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langchain-
...
cc/test_o3
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5cad3683b4 |
@@ -52,7 +52,7 @@ For these applications, LangChain simplifies the entire application lifecycle:
|
||||
- **Integration packages** (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
|
||||
- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
|
||||
- **`langchain-community`**: Third-party integrations that are community maintained.
|
||||
- **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
|
||||
- **[LangGraph](https://langchain-ai.github.io/langgraph)**: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
|
||||
|
||||
### Productionization:
|
||||
|
||||
|
||||
@@ -39,7 +39,7 @@ The interface consists of basic methods for writing, deleting and searching for
|
||||
The key methods are:
|
||||
|
||||
- `add_documents`: Add a list of texts to the vector store.
|
||||
- `delete_documents`: Delete a list of documents from the vector store.
|
||||
- `delete`: Delete a list of documents from the vector store.
|
||||
- `similarity_search`: Search for similar documents to a given query.
|
||||
|
||||
|
||||
@@ -89,10 +89,10 @@ vector_store.add_documents(documents=documents, ids=["doc1", "doc2"])
|
||||
|
||||
## Delete
|
||||
|
||||
To delete documents, use the `delete_documents` method which takes a list of document IDs to delete.
|
||||
To delete documents, use the `delete` method which takes a list of document IDs to delete.
|
||||
|
||||
```python
|
||||
vector_store.delete_documents(ids=["doc1"])
|
||||
vector_store.delete(ids=["doc1"])
|
||||
```
|
||||
|
||||
## Search
|
||||
|
||||
@@ -477,7 +477,7 @@
|
||||
"2) Make the file executable\n",
|
||||
"3) Run the file\n",
|
||||
"\n",
|
||||
"llamafiles bundle model weights and a [specially-compiled](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#technical-details) version of [`llama.cpp`](https://github.com/ggerganov/llama.cpp) into a single file that can run on most computers any additional dependencies. They also come with an embedded inference server that provides an [API](https://github.com/Mozilla-Ocho/llamafile/blob/main/llama.cpp/server/README.md#api-endpoints) for interacting with your model. \n",
|
||||
"llamafiles bundle model weights and a [specially-compiled](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#technical-details) version of [`llama.cpp`](https://github.com/ggerganov/llama.cpp) into a single file that can run on most computers without any additional dependencies. They also come with an embedded inference server that provides an [API](https://github.com/Mozilla-Ocho/llamafile/blob/main/llama.cpp/server/README.md#api-endpoints) for interacting with your model. \n",
|
||||
"\n",
|
||||
"Here's a simple bash script that shows all 3 setup steps:\n",
|
||||
"\n",
|
||||
|
||||
@@ -36,13 +36,18 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-21T08:00:08.878423Z",
|
||||
"start_time": "2025-01-21T08:00:08.876042Z"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
|
||||
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
|
||||
]
|
||||
],
|
||||
"outputs": [],
|
||||
"execution_count": 1
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -54,17 +59,28 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-21T08:00:12.003718Z",
|
||||
"start_time": "2025-01-21T08:00:10.291617Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install -qU langchain_community"
|
||||
]
|
||||
"source": "%pip install -qU langchain_community pypdf pillow",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 2
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Initialization\n",
|
||||
"\n",
|
||||
@@ -72,10 +88,13 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-21T08:00:18.512061Z",
|
||||
"start_time": "2025-01-21T08:00:17.313969Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.document_loaders import PyPDFDirectoryLoader\n",
|
||||
"\n",
|
||||
@@ -83,7 +102,9 @@
|
||||
" \"../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf\"\n",
|
||||
")\n",
|
||||
"loader = PyPDFDirectoryLoader(\"example_data/\")"
|
||||
]
|
||||
],
|
||||
"outputs": [],
|
||||
"execution_count": 3
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -94,41 +115,51 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-21T08:00:23.549752Z",
|
||||
"start_time": "2025-01-21T08:00:23.129010Z"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"docs = loader.load()\n",
|
||||
"docs[0]"
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Document(metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0}, page_content='LayoutParser : A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1( \\x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1Allen Institute for AI\\nshannons@allenai.org\\n2Brown University\\nruochen zhang@brown.edu\\n3Harvard University\\n{melissadell,jacob carlson }@fas.harvard.edu\\n4University of Washington\\nbcgl@cs.washington.edu\\n5University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser , an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io .\\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\\n·Character Recognition ·Open Source library ·Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [ 11,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021')"
|
||||
"Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\\x00 ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021')"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"docs = loader.load()\n",
|
||||
"docs[0]"
|
||||
]
|
||||
"execution_count": 4
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-21T08:00:26.612346Z",
|
||||
"start_time": "2025-01-21T08:00:26.609051Z"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"print(docs[0].metadata)"
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'source': 'example_data/layout-parser-paper.pdf', 'page': 0}\n"
|
||||
"{'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(docs[0].metadata)"
|
||||
]
|
||||
"execution_count": 5
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -139,9 +170,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-01-21T08:00:30.251598Z",
|
||||
"start_time": "2025-01-21T08:00:29.972141Z"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"page = []\n",
|
||||
"for doc in loader.lazy_load():\n",
|
||||
@@ -151,7 +185,9 @@
|
||||
" # index.upsert(page)\n",
|
||||
"\n",
|
||||
" page = []"
|
||||
]
|
||||
],
|
||||
"outputs": [],
|
||||
"execution_count": 6
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -161,6 +197,13 @@
|
||||
"\n",
|
||||
"For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"cell_type": "code",
|
||||
"outputs": [],
|
||||
"execution_count": null,
|
||||
"source": ""
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
17
docs/docs/integrations/providers/jenkins.mdx
Normal file
17
docs/docs/integrations/providers/jenkins.mdx
Normal file
@@ -0,0 +1,17 @@
|
||||
# Jenkins
|
||||
|
||||
[Jenkins](https://www.jenkins.io/) is an open-source automation platform that enables
|
||||
software teams to streamline their development workflows. It's widely adopted in the
|
||||
DevOps community as a tool for automating the building, testing, and deployment of
|
||||
applications through CI/CD pipelines.
|
||||
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
```bash
|
||||
pip install langchain-jenkins
|
||||
```
|
||||
|
||||
## Tools
|
||||
|
||||
See detail on available tools [here](/docs/integrations/tools/jenkins).
