': 'file'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n - Gravitational Wave Detector Kit: $800\\n - Exoplanet Terrarium: $120\\nTotal: $920')]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever = BoxRetriever(\n",
" box_developer_token=box_developer_token,\n",
" box_file_ids=box_file_ids,\n",
" answer=False,\n",
" citations=True,\n",
")\n",
"\n",
"retriever.invoke(query)"
]
},
{
"cell_type": "markdown",
"id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
@ -260,7 +334,7 @@
"metadata": {},
"outputs": [
{
"name": "stdin",
"name": "stdout",
"output_type": "stream",
"text": [
"Enter your OpenAI key: ········\n"

44
docs/docs/integrations/vectorstores/google_spanner.ipynb
View File

@ -52,7 +52,7 @@
}
],
"source": [
"%pip install --upgrade --quiet langchain-google-spanner"
"%pip install --upgrade --quiet langchain-google-spanner langchain-google-vertexai"
]
},
{
@ -124,7 +124,8 @@
"PROJECT_ID = \"my-project-id\" # @param {type:\"string\"}\n",
"\n",
"# Set the project id\n",
"!gcloud config set project {PROJECT_ID}"
"!gcloud config set project {PROJECT_ID}\n",
"%env GOOGLE_CLOUD_PROJECT={PROJECT_ID}"
]
},
{
@ -194,14 +195,16 @@
" instance_id=INSTANCE,\n",
" database_id=DATABASE,\n",
" table_name=TABLE_NAME,\n",
" id_column=\"row_id\",\n",
" metadata_columns=[\n",
" TableColumn(name=\"metadata\", type=\"JSON\", is_null=True),\n",
" TableColumn(name=\"title\", type=\"STRING(MAX)\", is_null=False),\n",
" ],\n",
" secondary_indexes=[\n",
" SecondaryIndex(index_name=\"row_id_and_title\", columns=[\"row_id\", \"title\"])\n",
" ],\n",
" # Customize the table creation\n",
" # id_column=\"row_id\",\n",
" # content_column=\"content_column\",\n",
" # metadata_columns=[\n",
" # TableColumn(name=\"metadata\", type=\"JSON\", is_null=True),\n",
" # TableColumn(name=\"title\", type=\"STRING(MAX)\", is_null=False),\n",
" # ],\n",
" # secondary_indexes=[\n",
" # SecondaryIndex(index_name=\"row_id_and_title\", columns=[\"row_id\", \"title\"])\n",
" # ],\n",
")"
]
},
@ -262,9 +265,11 @@
" instance_id=INSTANCE,\n",
" database_id=DATABASE,\n",
" table_name=TABLE_NAME,\n",
" ignore_metadata_columns=[],\n",
" embedding_service=embeddings,\n",
" metadata_json_column=\"metadata\",\n",
" # Connect to a custom vector store table\n",
" # id_column=\"row_id\",\n",
" # content_column=\"content\",\n",
" # metadata_columns=[\"metadata\", \"title\"],\n",
")"
]
},
@ -272,7 +277,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 🔐 Add Documents\n",
"#### Add Documents\n",
"To add documents in the vector store."
]
},
@ -289,14 +294,15 @@
"loader = HNLoader(\"https://news.ycombinator.com/item?id=34817881\")\n",
"\n",
"documents = loader.load()\n",
"ids = [str(uuid.uuid4()) for _ in range(len(documents))]"
"ids = [str(uuid.uuid4()) for _ in range(len(documents))]\n",
"db.add_documents(documents, ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 🔐 Search Documents\n",
"#### Search Documents\n",
"To search documents in the vector store with similarity search."
]
},
@ -313,7 +319,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 🔐 Search Documents\n",
"#### Search Documents\n",
"To search documents in the vector store with max marginal relevance search."
]
},
@ -330,7 +336,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 🔐 Delete Documents\n",
"#### Delete Documents\n",
"To remove documents from the vector store, use the IDs that correspond to the values in the `row_id`` column when initializing the VectorStore."
]
},
@ -347,7 +353,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 🔐 Delete Documents\n",
"#### Delete Documents\n",
"To remove documents from the vector store, you can utilize the documents themselves. The content column and metadata columns provided during VectorStore initialization will be used to find out the rows corresponding to the documents. Any matching rows will then be deleted."
]
},
@ -377,7 +383,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
"version": "3.11.8"
}
},
"nbformat": 4,

2
docs/docs/tutorials/local_rag.ipynb
View File

@ -60,7 +60,7 @@
"%pip install -qU langchain_ollama\n",
"\n",
"# Web Loader\n",
"% pip install -qU beautifulsoup4"
"%pip install -qU beautifulsoup4"
]
},
{

2
docs/docs/versions/migrating_chains/map_reduce_chain.ipynb
View File

@ -196,7 +196,7 @@
"metadata": {},
"outputs": [],
"source": [
"% pip install -qU langgraph"
"%pip install -qU langgraph"
]
},
{

169
docs/scripts/generate_api_reference_links.py
View File

@ -20,111 +20,53 @@ _LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
code_block_re = re.compile(r"^(```\s?python\n)(.*?)(```)", re.DOTALL | re.MULTILINE)
# (alias/re-exported modules, source module, class, docs namespace)
MANUAL_API_REFERENCES_LANGGRAPH = [
("langgraph.prebuilt", "create_react_agent"),
(
"langgraph.prebuilt",
"ToolNode",
["langgraph.prebuilt"],
"langgraph.prebuilt.chat_agent_executor",
"create_react_agent",
"prebuilt",
),
(["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
(
["langgraph.prebuilt"],
"langgraph.prebuilt.tool_node",
"tools_condition",
"prebuilt",
),
(
"langgraph.prebuilt",
"ToolExecutor",
),
(
"langgraph.prebuilt",
"ToolInvocation",
),
("langgraph.prebuilt", "tools_condition"),
(
"langgraph.prebuilt",
"ValidationNode",
),
(
"langgraph.prebuilt",
["langgraph.prebuilt"],
"langgraph.prebuilt.tool_node",
"InjectedState",
"prebuilt",
),
# Graph
(
"langgraph.graph",
"StateGraph",
),
(
"langgraph.graph.message",
"MessageGraph",
),
("langgraph.graph.message", "add_messages"),
(
"langgraph.graph.graph",
"CompiledGraph",
),
(
"langgraph.types",
"StreamMode",
),
(
"langgraph.graph",
"START",
),
(
"langgraph.graph",
"END",
),
(
"langgraph.types",
"Send",
),
(
"langgraph.types",
"Interrupt",
),
(
"langgraph.types",
"RetryPolicy",
),
(
"langgraph.checkpoint.base",
"Checkpoint",
),
(
"langgraph.checkpoint.base",
"CheckpointMetadata",
),
(
"langgraph.checkpoint.base",
"BaseCheckpointSaver",
),
(
"langgraph.checkpoint.base",
"SerializerProtocol",
),
(
"langgraph.checkpoint.serde.jsonplus",
"JsonPlusSerializer",
),
(
"langgraph.checkpoint.memory",
"MemorySaver",
),
(
"langgraph.checkpoint.sqlite.aio",
"AsyncSqliteSaver",
),
(
"langgraph.checkpoint.sqlite",
"SqliteSaver",
),
(
"langgraph.checkpoint.postgres.aio",
"AsyncPostgresSaver",
),
(
"langgraph.checkpoint.postgres",
"PostgresSaver",
),
(["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
(["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
(["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
([], "langgraph.types", "StreamMode", "types"),
(["langgraph.graph"], "langgraph.constants", "START", "constants"),
(["langgraph.graph"], "langgraph.constants", "END", "constants"),
(["langgraph.constants"], "langgraph.types", "Send", "types"),
(["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
([], "langgraph.types", "RetryPolicy", "types"),
([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
]
WELL_KNOWN_LANGGRAPH_OBJECTS = {
(module_, class_) for module_, class_ in MANUAL_API_REFERENCES_LANGGRAPH
(module_, class_): (source_module, namespace)
for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
for module_ in modules + [source_module]
}
@ -308,34 +250,21 @@ def _get_imports(
+ ".html"
)
elif package_ecosystem == "langgraph":
if module.startswith("langgraph.checkpoint"):
namespace = "checkpoints"
elif module.startswith("langgraph.graph"):
namespace = "graphs"
elif module.startswith("langgraph.prebuilt"):
namespace = "prebuilt"
elif module.startswith("langgraph.errors"):
namespace = "errors"
else:
if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
# Likely not documented yet
# Unable to determine the namespace
continue
if module.startswith("langgraph.errors"):
# Has different URL structure than other modules
url = (
_LANGGRAPH_API_REFERENCE
+ namespace
+ "/#langgraph.errors."
+ class_name # Uses the actual class name here.
)
else:
if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
# Likely not documented yet
continue
url = (
_LANGGRAPH_API_REFERENCE + namespace + "/#" + class_name.lower()
)
source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
(module, class_name)
]
url = (
_LANGGRAPH_API_REFERENCE
+ namespace
+ "/#"
+ source_module
+ "."
+ class_name
)
else:
raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")

4
libs/community/langchain_community/callbacks/openai_info.py
View File

@ -27,11 +27,11 @@ MODEL_COST_PER_1K_TOKENS = {
"gpt-4o-mini-completion": 0.0006,
"gpt-4o-mini-2024-07-18-completion": 0.0006,
# GPT-4o input
"gpt-4o": 0.005,
"gpt-4o": 0.0025,
"gpt-4o-2024-05-13": 0.005,
"gpt-4o-2024-08-06": 0.0025,
# GPT-4o output
"gpt-4o-completion": 0.015,
"gpt-4o-completion": 0.01,
"gpt-4o-2024-05-13-completion": 0.015,
"gpt-4o-2024-08-06-completion": 0.01,
# GPT-4 input

3
libs/community/langchain_community/chat_models/__init__.py
View File

@ -149,6 +149,7 @@ if TYPE_CHECKING:
)
from langchain_community.chat_models.sambanova import (
ChatSambaNovaCloud,
ChatSambaStudio,
)
from langchain_community.chat_models.snowflake import (
ChatSnowflakeCortex,
@ -215,6 +216,7 @@ __all__ = [
"ChatPerplexity",
"ChatPremAI",
"ChatSambaNovaCloud",
"ChatSambaStudio",
"ChatSparkLLM",
"ChatSnowflakeCortex",
"ChatTongyi",
@ -274,6 +276,7 @@ _module_lookup = {
"ChatOpenAI": "langchain_community.chat_models.openai",
"ChatPerplexity": "langchain_community.chat_models.perplexity",
"ChatSambaNovaCloud": "langchain_community.chat_models.sambanova",
"ChatSambaStudio": "langchain_community.chat_models.sambanova",
"ChatSnowflakeCortex": "langchain_community.chat_models.snowflake",
"ChatSparkLLM": "langchain_community.chat_models.sparkllm",
"ChatTongyi": "langchain_community.chat_models.tongyi",

903
libs/community/langchain_community/chat_models/sambanova.py
View File

File diff suppressed because it is too large Load Diff

7
libs/community/langchain_community/chat_models/snowflake.py
View File

@ -17,7 +17,7 @@ from langchain_core.utils import (
get_pydantic_field_names,
pre_init,
)
from langchain_core.utils.utils import build_extra_kwargs
from langchain_core.utils.utils import _build_model_kwargs
from pydantic import Field, SecretStr, model_validator
SUPPORTED_ROLES: List[str] = [
@ -131,10 +131,7 @@ class ChatSnowflakeCortex(BaseChatModel):
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@pre_init

7
libs/community/langchain_community/llms/anthropic.py
View File

@ -26,7 +26,7 @@ from langchain_core.utils import (
get_pydantic_field_names,
pre_init,
)
from langchain_core.utils.utils import build_extra_kwargs, convert_to_secret_str
from langchain_core.utils.utils import _build_model_kwargs, convert_to_secret_str
from pydantic import ConfigDict, Field, SecretStr, model_validator
@ -69,11 +69,8 @@ class _AnthropicCommon(BaseLanguageModel):
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict) -> Any:
extra = values.get("model_kwargs", {})
all_required_field_names = get_pydantic_field_names(cls)
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@pre_init

4
libs/community/langchain_community/llms/azureml_endpoint.py
View File

@ -9,7 +9,7 @@ from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs import Generation, LLMResult
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from pydantic import BaseModel, SecretStr, model_validator, validator
from pydantic import BaseModel, ConfigDict, SecretStr, model_validator, validator
DEFAULT_TIMEOUT = 50
@ -382,6 +382,8 @@ class AzureMLBaseEndpoint(BaseModel):
model_kwargs: Optional[dict] = None
"""Keyword arguments to pass to the model."""
model_config = ConfigDict(protected_namespaces=())
@model_validator(mode="before")
@classmethod
def validate_environ(cls, values: Dict) -> Any:

2
libs/community/langchain_community/llms/bedrock.py
View File

@ -295,6 +295,8 @@ class LLMInputOutputAdapter:
class BedrockBase(BaseModel, ABC):
"""Base class for Bedrock models."""
model_config = ConfigDict(protected_namespaces=())
client: Any = Field(exclude=True) #: :meta private:
region_name: Optional[str] = None

7
libs/community/langchain_community/llms/llamacpp.py
View File

@ -8,7 +8,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.utils import get_pydantic_field_names, pre_init
from langchain_core.utils.utils import build_extra_kwargs
from langchain_core.utils.utils import _build_model_kwargs
from pydantic import Field, model_validator
logger = logging.getLogger(__name__)
@ -199,10 +199,7 @@ class LlamaCpp(LLM):
def build_model_kwargs(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@property

4
libs/community/langchain_community/llms/minimax.py
View File

@ -16,7 +16,7 @@ from langchain_core.callbacks import (
)
from langchain_core.language_models.llms import LLM
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
from pydantic import BaseModel, Field, SecretStr, model_validator
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from langchain_community.llms.utils import enforce_stop_tokens
@ -58,6 +58,8 @@ class _MinimaxEndpointClient(BaseModel):
class MinimaxCommon(BaseModel):
"""Common parameters for Minimax large language models."""
model_config = ConfigDict(protected_namespaces=())
_client: _MinimaxEndpointClient
model: str = "abab5.5-chat"
"""Model name to use."""

7
libs/community/langchain_community/llms/openai.py
View File

@ -34,7 +34,7 @@ from langchain_core.utils import (
pre_init,
)
from langchain_core.utils.pydantic import get_fields
from langchain_core.utils.utils import build_extra_kwargs
from langchain_core.utils.utils import _build_model_kwargs
from pydantic import ConfigDict, Field, model_validator
from langchain_community.utils.openai import is_openai_v1
@ -268,10 +268,7 @@ class BaseOpenAI(BaseLLM):
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@pre_init

4
libs/community/langchain_community/llms/vertexai.py
View File

@ -11,7 +11,7 @@ from langchain_core.callbacks.manager import (
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.utils import pre_init
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
from langchain_community.utilities.vertexai import (
create_retry_decorator,
@ -100,6 +100,8 @@ async def acompletion_with_retry(
class _VertexAIBase(BaseModel):
model_config = ConfigDict(protected_namespaces=())
project: Optional[str] = None
"The default GCP project to use when making Vertex API calls."
location: str = "us-central1"

4
libs/community/langchain_community/llms/volcengine_maas.py
View File

@ -6,12 +6,14 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
from pydantic import BaseModel, Field, SecretStr
from pydantic import BaseModel, ConfigDict, Field, SecretStr
class VolcEngineMaasBase(BaseModel):
"""Base class for VolcEngineMaas models."""
model_config = ConfigDict(protected_namespaces=())
client: Any = None
volc_engine_maas_ak: Optional[SecretStr] = None

10
libs/community/langchain_community/vectorstores/usearch.py
View File

@ -1,6 +1,6 @@
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional, Tuple
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from langchain_core.documents import Document
@ -42,7 +42,7 @@ class USearch(VectorStore):
self,
texts: Iterable[str],
metadatas: Optional[List[Dict]] = None,
ids: Optional[np.ndarray] = None,
ids: Optional[Union[np.ndarray, list[str]]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
@ -69,6 +69,8 @@ class USearch(VectorStore):
last_id = int(self.ids[-1]) + 1
if ids is None:
ids = np.array([str(last_id + id) for id, _ in enumerate(texts)])
elif isinstance(ids, list):
ids = np.array(ids)
self.index.add(np.array(ids), np.array(embeddings))
self.docstore.add(dict(zip(ids, documents)))
@ -134,7 +136,7 @@ class USearch(VectorStore):
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict]] = None,
ids: Optional[np.ndarray] = None,
ids: Optional[Union[np.ndarray, list[str]]] = None,
metric: str = "cos",
**kwargs: Any,
) -> USearch:
@ -159,6 +161,8 @@ class USearch(VectorStore):
documents: List[Document] = []
if ids is None:
ids = np.array([str(id) for id, _ in enumerate(texts)])
elif isinstance(ids, list):
ids = np.array(ids)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))

13
libs/community/tests/integration_tests/chat_models/test_sambanova.py
View File

@ -1,6 +1,9 @@
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.chat_models.sambanova import ChatSambaNovaCloud
from langchain_community.chat_models.sambanova import (
ChatSambaNovaCloud,
ChatSambaStudio,
)
def test_chat_sambanova_cloud() -> None:
@ -9,3 +12,11 @@ def test_chat_sambanova_cloud() -> None:
response = chat.invoke([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_chat_sambastudio() -> None:
chat = ChatSambaStudio()
message = HumanMessage(content="Hello")
response = chat.invoke([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)

9
libs/community/tests/unit_tests/chat_models/test_anthropic.py
View File

@ -33,9 +33,12 @@ def test_anthropic_model_kwargs() -> None:
@pytest.mark.requires("anthropic")
def test_anthropic_invalid_model_kwargs() -> None:
with pytest.raises(ValueError):
ChatAnthropic(model_kwargs={"max_tokens_to_sample": 5})
def test_anthropic_fields_in_model_kwargs() -> None:
"""Test that for backwards compatibility fields can be passed in as model_kwargs."""
llm = ChatAnthropic(model_kwargs={"max_tokens_to_sample": 5})
assert llm.max_tokens_to_sample == 5
llm = ChatAnthropic(model_kwargs={"max_tokens": 5})
assert llm.max_tokens_to_sample == 5
@pytest.mark.requires("anthropic")

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

@ -35,6 +35,7 @@ EXPECTED_ALL = [
"ChatPerplexity",
"ChatPremAI",
"ChatSambaNovaCloud",
"ChatSambaStudio",
"ChatSparkLLM",
"ChatTongyi",
"ChatVertexAI",

13
libs/community/tests/unit_tests/llms/test_openai.py
View File

@ -26,13 +26,12 @@ def test_openai_model_kwargs() -> None:
@pytest.mark.requires("openai")
def test_openai_invalid_model_kwargs() -> None:
with pytest.raises(ValueError):
OpenAI(model_kwargs={"model_name": "foo"})
# Test that "model" cannot be specified in kwargs
with pytest.raises(ValueError):
OpenAI(model_kwargs={"model": "gpt-3.5-turbo-instruct"})
def test_openai_fields_model_kwargs() -> None:
"""Test that for backwards compatibility fields can be passed in as model_kwargs."""
llm = OpenAI(model_kwargs={"model_name": "foo"}, api_key="foo")
assert llm.model_name == "foo"
llm = OpenAI(model_kwargs={"model": "foo"}, api_key="foo")
assert llm.model_name == "foo"
@pytest.mark.requires("openai")

80
libs/core/langchain_core/callbacks/manager.py
View File

@ -1729,7 +1729,12 @@ class AsyncCallbackManager(BaseCallbackManager):
to each prompt.
"""
tasks = []
inline_tasks = []
non_inline_tasks = []
inline_handlers = [handler for handler in self.handlers if handler.run_inline]
non_inline_handlers = [
handler for handler in self.handlers if not handler.run_inline
]
managers = []
for prompt in prompts:
@ -1739,20 +1744,36 @@ class AsyncCallbackManager(BaseCallbackManager):
else:
run_id_ = uuid.uuid4()
tasks.append(
ahandle_event(
self.handlers,
"on_llm_start",
"ignore_llm",
serialized,
[prompt],
run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
if inline_handlers:
inline_tasks.append(
ahandle_event(
inline_handlers,
"on_llm_start",
"ignore_llm",
serialized,
[prompt],
run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
)
else:
non_inline_tasks.append(
ahandle_event(
non_inline_handlers,
"on_llm_start",
"ignore_llm",
serialized,
[prompt],
run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
)
)
managers.append(
AsyncCallbackManagerForLLMRun(
@ -1767,7 +1788,13 @@ class AsyncCallbackManager(BaseCallbackManager):
)
)
await asyncio.gather(*tasks)
# Run inline tasks sequentially
for inline_task in inline_tasks:
await inline_task
# Run non-inline tasks concurrently
if non_inline_tasks:
await asyncio.gather(*non_inline_tasks)
return managers
@ -1791,7 +1818,8 @@ class AsyncCallbackManager(BaseCallbackManager):
async callback managers, one for each LLM Run
corresponding to each inner message list.
"""
tasks = []
inline_tasks = []
non_inline_tasks = []
managers = []
for message_list in messages:
@ -1801,9 +1829,9 @@ class AsyncCallbackManager(BaseCallbackManager):
else:
run_id_ = uuid.uuid4()
tasks.append(
ahandle_event(
self.handlers,
for handler in self.handlers:
task = ahandle_event(
[handler],
"on_chat_model_start",
"ignore_chat_model",
serialized,
@ -1814,7 +1842,10 @@ class AsyncCallbackManager(BaseCallbackManager):
metadata=self.metadata,
**kwargs,
)
)
if handler.run_inline:
inline_tasks.append(task)
else:
non_inline_tasks.append(task)
managers.append(
AsyncCallbackManagerForLLMRun(
@ -1829,7 +1860,14 @@ class AsyncCallbackManager(BaseCallbackManager):
)
)
await asyncio.gather(*tasks)
# Run inline tasks sequentially
for task in inline_tasks:
await task
# Run non-inline tasks concurrently
if non_inline_tasks:
await asyncio.gather(*non_inline_tasks)
return managers
async def on_chain_start(

34
libs/core/langchain_core/indexing/api.py
View File

@ -198,6 +198,7 @@ def index(
source_id_key: Union[str, Callable[[Document], str], None] = None,
cleanup_batch_size: int = 1_000,
force_update: bool = False,
upsert_kwargs: Optional[dict[str, Any]] = None,
) -> IndexingResult:
"""Index data from the loader into the vector store.
@ -249,6 +250,12 @@ def index(
force_update: Force update documents even if they are present in the
record manager. Useful if you are re-indexing with updated embeddings.
Default is False.
upsert_kwargs: Additional keyword arguments to pass to the add_documents
method of the VectorStore or the upsert method of the
DocumentIndex. For example, you can use this to
specify a custom vector_field:
upsert_kwargs={"vector_field": "embedding"}
.. versionadded:: 0.3.10
Returns:
Indexing result which contains information about how many documents
@ -363,10 +370,16 @@ def index(
if docs_to_index:
if isinstance(destination, VectorStore):
destination.add_documents(
docs_to_index, ids=uids, batch_size=batch_size
docs_to_index,
ids=uids,
batch_size=batch_size,
**(upsert_kwargs or {}),
)
elif isinstance(destination, DocumentIndex):
destination.upsert(docs_to_index)
destination.upsert(
docs_to_index,
**(upsert_kwargs or {}),
)
num_added += len(docs_to_index) - len(seen_docs)
num_updated += len(seen_docs)
@ -438,6 +451,7 @@ async def aindex(
source_id_key: Union[str, Callable[[Document], str], None] = None,
cleanup_batch_size: int = 1_000,
force_update: bool = False,
upsert_kwargs: Optional[dict[str, Any]] = None,
) -> IndexingResult:
"""Async index data from the loader into the vector store.
@ -480,6 +494,12 @@ async def aindex(
force_update: Force update documents even if they are present in the
record manager. Useful if you are re-indexing with updated embeddings.
Default is False.
upsert_kwargs: Additional keyword arguments to pass to the aadd_documents
method of the VectorStore or the aupsert method of the
DocumentIndex. For example, you can use this to
specify a custom vector_field:
upsert_kwargs={"vector_field": "embedding"}
.. versionadded:: 0.3.10
Returns:
Indexing result which contains information about how many documents
@ -604,10 +624,16 @@ async def aindex(
if docs_to_index:
if isinstance(destination, VectorStore):
await destination.aadd_documents(
docs_to_index, ids=uids, batch_size=batch_size
docs_to_index,
ids=uids,
batch_size=batch_size,
**(upsert_kwargs or {}),
)
elif isinstance(destination, DocumentIndex):
await destination.aupsert(docs_to_index)
await destination.aupsert(
docs_to_index,
**(upsert_kwargs or {}),
)
num_added += len(docs_to_index) - len(seen_docs)
num_updated += len(seen_docs)

2
libs/core/langchain_core/language_models/base.py
View File

@ -98,7 +98,7 @@ class BaseLanguageModel(
All language model wrappers inherited from BaseLanguageModel.
"""
cache: Union[BaseCache, bool, None] = None
cache: Union[BaseCache, bool, None] = Field(default=None, exclude=True)
"""Whether to cache the response.
* If true, will use the global cache.

28
libs/core/langchain_core/language_models/fake_chat_models.py
View File

@ -13,6 +13,7 @@ from langchain_core.callbacks import (
from langchain_core.language_models.chat_models import BaseChatModel, SimpleChatModel
from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import RunnableConfig
class FakeMessagesListChatModel(BaseChatModel):
@ -128,6 +129,33 @@ class FakeListChatModel(SimpleChatModel):
def _identifying_params(self) -> dict[str, Any]:
return {"responses": self.responses}
# manually override batch to preserve batch ordering with no concurrency
def batch(
self,
inputs: list[Any],
config: Optional[Union[RunnableConfig, list[RunnableConfig]]] = None,
*,
return_exceptions: bool = False,
**kwargs: Any,
) -> list[BaseMessage]:
if isinstance(config, list):
return [self.invoke(m, c, **kwargs) for m, c in zip(inputs, config)]
return [self.invoke(m, config, **kwargs) for m in inputs]
async def abatch(
self,
inputs: list[Any],
config: Optional[Union[RunnableConfig, list[RunnableConfig]]] = None,
*,
return_exceptions: bool = False,
**kwargs: Any,
) -> list[BaseMessage]:
if isinstance(config, list):
# do Not use an async iterator here because need explicit ordering
return [await self.ainvoke(m, c, **kwargs) for m, c in zip(inputs, config)]
# do Not use an async iterator here because need explicit ordering
return [await self.ainvoke(m, config, **kwargs) for m in inputs]
class FakeChatModel(SimpleChatModel):
"""Fake Chat Model wrapper for testing purposes."""

97
libs/core/langchain_core/messages/ai.py
View File

@ -2,7 +2,7 @@ import json
from typing import Any, Literal, Optional, Union
from pydantic import model_validator
from typing_extensions import Self, TypedDict
from typing_extensions import NotRequired, Self, TypedDict
from langchain_core.messages.base import (
BaseMessage,
@ -29,6 +29,66 @@ from langchain_core.utils._merge import merge_dicts, merge_lists
from langchain_core.utils.json import parse_partial_json
class InputTokenDetails(TypedDict, total=False):
"""Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
"""
audio: int
"""Audio input tokens."""
cache_creation: int
"""Input tokens that were cached and there was a cache miss.
Since there was a cache miss, the cache was created from these tokens.
"""
cache_read: int
"""Input tokens that were cached and there was a cache hit.
Since there was a cache hit, the tokens were read from the cache. More precisely,
the model state given these tokens was read from the cache.
"""
class OutputTokenDetails(TypedDict, total=False):
"""Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
"""
audio: int
"""Audio output tokens."""
reasoning: int
"""Reasoning output tokens.
Tokens generated by the model in a chain of thought process (i.e. by OpenAI's o1
models) that are not returned as part of model output.
"""
class UsageMetadata(TypedDict):
"""Usage metadata for a message, such as token counts.
@ -39,18 +99,41 @@ class UsageMetadata(TypedDict):
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
"""
input_tokens: int
"""Count of input (or prompt) tokens."""
"""Count of input (or prompt) tokens. Sum of all input token types."""
output_tokens: int
"""Count of output (or completion) tokens."""
"""Count of output (or completion) tokens. Sum of all output token types."""
total_tokens: int
"""Total token count."""
"""Total token count. Sum of input_tokens + output_tokens."""
input_token_details: NotRequired[InputTokenDetails]
"""Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
"""
output_token_details: NotRequired[OutputTokenDetails]
"""Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
"""
class AIMessage(BaseMessage):

2
libs/core/langchain_core/runnables/base.py
View File

@ -1161,7 +1161,7 @@ class Runnable(Generic[Input, Output], ABC):
- ``data``: **Dict[str, Any]**
Below is a table that illustrates some evens that might be emitted by various
Below is a table that illustrates some events that might be emitted by various
chains. Metadata fields have been omitted from the table for brevity.
Chain definitions have been included after the table.

