diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml
index 5cb09453a37..9faaaa1c20a 100644
--- a/.github/workflows/api_doc_build.yml
+++ b/.github/workflows/api_doc_build.yml
@@ -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
diff --git a/MIGRATE.md b/MIGRATE.md
index 7254e9b7e3e..65008650764 100644
--- a/MIGRATE.md
+++ b/MIGRATE.md
@@ -1,70 +1,11 @@
# Migrating
-## π¨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.
diff --git a/docs/docs/concepts/index.mdx b/docs/docs/concepts/index.mdx
index a2678549369..fa6e7bcf3cf 100644
--- a/docs/docs/concepts/index.mdx
+++ b/docs/docs/concepts/index.mdx
@@ -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
diff --git a/docs/docs/integrations/chat/sambastudio.ipynb b/docs/docs/integrations/chat/sambastudio.ipynb
new file mode 100644
index 00000000000..9301d68fe33
--- /dev/null
+++ b/docs/docs/integrations/chat/sambastudio.ipynb
@@ -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) | β | β | β |  |  |\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",
+ "| β | β | β | β | β | β | β
| β
| β
| β | \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
+}
diff --git a/docs/docs/integrations/document_loaders/airbyte.ipynb b/docs/docs/integrations/document_loaders/airbyte.ipynb
index 1c992782f19..e0afa4e603b 100644
--- a/docs/docs/integrations/document_loaders/airbyte.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte.ipynb
@@ -29,7 +29,7 @@
"metadata": {},
"outputs": [],
"source": [
- "% pip install -qU langchain-airbyte"
+ "%pip install -qU langchain-airbyte"
]
},
{
diff --git a/docs/docs/integrations/document_loaders/browserbase.ipynb b/docs/docs/integrations/document_loaders/browserbase.ipynb
index ae497ba6dad..149fef73861 100644
--- a/docs/docs/integrations/document_loaders/browserbase.ipynb
+++ b/docs/docs/integrations/document_loaders/browserbase.ipynb
@@ -26,7 +26,7 @@
"metadata": {},
"outputs": [],
"source": [
- "% pip install browserbase"
+ "%pip install browserbase"
]
},
{
diff --git a/docs/docs/integrations/document_loaders/upstage.ipynb b/docs/docs/integrations/document_loaders/upstage.ipynb
index c6348b758a8..6eb29aec203 100644
--- a/docs/docs/integrations/document_loaders/upstage.ipynb
+++ b/docs/docs/integrations/document_loaders/upstage.ipynb
@@ -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",
diff --git a/docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb b/docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb
new file mode 100644
index 00000000000..190cf8db74d
--- /dev/null
+++ b/docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb
@@ -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 NVIDIAβs 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) | β
| beta | β |  |  |\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",
+ "| β | β
| β | β | β
| β | β | β | \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
+}
diff --git a/docs/docs/integrations/providers/upstage.ipynb b/docs/docs/integrations/providers/upstage.ipynb
index 9dfce63e351..8e5cb603109 100644
--- a/docs/docs/integrations/providers/upstage.ipynb
+++ b/docs/docs/integrations/providers/upstage.ipynb
@@ -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",
diff --git a/docs/docs/integrations/retrievers/box.ipynb b/docs/docs/integrations/retrievers/box.ipynb
index 8b25c1089e8..a4abf132ac5 100644
--- a/docs/docs/integrations/retrievers/box.ipynb
+++ b/docs/docs/integrations/retrievers/box.ipynb
@@ -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: Β·Β·Β·Β·Β·Β·Β·Β·\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: Β·Β·Β·Β·Β·Β·Β·Β·\n"
diff --git a/docs/docs/integrations/vectorstores/google_spanner.ipynb b/docs/docs/integrations/vectorstores/google_spanner.ipynb
index c7c0d90299c..fb06a692964 100644
--- a/docs/docs/integrations/vectorstores/google_spanner.ipynb
+++ b/docs/docs/integrations/vectorstores/google_spanner.ipynb
@@ -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": [
- "#### π 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": [
- "#### π 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": [
- "#### π 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": [
- "#### π 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": [
- "#### π 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,
diff --git a/docs/docs/tutorials/local_rag.ipynb b/docs/docs/tutorials/local_rag.ipynb
index bdff9dfdfcf..01712fa94a5 100644
--- a/docs/docs/tutorials/local_rag.ipynb
+++ b/docs/docs/tutorials/local_rag.ipynb
@@ -60,7 +60,7 @@
"%pip install -qU langchain_ollama\n",
"\n",
"# Web Loader\n",
- "% pip install -qU beautifulsoup4"
+ "%pip install -qU beautifulsoup4"
]
},
{
diff --git a/docs/docs/versions/migrating_chains/map_reduce_chain.ipynb b/docs/docs/versions/migrating_chains/map_reduce_chain.ipynb
index 40ca0040082..4f03f98092e 100644
--- a/docs/docs/versions/migrating_chains/map_reduce_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/map_reduce_chain.ipynb
@@ -196,7 +196,7 @@
"metadata": {},
"outputs": [],
"source": [
- "% pip install -qU langgraph"
+ "%pip install -qU langgraph"
]
},
{
diff --git a/docs/scripts/generate_api_reference_links.py b/docs/scripts/generate_api_reference_links.py
index 2f94390838b..662d1dd12f7 100644
--- a/docs/scripts/generate_api_reference_links.py
+++ b/docs/scripts/generate_api_reference_links.py
@@ -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}")
diff --git a/libs/community/langchain_community/callbacks/openai_info.py b/libs/community/langchain_community/callbacks/openai_info.py
index 7fbb0d097e1..9e2c070ccd2 100644
--- a/libs/community/langchain_community/callbacks/openai_info.py
+++ b/libs/community/langchain_community/callbacks/openai_info.py
@@ -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
diff --git a/libs/community/langchain_community/chat_models/__init__.py b/libs/community/langchain_community/chat_models/__init__.py
index 021f6f5c9d1..db507637528 100644
--- a/libs/community/langchain_community/chat_models/__init__.py
+++ b/libs/community/langchain_community/chat_models/__init__.py
@@ -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",
diff --git a/libs/community/langchain_community/chat_models/sambanova.py b/libs/community/langchain_community/chat_models/sambanova.py
index deaee597879..cd95f4beefa 100644
--- a/libs/community/langchain_community/chat_models/sambanova.py
+++ b/libs/community/langchain_community/chat_models/sambanova.py
@@ -1,5 +1,5 @@
import json
-from typing import Any, Dict, Iterator, List, Optional
+from typing import Any, Dict, Iterator, List, Optional, Tuple
import requests
from langchain_core.callbacks import (
@@ -13,6 +13,7 @@ from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
+ BaseMessageChunk,
ChatMessage,
HumanMessage,
SystemMessage,
@@ -21,6 +22,46 @@ from langchain_core.messages import (
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from pydantic import Field, SecretStr
+from requests import Response
+
+
+def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
+ """
+ convert a BaseMessage to a dictionary with Role / content
+
+ Args:
+ message: BaseMessage
+
+ Returns:
+ messages_dict: role / content dict
+ """
+ if isinstance(message, ChatMessage):
+ message_dict = {"role": message.role, "content": message.content}
+ elif isinstance(message, SystemMessage):
+ message_dict = {"role": "system", "content": message.content}
+ elif isinstance(message, HumanMessage):
+ message_dict = {"role": "user", "content": message.content}
+ elif isinstance(message, AIMessage):
+ message_dict = {"role": "assistant", "content": message.content}
+ elif isinstance(message, ToolMessage):
+ message_dict = {"role": "tool", "content": message.content}
+ else:
+ raise TypeError(f"Got unknown type {message}")
+ return message_dict
+
+
+def _create_message_dicts(messages: List[BaseMessage]) -> List[Dict[str, Any]]:
+ """
+ Convert a list of BaseMessages to a list of dictionaries with Role / content
+
+ Args:
+ messages: list of BaseMessages
+
+ Returns:
+ messages_dicts: list of role / content dicts
+ """
+ message_dicts = [_convert_message_to_dict(m) for m in messages]
+ return message_dicts
class ChatSambaNovaCloud(BaseChatModel):
@@ -28,7 +69,7 @@ class ChatSambaNovaCloud(BaseChatModel):
SambaNova Cloud chat model.