|
||||
225
docs/docs/integrations/tools/jenkins.ipynb
Normal file
225
docs/docs/integrations/tools/jenkins.ipynb
Normal file
@@ -0,0 +1,225 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Jenkins\n",
|
||||
"\n",
|
||||
"Tools for interacting with [Jenkins](https://www.jenkins.io/).\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Overview\n",
|
||||
"\n",
|
||||
"The `langchain-jenkins` package allows you to execute and control CI/CD pipelines with\n",
|
||||
"Jenkins."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Setup\n",
|
||||
"\n",
|
||||
"Install `langchain-jenkins`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install --upgrade --quiet langchain-jenkins"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Credentials\n",
|
||||
"\n",
|
||||
"You'll need to setup or obtain authorization to access Jenkins server."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def _set_env(var: str):\n",
|
||||
" if not os.environ.get(var):\n",
|
||||
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"_set_env(\"PASSWORD\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Instantiation\n",
|
||||
"To disable the SSL Verify, set `os.environ[\"PYTHONHTTPSVERIFY\"] = \"0\"`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_jenkins import JenkinsAPIWrapper, JenkinsJobRun\n",
|
||||
"\n",
|
||||
"tools = [\n",
|
||||
" JenkinsJobRun(\n",
|
||||
" api_wrapper=JenkinsAPIWrapper(\n",
|
||||
" jenkins_server=\"https://example.com\",\n",
|
||||
" username=\"admin\",\n",
|
||||
" password=os.environ[\"PASSWORD\"],\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Invocation\n",
|
||||
"You can now call invoke and pass arguments."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"1. Create the Jenkins job"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"jenkins_job_content = \"\"\n",
|
||||
"src_file = \"job1.xml\"\n",
|
||||
"with open(src_file) as fread:\n",
|
||||
" jenkins_job_content = fread.read()\n",
|
||||
"tools[0].invoke({\"job\": \"job01\", \"config_xml\": jenkins_job_content, \"action\": \"create\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"2. Run the Jenkins Job"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tools[0].invoke({\"job\": \"job01\", \"parameters\": {}, \"action\": \"run\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"3. Get job info"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"resp = tools[0].invoke({\"job\": \"job01\", \"number\": 1, \"action\": \"status\"})\n",
|
||||
"if not resp[\"inProgress\"]:\n",
|
||||
" print(resp[\"result\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"4. Delete the jenkins job"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tools[0].invoke({\"job\": \"job01\", \"action\": \"delete\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Chaining\n",
|
||||
"\n",
|
||||
"TODO.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For detailed documentation [API reference](https://python.langchain.com/docs/integrations/tools/jenkins/)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.9.1"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "3929050b09828356c9f5ebaf862d05c053d8228eddbc70f990c168e54dd824ba"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -104,7 +104,7 @@ Head to the reference section for full documentation of all classes and methods
|
||||
Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
|
||||
|
||||
### [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph)
|
||||
Build stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
|
||||
Build stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it. LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more.
|
||||
|
||||
## Additional resources
|
||||
|
||||
|
||||
@@ -18,6 +18,7 @@ from typing import (
|
||||
Optional,
|
||||
Sequence,
|
||||
Union,
|
||||
cast,
|
||||
)
|
||||
from urllib.parse import urlparse
|
||||
|
||||
@@ -240,86 +241,226 @@ def _merge_text_and_extras(extras: list[str], text_from_page: str) -> str:
|
||||
|
||||
|
||||
class PyPDFParser(BaseBlobParser):
|
||||
"""Load `PDF` using `pypdf`"""
|
||||
"""Parse a blob from a PDF using `pypdf` library.
|
||||
|
||||
This class provides methods to parse a blob from a PDF document, supporting various
|
||||
configurations such as handling password-protected PDFs, extracting images.
|
||||
It integrates the 'pypdf' library for PDF processing and offers synchronous blob
|
||||
parsing.
|
||||
|
||||
Examples:
|
||||
Setup:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install -U langchain-community pypdf
|
||||
|
||||
Load a blob from a PDF file:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_core.documents.base import Blob
|
||||
|
||||
blob = Blob.from_path("./example_data/layout-parser-paper.pdf")
|
||||
|
||||
Instantiate the parser:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.document_loaders.parsers import PyPDFParser
|
||||
|
||||
parser = PyPDFParser(
|
||||
# password = None,
|
||||
mode = "single",
|
||||
pages_delimiter = "\n\f",
|
||||
# extract_images = True,
|
||||
# images_parser = TesseractBlobParser(),
|
||||
)
|
||||
|
||||
Lazily parse the blob:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
docs = []
|
||||
docs_lazy = parser.lazy_parse(blob)
|
||||
|
||||
for doc in docs_lazy:
|
||||
docs.append(doc)
|
||||
print(docs[0].page_content[:100])
|
||||
print(docs[0].metadata)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
password: Optional[Union[str, bytes]] = None,
|
||||
extract_images: bool = False,
|
||||
*,
|
||||
extraction_mode: str = "plain",
|
||||
mode: Literal["single", "page"] = "page",
|
||||
pages_delimiter: str = _DEFAULT_PAGES_DELIMITER,
|
||||
images_parser: Optional[BaseImageBlobParser] = None,
|
||||
images_inner_format: Literal["text", "markdown-img", "html-img"] = "text",
|
||||
extraction_mode: Literal["plain", "layout"] = "plain",
|
||||
extraction_kwargs: Optional[dict[str, Any]] = None,
|
||||
):
|
||||
self.password = password
|
||||
"""Initialize a parser based on PyPDF.
|
||||
|
||||
Args:
|
||||
password: Optional password for opening encrypted PDFs.
|
||||
extract_images: Whether to extract images from the PDF.
|
||||
mode: The extraction mode, either "single" for the entire document or "page"
|
||||
for page-wise extraction.
|
||||
pages_delimiter: A string delimiter to separate pages in single-mode
|
||||
extraction.
|
||||
images_parser: Optional image blob parser.
|
||||
images_inner_format: The format for the parsed output.
|
||||
- "text" = return the content as is
|
||||
- "markdown-img" = wrap the content into an image markdown link, w/ link
|
||||
pointing to (`![body)(#)`]
|
||||
- "html-img" = wrap the content as the `alt` text of an tag and link to
|
||||
(`<img alt="{body}" src="#"/>`)
|
||||
extraction_mode: “plain” for legacy functionality, “layout” extract text
|
||||
in a fixed width format that closely adheres to the rendered layout in
|
||||
the source pdf.