2
libs/core/langchain_core/utils/__init__.py
View File

@ -32,6 +32,7 @@ from langchain_core.utils.utils import (
)
__all__ = [
"build_extra_kwargs",
"StrictFormatter",
"check_package_version",
"convert_to_secret_str",
@ -46,7 +47,6 @@ __all__ = [
"raise_for_status_with_text",
"xor_args",
"try_load_from_hub",
"build_extra_kwargs",
"image",
"get_from_env",
"get_from_dict_or_env",

45
libs/core/langchain_core/utils/utils.py
View File

@ -210,6 +210,51 @@ def get_pydantic_field_names(pydantic_cls: Any) -> set[str]:
return all_required_field_names
def _build_model_kwargs(
values: dict[str, Any],
all_required_field_names: set[str],
) -> dict[str, Any]:
"""Build "model_kwargs" param from Pydanitc constructor values.
Args:
values: All init args passed in by user.
all_required_field_names: All required field names for the pydantic class.
Returns:
Dict[str, Any]: Extra kwargs.
Raises:
ValueError: If a field is specified in both values and extra_kwargs.
ValueError: If a field is specified in model_kwargs.
"""
extra_kwargs = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra_kwargs:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
warnings.warn(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended.""",
stacklevel=7,
)
extra_kwargs[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra_kwargs.keys())
if invalid_model_kwargs:
warnings.warn(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter.",
stacklevel=7,
)
for k in invalid_model_kwargs:
values[k] = extra_kwargs.pop(k)
values["model_kwargs"] = extra_kwargs
return values
# DON'T USE! Kept for backwards-compatibility but should never have been public.
def build_extra_kwargs(
extra_kwargs: dict[str, Any],
values: dict[str, Any],

55
libs/core/langchain_core/vectorstores/base.py
View File

@ -25,7 +25,7 @@ import logging
import math
import warnings
from abc import ABC, abstractmethod
from collections.abc import Collection, Iterable, Sequence
from collections.abc import Collection, Iterable, Iterator, Sequence
from itertools import cycle
from typing import (
TYPE_CHECKING,
@ -61,10 +61,8 @@ class VectorStore(ABC):
self,
texts: Iterable[str],
metadatas: Optional[list[dict]] = None,
# One of the kwargs should be `ids` which is a list of ids
# associated with the texts.
# This is not yet enforced in the type signature for backwards compatibility
# with existing implementations.
*,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> list[str]:
"""Run more texts through the embeddings and add to the vectorstore.
@ -72,6 +70,7 @@ class VectorStore(ABC):
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of IDs associated with the texts.
**kwargs: vectorstore specific parameters.
One of the kwargs should be `ids` which is a list of ids
associated with the texts.
@ -99,10 +98,14 @@ class VectorStore(ABC):
f"Got {len(metadatas)} metadatas and {len(texts_)} texts."
)
metadatas_ = iter(metadatas) if metadatas else cycle([{}])
ids_: Iterator[Optional[str]] = iter(ids) if ids else cycle([None])
docs = [
Document(page_content=text, metadata=metadata_)
for text, metadata_ in zip(texts, metadatas_)
Document(id=id_, page_content=text, metadata=metadata_)
for text, metadata_, id_ in zip(texts, metadatas_, ids_)
]
if ids is not None:
# For backward compatibility
kwargs["ids"] = ids
return self.add_documents(docs, **kwargs)
raise NotImplementedError(
@ -206,6 +209,8 @@ class VectorStore(ABC):
self,
texts: Iterable[str],
metadatas: Optional[list[dict]] = None,
*,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> list[str]:
"""Async run more texts through the embeddings and add to the vectorstore.
@ -214,6 +219,7 @@ class VectorStore(ABC):
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Default is None.
ids: Optional list
**kwargs: vectorstore specific parameters.
Returns:
@ -223,6 +229,9 @@ class VectorStore(ABC):
ValueError: If the number of metadatas does not match the number of texts.
ValueError: If the number of ids does not match the number of texts.
"""
if ids is not None:
# For backward compatibility
kwargs["ids"] = ids
if type(self).aadd_documents != VectorStore.aadd_documents:
# Import document in local scope to avoid circular imports
from langchain_core.documents import Document
@ -239,12 +248,12 @@ class VectorStore(ABC):
f"Got {len(metadatas)} metadatas and {len(texts_)} texts."
)
metadatas_ = iter(metadatas) if metadatas else cycle([{}])
ids_: Iterator[Optional[str]] = iter(ids) if ids else cycle([None])
docs = [
Document(page_content=text, metadata=metadata_)
for text, metadata_ in zip(texts, metadatas_)
Document(id=id_, page_content=text, metadata=metadata_)
for text, metadata_, id_ in zip(texts, metadatas_, ids_)
]
return await self.aadd_documents(docs, **kwargs)
return await run_in_executor(None, self.add_texts, texts, metadatas, **kwargs)
@ -827,6 +836,15 @@ class VectorStore(ABC):
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
if "ids" not in kwargs:
ids = [doc.id for doc in documents]
# If there's at least one valid ID, we'll assume that IDs
# should be used.
if any(ids):
kwargs["ids"] = ids
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
@classmethod
@ -848,6 +866,15 @@ class VectorStore(ABC):
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
if "ids" not in kwargs:
ids = [doc.id for doc in documents]
# If there's at least one valid ID, we'll assume that IDs
# should be used.
if any(ids):
kwargs["ids"] = ids
return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)
@classmethod
@ -857,6 +884,8 @@ class VectorStore(ABC):
texts: list[str],
embedding: Embeddings,
metadatas: Optional[list[dict]] = None,
*,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings.
@ -866,6 +895,7 @@ class VectorStore(ABC):
embedding: Embedding function to use.
metadatas: Optional list of metadatas associated with the texts.
Default is None.
ids: Optional list of IDs associated with the texts.
kwargs: Additional keyword arguments.
Returns:
@ -878,6 +908,8 @@ class VectorStore(ABC):
texts: list[str],
embedding: Embeddings,
metadatas: Optional[list[dict]] = None,
*,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> VST:
"""Async return VectorStore initialized from texts and embeddings.
@ -887,11 +919,14 @@ class VectorStore(ABC):
embedding: Embedding function to use.
metadatas: Optional list of metadatas associated with the texts.
Default is None.
ids: Optional list of IDs associated with the texts.
kwargs: Additional keyword arguments.
Returns:
VectorStore: VectorStore initialized from texts and embeddings.
"""
if ids is not None:
kwargs["ids"] = ids
return await run_in_executor(
None, cls.from_texts, texts, embedding, metadatas, **kwargs
)

4
libs/core/pyproject.toml
View File

@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-core"
version = "0.3.8"
version = "0.3.9"
description = "Building applications with LLMs through composability"
authors = []
license = "MIT"
@ -45,7 +45,7 @@ python = ">=3.12.4"
[tool.ruff.lint]
select = [ "B", "C4", "E", "F", "I", "N", "PIE", "SIM", "T201", "UP", "W",]
ignore = [ "UP007",]
ignore = [ "UP007", "W293",]
[tool.coverage.run]
omit = [ "tests/*",]

148
libs/core/tests/unit_tests/callbacks/test_async_callback_manager.py Normal file
View File

@ -0,0 +1,148 @@
"""Unit tests for verifying event dispatching.
Much of this code is indirectly tested already through many end-to-end tests
that generate traces based on the callbacks. The traces are all verified
via snapshot testing (e.g., see unit tests for runnables).
"""
import contextvars
from contextlib import asynccontextmanager
from typing import Any, Optional
from uuid import UUID
from langchain_core.callbacks import (
AsyncCallbackHandler,
AsyncCallbackManager,
BaseCallbackHandler,
)
async def test_inline_handlers_share_parent_context() -> None:
"""Verify that handlers that are configured to run_inline can update parent context.
This test was created because some of the inline handlers were getting
their own context as the handling logic was kicked off using asyncio.gather
which does not automatically propagate the parent context (by design).
This issue was affecting only a few specific handlers:
* on_llm_start
* on_chat_model_start
which in some cases were triggered with multiple prompts and as a result
triggering multiple tasks that were launched in parallel.
"""
some_var: contextvars.ContextVar[str] = contextvars.ContextVar("some_var")
class CustomHandler(AsyncCallbackHandler):
"""A handler that sets the context variable.
The handler sets the context variable to the name of the callback that was
called.
"""
def __init__(self, run_inline: bool) -> None:
"""Initialize the handler."""
self.run_inline = run_inline
async def on_llm_start(self, *args: Any, **kwargs: Any) -> None:
"""Update the callstack with the name of the callback."""
some_var.set("on_llm_start")
# The manager serves as a callback dispatcher.
# It's responsible for dispatching callbacks to all registered handlers.
manager = AsyncCallbackManager(handlers=[CustomHandler(run_inline=True)])
# Check on_llm_start
some_var.set("unset")
await manager.on_llm_start({}, ["prompt 1"])
assert some_var.get() == "on_llm_start"
# Check what happens when run_inline is False
# We don't expect the context to be updated
manager2 = AsyncCallbackManager(
handlers=[
CustomHandler(run_inline=False),
]
)
some_var.set("unset")
await manager2.on_llm_start({}, ["prompt 1"])
# Will not be updated because the handler is not inline
assert some_var.get() == "unset"
async def test_inline_handlers_share_parent_context_multiple() -> None:
"""A slightly more complex variation of the test unit test above.
This unit test verifies that things work correctly when there are multiple prompts,
and multiple handlers that are configured to run inline.
"""
counter_var = contextvars.ContextVar("counter", default=0)
shared_stack = []
@asynccontextmanager
async def set_counter_var() -> Any:
token = counter_var.set(0)
try:
yield
finally:
counter_var.reset(token)
class StatefulAsyncCallbackHandler(AsyncCallbackHandler):
def __init__(self, name: str, run_inline: bool = True):
self.name = name
self.run_inline = run_inline
async def on_llm_start(
self,
serialized: dict[str, Any],
prompts: list[str],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
if self.name == "StateModifier":
current_counter = counter_var.get()
counter_var.set(current_counter + 1)
state = counter_var.get()
elif self.name == "StateReader":
state = counter_var.get()
else:
state = None
shared_stack.append(state)
await super().on_llm_start(
serialized,
prompts,
run_id=run_id,
parent_run_id=parent_run_id,
**kwargs,
)
handlers: list[BaseCallbackHandler] = [
StatefulAsyncCallbackHandler("StateModifier", run_inline=True),
StatefulAsyncCallbackHandler("StateReader", run_inline=True),
StatefulAsyncCallbackHandler("NonInlineHandler", run_inline=False),
]
prompts = ["Prompt1", "Prompt2", "Prompt3"]
async with set_counter_var():
shared_stack.clear()
manager = AsyncCallbackManager(handlers=handlers)
await manager.on_llm_start({}, prompts)
# Assert the order of states
states = [entry for entry in shared_stack if entry is not None]
assert states == [
1,
1,
2,
2,
3,
3,
], f"Expected order of states was broken due to context loss. Got {states}"

19
libs/core/tests/unit_tests/fake/test_fake_chat_model.py
View File

@ -5,7 +5,11 @@ from typing import Any, Optional, Union
from uuid import UUID
from langchain_core.callbacks.base import AsyncCallbackHandler
from langchain_core.language_models import GenericFakeChatModel, ParrotFakeChatModel
from langchain_core.language_models import (
FakeListChatModel,
GenericFakeChatModel,
ParrotFakeChatModel,
)
from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
from langchain_core.outputs import ChatGenerationChunk, GenerationChunk
from tests.unit_tests.stubs import (
@ -205,3 +209,16 @@ def test_chat_model_inputs() -> None:
assert fake.invoke([AIMessage(content="blah")]) == _any_id_ai_message(
content="blah"
)
def test_fake_list_chat_model_batch() -> None:
expected = [
_any_id_ai_message(content="a"),
_any_id_ai_message(content="b"),
_any_id_ai_message(content="c"),
]
for _ in range(20):
# run this 20 times to test race condition in batch
fake = FakeListChatModel(responses=["a", "b", "c"])
resp = fake.batch(["1", "2", "3"])
assert resp == expected

178
libs/core/tests/unit_tests/indexing/test_indexing.py
View File

@ -7,6 +7,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
import pytest
import pytest_asyncio
from pytest_mock import MockerFixture
from langchain_core.document_loaders.base import BaseLoader
from langchain_core.documents import Document
@ -1728,3 +1729,180 @@ async def test_incremental_aindexing_with_batch_size_with_optimization(
for uid in vector_store.store
}
assert doc_texts == {"updated 1", "2", "3", "updated 4"}
def test_index_with_upsert_kwargs(
record_manager: InMemoryRecordManager, upserting_vector_store: InMemoryVectorStore
) -> None:
"""Test indexing with upsert_kwargs parameter."""
mock_add_documents = MagicMock()
with patch.object(upserting_vector_store, "add_documents", mock_add_documents):
docs = [
Document(
page_content="Test document 1",
metadata={"source": "1"},
),
Document(
page_content="Test document 2",
metadata={"source": "2"},
),
]
upsert_kwargs = {"vector_field": "embedding"}
index(docs, record_manager, upserting_vector_store, upsert_kwargs=upsert_kwargs)
# Assert that add_documents was called with the correct arguments
mock_add_documents.assert_called_once()
call_args = mock_add_documents.call_args
assert call_args is not None
args, kwargs = call_args
# Check that the documents are correct (ignoring ids)
assert len(args[0]) == 2
assert all(isinstance(doc, Document) for doc in args[0])
assert [doc.page_content for doc in args[0]] == [
"Test document 1",
"Test document 2",
]
assert [doc.metadata for doc in args[0]] == [{"source": "1"}, {"source": "2"}]
# Check that ids are present
assert "ids" in kwargs
assert isinstance(kwargs["ids"], list)
assert len(kwargs["ids"]) == 2
# Check other arguments
assert kwargs["batch_size"] == 100
assert kwargs["vector_field"] == "embedding"
def test_index_with_upsert_kwargs_for_document_indexer(
record_manager: InMemoryRecordManager,
mocker: MockerFixture,
) -> None:
"""Test that kwargs are passed to the upsert method of the document indexer."""
document_index = InMemoryDocumentIndex()
upsert_spy = mocker.spy(document_index.__class__, "upsert")
docs = [
Document(
page_content="This is a test document.",
metadata={"source": "1"},
),
Document(
page_content="This is another document.",
metadata={"source": "2"},
),
]
upsert_kwargs = {"vector_field": "embedding"}
assert index(
docs,
record_manager,
document_index,
cleanup="full",
upsert_kwargs=upsert_kwargs,
) == {
"num_added": 2,
"num_deleted": 0,
"num_skipped": 0,
"num_updated": 0,
}
assert upsert_spy.call_count == 1
# assert call kwargs were passed as kwargs
assert upsert_spy.call_args.kwargs == upsert_kwargs
async def test_aindex_with_upsert_kwargs_for_document_indexer(
arecord_manager: InMemoryRecordManager,
mocker: MockerFixture,
) -> None:
"""Test that kwargs are passed to the upsert method of the document indexer."""
document_index = InMemoryDocumentIndex()
upsert_spy = mocker.spy(document_index.__class__, "aupsert")
docs = [
Document(
page_content="This is a test document.",
metadata={"source": "1"},
),
Document(
page_content="This is another document.",
metadata={"source": "2"},
),
]
upsert_kwargs = {"vector_field": "embedding"}
assert await aindex(
docs,
arecord_manager,
document_index,
cleanup="full",
upsert_kwargs=upsert_kwargs,
) == {
"num_added": 2,
"num_deleted": 0,
"num_skipped": 0,
"num_updated": 0,
}
assert upsert_spy.call_count == 1
# assert call kwargs were passed as kwargs
assert upsert_spy.call_args.kwargs == upsert_kwargs
async def test_aindex_with_upsert_kwargs(
arecord_manager: InMemoryRecordManager, upserting_vector_store: InMemoryVectorStore
) -> None:
"""Test async indexing with upsert_kwargs parameter."""
mock_aadd_documents = AsyncMock()
with patch.object(upserting_vector_store, "aadd_documents", mock_aadd_documents):
docs = [
Document(
page_content="Async test document 1",
metadata={"source": "1"},
),
Document(
page_content="Async test document 2",
metadata={"source": "2"},
),
]
upsert_kwargs = {"vector_field": "embedding"}
await aindex(
docs,
arecord_manager,
upserting_vector_store,
upsert_kwargs=upsert_kwargs,
)
# Assert that aadd_documents was called with the correct arguments
mock_aadd_documents.assert_called_once()
call_args = mock_aadd_documents.call_args
assert call_args is not None
args, kwargs = call_args
# Check that the documents are correct (ignoring ids)
assert len(args[0]) == 2
assert all(isinstance(doc, Document) for doc in args[0])
assert [doc.page_content for doc in args[0]] == [
"Async test document 1",
"Async test document 2",
]
assert [doc.metadata for doc in args[0]] == [{"source": "1"}, {"source": "2"}]
# Check that ids are present
assert "ids" in kwargs
assert isinstance(kwargs["ids"], list)
assert len(kwargs["ids"]) == 2
# Check other arguments
assert kwargs["batch_size"] == 100
assert kwargs["vector_field"] == "embedding"

40
libs/core/tests/unit_tests/language_models/chat_models/test_cache.py
View File

@ -199,19 +199,13 @@ async def test_global_cache_abatch() -> None:
assert results[0].content == "hello"
assert results[1].content == "hello"
## RACE CONDITION -- note behavior is different from sync
# Now, reset cache and test the race condition
# For now we just hard-code the result, if this changes
# we can investigate further
global_cache = InMemoryCache()
set_llm_cache(global_cache)
assert global_cache._cache == {}
results = await chat_model.abatch(["prompt", "prompt"])
# suspecting that tasks will be scheduled and executed in order
# if this ever fails, we can relax to a set comparison
# Cache misses likely guaranteed?
assert results[0].content == "meow"
assert results[1].content == "woof"
assert results[1].content == "meow"
finally:
set_llm_cache(None)
@ -303,9 +297,7 @@ def test_llm_representation_for_serializable() -> None:
chat = CustomChat(cache=cache, messages=iter([]))
assert chat._get_llm_string() == (
'{"id": ["tests", "unit_tests", "language_models", "chat_models", '
'"test_cache", "CustomChat"], "kwargs": {"cache": {"id": ["tests", '
'"unit_tests", "language_models", "chat_models", "test_cache", '
'"InMemoryCache"], "lc": 1, "type": "not_implemented"}, "messages": {"id": '
'"test_cache", "CustomChat"], "kwargs": {"messages": {"id": '
'["builtins", "list_iterator"], "lc": 1, "type": "not_implemented"}}, "lc": '
'1, "name": "CustomChat", "type": "constructor"}---[(\'stop\', None)]'
)
@ -324,20 +316,6 @@ def test_cleanup_serialized() -> None:
"CustomChat",
],
"kwargs": {
"cache": {
"lc": 1,
"type": "not_implemented",
"id": [
"tests",
"unit_tests",
"language_models",
"chat_models",
"test_cache",
"InMemoryCache",
],
"repr": "<tests.unit_tests.language_models.chat_models."
"test_cache.InMemoryCache object at 0x79ff437fe7d0>",
},
"messages": {
"lc": 1,
"type": "not_implemented",
@ -380,18 +358,6 @@ def test_cleanup_serialized() -> None:
"CustomChat",
],
"kwargs": {
"cache": {
"id": [
"tests",
"unit_tests",
"language_models",
"chat_models",
"test_cache",
"InMemoryCache",
],
"lc": 1,
"type": "not_implemented",
},
"messages": {
"id": ["builtins", "list_iterator"],
"lc": 1,

180
libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr
View File

@ -677,6 +677,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -743,6 +778,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'SystemMessage': dict({
'additionalProperties': True,
'description': '''
@ -1245,16 +1310,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/$defs/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/$defs/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',
@ -2008,6 +2092,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -2074,6 +2193,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'SystemMessage': dict({
'additionalProperties': True,
'description': '''
@ -2576,16 +2725,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/$defs/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/$defs/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',

90
libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr
View File

@ -1037,6 +1037,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -1103,6 +1138,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'SystemMessage': dict({
'additionalProperties': True,
'description': '''
@ -1605,16 +1670,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/$defs/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/$defs/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',

720
libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
View File

@ -2639,6 +2639,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -2705,6 +2740,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'SystemMessage': dict({
'additionalProperties': True,
'description': '''
@ -3207,16 +3272,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/$defs/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/$defs/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',
@ -4028,6 +4112,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -4094,6 +4213,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'StringPromptValue': dict({
'description': 'String prompt value.',
'properties': dict({
@ -4615,16 +4764,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/$defs/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/$defs/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',
@ -5448,6 +5616,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -5514,6 +5717,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'StringPromptValue': dict({
'description': 'String prompt value.',
'properties': dict({
@ -6035,16 +6268,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/definitions/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/definitions/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',
@ -6744,6 +6996,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -6810,6 +7097,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'SystemMessage': dict({
'additionalProperties': True,
'description': '''
@ -7312,16 +7629,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/definitions/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/definitions/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',
@ -8175,6 +8511,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -8241,6 +8612,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'StringPromptValue': dict({
'description': 'String prompt value.',
'properties': dict({
@ -8762,16 +9163,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/definitions/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/definitions/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',
@ -9516,6 +9936,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -9582,6 +10037,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'SystemMessage': dict({
'additionalProperties': True,
'description': '''
@ -10084,16 +10569,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/definitions/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/definitions/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',
@ -10855,6 +11359,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -10921,6 +11460,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'PromptTemplateOutput': dict({
'anyOf': list([
dict({
@ -11453,16 +12022,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/definitions/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/definitions/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',
@ -12236,6 +12824,41 @@
'title': 'HumanMessageChunk',
'type': 'object',
}),
'InputTokenDetails': dict({
'description': '''
Breakdown of input token counts.
Does *not* need to sum to full input token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'cache_creation': dict({
'title': 'Cache Creation',
'type': 'integer',
}),
'cache_read': dict({
'title': 'Cache Read',
'type': 'integer',
}),
}),
'title': 'InputTokenDetails',
'type': 'object',
}),
'InvalidToolCall': dict({
'description': '''
Allowance for errors made by LLM.
@ -12302,6 +12925,36 @@
'title': 'InvalidToolCall',
'type': 'object',
}),
'OutputTokenDetails': dict({
'description': '''
Breakdown of output token counts.
Does *not* need to sum to full output token count. Does *not* need to have all keys.
Example:
.. code-block:: python
{
"audio": 10,
"reasoning": 200,
}
.. versionadded:: 0.3.9
''',
'properties': dict({
'audio': dict({
'title': 'Audio',
'type': 'integer',
}),
'reasoning': dict({
'title': 'Reasoning',
'type': 'integer',
}),
}),
'title': 'OutputTokenDetails',
'type': 'object',
}),
'StringPromptValue': dict({
'description': 'String prompt value.',
'properties': dict({
@ -12823,16 +13476,35 @@
.. code-block:: python
{
"input_tokens": 10,
"output_tokens": 20,
"total_tokens": 30
"input_tokens": 350,
"output_tokens": 240,
"total_tokens": 590,
"input_token_details": {
"audio": 10,
"cache_creation": 200,
"cache_read": 100,
},
"output_token_details": {
"audio": 10,
"reasoning": 200,
}
}
.. versionchanged:: 0.3.9
Added ``input_token_details`` and ``output_token_details``.
''',
'properties': dict({
'input_token_details': dict({
'$ref': '#/definitions/InputTokenDetails',
}),
'input_tokens': dict({
'title': 'Input Tokens',
'type': 'integer',
}),
'output_token_details': dict({
'$ref': '#/definitions/OutputTokenDetails',
}),
'output_tokens': dict({
'title': 'Output Tokens',
'type': 'integer',

2
libs/core/tests/unit_tests/utils/test_imports.py
View File

@ -17,8 +17,8 @@ EXPECTED_ALL = [
"raise_for_status_with_text",
"xor_args",
"try_load_from_hub",
"build_extra_kwargs",
"image",
"build_extra_kwargs",
"get_from_dict_or_env",
"get_from_env",
"stringify_dict",

140
libs/core/tests/unit_tests/vectorstores/test_vectorstore.py
View File

@ -10,8 +10,10 @@ import uuid
from collections.abc import Iterable, Sequence
from typing import Any, Optional
import pytest
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.embeddings import Embeddings, FakeEmbeddings
from langchain_core.vectorstores import VectorStore
@ -25,10 +27,6 @@ class CustomAddTextsVectorstore(VectorStore):
self,
texts: Iterable[str],
metadatas: Optional[list[dict]] = None,
# One of the kwargs should be `ids` which is a list of ids
# associated with the texts.
# This is not yet enforced in the type signature for backwards compatibility
# with existing implementations.
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> list[str]:
@ -68,12 +66,59 @@ class CustomAddTextsVectorstore(VectorStore):
raise NotImplementedError()
def test_default_add_documents() -> None:
class CustomAddDocumentsVectorstore(VectorStore):
"""A vectorstore that only implements add documents."""
def __init__(self) -> None:
self.store: dict[str, Document] = {}
def add_documents(
self,
documents: list[Document],
*,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> list[str]:
ids_ = []
ids_iter = iter(ids or [])
for document in documents:
id_ = next(ids_iter) if ids else document.id or str(uuid.uuid4())
self.store[id_] = Document(
id=id_, page_content=document.page_content, metadata=document.metadata
)
ids_.append(id_)
return ids_
def get_by_ids(self, ids: Sequence[str], /) -> list[Document]:
return [self.store[id] for id in ids if id in self.store]
@classmethod
def from_texts( # type: ignore
cls,
texts: list[str],
embedding: Embeddings,
metadatas: Optional[list[dict]] = None,
**kwargs: Any,
) -> CustomAddDocumentsVectorstore:
vectorstore = CustomAddDocumentsVectorstore()
vectorstore.add_texts(texts, metadatas=metadatas, **kwargs)
return vectorstore
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> list[Document]:
raise NotImplementedError()
@pytest.mark.parametrize(
"vs_class", [CustomAddTextsVectorstore, CustomAddDocumentsVectorstore]
)
def test_default_add_documents(vs_class: type[VectorStore]) -> None:
"""Test that we can implement the upsert method of the CustomVectorStore
class without violating the Liskov Substitution Principle.
"""
store = CustomAddTextsVectorstore()
store = vs_class()
# Check upsert with id
assert store.add_documents([Document(id="1", page_content="hello")]) == ["1"]
@ -95,8 +140,11 @@ def test_default_add_documents() -> None:
assert store.get_by_ids(["6"]) == [Document(id="6", page_content="baz")]
def test_default_add_texts() -> None:
store = CustomAddTextsVectorstore()
@pytest.mark.parametrize(
"vs_class", [CustomAddTextsVectorstore, CustomAddDocumentsVectorstore]
)
def test_default_add_texts(vs_class: type[VectorStore]) -> None:
store = vs_class()
# Check that default implementation of add_texts works
assert store.add_texts(["hello", "world"], ids=["3", "4"]) == ["3", "4"]
@ -122,9 +170,12 @@ def test_default_add_texts() -> None:
]
async def test_default_aadd_documents() -> None:
@pytest.mark.parametrize(
"vs_class", [CustomAddTextsVectorstore, CustomAddDocumentsVectorstore]
)
async def test_default_aadd_documents(vs_class: type[VectorStore]) -> None:
"""Test delegation to the synchronous method."""
store = CustomAddTextsVectorstore()
store = vs_class()
# Check upsert with id
assert await store.aadd_documents([Document(id="1", page_content="hello")]) == ["1"]
@ -146,10 +197,13 @@ async def test_default_aadd_documents() -> None:
assert await store.aget_by_ids(["6"]) == [Document(id="6", page_content="baz")]
async def test_default_aadd_texts() -> None:
@pytest.mark.parametrize(
"vs_class", [CustomAddTextsVectorstore, CustomAddDocumentsVectorstore]
)
async def test_default_aadd_texts(vs_class: type[VectorStore]) -> None:
"""Test delegation to the synchronous method."""
store = CustomAddTextsVectorstore()
# Check that default implementation of add_texts works
store = vs_class()
# Check that default implementation of aadd_texts works
assert await store.aadd_texts(["hello", "world"], ids=["3", "4"]) == ["3", "4"]
assert await store.aget_by_ids(["3", "4"]) == [
@ -172,3 +226,61 @@ async def test_default_aadd_texts() -> None:
Document(id=ids_2[0], page_content="foo", metadata={"foo": "bar"}),
Document(id=ids_2[1], page_content="bar", metadata={"foo": "bar"}),
]
@pytest.mark.parametrize(
"vs_class", [CustomAddTextsVectorstore, CustomAddDocumentsVectorstore]
)
def test_default_from_documents(vs_class: type[VectorStore]) -> None:
embeddings = FakeEmbeddings(size=1)
store = vs_class.from_documents(
[Document(id="1", page_content="hello", metadata={"foo": "bar"})], embeddings
)
assert store.get_by_ids(["1"]) == [
Document(id="1", page_content="hello", metadata={"foo": "bar"})
]
# from_documents with ids in args
store = vs_class.from_documents(
[Document(page_content="hello", metadata={"foo": "bar"})], embeddings, ids=["1"]
)
assert store.get_by_ids(["1"]) == [
Document(id="1", page_content="hello", metadata={"foo": "bar"})
]
# Test from_documents with id specified in both document and ids
original_document = Document(id="7", page_content="baz")
store = vs_class.from_documents([original_document], embeddings, ids=["6"])
assert original_document.id == "7" # original document should not be modified
assert store.get_by_ids(["6"]) == [Document(id="6", page_content="baz")]
@pytest.mark.parametrize(
"vs_class", [CustomAddTextsVectorstore, CustomAddDocumentsVectorstore]
)
async def test_default_afrom_documents(vs_class: type[VectorStore]) -> None:
embeddings = FakeEmbeddings(size=1)
store = await vs_class.afrom_documents(
[Document(id="1", page_content="hello", metadata={"foo": "bar"})], embeddings
)
assert await store.aget_by_ids(["1"]) == [
Document(id="1", page_content="hello", metadata={"foo": "bar"})
]
# from_documents with ids in args
store = await vs_class.afrom_documents(
[Document(page_content="hello", metadata={"foo": "bar"})], embeddings, ids=["1"]
)
assert await store.aget_by_ids(["1"]) == [
Document(id="1", page_content="hello", metadata={"foo": "bar"})
]
# Test afrom_documents with id specified in both document and ids
original_document = Document(id="7", page_content="baz")
store = await vs_class.afrom_documents([original_document], embeddings, ids=["6"])
assert original_document.id == "7" # original document should not be modified
assert await store.aget_by_ids(["6"]) == [Document(id="6", page_content="baz")]

2
libs/langchain/tests/unit_tests/chat_models/test_base.py
View File

@ -101,7 +101,6 @@ def test_configurable() -> None:
"name": None,
"bound": {
"name": None,
"cache": None,
"disable_streaming": False,
"disabled_params": None,
"model_name": "gpt-4o",
@ -189,7 +188,6 @@ def test_configurable_with_default() -> None:
"name": None,
"bound": {
"name": None,
"cache": None,
"disable_streaming": False,
"model": "claude-3-sonnet-20240229",
"max_tokens": 1024,

67
libs/partners/anthropic/langchain_anthropic/chat_models.py
View File

@ -41,7 +41,7 @@ from langchain_core.messages import (
ToolCall,
ToolMessage,
)
from langchain_core.messages.ai import UsageMetadata
from langchain_core.messages.ai import InputTokenDetails, UsageMetadata
from langchain_core.messages.tool import tool_call_chunk as create_tool_call_chunk
from langchain_core.output_parsers import (
JsonOutputKeyToolsParser,
@ -56,13 +56,13 @@ from langchain_core.runnables import (
)
from langchain_core.tools import BaseTool
from langchain_core.utils import (
build_extra_kwargs,
from_env,
get_pydantic_field_names,
secret_from_env,
)
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.utils.pydantic import is_basemodel_subclass
from langchain_core.utils.utils import _build_model_kwargs
from pydantic import (
BaseModel,
ConfigDict,
@ -118,9 +118,13 @@ def _merge_messages(
for curr in messages:
curr = curr.model_copy(deep=True)
if isinstance(curr, ToolMessage):
if isinstance(curr.content, list) and all(
isinstance(block, dict) and block.get("type") == "tool_result"
for block in curr.content
if (
isinstance(curr.content, list)
and curr.content
and all(
isinstance(block, dict) and block.get("type") == "tool_result"
for block in curr.content
)
):
curr = HumanMessage(curr.content) # type: ignore[misc]
else:
@ -291,7 +295,7 @@ class ChatAnthropic(BaseChatModel):
Name of Anthropic model to use. E.g. "claude-3-sonnet-20240229".
temperature: float
Sampling temperature. Ranges from 0.0 to 1.0.
max_tokens: Optional[int]
max_tokens: int
Max number of tokens to generate.
Key init args — client params:
@ -646,11 +650,8 @@ class ChatAnthropic(BaseChatModel):
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict) -> Any:
extra = values.get("model_kwargs", {})
all_required_field_names = get_pydantic_field_names(cls)
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="after")
@ -766,12 +767,7 @@ class ChatAnthropic(BaseChatModel):
)
else:
msg = AIMessage(content=content)
# Collect token usage
msg.usage_metadata = {
"input_tokens": data.usage.input_tokens,
"output_tokens": data.usage.output_tokens,
"total_tokens": data.usage.input_tokens + data.usage.output_tokens,
}
msg.usage_metadata = _create_usage_metadata(data.usage)
return ChatResult(
generations=[ChatGeneration(message=msg)],
llm_output=llm_output,
@ -1182,14 +1178,10 @@ def _make_message_chunk_from_anthropic_event(
message_chunk: Optional[AIMessageChunk] = None
# See https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/lib/streaming/_messages.py # noqa: E501
if event.type == "message_start" and stream_usage:
input_tokens = event.message.usage.input_tokens
usage_metadata = _create_usage_metadata(event.message.usage)
message_chunk = AIMessageChunk(
content="" if coerce_content_to_string else [],
usage_metadata=UsageMetadata(
input_tokens=input_tokens,
output_tokens=0,
total_tokens=input_tokens,
),
usage_metadata=usage_metadata,
)
elif (
event.type == "content_block_start"
@ -1235,14 +1227,10 @@ def _make_message_chunk_from_anthropic_event(
tool_call_chunks=[tool_call_chunk], # type: ignore
)
elif event.type == "message_delta" and stream_usage:
output_tokens = event.usage.output_tokens
usage_metadata = _create_usage_metadata(event.usage)
message_chunk = AIMessageChunk(
content="",
usage_metadata=UsageMetadata(
input_tokens=0,
output_tokens=output_tokens,
total_tokens=output_tokens,
),
usage_metadata=usage_metadata,
response_metadata={
"stop_reason": event.delta.stop_reason,
"stop_sequence": event.delta.stop_sequence,
@ -1257,3 +1245,26 @@ def _make_message_chunk_from_anthropic_event(
@deprecated(since="0.1.0", removal="0.3.0", alternative="ChatAnthropic")
class ChatAnthropicMessages(ChatAnthropic):
pass
def _create_usage_metadata(anthropic_usage: BaseModel) -> UsageMetadata:
input_token_details: Dict = {
"cache_read": getattr(anthropic_usage, "cache_read_input_tokens", None),
"cache_creation": getattr(anthropic_usage, "cache_creation_input_tokens", None),
}
# Anthropic input_tokens exclude cached token counts.
input_tokens = (
getattr(anthropic_usage, "input_tokens", 0)
+ (input_token_details["cache_read"] or 0)
+ (input_token_details["cache_creation"] or 0)
)
output_tokens = getattr(anthropic_usage, "output_tokens", 0)
return UsageMetadata(
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=input_tokens + output_tokens,
input_token_details=InputTokenDetails(
**{k: v for k, v in input_token_details.items() if v is not None}
),
)