Setup:
- To use, you should have the environment variables
+ To use, you should have the environment variables:
``SAMBANOVA_URL`` set with your SambaNova Cloud URL.
``SAMBANOVA_API_KEY`` set with your SambaNova Cloud API Key.
http://cloud.sambanova.ai/
@@ -38,7 +79,6 @@ class ChatSambaNovaCloud(BaseChatModel):
sambanova_url = SambaNova cloud endpoint URL,
sambanova_api_key = set with your SambaNova cloud API key,
model = model name,
- streaming = set True for use streaming API
max_tokens = max number of tokens to generate,
temperature = model temperature,
top_p = model top p,
@@ -48,9 +88,9 @@ class ChatSambaNovaCloud(BaseChatModel):
Key init args β completion params:
model: str
- The name of the model to use, e.g., llama3-8b.
+ The name of the model to use, e.g., Meta-Llama-3-70B-Instruct.
streaming: bool
- Whether to use streaming or not
+ Whether to use streaming handler when using non streaming methods
max_tokens: int
max tokens to generate
temperature: float
@@ -77,7 +117,6 @@ class ChatSambaNovaCloud(BaseChatModel):
sambanova_url = SambaNova cloud endpoint URL,
sambanova_api_key = set with your SambaNova cloud API key,
model = model name,
- streaming = set True for streaming
max_tokens = max number of tokens to generate,
temperature = model temperature,
top_p = model top p,
@@ -123,11 +162,11 @@ class ChatSambaNovaCloud(BaseChatModel):
sambanova_api_key: SecretStr = Field(default="")
"""SambaNova Cloud api key"""
- model: str = Field(default="llama3-8b")
+ model: str = Field(default="Meta-Llama-3.1-8B-Instruct")
"""The name of the model"""
streaming: bool = Field(default=False)
- """Whether to use streaming or not"""
+ """Whether to use streaming handler when using non streaming methods"""
max_tokens: int = Field(default=1024)
"""max tokens to generate"""
@@ -135,10 +174,10 @@ class ChatSambaNovaCloud(BaseChatModel):
temperature: float = Field(default=0.7)
"""model temperature"""
- top_p: float = Field(default=0.0)
+ top_p: Optional[float] = Field()
"""model top p"""
- top_k: int = Field(default=1)
+ top_k: Optional[int] = Field()
"""model top k"""
stream_options: dict = Field(default={"include_usage": True})
@@ -225,15 +264,15 @@ class ChatSambaNovaCloud(BaseChatModel):
if response.status_code != 200:
raise RuntimeError(
f"Sambanova /complete call failed with status code "
- f"{response.status_code}."
- f"{response.text}."
+ f"{response.status_code}.",
+ f"{response.text}.",
)
response_dict = response.json()
if response_dict.get("error"):
raise RuntimeError(
f"Sambanova /complete call failed with status code "
- f"{response.status_code}."
- f"{response_dict}."
+ f"{response.status_code}.",
+ f"{response_dict}.",
)
return response_dict
@@ -247,7 +286,7 @@ class ChatSambaNovaCloud(BaseChatModel):
messages_dicts: List of role / content dicts to use as input.
stop: list of stop tokens
- Returns:
+ Yields:
An iterator of response dicts.
"""
try:
@@ -289,82 +328,38 @@ class ChatSambaNovaCloud(BaseChatModel):
)
for event in client.events():
- chunk = {
- "event": event.event,
- "data": event.data,
- "status_code": response.status_code,
- }
-
- if chunk["event"] == "error_event" or chunk["status_code"] != 200:
+ if event.event == "error_event":
raise RuntimeError(
f"Sambanova /complete call failed with status code "
- f"{chunk['status_code']}."
- f"{chunk}."
+ f"{response.status_code}."
+ f"{event.data}."
)
try:
# check if the response is a final event
# in that case event data response is '[DONE]'
- if chunk["data"] != "[DONE]":
- if isinstance(chunk["data"], str):
- data = json.loads(chunk["data"])
+ if event.data != "[DONE]":
+ if isinstance(event.data, str):
+ data = json.loads(event.data)
else:
raise RuntimeError(
f"Sambanova /complete call failed with status code "
- f"{chunk['status_code']}."
- f"{chunk}."
+ f"{response.status_code}."
+ f"{event.data}."
)
if data.get("error"):
raise RuntimeError(
f"Sambanova /complete call failed with status code "
- f"{chunk['status_code']}."
- f"{chunk}."
+ f"{response.status_code}."
+ f"{event.data}."
)
yield data
- except Exception:
- raise Exception(
- f"Error getting content chunk raw streamed response: {chunk}"
+ except Exception as e:
+ raise RuntimeError(
+ f"Error getting content chunk raw streamed response: {e}"
+ f"data: {event.data}"
)
- def _convert_message_to_dict(self, message: BaseMessage) -> Dict[str, Any]:
- """
- convert a BaseMessage to a dictionary with Role / content
-
- Args:
- message: BaseMessage
-
- Returns:
- messages_dict: role / content dict
- """
- if isinstance(message, ChatMessage):
- message_dict = {"role": message.role, "content": message.content}
- elif isinstance(message, SystemMessage):
- message_dict = {"role": "system", "content": message.content}
- elif isinstance(message, HumanMessage):
- message_dict = {"role": "user", "content": message.content}
- elif isinstance(message, AIMessage):
- message_dict = {"role": "assistant", "content": message.content}
- elif isinstance(message, ToolMessage):
- message_dict = {"role": "tool", "content": message.content}
- else:
- raise TypeError(f"Got unknown type {message}")
- return message_dict
-
- def _create_message_dicts(
- self, messages: List[BaseMessage]
- ) -> List[Dict[str, Any]]:
- """
- convert a lit of BaseMessages to a list of dictionaries with Role / content
-
- Args:
- messages: list of BaseMessages
-
- Returns:
- messages_dicts: list of role / content dicts
- """
- message_dicts = [self._convert_message_to_dict(m) for m in messages]
- return message_dicts
-
def _generate(
self,
messages: List[BaseMessage],
@@ -373,9 +368,7 @@ class ChatSambaNovaCloud(BaseChatModel):
**kwargs: Any,
) -> ChatResult:
"""
- SambaNovaCloud chat model logic.
-
- Call SambaNovaCloud API.
+ Call SambaNovaCloud models.
Args:
messages: the prompt composed of a list of messages.