|
||||
extraction_kwargs: Optional additional parameters for the extraction
|
||||
process.
|
||||
|
||||
Raises:
|
||||
ValueError: If the `mode` is not "single" or "page".
|
||||
"""
|
||||
super().__init__()
|
||||
if mode not in ["single", "page"]:
|
||||
raise ValueError("mode must be single or page")
|
||||
self.extract_images = extract_images
|
||||
if extract_images and not images_parser:
|
||||
images_parser = RapidOCRBlobParser()
|
||||
self.images_parser = images_parser
|
||||
self.images_inner_format = images_inner_format
|
||||
self.password = password
|
||||
self.mode = mode
|
||||
self.pages_delimiter = pages_delimiter
|
||||
self.extraction_mode = extraction_mode
|
||||
self.extraction_kwargs = extraction_kwargs or {}
|
||||
|
||||
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
|
||||
"""Lazily parse the blob."""
|
||||
"""
|
||||
Lazily parse the blob.
|
||||
Insert image, if possible, between two paragraphs.
|
||||
In this way, a paragraph can be continued on the next page.
|
||||
|
||||
Args:
|
||||
blob: The blob to parse.
|
||||
|
||||
Raises:
|
||||
ImportError: If the `pypdf` package is not found.
|
||||
|
||||
Yield:
|
||||
An iterator over the parsed documents.
|
||||
"""
|
||||
try:
|
||||
import pypdf
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"`pypdf` package not found, please install it with "
|
||||
"`pip install pypdf`"
|
||||
"pypdf package not found, please install it with `pip install pypdf`"
|
||||
)
|
||||
|
||||
def _extract_text_from_page(page: pypdf.PageObject) -> str:
|
||||
"""Extract text from image given the version of pypdf."""
|
||||
"""
|
||||
Extract text from image given the version of pypdf.
|
||||
|
||||
Args:
|
||||
page: The page object to extract text from.
|
||||
|
||||
Returns:
|
||||
str: The extracted text.
|
||||
"""
|
||||
if pypdf.__version__.startswith("3"):
|
||||
return page.extract_text()
|
||||
else:
|
||||
return page.extract_text(
|
||||
extraction_mode=self.extraction_mode, # type: ignore[arg-type]
|
||||
**self.extraction_kwargs, # type: ignore[arg-type]
|
||||
extraction_mode=self.extraction_mode,
|
||||
**self.extraction_kwargs,
|
||||
)
|
||||
|
||||
with blob.as_bytes_io() as pdf_file_obj: # type: ignore[attr-defined]
|
||||
pdf_reader = pypdf.PdfReader(pdf_file_obj, password=self.password)
|
||||
|
||||
yield from [
|
||||
Document(
|
||||
page_content=_extract_text_from_page(page=page)
|
||||
+ self._extract_images_from_page(page),
|
||||
metadata={
|
||||
"source": blob.source,
|
||||
"page": page_number,
|
||||
"page_label": pdf_reader.page_labels[page_number],
|
||||
},
|
||||
# type: ignore[attr-defined]
|
||||
doc_metadata = _purge_metadata(
|
||||
{"producer": "PyPDF", "creator": "PyPDF", "creationdate": ""}
|
||||
| cast(dict, pdf_reader.metadata or {})
|
||||
| {
|
||||
"source": blob.source,
|
||||
"total_pages": len(pdf_reader.pages),
|
||||
}
|
||||
)
|
||||
single_texts = []
|
||||
for page_number, page in enumerate(pdf_reader.pages):
|
||||
text_from_page = _extract_text_from_page(page=page)
|
||||
images_from_page = self.extract_images_from_page(page)
|
||||
all_text = _merge_text_and_extras(
|
||||
[images_from_page], text_from_page
|
||||
).strip()
|
||||
if self.mode == "page":
|
||||
yield Document(
|
||||
page_content=all_text,
|
||||
metadata=_validate_metadata(
|
||||
doc_metadata
|
||||
| {
|
||||
"page": page_number,
|
||||
"page_label": pdf_reader.page_labels[page_number],
|
||||
}
|
||||
),
|
||||
)
|
||||
else:
|
||||
single_texts.append(all_text)
|
||||
if self.mode == "single":
|
||||
yield Document(
|
||||
page_content=self.pages_delimiter.join(single_texts),
|
||||
metadata=_validate_metadata(doc_metadata),
|
||||
)
|
||||
for page_number, page in enumerate(pdf_reader.pages)
|
||||
]
|
||||
|
||||
def _extract_images_from_page(self, page: pypdf.PageObject) -> str:
|
||||
"""Extract images from page and get the text with RapidOCR."""
|
||||
if not self.extract_images or "/XObject" not in page["/Resources"].keys(): # type: ignore[attr-defined]
|
||||
def extract_images_from_page(self, page: pypdf._page.PageObject) -> str:
|
||||
"""Extract images from a PDF page and get the text using images_to_text.
|
||||
|
||||
Args:
|
||||
page: The page object from which to extract images.
|
||||
|
||||
Returns:
|
||||
str: The extracted text from the images on the page.
|
||||
"""
|
||||
if not self.images_parser:
|
||||
return ""
|
||||
from PIL import Image
|
||||
|
||||
if "/XObject" not in cast(dict, page["/Resources"]).keys():
|
||||
return ""
|
||||
|
||||
xObject = page["/Resources"]["/XObject"].get_object() # type: ignore
|
||||
xObject = page["/Resources"]["/XObject"].get_object() # type: ignore[index]
|
||||
images = []
|
||||
for obj in xObject:
|
||||
np_image: Any = None
|
||||
if xObject[obj]["/Subtype"] == "/Image":
|
||||
if xObject[obj]["/Filter"][1:] in _PDF_FILTER_WITHOUT_LOSS:
|
||||
height, width = xObject[obj]["/Height"], xObject[obj]["/Width"]
|
||||
|
||||
images.append(
|
||||
np.frombuffer(xObject[obj].get_data(), dtype=np.uint8).reshape(
|
||||
height, width, -1
|
||||
)
|
||||
)
|
||||
np_image = np.frombuffer(
|
||||
xObject[obj].get_data(), dtype=np.uint8
|
||||
).reshape(height, width, -1)
|
||||
elif xObject[obj]["/Filter"][1:] in _PDF_FILTER_WITH_LOSS:
|
||||
images.append(xObject[obj].get_data())
|
||||
elif (
|
||||
isinstance(xObject[obj]["/Filter"], list)
|
||||
and xObject[obj]["/Filter"]
|
||||
and xObject[obj]["/Filter"][0][1:] in _PDF_FILTER_WITH_LOSS
|
||||
):
|
||||
images.append(xObject[obj].get_data())
|
||||
np_image = np.array(Image.open(io.BytesIO(xObject[obj].get_data())))
|
||||
|
||||
else:
|
||||
warnings.warn("Unknown PDF Filter!")