7
libs/partners/anthropic/langchain_anthropic/llms.py
View File

@ -25,7 +25,7 @@ from langchain_core.utils import (
get_pydantic_field_names,
)
from langchain_core.utils.utils import (
build_extra_kwargs,
_build_model_kwargs,
from_env,
secret_from_env,
)
@ -88,11 +88,8 @@ class _AnthropicCommon(BaseLanguageModel):
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict) -> Any:
extra = values.get("model_kwargs", {})
all_required_field_names = get_pydantic_field_names(cls)
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="after")

131
libs/partners/anthropic/poetry.lock generated
View File

@ -13,13 +13,13 @@ files = [
[[package]]
name = "anthropic"
version = "0.34.2"
version = "0.35.0"
description = "The official Python library for the anthropic API"
optional = false
python-versions = ">=3.7"
files = [
{file = "anthropic-0.34.2-py3-none-any.whl", hash = "sha256:f50a628eb71e2c76858b106c8cbea278c45c6bd2077cb3aff716a112abddc9fc"},
{file = "anthropic-0.34.2.tar.gz", hash = "sha256:808ea19276f26646bfde9ee535669735519376e4eeb301a2974fc69892be1d6e"},
{file = "anthropic-0.35.0-py3-none-any.whl", hash = "sha256:777983989ed9e444eb4a6d92dad84027f14a6639cba6f48772c0078d51959828"},
{file = "anthropic-0.35.0.tar.gz", hash = "sha256:d2f998246413c309a7770d1faa617500f505377a04ab45a13a66f8559daf3742"},
]
[package.dependencies]
@ -38,13 +38,13 @@ vertex = ["google-auth (>=2,<3)"]
[[package]]
name = "anyio"
version = "4.4.0"
version = "4.6.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
{file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
{file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
{file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
]
[package.dependencies]
@ -54,9 +54,9 @@ sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (>=0.23)"]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
trio = ["trio (>=0.26.1)"]
[[package]]
name = "certifi"
@ -314,13 +314,13 @@ files = [
[[package]]
name = "httpcore"
version = "1.0.5"
version = "1.0.6"
description = "A minimal low-level HTTP client."
optional = false
python-versions = ">=3.8"
files = [
{file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
{file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
{file = "httpcore-1.0.6-py3-none-any.whl", hash = "sha256:27b59625743b85577a8c0e10e55b50b5368a4f2cfe8cc7bcfa9cf00829c2682f"},
{file = "httpcore-1.0.6.tar.gz", hash = "sha256:73f6dbd6eb8c21bbf7ef8efad555481853f5f6acdeaff1edb0694289269ee17f"},
]
[package.dependencies]
@ -331,7 +331,7 @@ h11 = ">=0.13,<0.15"
asyncio = ["anyio (>=4.0,<5.0)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
trio = ["trio (>=0.22.0,<0.26.0)"]
trio = ["trio (>=0.22.0,<1.0)"]
[[package]]
name = "httpx"
@ -360,13 +360,13 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "huggingface-hub"
version = "0.25.0"
version = "0.25.1"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = false
python-versions = ">=3.8.0"
files = [
{file = "huggingface_hub-0.25.0-py3-none-any.whl", hash = "sha256:e2f357b35d72d5012cfd127108c4e14abcd61ba4ebc90a5a374dc2456cb34e12"},
{file = "huggingface_hub-0.25.0.tar.gz", hash = "sha256:fb5fbe6c12fcd99d187ec7db95db9110fb1a20505f23040a5449a717c1a0db4d"},
{file = "huggingface_hub-0.25.1-py3-none-any.whl", hash = "sha256:a5158ded931b3188f54ea9028097312cb0acd50bffaaa2612014c3c526b44972"},
{file = "huggingface_hub-0.25.1.tar.gz", hash = "sha256:9ff7cb327343211fbd06e2b149b8f362fd1e389454f3f14c6db75a4999ee20ff"},
]
[package.dependencies]
@ -514,7 +514,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.3.1"
version = "0.3.9"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@ -523,7 +523,7 @@ develop = true
[package.dependencies]
jsonpatch = "^1.33"
langsmith = "^0.1.117"
langsmith = "^0.1.125"
packaging = ">=23.2,<25"
pydantic = [
{version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
@ -558,13 +558,13 @@ url = "../../standard-tests"
[[package]]
name = "langsmith"
version = "0.1.121"
version = "0.1.131"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langsmith-0.1.121-py3-none-any.whl", hash = "sha256:fdb1ac8a671d3904201bfeea197d87bded46a10d08f1034af464211872e29893"},
{file = "langsmith-0.1.121.tar.gz", hash = "sha256:e9381b82a5bd484af9a51c3e96faea572746b8d617b070c1cda40cbbe48e33df"},
{file = "langsmith-0.1.131-py3-none-any.whl", hash = "sha256:80c106b1c42307195cc0bb3a596472c41ef91b79d15bcee9938307800336c563"},
{file = "langsmith-0.1.131.tar.gz", hash = "sha256:626101a3bf3ca481e5110d5155ace8aa066e4e9cc2fa7d96c8290ade0fbff797"},
]
[package.dependencies]
@ -575,6 +575,7 @@ pydantic = [
{version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
]
requests = ">=2,<3"
requests-toolbelt = ">=1.0.0,<2.0.0"
[[package]]
name = "mypy"
@ -1019,6 +1020,20 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
[[package]]
name = "requests-toolbelt"
version = "1.0.0"
description = "A utility belt for advanced users of python-requests"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
files = [
{file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
{file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"},
]
[package.dependencies]
requests = ">=2.0.1,<3.0.0"
[[package]]
name = "ruff"
version = "0.5.7"
@ -1216,13 +1231,13 @@ testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
[[package]]
name = "tomli"
version = "2.0.1"
version = "2.0.2"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
{file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
{file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
]
[[package]]
@ -1275,41 +1290,41 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "watchdog"
version = "5.0.2"
version = "5.0.3"
description = "Filesystem events monitoring"
optional = false
python-versions = ">=3.9"
files = [
{file = "watchdog-5.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d961f4123bb3c447d9fcdcb67e1530c366f10ab3a0c7d1c0c9943050936d4877"},
{file = "watchdog-5.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72990192cb63872c47d5e5fefe230a401b87fd59d257ee577d61c9e5564c62e5"},
{file = "watchdog-5.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6bec703ad90b35a848e05e1b40bf0050da7ca28ead7ac4be724ae5ac2653a1a0"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:dae7a1879918f6544201d33666909b040a46421054a50e0f773e0d870ed7438d"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c4a440f725f3b99133de610bfec93d570b13826f89616377715b9cd60424db6e"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f8b2918c19e0d48f5f20df458c84692e2a054f02d9df25e6c3c930063eca64c1"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:aa9cd6e24126d4afb3752a3e70fce39f92d0e1a58a236ddf6ee823ff7dba28ee"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f627c5bf5759fdd90195b0c0431f99cff4867d212a67b384442c51136a098ed7"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d7594a6d32cda2b49df3fd9abf9b37c8d2f3eab5df45c24056b4a671ac661619"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:ba32efcccfe2c58f4d01115440d1672b4eb26cdd6fc5b5818f1fb41f7c3e1889"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:963f7c4c91e3f51c998eeff1b3fb24a52a8a34da4f956e470f4b068bb47b78ee"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:8c47150aa12f775e22efff1eee9f0f6beee542a7aa1a985c271b1997d340184f"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:14dd4ed023d79d1f670aa659f449bcd2733c33a35c8ffd88689d9d243885198b"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b84bff0391ad4abe25c2740c7aec0e3de316fdf7764007f41e248422a7760a7f"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3e8d5ff39f0a9968952cce548e8e08f849141a4fcc1290b1c17c032ba697b9d7"},
{file = "watchdog-5.0.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:fb223456db6e5f7bd9bbd5cd969f05aae82ae21acc00643b60d81c770abd402b"},
{file = "watchdog-5.0.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:9814adb768c23727a27792c77812cf4e2fd9853cd280eafa2bcfa62a99e8bd6e"},
{file = "watchdog-5.0.2-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:901ee48c23f70193d1a7bc2d9ee297df66081dd5f46f0ca011be4f70dec80dab"},
{file = "watchdog-5.0.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:638bcca3d5b1885c6ec47be67bf712b00a9ab3d4b22ec0881f4889ad870bc7e8"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5597c051587f8757798216f2485e85eac583c3b343e9aa09127a3a6f82c65ee8"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_armv7l.whl", hash = "sha256:53ed1bf71fcb8475dd0ef4912ab139c294c87b903724b6f4a8bd98e026862e6d"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_i686.whl", hash = "sha256:29e4a2607bd407d9552c502d38b45a05ec26a8e40cc7e94db9bb48f861fa5abc"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_ppc64.whl", hash = "sha256:b6dc8f1d770a8280997e4beae7b9a75a33b268c59e033e72c8a10990097e5fde"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:d2ab34adc9bf1489452965cdb16a924e97d4452fcf88a50b21859068b50b5c3b"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_s390x.whl", hash = "sha256:7d1aa7e4bb0f0c65a1a91ba37c10e19dabf7eaaa282c5787e51371f090748f4b"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:726eef8f8c634ac6584f86c9c53353a010d9f311f6c15a034f3800a7a891d941"},
{file = "watchdog-5.0.2-py3-none-win32.whl", hash = "sha256:bda40c57115684d0216556671875e008279dea2dc00fcd3dde126ac8e0d7a2fb"},
{file = "watchdog-5.0.2-py3-none-win_amd64.whl", hash = "sha256:d010be060c996db725fbce7e3ef14687cdcc76f4ca0e4339a68cc4532c382a73"},
{file = "watchdog-5.0.2-py3-none-win_ia64.whl", hash = "sha256:3960136b2b619510569b90f0cd96408591d6c251a75c97690f4553ca88889769"},
{file = "watchdog-5.0.2.tar.gz", hash = "sha256:dcebf7e475001d2cdeb020be630dc5b687e9acdd60d16fea6bb4508e7b94cf76"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:85527b882f3facda0579bce9d743ff7f10c3e1e0db0a0d0e28170a7d0e5ce2ea"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:53adf73dcdc0ef04f7735066b4a57a4cd3e49ef135daae41d77395f0b5b692cb"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e25adddab85f674acac303cf1f5835951345a56c5f7f582987d266679979c75b"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:f01f4a3565a387080dc49bdd1fefe4ecc77f894991b88ef927edbfa45eb10818"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:91b522adc25614cdeaf91f7897800b82c13b4b8ac68a42ca959f992f6990c490"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d52db5beb5e476e6853da2e2d24dbbbed6797b449c8bf7ea118a4ee0d2c9040e"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:94d11b07c64f63f49876e0ab8042ae034674c8653bfcdaa8c4b32e71cfff87e8"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:349c9488e1d85d0a58e8cb14222d2c51cbc801ce11ac3936ab4c3af986536926"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:53a3f10b62c2d569e260f96e8d966463dec1a50fa4f1b22aec69e3f91025060e"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:950f531ec6e03696a2414b6308f5c6ff9dab7821a768c9d5788b1314e9a46ca7"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ae6deb336cba5d71476caa029ceb6e88047fc1dc74b62b7c4012639c0b563906"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1021223c08ba8d2d38d71ec1704496471ffd7be42cfb26b87cd5059323a389a1"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:752fb40efc7cc8d88ebc332b8f4bcbe2b5cc7e881bccfeb8e25054c00c994ee3"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a2e8f3f955d68471fa37b0e3add18500790d129cc7efe89971b8a4cc6fdeb0b2"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b8ca4d854adcf480bdfd80f46fdd6fb49f91dd020ae11c89b3a79e19454ec627"},
{file = "watchdog-5.0.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:90a67d7857adb1d985aca232cc9905dd5bc4803ed85cfcdcfcf707e52049eda7"},
{file = "watchdog-5.0.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:720ef9d3a4f9ca575a780af283c8fd3a0674b307651c1976714745090da5a9e8"},
{file = "watchdog-5.0.3-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:223160bb359281bb8e31c8f1068bf71a6b16a8ad3d9524ca6f523ac666bb6a1e"},
{file = "watchdog-5.0.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:560135542c91eaa74247a2e8430cf83c4342b29e8ad4f520ae14f0c8a19cfb5b"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:dd021efa85970bd4824acacbb922066159d0f9e546389a4743d56919b6758b91"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_armv7l.whl", hash = "sha256:78864cc8f23dbee55be34cc1494632a7ba30263951b5b2e8fc8286b95845f82c"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_i686.whl", hash = "sha256:1e9679245e3ea6498494b3028b90c7b25dbb2abe65c7d07423ecfc2d6218ff7c"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_ppc64.whl", hash = "sha256:9413384f26b5d050b6978e6fcd0c1e7f0539be7a4f1a885061473c5deaa57221"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:294b7a598974b8e2c6123d19ef15de9abcd282b0fbbdbc4d23dfa812959a9e05"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_s390x.whl", hash = "sha256:26dd201857d702bdf9d78c273cafcab5871dd29343748524695cecffa44a8d97"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:0f9332243355643d567697c3e3fa07330a1d1abf981611654a1f2bf2175612b7"},
{file = "watchdog-5.0.3-py3-none-win32.whl", hash = "sha256:c66f80ee5b602a9c7ab66e3c9f36026590a0902db3aea414d59a2f55188c1f49"},
{file = "watchdog-5.0.3-py3-none-win_amd64.whl", hash = "sha256:f00b4cf737f568be9665563347a910f8bdc76f88c2970121c86243c8cfdf90e9"},
{file = "watchdog-5.0.3-py3-none-win_ia64.whl", hash = "sha256:49f4d36cb315c25ea0d946e018c01bb028048023b9e103d3d3943f58e109dd45"},
{file = "watchdog-5.0.3.tar.gz", hash = "sha256:108f42a7f0345042a854d4d0ad0834b741d421330d5f575b81cb27b883500176"},
]
[package.extras]
@ -1318,4 +1333,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "3e91787d600949b4f2530c3eb00c854945aec2a8ce8977b724360b4d084db133"
content-hash = "ff45241f112ccfe1ca845dfc9685f7b63d7cb0a362acffbb17d81ff4691af9d4"

4
libs/partners/anthropic/pyproject.toml
View File

@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-anthropic"
version = "0.2.1"
version = "0.2.3"
description = "An integration package connecting AnthropicMessages and LangChain"
authors = []
readme = "README.md"
@ -21,7 +21,7 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
anthropic = ">=0.30.0,<1"
langchain-core = "^0.3.1"
langchain-core = "^0.3.9"
pydantic = "^2.7.4"
[tool.ruff.lint]

112
libs/partners/anthropic/tests/integration_tests/test_standard.py
View File

@ -1,12 +1,16 @@
"""Standard LangChain interface tests"""
from typing import Type
from pathlib import Path
from typing import Dict, List, Literal, Type, cast
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_standard_tests.integration_tests import ChatModelIntegrationTests
from langchain_anthropic import ChatAnthropic
REPO_ROOT_DIR = Path(__file__).parents[5]
class TestAnthropicStandard(ChatModelIntegrationTests):
@property
@ -28,3 +32,109 @@ class TestAnthropicStandard(ChatModelIntegrationTests):
@property
def supports_anthropic_inputs(self) -> bool:
return True
@property
def supported_usage_metadata_details(
self,
) -> Dict[
Literal["invoke", "stream"],
List[
Literal[
"audio_input",
"audio_output",
"reasoning_output",
"cache_read_input",
"cache_creation_input",
]
],
]:
return {
"invoke": ["cache_read_input", "cache_creation_input"],
"stream": ["cache_read_input", "cache_creation_input"],
}
def invoke_with_cache_creation_input(self, *, stream: bool = False) -> AIMessage:
llm = ChatAnthropic(
model="claude-3-5-sonnet-20240620", # type: ignore[call-arg]
extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"}, # type: ignore[call-arg]
)
with open(REPO_ROOT_DIR / "README.md", "r") as f:
readme = f.read()
input_ = f"""What's langchain? Here's the langchain README:
{readme}
"""
return _invoke(
llm,
[
{
"role": "user",
"content": [
{
"type": "text",
"text": input_,
"cache_control": {"type": "ephemeral"},
}
],
}
],
stream,
)
def invoke_with_cache_read_input(self, *, stream: bool = False) -> AIMessage:
llm = ChatAnthropic(
model="claude-3-5-sonnet-20240620", # type: ignore[call-arg]
extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"}, # type: ignore[call-arg]
)
with open(REPO_ROOT_DIR / "README.md", "r") as f:
readme = f.read()
input_ = f"""What's langchain? Here's the langchain README:
{readme}
"""
# invoke twice so first invocation is cached
_invoke(
llm,
[
{
"role": "user",
"content": [
{
"type": "text",
"text": input_,
"cache_control": {"type": "ephemeral"},
}
],
}
],
stream,
)
return _invoke(
llm,
[
{
"role": "user",
"content": [
{
"type": "text",
"text": input_,
"cache_control": {"type": "ephemeral"},
}
],
}
],
stream,
)
def _invoke(llm: ChatAnthropic, input_: list, stream: bool) -> AIMessage:
if stream:
full = None
for chunk in llm.stream(input_):
full = full + chunk if full else chunk # type: ignore[operator]
return cast(AIMessage, full)
else:
return cast(AIMessage, llm.invoke(input_))

94
libs/partners/anthropic/tests/unit_tests/test_chat_models.py
View File

@ -5,8 +5,11 @@ from typing import Any, Callable, Dict, Literal, Type, cast
import pytest
from anthropic.types import Message, TextBlock, Usage
from anthropic.types.beta.prompt_caching import (
PromptCachingBetaMessage,
PromptCachingBetaUsage,
)
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.runnables import RunnableBinding
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field, SecretStr
@ -58,9 +61,12 @@ def test_anthropic_model_kwargs() -> None:
@pytest.mark.requires("anthropic")
def test_anthropic_invalid_model_kwargs() -> None:
with pytest.raises(ValueError):
ChatAnthropic(model="foo", model_kwargs={"max_tokens_to_sample": 5}) # type: ignore[call-arg]
def test_anthropic_fields_in_model_kwargs() -> None:
"""Test that for backwards compatibility fields can be passed in as model_kwargs."""
llm = ChatAnthropic(model="foo", model_kwargs={"max_tokens_to_sample": 5}) # type: ignore[call-arg]
assert llm.max_tokens == 5
llm = ChatAnthropic(model="foo", model_kwargs={"max_tokens": 5}) # type: ignore[call-arg]
assert llm.max_tokens == 5
@pytest.mark.requires("anthropic")
@ -89,30 +95,49 @@ def test__format_output() -> None:
usage=Usage(input_tokens=2, output_tokens=1),
type="message",
)
expected = ChatResult(
generations=[
ChatGeneration(
message=AIMessage( # type: ignore[misc]
"bar",
usage_metadata={
"input_tokens": 2,
"output_tokens": 1,
"total_tokens": 3,
},
)
),
],
llm_output={
"id": "foo",
"model": "baz",
"stop_reason": None,
"stop_sequence": None,
"usage": {"input_tokens": 2, "output_tokens": 1},
expected = AIMessage( # type: ignore[misc]
"bar",
usage_metadata={
"input_tokens": 2,
"output_tokens": 1,
"total_tokens": 3,
"input_token_details": {},
},
)
llm = ChatAnthropic(model="test", anthropic_api_key="test") # type: ignore[call-arg, call-arg]
actual = llm._format_output(anthropic_msg)
assert expected == actual
assert actual.generations[0].message == expected
def test__format_output_cached() -> None:
anthropic_msg = PromptCachingBetaMessage(
id="foo",
content=[TextBlock(type="text", text="bar")],
model="baz",
role="assistant",
stop_reason=None,
stop_sequence=None,
usage=PromptCachingBetaUsage(
input_tokens=2,
output_tokens=1,
cache_creation_input_tokens=3,
cache_read_input_tokens=4,
),
type="message",
)
expected = AIMessage( # type: ignore[misc]
"bar",
usage_metadata={
"input_tokens": 9,
"output_tokens": 1,
"total_tokens": 10,
"input_token_details": {"cache_creation": 3, "cache_read": 4},
},
)
llm = ChatAnthropic(model="test", anthropic_api_key="test") # type: ignore[call-arg, call-arg]
actual = llm._format_output(anthropic_msg)
assert actual.generations[0].message == expected
def test__merge_messages() -> None:
@ -137,6 +162,13 @@ def test__merge_messages() -> None:
"text": None,
"name": "blah",
},
{
"tool_input": {"a": "c"},
"type": "tool_use",
"id": "3",
"text": None,
"name": "blah",
},
]
),
ToolMessage("buz output", tool_call_id="1", status="error"), # type: ignore[misc]
@ -153,6 +185,7 @@ def test__merge_messages() -> None:
],
tool_call_id="2",
), # type: ignore[misc]
ToolMessage([], tool_call_id="3"), # type: ignore[misc]
HumanMessage("next thing"), # type: ignore[misc]
]
expected = [
@ -176,6 +209,13 @@ def test__merge_messages() -> None:
"text": None,
"name": "blah",
},
{
"tool_input": {"a": "c"},
"type": "tool_use",
"id": "3",
"text": None,
"name": "blah",
},
]
),
HumanMessage( # type: ignore[misc]
@ -201,6 +241,12 @@ def test__merge_messages() -> None:
"tool_use_id": "2",
"is_error": False,
},
{
"type": "tool_result",
"content": [],
"tool_use_id": "3",
"is_error": False,
},
{"type": "text", "text": "next thing"},
]
),

24
libs/partners/box/langchain_box/retrievers/box.py
View File

@ -3,7 +3,8 @@ from typing import List, Optional
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from pydantic import ConfigDict, model_validator
from langchain_core.utils import from_env
from pydantic import ConfigDict, Field, model_validator
from typing_extensions import Self
from langchain_box.utilities import BoxAuth, BoxSearchOptions, _BoxAPIWrapper
@ -113,8 +114,9 @@ class BoxRetriever(BaseRetriever):
he decides to go to the pool with Carlos.'
""" # noqa: E501
box_developer_token: Optional[str] = None
"""String containing the Box Developer Token generated in the developer console"""
box_developer_token: Optional[str] = Field(
default_factory=from_env("BOX_DEVELOPER_TOKEN", default=None)
)
box_auth: Optional[BoxAuth] = None
"""Configured
@ -131,6 +133,15 @@ class BoxRetriever(BaseRetriever):
box_search_options: Optional[BoxSearchOptions] = None
"""Search options to configure BoxRetriever to narrow search results."""
answer: Optional[bool] = True
"""When using Box AI, return the answer to the prompt as a `Document`
object. Returned as `List[Document`]. Default is `True`."""
citations: Optional[bool] = False
"""When using Box AI, return the citations from to the prompt as
`Document` objects. Can be used with answer. Returned as `List[Document`].
Default is `False`."""
_box: Optional[_BoxAPIWrapper]
model_config = ConfigDict(
@ -164,6 +175,11 @@ class BoxRetriever(BaseRetriever):
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
if self.box_file_ids: # If using Box AI
return self._box.ask_box_ai(query=query, box_file_ids=self.box_file_ids) # type: ignore[union-attr]
return self._box.ask_box_ai( # type: ignore[union-attr]
query=query,
box_file_ids=self.box_file_ids,
answer=self.answer, # type: ignore[arg-type]
citations=self.citations, # type: ignore[arg-type]
)
else: # If using Search
return self._box.search_box(query=query) # type: ignore[union-attr]

47
libs/partners/box/langchain_box/utilities/box.py
View File

@ -805,7 +805,13 @@ class _BoxAPIWrapper(BaseModel):
f"BoxSDKError: Error getting search results: {bse.message}"
)
def ask_box_ai(self, query: str, box_file_ids: List[str]) -> List[Document]:
def ask_box_ai(
self,
query: str,
box_file_ids: List[str],
answer: bool = True,
citations: bool = False,
) -> List[Document]:
if self._box is None:
self.get_box_client()
@ -819,13 +825,16 @@ class _BoxAPIWrapper(BaseModel):
items = []
for file_id in box_file_ids:
item = box_sdk_gen.CreateAiAskItems(
id=file_id, type=box_sdk_gen.CreateAiAskItemsTypeField.FILE.value
item = box_sdk_gen.AiItemBase(
id=file_id, type=box_sdk_gen.AiItemBaseTypeField.FILE.value
)
items.append(item)
try:
response = self._box.ai.create_ai_ask(ai_mode, query, items) # type: ignore[union-attr]
response = self._box.ai.create_ai_ask( # type: ignore[union-attr]
mode=ai_mode, prompt=query, items=items, include_citations=citations
)
except box_sdk_gen.BoxAPIError as bae:
raise RuntimeError(
f"BoxAPIError: Error getting Box AI result: {bae.message}"
@ -835,8 +844,32 @@ class _BoxAPIWrapper(BaseModel):
f"BoxSDKError: Error getting Box AI result: {bse.message}"
)
content = response.answer
docs = []
metadata = {"source": "Box AI", "title": f"Box AI {query}"}
if answer:
content = response.answer
metadata = {"source": "Box AI", "title": f"Box AI {query}"}
return [Document(page_content=content, metadata=metadata)]
document = Document(page_content=content, metadata=metadata)
docs.append(document)
if citations:
box_citations = response.citations
for citation in box_citations:
content = citation.content
file_name = citation.name
file_id = citation.id
file_type = citation.type.value
metadata = {
"source": f"Box AI {query}",
"file_name": file_name,
"file_id": file_id,
"file_type": file_type,
}
document = Document(page_content=content, metadata=metadata)
docs.append(document)
return docs

32
libs/partners/box/poetry.lock generated
View File

@ -13,13 +13,13 @@ files = [
[[package]]
name = "anyio"
version = "4.4.0"
version = "4.6.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
{file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
{file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
{file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
]
[package.dependencies]
@ -29,19 +29,19 @@ sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (>=0.23)"]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
trio = ["trio (>=0.26.1)"]
[[package]]
name = "box-sdk-gen"
version = "1.5.0"
version = "1.5.1"
description = "Official Box Python Generated SDK"
optional = false
python-versions = "*"
files = [
{file = "box_sdk_gen-1.5.0-py3-none-any.whl", hash = "sha256:37763ace35fcb02ef84e28b4f61a93fe0d74ae22ceb9e1815ec1309b737cc016"},
{file = "box_sdk_gen-1.5.0.tar.gz", hash = "sha256:49440a5dc4744261a877d149f0cad0a68f6096c2ad2a0c3392fe443269adca5f"},
{file = "box_sdk_gen-1.5.1-py3-none-any.whl", hash = "sha256:3aba4615940566df86a236781ac34defd33ac127b9027a8a73775997b6a1ef97"},
{file = "box_sdk_gen-1.5.1.tar.gz", hash = "sha256:2171b5a9b9d93014aecd4a883767459839515ecab18c6358868a5457401d896e"},
]
[package.dependencies]
@ -444,7 +444,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.3.1"
version = "0.3.6"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@ -453,7 +453,7 @@ develop = true
[package.dependencies]
jsonpatch = "^1.33"
langsmith = "^0.1.117"
langsmith = "^0.1.125"
packaging = ">=23.2,<25"
pydantic = [
{version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
@ -469,13 +469,13 @@ url = "../../core"
[[package]]
name = "langsmith"
version = "0.1.122"
version = "0.1.129"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langsmith-0.1.122-py3-none-any.whl", hash = "sha256:9c9cde442d7321e8557f5c45c14b1b643b8aa28acc3f844d3a0021a9571aad7c"},
{file = "langsmith-0.1.122.tar.gz", hash = "sha256:56dff727ca529fe8df300e6e4759dc920efe10ab8cd602b4d6b51e33599214e6"},
{file = "langsmith-0.1.129-py3-none-any.whl", hash = "sha256:31393fbbb17d6be5b99b9b22d530450094fab23c6c37281a6a6efb2143d05347"},
{file = "langsmith-0.1.129.tar.gz", hash = "sha256:6c3ba66471bef41b9f87da247cc0b493268b3f54656f73648a256a205261b6a0"},
]
[package.dependencies]
@ -1076,4 +1076,4 @@ zstd = ["zstandard (>=0.18.0)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9.0,<3.13"
content-hash = "5fc4a4313b1be5863c4abe9d237ce4eef744c4ad9173a7496e138020db6673de"
content-hash = "fe5ad1ad0e68ef281c8fc11b19ddb9494343dd4931a9b33804bb9698fa5d3b3d"

2
libs/partners/box/pyproject.toml
View File

@ -14,7 +14,7 @@ license = "MIT"
[tool.poetry.dependencies]
python = ">=3.9.0,<3.13"
langchain-core = "^0.3.1"
box-sdk-gen = { extras = ["jwt"], version = "^1.1.0" }
box-sdk-gen = { extras = ["jwt"], version = "^1.5.0" }
pydantic = "^2"
[tool.poetry.group.test]

70
libs/partners/box/tests/unit_tests/retrievers/test_box_retriever.py
View File

@ -131,3 +131,73 @@ def test_ai(mocker: MockerFixture) -> None:
metadata={"title": "Testing Files"},
)
]
# test ai retrieval with answer and citations
def test_ai_answer_citations(mocker: MockerFixture) -> None:
mocker.patch(
"langchain_box.utilities._BoxAPIWrapper.ask_box_ai",
return_value=(
[
Document(
page_content="Test file mode\ndocument contents",
metadata={"title": "Testing Files"},
),
Document(page_content="citation 1", metadata={"source": "source 1"}),
Document(page_content="citation 2", metadata={"source": "source 2"}),
Document(page_content="citation 3", metadata={"source": "source 3"}),
Document(page_content="citation 4", metadata={"source": "source 4"}),
Document(page_content="citation 5", metadata={"source": "source 5"}),
]
),
)
retriever = BoxRetriever( # type: ignore[call-arg]
box_developer_token="box_developer_token",
box_file_ids=["box_file_ids"],
citations=True,
)
documents = retriever.invoke("query")
assert documents == [
Document(
page_content="Test file mode\ndocument contents",
metadata={"title": "Testing Files"},
),
Document(page_content="citation 1", metadata={"source": "source 1"}),
Document(page_content="citation 2", metadata={"source": "source 2"}),
Document(page_content="citation 3", metadata={"source": "source 3"}),
Document(page_content="citation 4", metadata={"source": "source 4"}),
Document(page_content="citation 5", metadata={"source": "source 5"}),
]
# test ai retrieval with citations only
def test_ai_citations_only(mocker: MockerFixture) -> None:
mocker.patch(
"langchain_box.utilities._BoxAPIWrapper.ask_box_ai",
return_value=(
[
Document(page_content="citation 1", metadata={"source": "source 1"}),
Document(page_content="citation 2", metadata={"source": "source 2"}),
Document(page_content="citation 3", metadata={"source": "source 3"}),
Document(page_content="citation 4", metadata={"source": "source 4"}),
Document(page_content="citation 5", metadata={"source": "source 5"}),
]
),
)
retriever = BoxRetriever( # type: ignore[call-arg]
box_developer_token="box_developer_token",
box_file_ids=["box_file_ids"],
citations=True,
)
documents = retriever.invoke("query")
assert documents == [
Document(page_content="citation 1", metadata={"source": "source 1"}),
Document(page_content="citation 2", metadata={"source": "source 2"}),
Document(page_content="citation 3", metadata={"source": "source 3"}),
Document(page_content="citation 4", metadata={"source": "source 4"}),
Document(page_content="citation 5", metadata={"source": "source 5"}),
]

7
libs/partners/fireworks/langchain_fireworks/chat_models.py
View File

@ -79,7 +79,7 @@ from langchain_core.utils.function_calling import (
convert_to_openai_tool,
)
from langchain_core.utils.pydantic import is_basemodel_subclass
from langchain_core.utils.utils import build_extra_kwargs, from_env, secret_from_env
from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env
from pydantic import (
BaseModel,
ConfigDict,
@ -366,10 +366,7 @@ class ChatFireworks(BaseChatModel):
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="after")

7
libs/partners/fireworks/langchain_fireworks/llms.py
View File

@ -11,7 +11,7 @@ from langchain_core.callbacks import (
)
from langchain_core.language_models.llms import LLM
from langchain_core.utils import get_pydantic_field_names
from langchain_core.utils.utils import build_extra_kwargs, secret_from_env
from langchain_core.utils.utils import _build_model_kwargs, secret_from_env
from pydantic import ConfigDict, Field, SecretStr, model_validator
from langchain_fireworks.version import __version__
@ -93,10 +93,7 @@ class Fireworks(LLM):
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@property