@@ -386,6 +379,9 @@ class ChatSambaNovaCloud(BaseChatModel):
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
+
+ Returns:
+ result: ChatResult with model generation
"""
if self.streaming:
stream_iter = self._stream(
@@ -393,7 +389,7 @@ class ChatSambaNovaCloud(BaseChatModel):
)
if stream_iter:
return generate_from_stream(stream_iter)
- messages_dicts = self._create_message_dicts(messages)
+ messages_dicts = _create_message_dicts(messages)
response = self._handle_request(messages_dicts, stop)
message = AIMessage(
content=response["choices"][0]["message"]["content"],
@@ -430,8 +426,11 @@ class ChatSambaNovaCloud(BaseChatModel):
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
+
+ Yields:
+ chunk: ChatGenerationChunk with model partial generation
"""
- messages_dicts = self._create_message_dicts(messages)
+ messages_dicts = _create_message_dicts(messages)
finish_reason = None
for partial_response in self._handle_streaming_request(messages_dicts, stop):
if len(partial_response["choices"]) > 0:
@@ -463,3 +462,751 @@ class ChatSambaNovaCloud(BaseChatModel):
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
+
+
+class ChatSambaStudio(BaseChatModel):
+ """
+ SambaStudio chat model.
+
+ Setup:
+ To use, you should have the environment variables:
+ ``SAMBASTUDIO_URL`` set with your SambaStudio deployed endpoint URL.
+ ``SAMBASTUDIO_API_KEY`` set with your SambaStudio deployed endpoint Key.
+ https://docs.sambanova.ai/sambastudio/latest/index.html
+ Example:
+ .. code-block:: python
+ ChatSambaStudio(
+ sambastudio_url = set with your SambaStudio deployed endpoint URL,
+ sambastudio_api_key = set with your SambaStudio deployed endpoint Key.
+ model = model or expert name (set for CoE endpoints),
+ max_tokens = max number of tokens to generate,
+ temperature = model temperature,
+ top_p = model top p,
+ top_k = model top k,
+ do_sample = wether to do sample
+ process_prompt = wether to process prompt
+ (set for CoE generic v1 and v2 endpoints)
+ stream_options = include usage to get generation metrics
+ special_tokens = start, start_role, end_role, end special tokens
+ (set for CoE generic v1 and v2 endpoints when process prompt
+ set to false or for StandAlone v1 and v2 endpoints)
+ model_kwargs: Optional = Extra Key word arguments to pass to the model.
+ )
+
+ Key init args β completion params:
+ model: str
+ The name of the model to use, e.g., Meta-Llama-3-70B-Instruct-4096
+ (set for CoE endpoints).
+ streaming: bool
+ Whether to use streaming
+ max_tokens: inthandler when using non streaming methods
+ max tokens to generate
+ temperature: float
+ model temperature
+ top_p: float
+ model top p
+ top_k: int
+ model top k
+ do_sample: bool
+ wether to do sample
+ process_prompt:
+ wether to process prompt (set for CoE generic v1 and v2 endpoints)
+ stream_options: dict
+ stream options, include usage to get generation metrics
+ special_tokens: dict
+ start, start_role, end_role and end special tokens
+ (set for CoE generic v1 and v2 endpoints when process prompt set to false
+ or for StandAlone v1 and v2 endpoints) default to llama3 special tokens
+ model_kwargs: dict
+ Extra Key word arguments to pass to the model.
+
+ Key init args β client params:
+ sambastudio_url: str
+ SambaStudio endpoint Url
+ sambastudio_api_key: str
+ SambaStudio endpoint api key
+
+ Instantiate:
+ .. code-block:: python
+
+ from langchain_community.chat_models import ChatSambaStudio
+
+ chat = ChatSambaStudio=(
+ sambastudio_url = set with your SambaStudio deployed endpoint URL,
+ sambastudio_api_key = set with your SambaStudio deployed endpoint Key.
+ model = model or expert name (set for CoE endpoints),
+ max_tokens = max number of tokens to generate,
+ temperature = model temperature,
+ top_p = model top p,
+ top_k = model top k,
+ do_sample = wether to do sample
+ process_prompt = wether to process prompt
+ (set for CoE generic v1 and v2 endpoints)
+ stream_options = include usage to get generation metrics
+ special_tokens = start, start_role, end_role, and special tokens
+ (set for CoE generic v1 and v2 endpoints when process prompt
+ set to false or for StandAlone v1 and v2 endpoints)
+ model_kwargs: Optional = Extra Key word arguments to pass to the model.
+ )
+ Invoke:
+ .. code-block:: python
+ messages = [
+ SystemMessage(content="your are an AI assistant."),
+ HumanMessage(content="tell me a joke."),
+ ]
+ response = chat.invoke(messages)
+
+ Stream:
+ .. code-block:: python
+
+ for chunk in chat.stream(messages):
+ print(chunk.content, end="", flush=True)
+
+ Async:
+ .. code-block:: python
+
+ response = chat.ainvoke(messages)
+ await response
+
+ Token usage:
+ .. code-block:: python
+ response = chat.invoke(messages)
+ print(response.response_metadata["usage"]["prompt_tokens"]
+ print(response.response_metadata["usage"]["total_tokens"]
+
+ Response metadata
+ .. code-block:: python
+
+ response = chat.invoke(messages)
+ print(response.response_metadata)
+ """
+
+ sambastudio_url: str = Field(default="")
+ """SambaStudio Url"""
+
+ sambastudio_api_key: SecretStr = Field(default="")
+ """SambaStudio api key"""
+
+ base_url: str = Field(default="", exclude=True)
+ """SambaStudio non streaming Url"""
+
+ streaming_url: str = Field(default="", exclude=True)
+ """SambaStudio streaming Url"""
+
+ model: Optional[str] = Field()
+ """The name of the model or expert to use (for CoE endpoints)"""
+
+ streaming: bool = Field(default=False)
+ """Whether to use streaming handler when using non streaming methods"""
+
+ max_tokens: int = Field(default=1024)
+ """max tokens to generate"""
+
+ temperature: Optional[float] = Field(default=0.7)
+ """model temperature"""
+
+ top_p: Optional[float] = Field()
+ """model top p"""
+
+ top_k: Optional[int] = Field()
+ """model top k"""
+
+ do_sample: Optional[bool] = Field()
+ """whether to do sampling"""
+
+ process_prompt: Optional[bool] = Field()
+ """whether process prompt (for CoE generic v1 and v2 endpoints)"""
+
+ stream_options: dict = Field(default={"include_usage": True})
+ """stream options, include usage to get generation metrics"""
+
+ special_tokens: dict = Field(
+ default={
+ "start": "<|begin_of_text|>",
+ "start_role": "<|begin_of_text|><|start_header_id|>{role}<|end_header_id|>",
+ "end_role": "<|eot_id|>",
+ "end": "<|start_header_id|>assistant<|end_header_id|>\n",
+ }
+ )
+ """start, start_role, end_role and end special tokens
+ (set for CoE generic v1 and v2 endpoints when process prompt set to false
+ or for StandAlone v1 and v2 endpoints)
+ default to llama3 special tokens"""
+
+ model_kwargs: Optional[Dict[str, Any]] = None
+ """Key word arguments to pass to the model."""