|
||||
return extract_from_images_with_rapidocr(images)
|
||||
logger.warning("Unknown PDF Filter!")
|
||||
if np_image is not None:
|
||||
image_bytes = io.BytesIO()
|
||||
Image.fromarray(np_image).save(image_bytes, format="PNG")
|
||||
blob = Blob.from_data(image_bytes.getvalue(), mime_type="image/png")
|
||||
image_text = next(self.images_parser.lazy_parse(blob)).page_content
|
||||
images.append(
|
||||
_format_inner_image(blob, image_text, self.images_inner_format)
|
||||
)
|
||||
return _FORMAT_IMAGE_STR.format(
|
||||
image_text=_JOIN_IMAGES.join(filter(None, images))
|
||||
)
|
||||
|
||||
|
||||
class PDFMinerParser(BaseBlobParser):
|
||||
|
||||
@@ -184,64 +184,56 @@ class OnlinePDFLoader(BasePDFLoader):
|
||||
|
||||
|
||||
class PyPDFLoader(BasePDFLoader):
|
||||
"""PyPDFLoader document loader integration
|
||||
"""Load and parse a PDF file using 'pypdf' library.
|
||||
|
||||
Setup:
|
||||
Install ``langchain-community``.
|
||||
This class provides methods to load and parse PDF documents, supporting various
|
||||
configurations such as handling password-protected files, extracting images, and
|
||||
defining extraction mode. It integrates the `pypdf` library for PDF processing and
|
||||
offers both synchronous and asynchronous document loading.
|
||||
|
||||
Examples:
|
||||
Setup:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install -U langchain-community
|
||||
pip install -U langchain-community pypdf
|
||||
|
||||
Instantiate the loader:
|
||||
|
||||
Instantiate:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.document_loaders import PyPDFLoader
|
||||
|
||||
loader = PyPDFLoader(
|
||||
file_path = "./example_data/layout-parser-paper.pdf",
|
||||
password = "my-password",
|
||||
extract_images = True,
|
||||
# headers = None
|
||||
# extraction_mode = "plain",
|
||||
# extraction_kwargs = None,
|
||||
# password = None,
|
||||
mode = "single",
|
||||
pages_delimiter = "\n\f",
|
||||
# extract_images = True,
|
||||
# images_parser = RapidOCRBlobParser(),
|
||||
)
|
||||
|
||||
Lazy load:
|
||||
Lazy load documents:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
docs = []
|
||||
docs_lazy = loader.lazy_load()
|
||||
|
||||
# async variant:
|
||||
# docs_lazy = await loader.alazy_load()
|
||||
|
||||
for doc in docs_lazy:
|
||||
docs.append(doc)
|
||||
print(docs[0].page_content[:100])
|
||||
print(docs[0].metadata)
|
||||
|
||||
.. code-block:: python
|
||||
Load documents asynchronously:
|
||||
|
||||
LayoutParser : A Unified Toolkit for Deep
|
||||
Learning Based Document Image Analysis
|
||||
Zejiang Shen1( ), R
|
||||
{'source': './example_data/layout-parser-paper.pdf', 'page': 0}
|
||||
|
||||
Async load:
|
||||
.. code-block:: python
|
||||
|
||||
docs = await loader.aload()
|
||||
print(docs[0].page_content[:100])
|
||||
print(docs[0].metadata)
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
LayoutParser : A Unified Toolkit for Deep
|
||||
Learning Based Document Image Analysis
|
||||
Zejiang Shen1( ), R
|
||||
{'source': './example_data/layout-parser-paper.pdf', 'page': 0}
|
||||
""" # noqa: E501
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@@ -250,20 +242,50 @@ class PyPDFLoader(BasePDFLoader):
|
||||
headers: Optional[dict] = None,
|
||||
extract_images: bool = False,
|
||||
*,
|
||||
extraction_mode: str = "plain",
|
||||
mode: Literal["single", "page"] = "page",
|
||||
images_parser: Optional[BaseImageBlobParser] = None,
|
||||
images_inner_format: Literal["text", "markdown-img", "html-img"] = "text",
|
||||
pages_delimiter: str = _DEFAULT_PAGES_DELIMITER,
|
||||
extraction_mode: Literal["plain", "layout"] = "plain",
|
||||
extraction_kwargs: Optional[dict] = None,
|
||||
) -> None:
|
||||
"""Initialize with a file path."""
|
||||
try:
|
||||
import pypdf # noqa:F401
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"pypdf package not found, please install it with `pip install pypdf`"
|
||||
)
|
||||
"""Initialize with a file path.
|
||||
|
||||
Args:
|
||||
file_path: The path to the PDF file to be loaded.
|
||||
headers: Optional headers to use for GET request to download a file from a
|
||||
web path.
|
||||
password: Optional password for opening encrypted PDFs.
|
||||
mode: The extraction mode, either "single" for the entire document or "page"
|
||||
for page-wise extraction.
|
||||
pages_delimiter: A string delimiter to separate pages in single-mode
|
||||
extraction.
|
||||
extract_images: Whether to extract images from the PDF.
|
||||
images_parser: Optional image blob parser.
|
||||
images_inner_format: The format for the parsed output.
|
||||
- "text" = return the content as is
|
||||
- "markdown-img" = wrap the content into an image markdown link, w/ link
|
||||
pointing to (`![body)(#)`]
|
||||
- "html-img" = wrap the content as the `alt` text of an tag and link to
|
||||
(`<img alt="{body}" src="#"/>`)
|
||||
extraction_mode: “plain” for legacy functionality, “layout” extract text
|
||||
in a fixed width format that closely adheres to the rendered layout in
|
||||
the source pdf
|
||||
extraction_kwargs: Optional additional parameters for the extraction
|
||||
process.
|
||||
|
||||
Returns:
|
||||
This method does not directly return data. Use the `load`, `lazy_load` or
|
||||
`aload` methods to retrieve parsed documents with content and metadata.