716
libs/partners/fireworks/poetry.lock generated
View File

@ -1,114 +1,114 @@
# This file is automatically @generated by Poetry 1.8.3 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 = "aiohappyeyeballs"
version = "2.4.0"
version = "2.4.3"
description = "Happy Eyeballs for asyncio"
optional = false
python-versions = ">=3.8"
files = [
{file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"},
{file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
{file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
{file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
]
[[package]]
name = "aiohttp"
version = "3.10.5"
version = "3.10.9"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
files = [
{file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:18a01eba2574fb9edd5f6e5fb25f66e6ce061da5dab5db75e13fe1558142e0a3"},
{file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:94fac7c6e77ccb1ca91e9eb4cb0ac0270b9fb9b289738654120ba8cebb1189c6"},
{file = "aiohttp-3.10.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2f1f1c75c395991ce9c94d3e4aa96e5c59c8356a15b1c9231e783865e2772699"},
{file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f7acae3cf1a2a2361ec4c8e787eaaa86a94171d2417aae53c0cca6ca3118ff6"},
{file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:94c4381ffba9cc508b37d2e536b418d5ea9cfdc2848b9a7fea6aebad4ec6aac1"},
{file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c31ad0c0c507894e3eaa843415841995bf8de4d6b2d24c6e33099f4bc9fc0d4f"},
{file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0912b8a8fadeb32ff67a3ed44249448c20148397c1ed905d5dac185b4ca547bb"},
{file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0d93400c18596b7dc4794d48a63fb361b01a0d8eb39f28800dc900c8fbdaca91"},
{file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d00f3c5e0d764a5c9aa5a62d99728c56d455310bcc288a79cab10157b3af426f"},
{file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:d742c36ed44f2798c8d3f4bc511f479b9ceef2b93f348671184139e7d708042c"},
{file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:814375093edae5f1cb31e3407997cf3eacefb9010f96df10d64829362ae2df69"},
{file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:8224f98be68a84b19f48e0bdc14224b5a71339aff3a27df69989fa47d01296f3"},
{file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d9a487ef090aea982d748b1b0d74fe7c3950b109df967630a20584f9a99c0683"},
{file = "aiohttp-3.10.5-cp310-cp310-win32.whl", hash = "sha256:d9ef084e3dc690ad50137cc05831c52b6ca428096e6deb3c43e95827f531d5ef"},
{file = "aiohttp-3.10.5-cp310-cp310-win_amd64.whl", hash = "sha256:66bf9234e08fe561dccd62083bf67400bdbf1c67ba9efdc3dac03650e97c6088"},
{file = "aiohttp-3.10.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:8c6a4e5e40156d72a40241a25cc226051c0a8d816610097a8e8f517aeacd59a2"},
{file = "aiohttp-3.10.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:2c634a3207a5445be65536d38c13791904fda0748b9eabf908d3fe86a52941cf"},
{file = "aiohttp-3.10.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4aff049b5e629ef9b3e9e617fa6e2dfeda1bf87e01bcfecaf3949af9e210105e"},
{file = "aiohttp-3.10.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1942244f00baaacaa8155eca94dbd9e8cc7017deb69b75ef67c78e89fdad3c77"},
{file = "aiohttp-3.10.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e04a1f2a65ad2f93aa20f9ff9f1b672bf912413e5547f60749fa2ef8a644e061"},
{file = "aiohttp-3.10.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7f2bfc0032a00405d4af2ba27f3c429e851d04fad1e5ceee4080a1c570476697"},
{file = "aiohttp-3.10.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:424ae21498790e12eb759040bbb504e5e280cab64693d14775c54269fd1d2bb7"},
{file = "aiohttp-3.10.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:975218eee0e6d24eb336d0328c768ebc5d617609affaca5dbbd6dd1984f16ed0"},
{file = "aiohttp-3.10.5-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:4120d7fefa1e2d8fb6f650b11489710091788de554e2b6f8347c7a20ceb003f5"},
{file = "aiohttp-3.10.5-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:b90078989ef3fc45cf9221d3859acd1108af7560c52397ff4ace8ad7052a132e"},
{file = "aiohttp-3.10.5-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:ba5a8b74c2a8af7d862399cdedce1533642fa727def0b8c3e3e02fcb52dca1b1"},
{file = "aiohttp-3.10.5-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:02594361128f780eecc2a29939d9dfc870e17b45178a867bf61a11b2a4367277"},
{file = "aiohttp-3.10.5-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:8fb4fc029e135859f533025bc82047334e24b0d489e75513144f25408ecaf058"},
{file = "aiohttp-3.10.5-cp311-cp311-win32.whl", hash = "sha256:e1ca1ef5ba129718a8fc827b0867f6aa4e893c56eb00003b7367f8a733a9b072"},
{file = "aiohttp-3.10.5-cp311-cp311-win_amd64.whl", hash = "sha256:349ef8a73a7c5665cca65c88ab24abe75447e28aa3bc4c93ea5093474dfdf0ff"},
{file = "aiohttp-3.10.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:305be5ff2081fa1d283a76113b8df7a14c10d75602a38d9f012935df20731487"},
{file = "aiohttp-3.10.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:3a1c32a19ee6bbde02f1cb189e13a71b321256cc1d431196a9f824050b160d5a"},
{file = "aiohttp-3.10.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:61645818edd40cc6f455b851277a21bf420ce347baa0b86eaa41d51ef58ba23d"},
{file = "aiohttp-3.10.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6c225286f2b13bab5987425558baa5cbdb2bc925b2998038fa028245ef421e75"},
{file = "aiohttp-3.10.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8ba01ebc6175e1e6b7275c907a3a36be48a2d487549b656aa90c8a910d9f3178"},
{file = "aiohttp-3.10.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8eaf44ccbc4e35762683078b72bf293f476561d8b68ec8a64f98cf32811c323e"},
{file = "aiohttp-3.10.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1c43eb1ab7cbf411b8e387dc169acb31f0ca0d8c09ba63f9eac67829585b44f"},
{file = "aiohttp-3.10.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:de7a5299827253023c55ea549444e058c0eb496931fa05d693b95140a947cb73"},
{file = "aiohttp-3.10.5-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:4790f0e15f00058f7599dab2b206d3049d7ac464dc2e5eae0e93fa18aee9e7bf"},
{file = "aiohttp-3.10.5-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:44b324a6b8376a23e6ba25d368726ee3bc281e6ab306db80b5819999c737d820"},
{file = "aiohttp-3.10.5-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:0d277cfb304118079e7044aad0b76685d30ecb86f83a0711fc5fb257ffe832ca"},
{file = "aiohttp-3.10.5-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:54d9ddea424cd19d3ff6128601a4a4d23d54a421f9b4c0fff740505813739a91"},
{file = "aiohttp-3.10.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4f1c9866ccf48a6df2b06823e6ae80573529f2af3a0992ec4fe75b1a510df8a6"},
{file = "aiohttp-3.10.5-cp312-cp312-win32.whl", hash = "sha256:dc4826823121783dccc0871e3f405417ac116055bf184ac04c36f98b75aacd12"},
{file = "aiohttp-3.10.5-cp312-cp312-win_amd64.whl", hash = "sha256:22c0a23a3b3138a6bf76fc553789cb1a703836da86b0f306b6f0dc1617398abc"},
{file = "aiohttp-3.10.5-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:7f6b639c36734eaa80a6c152a238242bedcee9b953f23bb887e9102976343092"},
{file = "aiohttp-3.10.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f29930bc2921cef955ba39a3ff87d2c4398a0394ae217f41cb02d5c26c8b1b77"},
{file = "aiohttp-3.10.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f489a2c9e6455d87eabf907ac0b7d230a9786be43fbe884ad184ddf9e9c1e385"},
{file = "aiohttp-3.10.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:123dd5b16b75b2962d0fff566effb7a065e33cd4538c1692fb31c3bda2bfb972"},
{file = "aiohttp-3.10.5-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b98e698dc34966e5976e10bbca6d26d6724e6bdea853c7c10162a3235aba6e16"},
{file = "aiohttp-3.10.5-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c3b9162bab7e42f21243effc822652dc5bb5e8ff42a4eb62fe7782bcbcdfacf6"},
{file = "aiohttp-3.10.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1923a5c44061bffd5eebeef58cecf68096e35003907d8201a4d0d6f6e387ccaa"},
{file = "aiohttp-3.10.5-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d55f011da0a843c3d3df2c2cf4e537b8070a419f891c930245f05d329c4b0689"},
{file = "aiohttp-3.10.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:afe16a84498441d05e9189a15900640a2d2b5e76cf4efe8cbb088ab4f112ee57"},
{file = "aiohttp-3.10.5-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:f8112fb501b1e0567a1251a2fd0747baae60a4ab325a871e975b7bb67e59221f"},
{file = "aiohttp-3.10.5-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:1e72589da4c90337837fdfe2026ae1952c0f4a6e793adbbfbdd40efed7c63599"},
{file = "aiohttp-3.10.5-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:4d46c7b4173415d8e583045fbc4daa48b40e31b19ce595b8d92cf639396c15d5"},
{file = "aiohttp-3.10.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:33e6bc4bab477c772a541f76cd91e11ccb6d2efa2b8d7d7883591dfb523e5987"},
{file = "aiohttp-3.10.5-cp313-cp313-win32.whl", hash = "sha256:c58c6837a2c2a7cf3133983e64173aec11f9c2cd8e87ec2fdc16ce727bcf1a04"},
{file = "aiohttp-3.10.5-cp313-cp313-win_amd64.whl", hash = "sha256:38172a70005252b6893088c0f5e8a47d173df7cc2b2bd88650957eb84fcf5022"},
{file = "aiohttp-3.10.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:f6f18898ace4bcd2d41a122916475344a87f1dfdec626ecde9ee802a711bc569"},
{file = "aiohttp-3.10.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5ede29d91a40ba22ac1b922ef510aab871652f6c88ef60b9dcdf773c6d32ad7a"},
{file = "aiohttp-3.10.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:673f988370f5954df96cc31fd99c7312a3af0a97f09e407399f61583f30da9bc"},
{file = "aiohttp-3.10.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:58718e181c56a3c02d25b09d4115eb02aafe1a732ce5714ab70326d9776457c3"},
{file = "aiohttp-3.10.5-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4b38b1570242fbab8d86a84128fb5b5234a2f70c2e32f3070143a6d94bc854cf"},
{file = "aiohttp-3.10.5-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:074d1bff0163e107e97bd48cad9f928fa5a3eb4b9d33366137ffce08a63e37fe"},
{file = "aiohttp-3.10.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd31f176429cecbc1ba499d4aba31aaccfea488f418d60376b911269d3b883c5"},
{file = "aiohttp-3.10.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7384d0b87d4635ec38db9263e6a3f1eb609e2e06087f0aa7f63b76833737b471"},
{file = "aiohttp-3.10.5-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:8989f46f3d7ef79585e98fa991e6ded55d2f48ae56d2c9fa5e491a6e4effb589"},
{file = "aiohttp-3.10.5-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:c83f7a107abb89a227d6c454c613e7606c12a42b9a4ca9c5d7dad25d47c776ae"},
{file = "aiohttp-3.10.5-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:cde98f323d6bf161041e7627a5fd763f9fd829bcfcd089804a5fdce7bb6e1b7d"},
{file = "aiohttp-3.10.5-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:676f94c5480d8eefd97c0c7e3953315e4d8c2b71f3b49539beb2aa676c58272f"},
{file = "aiohttp-3.10.5-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:2d21ac12dc943c68135ff858c3a989f2194a709e6e10b4c8977d7fcd67dfd511"},
{file = "aiohttp-3.10.5-cp38-cp38-win32.whl", hash = "sha256:17e997105bd1a260850272bfb50e2a328e029c941c2708170d9d978d5a30ad9a"},
{file = "aiohttp-3.10.5-cp38-cp38-win_amd64.whl", hash = "sha256:1c19de68896747a2aa6257ae4cf6ef59d73917a36a35ee9d0a6f48cff0f94db8"},
{file = "aiohttp-3.10.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7e2fe37ac654032db1f3499fe56e77190282534810e2a8e833141a021faaab0e"},
{file = "aiohttp-3.10.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f5bf3ead3cb66ab990ee2561373b009db5bc0e857549b6c9ba84b20bc462e172"},
{file = "aiohttp-3.10.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1b2c16a919d936ca87a3c5f0e43af12a89a3ce7ccbce59a2d6784caba945b68b"},
{file = "aiohttp-3.10.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ad146dae5977c4dd435eb31373b3fe9b0b1bf26858c6fc452bf6af394067e10b"},
{file = "aiohttp-3.10.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8c5c6fa16412b35999320f5c9690c0f554392dc222c04e559217e0f9ae244b92"},
{file = "aiohttp-3.10.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:95c4dc6f61d610bc0ee1edc6f29d993f10febfe5b76bb470b486d90bbece6b22"},
{file = "aiohttp-3.10.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da452c2c322e9ce0cfef392e469a26d63d42860f829026a63374fde6b5c5876f"},
{file = "aiohttp-3.10.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:898715cf566ec2869d5cb4d5fb4be408964704c46c96b4be267442d265390f32"},
{file = "aiohttp-3.10.5-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:391cc3a9c1527e424c6865e087897e766a917f15dddb360174a70467572ac6ce"},
{file = "aiohttp-3.10.5-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:380f926b51b92d02a34119d072f178d80bbda334d1a7e10fa22d467a66e494db"},
{file = "aiohttp-3.10.5-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:ce91db90dbf37bb6fa0997f26574107e1b9d5ff939315247b7e615baa8ec313b"},
{file = "aiohttp-3.10.5-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:9093a81e18c45227eebe4c16124ebf3e0d893830c6aca7cc310bfca8fe59d857"},
{file = "aiohttp-3.10.5-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:ee40b40aa753d844162dcc80d0fe256b87cba48ca0054f64e68000453caead11"},
{file = "aiohttp-3.10.5-cp39-cp39-win32.whl", hash = "sha256:03f2645adbe17f274444953bdea69f8327e9d278d961d85657cb0d06864814c1"},
{file = "aiohttp-3.10.5-cp39-cp39-win_amd64.whl", hash = "sha256:d17920f18e6ee090bdd3d0bfffd769d9f2cb4c8ffde3eb203777a3895c128862"},
{file = "aiohttp-3.10.5.tar.gz", hash = "sha256:f071854b47d39591ce9a17981c46790acb30518e2f83dfca8db2dfa091178691"},
{file = "aiohttp-3.10.9-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8b3fb28a9ac8f2558760d8e637dbf27aef1e8b7f1d221e8669a1074d1a266bb2"},
{file = "aiohttp-3.10.9-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:91aa966858593f64c8a65cdefa3d6dc8fe3c2768b159da84c1ddbbb2c01ab4ef"},
{file = "aiohttp-3.10.9-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:63649309da83277f06a15bbdc2a54fbe75efb92caa2c25bb57ca37762789c746"},
{file = "aiohttp-3.10.9-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e3e7fabedb3fe06933f47f1538df7b3a8d78e13d7167195f51ca47ee12690373"},
{file = "aiohttp-3.10.9-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5c070430fda1a550a1c3a4c2d7281d3b8cfc0c6715f616e40e3332201a253067"},
{file = "aiohttp-3.10.9-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:51d0a4901b27272ae54e42067bc4b9a90e619a690b4dc43ea5950eb3070afc32"},
{file = "aiohttp-3.10.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fec5fac7aea6c060f317f07494961236434928e6f4374e170ef50b3001e14581"},
{file = "aiohttp-3.10.9-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:172ad884bb61ad31ed7beed8be776eb17e7fb423f1c1be836d5cb357a096bf12"},
{file = "aiohttp-3.10.9-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d646fdd74c25bbdd4a055414f0fe32896c400f38ffbdfc78c68e62812a9e0257"},
{file = "aiohttp-3.10.9-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:e86260b76786c28acf0b5fe31c8dca4c2add95098c709b11e8c35b424ebd4f5b"},
{file = "aiohttp-3.10.9-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:c7d7cafc11d70fdd8801abfc2ff276744ae4cb39d8060b6b542c7e44e5f2cfc2"},
{file = "aiohttp-3.10.9-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:fc262c3df78c8ff6020c782d9ce02e4bcffe4900ad71c0ecdad59943cba54442"},
{file = "aiohttp-3.10.9-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:482c85cf3d429844396d939b22bc2a03849cb9ad33344689ad1c85697bcba33a"},
{file = "aiohttp-3.10.9-cp310-cp310-win32.whl", hash = "sha256:aeebd3061f6f1747c011e1d0b0b5f04f9f54ad1a2ca183e687e7277bef2e0da2"},
{file = "aiohttp-3.10.9-cp310-cp310-win_amd64.whl", hash = "sha256:fa430b871220dc62572cef9c69b41e0d70fcb9d486a4a207a5de4c1f25d82593"},
{file = "aiohttp-3.10.9-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:16e6a51d8bc96b77f04a6764b4ad03eeef43baa32014fce71e882bd71302c7e4"},
{file = "aiohttp-3.10.9-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8bd9125dd0cc8ebd84bff2be64b10fdba7dc6fd7be431b5eaf67723557de3a31"},
{file = "aiohttp-3.10.9-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dcf354661f54e6a49193d0b5653a1b011ba856e0b7a76bda2c33e4c6892f34ea"},
{file = "aiohttp-3.10.9-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42775de0ca04f90c10c5c46291535ec08e9bcc4756f1b48f02a0657febe89b10"},
{file = "aiohttp-3.10.9-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:87d1e4185c5d7187684d41ebb50c9aeaaaa06ca1875f4c57593071b0409d2444"},
{file = "aiohttp-3.10.9-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c2695c61cf53a5d4345a43d689f37fc0f6d3a2dc520660aec27ec0f06288d1f9"},
{file = "aiohttp-3.10.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8a3f063b41cc06e8d0b3fcbbfc9c05b7420f41287e0cd4f75ce0a1f3d80729e6"},
{file = "aiohttp-3.10.9-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2d37f4718002863b82c6f391c8efd4d3a817da37030a29e2682a94d2716209de"},
{file = "aiohttp-3.10.9-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:2746d8994ebca1bdc55a1e998feff4e94222da709623bb18f6e5cfec8ec01baf"},
{file = "aiohttp-3.10.9-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:6f3c6648aa123bcd73d6f26607d59967b607b0da8ffcc27d418a4b59f4c98c7c"},
{file = "aiohttp-3.10.9-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:558b3d223fd631ad134d89adea876e7fdb4c93c849ef195049c063ada82b7d08"},
{file = "aiohttp-3.10.9-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:4e6cb75f8ddd9c2132d00bc03c9716add57f4beff1263463724f6398b813e7eb"},
{file = "aiohttp-3.10.9-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:608cecd8d58d285bfd52dbca5b6251ca8d6ea567022c8a0eaae03c2589cd9af9"},
{file = "aiohttp-3.10.9-cp311-cp311-win32.whl", hash = "sha256:36d4fba838be5f083f5490ddd281813b44d69685db910907636bc5dca6322316"},
{file = "aiohttp-3.10.9-cp311-cp311-win_amd64.whl", hash = "sha256:8be1a65487bdfc285bd5e9baf3208c2132ca92a9b4020e9f27df1b16fab998a9"},
{file = "aiohttp-3.10.9-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:4fd16b30567c5b8e167923be6e027eeae0f20cf2b8a26b98a25115f28ad48ee0"},
{file = "aiohttp-3.10.9-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:40ff5b7660f903dc587ed36ef08a88d46840182d9d4b5694e7607877ced698a1"},
{file = "aiohttp-3.10.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4edc3fd701e2b9a0d605a7b23d3de4ad23137d23fc0dbab726aa71d92f11aaaf"},
{file = "aiohttp-3.10.9-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e525b69ee8a92c146ae5b4da9ecd15e518df4d40003b01b454ad694a27f498b5"},
{file = "aiohttp-3.10.9-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5002a02c17fcfd796d20bac719981d2fca9c006aac0797eb8f430a58e9d12431"},
{file = "aiohttp-3.10.9-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fd4ceeae2fb8cabdd1b71c82bfdd39662473d3433ec95b962200e9e752fb70d0"},
{file = "aiohttp-3.10.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d6e395c3d1f773cf0651cd3559e25182eb0c03a2777b53b4575d8adc1149c6e9"},
{file = "aiohttp-3.10.9-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bbdb8def5268f3f9cd753a265756f49228a20ed14a480d151df727808b4531dd"},
{file = "aiohttp-3.10.9-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f82ace0ec57c94aaf5b0e118d4366cff5889097412c75aa14b4fd5fc0c44ee3e"},
{file = "aiohttp-3.10.9-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:6ebdc3b3714afe1b134b3bbeb5f745eed3ecbcff92ab25d80e4ef299e83a5465"},
{file = "aiohttp-3.10.9-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:f9ca09414003c0e96a735daa1f071f7d7ed06962ef4fa29ceb6c80d06696d900"},
{file = "aiohttp-3.10.9-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:1298b854fd31d0567cbb916091be9d3278168064fca88e70b8468875ef9ff7e7"},
{file = "aiohttp-3.10.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:60ad5b8a7452c0f5645c73d4dad7490afd6119d453d302cd5b72b678a85d6044"},
{file = "aiohttp-3.10.9-cp312-cp312-win32.whl", hash = "sha256:1a0ee6c0d590c917f1b9629371fce5f3d3f22c317aa96fbdcce3260754d7ea21"},
{file = "aiohttp-3.10.9-cp312-cp312-win_amd64.whl", hash = "sha256:c46131c6112b534b178d4e002abe450a0a29840b61413ac25243f1291613806a"},
{file = "aiohttp-3.10.9-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:2bd9f3eac515c16c4360a6a00c38119333901b8590fe93c3257a9b536026594d"},
{file = "aiohttp-3.10.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8cc0d13b4e3b1362d424ce3f4e8c79e1f7247a00d792823ffd640878abf28e56"},
{file = "aiohttp-3.10.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:ba1a599255ad6a41022e261e31bc2f6f9355a419575b391f9655c4d9e5df5ff5"},
{file = "aiohttp-3.10.9-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:776e9f3c9b377fcf097c4a04b241b15691e6662d850168642ff976780609303c"},
{file = "aiohttp-3.10.9-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8debb45545ad95b58cc16c3c1cc19ad82cffcb106db12b437885dbee265f0ab5"},
{file = "aiohttp-3.10.9-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c2555e4949c8d8782f18ef20e9d39730d2656e218a6f1a21a4c4c0b56546a02e"},
{file = "aiohttp-3.10.9-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c54dc329cd44f7f7883a9f4baaefe686e8b9662e2c6c184ea15cceee587d8d69"},
{file = "aiohttp-3.10.9-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e709d6ac598c5416f879bb1bae3fd751366120ac3fa235a01de763537385d036"},
{file = "aiohttp-3.10.9-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:17c272cfe7b07a5bb0c6ad3f234e0c336fb53f3bf17840f66bd77b5815ab3d16"},
{file = "aiohttp-3.10.9-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:0c21c82df33b264216abffff9f8370f303dab65d8eee3767efbbd2734363f677"},
{file = "aiohttp-3.10.9-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:9331dd34145ff105177855017920dde140b447049cd62bb589de320fd6ddd582"},
{file = "aiohttp-3.10.9-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:ac3196952c673822ebed8871cf8802e17254fff2a2ed4835d9c045d9b88c5ec7"},
{file = "aiohttp-3.10.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2c33fa6e10bb7ed262e3ff03cc69d52869514f16558db0626a7c5c61dde3c29f"},
{file = "aiohttp-3.10.9-cp313-cp313-win32.whl", hash = "sha256:a14e4b672c257a6b94fe934ee62666bacbc8e45b7876f9dd9502d0f0fe69db16"},
{file = "aiohttp-3.10.9-cp313-cp313-win_amd64.whl", hash = "sha256:a35ed3d03910785f7d9d6f5381f0c24002b2b888b298e6f941b2fc94c5055fcd"},
{file = "aiohttp-3.10.9-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5f392ef50e22c31fa49b5a46af7f983fa3f118f3eccb8522063bee8bfa6755f8"},
{file = "aiohttp-3.10.9-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d1f5c9169e26db6a61276008582d945405b8316aae2bb198220466e68114a0f5"},
{file = "aiohttp-3.10.9-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:8d9d10d10ec27c0d46ddaecc3c5598c4db9ce4e6398ca872cdde0525765caa2f"},
{file = "aiohttp-3.10.9-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d97273a52d7f89a75b11ec386f786d3da7723d7efae3034b4dda79f6f093edc1"},
{file = "aiohttp-3.10.9-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d271f770b52e32236d945911b2082f9318e90ff835d45224fa9e28374303f729"},
{file = "aiohttp-3.10.9-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7003f33f5f7da1eb02f0446b0f8d2ccf57d253ca6c2e7a5732d25889da82b517"},
{file = "aiohttp-3.10.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6e00c8a92e7663ed2be6fcc08a2997ff06ce73c8080cd0df10cc0321a3168d7"},
{file = "aiohttp-3.10.9-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a61df62966ce6507aafab24e124e0c3a1cfbe23c59732987fc0fd0d71daa0b88"},
{file = "aiohttp-3.10.9-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:60555211a006d26e1a389222e3fab8cd379f28e0fbf7472ee55b16c6c529e3a6"},
{file = "aiohttp-3.10.9-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:d15a29424e96fad56dc2f3abed10a89c50c099f97d2416520c7a543e8fddf066"},
{file = "aiohttp-3.10.9-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:a19caae0d670771ea7854ca30df76f676eb47e0fd9b2ee4392d44708f272122d"},
{file = "aiohttp-3.10.9-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:99f9678bf0e2b1b695e8028fedac24ab6770937932eda695815d5a6618c37e04"},
{file = "aiohttp-3.10.9-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:2914caa46054f3b5ff910468d686742ff8cff54b8a67319d75f5d5945fd0a13d"},
{file = "aiohttp-3.10.9-cp38-cp38-win32.whl", hash = "sha256:0bc059ecbce835630e635879f5f480a742e130d9821fbe3d2f76610a6698ee25"},
{file = "aiohttp-3.10.9-cp38-cp38-win_amd64.whl", hash = "sha256:e883b61b75ca6efc2541fcd52a5c8ccfe288b24d97e20ac08fdf343b8ac672ea"},
{file = "aiohttp-3.10.9-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:fcd546782d03181b0b1d20b43d612429a90a68779659ba8045114b867971ab71"},
{file = "aiohttp-3.10.9-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:85711eec2d875cd88c7eb40e734c4ca6d9ae477d6f26bd2b5bb4f7f60e41b156"},
{file = "aiohttp-3.10.9-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:02d1d6610588bcd743fae827bd6f2e47e0d09b346f230824b4c6fb85c6065f9c"},
{file = "aiohttp-3.10.9-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3668d0c2a4d23fb136a753eba42caa2c0abbd3d9c5c87ee150a716a16c6deec1"},
{file = "aiohttp-3.10.9-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d7c071235a47d407b0e93aa6262b49422dbe48d7d8566e1158fecc91043dd948"},
{file = "aiohttp-3.10.9-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ac74e794e3aee92ae8f571bfeaa103a141e409863a100ab63a253b1c53b707eb"},
{file = "aiohttp-3.10.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2bbf94d4a0447705b7775417ca8bb8086cc5482023a6e17cdc8f96d0b1b5aba6"},
{file = "aiohttp-3.10.9-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cb0b2d5d51f96b6cc19e6ab46a7b684be23240426ae951dcdac9639ab111b45e"},
{file = "aiohttp-3.10.9-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:e83dfefb4f7d285c2d6a07a22268344a97d61579b3e0dce482a5be0251d672ab"},
{file = "aiohttp-3.10.9-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:f0a44bb40b6aaa4fb9a5c1ee07880570ecda2065433a96ccff409c9c20c1624a"},
{file = "aiohttp-3.10.9-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:c2b627d3c8982691b06d89d31093cee158c30629fdfebe705a91814d49b554f8"},
{file = "aiohttp-3.10.9-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:03690541e4cc866eef79626cfa1ef4dd729c5c1408600c8cb9e12e1137eed6ab"},
{file = "aiohttp-3.10.9-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:ad3675c126f2a95bde637d162f8231cff6bc0bc9fbe31bd78075f9ff7921e322"},
{file = "aiohttp-3.10.9-cp39-cp39-win32.whl", hash = "sha256:1321658f12b6caffafdc35cfba6c882cb014af86bef4e78c125e7e794dfb927b"},
{file = "aiohttp-3.10.9-cp39-cp39-win_amd64.whl", hash = "sha256:9fdf5c839bf95fc67be5794c780419edb0dbef776edcfc6c2e5e2ffd5ee755fa"},
{file = "aiohttp-3.10.9.tar.gz", hash = "sha256:143b0026a9dab07a05ad2dd9e46aa859bffdd6348ddc5967b42161168c24f857"},
]
[package.dependencies]
@ -118,7 +118,7 @@ async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
attrs = ">=17.3.0"
frozenlist = ">=1.1.1"
multidict = ">=4.5,<7.0"
yarl = ">=1.0,<2.0"
yarl = ">=1.12.0,<2.0"
[package.extras]
speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
@ -150,13 +150,13 @@ files = [
[[package]]
name = "anyio"
version = "4.4.0"
version = "4.6.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
{file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
{file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
{file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
]
[package.dependencies]
@ -166,9 +166,9 @@ sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (>=0.23)"]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
trio = ["trio (>=0.26.1)"]
[[package]]
name = "async-timeout"
@ -365,13 +365,13 @@ test = ["pytest (>=6)"]
[[package]]
name = "fireworks-ai"
version = "0.15.1"
version = "0.15.4"
description = "Python client library for the Fireworks.ai Generative AI Platform"
optional = false
python-versions = ">=3.7"
files = [
{file = "fireworks_ai-0.15.1-py3-none-any.whl", hash = "sha256:56aeeb694019db95377ba7cecc45dbc575c4de56cb411a5bda5ca78e6871ab22"},
{file = "fireworks_ai-0.15.1.tar.gz", hash = "sha256:fb78df46eef3a23b9cc63cb9bbb5242c3851bce40d9c1ae059161cd6b3e430b8"},
{file = "fireworks_ai-0.15.4-py3-none-any.whl", hash = "sha256:fb2e5c6aef53ee4bf489110c3fd838bfc73f0c7f129611771037d0d30cf4f920"},
{file = "fireworks_ai-0.15.4.tar.gz", hash = "sha256:414c6a791257eecd6dc571eb33a86e53dcc3768322aef82a5ac3151b1c2a209f"},
]
[package.dependencies]
@ -493,13 +493,13 @@ files = [
[[package]]
name = "httpcore"
version = "1.0.5"
version = "1.0.6"
description = "A minimal low-level HTTP client."
optional = false
python-versions = ">=3.8"
files = [
{file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
{file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
{file = "httpcore-1.0.6-py3-none-any.whl", hash = "sha256:27b59625743b85577a8c0e10e55b50b5368a4f2cfe8cc7bcfa9cf00829c2682f"},
{file = "httpcore-1.0.6.tar.gz", hash = "sha256:73f6dbd6eb8c21bbf7ef8efad555481853f5f6acdeaff1edb0694289269ee17f"},
]
[package.dependencies]
@ -510,7 +510,7 @@ h11 = ">=0.13,<0.15"
asyncio = ["anyio (>=4.0,<5.0)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
trio = ["trio (>=0.22.0,<0.26.0)"]
trio = ["trio (>=0.22.0,<1.0)"]
[[package]]
name = "httpx"
@ -550,15 +550,18 @@ files = [
[[package]]
name = "idna"
version = "3.8"
version = "3.10"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.6"
files = [
{file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"},
{file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"},
{file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
{file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
]
[package.extras]
all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
[[package]]
name = "iniconfig"
version = "2.0.0"
@ -667,7 +670,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.3.0"
version = "0.3.9"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@ -676,7 +679,7 @@ develop = true
[package.dependencies]
jsonpatch = "^1.33"
langsmith = "^0.1.117"
langsmith = "^0.1.125"
packaging = ">=23.2,<25"
pydantic = [
{version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
@ -701,7 +704,7 @@ develop = true
[package.dependencies]
httpx = "^0.27.0"
langchain-core = ">=0.3.0.dev1"
langchain-core = "^0.3.0"
pytest = ">=7,<9"
syrupy = "^4"
@ -711,13 +714,13 @@ url = "../../standard-tests"
[[package]]
name = "langsmith"
version = "0.1.120"
version = "0.1.131"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langsmith-0.1.120-py3-none-any.whl", hash = "sha256:54d2785e301646c0988e0a69ebe4d976488c87b41928b358cb153b6ddd8db62b"},
{file = "langsmith-0.1.120.tar.gz", hash = "sha256:25499ca187b41bd89d784b272b97a8d76f60e0e21bdf20336e8a2aa6a9b23ac9"},