+
+ class Config:
+ populate_by_name = True
+
+ @classmethod
+ def is_lc_serializable(cls) -> bool:
+ """Return whether this model can be serialized by Langchain."""
+ return False
+
+ @property
+ def lc_secrets(self) -> Dict[str, str]:
+ return {
+ "sambastudio_url": "sambastudio_url",
+ "sambastudio_api_key": "sambastudio_api_key",
+ }
+
+ @property
+ def _identifying_params(self) -> Dict[str, Any]:
+ """Return a dictionary of identifying parameters.
+
+ This information is used by the LangChain callback system, which
+ is used for tracing purposes make it possible to monitor LLMs.
+ """
+ return {
+ "model": self.model,
+ "streaming": self.streaming,
+ "max_tokens": self.max_tokens,
+ "temperature": self.temperature,
+ "top_p": self.top_p,
+ "top_k": self.top_k,
+ "do_sample": self.do_sample,
+ "process_prompt": self.process_prompt,
+ "stream_options": self.stream_options,
+ "special_tokens": self.special_tokens,
+ "model_kwargs": self.model_kwargs,
+ }
+
+ @property
+ def _llm_type(self) -> str:
+ """Get the type of language model used by this chat model."""
+ return "sambastudio-chatmodel"
+
+ def __init__(self, **kwargs: Any) -> None:
+ """init and validate environment variables"""
+ kwargs["sambastudio_url"] = get_from_dict_or_env(
+ kwargs, "sambastudio_url", "SAMBASTUDIO_URL"
+ )
+
+ kwargs["sambastudio_api_key"] = convert_to_secret_str(
+ get_from_dict_or_env(kwargs, "sambastudio_api_key", "SAMBASTUDIO_API_KEY")
+ )
+ kwargs["base_url"], kwargs["streaming_url"] = self._get_sambastudio_urls(
+ kwargs["sambastudio_url"]
+ )
+ super().__init__(**kwargs)
+
+ def _get_role(self, message: BaseMessage) -> str:
+ """
+ Get the role of LangChain BaseMessage
+
+ Args:
+ message: LangChain BaseMessage
+
+ Returns:
+ str: Role of the LangChain BaseMessage
+ """
+ if isinstance(message, ChatMessage):
+ role = message.role
+ elif isinstance(message, SystemMessage):
+ role = "system"
+ elif isinstance(message, HumanMessage):
+ role = "user"
+ elif isinstance(message, AIMessage):
+ role = "assistant"
+ elif isinstance(message, ToolMessage):
+ role = "tool"
+ else:
+ raise TypeError(f"Got unknown type {message}")
+ return role
+
+ def _messages_to_string(self, messages: List[BaseMessage]) -> str:
+ """
+ Convert a list of BaseMessages to a:
+ - dumped json string with Role / content dict structure
+ when process_prompt is true,
+ - string with special tokens if process_prompt is false
+ for generic V1 and V2 endpoints
+
+ Args:
+ messages: list of BaseMessages
+
+ Returns:
+ str: string to send as model input depending on process_prompt param
+ """
+ if self.process_prompt:
+ messages_dict: Dict[str, Any] = {
+ "conversation_id": "sambaverse-conversation-id",
+ "messages": [],
+ }
+ for message in messages:
+ messages_dict["messages"].append(
+ {
+ "message_id": message.id,
+ "role": self._get_role(message),
+ "content": message.content,
+ }
+ )
+ messages_string = json.dumps(messages_dict)
+ else:
+ messages_string = self.special_tokens["start"]
+ for message in messages:
+ messages_string += self.special_tokens["start_role"].format(
+ role=self._get_role(message)
+ )
+ messages_string += f" {message.content} "
+ messages_string += self.special_tokens["end_role"]
+ messages_string += self.special_tokens["end"]
+
+ return messages_string
+
+ def _get_sambastudio_urls(self, url: str) -> Tuple[str, str]:
+ """
+ Get streaming and non streaming URLs from the given URL
+
+ Args:
+ url: string with sambastudio base or streaming endpoint url
+
+ Returns:
+ base_url: string with url to do non streaming calls
+ streaming_url: string with url to do streaming calls
+ """
+ if "openai" in url:
+ base_url = url
+ stream_url = url
+ else:
+ if "stream" in url:
+ base_url = url.replace("stream/", "")
+ stream_url = url
+ else:
+ base_url = url
+ if "generic" in url:
+ stream_url = "generic/stream".join(url.split("generic"))
+ else:
+ raise ValueError("Unsupported URL")
+ return base_url, stream_url
+
+ def _handle_request(
+ self,
+ messages: List[BaseMessage],
+ stop: Optional[List[str]] = None,
+ streaming: Optional[bool] = False,
+ ) -> Response:
+ """
+ Performs a post request to the LLM API.
+
+ Args:
+ messages_dicts: List of role / content dicts to use as input.
+ stop: list of stop tokens
+ streaming: wether to do a streaming call
+
+ Returns:
+ A request Response object
+ """
+
+ # create request payload for openai compatible API
+ if "openai" in self.sambastudio_url:
+ messages_dicts = _create_message_dicts(messages)
+ data = {
+ "messages": messages_dicts,
+ "max_tokens": self.max_tokens,
+ "stop": stop,
+ "model": self.model,
+ "temperature": self.temperature,
+ "top_p": self.top_p,
+ "top_k": self.top_k,
+ "stream": streaming,
+ "stream_options": self.stream_options,
+ }
+ data = {key: value for key, value in data.items() if value is not None}
+ headers = {
+ "Authorization": f"Bearer "
+ f"{self.sambastudio_api_key.get_secret_value()}",
+ "Content-Type": "application/json",
+ }
+
+ # create request payload for generic v1 API
+ elif "api/v2/predict/generic" in self.sambastudio_url:
+ items = [{"id": "item0", "value": self._messages_to_string(messages)}]
+ params: Dict[str, Any] = {
+ "select_expert": self.model,
+ "process_prompt": self.process_prompt,
+ "max_tokens_to_generate": self.max_tokens,
+ "temperature": self.temperature,
+ "top_p": self.top_p,
+ "top_k": self.top_k,
+ "do_sample": self.do_sample,
+ }
+ if self.model_kwargs is not None:
+ params = {**params, **self.model_kwargs}
+ params = {key: value for key, value in params.items() if value is not None}
+ data = {"items": items, "params": params}
+ headers = {"key": self.sambastudio_api_key.get_secret_value()}
+
+ # create request payload for generic v1 API
+ elif "api/predict/generic" in self.sambastudio_url:
+ params = {
+ "select_expert": self.model,
+ "process_prompt": self.process_prompt,
+ "max_tokens_to_generate": self.max_tokens,
+ "temperature": self.temperature,
+ "top_p": self.top_p,
+ "top_k": self.top_k,
+ "do_sample": self.do_sample,
+ }
+ if self.model_kwargs is not None:
+ params = {**params, **self.model_kwargs}
+ params = {
+ key: {"type": type(value).__name__, "value": str(value)}
+ for key, value in params.items()
+ if value is not None
+ }
+ if streaming:
+ data = {
+ "instance": self._messages_to_string(messages),
+ "params": params,
+ }
+ else:
+ data = {
+ "instances": [self._messages_to_string(messages)],
+ "params": params,
+ }
+ headers = {"key": self.sambastudio_api_key.get_secret_value()}
+
+ else:
+ raise ValueError(
+ f"Unsupported URL{self.sambastudio_url}"
+ "only openai, generic v1 and generic v2 APIs are supported"
+ )
+
+ http_session = requests.Session()
+ if streaming:
+ response = http_session.post(
+ self.streaming_url, headers=headers, json=data, stream=True
+ )
+ else:
+ response = http_session.post(
+ self.base_url, headers=headers, json=data, stream=False
+ )
+ if response.status_code != 200:
+ raise RuntimeError(
+ f"Sambanova /complete call failed with status code "
+ f"{response.status_code}."