|
||||
"""
|
||||
super().__init__(file_path, headers=headers)
|
||||
self.parser = PyPDFParser(
|
||||
password=password,
|
||||
mode=mode,
|
||||
extract_images=extract_images,
|
||||
images_parser=images_parser,
|
||||
images_inner_format=images_inner_format,
|
||||
pages_delimiter=pages_delimiter,
|
||||
extraction_mode=extraction_mode,
|
||||
extraction_kwargs=extraction_kwargs,
|
||||
)
|
||||
@@ -271,12 +293,18 @@ class PyPDFLoader(BasePDFLoader):
|
||||
def lazy_load(
|
||||
self,
|
||||
) -> Iterator[Document]:
|
||||
"""Lazy load given path as pages."""
|
||||
"""
|
||||
Lazy load given path as pages.
|
||||
Insert image, if possible, between two paragraphs.
|
||||
In this way, a paragraph can be continued on the next page.
|
||||
"""
|
||||
if self.web_path:
|
||||
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path) # type: ignore[attr-defined]
|
||||
blob = Blob.from_data( # type: ignore[attr-defined]
|
||||
open(self.file_path, "rb").read(), path=self.web_path
|
||||
)
|
||||
else:
|
||||
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
|
||||
yield from self.parser.parse(blob)
|
||||
yield from self.parser.lazy_parse(blob)
|
||||
|
||||
|
||||
class PyPDFium2Loader(BasePDFLoader):
|
||||
@@ -305,9 +333,56 @@ class PyPDFium2Loader(BasePDFLoader):
|
||||
|
||||
|
||||
class PyPDFDirectoryLoader(BaseLoader):
|
||||
"""Load a directory with `PDF` files using `pypdf` and chunks at character level.
|
||||
"""Load and parse a directory of PDF files using 'pypdf' library.
|
||||
|
||||
Loader also stores page numbers in metadata.
|
||||
This class provides methods to load and parse multiple PDF documents in a directory,
|
||||
supporting options for recursive search, handling password-protected files,
|
||||
extracting images, and defining extraction modes. It integrates the `pypdf` library
|
||||
for PDF processing and offers synchronous document loading.
|
||||
|
||||
Examples:
|
||||
Setup:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install -U langchain-community pypdf
|
||||
|
||||
Instantiate the loader:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.document_loaders import PyPDFDirectoryLoader
|
||||
|
||||
loader = PyPDFDirectoryLoader(
|
||||
path = "./example_data/",
|
||||
glob = "**/[!.]*.pdf",
|
||||
silent_errors = False,
|
||||
load_hidden = False,
|
||||
recursive = False,
|
||||
extract_images = False,
|
||||
password = None,
|
||||
mode = "page",
|
||||
images_to_text = None,
|
||||
headers = None,
|
||||
extraction_mode = "plain",
|
||||
# extraction_kwargs = None,
|
||||
)
|
||||
|
||||
Load documents:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
docs = loader.load()
|
||||
print(docs[0].page_content[:100])
|
||||
print(docs[0].metadata)
|
||||
|
||||
Load documents asynchronously:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
docs = await loader.aload()
|
||||
print(docs[0].page_content[:100])
|
||||
print(docs[0].metadata)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
@@ -318,16 +393,53 @@ class PyPDFDirectoryLoader(BaseLoader):
|
||||
load_hidden: bool = False,
|
||||
recursive: bool = False,
|
||||
extract_images: bool = False,
|
||||
*,
|
||||
password: Optional[str] = None,
|
||||
mode: Literal["single", "page"] = "page",
|
||||
images_parser: Optional[BaseImageBlobParser] = None,
|
||||
headers: Optional[dict] = None,
|
||||
extraction_mode: Literal["plain", "layout"] = "plain",
|
||||
extraction_kwargs: Optional[dict] = None,
|
||||
):
|
||||
"""Initialize with a directory path.
|
||||
|
||||
Args:
|
||||
path: The path to the directory containing PDF files to be loaded.
|
||||
glob: The glob pattern to match files in the directory.
|
||||
silent_errors: Whether to log errors instead of raising them.
|
||||
load_hidden: Whether to include hidden files in the search.
|
||||
recursive: Whether to search subdirectories recursively.
|
||||
extract_images: Whether to extract images from PDFs.
|
||||
password: Optional password for opening encrypted PDFs.
|
||||
mode: The extraction mode, either "single" for extracting the entire
|
||||
document or "page" for page-wise extraction.
|
||||
images_parser: Optional image blob parser..
|
||||
headers: Optional headers to use for GET request to download a file from a
|
||||
web path.
|
||||
extraction_mode: “plain” for legacy functionality, “layout” for
|
||||
experimental layout mode functionality
|
||||
extraction_kwargs: Optional additional parameters for the extraction
|
||||
process.
|
||||
|
||||
Returns:
|
||||
This method does not directly return data. Use the `load` method to
|
||||
retrieve parsed documents with content and metadata.
|
||||
"""
|
||||
self.password = password
|
||||
self.mode = mode
|
||||
self.path = path
|
||||
self.glob = glob
|
||||
self.load_hidden = load_hidden
|
||||
self.recursive = recursive
|
||||
self.silent_errors = silent_errors
|
||||
self.extract_images = extract_images
|
||||
self.images_parser = images_parser
|
||||
self.headers = headers
|
||||
self.extraction_mode = extraction_mode
|
||||
self.extraction_kwargs = extraction_kwargs
|
||||
|
||||
@staticmethod
|
||||
def _is_visible(path: Path) -> bool:
|
||||
def _is_visible(path: PurePath) -> bool:
|
||||
return not any(part.startswith(".") for part in path.parts)
|
||||
|
||||
def load(self) -> list[Document]:
|
||||
@@ -338,7 +450,16 @@ class PyPDFDirectoryLoader(BaseLoader):
|
||||
if i.is_file():
|
||||
if self._is_visible(i.relative_to(p)) or self.load_hidden:
|
||||
try:
|
||||
loader = PyPDFLoader(str(i), extract_images=self.extract_images)
|
||||
loader = PyPDFLoader(
|
||||
str(i),
|
||||
password=self.password,
|
||||
mode=self.mode,
|
||||
extract_images=self.extract_images,
|
||||
images_parser=self.images_parser,
|
||||
headers=self.headers,
|
||||
extraction_mode=self.extraction_mode,
|
||||
extraction_kwargs=self.extraction_kwargs,
|
||||
)
|
||||
sub_docs = loader.load()
|
||||
for doc in sub_docs:
|
||||
doc.metadata["source"] = str(i)
|
||||
|
||||
@@ -36,7 +36,16 @@ class YahooFinanceNewsTool(BaseTool): # type: ignore[override, override]
|
||||
query: str,
|
||||
run_manager: Optional[CallbackManagerForToolRun] = None,
|
||||
) -> str:
|
||||
"""Use the Yahoo Finance News tool."""