{file = "langsmith-0.1.131-py3-none-any.whl", hash = "sha256:80c106b1c42307195cc0bb3a596472c41ef91b79d15bcee9938307800336c563"},
{file = "langsmith-0.1.131.tar.gz", hash = "sha256:626101a3bf3ca481e5110d5155ace8aa066e4e9cc2fa7d96c8290ade0fbff797"},
]
[package.dependencies]
@ -728,6 +731,7 @@ pydantic = [
{version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
]
requests = ">=2,<3"
requests-toolbelt = ">=1.0.0,<2.0.0"
[[package]]
name = "multidict"
@ -893,13 +897,13 @@ files = [
[[package]]
name = "openai"
version = "1.45.0"
version = "1.51.0"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.7.1"
files = [
{file = "openai-1.45.0-py3-none-any.whl", hash = "sha256:2f1f7b7cf90f038a9f1c24f0d26c0f1790c102ec5acd07ffd70a9b7feac1ff4e"},
{file = "openai-1.45.0.tar.gz", hash = "sha256:731207d10637335413aa3c0955f8f8df30d7636a4a0f9c381f2209d32cf8de97"},
{file = "openai-1.51.0-py3-none-any.whl", hash = "sha256:d9affafb7e51e5a27dce78589d4964ce4d6f6d560307265933a94b2e3f3c5d2c"},
{file = "openai-1.51.0.tar.gz", hash = "sha256:8dc4f9d75ccdd5466fc8c99a952186eddceb9fd6ba694044773f3736a847149d"},
]
[package.dependencies]
@ -1106,18 +1110,18 @@ testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "pydantic"
version = "2.9.1"
version = "2.9.2"
description = "Data validation using Python type hints"
optional = false
python-versions = ">=3.8"
files = [
{file = "pydantic-2.9.1-py3-none-any.whl", hash = "sha256:7aff4db5fdf3cf573d4b3c30926a510a10e19a0774d38fc4967f78beb6deb612"},
{file = "pydantic-2.9.1.tar.gz", hash = "sha256:1363c7d975c7036df0db2b4a61f2e062fbc0aa5ab5f2772e0ffc7191a4f4bce2"},
{file = "pydantic-2.9.2-py3-none-any.whl", hash = "sha256:f048cec7b26778210e28a0459867920654d48e5e62db0958433636cde4254f12"},
{file = "pydantic-2.9.2.tar.gz", hash = "sha256:d155cef71265d1e9807ed1c32b4c8deec042a44a50a4188b25ac67ecd81a9c0f"},
]
[package.dependencies]
annotated-types = ">=0.6.0"
pydantic-core = "2.23.3"
pydantic-core = "2.23.4"
typing-extensions = [
{version = ">=4.6.1", markers = "python_version < \"3.13\""},
{version = ">=4.12.2", markers = "python_version >= \"3.13\""},
@ -1129,100 +1133,100 @@ timezone = ["tzdata"]
[[package]]
name = "pydantic-core"
version = "2.23.3"
version = "2.23.4"
description = "Core functionality for Pydantic validation and serialization"
optional = false
python-versions = ">=3.8"
files = [
{file = "pydantic_core-2.23.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:7f10a5d1b9281392f1bf507d16ac720e78285dfd635b05737c3911637601bae6"},
{file = "pydantic_core-2.23.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3c09a7885dd33ee8c65266e5aa7fb7e2f23d49d8043f089989726391dd7350c5"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6470b5a1ec4d1c2e9afe928c6cb37eb33381cab99292a708b8cb9aa89e62429b"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9172d2088e27d9a185ea0a6c8cebe227a9139fd90295221d7d495944d2367700"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86fc6c762ca7ac8fbbdff80d61b2c59fb6b7d144aa46e2d54d9e1b7b0e780e01"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f0cb80fd5c2df4898693aa841425ea1727b1b6d2167448253077d2a49003e0ed"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:03667cec5daf43ac4995cefa8aaf58f99de036204a37b889c24a80927b629cec"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:047531242f8e9c2db733599f1c612925de095e93c9cc0e599e96cf536aaf56ba"},
{file = "pydantic_core-2.23.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:5499798317fff7f25dbef9347f4451b91ac2a4330c6669821c8202fd354c7bee"},
{file = "pydantic_core-2.23.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bbb5e45eab7624440516ee3722a3044b83fff4c0372efe183fd6ba678ff681fe"},
{file = "pydantic_core-2.23.3-cp310-none-win32.whl", hash = "sha256:8b5b3ed73abb147704a6e9f556d8c5cb078f8c095be4588e669d315e0d11893b"},
{file = "pydantic_core-2.23.3-cp310-none-win_amd64.whl", hash = "sha256:2b603cde285322758a0279995b5796d64b63060bfbe214b50a3ca23b5cee3e83"},
{file = "pydantic_core-2.23.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:c889fd87e1f1bbeb877c2ee56b63bb297de4636661cc9bbfcf4b34e5e925bc27"},
{file = "pydantic_core-2.23.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ea85bda3189fb27503af4c45273735bcde3dd31c1ab17d11f37b04877859ef45"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a7f7f72f721223f33d3dc98a791666ebc6a91fa023ce63733709f4894a7dc611"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2b2b55b0448e9da68f56b696f313949cda1039e8ec7b5d294285335b53104b61"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c24574c7e92e2c56379706b9a3f07c1e0c7f2f87a41b6ee86653100c4ce343e5"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f2b05e6ccbee333a8f4b8f4d7c244fdb7a979e90977ad9c51ea31261e2085ce0"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2c409ce1c219c091e47cb03feb3c4ed8c2b8e004efc940da0166aaee8f9d6c8"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d965e8b325f443ed3196db890d85dfebbb09f7384486a77461347f4adb1fa7f8"},
{file = "pydantic_core-2.23.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f56af3a420fb1ffaf43ece3ea09c2d27c444e7c40dcb7c6e7cf57aae764f2b48"},
{file = "pydantic_core-2.23.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5b01a078dd4f9a52494370af21aa52964e0a96d4862ac64ff7cea06e0f12d2c5"},
{file = "pydantic_core-2.23.3-cp311-none-win32.whl", hash = "sha256:560e32f0df04ac69b3dd818f71339983f6d1f70eb99d4d1f8e9705fb6c34a5c1"},
{file = "pydantic_core-2.23.3-cp311-none-win_amd64.whl", hash = "sha256:c744fa100fdea0d000d8bcddee95213d2de2e95b9c12be083370b2072333a0fa"},
{file = "pydantic_core-2.23.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:e0ec50663feedf64d21bad0809f5857bac1ce91deded203efc4a84b31b2e4305"},
{file = "pydantic_core-2.23.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:db6e6afcb95edbe6b357786684b71008499836e91f2a4a1e55b840955b341dbb"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:98ccd69edcf49f0875d86942f4418a4e83eb3047f20eb897bffa62a5d419c8fa"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a678c1ac5c5ec5685af0133262103defb427114e62eafeda12f1357a12140162"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:01491d8b4d8db9f3391d93b0df60701e644ff0894352947f31fff3e52bd5c801"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fcf31facf2796a2d3b7fe338fe8640aa0166e4e55b4cb108dbfd1058049bf4cb"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7200fd561fb3be06827340da066df4311d0b6b8eb0c2116a110be5245dceb326"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:dc1636770a809dee2bd44dd74b89cc80eb41172bcad8af75dd0bc182c2666d4c"},
{file = "pydantic_core-2.23.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:67a5def279309f2e23014b608c4150b0c2d323bd7bccd27ff07b001c12c2415c"},
{file = "pydantic_core-2.23.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:748bdf985014c6dd3e1e4cc3db90f1c3ecc7246ff5a3cd4ddab20c768b2f1dab"},
{file = "pydantic_core-2.23.3-cp312-none-win32.whl", hash = "sha256:255ec6dcb899c115f1e2a64bc9ebc24cc0e3ab097775755244f77360d1f3c06c"},
{file = "pydantic_core-2.23.3-cp312-none-win_amd64.whl", hash = "sha256:40b8441be16c1e940abebed83cd006ddb9e3737a279e339dbd6d31578b802f7b"},
{file = "pydantic_core-2.23.3-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:6daaf5b1ba1369a22c8b050b643250e3e5efc6a78366d323294aee54953a4d5f"},
{file = "pydantic_core-2.23.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d015e63b985a78a3d4ccffd3bdf22b7c20b3bbd4b8227809b3e8e75bc37f9cb2"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a3fc572d9b5b5cfe13f8e8a6e26271d5d13f80173724b738557a8c7f3a8a3791"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f6bd91345b5163ee7448bee201ed7dd601ca24f43f439109b0212e296eb5b423"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fc379c73fd66606628b866f661e8785088afe2adaba78e6bbe80796baf708a63"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fbdce4b47592f9e296e19ac31667daed8753c8367ebb34b9a9bd89dacaa299c9"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc3cf31edf405a161a0adad83246568647c54404739b614b1ff43dad2b02e6d5"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8e22b477bf90db71c156f89a55bfe4d25177b81fce4aa09294d9e805eec13855"},
{file = "pydantic_core-2.23.3-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:0a0137ddf462575d9bce863c4c95bac3493ba8e22f8c28ca94634b4a1d3e2bb4"},
{file = "pydantic_core-2.23.3-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:203171e48946c3164fe7691fc349c79241ff8f28306abd4cad5f4f75ed80bc8d"},
{file = "pydantic_core-2.23.3-cp313-none-win32.whl", hash = "sha256:76bdab0de4acb3f119c2a4bff740e0c7dc2e6de7692774620f7452ce11ca76c8"},
{file = "pydantic_core-2.23.3-cp313-none-win_amd64.whl", hash = "sha256:37ba321ac2a46100c578a92e9a6aa33afe9ec99ffa084424291d84e456f490c1"},
{file = "pydantic_core-2.23.3-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:d063c6b9fed7d992bcbebfc9133f4c24b7a7f215d6b102f3e082b1117cddb72c"},
{file = "pydantic_core-2.23.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:6cb968da9a0746a0cf521b2b5ef25fc5a0bee9b9a1a8214e0a1cfaea5be7e8a4"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:edbefe079a520c5984e30e1f1f29325054b59534729c25b874a16a5048028d16"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:cbaaf2ef20d282659093913da9d402108203f7cb5955020bd8d1ae5a2325d1c4"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fb539d7e5dc4aac345846f290cf504d2fd3c1be26ac4e8b5e4c2b688069ff4cf"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7e6f33503c5495059148cc486867e1d24ca35df5fc064686e631e314d959ad5b"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:04b07490bc2f6f2717b10c3969e1b830f5720b632f8ae2f3b8b1542394c47a8e"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:03795b9e8a5d7fda05f3873efc3f59105e2dcff14231680296b87b80bb327295"},
{file = "pydantic_core-2.23.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:c483dab0f14b8d3f0df0c6c18d70b21b086f74c87ab03c59250dbf6d3c89baba"},
{file = "pydantic_core-2.23.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8b2682038e255e94baf2c473dca914a7460069171ff5cdd4080be18ab8a7fd6e"},
{file = "pydantic_core-2.23.3-cp38-none-win32.whl", hash = "sha256:f4a57db8966b3a1d1a350012839c6a0099f0898c56512dfade8a1fe5fb278710"},
{file = "pydantic_core-2.23.3-cp38-none-win_amd64.whl", hash = "sha256:13dd45ba2561603681a2676ca56006d6dee94493f03d5cadc055d2055615c3ea"},
{file = "pydantic_core-2.23.3-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:82da2f4703894134a9f000e24965df73cc103e31e8c31906cc1ee89fde72cbd8"},
{file = "pydantic_core-2.23.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:dd9be0a42de08f4b58a3cc73a123f124f65c24698b95a54c1543065baca8cf0e"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:89b731f25c80830c76fdb13705c68fef6a2b6dc494402987c7ea9584fe189f5d"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c6de1ec30c4bb94f3a69c9f5f2182baeda5b809f806676675e9ef6b8dc936f28"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bb68b41c3fa64587412b104294b9cbb027509dc2f6958446c502638d481525ef"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1c3980f2843de5184656aab58698011b42763ccba11c4a8c35936c8dd6c7068c"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:94f85614f2cba13f62c3c6481716e4adeae48e1eaa7e8bac379b9d177d93947a"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:510b7fb0a86dc8f10a8bb43bd2f97beb63cffad1203071dc434dac26453955cd"},
{file = "pydantic_core-2.23.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:1eba2f7ce3e30ee2170410e2171867ea73dbd692433b81a93758ab2de6c64835"},
{file = "pydantic_core-2.23.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:4b259fd8409ab84b4041b7b3f24dcc41e4696f180b775961ca8142b5b21d0e70"},
{file = "pydantic_core-2.23.3-cp39-none-win32.whl", hash = "sha256:40d9bd259538dba2f40963286009bf7caf18b5112b19d2b55b09c14dde6db6a7"},
{file = "pydantic_core-2.23.3-cp39-none-win_amd64.whl", hash = "sha256:5a8cd3074a98ee70173a8633ad3c10e00dcb991ecec57263aacb4095c5efb958"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:f399e8657c67313476a121a6944311fab377085ca7f490648c9af97fc732732d"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:6b5547d098c76e1694ba85f05b595720d7c60d342f24d5aad32c3049131fa5c4"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0dda0290a6f608504882d9f7650975b4651ff91c85673341789a476b1159f211"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:65b6e5da855e9c55a0c67f4db8a492bf13d8d3316a59999cfbaf98cc6e401961"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:09e926397f392059ce0afdcac920df29d9c833256354d0c55f1584b0b70cf07e"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:87cfa0ed6b8c5bd6ae8b66de941cece179281239d482f363814d2b986b79cedc"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e61328920154b6a44d98cabcb709f10e8b74276bc709c9a513a8c37a18786cc4"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ce3317d155628301d649fe5e16a99528d5680af4ec7aa70b90b8dacd2d725c9b"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:e89513f014c6be0d17b00a9a7c81b1c426f4eb9224b15433f3d98c1a071f8433"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:4f62c1c953d7ee375df5eb2e44ad50ce2f5aff931723b398b8bc6f0ac159791a"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2718443bc671c7ac331de4eef9b673063b10af32a0bb385019ad61dcf2cc8f6c"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0d90e08b2727c5d01af1b5ef4121d2f0c99fbee692c762f4d9d0409c9da6541"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2b676583fc459c64146debea14ba3af54e540b61762dfc0613dc4e98c3f66eeb"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:50e4661f3337977740fdbfbae084ae5693e505ca2b3130a6d4eb0f2281dc43b8"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:68f4cf373f0de6abfe599a38307f4417c1c867ca381c03df27c873a9069cda25"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:59d52cf01854cb26c46958552a21acb10dd78a52aa34c86f284e66b209db8cab"},
{file = "pydantic_core-2.23.3.tar.gz", hash = "sha256:3cb0f65d8b4121c1b015c60104a685feb929a29d7cf204387c7f2688c7974690"},
{file = "pydantic_core-2.23.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b10bd51f823d891193d4717448fab065733958bdb6a6b351967bd349d48d5c9b"},
{file = "pydantic_core-2.23.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4fc714bdbfb534f94034efaa6eadd74e5b93c8fa6315565a222f7b6f42ca1166"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:63e46b3169866bd62849936de036f901a9356e36376079b05efa83caeaa02ceb"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ed1a53de42fbe34853ba90513cea21673481cd81ed1be739f7f2efb931b24916"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cfdd16ab5e59fc31b5e906d1a3f666571abc367598e3e02c83403acabc092e07"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:255a8ef062cbf6674450e668482456abac99a5583bbafb73f9ad469540a3a232"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4a7cd62e831afe623fbb7aabbb4fe583212115b3ef38a9f6b71869ba644624a2"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f09e2ff1f17c2b51f2bc76d1cc33da96298f0a036a137f5440ab3ec5360b624f"},
{file = "pydantic_core-2.23.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e38e63e6f3d1cec5a27e0afe90a085af8b6806ee208b33030e65b6516353f1a3"},
{file = "pydantic_core-2.23.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:0dbd8dbed2085ed23b5c04afa29d8fd2771674223135dc9bc937f3c09284d071"},
{file = "pydantic_core-2.23.4-cp310-none-win32.whl", hash = "sha256:6531b7ca5f951d663c339002e91aaebda765ec7d61b7d1e3991051906ddde119"},
{file = "pydantic_core-2.23.4-cp310-none-win_amd64.whl", hash = "sha256:7c9129eb40958b3d4500fa2467e6a83356b3b61bfff1b414c7361d9220f9ae8f"},
{file = "pydantic_core-2.23.4-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:77733e3892bb0a7fa797826361ce8a9184d25c8dffaec60b7ffe928153680ba8"},
{file = "pydantic_core-2.23.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1b84d168f6c48fabd1f2027a3d1bdfe62f92cade1fb273a5d68e621da0e44e6d"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:df49e7a0861a8c36d089c1ed57d308623d60416dab2647a4a17fe050ba85de0e"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ff02b6d461a6de369f07ec15e465a88895f3223eb75073ffea56b84d9331f607"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:996a38a83508c54c78a5f41456b0103c30508fed9abcad0a59b876d7398f25fd"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d97683ddee4723ae8c95d1eddac7c192e8c552da0c73a925a89fa8649bf13eea"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:216f9b2d7713eb98cb83c80b9c794de1f6b7e3145eef40400c62e86cee5f4e1e"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6f783e0ec4803c787bcea93e13e9932edab72068f68ecffdf86a99fd5918878b"},
{file = "pydantic_core-2.23.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:d0776dea117cf5272382634bd2a5c1b6eb16767c223c6a5317cd3e2a757c61a0"},
{file = "pydantic_core-2.23.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d5f7a395a8cf1621939692dba2a6b6a830efa6b3cee787d82c7de1ad2930de64"},
{file = "pydantic_core-2.23.4-cp311-none-win32.whl", hash = "sha256:74b9127ffea03643e998e0c5ad9bd3811d3dac8c676e47db17b0ee7c3c3bf35f"},
{file = "pydantic_core-2.23.4-cp311-none-win_amd64.whl", hash = "sha256:98d134c954828488b153d88ba1f34e14259284f256180ce659e8d83e9c05eaa3"},
{file = "pydantic_core-2.23.4-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:f3e0da4ebaef65158d4dfd7d3678aad692f7666877df0002b8a522cdf088f231"},
{file = "pydantic_core-2.23.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f69a8e0b033b747bb3e36a44e7732f0c99f7edd5cea723d45bc0d6e95377ffee"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:723314c1d51722ab28bfcd5240d858512ffd3116449c557a1336cbe3919beb87"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bb2802e667b7051a1bebbfe93684841cc9351004e2badbd6411bf357ab8d5ac8"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d18ca8148bebe1b0a382a27a8ee60350091a6ddaf475fa05ef50dc35b5df6327"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:33e3d65a85a2a4a0dc3b092b938a4062b1a05f3a9abde65ea93b233bca0e03f2"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:128585782e5bfa515c590ccee4b727fb76925dd04a98864182b22e89a4e6ed36"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:68665f4c17edcceecc112dfed5dbe6f92261fb9d6054b47d01bf6371a6196126"},
{file = "pydantic_core-2.23.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:20152074317d9bed6b7a95ade3b7d6054845d70584216160860425f4fbd5ee9e"},
{file = "pydantic_core-2.23.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:9261d3ce84fa1d38ed649c3638feefeae23d32ba9182963e465d58d62203bd24"},
{file = "pydantic_core-2.23.4-cp312-none-win32.whl", hash = "sha256:4ba762ed58e8d68657fc1281e9bb72e1c3e79cc5d464be146e260c541ec12d84"},
{file = "pydantic_core-2.23.4-cp312-none-win_amd64.whl", hash = "sha256:97df63000f4fea395b2824da80e169731088656d1818a11b95f3b173747b6cd9"},
{file = "pydantic_core-2.23.4-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:7530e201d10d7d14abce4fb54cfe5b94a0aefc87da539d0346a484ead376c3cc"},
{file = "pydantic_core-2.23.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:df933278128ea1cd77772673c73954e53a1c95a4fdf41eef97c2b779271bd0bd"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0cb3da3fd1b6a5d0279a01877713dbda118a2a4fc6f0d821a57da2e464793f05"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:42c6dcb030aefb668a2b7009c85b27f90e51e6a3b4d5c9bc4c57631292015b0d"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:696dd8d674d6ce621ab9d45b205df149399e4bb9aa34102c970b721554828510"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2971bb5ffe72cc0f555c13e19b23c85b654dd2a8f7ab493c262071377bfce9f6"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8394d940e5d400d04cad4f75c0598665cbb81aecefaca82ca85bd28264af7f9b"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:0dff76e0602ca7d4cdaacc1ac4c005e0ce0dcfe095d5b5259163a80d3a10d327"},
{file = "pydantic_core-2.23.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:7d32706badfe136888bdea71c0def994644e09fff0bfe47441deaed8e96fdbc6"},
{file = "pydantic_core-2.23.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:ed541d70698978a20eb63d8c5d72f2cc6d7079d9d90f6b50bad07826f1320f5f"},
{file = "pydantic_core-2.23.4-cp313-none-win32.whl", hash = "sha256:3d5639516376dce1940ea36edf408c554475369f5da2abd45d44621cb616f769"},
{file = "pydantic_core-2.23.4-cp313-none-win_amd64.whl", hash = "sha256:5a1504ad17ba4210df3a045132a7baeeba5a200e930f57512ee02909fc5c4cb5"},
{file = "pydantic_core-2.23.4-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:d4488a93b071c04dc20f5cecc3631fc78b9789dd72483ba15d423b5b3689b555"},
{file = "pydantic_core-2.23.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:81965a16b675b35e1d09dd14df53f190f9129c0202356ed44ab2728b1c905658"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4ffa2ebd4c8530079140dd2d7f794a9d9a73cbb8e9d59ffe24c63436efa8f271"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:61817945f2fe7d166e75fbfb28004034b48e44878177fc54d81688e7b85a3665"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:29d2c342c4bc01b88402d60189f3df065fb0dda3654744d5a165a5288a657368"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5e11661ce0fd30a6790e8bcdf263b9ec5988e95e63cf901972107efc49218b13"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9d18368b137c6295db49ce7218b1a9ba15c5bc254c96d7c9f9e924a9bc7825ad"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ec4e55f79b1c4ffb2eecd8a0cfba9955a2588497d96851f4c8f99aa4a1d39b12"},
{file = "pydantic_core-2.23.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:374a5e5049eda9e0a44c696c7ade3ff355f06b1fe0bb945ea3cac2bc336478a2"},
{file = "pydantic_core-2.23.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:5c364564d17da23db1106787675fc7af45f2f7b58b4173bfdd105564e132e6fb"},
{file = "pydantic_core-2.23.4-cp38-none-win32.whl", hash = "sha256:d7a80d21d613eec45e3d41eb22f8f94ddc758a6c4720842dc74c0581f54993d6"},
{file = "pydantic_core-2.23.4-cp38-none-win_amd64.whl", hash = "sha256:5f5ff8d839f4566a474a969508fe1c5e59c31c80d9e140566f9a37bba7b8d556"},
{file = "pydantic_core-2.23.4-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:a4fa4fc04dff799089689f4fd502ce7d59de529fc2f40a2c8836886c03e0175a"},
{file = "pydantic_core-2.23.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0a7df63886be5e270da67e0966cf4afbae86069501d35c8c1b3b6c168f42cb36"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dcedcd19a557e182628afa1d553c3895a9f825b936415d0dbd3cd0bbcfd29b4b"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5f54b118ce5de9ac21c363d9b3caa6c800341e8c47a508787e5868c6b79c9323"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86d2f57d3e1379a9525c5ab067b27dbb8a0642fb5d454e17a9ac434f9ce523e3"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:de6d1d1b9e5101508cb37ab0d972357cac5235f5c6533d1071964c47139257df"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1278e0d324f6908e872730c9102b0112477a7f7cf88b308e4fc36ce1bdb6d58c"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9a6b5099eeec78827553827f4c6b8615978bb4b6a88e5d9b93eddf8bb6790f55"},
{file = "pydantic_core-2.23.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:e55541f756f9b3ee346b840103f32779c695a19826a4c442b7954550a0972040"},
{file = "pydantic_core-2.23.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a5c7ba8ffb6d6f8f2ab08743be203654bb1aaa8c9dcb09f82ddd34eadb695605"},
{file = "pydantic_core-2.23.4-cp39-none-win32.whl", hash = "sha256:37b0fe330e4a58d3c58b24d91d1eb102aeec675a3db4c292ec3928ecd892a9a6"},
{file = "pydantic_core-2.23.4-cp39-none-win_amd64.whl", hash = "sha256:1498bec4c05c9c787bde9125cfdcc63a41004ff167f495063191b863399b1a29"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:f455ee30a9d61d3e1a15abd5068827773d6e4dc513e795f380cdd59932c782d5"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1e90d2e3bd2c3863d48525d297cd143fe541be8bbf6f579504b9712cb6b643ec"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e203fdf807ac7e12ab59ca2bfcabb38c7cf0b33c41efeb00f8e5da1d86af480"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e08277a400de01bc72436a0ccd02bdf596631411f592ad985dcee21445bd0068"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f220b0eea5965dec25480b6333c788fb72ce5f9129e8759ef876a1d805d00801"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:d06b0c8da4f16d1d1e352134427cb194a0a6e19ad5db9161bf32b2113409e728"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ba1a0996f6c2773bd83e63f18914c1de3c9dd26d55f4ac302a7efe93fb8e7433"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:9a5bce9d23aac8f0cf0836ecfc033896aa8443b501c58d0602dbfd5bd5b37753"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:78ddaaa81421a29574a682b3179d4cf9e6d405a09b99d93ddcf7e5239c742e21"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:883a91b5dd7d26492ff2f04f40fbb652de40fcc0afe07e8129e8ae779c2110eb"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88ad334a15b32a791ea935af224b9de1bf99bcd62fabf745d5f3442199d86d59"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:233710f069d251feb12a56da21e14cca67994eab08362207785cf8c598e74577"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:19442362866a753485ba5e4be408964644dd6a09123d9416c54cd49171f50744"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:624e278a7d29b6445e4e813af92af37820fafb6dcc55c012c834f9e26f9aaaef"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f5ef8f42bec47f21d07668a043f077d507e5bf4e668d5c6dfe6aaba89de1a5b8"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:aea443fffa9fbe3af1a9ba721a87f926fe548d32cab71d188a6ede77d0ff244e"},
{file = "pydantic_core-2.23.4.tar.gz", hash = "sha256:2584f7cf844ac4d970fba483a717dbe10c1c1c96a969bf65d61ffe94df1b2863"},
]
[package.dependencies]
@ -1397,6 +1401,20 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
[[package]]
name = "requests-toolbelt"
version = "1.0.0"
description = "A utility belt for advanced users of python-requests"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
files = [
{file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
{file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"},
]
[package.dependencies]
requests = ">=2.0.1,<3.0.0"
[[package]]
name = "ruff"
version = "0.5.7"
@ -1477,13 +1495,13 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
[[package]]
name = "tomli"
version = "2.0.1"
version = "2.0.2"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
{file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
{file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
]
[[package]]
@ -1508,13 +1526,13 @@ telegram = ["requests"]
[[package]]
name = "types-requests"
version = "2.32.0.20240907"
version = "2.32.0.20240914"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
{file = "types-requests-2.32.0.20240907.tar.gz", hash = "sha256:ff33935f061b5e81ec87997e91050f7b4af4f82027a7a7a9d9aaea04a963fdf8"},
{file = "types_requests-2.32.0.20240907-py3-none-any.whl", hash = "sha256:1d1e79faeaf9d42def77f3c304893dea17a97cae98168ac69f3cb465516ee8da"},
{file = "types-requests-2.32.0.20240914.tar.gz", hash = "sha256:2850e178db3919d9bf809e434eef65ba49d0e7e33ac92d588f4a5e295fffd405"},
{file = "types_requests-2.32.0.20240914-py3-none-any.whl", hash = "sha256:59c2f673eb55f32a99b2894faf6020e1a9f4a402ad0f192bfee0b64469054310"},
]
[package.dependencies]
@ -1550,41 +1568,41 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "watchdog"
version = "5.0.2"
version = "5.0.3"
description = "Filesystem events monitoring"
optional = false
python-versions = ">=3.9"
files = [
{file = "watchdog-5.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d961f4123bb3c447d9fcdcb67e1530c366f10ab3a0c7d1c0c9943050936d4877"},
{file = "watchdog-5.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72990192cb63872c47d5e5fefe230a401b87fd59d257ee577d61c9e5564c62e5"},
{file = "watchdog-5.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6bec703ad90b35a848e05e1b40bf0050da7ca28ead7ac4be724ae5ac2653a1a0"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:dae7a1879918f6544201d33666909b040a46421054a50e0f773e0d870ed7438d"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c4a440f725f3b99133de610bfec93d570b13826f89616377715b9cd60424db6e"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f8b2918c19e0d48f5f20df458c84692e2a054f02d9df25e6c3c930063eca64c1"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:aa9cd6e24126d4afb3752a3e70fce39f92d0e1a58a236ddf6ee823ff7dba28ee"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f627c5bf5759fdd90195b0c0431f99cff4867d212a67b384442c51136a098ed7"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d7594a6d32cda2b49df3fd9abf9b37c8d2f3eab5df45c24056b4a671ac661619"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:ba32efcccfe2c58f4d01115440d1672b4eb26cdd6fc5b5818f1fb41f7c3e1889"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:963f7c4c91e3f51c998eeff1b3fb24a52a8a34da4f956e470f4b068bb47b78ee"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:8c47150aa12f775e22efff1eee9f0f6beee542a7aa1a985c271b1997d340184f"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:14dd4ed023d79d1f670aa659f449bcd2733c33a35c8ffd88689d9d243885198b"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b84bff0391ad4abe25c2740c7aec0e3de316fdf7764007f41e248422a7760a7f"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3e8d5ff39f0a9968952cce548e8e08f849141a4fcc1290b1c17c032ba697b9d7"},
{file = "watchdog-5.0.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:fb223456db6e5f7bd9bbd5cd969f05aae82ae21acc00643b60d81c770abd402b"},
{file = "watchdog-5.0.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:9814adb768c23727a27792c77812cf4e2fd9853cd280eafa2bcfa62a99e8bd6e"},
{file = "watchdog-5.0.2-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:901ee48c23f70193d1a7bc2d9ee297df66081dd5f46f0ca011be4f70dec80dab"},
{file = "watchdog-5.0.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:638bcca3d5b1885c6ec47be67bf712b00a9ab3d4b22ec0881f4889ad870bc7e8"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5597c051587f8757798216f2485e85eac583c3b343e9aa09127a3a6f82c65ee8"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_armv7l.whl", hash = "sha256:53ed1bf71fcb8475dd0ef4912ab139c294c87b903724b6f4a8bd98e026862e6d"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_i686.whl", hash = "sha256:29e4a2607bd407d9552c502d38b45a05ec26a8e40cc7e94db9bb48f861fa5abc"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_ppc64.whl", hash = "sha256:b6dc8f1d770a8280997e4beae7b9a75a33b268c59e033e72c8a10990097e5fde"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:d2ab34adc9bf1489452965cdb16a924e97d4452fcf88a50b21859068b50b5c3b"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_s390x.whl", hash = "sha256:7d1aa7e4bb0f0c65a1a91ba37c10e19dabf7eaaa282c5787e51371f090748f4b"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:726eef8f8c634ac6584f86c9c53353a010d9f311f6c15a034f3800a7a891d941"},