+ f"{response.text}."
+ )
+ return response
+
+ def _process_response(self, response: Response) -> AIMessage:
+ """
+ Process a non streaming response from the api
+
+ Args:
+ response: A request Response object
+
+ Returns
+ generation: an AIMessage with model generation
+ """
+
+ # Extract json payload form response
+ try:
+ response_dict = response.json()
+ except Exception as e:
+ raise RuntimeError(
+ f"Sambanova /complete call failed couldn't get JSON response {e}"
+ f"response: {response.text}"
+ )
+
+ # process response payload for openai compatible API
+ if "openai" in self.sambastudio_url:
+ content = response_dict["choices"][0]["message"]["content"]
+ id = response_dict["id"]
+ response_metadata = {
+ "finish_reason": response_dict["choices"][0]["finish_reason"],
+ "usage": response_dict.get("usage"),
+ "model_name": response_dict["model"],
+ "system_fingerprint": response_dict["system_fingerprint"],
+ "created": response_dict["created"],
+ }
+
+ # process response payload for generic v2 API
+ elif "api/v2/predict/generic" in self.sambastudio_url:
+ content = response_dict["items"][0]["value"]["completion"]
+ id = response_dict["items"][0]["id"]
+ response_metadata = response_dict["items"][0]
+
+ # process response payload for generic v1 API
+ elif "api/predict/generic" in self.sambastudio_url:
+ content = response_dict["predictions"][0]["completion"]
+ id = None
+ response_metadata = response_dict
+
+ else:
+ raise ValueError(
+ f"Unsupported URL{self.sambastudio_url}"
+ "only openai, generic v1 and generic v2 APIs are supported"
+ )
+
+ return AIMessage(
+ content=content,
+ additional_kwargs={},
+ response_metadata=response_metadata,
+ id=id,
+ )
+
+ def _process_stream_response(
+ self, response: Response
+ ) -> Iterator[BaseMessageChunk]:
+ """
+ Process a streaming response from the api
+
+ Args:
+ response: An iterable request Response object
+
+ Yields:
+ generation: an AIMessageChunk with model partial generation
+ """
+
+ try:
+ import sseclient
+ except ImportError:
+ raise ImportError(
+ "could not import sseclient library"
+ "Please install it with `pip install sseclient-py`."
+ )
+
+ # process response payload for openai compatible API
+ if "openai" in self.sambastudio_url:
+ finish_reason = ""
+ client = sseclient.SSEClient(response)
+ for event in client.events():
+ if event.event == "error_event":
+ raise RuntimeError(
+ f"Sambanova /complete call failed with status code "
+ f"{response.status_code}."
+ f"{event.data}."
+ )
+ try:
+ # check if the response is not a final event ("[DONE]")
+ if event.data != "[DONE]":
+ if isinstance(event.data, str):
+ data = json.loads(event.data)
+ else:
+ raise RuntimeError(
+ f"Sambanova /complete call failed with status code "
+ f"{response.status_code}."
+ f"{event.data}."
+ )
+ if data.get("error"):
+ raise RuntimeError(
+ f"Sambanova /complete call failed with status code "
+ f"{response.status_code}."
+ f"{event.data}."
+ )
+ if len(data["choices"]) > 0:
+ finish_reason = data["choices"][0].get("finish_reason")
+ content = data["choices"][0]["delta"]["content"]
+ id = data["id"]
+ metadata = {}
+ else:
+ content = ""
+ id = data["id"]
+ metadata = {
+ "finish_reason": finish_reason,
+ "usage": data.get("usage"),
+ "model_name": data["model"],
+ "system_fingerprint": data["system_fingerprint"],
+ "created": data["created"],
+ }
+ yield AIMessageChunk(
+ content=content,
+ id=id,
+ response_metadata=metadata,
+ additional_kwargs={},
+ )
+
+ except Exception as e:
+ raise RuntimeError(
+ f"Error getting content chunk raw streamed response: {e}"
+ f"data: {event.data}"
+ )
+
+ # process response payload for generic v2 API
+ elif "api/v2/predict/generic" in self.sambastudio_url:
+ for line in response.iter_lines():
+ try:
+ data = json.loads(line)
+ content = data["result"]["items"][0]["value"]["stream_token"]
+ id = data["result"]["items"][0]["id"]
+ if data["result"]["items"][0]["value"]["is_last_response"]:
+ metadata = {
+ "finish_reason": data["result"]["items"][0]["value"].get(
+ "stop_reason"
+ ),
+ "prompt": data["result"]["items"][0]["value"].get("prompt"),
+ "usage": {
+ "prompt_tokens_count": data["result"]["items"][0][
+ "value"
+ ].get("prompt_tokens_count"),
+ "completion_tokens_count": data["result"]["items"][0][
+ "value"
+ ].get("completion_tokens_count"),
+ "total_tokens_count": data["result"]["items"][0][
+ "value"
+ ].get("total_tokens_count"),
+ "start_time": data["result"]["items"][0]["value"].get(
+ "start_time"
+ ),
+ "end_time": data["result"]["items"][0]["value"].get(
+ "end_time"
+ ),
+ "model_execution_time": data["result"]["items"][0][
+ "value"
+ ].get("model_execution_time"),
+ "time_to_first_token": data["result"]["items"][0][
+ "value"
+ ].get("time_to_first_token"),
+ "throughput_after_first_token": data["result"]["items"][
+ 0
+ ]["value"].get("throughput_after_first_token"),
+ "batch_size_used": data["result"]["items"][0][
+ "value"
+ ].get("batch_size_used"),
+ },
+ }
+ else:
+ metadata = {}
+ yield AIMessageChunk(
+ content=content,
+ id=id,
+ response_metadata=metadata,
+ additional_kwargs={},
+ )
+
+ except Exception as e:
+ raise RuntimeError(
+ f"Error getting content chunk raw streamed response: {e}"
+ f"line: {line}"
+ )
+
+ # process response payload for generic v1 API
+ elif "api/predict/generic" in self.sambastudio_url:
+ for line in response.iter_lines():
+ try:
+ data = json.loads(line)
+ content = data["result"]["responses"][0]["stream_token"]
+ id = None
+ if data["result"]["responses"][0]["is_last_response"]:
+ metadata = {
+ "finish_reason": data["result"]["responses"][0].get(
+ "stop_reason"
+ ),
+ "prompt": data["result"]["responses"][0].get("prompt"),
+ "usage": {
+ "prompt_tokens_count": data["result"]["responses"][
+ 0
+ ].get("prompt_tokens_count"),
+ "completion_tokens_count": data["result"]["responses"][
+ 0
+ ].get("completion_tokens_count"),
+ "total_tokens_count": data["result"]["responses"][
+ 0
+ ].get("total_tokens_count"),
+ "start_time": data["result"]["responses"][0].get(
+ "start_time"
+ ),
+ "end_time": data["result"]["responses"][0].get(
+ "end_time"
+ ),
+ "model_execution_time": data["result"]["responses"][
+ 0
+ ].get("model_execution_time"),
+ "time_to_first_token": data["result"]["responses"][
+ 0
+ ].get("time_to_first_token"),
+ "throughput_after_first_token": data["result"][
+ "responses"
+ ][0].get("throughput_after_first_token"),
+ "batch_size_used": data["result"]["responses"][0].get(
+ "batch_size_used"
+ ),
+ },
+ }
+ else:
+ metadata = {}
+ yield AIMessageChunk(
+ content=content,
+ id=id,
+ response_metadata=metadata,
+ additional_kwargs={},
+ )
+
+ except Exception as e:
+ raise RuntimeError(
+ f"Error getting content chunk raw streamed response: {e}"
+ f"line: {line}"
+ )
+
+ else:
+ raise ValueError(
+ f"Unsupported URL{self.sambastudio_url}"
+ "only openai, generic v1 and generic v2 APIs are supported"
+ )
+
+ def _generate(
+ self,
+ messages: List[BaseMessage],
+ stop: Optional[List[str]] = None,
+ run_manager: Optional[CallbackManagerForLLMRun] = None,
+ **kwargs: Any,
+ ) -> ChatResult:
+ """
+ Call SambaStudio models.