|
||||
"""
|
||||
Use the Yahoo Finance News tool.
|
||||
|
||||
Args:
|
||||
query: Company ticker symbol (e.g., 'AAPL' for Apple).
|
||||
run_manager: Optional callback manager.
|
||||
|
||||
Returns:
|
||||
str: Formatted news results or error message.
|
||||
"""
|
||||
try:
|
||||
import yfinance
|
||||
except ImportError:
|
||||
@@ -53,7 +62,11 @@ class YahooFinanceNewsTool(BaseTool): # type: ignore[override, override]
|
||||
|
||||
links = []
|
||||
try:
|
||||
links = [n["link"] for n in company.news if n["type"] == "STORY"]
|
||||
links = [
|
||||
n["content"]["canonicalUrl"]["url"]
|
||||
for n in company.news
|
||||
if n["content"]["contentType"] == "STORY"
|
||||
]
|
||||
except (HTTPError, ReadTimeout, ConnectionError):
|
||||
if not links:
|
||||
return f"No news found for company that searched with {query} ticker."
|
||||
@@ -69,8 +82,9 @@ class YahooFinanceNewsTool(BaseTool): # type: ignore[override, override]
|
||||
@staticmethod
|
||||
def _format_results(docs: Iterable[Document], query: str) -> str:
|
||||
doc_strings = [
|
||||
"\n".join([doc.metadata["title"], doc.metadata["description"]])
|
||||
"\n".join([doc.metadata["title"], doc.metadata.get("description", "")])
|
||||
for doc in docs
|
||||
if query in doc.metadata["description"] or query in doc.metadata["title"]
|
||||
if query in doc.metadata.get("description", "")
|
||||
or query in doc.metadata["title"]
|
||||
]
|
||||
return "\n\n".join(doc_strings)
|
||||
|
||||
@@ -14,7 +14,6 @@ from langchain_community.document_loaders.parsers import (
|
||||
PDFMinerParser,
|
||||
PDFPlumberParser,
|
||||
PyPDFium2Parser,
|
||||
PyPDFParser,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@@ -98,11 +97,6 @@ def _assert_with_duplicate_parser(parser: BaseBlobParser, dedupe: bool = False)
|
||||
assert "11000000 SSeerriieess" == docs[0].page_content.split("\n")[0]
|
||||
|
||||
|
||||
def test_pypdf_parser() -> None:
|
||||
"""Test PyPDF parser."""
|
||||
_assert_with_parser(PyPDFParser())
|
||||
|
||||
|
||||
def test_pdfminer_parser() -> None:
|
||||
"""Test PDFMiner parser."""
|
||||
# Does not follow defaults to split by page.
|
||||
@@ -122,11 +116,6 @@ def test_pdfplumber_parser() -> None:
|
||||
_assert_with_duplicate_parser(PDFPlumberParser(dedupe=True), dedupe=True)
|
||||
|
||||
|
||||
def test_extract_images_text_from_pdf_pypdfparser() -> None:
|
||||
"""Test extract image from pdf and recognize text with rapid ocr - PyPDFParser"""
|
||||
_assert_with_parser(PyPDFParser(extract_images=True))
|
||||
|
||||
|
||||
def test_extract_images_text_from_pdf_pdfminerparser() -> None:
|
||||
"""Test extract image from pdf and recognize text with rapid ocr - PDFMinerParser"""
|
||||
_assert_with_parser(PDFMinerParser(extract_images=True))
|
||||
@@ -150,6 +139,8 @@ class EmptyImageBlobParser(BaseImageBlobParser):
|
||||
"parser_factory,params",
|
||||
[
|
||||
("PyMuPDFParser", {}),
|
||||
("PyPDFParser", {"extraction_mode": "plain"}),
|
||||
("PyPDFParser", {"extraction_mode": "layout"}),
|
||||
],
|
||||
)
|
||||
@pytest.mark.requires("pillow")
|
||||
@@ -176,6 +167,8 @@ def test_mode_and_extract_images_variations(
|
||||
"parser_factory,params",
|
||||
[
|
||||
("PyMuPDFParser", {}),
|
||||
("PyPDFParser", {"extraction_mode": "plain"}),
|
||||
("PyPDFParser", {"extraction_mode": "layout"}),
|
||||
],
|
||||
)
|
||||
@pytest.mark.requires("pillow")
|
||||
|
||||
@@ -212,6 +212,7 @@ def test_amazontextract_loader_failures() -> None:
|
||||
"parser_factory,params",
|
||||
[
|
||||
("PyMuPDFLoader", {}),
|
||||
("PyPDFLoader", {}),
|
||||
],
|
||||
)
|
||||
def test_standard_parameters(
|
||||
@@ -229,12 +230,10 @@ def test_standard_parameters(
|
||||
loader = loader_class(
|
||||
file_path,
|
||||
mode="page",
|
||||
page_delimiter="---",
|
||||
pages_delimiter="---",
|
||||
images_parser=None,
|
||||
images_inner_format="text",
|
||||
password=None,
|
||||
extract_tables=None,
|
||||
extract_tables_settings=None,
|
||||
)
|
||||
docs = loader.load()
|
||||
assert len(docs) == 16
|
||||
|
||||
@@ -9,7 +9,7 @@ yfinance = pytest.importorskip("yfinance")
|
||||
def test_success() -> None:
|
||||
"""Test that the tool runs successfully."""