{file = "watchdog-5.0.2-py3-none-win32.whl", hash = "sha256:bda40c57115684d0216556671875e008279dea2dc00fcd3dde126ac8e0d7a2fb"},
{file = "watchdog-5.0.2-py3-none-win_amd64.whl", hash = "sha256:d010be060c996db725fbce7e3ef14687cdcc76f4ca0e4339a68cc4532c382a73"},
{file = "watchdog-5.0.2-py3-none-win_ia64.whl", hash = "sha256:3960136b2b619510569b90f0cd96408591d6c251a75c97690f4553ca88889769"},
{file = "watchdog-5.0.2.tar.gz", hash = "sha256:dcebf7e475001d2cdeb020be630dc5b687e9acdd60d16fea6bb4508e7b94cf76"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:85527b882f3facda0579bce9d743ff7f10c3e1e0db0a0d0e28170a7d0e5ce2ea"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:53adf73dcdc0ef04f7735066b4a57a4cd3e49ef135daae41d77395f0b5b692cb"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e25adddab85f674acac303cf1f5835951345a56c5f7f582987d266679979c75b"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:f01f4a3565a387080dc49bdd1fefe4ecc77f894991b88ef927edbfa45eb10818"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:91b522adc25614cdeaf91f7897800b82c13b4b8ac68a42ca959f992f6990c490"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d52db5beb5e476e6853da2e2d24dbbbed6797b449c8bf7ea118a4ee0d2c9040e"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:94d11b07c64f63f49876e0ab8042ae034674c8653bfcdaa8c4b32e71cfff87e8"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:349c9488e1d85d0a58e8cb14222d2c51cbc801ce11ac3936ab4c3af986536926"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:53a3f10b62c2d569e260f96e8d966463dec1a50fa4f1b22aec69e3f91025060e"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:950f531ec6e03696a2414b6308f5c6ff9dab7821a768c9d5788b1314e9a46ca7"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ae6deb336cba5d71476caa029ceb6e88047fc1dc74b62b7c4012639c0b563906"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1021223c08ba8d2d38d71ec1704496471ffd7be42cfb26b87cd5059323a389a1"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:752fb40efc7cc8d88ebc332b8f4bcbe2b5cc7e881bccfeb8e25054c00c994ee3"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a2e8f3f955d68471fa37b0e3add18500790d129cc7efe89971b8a4cc6fdeb0b2"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b8ca4d854adcf480bdfd80f46fdd6fb49f91dd020ae11c89b3a79e19454ec627"},
{file = "watchdog-5.0.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:90a67d7857adb1d985aca232cc9905dd5bc4803ed85cfcdcfcf707e52049eda7"},
{file = "watchdog-5.0.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:720ef9d3a4f9ca575a780af283c8fd3a0674b307651c1976714745090da5a9e8"},
{file = "watchdog-5.0.3-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:223160bb359281bb8e31c8f1068bf71a6b16a8ad3d9524ca6f523ac666bb6a1e"},
{file = "watchdog-5.0.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:560135542c91eaa74247a2e8430cf83c4342b29e8ad4f520ae14f0c8a19cfb5b"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:dd021efa85970bd4824acacbb922066159d0f9e546389a4743d56919b6758b91"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_armv7l.whl", hash = "sha256:78864cc8f23dbee55be34cc1494632a7ba30263951b5b2e8fc8286b95845f82c"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_i686.whl", hash = "sha256:1e9679245e3ea6498494b3028b90c7b25dbb2abe65c7d07423ecfc2d6218ff7c"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_ppc64.whl", hash = "sha256:9413384f26b5d050b6978e6fcd0c1e7f0539be7a4f1a885061473c5deaa57221"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:294b7a598974b8e2c6123d19ef15de9abcd282b0fbbdbc4d23dfa812959a9e05"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_s390x.whl", hash = "sha256:26dd201857d702bdf9d78c273cafcab5871dd29343748524695cecffa44a8d97"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:0f9332243355643d567697c3e3fa07330a1d1abf981611654a1f2bf2175612b7"},
{file = "watchdog-5.0.3-py3-none-win32.whl", hash = "sha256:c66f80ee5b602a9c7ab66e3c9f36026590a0902db3aea414d59a2f55188c1f49"},
{file = "watchdog-5.0.3-py3-none-win_amd64.whl", hash = "sha256:f00b4cf737f568be9665563347a910f8bdc76f88c2970121c86243c8cfdf90e9"},
{file = "watchdog-5.0.3-py3-none-win_ia64.whl", hash = "sha256:49f4d36cb315c25ea0d946e018c01bb028048023b9e103d3d3943f58e109dd45"},
{file = "watchdog-5.0.3.tar.gz", hash = "sha256:108f42a7f0345042a854d4d0ad0834b741d421330d5f575b81cb27b883500176"},
]
[package.extras]
@ -1592,103 +1610,103 @@ watchmedo = ["PyYAML (>=3.10)"]
[[package]]
name = "yarl"
version = "1.11.1"
version = "1.13.1"
description = "Yet another URL library"
optional = false
python-versions = ">=3.8"
files = [
{file = "yarl-1.11.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:400cd42185f92de559d29eeb529e71d80dfbd2f45c36844914a4a34297ca6f00"},
{file = "yarl-1.11.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8258c86f47e080a258993eed877d579c71da7bda26af86ce6c2d2d072c11320d"},
{file = "yarl-1.11.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2164cd9725092761fed26f299e3f276bb4b537ca58e6ff6b252eae9631b5c96e"},
{file = "yarl-1.11.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08ea567c16f140af8ddc7cb58e27e9138a1386e3e6e53982abaa6f2377b38cc"},
{file = "yarl-1.11.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:768ecc550096b028754ea28bf90fde071c379c62c43afa574edc6f33ee5daaec"},
{file = "yarl-1.11.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2909fa3a7d249ef64eeb2faa04b7957e34fefb6ec9966506312349ed8a7e77bf"},
{file = "yarl-1.11.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:01a8697ec24f17c349c4f655763c4db70eebc56a5f82995e5e26e837c6eb0e49"},
{file = "yarl-1.11.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e286580b6511aac7c3268a78cdb861ec739d3e5a2a53b4809faef6b49778eaff"},
{file = "yarl-1.11.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:4179522dc0305c3fc9782549175c8e8849252fefeb077c92a73889ccbcd508ad"},
{file = "yarl-1.11.1-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:27fcb271a41b746bd0e2a92182df507e1c204759f460ff784ca614e12dd85145"},
{file = "yarl-1.11.1-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:f61db3b7e870914dbd9434b560075e0366771eecbe6d2b5561f5bc7485f39efd"},
{file = "yarl-1.11.1-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:c92261eb2ad367629dc437536463dc934030c9e7caca861cc51990fe6c565f26"},
{file = "yarl-1.11.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d95b52fbef190ca87d8c42f49e314eace4fc52070f3dfa5f87a6594b0c1c6e46"},
{file = "yarl-1.11.1-cp310-cp310-win32.whl", hash = "sha256:489fa8bde4f1244ad6c5f6d11bb33e09cf0d1d0367edb197619c3e3fc06f3d91"},
{file = "yarl-1.11.1-cp310-cp310-win_amd64.whl", hash = "sha256:476e20c433b356e16e9a141449f25161e6b69984fb4cdbd7cd4bd54c17844998"},
{file = "yarl-1.11.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:946eedc12895873891aaceb39bceb484b4977f70373e0122da483f6c38faaa68"},
{file = "yarl-1.11.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:21a7c12321436b066c11ec19c7e3cb9aec18884fe0d5b25d03d756a9e654edfe"},
{file = "yarl-1.11.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c35f493b867912f6fda721a59cc7c4766d382040bdf1ddaeeaa7fa4d072f4675"},
{file = "yarl-1.11.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25861303e0be76b60fddc1250ec5986c42f0a5c0c50ff57cc30b1be199c00e63"},
{file = "yarl-1.11.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e4b53f73077e839b3f89c992223f15b1d2ab314bdbdf502afdc7bb18e95eae27"},
{file = "yarl-1.11.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:327c724b01b8641a1bf1ab3b232fb638706e50f76c0b5bf16051ab65c868fac5"},
{file = "yarl-1.11.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4307d9a3417eea87715c9736d050c83e8c1904e9b7aada6ce61b46361b733d92"},
{file = "yarl-1.11.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:48a28bed68ab8fb7e380775f0029a079f08a17799cb3387a65d14ace16c12e2b"},
{file = "yarl-1.11.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:067b961853c8e62725ff2893226fef3d0da060656a9827f3f520fb1d19b2b68a"},
{file = "yarl-1.11.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:8215f6f21394d1f46e222abeb06316e77ef328d628f593502d8fc2a9117bde83"},
{file = "yarl-1.11.1-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:498442e3af2a860a663baa14fbf23fb04b0dd758039c0e7c8f91cb9279799bff"},
{file = "yarl-1.11.1-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:69721b8effdb588cb055cc22f7c5105ca6fdaa5aeb3ea09021d517882c4a904c"},
{file = "yarl-1.11.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:1e969fa4c1e0b1a391f3fcbcb9ec31e84440253325b534519be0d28f4b6b533e"},
{file = "yarl-1.11.1-cp311-cp311-win32.whl", hash = "sha256:7d51324a04fc4b0e097ff8a153e9276c2593106a811704025bbc1d6916f45ca6"},
{file = "yarl-1.11.1-cp311-cp311-win_amd64.whl", hash = "sha256:15061ce6584ece023457fb8b7a7a69ec40bf7114d781a8c4f5dcd68e28b5c53b"},
{file = "yarl-1.11.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:a4264515f9117be204935cd230fb2a052dd3792789cc94c101c535d349b3dab0"},
{file = "yarl-1.11.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f41fa79114a1d2eddb5eea7b912d6160508f57440bd302ce96eaa384914cd265"},
{file = "yarl-1.11.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:02da8759b47d964f9173c8675710720b468aa1c1693be0c9c64abb9d8d9a4867"},
{file = "yarl-1.11.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9361628f28f48dcf8b2f528420d4d68102f593f9c2e592bfc842f5fb337e44fd"},
{file = "yarl-1.11.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b91044952da03b6f95fdba398d7993dd983b64d3c31c358a4c89e3c19b6f7aef"},
{file = "yarl-1.11.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:74db2ef03b442276d25951749a803ddb6e270d02dda1d1c556f6ae595a0d76a8"},
{file = "yarl-1.11.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e975a2211952a8a083d1b9d9ba26472981ae338e720b419eb50535de3c02870"},
{file = "yarl-1.11.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8aef97ba1dd2138112890ef848e17d8526fe80b21f743b4ee65947ea184f07a2"},
{file = "yarl-1.11.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a7915ea49b0c113641dc4d9338efa9bd66b6a9a485ffe75b9907e8573ca94b84"},
{file = "yarl-1.11.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:504cf0d4c5e4579a51261d6091267f9fd997ef58558c4ffa7a3e1460bd2336fa"},
{file = "yarl-1.11.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:3de5292f9f0ee285e6bd168b2a77b2a00d74cbcfa420ed078456d3023d2f6dff"},
{file = "yarl-1.11.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:a34e1e30f1774fa35d37202bbeae62423e9a79d78d0874e5556a593479fdf239"},
{file = "yarl-1.11.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:66b63c504d2ca43bf7221a1f72fbe981ff56ecb39004c70a94485d13e37ebf45"},
{file = "yarl-1.11.1-cp312-cp312-win32.whl", hash = "sha256:a28b70c9e2213de425d9cba5ab2e7f7a1c8ca23a99c4b5159bf77b9c31251447"},
{file = "yarl-1.11.1-cp312-cp312-win_amd64.whl", hash = "sha256:17b5a386d0d36fb828e2fb3ef08c8829c1ebf977eef88e5367d1c8c94b454639"},
{file = "yarl-1.11.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1fa2e7a406fbd45b61b4433e3aa254a2c3e14c4b3186f6e952d08a730807fa0c"},
{file = "yarl-1.11.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:750f656832d7d3cb0c76be137ee79405cc17e792f31e0a01eee390e383b2936e"},
{file = "yarl-1.11.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0b8486f322d8f6a38539136a22c55f94d269addb24db5cb6f61adc61eabc9d93"},
{file = "yarl-1.11.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3fce4da3703ee6048ad4138fe74619c50874afe98b1ad87b2698ef95bf92c96d"},
{file = "yarl-1.11.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8ed653638ef669e0efc6fe2acb792275cb419bf9cb5c5049399f3556995f23c7"},
{file = "yarl-1.11.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18ac56c9dd70941ecad42b5a906820824ca72ff84ad6fa18db33c2537ae2e089"},
{file = "yarl-1.11.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:688654f8507464745ab563b041d1fb7dab5d9912ca6b06e61d1c4708366832f5"},
{file = "yarl-1.11.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4973eac1e2ff63cf187073cd4e1f1148dcd119314ab79b88e1b3fad74a18c9d5"},
{file = "yarl-1.11.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:964a428132227edff96d6f3cf261573cb0f1a60c9a764ce28cda9525f18f7786"},
{file = "yarl-1.11.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:6d23754b9939cbab02c63434776df1170e43b09c6a517585c7ce2b3d449b7318"},
{file = "yarl-1.11.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:c2dc4250fe94d8cd864d66018f8344d4af50e3758e9d725e94fecfa27588ff82"},
{file = "yarl-1.11.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:09696438cb43ea6f9492ef237761b043f9179f455f405279e609f2bc9100212a"},
{file = "yarl-1.11.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:999bfee0a5b7385a0af5ffb606393509cfde70ecca4f01c36985be6d33e336da"},
{file = "yarl-1.11.1-cp313-cp313-win32.whl", hash = "sha256:ce928c9c6409c79e10f39604a7e214b3cb69552952fbda8d836c052832e6a979"},
{file = "yarl-1.11.1-cp313-cp313-win_amd64.whl", hash = "sha256:501c503eed2bb306638ccb60c174f856cc3246c861829ff40eaa80e2f0330367"},
{file = "yarl-1.11.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:dae7bd0daeb33aa3e79e72877d3d51052e8b19c9025ecf0374f542ea8ec120e4"},
{file = "yarl-1.11.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:3ff6b1617aa39279fe18a76c8d165469c48b159931d9b48239065767ee455b2b"},
{file = "yarl-1.11.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:3257978c870728a52dcce8c2902bf01f6c53b65094b457bf87b2644ee6238ddc"},
{file = "yarl-1.11.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0f351fa31234699d6084ff98283cb1e852270fe9e250a3b3bf7804eb493bd937"},
{file = "yarl-1.11.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8aef1b64da41d18026632d99a06b3fefe1d08e85dd81d849fa7c96301ed22f1b"},
{file = "yarl-1.11.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7175a87ab8f7fbde37160a15e58e138ba3b2b0e05492d7351314a250d61b1591"},
{file = "yarl-1.11.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba444bdd4caa2a94456ef67a2f383710928820dd0117aae6650a4d17029fa25e"},
{file = "yarl-1.11.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0ea9682124fc062e3d931c6911934a678cb28453f957ddccf51f568c2f2b5e05"},
{file = "yarl-1.11.1-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:8418c053aeb236b20b0ab8fa6bacfc2feaaf7d4683dd96528610989c99723d5f"},
{file = "yarl-1.11.1-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:61a5f2c14d0a1adfdd82258f756b23a550c13ba4c86c84106be4c111a3a4e413"},
{file = "yarl-1.11.1-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:f3a6d90cab0bdf07df8f176eae3a07127daafcf7457b997b2bf46776da2c7eb7"},
{file = "yarl-1.11.1-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:077da604852be488c9a05a524068cdae1e972b7dc02438161c32420fb4ec5e14"},
{file = "yarl-1.11.1-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:15439f3c5c72686b6c3ff235279630d08936ace67d0fe5c8d5bbc3ef06f5a420"},
{file = "yarl-1.11.1-cp38-cp38-win32.whl", hash = "sha256:238a21849dd7554cb4d25a14ffbfa0ef380bb7ba201f45b144a14454a72ffa5a"},
{file = "yarl-1.11.1-cp38-cp38-win_amd64.whl", hash = "sha256:67459cf8cf31da0e2cbdb4b040507e535d25cfbb1604ca76396a3a66b8ba37a6"},
{file = "yarl-1.11.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:884eab2ce97cbaf89f264372eae58388862c33c4f551c15680dd80f53c89a269"},
{file = "yarl-1.11.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8a336eaa7ee7e87cdece3cedb395c9657d227bfceb6781295cf56abcd3386a26"},
{file = "yarl-1.11.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:87f020d010ba80a247c4abc335fc13421037800ca20b42af5ae40e5fd75e7909"},
{file = "yarl-1.11.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:637c7ddb585a62d4469f843dac221f23eec3cbad31693b23abbc2c366ad41ff4"},
{file = "yarl-1.11.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:48dfd117ab93f0129084577a07287376cc69c08138694396f305636e229caa1a"},
{file = "yarl-1.11.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:75e0ae31fb5ccab6eda09ba1494e87eb226dcbd2372dae96b87800e1dcc98804"},
{file = "yarl-1.11.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f46f81501160c28d0c0b7333b4f7be8983dbbc161983b6fb814024d1b4952f79"},
{file = "yarl-1.11.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:04293941646647b3bfb1719d1d11ff1028e9c30199509a844da3c0f5919dc520"},
{file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:250e888fa62d73e721f3041e3a9abf427788a1934b426b45e1b92f62c1f68366"},
{file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:e8f63904df26d1a66aabc141bfd258bf738b9bc7bc6bdef22713b4f5ef789a4c"},
{file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:aac44097d838dda26526cffb63bdd8737a2dbdf5f2c68efb72ad83aec6673c7e"},
{file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:267b24f891e74eccbdff42241c5fb4f974de2d6271dcc7d7e0c9ae1079a560d9"},
{file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:6907daa4b9d7a688063ed098c472f96e8181733c525e03e866fb5db480a424df"},
{file = "yarl-1.11.1-cp39-cp39-win32.whl", hash = "sha256:14438dfc5015661f75f85bc5adad0743678eefee266ff0c9a8e32969d5d69f74"},
{file = "yarl-1.11.1-cp39-cp39-win_amd64.whl", hash = "sha256:94d0caaa912bfcdc702a4204cd5e2bb01eb917fc4f5ea2315aa23962549561b0"},
{file = "yarl-1.11.1-py3-none-any.whl", hash = "sha256:72bf26f66456baa0584eff63e44545c9f0eaed9b73cb6601b647c91f14c11f38"},
{file = "yarl-1.11.1.tar.gz", hash = "sha256:1bb2d9e212fb7449b8fb73bc461b51eaa17cc8430b4a87d87be7b25052d92f53"},
{file = "yarl-1.13.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:82e692fb325013a18a5b73a4fed5a1edaa7c58144dc67ad9ef3d604eccd451ad"},
{file = "yarl-1.13.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df4e82e68f43a07735ae70a2d84c0353e58e20add20ec0af611f32cd5ba43fb4"},
{file = "yarl-1.13.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ec9dd328016d8d25702a24ee274932aebf6be9787ed1c28d021945d264235b3c"},
{file = "yarl-1.13.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5820bd4178e6a639b3ef1db8b18500a82ceab6d8b89309e121a6859f56585b05"},
{file = "yarl-1.13.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86c438ce920e089c8c2388c7dcc8ab30dfe13c09b8af3d306bcabb46a053d6f7"},
{file = "yarl-1.13.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3de86547c820e4f4da4606d1c8ab5765dd633189791f15247706a2eeabc783ae"},
{file = "yarl-1.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8ca53632007c69ddcdefe1e8cbc3920dd88825e618153795b57e6ebcc92e752a"},
{file = "yarl-1.13.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d4ee1d240b84e2f213565f0ec08caef27a0e657d4c42859809155cf3a29d1735"},
{file = "yarl-1.13.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:c49f3e379177f4477f929097f7ed4b0622a586b0aa40c07ac8c0f8e40659a1ac"},
{file = "yarl-1.13.1-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:5c5e32fef09ce101fe14acd0f498232b5710effe13abac14cd95de9c274e689e"},
{file = "yarl-1.13.1-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:ab9524e45ee809a083338a749af3b53cc7efec458c3ad084361c1dbf7aaf82a2"},
{file = "yarl-1.13.1-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:b1481c048fe787f65e34cb06f7d6824376d5d99f1231eae4778bbe5c3831076d"},
{file = "yarl-1.13.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:31497aefd68036d8e31bfbacef915826ca2e741dbb97a8d6c7eac66deda3b606"},
{file = "yarl-1.13.1-cp310-cp310-win32.whl", hash = "sha256:1fa56f34b2236f5192cb5fceba7bbb09620e5337e0b6dfe2ea0ddbd19dd5b154"},
{file = "yarl-1.13.1-cp310-cp310-win_amd64.whl", hash = "sha256:1bbb418f46c7f7355084833051701b2301092e4611d9e392360c3ba2e3e69f88"},
{file = "yarl-1.13.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:216a6785f296169ed52cd7dcdc2612f82c20f8c9634bf7446327f50398732a51"},
{file = "yarl-1.13.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:40c6e73c03a6befb85b72da213638b8aaa80fe4136ec8691560cf98b11b8ae6e"},
{file = "yarl-1.13.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2430cf996113abe5aee387d39ee19529327205cda975d2b82c0e7e96e5fdabdc"},
{file = "yarl-1.13.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fb4134cc6e005b99fa29dbc86f1ea0a298440ab6b07c6b3ee09232a3b48f495"},
{file = "yarl-1.13.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:309c104ecf67626c033845b860d31594a41343766a46fa58c3309c538a1e22b2"},
{file = "yarl-1.13.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f90575e9fe3aae2c1e686393a9689c724cd00045275407f71771ae5d690ccf38"},
{file = "yarl-1.13.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9d2e1626be8712333a9f71270366f4a132f476ffbe83b689dd6dc0d114796c74"},
{file = "yarl-1.13.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5b66c87da3c6da8f8e8b648878903ca54589038a0b1e08dde2c86d9cd92d4ac9"},
{file = "yarl-1.13.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:cf1ad338620249f8dd6d4b6a91a69d1f265387df3697ad5dc996305cf6c26fb2"},
{file = "yarl-1.13.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:9915300fe5a0aa663c01363db37e4ae8e7c15996ebe2c6cce995e7033ff6457f"},
{file = "yarl-1.13.1-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:703b0f584fcf157ef87816a3c0ff868e8c9f3c370009a8b23b56255885528f10"},
{file = "yarl-1.13.1-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:1d8e3ca29f643dd121f264a7c89f329f0fcb2e4461833f02de6e39fef80f89da"},
{file = "yarl-1.13.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:7055bbade838d68af73aea13f8c86588e4bcc00c2235b4b6d6edb0dbd174e246"},
{file = "yarl-1.13.1-cp311-cp311-win32.whl", hash = "sha256:a3442c31c11088e462d44a644a454d48110f0588de830921fd201060ff19612a"},
{file = "yarl-1.13.1-cp311-cp311-win_amd64.whl", hash = "sha256:81bad32c8f8b5897c909bf3468bf601f1b855d12f53b6af0271963ee67fff0d2"},
{file = "yarl-1.13.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:f452cc1436151387d3d50533523291d5f77c6bc7913c116eb985304abdbd9ec9"},
{file = "yarl-1.13.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:9cec42a20eae8bebf81e9ce23fb0d0c729fc54cf00643eb251ce7c0215ad49fe"},
{file = "yarl-1.13.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d959fe96e5c2712c1876d69af0507d98f0b0e8d81bee14cfb3f6737470205419"},
{file = "yarl-1.13.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b8c837ab90c455f3ea8e68bee143472ee87828bff19ba19776e16ff961425b57"},
{file = "yarl-1.13.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:94a993f976cdcb2dc1b855d8b89b792893220db8862d1a619efa7451817c836b"},
{file = "yarl-1.13.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2b2442a415a5f4c55ced0fade7b72123210d579f7d950e0b5527fc598866e62c"},
{file = "yarl-1.13.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3fdbf0418489525231723cdb6c79e7738b3cbacbaed2b750cb033e4ea208f220"},
{file = "yarl-1.13.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6b7f6e699304717fdc265a7e1922561b02a93ceffdaefdc877acaf9b9f3080b8"},
{file = "yarl-1.13.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:bcd5bf4132e6a8d3eb54b8d56885f3d3a38ecd7ecae8426ecf7d9673b270de43"},
{file = "yarl-1.13.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:2a93a4557f7fc74a38ca5a404abb443a242217b91cd0c4840b1ebedaad8919d4"},
{file = "yarl-1.13.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:22b739f99c7e4787922903f27a892744189482125cc7b95b747f04dd5c83aa9f"},
{file = "yarl-1.13.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:2db874dd1d22d4c2c657807562411ffdfabec38ce4c5ce48b4c654be552759dc"},
{file = "yarl-1.13.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4feaaa4742517eaceafcbe74595ed335a494c84634d33961214b278126ec1485"},
{file = "yarl-1.13.1-cp312-cp312-win32.whl", hash = "sha256:bbf9c2a589be7414ac4a534d54e4517d03f1cbb142c0041191b729c2fa23f320"},
{file = "yarl-1.13.1-cp312-cp312-win_amd64.whl", hash = "sha256:d07b52c8c450f9366c34aa205754355e933922c79135125541daae6cbf31c799"},
{file = "yarl-1.13.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:95c6737f28069153c399d875317f226bbdea939fd48a6349a3b03da6829fb550"},
{file = "yarl-1.13.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:cd66152561632ed4b2a9192e7f8e5a1d41e28f58120b4761622e0355f0fe034c"},
{file = "yarl-1.13.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6a2acde25be0cf9be23a8f6cbd31734536a264723fca860af3ae5e89d771cd71"},
{file = "yarl-1.13.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9a18595e6a2ee0826bf7dfdee823b6ab55c9b70e8f80f8b77c37e694288f5de1"},
{file = "yarl-1.13.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a31d21089894942f7d9a8df166b495101b7258ff11ae0abec58e32daf8088813"},
{file = "yarl-1.13.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:45f209fb4bbfe8630e3d2e2052535ca5b53d4ce2d2026bed4d0637b0416830da"},
{file = "yarl-1.13.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f722f30366474a99745533cc4015b1781ee54b08de73260b2bbe13316079851"},
{file = "yarl-1.13.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f3bf60444269345d712838bb11cc4eadaf51ff1a364ae39ce87a5ca8ad3bb2c8"},
{file = "yarl-1.13.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:942c80a832a79c3707cca46bd12ab8aa58fddb34b1626d42b05aa8f0bcefc206"},
{file = "yarl-1.13.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:44b07e1690f010c3c01d353b5790ec73b2f59b4eae5b0000593199766b3f7a5c"},
{file = "yarl-1.13.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:396e59b8de7e4d59ff5507fb4322d2329865b909f29a7ed7ca37e63ade7f835c"},
{file = "yarl-1.13.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:3bb83a0f12701c0b91112a11148b5217617982e1e466069d0555be9b372f2734"},
{file = "yarl-1.13.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:c92b89bffc660f1274779cb6fbb290ec1f90d6dfe14492523a0667f10170de26"},
{file = "yarl-1.13.1-cp313-cp313-win32.whl", hash = "sha256:269c201bbc01d2cbba5b86997a1e0f73ba5e2f471cfa6e226bcaa7fd664b598d"},
{file = "yarl-1.13.1-cp313-cp313-win_amd64.whl", hash = "sha256:1d0828e17fa701b557c6eaed5edbd9098eb62d8838344486248489ff233998b8"},
{file = "yarl-1.13.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:8be8cdfe20787e6a5fcbd010f8066227e2bb9058331a4eccddec6c0db2bb85b2"},
{file = "yarl-1.13.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:08d7148ff11cb8e886d86dadbfd2e466a76d5dd38c7ea8ebd9b0e07946e76e4b"},
{file = "yarl-1.13.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4afdf84610ca44dcffe8b6c22c68f309aff96be55f5ea2fa31c0c225d6b83e23"},
{file = "yarl-1.13.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d0d12fe78dcf60efa205e9a63f395b5d343e801cf31e5e1dda0d2c1fb618073d"},
{file = "yarl-1.13.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:298c1eecfd3257aa16c0cb0bdffb54411e3e831351cd69e6b0739be16b1bdaa8"},
{file = "yarl-1.13.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c14c16831b565707149c742d87a6203eb5597f4329278446d5c0ae7a1a43928e"},
{file = "yarl-1.13.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5a9bacedbb99685a75ad033fd4de37129449e69808e50e08034034c0bf063f99"},
{file = "yarl-1.13.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:658e8449b84b92a4373f99305de042b6bd0d19bf2080c093881e0516557474a5"},
{file = "yarl-1.13.1-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:373f16f38721c680316a6a00ae21cc178e3a8ef43c0227f88356a24c5193abd6"},
{file = "yarl-1.13.1-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:45d23c4668d4925688e2ea251b53f36a498e9ea860913ce43b52d9605d3d8177"},
{file = "yarl-1.13.1-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:f7917697bcaa3bc3e83db91aa3a0e448bf5cde43c84b7fc1ae2427d2417c0224"},
{file = "yarl-1.13.1-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:5989a38ba1281e43e4663931a53fbf356f78a0325251fd6af09dd03b1d676a09"},
{file = "yarl-1.13.1-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:11b3ca8b42a024513adce810385fcabdd682772411d95bbbda3b9ed1a4257644"},
{file = "yarl-1.13.1-cp38-cp38-win32.whl", hash = "sha256:dcaef817e13eafa547cdfdc5284fe77970b891f731266545aae08d6cce52161e"},
{file = "yarl-1.13.1-cp38-cp38-win_amd64.whl", hash = "sha256:7addd26594e588503bdef03908fc207206adac5bd90b6d4bc3e3cf33a829f57d"},
{file = "yarl-1.13.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:a0ae6637b173d0c40b9c1462e12a7a2000a71a3258fa88756a34c7d38926911c"},
{file = "yarl-1.13.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:576365c9f7469e1f6124d67b001639b77113cfd05e85ce0310f5f318fd02fe85"},
{file = "yarl-1.13.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:78f271722423b2d4851cf1f4fa1a1c4833a128d020062721ba35e1a87154a049"},
{file = "yarl-1.13.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9d74f3c335cfe9c21ea78988e67f18eb9822f5d31f88b41aec3a1ec5ecd32da5"},
{file = "yarl-1.13.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1891d69a6ba16e89473909665cd355d783a8a31bc84720902c5911dbb6373465"},
{file = "yarl-1.13.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fb382fd7b4377363cc9f13ba7c819c3c78ed97c36a82f16f3f92f108c787cbbf"},
{file = "yarl-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9c8854b9f80693d20cec797d8e48a848c2fb273eb6f2587b57763ccba3f3bd4b"},
{file = "yarl-1.13.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bbf2c3f04ff50f16404ce70f822cdc59760e5e2d7965905f0e700270feb2bbfc"},
{file = "yarl-1.13.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:fb9f59f3848edf186a76446eb8bcf4c900fe147cb756fbbd730ef43b2e67c6a7"},
{file = "yarl-1.13.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:ef9b85fa1bc91c4db24407e7c4da93a5822a73dd4513d67b454ca7064e8dc6a3"},
{file = "yarl-1.13.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:098b870c18f1341786f290b4d699504e18f1cd050ed179af8123fd8232513424"},
{file = "yarl-1.13.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:8c723c91c94a3bc8033dd2696a0f53e5d5f8496186013167bddc3fb5d9df46a3"},
{file = "yarl-1.13.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:44a4c40a6f84e4d5955b63462a0e2a988f8982fba245cf885ce3be7618f6aa7d"},
{file = "yarl-1.13.1-cp39-cp39-win32.whl", hash = "sha256:84bbcdcf393139f0abc9f642bf03f00cac31010f3034faa03224a9ef0bb74323"},
{file = "yarl-1.13.1-cp39-cp39-win_amd64.whl", hash = "sha256:fc2931ac9ce9c61c9968989ec831d3a5e6fcaaff9474e7cfa8de80b7aff5a093"},
{file = "yarl-1.13.1-py3-none-any.whl", hash = "sha256:6a5185ad722ab4dd52d5fb1f30dcc73282eb1ed494906a92d1a228d3f89607b0"},
{file = "yarl-1.13.1.tar.gz", hash = "sha256:ec8cfe2295f3e5e44c51f57272afbd69414ae629ec7c6b27f5a410efc78b70a0"},
]
[package.dependencies]
@ -1698,4 +1716,4 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "bbe033083912607ce26fc35f7d73a95ff7d1d16d115a0fb9f2c8768d7d119788"
content-hash = "56f279717e189244fb82561676aac12e1be0d39400d261a1587302335563a091"