+
+ Args:
+ messages: the prompt composed of a list of messages.
+ stop: a list of strings on which the model should stop generating.
+ If generation stops due to a stop token, the stop token itself
+ SHOULD BE INCLUDED as part of the output. This is not enforced
+ across models right now, but it's a good practice to follow since
+ it makes it much easier to parse the output of the model
+ downstream and understand why generation stopped.
+ run_manager: A run manager with callbacks for the LLM.
+
+ Returns:
+ result: ChatResult with model generation
+ """
+ if self.streaming:
+ stream_iter = self._stream(
+ messages, stop=stop, run_manager=run_manager, **kwargs
+ )
+ if stream_iter:
+ return generate_from_stream(stream_iter)
+ response = self._handle_request(messages, stop, streaming=False)
+ message = self._process_response(response)
+ generation = ChatGeneration(message=message)
+ return ChatResult(generations=[generation])
+
+ def _stream(
+ self,
+ messages: List[BaseMessage],
+ stop: Optional[List[str]] = None,
+ run_manager: Optional[CallbackManagerForLLMRun] = None,
+ **kwargs: Any,
+ ) -> Iterator[ChatGenerationChunk]:
+ """
+ Stream the output of the SambaStudio model.
+
+ Args:
+ messages: the prompt composed of a list of messages.
+ stop: a list of strings on which the model should stop generating.
+ If generation stops due to a stop token, the stop token itself
+ SHOULD BE INCLUDED as part of the output. This is not enforced
+ across models right now, but it's a good practice to follow since
+ it makes it much easier to parse the output of the model
+ downstream and understand why generation stopped.
+ run_manager: A run manager with callbacks for the LLM.
+
+ Yields:
+ chunk: ChatGenerationChunk with model partial generation
+ """
+ response = self._handle_request(messages, stop, streaming=True)
+ for ai_message_chunk in self._process_stream_response(response):
+ chunk = ChatGenerationChunk(message=ai_message_chunk)
+ if run_manager:
+ run_manager.on_llm_new_token(chunk.text, chunk=chunk)
+ yield chunk
diff --git a/libs/community/langchain_community/chat_models/snowflake.py b/libs/community/langchain_community/chat_models/snowflake.py
index 52483818830..84883b2cd87 100644
--- a/libs/community/langchain_community/chat_models/snowflake.py
+++ b/libs/community/langchain_community/chat_models/snowflake.py
@@ -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
diff --git a/libs/community/langchain_community/llms/anthropic.py b/libs/community/langchain_community/llms/anthropic.py
index 07b40d6eda7..0a6af6799d8 100644
--- a/libs/community/langchain_community/llms/anthropic.py
+++ b/libs/community/langchain_community/llms/anthropic.py
@@ -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
diff --git a/libs/community/langchain_community/llms/azureml_endpoint.py b/libs/community/langchain_community/llms/azureml_endpoint.py
index f2b5eed2e1c..5c34ca15477 100644
--- a/libs/community/langchain_community/llms/azureml_endpoint.py
+++ b/libs/community/langchain_community/llms/azureml_endpoint.py
@@ -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:
diff --git a/libs/community/langchain_community/llms/bedrock.py b/libs/community/langchain_community/llms/bedrock.py
index 2436ca765b1..a0de3fb81e2 100644
--- a/libs/community/langchain_community/llms/bedrock.py
+++ b/libs/community/langchain_community/llms/bedrock.py
@@ -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
diff --git a/libs/community/langchain_community/llms/llamacpp.py b/libs/community/langchain_community/llms/llamacpp.py
index 15d1119fcad..a045878fd26 100644
--- a/libs/community/langchain_community/llms/llamacpp.py
+++ b/libs/community/langchain_community/llms/llamacpp.py
@@ -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
diff --git a/libs/community/langchain_community/llms/minimax.py b/libs/community/langchain_community/llms/minimax.py
index e508eb4f959..5a1822fffb2 100644
--- a/libs/community/langchain_community/llms/minimax.py
+++ b/libs/community/langchain_community/llms/minimax.py
@@ -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."""