|
||||
tool = YahooFinanceNewsTool()
|
||||
query = "Microsoft"
|
||||
query = "AAPL"
|
||||
result = tool.run(query)
|
||||
assert result is not None
|
||||
assert f"Company ticker {query} not found." not in result
|
||||
|
||||
@@ -12,7 +12,6 @@ from langchain_community.document_loaders.blob_loaders import Blob
|
||||
from langchain_community.document_loaders.parsers.pdf import (
|
||||
PDFMinerParser,
|
||||
PyPDFium2Parser,
|
||||
PyPDFParser,
|
||||
_merge_text_and_extras,
|
||||
)
|
||||
|
||||
@@ -76,12 +75,6 @@ def _assert_with_parser(parser: BaseBlobParser, *, splits_by_page: bool = True)
|
||||
assert int(metadata["page"]) == 0
|
||||
|
||||
|
||||
@pytest.mark.requires("pypdf")
|
||||
def test_pypdf_parser() -> None:
|
||||
"""Test PyPDF parser."""
|
||||
_assert_with_parser(PyPDFParser())
|
||||
|
||||
|
||||
@pytest.mark.requires("pdfminer")
|
||||
def test_pdfminer_parser() -> None:
|
||||
"""Test PDFMiner parser."""
|
||||
@@ -100,6 +93,7 @@ def test_pypdfium2_parser() -> None:
|
||||
"parser_factory,require,params",
|
||||
[
|
||||
("PyMuPDFParser", "pymupdf", {}),
|
||||
("PyPDFParser", "pypdf", {}),
|
||||
],
|
||||
)
|
||||
def test_parsers(
|
||||
|
||||
@@ -65,7 +65,8 @@ def test_pypdf_loader_with_layout() -> None:
|
||||
expected = path_to_layout_pdf_txt.read_text(encoding="utf-8")
|
||||
cleaned_first_page = re.sub(r"\x00", "", first_page)
|
||||
cleaned_expected = re.sub(r"\x00", "", expected)
|
||||
assert cleaned_first_page == cleaned_expected
|
||||
|
||||
assert cleaned_first_page == cleaned_expected.strip()
|
||||
|
||||
|
||||
@pytest.mark.requires("pypdf")
|
||||
|
||||
30
libs/core/poetry.lock
generated
30
libs/core/poetry.lock
generated
@@ -221,6 +221,20 @@ webencodings = "*"
|
||||
[package.extras]
|
||||
css = ["tinycss2 (>=1.1.0,<1.5)"]
|
||||
|
||||
[[package]]
|
||||
name = "blockbuster"
|
||||
version = "1.5.11"
|
||||
description = "Utility to detect blocking calls in the async event loop"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "blockbuster-1.5.11-py3-none-any.whl", hash = "sha256:34e56b2ff24c73d7b2857dcc20d49abe88f92a9cddd5f56432adeb3ef4aca9b8"},
|
||||
{file = "blockbuster-1.5.11.tar.gz", hash = "sha256:c5ed3da13216c80e26b755fb576c3d638d8125f893ba4709e56e28ecf3ee7254"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
forbiddenfruit = ">=0.1.4"
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2024.12.14"
|
||||
@@ -553,6 +567,16 @@ files = [
|
||||
[package.extras]
|
||||
devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
|
||||
|
||||
[[package]]
|
||||
name = "forbiddenfruit"
|
||||
version = "0.1.4"
|
||||
description = "Patch python built-in objects"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "forbiddenfruit-0.1.4.tar.gz", hash = "sha256:e3f7e66561a29ae129aac139a85d610dbf3dd896128187ed5454b6421f624253"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "fqdn"
|
||||
version = "1.5.1"
|
||||
@@ -1188,7 +1212,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain-tests"
|
||||
version = "0.3.9"
|
||||
version = "0.3.10"
|
||||
description = "Standard tests for LangChain implementations"
|
||||
optional = false
|
||||
python-versions = ">=3.9,<4.0"
|
||||
@@ -1197,7 +1221,7 @@ develop = true
|
||||
|
||||
[package.dependencies]
|
||||
httpx = ">=0.25.0,<1"
|
||||
langchain-core = "^0.3.31"
|
||||
langchain-core = "^0.3.33"
|
||||
numpy = [
|
||||
{version = ">=1.24.0,<2.0.0", markers = "python_version < \"3.12\""},
|
||||
{version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
|
||||
@@ -3247,4 +3271,4 @@ cffi = ["cffi (>=1.11)"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<4.0"
|
||||
content-hash = "2a262498da93ae3991e5eade787affab38f1ddef5212cb745eafdd0a40f0e986"
|
||||
content-hash = "7a90068bfdba1760bd1e80d65c8f83b7d940f3c49e463cc793d63f1be518eb72"
|
||||
|
||||
@@ -118,6 +118,7 @@ grandalf = "^0.8"
|
||||
responses = "^0.25.0"
|
||||
pytest-socket = "^0.7.0"
|
||||
pytest-xdist = "^3.6.1"
|
||||
blockbuster = "~1.5.11"
|
||||
[[tool.poetry.group.test.dependencies.numpy]]
|
||||
version = "^1.24.0"
|
||||
python = "<3.12"
|
||||
|
||||
@@ -1,14 +1,41 @@
|
||||
"""Configuration for unit tests."""
|
||||
|
||||
from collections.abc import Sequence
|
||||
from collections.abc import Iterator, Sequence
|
||||
from importlib import util
|
||||
from uuid import UUID
|
||||
|
||||
import pytest
|
||||
from blockbuster import BlockBuster, blockbuster_ctx
|
||||
from pytest import Config, Function, Parser
|
||||
from pytest_mock import MockerFixture
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def blockbuster() -> Iterator[BlockBuster]:
|
||||
with blockbuster_ctx("langchain_core") as bb:
|
||||
for func in ["os.stat", "os.path.abspath"]:
|
||||
(
|
||||
bb.functions[func]
|
||||
.can_block_in("langchain_core/_api/internal.py", "is_caller_internal")
|
||||
.can_block_in("langchain_core/runnables/base.py", "__repr__")
|
||||
.can_block_in(
|
||||
"langchain_core/beta/runnables/context.py", "aconfig_with_context"
|
||||
)
|
||||
)
|
||||
|
||||
for func in ["os.stat", "io.TextIOWrapper.read"]:
|
||||
bb.functions[func].can_block_in(
|
||||
"langsmith/client.py", "_default_retry_config"
|
||||
)
|
||||
|
||||
for bb_function in bb.functions.values():
|
||||
bb_function.can_block_in(
|
||||
"freezegun/api.py", "_get_cached_module_attributes"
|
||||
)
|
||||
|
||||
yield bb
|
||||
|
||||
|
||||
def pytest_addoption(parser: Parser) -> None:
|
||||
"""Add custom command line options to pytest."""