16
libs/partners/fireworks/pyproject.toml
View File

@ -1,10 +1,10 @@
[build-system]
requires = ["poetry-core>=1.0.0"]
requires = [ "poetry-core>=1.0.0",]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-fireworks"
version = "0.2.0"
version = "0.2.1"
description = "An integration package connecting Fireworks and LangChain"
authors = []
readme = "README.md"
@ -20,25 +20,21 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.0"
langchain-core = "^0.3.9"
fireworks-ai = ">=0.13.0"
openai = "^1.10.0"
requests = "^2"
aiohttp = "^3.9.1"
[tool.ruff.lint]
select = ["E", "F", "I", "T201"]
select = [ "E", "F", "I", "T201",]
[tool.coverage.run]
omit = ["tests/*"]
omit = [ "tests/*",]
[tool.pytest.ini_options]
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
markers = [
"requires: mark tests as requiring a specific library",
"asyncio: mark tests as requiring asyncio",
"compile: mark placeholder test used to compile integration tests without running them",
]
markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them",]
asyncio_mode = "auto"
[tool.poetry.group.test]

66
libs/partners/openai/langchain_openai/chat_models/base.py
View File

@ -63,7 +63,11 @@ from langchain_core.messages import (
ToolMessage,
ToolMessageChunk,
)
from langchain_core.messages.ai import UsageMetadata
from langchain_core.messages.ai import (
InputTokenDetails,
OutputTokenDetails,
UsageMetadata,
)
from langchain_core.messages.tool import tool_call_chunk
from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
from langchain_core.output_parsers.openai_tools import (
@ -86,7 +90,7 @@ from langchain_core.utils.pydantic import (
TypeBaseModel,
is_basemodel_subclass,
)
from langchain_core.utils.utils import build_extra_kwargs, from_env, secret_from_env
from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
@ -286,16 +290,10 @@ def _convert_chunk_to_generation_chunk(
) -> Optional[ChatGenerationChunk]:
token_usage = chunk.get("usage")
choices = chunk.get("choices", [])
usage_metadata: Optional[UsageMetadata] = (
UsageMetadata(
input_tokens=token_usage.get("prompt_tokens", 0),
output_tokens=token_usage.get("completion_tokens", 0),
total_tokens=token_usage.get("total_tokens", 0),
)
if token_usage
else None
)
usage_metadata: Optional[UsageMetadata] = (
_create_usage_metadata(token_usage) if token_usage else None
)
if len(choices) == 0:
# logprobs is implicitly None
generation_chunk = ChatGenerationChunk(
@ -479,10 +477,7 @@ class BaseChatOpenAI(BaseChatModel):
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="after")
@ -721,15 +716,11 @@ class BaseChatOpenAI(BaseChatModel):
if response_dict.get("error"):
raise ValueError(response_dict.get("error"))
token_usage = response_dict.get("usage", {})
token_usage = response_dict.get("usage")
for res in response_dict["choices"]:
message = _convert_dict_to_message(res["message"])
if token_usage and isinstance(message, AIMessage):
message.usage_metadata = {
"input_tokens": token_usage.get("prompt_tokens", 0),
"output_tokens": token_usage.get("completion_tokens", 0),
"total_tokens": token_usage.get("total_tokens", 0),
}
message.usage_metadata = _create_usage_metadata(token_usage)
generation_info = generation_info or {}
generation_info["finish_reason"] = (
res.get("finish_reason")
@ -2160,3 +2151,36 @@ class OpenAIRefusalError(Exception):
.. versionadded:: 0.1.21
"""
def _create_usage_metadata(oai_token_usage: dict) -> UsageMetadata:
input_tokens = oai_token_usage.get("prompt_tokens", 0)
output_tokens = oai_token_usage.get("completion_tokens", 0)
total_tokens = oai_token_usage.get("total_tokens", input_tokens + output_tokens)
input_token_details: dict = {
"audio": (oai_token_usage.get("prompt_tokens_details") or {}).get(
"audio_tokens"
),
"cache_read": (oai_token_usage.get("prompt_tokens_details") or {}).get(
"cached_tokens"
),
}
output_token_details: dict = {
"audio": (oai_token_usage.get("completion_tokens_details") or {}).get(
"audio_tokens"
),
"reasoning": (oai_token_usage.get("completion_tokens_details") or {}).get(
"reasoning_tokens"
),
}
return UsageMetadata(
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=total_tokens,
input_token_details=InputTokenDetails(
**{k: v for k, v in input_token_details.items() if v is not None}
),
output_token_details=OutputTokenDetails(
**{k: v for k, v in output_token_details.items() if v is not None}
),
)

7
libs/partners/openai/langchain_openai/llms/base.py
View File

@ -27,7 +27,7 @@ from langchain_core.callbacks import (
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.utils import get_pydantic_field_names
from langchain_core.utils.utils import build_extra_kwargs, from_env, secret_from_env
from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env
from pydantic import ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
@ -160,10 +160,7 @@ class BaseOpenAI(BaseLLM):
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="after")

395
libs/partners/openai/poetry.lock generated
View File

@ -13,13 +13,13 @@ files = [
[[package]]
name = "anyio"
version = "4.4.0"
version = "4.6.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
{file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
{file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
{file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
]
[package.dependencies]
@ -29,9 +29,9 @@ sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (>=0.23)"]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
trio = ["trio (>=0.26.1)"]
[[package]]
name = "certifi"
@ -310,13 +310,13 @@ files = [
[[package]]
name = "httpcore"
version = "1.0.5"
version = "1.0.6"
description = "A minimal low-level HTTP client."
optional = false
python-versions = ">=3.8"
files = [
{file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
{file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
{file = "httpcore-1.0.6-py3-none-any.whl", hash = "sha256:27b59625743b85577a8c0e10e55b50b5368a4f2cfe8cc7bcfa9cf00829c2682f"},
{file = "httpcore-1.0.6.tar.gz", hash = "sha256:73f6dbd6eb8c21bbf7ef8efad555481853f5f6acdeaff1edb0694289269ee17f"},
]
[package.dependencies]
@ -327,7 +327,7 @@ h11 = ">=0.13,<0.15"
asyncio = ["anyio (>=4.0,<5.0)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
trio = ["trio (>=0.22.0,<0.26.0)"]
trio = ["trio (>=0.22.0,<1.0)"]
[[package]]
name = "httpx"
@ -356,15 +356,18 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "idna"
version = "3.8"
version = "3.10"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.6"
files = [
{file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"},
{file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"},
{file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
{file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
]
[package.extras]
all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
[[package]]
name = "iniconfig"
version = "2.0.0"
@ -473,7 +476,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.3.0"
version = "0.3.9"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@ -482,7 +485,7 @@ develop = true
[package.dependencies]
jsonpatch = "^1.33"
langsmith = "^0.1.117"
langsmith = "^0.1.125"
packaging = ">=23.2,<25"
pydantic = [
{version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
@ -507,7 +510,7 @@ develop = true
[package.dependencies]
httpx = "^0.27.0"
langchain-core = ">=0.3.0.dev1"
langchain-core = "^0.3.0"
pytest = ">=7,<9"
syrupy = "^4"
@ -517,13 +520,13 @@ url = "../../standard-tests"
[[package]]
name = "langsmith"
version = "0.1.120"
version = "0.1.131"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langsmith-0.1.120-py3-none-any.whl", hash = "sha256:54d2785e301646c0988e0a69ebe4d976488c87b41928b358cb153b6ddd8db62b"},
{file = "langsmith-0.1.120.tar.gz", hash = "sha256:25499ca187b41bd89d784b272b97a8d76f60e0e21bdf20336e8a2aa6a9b23ac9"},
{file = "langsmith-0.1.131-py3-none-any.whl", hash = "sha256:80c106b1c42307195cc0bb3a596472c41ef91b79d15bcee9938307800336c563"},
{file = "langsmith-0.1.131.tar.gz", hash = "sha256:626101a3bf3ca481e5110d5155ace8aa066e4e9cc2fa7d96c8290ade0fbff797"},
]
[package.dependencies]
@ -534,6 +537,7 @@ pydantic = [
{version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
]
requests = ">=2,<3"
requests-toolbelt = ">=1.0.0,<2.0.0"
[[package]]
name = "mypy"
@ -640,13 +644,13 @@ files = [
[[package]]
name = "openai"
version = "1.45.0"
version = "1.51.0"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.7.1"
files = [
{file = "openai-1.45.0-py3-none-any.whl", hash = "sha256:2f1f7b7cf90f038a9f1c24f0d26c0f1790c102ec5acd07ffd70a9b7feac1ff4e"},
{file = "openai-1.45.0.tar.gz", hash = "sha256:731207d10637335413aa3c0955f8f8df30d7636a4a0f9c381f2209d32cf8de97"},
{file = "openai-1.51.0-py3-none-any.whl", hash = "sha256:d9affafb7e51e5a27dce78589d4964ce4d6f6d560307265933a94b2e3f3c5d2c"},
{file = "openai-1.51.0.tar.gz", hash = "sha256:8dc4f9d75ccdd5466fc8c99a952186eddceb9fd6ba694044773f3736a847149d"},
]
[package.dependencies]
@ -853,18 +857,18 @@ testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "pydantic"
version = "2.9.1"
version = "2.9.2"
description = "Data validation using Python type hints"
optional = false
python-versions = ">=3.8"
files = [
{file = "pydantic-2.9.1-py3-none-any.whl", hash = "sha256:7aff4db5fdf3cf573d4b3c30926a510a10e19a0774d38fc4967f78beb6deb612"},
{file = "pydantic-2.9.1.tar.gz", hash = "sha256:1363c7d975c7036df0db2b4a61f2e062fbc0aa5ab5f2772e0ffc7191a4f4bce2"},
{file = "pydantic-2.9.2-py3-none-any.whl", hash = "sha256:f048cec7b26778210e28a0459867920654d48e5e62db0958433636cde4254f12"},
{file = "pydantic-2.9.2.tar.gz", hash = "sha256:d155cef71265d1e9807ed1c32b4c8deec042a44a50a4188b25ac67ecd81a9c0f"},
]
[package.dependencies]
annotated-types = ">=0.6.0"
pydantic-core = "2.23.3"
pydantic-core = "2.23.4"
typing-extensions = [
{version = ">=4.6.1", markers = "python_version < \"3.13\""},
{version = ">=4.12.2", markers = "python_version >= \"3.13\""},
@ -876,100 +880,100 @@ timezone = ["tzdata"]
[[package]]
name = "pydantic-core"
version = "2.23.3"
version = "2.23.4"
description = "Core functionality for Pydantic validation and serialization"
optional = false
python-versions = ">=3.8"
files = [
{file = "pydantic_core-2.23.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:7f10a5d1b9281392f1bf507d16ac720e78285dfd635b05737c3911637601bae6"},
{file = "pydantic_core-2.23.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3c09a7885dd33ee8c65266e5aa7fb7e2f23d49d8043f089989726391dd7350c5"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6470b5a1ec4d1c2e9afe928c6cb37eb33381cab99292a708b8cb9aa89e62429b"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9172d2088e27d9a185ea0a6c8cebe227a9139fd90295221d7d495944d2367700"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86fc6c762ca7ac8fbbdff80d61b2c59fb6b7d144aa46e2d54d9e1b7b0e780e01"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f0cb80fd5c2df4898693aa841425ea1727b1b6d2167448253077d2a49003e0ed"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:03667cec5daf43ac4995cefa8aaf58f99de036204a37b889c24a80927b629cec"},
{file = "pydantic_core-2.23.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:047531242f8e9c2db733599f1c612925de095e93c9cc0e599e96cf536aaf56ba"},
{file = "pydantic_core-2.23.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:5499798317fff7f25dbef9347f4451b91ac2a4330c6669821c8202fd354c7bee"},
{file = "pydantic_core-2.23.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bbb5e45eab7624440516ee3722a3044b83fff4c0372efe183fd6ba678ff681fe"},
{file = "pydantic_core-2.23.3-cp310-none-win32.whl", hash = "sha256:8b5b3ed73abb147704a6e9f556d8c5cb078f8c095be4588e669d315e0d11893b"},
{file = "pydantic_core-2.23.3-cp310-none-win_amd64.whl", hash = "sha256:2b603cde285322758a0279995b5796d64b63060bfbe214b50a3ca23b5cee3e83"},
{file = "pydantic_core-2.23.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:c889fd87e1f1bbeb877c2ee56b63bb297de4636661cc9bbfcf4b34e5e925bc27"},
{file = "pydantic_core-2.23.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ea85bda3189fb27503af4c45273735bcde3dd31c1ab17d11f37b04877859ef45"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a7f7f72f721223f33d3dc98a791666ebc6a91fa023ce63733709f4894a7dc611"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2b2b55b0448e9da68f56b696f313949cda1039e8ec7b5d294285335b53104b61"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c24574c7e92e2c56379706b9a3f07c1e0c7f2f87a41b6ee86653100c4ce343e5"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f2b05e6ccbee333a8f4b8f4d7c244fdb7a979e90977ad9c51ea31261e2085ce0"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2c409ce1c219c091e47cb03feb3c4ed8c2b8e004efc940da0166aaee8f9d6c8"},
{file = "pydantic_core-2.23.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d965e8b325f443ed3196db890d85dfebbb09f7384486a77461347f4adb1fa7f8"},
{file = "pydantic_core-2.23.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f56af3a420fb1ffaf43ece3ea09c2d27c444e7c40dcb7c6e7cf57aae764f2b48"},
{file = "pydantic_core-2.23.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5b01a078dd4f9a52494370af21aa52964e0a96d4862ac64ff7cea06e0f12d2c5"},
{file = "pydantic_core-2.23.3-cp311-none-win32.whl", hash = "sha256:560e32f0df04ac69b3dd818f71339983f6d1f70eb99d4d1f8e9705fb6c34a5c1"},
{file = "pydantic_core-2.23.3-cp311-none-win_amd64.whl", hash = "sha256:c744fa100fdea0d000d8bcddee95213d2de2e95b9c12be083370b2072333a0fa"},
{file = "pydantic_core-2.23.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:e0ec50663feedf64d21bad0809f5857bac1ce91deded203efc4a84b31b2e4305"},
{file = "pydantic_core-2.23.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:db6e6afcb95edbe6b357786684b71008499836e91f2a4a1e55b840955b341dbb"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:98ccd69edcf49f0875d86942f4418a4e83eb3047f20eb897bffa62a5d419c8fa"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a678c1ac5c5ec5685af0133262103defb427114e62eafeda12f1357a12140162"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:01491d8b4d8db9f3391d93b0df60701e644ff0894352947f31fff3e52bd5c801"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fcf31facf2796a2d3b7fe338fe8640aa0166e4e55b4cb108dbfd1058049bf4cb"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7200fd561fb3be06827340da066df4311d0b6b8eb0c2116a110be5245dceb326"},
{file = "pydantic_core-2.23.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:dc1636770a809dee2bd44dd74b89cc80eb41172bcad8af75dd0bc182c2666d4c"},
{file = "pydantic_core-2.23.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:67a5def279309f2e23014b608c4150b0c2d323bd7bccd27ff07b001c12c2415c"},
{file = "pydantic_core-2.23.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:748bdf985014c6dd3e1e4cc3db90f1c3ecc7246ff5a3cd4ddab20c768b2f1dab"},
{file = "pydantic_core-2.23.3-cp312-none-win32.whl", hash = "sha256:255ec6dcb899c115f1e2a64bc9ebc24cc0e3ab097775755244f77360d1f3c06c"},
{file = "pydantic_core-2.23.3-cp312-none-win_amd64.whl", hash = "sha256:40b8441be16c1e940abebed83cd006ddb9e3737a279e339dbd6d31578b802f7b"},
{file = "pydantic_core-2.23.3-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:6daaf5b1ba1369a22c8b050b643250e3e5efc6a78366d323294aee54953a4d5f"},
{file = "pydantic_core-2.23.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d015e63b985a78a3d4ccffd3bdf22b7c20b3bbd4b8227809b3e8e75bc37f9cb2"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a3fc572d9b5b5cfe13f8e8a6e26271d5d13f80173724b738557a8c7f3a8a3791"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f6bd91345b5163ee7448bee201ed7dd601ca24f43f439109b0212e296eb5b423"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fc379c73fd66606628b866f661e8785088afe2adaba78e6bbe80796baf708a63"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fbdce4b47592f9e296e19ac31667daed8753c8367ebb34b9a9bd89dacaa299c9"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc3cf31edf405a161a0adad83246568647c54404739b614b1ff43dad2b02e6d5"},
{file = "pydantic_core-2.23.3-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8e22b477bf90db71c156f89a55bfe4d25177b81fce4aa09294d9e805eec13855"},
{file = "pydantic_core-2.23.3-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:0a0137ddf462575d9bce863c4c95bac3493ba8e22f8c28ca94634b4a1d3e2bb4"},
{file = "pydantic_core-2.23.3-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:203171e48946c3164fe7691fc349c79241ff8f28306abd4cad5f4f75ed80bc8d"},
{file = "pydantic_core-2.23.3-cp313-none-win32.whl", hash = "sha256:76bdab0de4acb3f119c2a4bff740e0c7dc2e6de7692774620f7452ce11ca76c8"},
{file = "pydantic_core-2.23.3-cp313-none-win_amd64.whl", hash = "sha256:37ba321ac2a46100c578a92e9a6aa33afe9ec99ffa084424291d84e456f490c1"},
{file = "pydantic_core-2.23.3-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:d063c6b9fed7d992bcbebfc9133f4c24b7a7f215d6b102f3e082b1117cddb72c"},
{file = "pydantic_core-2.23.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:6cb968da9a0746a0cf521b2b5ef25fc5a0bee9b9a1a8214e0a1cfaea5be7e8a4"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:edbefe079a520c5984e30e1f1f29325054b59534729c25b874a16a5048028d16"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:cbaaf2ef20d282659093913da9d402108203f7cb5955020bd8d1ae5a2325d1c4"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fb539d7e5dc4aac345846f290cf504d2fd3c1be26ac4e8b5e4c2b688069ff4cf"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7e6f33503c5495059148cc486867e1d24ca35df5fc064686e631e314d959ad5b"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:04b07490bc2f6f2717b10c3969e1b830f5720b632f8ae2f3b8b1542394c47a8e"},
{file = "pydantic_core-2.23.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:03795b9e8a5d7fda05f3873efc3f59105e2dcff14231680296b87b80bb327295"},
{file = "pydantic_core-2.23.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:c483dab0f14b8d3f0df0c6c18d70b21b086f74c87ab03c59250dbf6d3c89baba"},
{file = "pydantic_core-2.23.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8b2682038e255e94baf2c473dca914a7460069171ff5cdd4080be18ab8a7fd6e"},
{file = "pydantic_core-2.23.3-cp38-none-win32.whl", hash = "sha256:f4a57db8966b3a1d1a350012839c6a0099f0898c56512dfade8a1fe5fb278710"},
{file = "pydantic_core-2.23.3-cp38-none-win_amd64.whl", hash = "sha256:13dd45ba2561603681a2676ca56006d6dee94493f03d5cadc055d2055615c3ea"},
{file = "pydantic_core-2.23.3-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:82da2f4703894134a9f000e24965df73cc103e31e8c31906cc1ee89fde72cbd8"},
{file = "pydantic_core-2.23.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:dd9be0a42de08f4b58a3cc73a123f124f65c24698b95a54c1543065baca8cf0e"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:89b731f25c80830c76fdb13705c68fef6a2b6dc494402987c7ea9584fe189f5d"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c6de1ec30c4bb94f3a69c9f5f2182baeda5b809f806676675e9ef6b8dc936f28"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bb68b41c3fa64587412b104294b9cbb027509dc2f6958446c502638d481525ef"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1c3980f2843de5184656aab58698011b42763ccba11c4a8c35936c8dd6c7068c"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:94f85614f2cba13f62c3c6481716e4adeae48e1eaa7e8bac379b9d177d93947a"},
{file = "pydantic_core-2.23.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:510b7fb0a86dc8f10a8bb43bd2f97beb63cffad1203071dc434dac26453955cd"},
{file = "pydantic_core-2.23.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:1eba2f7ce3e30ee2170410e2171867ea73dbd692433b81a93758ab2de6c64835"},
{file = "pydantic_core-2.23.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:4b259fd8409ab84b4041b7b3f24dcc41e4696f180b775961ca8142b5b21d0e70"},
{file = "pydantic_core-2.23.3-cp39-none-win32.whl", hash = "sha256:40d9bd259538dba2f40963286009bf7caf18b5112b19d2b55b09c14dde6db6a7"},
{file = "pydantic_core-2.23.3-cp39-none-win_amd64.whl", hash = "sha256:5a8cd3074a98ee70173a8633ad3c10e00dcb991ecec57263aacb4095c5efb958"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:f399e8657c67313476a121a6944311fab377085ca7f490648c9af97fc732732d"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:6b5547d098c76e1694ba85f05b595720d7c60d342f24d5aad32c3049131fa5c4"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0dda0290a6f608504882d9f7650975b4651ff91c85673341789a476b1159f211"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:65b6e5da855e9c55a0c67f4db8a492bf13d8d3316a59999cfbaf98cc6e401961"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:09e926397f392059ce0afdcac920df29d9c833256354d0c55f1584b0b70cf07e"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:87cfa0ed6b8c5bd6ae8b66de941cece179281239d482f363814d2b986b79cedc"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e61328920154b6a44d98cabcb709f10e8b74276bc709c9a513a8c37a18786cc4"},
{file = "pydantic_core-2.23.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ce3317d155628301d649fe5e16a99528d5680af4ec7aa70b90b8dacd2d725c9b"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:e89513f014c6be0d17b00a9a7c81b1c426f4eb9224b15433f3d98c1a071f8433"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:4f62c1c953d7ee375df5eb2e44ad50ce2f5aff931723b398b8bc6f0ac159791a"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2718443bc671c7ac331de4eef9b673063b10af32a0bb385019ad61dcf2cc8f6c"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0d90e08b2727c5d01af1b5ef4121d2f0c99fbee692c762f4d9d0409c9da6541"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2b676583fc459c64146debea14ba3af54e540b61762dfc0613dc4e98c3f66eeb"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:50e4661f3337977740fdbfbae084ae5693e505ca2b3130a6d4eb0f2281dc43b8"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:68f4cf373f0de6abfe599a38307f4417c1c867ca381c03df27c873a9069cda25"},
{file = "pydantic_core-2.23.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:59d52cf01854cb26c46958552a21acb10dd78a52aa34c86f284e66b209db8cab"},
{file = "pydantic_core-2.23.3.tar.gz", hash = "sha256:3cb0f65d8b4121c1b015c60104a685feb929a29d7cf204387c7f2688c7974690"},
{file = "pydantic_core-2.23.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b10bd51f823d891193d4717448fab065733958bdb6a6b351967bd349d48d5c9b"},
{file = "pydantic_core-2.23.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4fc714bdbfb534f94034efaa6eadd74e5b93c8fa6315565a222f7b6f42ca1166"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:63e46b3169866bd62849936de036f901a9356e36376079b05efa83caeaa02ceb"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ed1a53de42fbe34853ba90513cea21673481cd81ed1be739f7f2efb931b24916"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cfdd16ab5e59fc31b5e906d1a3f666571abc367598e3e02c83403acabc092e07"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:255a8ef062cbf6674450e668482456abac99a5583bbafb73f9ad469540a3a232"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4a7cd62e831afe623fbb7aabbb4fe583212115b3ef38a9f6b71869ba644624a2"},
{file = "pydantic_core-2.23.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f09e2ff1f17c2b51f2bc76d1cc33da96298f0a036a137f5440ab3ec5360b624f"},
{file = "pydantic_core-2.23.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e38e63e6f3d1cec5a27e0afe90a085af8b6806ee208b33030e65b6516353f1a3"},
{file = "pydantic_core-2.23.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:0dbd8dbed2085ed23b5c04afa29d8fd2771674223135dc9bc937f3c09284d071"},
{file = "pydantic_core-2.23.4-cp310-none-win32.whl", hash = "sha256:6531b7ca5f951d663c339002e91aaebda765ec7d61b7d1e3991051906ddde119"},
{file = "pydantic_core-2.23.4-cp310-none-win_amd64.whl", hash = "sha256:7c9129eb40958b3d4500fa2467e6a83356b3b61bfff1b414c7361d9220f9ae8f"},
{file = "pydantic_core-2.23.4-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:77733e3892bb0a7fa797826361ce8a9184d25c8dffaec60b7ffe928153680ba8"},
{file = "pydantic_core-2.23.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1b84d168f6c48fabd1f2027a3d1bdfe62f92cade1fb273a5d68e621da0e44e6d"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:df49e7a0861a8c36d089c1ed57d308623d60416dab2647a4a17fe050ba85de0e"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ff02b6d461a6de369f07ec15e465a88895f3223eb75073ffea56b84d9331f607"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:996a38a83508c54c78a5f41456b0103c30508fed9abcad0a59b876d7398f25fd"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d97683ddee4723ae8c95d1eddac7c192e8c552da0c73a925a89fa8649bf13eea"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:216f9b2d7713eb98cb83c80b9c794de1f6b7e3145eef40400c62e86cee5f4e1e"},
{file = "pydantic_core-2.23.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6f783e0ec4803c787bcea93e13e9932edab72068f68ecffdf86a99fd5918878b"},
{file = "pydantic_core-2.23.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:d0776dea117cf5272382634bd2a5c1b6eb16767c223c6a5317cd3e2a757c61a0"},
{file = "pydantic_core-2.23.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d5f7a395a8cf1621939692dba2a6b6a830efa6b3cee787d82c7de1ad2930de64"},
{file = "pydantic_core-2.23.4-cp311-none-win32.whl", hash = "sha256:74b9127ffea03643e998e0c5ad9bd3811d3dac8c676e47db17b0ee7c3c3bf35f"},
{file = "pydantic_core-2.23.4-cp311-none-win_amd64.whl", hash = "sha256:98d134c954828488b153d88ba1f34e14259284f256180ce659e8d83e9c05eaa3"},
{file = "pydantic_core-2.23.4-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:f3e0da4ebaef65158d4dfd7d3678aad692f7666877df0002b8a522cdf088f231"},
{file = "pydantic_core-2.23.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f69a8e0b033b747bb3e36a44e7732f0c99f7edd5cea723d45bc0d6e95377ffee"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:723314c1d51722ab28bfcd5240d858512ffd3116449c557a1336cbe3919beb87"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bb2802e667b7051a1bebbfe93684841cc9351004e2badbd6411bf357ab8d5ac8"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d18ca8148bebe1b0a382a27a8ee60350091a6ddaf475fa05ef50dc35b5df6327"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:33e3d65a85a2a4a0dc3b092b938a4062b1a05f3a9abde65ea93b233bca0e03f2"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:128585782e5bfa515c590ccee4b727fb76925dd04a98864182b22e89a4e6ed36"},
{file = "pydantic_core-2.23.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:68665f4c17edcceecc112dfed5dbe6f92261fb9d6054b47d01bf6371a6196126"},
{file = "pydantic_core-2.23.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:20152074317d9bed6b7a95ade3b7d6054845d70584216160860425f4fbd5ee9e"},
{file = "pydantic_core-2.23.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:9261d3ce84fa1d38ed649c3638feefeae23d32ba9182963e465d58d62203bd24"},
{file = "pydantic_core-2.23.4-cp312-none-win32.whl", hash = "sha256:4ba762ed58e8d68657fc1281e9bb72e1c3e79cc5d464be146e260c541ec12d84"},
{file = "pydantic_core-2.23.4-cp312-none-win_amd64.whl", hash = "sha256:97df63000f4fea395b2824da80e169731088656d1818a11b95f3b173747b6cd9"},
{file = "pydantic_core-2.23.4-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:7530e201d10d7d14abce4fb54cfe5b94a0aefc87da539d0346a484ead376c3cc"},
{file = "pydantic_core-2.23.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:df933278128ea1cd77772673c73954e53a1c95a4fdf41eef97c2b779271bd0bd"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0cb3da3fd1b6a5d0279a01877713dbda118a2a4fc6f0d821a57da2e464793f05"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:42c6dcb030aefb668a2b7009c85b27f90e51e6a3b4d5c9bc4c57631292015b0d"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:696dd8d674d6ce621ab9d45b205df149399e4bb9aa34102c970b721554828510"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2971bb5ffe72cc0f555c13e19b23c85b654dd2a8f7ab493c262071377bfce9f6"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8394d940e5d400d04cad4f75c0598665cbb81aecefaca82ca85bd28264af7f9b"},
{file = "pydantic_core-2.23.4-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:0dff76e0602ca7d4cdaacc1ac4c005e0ce0dcfe095d5b5259163a80d3a10d327"},
{file = "pydantic_core-2.23.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:7d32706badfe136888bdea71c0def994644e09fff0bfe47441deaed8e96fdbc6"},
{file = "pydantic_core-2.23.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:ed541d70698978a20eb63d8c5d72f2cc6d7079d9d90f6b50bad07826f1320f5f"},
{file = "pydantic_core-2.23.4-cp313-none-win32.whl", hash = "sha256:3d5639516376dce1940ea36edf408c554475369f5da2abd45d44621cb616f769"},
{file = "pydantic_core-2.23.4-cp313-none-win_amd64.whl", hash = "sha256:5a1504ad17ba4210df3a045132a7baeeba5a200e930f57512ee02909fc5c4cb5"},
{file = "pydantic_core-2.23.4-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:d4488a93b071c04dc20f5cecc3631fc78b9789dd72483ba15d423b5b3689b555"},
{file = "pydantic_core-2.23.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:81965a16b675b35e1d09dd14df53f190f9129c0202356ed44ab2728b1c905658"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4ffa2ebd4c8530079140dd2d7f794a9d9a73cbb8e9d59ffe24c63436efa8f271"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:61817945f2fe7d166e75fbfb28004034b48e44878177fc54d81688e7b85a3665"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:29d2c342c4bc01b88402d60189f3df065fb0dda3654744d5a165a5288a657368"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5e11661ce0fd30a6790e8bcdf263b9ec5988e95e63cf901972107efc49218b13"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9d18368b137c6295db49ce7218b1a9ba15c5bc254c96d7c9f9e924a9bc7825ad"},
{file = "pydantic_core-2.23.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ec4e55f79b1c4ffb2eecd8a0cfba9955a2588497d96851f4c8f99aa4a1d39b12"},
{file = "pydantic_core-2.23.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:374a5e5049eda9e0a44c696c7ade3ff355f06b1fe0bb945ea3cac2bc336478a2"},
{file = "pydantic_core-2.23.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:5c364564d17da23db1106787675fc7af45f2f7b58b4173bfdd105564e132e6fb"},
{file = "pydantic_core-2.23.4-cp38-none-win32.whl", hash = "sha256:d7a80d21d613eec45e3d41eb22f8f94ddc758a6c4720842dc74c0581f54993d6"},
{file = "pydantic_core-2.23.4-cp38-none-win_amd64.whl", hash = "sha256:5f5ff8d839f4566a474a969508fe1c5e59c31c80d9e140566f9a37bba7b8d556"},
{file = "pydantic_core-2.23.4-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:a4fa4fc04dff799089689f4fd502ce7d59de529fc2f40a2c8836886c03e0175a"},
{file = "pydantic_core-2.23.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0a7df63886be5e270da67e0966cf4afbae86069501d35c8c1b3b6c168f42cb36"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dcedcd19a557e182628afa1d553c3895a9f825b936415d0dbd3cd0bbcfd29b4b"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5f54b118ce5de9ac21c363d9b3caa6c800341e8c47a508787e5868c6b79c9323"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86d2f57d3e1379a9525c5ab067b27dbb8a0642fb5d454e17a9ac434f9ce523e3"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:de6d1d1b9e5101508cb37ab0d972357cac5235f5c6533d1071964c47139257df"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1278e0d324f6908e872730c9102b0112477a7f7cf88b308e4fc36ce1bdb6d58c"},
{file = "pydantic_core-2.23.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9a6b5099eeec78827553827f4c6b8615978bb4b6a88e5d9b93eddf8bb6790f55"},
{file = "pydantic_core-2.23.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:e55541f756f9b3ee346b840103f32779c695a19826a4c442b7954550a0972040"},
{file = "pydantic_core-2.23.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a5c7ba8ffb6d6f8f2ab08743be203654bb1aaa8c9dcb09f82ddd34eadb695605"},
{file = "pydantic_core-2.23.4-cp39-none-win32.whl", hash = "sha256:37b0fe330e4a58d3c58b24d91d1eb102aeec675a3db4c292ec3928ecd892a9a6"},
{file = "pydantic_core-2.23.4-cp39-none-win_amd64.whl", hash = "sha256:1498bec4c05c9c787bde9125cfdcc63a41004ff167f495063191b863399b1a29"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:f455ee30a9d61d3e1a15abd5068827773d6e4dc513e795f380cdd59932c782d5"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1e90d2e3bd2c3863d48525d297cd143fe541be8bbf6f579504b9712cb6b643ec"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e203fdf807ac7e12ab59ca2bfcabb38c7cf0b33c41efeb00f8e5da1d86af480"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e08277a400de01bc72436a0ccd02bdf596631411f592ad985dcee21445bd0068"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f220b0eea5965dec25480b6333c788fb72ce5f9129e8759ef876a1d805d00801"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:d06b0c8da4f16d1d1e352134427cb194a0a6e19ad5db9161bf32b2113409e728"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ba1a0996f6c2773bd83e63f18914c1de3c9dd26d55f4ac302a7efe93fb8e7433"},
{file = "pydantic_core-2.23.4-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:9a5bce9d23aac8f0cf0836ecfc033896aa8443b501c58d0602dbfd5bd5b37753"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:78ddaaa81421a29574a682b3179d4cf9e6d405a09b99d93ddcf7e5239c742e21"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:883a91b5dd7d26492ff2f04f40fbb652de40fcc0afe07e8129e8ae779c2110eb"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88ad334a15b32a791ea935af224b9de1bf99bcd62fabf745d5f3442199d86d59"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:233710f069d251feb12a56da21e14cca67994eab08362207785cf8c598e74577"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:19442362866a753485ba5e4be408964644dd6a09123d9416c54cd49171f50744"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:624e278a7d29b6445e4e813af92af37820fafb6dcc55c012c834f9e26f9aaaef"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f5ef8f42bec47f21d07668a043f077d507e5bf4e668d5c6dfe6aaba89de1a5b8"},
{file = "pydantic_core-2.23.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:aea443fffa9fbe3af1a9ba721a87f926fe548d32cab71d188a6ede77d0ff244e"},
{file = "pydantic_core-2.23.4.tar.gz", hash = "sha256:2584f7cf844ac4d970fba483a717dbe10c1c1c96a969bf65d61ffe94df1b2863"},
]
[package.dependencies]
@ -1265,6 +1269,20 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
[[package]]
name = "requests-toolbelt"
version = "1.0.0"
description = "A utility belt for advanced users of python-requests"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
files = [
{file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
{file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"},
]
[package.dependencies]
requests = ">=2.0.1,<3.0.0"
[[package]]
name = "ruff"
version = "0.5.7"
@ -1345,47 +1363,42 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
[[package]]
name = "tiktoken"
version = "0.7.0"
version = "0.8.0"
description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "tiktoken-0.7.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:485f3cc6aba7c6b6ce388ba634fbba656d9ee27f766216f45146beb4ac18b25f"},
{file = "tiktoken-0.7.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e54be9a2cd2f6d6ffa3517b064983fb695c9a9d8aa7d574d1ef3c3f931a99225"},
{file = "tiktoken-0.7.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79383a6e2c654c6040e5f8506f3750db9ddd71b550c724e673203b4f6b4b4590"},
{file = "tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d4511c52caacf3c4981d1ae2df85908bd31853f33d30b345c8b6830763f769c"},
{file = "tiktoken-0.7.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:13c94efacdd3de9aff824a788353aa5749c0faee1fbe3816df365ea450b82311"},
{file = "tiktoken-0.7.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8e58c7eb29d2ab35a7a8929cbeea60216a4ccdf42efa8974d8e176d50c9a3df5"},
{file = "tiktoken-0.7.0-cp310-cp310-win_amd64.whl", hash = "sha256:21a20c3bd1dd3e55b91c1331bf25f4af522c525e771691adbc9a69336fa7f702"},
{file = "tiktoken-0.7.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:10c7674f81e6e350fcbed7c09a65bca9356eaab27fb2dac65a1e440f2bcfe30f"},
{file = "tiktoken-0.7.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:084cec29713bc9d4189a937f8a35dbdfa785bd1235a34c1124fe2323821ee93f"},
{file = "tiktoken-0.7.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:811229fde1652fedcca7c6dfe76724d0908775b353556d8a71ed74d866f73f7b"},
{file = "tiktoken-0.7.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86b6e7dc2e7ad1b3757e8a24597415bafcfb454cebf9a33a01f2e6ba2e663992"},
{file = "tiktoken-0.7.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1063c5748be36344c7e18c7913c53e2cca116764c2080177e57d62c7ad4576d1"},
{file = "tiktoken-0.7.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:20295d21419bfcca092644f7e2f2138ff947a6eb8cfc732c09cc7d76988d4a89"},
{file = "tiktoken-0.7.0-cp311-cp311-win_amd64.whl", hash = "sha256:959d993749b083acc57a317cbc643fb85c014d055b2119b739487288f4e5d1cb"},
{file = "tiktoken-0.7.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:71c55d066388c55a9c00f61d2c456a6086673ab7dec22dd739c23f77195b1908"},
{file = "tiktoken-0.7.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:09ed925bccaa8043e34c519fbb2f99110bd07c6fd67714793c21ac298e449410"},
{file = "tiktoken-0.7.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:03c6c40ff1db0f48a7b4d2dafeae73a5607aacb472fa11f125e7baf9dce73704"},
{file = "tiktoken-0.7.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d20b5c6af30e621b4aca094ee61777a44118f52d886dbe4f02b70dfe05c15350"},
{file = "tiktoken-0.7.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d427614c3e074004efa2f2411e16c826f9df427d3c70a54725cae860f09e4bf4"},
{file = "tiktoken-0.7.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8c46d7af7b8c6987fac9b9f61041b452afe92eb087d29c9ce54951280f899a97"},
{file = "tiktoken-0.7.0-cp312-cp312-win_amd64.whl", hash = "sha256:0bc603c30b9e371e7c4c7935aba02af5994a909fc3c0fe66e7004070858d3f8f"},
{file = "tiktoken-0.7.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2398fecd38c921bcd68418675a6d155fad5f5e14c2e92fcf5fe566fa5485a858"},
{file = "tiktoken-0.7.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:8f5f6afb52fb8a7ea1c811e435e4188f2bef81b5e0f7a8635cc79b0eef0193d6"},
{file = "tiktoken-0.7.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:861f9ee616766d736be4147abac500732b505bf7013cfaf019b85892637f235e"},
{file = "tiktoken-0.7.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:54031f95c6939f6b78122c0aa03a93273a96365103793a22e1793ee86da31685"},
{file = "tiktoken-0.7.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:fffdcb319b614cf14f04d02a52e26b1d1ae14a570f90e9b55461a72672f7b13d"},
{file = "tiktoken-0.7.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:c72baaeaefa03ff9ba9688624143c858d1f6b755bb85d456d59e529e17234769"},
{file = "tiktoken-0.7.0-cp38-cp38-win_amd64.whl", hash = "sha256:131b8aeb043a8f112aad9f46011dced25d62629091e51d9dc1adbf4a1cc6aa98"},
{file = "tiktoken-0.7.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cabc6dc77460df44ec5b879e68692c63551ae4fae7460dd4ff17181df75f1db7"},
{file = "tiktoken-0.7.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8d57f29171255f74c0aeacd0651e29aa47dff6f070cb9f35ebc14c82278f3b25"},
{file = "tiktoken-0.7.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2ee92776fdbb3efa02a83f968c19d4997a55c8e9ce7be821ceee04a1d1ee149c"},
{file = "tiktoken-0.7.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e215292e99cb41fbc96988ef62ea63bb0ce1e15f2c147a61acc319f8b4cbe5bf"},
{file = "tiktoken-0.7.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:8a81bac94769cab437dd3ab0b8a4bc4e0f9cf6835bcaa88de71f39af1791727a"},
{file = "tiktoken-0.7.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:d6d73ea93e91d5ca771256dfc9d1d29f5a554b83821a1dc0891987636e0ae226"},
{file = "tiktoken-0.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:2bcb28ddf79ffa424f171dfeef9a4daff61a94c631ca6813f43967cb263b83b9"},
{file = "tiktoken-0.7.0.tar.gz", hash = "sha256:1077266e949c24e0291f6c350433c6f0971365ece2b173a23bc3b9f9defef6b6"},
{file = "tiktoken-0.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b07e33283463089c81ef1467180e3e00ab00d46c2c4bbcef0acab5f771d6695e"},
{file = "tiktoken-0.8.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9269348cb650726f44dd3bbb3f9110ac19a8dcc8f54949ad3ef652ca22a38e21"},
{file = "tiktoken-0.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25e13f37bc4ef2d012731e93e0fef21dc3b7aea5bb9009618de9a4026844e560"},
{file = "tiktoken-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f13d13c981511331eac0d01a59b5df7c0d4060a8be1e378672822213da51e0a2"},
{file = "tiktoken-0.8.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:6b2ddbc79a22621ce8b1166afa9f9a888a664a579350dc7c09346a3b5de837d9"},
{file = "tiktoken-0.8.0-cp310-cp310-win_amd64.whl", hash = "sha256:d8c2d0e5ba6453a290b86cd65fc51fedf247e1ba170191715b049dac1f628005"},
{file = "tiktoken-0.8.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d622d8011e6d6f239297efa42a2657043aaed06c4f68833550cac9e9bc723ef1"},
{file = "tiktoken-0.8.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2efaf6199717b4485031b4d6edb94075e4d79177a172f38dd934d911b588d54a"},
{file = "tiktoken-0.8.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5637e425ce1fc49cf716d88df3092048359a4b3bbb7da762840426e937ada06d"},
{file = "tiktoken-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fb0e352d1dbe15aba082883058b3cce9e48d33101bdaac1eccf66424feb5b47"},
{file = "tiktoken-0.8.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:56edfefe896c8f10aba372ab5706b9e3558e78db39dd497c940b47bf228bc419"},
{file = "tiktoken-0.8.0-cp311-cp311-win_amd64.whl", hash = "sha256:326624128590def898775b722ccc327e90b073714227175ea8febbc920ac0a99"},
{file = "tiktoken-0.8.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:881839cfeae051b3628d9823b2e56b5cc93a9e2efb435f4cf15f17dc45f21586"},
{file = "tiktoken-0.8.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fe9399bdc3f29d428f16a2f86c3c8ec20be3eac5f53693ce4980371c3245729b"},
{file = "tiktoken-0.8.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9a58deb7075d5b69237a3ff4bb51a726670419db6ea62bdcd8bd80c78497d7ab"},
{file = "tiktoken-0.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2908c0d043a7d03ebd80347266b0e58440bdef5564f84f4d29fb235b5df3b04"},
{file = "tiktoken-0.8.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:294440d21a2a51e12d4238e68a5972095534fe9878be57d905c476017bff99fc"},
{file = "tiktoken-0.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:d8f3192733ac4d77977432947d563d7e1b310b96497acd3c196c9bddb36ed9db"},
{file = "tiktoken-0.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:02be1666096aff7da6cbd7cdaa8e7917bfed3467cd64b38b1f112e96d3b06a24"},
{file = "tiktoken-0.8.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c94ff53c5c74b535b2cbf431d907fc13c678bbd009ee633a2aca269a04389f9a"},
{file = "tiktoken-0.8.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6b231f5e8982c245ee3065cd84a4712d64692348bc609d84467c57b4b72dcbc5"},
{file = "tiktoken-0.8.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4177faa809bd55f699e88c96d9bb4635d22e3f59d635ba6fd9ffedf7150b9953"},
{file = "tiktoken-0.8.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5376b6f8dc4753cd81ead935c5f518fa0fbe7e133d9e25f648d8c4dabdd4bad7"},
{file = "tiktoken-0.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:18228d624807d66c87acd8f25fc135665617cab220671eb65b50f5d70fa51f69"},
{file = "tiktoken-0.8.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7e17807445f0cf1f25771c9d86496bd8b5c376f7419912519699f3cc4dc5c12e"},
{file = "tiktoken-0.8.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:886f80bd339578bbdba6ed6d0567a0d5c6cfe198d9e587ba6c447654c65b8edc"},
{file = "tiktoken-0.8.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6adc8323016d7758d6de7313527f755b0fc6c72985b7d9291be5d96d73ecd1e1"},
{file = "tiktoken-0.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b591fb2b30d6a72121a80be24ec7a0e9eb51c5500ddc7e4c2496516dd5e3816b"},
{file = "tiktoken-0.8.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:845287b9798e476b4d762c3ebda5102be87ca26e5d2c9854002825d60cdb815d"},
{file = "tiktoken-0.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:1473cfe584252dc3fa62adceb5b1c763c1874e04511b197da4e6de51d6ce5a02"},
{file = "tiktoken-0.8.0.tar.gz", hash = "sha256:9ccbb2740f24542534369c5635cfd9b2b3c2490754a78ac8831d99f89f94eeb2"},
]
[package.dependencies]
@ -1397,13 +1410,13 @@ blobfile = ["blobfile (>=2)"]
[[package]]
name = "tomli"
version = "2.0.1"
version = "2.0.2"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
{file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
{file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
]
[[package]]
@ -1467,41 +1480,41 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "watchdog"
version = "5.0.2"
version = "5.0.3"
description = "Filesystem events monitoring"
optional = false
python-versions = ">=3.9"
files = [
{file = "watchdog-5.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d961f4123bb3c447d9fcdcb67e1530c366f10ab3a0c7d1c0c9943050936d4877"},
{file = "watchdog-5.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72990192cb63872c47d5e5fefe230a401b87fd59d257ee577d61c9e5564c62e5"},
{file = "watchdog-5.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6bec703ad90b35a848e05e1b40bf0050da7ca28ead7ac4be724ae5ac2653a1a0"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:dae7a1879918f6544201d33666909b040a46421054a50e0f773e0d870ed7438d"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c4a440f725f3b99133de610bfec93d570b13826f89616377715b9cd60424db6e"},
{file = "watchdog-5.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f8b2918c19e0d48f5f20df458c84692e2a054f02d9df25e6c3c930063eca64c1"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:aa9cd6e24126d4afb3752a3e70fce39f92d0e1a58a236ddf6ee823ff7dba28ee"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f627c5bf5759fdd90195b0c0431f99cff4867d212a67b384442c51136a098ed7"},
{file = "watchdog-5.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d7594a6d32cda2b49df3fd9abf9b37c8d2f3eab5df45c24056b4a671ac661619"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:ba32efcccfe2c58f4d01115440d1672b4eb26cdd6fc5b5818f1fb41f7c3e1889"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:963f7c4c91e3f51c998eeff1b3fb24a52a8a34da4f956e470f4b068bb47b78ee"},
{file = "watchdog-5.0.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:8c47150aa12f775e22efff1eee9f0f6beee542a7aa1a985c271b1997d340184f"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:14dd4ed023d79d1f670aa659f449bcd2733c33a35c8ffd88689d9d243885198b"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b84bff0391ad4abe25c2740c7aec0e3de316fdf7764007f41e248422a7760a7f"},
{file = "watchdog-5.0.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3e8d5ff39f0a9968952cce548e8e08f849141a4fcc1290b1c17c032ba697b9d7"},
{file = "watchdog-5.0.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:fb223456db6e5f7bd9bbd5cd969f05aae82ae21acc00643b60d81c770abd402b"},
{file = "watchdog-5.0.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:9814adb768c23727a27792c77812cf4e2fd9853cd280eafa2bcfa62a99e8bd6e"},
{file = "watchdog-5.0.2-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:901ee48c23f70193d1a7bc2d9ee297df66081dd5f46f0ca011be4f70dec80dab"},
{file = "watchdog-5.0.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:638bcca3d5b1885c6ec47be67bf712b00a9ab3d4b22ec0881f4889ad870bc7e8"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5597c051587f8757798216f2485e85eac583c3b343e9aa09127a3a6f82c65ee8"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_armv7l.whl", hash = "sha256:53ed1bf71fcb8475dd0ef4912ab139c294c87b903724b6f4a8bd98e026862e6d"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_i686.whl", hash = "sha256:29e4a2607bd407d9552c502d38b45a05ec26a8e40cc7e94db9bb48f861fa5abc"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_ppc64.whl", hash = "sha256:b6dc8f1d770a8280997e4beae7b9a75a33b268c59e033e72c8a10990097e5fde"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:d2ab34adc9bf1489452965cdb16a924e97d4452fcf88a50b21859068b50b5c3b"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_s390x.whl", hash = "sha256:7d1aa7e4bb0f0c65a1a91ba37c10e19dabf7eaaa282c5787e51371f090748f4b"},
{file = "watchdog-5.0.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:726eef8f8c634ac6584f86c9c53353a010d9f311f6c15a034f3800a7a891d941"},
{file = "watchdog-5.0.2-py3-none-win32.whl", hash = "sha256:bda40c57115684d0216556671875e008279dea2dc00fcd3dde126ac8e0d7a2fb"},
{file = "watchdog-5.0.2-py3-none-win_amd64.whl", hash = "sha256:d010be060c996db725fbce7e3ef14687cdcc76f4ca0e4339a68cc4532c382a73"},
{file = "watchdog-5.0.2-py3-none-win_ia64.whl", hash = "sha256:3960136b2b619510569b90f0cd96408591d6c251a75c97690f4553ca88889769"},
{file = "watchdog-5.0.2.tar.gz", hash = "sha256:dcebf7e475001d2cdeb020be630dc5b687e9acdd60d16fea6bb4508e7b94cf76"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:85527b882f3facda0579bce9d743ff7f10c3e1e0db0a0d0e28170a7d0e5ce2ea"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:53adf73dcdc0ef04f7735066b4a57a4cd3e49ef135daae41d77395f0b5b692cb"},
{file = "watchdog-5.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e25adddab85f674acac303cf1f5835951345a56c5f7f582987d266679979c75b"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:f01f4a3565a387080dc49bdd1fefe4ecc77f894991b88ef927edbfa45eb10818"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:91b522adc25614cdeaf91f7897800b82c13b4b8ac68a42ca959f992f6990c490"},
{file = "watchdog-5.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d52db5beb5e476e6853da2e2d24dbbbed6797b449c8bf7ea118a4ee0d2c9040e"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:94d11b07c64f63f49876e0ab8042ae034674c8653bfcdaa8c4b32e71cfff87e8"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:349c9488e1d85d0a58e8cb14222d2c51cbc801ce11ac3936ab4c3af986536926"},
{file = "watchdog-5.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:53a3f10b62c2d569e260f96e8d966463dec1a50fa4f1b22aec69e3f91025060e"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:950f531ec6e03696a2414b6308f5c6ff9dab7821a768c9d5788b1314e9a46ca7"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ae6deb336cba5d71476caa029ceb6e88047fc1dc74b62b7c4012639c0b563906"},
{file = "watchdog-5.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1021223c08ba8d2d38d71ec1704496471ffd7be42cfb26b87cd5059323a389a1"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:752fb40efc7cc8d88ebc332b8f4bcbe2b5cc7e881bccfeb8e25054c00c994ee3"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a2e8f3f955d68471fa37b0e3add18500790d129cc7efe89971b8a4cc6fdeb0b2"},
{file = "watchdog-5.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b8ca4d854adcf480bdfd80f46fdd6fb49f91dd020ae11c89b3a79e19454ec627"},
{file = "watchdog-5.0.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:90a67d7857adb1d985aca232cc9905dd5bc4803ed85cfcdcfcf707e52049eda7"},
{file = "watchdog-5.0.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:720ef9d3a4f9ca575a780af283c8fd3a0674b307651c1976714745090da5a9e8"},
{file = "watchdog-5.0.3-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:223160bb359281bb8e31c8f1068bf71a6b16a8ad3d9524ca6f523ac666bb6a1e"},
{file = "watchdog-5.0.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:560135542c91eaa74247a2e8430cf83c4342b29e8ad4f520ae14f0c8a19cfb5b"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:dd021efa85970bd4824acacbb922066159d0f9e546389a4743d56919b6758b91"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_armv7l.whl", hash = "sha256:78864cc8f23dbee55be34cc1494632a7ba30263951b5b2e8fc8286b95845f82c"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_i686.whl", hash = "sha256:1e9679245e3ea6498494b3028b90c7b25dbb2abe65c7d07423ecfc2d6218ff7c"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_ppc64.whl", hash = "sha256:9413384f26b5d050b6978e6fcd0c1e7f0539be7a4f1a885061473c5deaa57221"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:294b7a598974b8e2c6123d19ef15de9abcd282b0fbbdbc4d23dfa812959a9e05"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_s390x.whl", hash = "sha256:26dd201857d702bdf9d78c273cafcab5871dd29343748524695cecffa44a8d97"},
{file = "watchdog-5.0.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:0f9332243355643d567697c3e3fa07330a1d1abf981611654a1f2bf2175612b7"},
{file = "watchdog-5.0.3-py3-none-win32.whl", hash = "sha256:c66f80ee5b602a9c7ab66e3c9f36026590a0902db3aea414d59a2f55188c1f49"},
{file = "watchdog-5.0.3-py3-none-win_amd64.whl", hash = "sha256:f00b4cf737f568be9665563347a910f8bdc76f88c2970121c86243c8cfdf90e9"},
{file = "watchdog-5.0.3-py3-none-win_ia64.whl", hash = "sha256:49f4d36cb315c25ea0d946e018c01bb028048023b9e103d3d3943f58e109dd45"},
{file = "watchdog-5.0.3.tar.gz", hash = "sha256:108f42a7f0345042a854d4d0ad0834b741d421330d5f575b81cb27b883500176"},
]
[package.extras]
@ -1510,4 +1523,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "d166346e440b35690981e166fd6d92f254afcdd57a661ba9f811842cc96622e7"
content-hash = "fec30c155fdf4518310435f840b374ff86183936452fed3f59ad8ee0fcfefac5"