diff --git a/libs/community/langchain_community/llms/openai.py b/libs/community/langchain_community/llms/openai.py
index aeb16883fd4..cc33d07ac4e 100644
--- a/libs/community/langchain_community/llms/openai.py
+++ b/libs/community/langchain_community/llms/openai.py
@@ -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
diff --git a/libs/community/langchain_community/llms/vertexai.py b/libs/community/langchain_community/llms/vertexai.py
index 35fd883fdd7..0b42bd535ef 100644
--- a/libs/community/langchain_community/llms/vertexai.py
+++ b/libs/community/langchain_community/llms/vertexai.py
@@ -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"
diff --git a/libs/community/langchain_community/llms/volcengine_maas.py b/libs/community/langchain_community/llms/volcengine_maas.py
index c62c3523bb3..e737a0a5569 100644
--- a/libs/community/langchain_community/llms/volcengine_maas.py
+++ b/libs/community/langchain_community/llms/volcengine_maas.py
@@ -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
diff --git a/libs/community/langchain_community/vectorstores/usearch.py b/libs/community/langchain_community/vectorstores/usearch.py
index fa94d19de00..c59446de54d 100644
--- a/libs/community/langchain_community/vectorstores/usearch.py
+++ b/libs/community/langchain_community/vectorstores/usearch.py
@@ -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))
diff --git a/libs/community/tests/integration_tests/chat_models/test_sambanova.py b/libs/community/tests/integration_tests/chat_models/test_sambanova.py
index 965a8156f2f..2683b5dc399 100644
--- a/libs/community/tests/integration_tests/chat_models/test_sambanova.py
+++ b/libs/community/tests/integration_tests/chat_models/test_sambanova.py
@@ -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)
diff --git a/libs/community/tests/unit_tests/chat_models/test_anthropic.py b/libs/community/tests/unit_tests/chat_models/test_anthropic.py
index 41452ce0b83..8e6a659743b 100644
--- a/libs/community/tests/unit_tests/chat_models/test_anthropic.py
+++ b/libs/community/tests/unit_tests/chat_models/test_anthropic.py
@@ -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")
diff --git a/libs/community/tests/unit_tests/chat_models/test_imports.py b/libs/community/tests/unit_tests/chat_models/test_imports.py
index d8399ed8315..6be5a41d9ec 100644
--- a/libs/community/tests/unit_tests/chat_models/test_imports.py
+++ b/libs/community/tests/unit_tests/chat_models/test_imports.py
@@ -35,6 +35,7 @@ EXPECTED_ALL = [
"ChatPerplexity",
"ChatPremAI",
"ChatSambaNovaCloud",
+ "ChatSambaStudio",
"ChatSparkLLM",
"ChatTongyi",
"ChatVertexAI",
diff --git a/libs/community/tests/unit_tests/llms/test_openai.py b/libs/community/tests/unit_tests/llms/test_openai.py
index 83b229d8c2e..bd5f8b3bd90 100644
--- a/libs/community/tests/unit_tests/llms/test_openai.py
+++ b/libs/community/tests/unit_tests/llms/test_openai.py
@@ -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")
diff --git a/libs/core/langchain_core/callbacks/manager.py b/libs/core/langchain_core/callbacks/manager.py
index 05dd786591d..604e17c10af 100644
--- a/libs/core/langchain_core/callbacks/manager.py
+++ b/libs/core/langchain_core/callbacks/manager.py
@@ -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(
diff --git a/libs/core/langchain_core/indexing/api.py b/libs/core/langchain_core/indexing/api.py
index 814356b17c3..26566b1be80 100644
--- a/libs/core/langchain_core/indexing/api.py
+++ b/libs/core/langchain_core/indexing/api.py
@@ -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)
diff --git a/libs/core/langchain_core/language_models/base.py b/libs/core/langchain_core/language_models/base.py
index 2df436f780f..93ebe523d89 100644
--- a/libs/core/langchain_core/language_models/base.py
+++ b/libs/core/langchain_core/language_models/base.py
@@ -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.
diff --git a/libs/core/langchain_core/language_models/fake_chat_models.py b/libs/core/langchain_core/language_models/fake_chat_models.py
index 6476db5f825..8dc4e55695c 100644
--- a/libs/core/langchain_core/language_models/fake_chat_models.py
+++ b/libs/core/langchain_core/language_models/fake_chat_models.py
@@ -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."""
diff --git a/libs/core/langchain_core/messages/ai.py b/libs/core/langchain_core/messages/ai.py
index 03a22e79764..dece1e575ee 100644
--- a/libs/core/langchain_core/messages/ai.py
+++ b/libs/core/langchain_core/messages/ai.py
@@ -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):
diff --git a/libs/core/langchain_core/runnables/base.py b/libs/core/langchain_core/runnables/base.py
index 730cd38931d..5dd5d35d1d0 100644
--- a/libs/core/langchain_core/runnables/base.py
+++ b/libs/core/langchain_core/runnables/base.py
@@ -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.
diff --git a/libs/core/langchain_core/utils/__init__.py b/libs/core/langchain_core/utils/__init__.py
index 2e560b21e96..7822d3b6251 100644
--- a/libs/core/langchain_core/utils/__init__.py
+++ b/libs/core/langchain_core/utils/__init__.py
@@ -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",
diff --git a/libs/core/langchain_core/utils/utils.py b/libs/core/langchain_core/utils/utils.py
index e8d7b34bd26..7bbea2d4e0c 100644
--- a/libs/core/langchain_core/utils/utils.py
+++ b/libs/core/langchain_core/utils/utils.py
@@ -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],
diff --git a/libs/core/langchain_core/vectorstores/base.py b/libs/core/langchain_core/vectorstores/base.py
index aacf897ffb9..ccc966d9f2a 100644
--- a/libs/core/langchain_core/vectorstores/base.py
+++ b/libs/core/langchain_core/vectorstores/base.py
@@ -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
)
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 11a2e85aa37..36a6cb9f593 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -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/*",]
diff --git a/libs/core/tests/unit_tests/callbacks/test_async_callback_manager.py b/libs/core/tests/unit_tests/callbacks/test_async_callback_manager.py
new file mode 100644
index 00000000000..38350f9d82f
--- /dev/null
+++ b/libs/core/tests/unit_tests/callbacks/test_async_callback_manager.py
@@ -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}"
diff --git a/libs/core/tests/unit_tests/fake/test_fake_chat_model.py b/libs/core/tests/unit_tests/fake/test_fake_chat_model.py
index 1829bf773ce..7502e17c50f 100644
--- a/libs/core/tests/unit_tests/fake/test_fake_chat_model.py
+++ b/libs/core/tests/unit_tests/fake/test_fake_chat_model.py
@@ -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
diff --git a/libs/core/tests/unit_tests/indexing/test_indexing.py b/libs/core/tests/unit_tests/indexing/test_indexing.py
index 96d3584dad8..287b6b49f66 100644
--- a/libs/core/tests/unit_tests/indexing/test_indexing.py
+++ b/libs/core/tests/unit_tests/indexing/test_indexing.py
@@ -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"
diff --git a/libs/core/tests/unit_tests/language_models/chat_models/test_cache.py b/libs/core/tests/unit_tests/language_models/chat_models/test_cache.py
index 3992420621a..0d5b89de7c3 100644
--- a/libs/core/tests/unit_tests/language_models/chat_models/test_cache.py
+++ b/libs/core/tests/unit_tests/language_models/chat_models/test_cache.py
@@ -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,
diff --git a/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr b/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr
index 5a235a0f9f9..7e35bd6c465 100644
--- a/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr
+++ b/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr
@@ -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',
diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr
index aa70b9bb5a3..ba9d742b374 100644
--- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr
+++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr
@@ -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',
diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
index 045b9eced27..85b53552716 100644
--- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
+++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
@@ -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',
diff --git a/libs/core/tests/unit_tests/utils/test_imports.py b/libs/core/tests/unit_tests/utils/test_imports.py
index f33491ed295..67fe97e6569 100644
--- a/libs/core/tests/unit_tests/utils/test_imports.py
+++ b/libs/core/tests/unit_tests/utils/test_imports.py
@@ -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",
diff --git a/libs/core/tests/unit_tests/vectorstores/test_vectorstore.py b/libs/core/tests/unit_tests/vectorstores/test_vectorstore.py
index 29746bbe67d..aba1481c623 100644
--- a/libs/core/tests/unit_tests/vectorstores/test_vectorstore.py
+++ b/libs/core/tests/unit_tests/vectorstores/test_vectorstore.py
@@ -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")]
diff --git a/libs/langchain/tests/unit_tests/chat_models/test_base.py b/libs/langchain/tests/unit_tests/chat_models/test_base.py
index b82dead3099..8b7f6445697 100644
--- a/libs/langchain/tests/unit_tests/chat_models/test_base.py
+++ b/libs/langchain/tests/unit_tests/chat_models/test_base.py
@@ -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,
diff --git a/libs/partners/anthropic/langchain_anthropic/chat_models.py b/libs/partners/anthropic/langchain_anthropic/chat_models.py
index 08c3ff66ce2..d7f2643f0f7 100644
--- a/libs/partners/anthropic/langchain_anthropic/chat_models.py
+++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py
@@ -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 β 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}
+ ),
+ )
diff --git a/libs/partners/anthropic/langchain_anthropic/llms.py b/libs/partners/anthropic/langchain_anthropic/llms.py
index 99e7df965fb..5b53663d7e2 100644
--- a/libs/partners/anthropic/langchain_anthropic/llms.py
+++ b/libs/partners/anthropic/langchain_anthropic/llms.py
@@ -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")
diff --git a/libs/partners/anthropic/poetry.lock b/libs/partners/anthropic/poetry.lock
index e5ec26cb27c..5e783dda5a4 100644
--- a/libs/partners/anthropic/poetry.lock
+++ b/libs/partners/anthropic/poetry.lock
@@ -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"},
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[[package]]
name = "certifi"
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python-versions = ">=3.9,<4.0"
-content-hash = "3e91787d600949b4f2530c3eb00c854945aec2a8ce8977b724360b4d084db133"
+content-hash = "ff45241f112ccfe1ca845dfc9685f7b63d7cb0a362acffbb17d81ff4691af9d4"
diff --git a/libs/partners/anthropic/pyproject.toml b/libs/partners/anthropic/pyproject.toml
index 76c802cf41a..12bec1e3ae2 100644
--- a/libs/partners/anthropic/pyproject.toml
+++ b/libs/partners/anthropic/pyproject.toml
@@ -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]
diff --git a/libs/partners/anthropic/tests/integration_tests/test_standard.py b/libs/partners/anthropic/tests/integration_tests/test_standard.py
index e439fffc74f..241588f32a3 100644
--- a/libs/partners/anthropic/tests/integration_tests/test_standard.py
+++ b/libs/partners/anthropic/tests/integration_tests/test_standard.py
@@ -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_))
diff --git a/libs/partners/anthropic/tests/unit_tests/test_chat_models.py b/libs/partners/anthropic/tests/unit_tests/test_chat_models.py
index 781a3b6e747..859a2fa5acf 100644
--- a/libs/partners/anthropic/tests/unit_tests/test_chat_models.py
+++ b/libs/partners/anthropic/tests/unit_tests/test_chat_models.py
@@ -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"},
]
),
diff --git a/libs/partners/box/langchain_box/retrievers/box.py b/libs/partners/box/langchain_box/retrievers/box.py
index b216115af3d..1c8dc550665 100644
--- a/libs/partners/box/langchain_box/retrievers/box.py
+++ b/libs/partners/box/langchain_box/retrievers/box.py
@@ -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]
diff --git a/libs/partners/box/langchain_box/utilities/box.py b/libs/partners/box/langchain_box/utilities/box.py
index 758f454a9da..8c87a8dc4cd 100644
--- a/libs/partners/box/langchain_box/utilities/box.py
+++ b/libs/partners/box/langchain_box/utilities/box.py
@@ -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
diff --git a/libs/partners/box/poetry.lock b/libs/partners/box/poetry.lock
index 49e70f6fca4..c09158c9e79 100644
--- a/libs/partners/box/poetry.lock
+++ b/libs/partners/box/poetry.lock
@@ -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"
diff --git a/libs/partners/box/pyproject.toml b/libs/partners/box/pyproject.toml
index 7487325be3c..af8cd16502b 100644
--- a/libs/partners/box/pyproject.toml
+++ b/libs/partners/box/pyproject.toml
@@ -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]
diff --git a/libs/partners/box/tests/unit_tests/retrievers/test_box_retriever.py b/libs/partners/box/tests/unit_tests/retrievers/test_box_retriever.py
index 82a0a304207..054f7ed205c 100644
--- a/libs/partners/box/tests/unit_tests/retrievers/test_box_retriever.py
+++ b/libs/partners/box/tests/unit_tests/retrievers/test_box_retriever.py
@@ -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"}),
+ ]
diff --git a/libs/partners/fireworks/langchain_fireworks/chat_models.py b/libs/partners/fireworks/langchain_fireworks/chat_models.py
index a07398f6d35..11b24197f60 100644
--- a/libs/partners/fireworks/langchain_fireworks/chat_models.py
+++ b/libs/partners/fireworks/langchain_fireworks/chat_models.py
@@ -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")
diff --git a/libs/partners/fireworks/langchain_fireworks/llms.py b/libs/partners/fireworks/langchain_fireworks/llms.py
index 3189483c914..fad10e9039f 100644
--- a/libs/partners/fireworks/langchain_fireworks/llms.py
+++ b/libs/partners/fireworks/langchain_fireworks/llms.py
@@ -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
diff --git a/libs/partners/fireworks/poetry.lock b/libs/partners/fireworks/poetry.lock
index 6773ca4f626..5f424993371 100644
--- a/libs/partners/fireworks/poetry.lock
+++ b/libs/partners/fireworks/poetry.lock
@@ -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 = [
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- {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"
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+ {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"
diff --git a/libs/partners/fireworks/pyproject.toml b/libs/partners/fireworks/pyproject.toml
index 44f26c99710..dd44828ff59 100644
--- a/libs/partners/fireworks/pyproject.toml
+++ b/libs/partners/fireworks/pyproject.toml
@@ -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]
diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py
index 21754937d18..baaa74f637b 100644
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -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}
+ ),
+ )
diff --git a/libs/partners/openai/langchain_openai/llms/base.py b/libs/partners/openai/langchain_openai/llms/base.py
index 0c773b347a6..633d473ae81 100644
--- a/libs/partners/openai/langchain_openai/llms/base.py
+++ b/libs/partners/openai/langchain_openai/llms/base.py
@@ -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")
diff --git a/libs/partners/openai/poetry.lock b/libs/partners/openai/poetry.lock
index 0f9b08324cc..9d9e88d6a47 100644
--- a/libs/partners/openai/poetry.lock
+++ b/libs/partners/openai/poetry.lock
@@ -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"
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]
[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"
diff --git a/libs/partners/openai/pyproject.toml b/libs/partners/openai/pyproject.toml
index 24ea6eb1536..d4c3a5875d7 100644
--- a/libs/partners/openai/pyproject.toml
+++ b/libs/partners/openai/pyproject.toml
@@ -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"
diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
index fb2a150efd2..950a52ce3ee 100644
--- a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
@@ -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."""
diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_base_standard.py b/libs/partners/openai/tests/integration_tests/chat_models/test_base_standard.py
index 22b305c9753..b021603aace 100644
--- a/libs/partners/openai/tests/integration_tests/chat_models/test_base_standard.py
+++ b/libs/partners/openai/tests/integration_tests/chat_models/test_base_standard.py
@@ -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_))
diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
index d2052767040..cc03698e5ef 100644
--- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
@@ -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={},
+ )
diff --git a/libs/partners/openai/tests/unit_tests/llms/test_base.py b/libs/partners/openai/tests/unit_tests/llms/test_base.py
index 45955b097c3..e9b43190cd6 100644
--- a/libs/partners/openai/tests/unit_tests/llms/test_base.py
+++ b/libs/partners/openai/tests/unit_tests/llms/test_base.py
@@ -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:
diff --git a/libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py b/libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py
index c5ac855cdc6..b5e0a9a0bd4 100644
--- a/libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py
+++ b/libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py
@@ -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()
diff --git a/libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py b/libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py
index 1a611f1800f..1321eb62151 100644
--- a/libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py
+++ b/libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py
@@ -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