4
libs/partners/openai/pyproject.toml
View File

@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-openai"
version = "0.2.1"
version = "0.2.2"
description = "An integration package connecting OpenAI and LangChain"
authors = []
readme = "README.md"
@ -23,7 +23,7 @@ ignore_missing_imports = true
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3"
langchain-core = "^0.3.9"
openai = "^1.40.0"
tiktoken = ">=0.7,<1"

24
libs/partners/openai/tests/integration_tests/chat_models/test_base.py
View File

@ -238,30 +238,6 @@ async def test_async_chat_openai_bind_functions() -> None:
assert isinstance(generation, AIMessage)
def test_chat_openai_extra_kwargs() -> None:
"""Test extra kwargs to chat openai."""
# Check that foo is saved in extra_kwargs.
llm = ChatOpenAI(foo=3, max_tokens=10) # type: ignore[call-arg]
assert llm.max_tokens == 10
assert llm.model_kwargs == {"foo": 3}
# Test that if extra_kwargs are provided, they are added to it.
llm = ChatOpenAI(foo=3, model_kwargs={"bar": 2}) # type: ignore[call-arg]
assert llm.model_kwargs == {"foo": 3, "bar": 2}
# Test that if provided twice it errors
with pytest.raises(ValueError):
ChatOpenAI(foo=3, model_kwargs={"foo": 2}) # type: ignore[call-arg]
# Test that if explicit param is specified in kwargs it errors
with pytest.raises(ValueError):
ChatOpenAI(model_kwargs={"temperature": 0.2})
# Test that "model" cannot be specified in kwargs
with pytest.raises(ValueError):
ChatOpenAI(model_kwargs={"model": "gpt-3.5-turbo-instruct"})
@pytest.mark.scheduled
def test_openai_streaming() -> None:
"""Test streaming tokens from OpenAI."""

54
libs/partners/openai/tests/integration_tests/chat_models/test_base_standard.py
View File

@ -1,12 +1,16 @@
"""Standard LangChain interface tests"""
from typing import Type
from pathlib import Path
from typing import Dict, List, Literal, Type, cast
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_standard_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import ChatOpenAI
REPO_ROOT_DIR = Path(__file__).parents[6]
class TestOpenAIStandard(ChatModelIntegrationTests):
@property
@ -20,3 +24,51 @@ class TestOpenAIStandard(ChatModelIntegrationTests):
@property
def supports_image_inputs(self) -> bool:
return True
@property
def supported_usage_metadata_details(
self,
) -> Dict[
Literal["invoke", "stream"],
List[
Literal[
"audio_input",
"audio_output",
"reasoning_output",
"cache_read_input",
"cache_creation_input",
]
],
]:
return {"invoke": ["reasoning_output", "cache_read_input"], "stream": []}
def invoke_with_cache_read_input(self, *, stream: bool = False) -> AIMessage:
with open(REPO_ROOT_DIR / "README.md", "r") as f:
readme = f.read()
input_ = f"""What's langchain? Here's the langchain README:
{readme}
"""
llm = ChatOpenAI(model="gpt-4o-mini", stream_usage=True)
_invoke(llm, input_, stream)
# invoke twice so first invocation is cached
return _invoke(llm, input_, stream)
def invoke_with_reasoning_output(self, *, stream: bool = False) -> AIMessage:
llm = ChatOpenAI(model="o1-mini", stream_usage=True, temperature=1)
input_ = (
"explain the relationship between the 2008/9 economic crisis and the "
"startup ecosystem in the early 2010s"
)
return _invoke(llm, input_, stream)
def _invoke(llm: ChatOpenAI, input_: str, stream: bool) -> AIMessage:
if stream:
full = None
for chunk in llm.stream(input_):
full = full + chunk if full else chunk # type: ignore[operator]
return cast(AIMessage, full)
else:
return cast(AIMessage, llm.invoke(input_))

19
libs/partners/openai/tests/unit_tests/chat_models/test_base.py
View File

@ -23,6 +23,7 @@ from langchain_openai import ChatOpenAI
from langchain_openai.chat_models.base import (
_convert_dict_to_message,
_convert_message_to_dict,
_create_usage_metadata,
_format_message_content,
)
@ -730,3 +731,21 @@ def test_schema_from_with_structured_output(schema: Type) -> None:
}
actual = structured_llm.get_output_schema().model_json_schema()
assert actual == expected
def test__create_usage_metadata() -> None:
usage_metadata = {
"completion_tokens": 15,
"prompt_tokens_details": None,
"completion_tokens_details": None,
"prompt_tokens": 11,
"total_tokens": 26,
}
result = _create_usage_metadata(usage_metadata)
assert result == UsageMetadata(
output_tokens=15,
input_tokens=11,
total_tokens=26,
input_token_details={},
output_token_details={},
)

9
libs/partners/openai/tests/unit_tests/llms/test_base.py
View File

@ -30,9 +30,12 @@ def test_openai_model_kwargs() -> None:
assert llm.model_kwargs == {"foo": "bar"}
def test_openai_invalid_model_kwargs() -> None:
with pytest.raises(ValueError):
OpenAI(model_kwargs={"model_name": "foo"})
def test_openai_fields_in_model_kwargs() -> None:
"""Test that for backwards compatibility fields can be passed in as model_kwargs."""
llm = OpenAI(model_kwargs={"model_name": "foo"})
assert llm.model_name == "foo"
llm = OpenAI(model_kwargs={"model": "foo"})
assert llm.model_name == "foo"
def test_openai_incorrect_field() -> None:

95
libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py
View File

@ -151,6 +151,61 @@ class ChatModelIntegrationTests(ChatModelTests):
assert isinstance(result.usage_metadata["output_tokens"], int)
assert isinstance(result.usage_metadata["total_tokens"], int)
if "audio_input" in self.supported_usage_metadata_details["invoke"]:
msg = self.invoke_with_audio_input()
assert msg.usage_metadata is not None
assert msg.usage_metadata["input_token_details"] is not None
assert isinstance(msg.usage_metadata["input_token_details"]["audio"], int)
assert msg.usage_metadata["input_tokens"] >= sum(
(v or 0) # type: ignore[misc]
for v in msg.usage_metadata["input_token_details"].values()
)
if "audio_output" in self.supported_usage_metadata_details["invoke"]:
msg = self.invoke_with_audio_output()
assert msg.usage_metadata is not None
assert msg.usage_metadata["output_token_details"] is not None
assert isinstance(msg.usage_metadata["output_token_details"]["audio"], int)
assert int(msg.usage_metadata["output_tokens"]) >= sum(
(v or 0) # type: ignore[misc]
for v in msg.usage_metadata["output_token_details"].values()
)
if "reasoning_output" in self.supported_usage_metadata_details["invoke"]:
msg = self.invoke_with_reasoning_output()
assert msg.usage_metadata is not None
assert msg.usage_metadata["output_token_details"] is not None
assert isinstance(
msg.usage_metadata["output_token_details"]["reasoning"],
int,
)
assert msg.usage_metadata["output_tokens"] >= sum(
(v or 0) # type: ignore[misc]
for v in msg.usage_metadata["output_token_details"].values()
)
if "cache_read_input" in self.supported_usage_metadata_details["invoke"]:
msg = self.invoke_with_cache_read_input()
assert msg.usage_metadata is not None
assert msg.usage_metadata["input_token_details"] is not None
assert isinstance(
msg.usage_metadata["input_token_details"]["cache_read"],
int,
)
assert msg.usage_metadata["input_tokens"] >= sum(
(v or 0) # type: ignore[misc]
for v in msg.usage_metadata["input_token_details"].values()
)
if "cache_creation_input" in self.supported_usage_metadata_details["invoke"]:
msg = self.invoke_with_cache_creation_input()
assert msg.usage_metadata is not None
assert msg.usage_metadata["input_token_details"] is not None
assert isinstance(
msg.usage_metadata["input_token_details"]["cache_creation"],
int,
)
assert msg.usage_metadata["input_tokens"] >= sum(
(v or 0) # type: ignore[misc]
for v in msg.usage_metadata["input_token_details"].values()
)
def test_usage_metadata_streaming(self, model: BaseChatModel) -> None:
if not self.returns_usage_metadata:
pytest.skip("Not implemented.")
@ -164,6 +219,31 @@ class ChatModelIntegrationTests(ChatModelTests):
assert isinstance(full.usage_metadata["output_tokens"], int)
assert isinstance(full.usage_metadata["total_tokens"], int)
if "audio_input" in self.supported_usage_metadata_details["stream"]:
msg = self.invoke_with_audio_input(stream=True)
assert isinstance(msg.usage_metadata["input_token_details"]["audio"], int) # type: ignore[index]
if "audio_output" in self.supported_usage_metadata_details["stream"]:
msg = self.invoke_with_audio_output(stream=True)
assert isinstance(msg.usage_metadata["output_token_details"]["audio"], int) # type: ignore[index]
if "reasoning_output" in self.supported_usage_metadata_details["stream"]:
msg = self.invoke_with_reasoning_output(stream=True)
assert isinstance(
msg.usage_metadata["output_token_details"]["reasoning"], # type: ignore[index]
int,
)
if "cache_read_input" in self.supported_usage_metadata_details["stream"]:
msg = self.invoke_with_cache_read_input(stream=True)
assert isinstance(
msg.usage_metadata["input_token_details"]["cache_read"], # type: ignore[index]
int,
)
if "cache_creation_input" in self.supported_usage_metadata_details["stream"]:
msg = self.invoke_with_cache_creation_input(stream=True)
assert isinstance(
msg.usage_metadata["input_token_details"]["cache_creation"], # type: ignore[index]
int,
)
def test_stop_sequence(self, model: BaseChatModel) -> None:
result = model.invoke("hi", stop=["you"])
assert isinstance(result, AIMessage)
@ -608,3 +688,18 @@ class ChatModelIntegrationTests(ChatModelTests):
assert isinstance(result, AIMessage)
assert isinstance(result.content, str)
assert len(result.content) > 0
def invoke_with_audio_input(self, *, stream: bool = False) -> AIMessage:
raise NotImplementedError()
def invoke_with_audio_output(self, *, stream: bool = False) -> AIMessage:
raise NotImplementedError()
def invoke_with_reasoning_output(self, *, stream: bool = False) -> AIMessage:
raise NotImplementedError()
def invoke_with_cache_read_input(self, *, stream: bool = False) -> AIMessage:
raise NotImplementedError()
def invoke_with_cache_creation_input(self, *, stream: bool = False) -> AIMessage:
raise NotImplementedError()

19
libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py
View File

@ -2,7 +2,7 @@
import os
from abc import abstractmethod
from typing import Any, List, Literal, Optional, Tuple, Type
from typing import Any, Dict, List, Literal, Optional, Tuple, Type
from unittest import mock
import pytest
@ -138,6 +138,23 @@ class ChatModelTests(BaseStandardTests):
def supports_image_tool_message(self) -> bool:
return False
@property
def supported_usage_metadata_details(
self,
) -> Dict[
Literal["invoke", "stream"],
List[
Literal[
"audio_input",
"audio_output",
"reasoning_output",
"cache_read_input",
"cache_creation_input",
]
],
]:
return {"invoke": [], "stream": []}
class ChatModelUnitTests(ChatModelTests):
@property