|
||||
parser.addoption(
|
||||
|
||||
@@ -191,7 +191,12 @@ async def test_callback_handlers() -> None:
|
||||
model = GenericFakeChatModel(messages=infinite_cycle)
|
||||
tokens: list[str] = []
|
||||
# New model
|
||||
results = list(model.stream("meow", {"callbacks": [MyCustomAsyncHandler(tokens)]}))
|
||||
results = [
|
||||
chunk
|
||||
async for chunk in model.astream(
|
||||
"meow", {"callbacks": [MyCustomAsyncHandler(tokens)]}
|
||||
)
|
||||
]
|
||||
assert results == [
|
||||
_any_id_ai_message_chunk(content="hello"),
|
||||
_any_id_ai_message_chunk(content=" "),
|
||||
|
||||
@@ -18,6 +18,7 @@ from langchain_core.messages import (
|
||||
)
|
||||
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
|
||||
from langchain_core.outputs.llm_result import LLMResult
|
||||
from langchain_core.tracers import LogStreamCallbackHandler
|
||||
from langchain_core.tracers.base import BaseTracer
|
||||
from langchain_core.tracers.context import collect_runs
|
||||
from langchain_core.tracers.event_stream import _AstreamEventsCallbackHandler
|
||||
@@ -303,39 +304,48 @@ class StreamingModel(NoStreamingModel):
|
||||
|
||||
|
||||
@pytest.mark.parametrize("disable_streaming", [True, False, "tool_calling"])
|
||||
async def test_disable_streaming(
|
||||
def test_disable_streaming(
|
||||
disable_streaming: Union[bool, Literal["tool_calling"]],
|
||||
) -> None:
|
||||
model = StreamingModel(disable_streaming=disable_streaming)
|
||||
assert model.invoke([]).content == "invoke"
|
||||
assert (await model.ainvoke([])).content == "invoke"
|
||||
|
||||
expected = "invoke" if disable_streaming is True else "stream"
|
||||
assert next(model.stream([])).content == expected
|
||||
async for c in model.astream([]):
|
||||
assert c.content == expected
|
||||
break
|
||||
assert (
|
||||
model.invoke([], config={"callbacks": [LogStreamCallbackHandler()]}).content
|
||||
== expected
|
||||
)
|
||||
|
||||
expected = "invoke" if disable_streaming in ("tool_calling", True) else "stream"
|
||||
assert next(model.stream([], tools=[{"type": "function"}])).content == expected
|
||||
assert (
|
||||
model.invoke(
|
||||
[], config={"callbacks": [_AstreamEventsCallbackHandler()]}
|
||||
[], config={"callbacks": [LogStreamCallbackHandler()]}, tools=[{}]
|
||||
).content
|
||||
== expected
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("disable_streaming", [True, False, "tool_calling"])
|
||||
async def test_disable_streaming_async(
|
||||
disable_streaming: Union[bool, Literal["tool_calling"]],
|
||||
) -> None:
|
||||
model = StreamingModel(disable_streaming=disable_streaming)
|
||||
assert (await model.ainvoke([])).content == "invoke"
|
||||
|
||||
expected = "invoke" if disable_streaming is True else "stream"
|
||||
async for c in model.astream([]):
|
||||
assert c.content == expected
|
||||
break
|
||||
assert (
|
||||
await model.ainvoke([], config={"callbacks": [_AstreamEventsCallbackHandler()]})
|
||||
).content == expected
|
||||
|
||||
expected = "invoke" if disable_streaming in ("tool_calling", True) else "stream"
|
||||
assert next(model.stream([], tools=[{"type": "function"}])).content == expected
|
||||
async for c in model.astream([], tools=[{}]):
|
||||
assert c.content == expected
|
||||
break
|
||||
assert (
|
||||
model.invoke(
|
||||
[], config={"callbacks": [_AstreamEventsCallbackHandler()]}, tools=[{}]
|
||||
).content
|
||||
== expected
|
||||
)
|
||||
assert (
|
||||
await model.ainvoke(
|
||||
[], config={"callbacks": [_AstreamEventsCallbackHandler()]}, tools=[{}]
|
||||
@@ -344,26 +354,31 @@ async def test_disable_streaming(
|
||||
|
||||
|
||||
@pytest.mark.parametrize("disable_streaming", [True, False, "tool_calling"])
|
||||
async def test_disable_streaming_no_streaming_model(
|
||||
def test_disable_streaming_no_streaming_model(
|
||||
disable_streaming: Union[bool, Literal["tool_calling"]],
|
||||
) -> None:
|
||||
model = NoStreamingModel(disable_streaming=disable_streaming)
|
||||
assert model.invoke([]).content == "invoke"
|
||||
assert (await model.ainvoke([])).content == "invoke"
|
||||
assert next(model.stream([])).content == "invoke"
|
||||
assert (
|
||||
model.invoke([], config={"callbacks": [LogStreamCallbackHandler()]}).content
|
||||
== "invoke"
|
||||
)
|
||||
assert next(model.stream([], tools=[{}])).content == "invoke"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("disable_streaming", [True, False, "tool_calling"])
|
||||
async def test_disable_streaming_no_streaming_model_async(
|
||||
disable_streaming: Union[bool, Literal["tool_calling"]],
|
||||
) -> None:
|
||||
model = NoStreamingModel(disable_streaming=disable_streaming)
|
||||
assert (await model.ainvoke([])).content == "invoke"
|
||||
async for c in model.astream([]):
|
||||
assert c.content == "invoke"
|
||||
break
|
||||
assert (
|
||||
model.invoke(
|
||||
[], config={"callbacks": [_AstreamEventsCallbackHandler()]}
|
||||
).content
|
||||
== "invoke"
|
||||
)
|
||||
assert (
|
||||
await model.ainvoke([], config={"callbacks": [_AstreamEventsCallbackHandler()]})
|
||||
).content == "invoke"
|
||||
assert next(model.stream([], tools=[{}])).content == "invoke"
|
||||
async for c in model.astream([], tools=[{}]):
|
||||
assert c.content == "invoke"
|
||||
break
|
||||
|
||||
@@ -1,11 +1,20 @@
|
||||
import time
|
||||
from typing import Optional as Optional
|
||||
|
||||
import pytest
|
||||
from blockbuster import BlockBuster
|
||||
|
||||
from langchain_core.caches import InMemoryCache
|
||||
from langchain_core.language_models import GenericFakeChatModel
|
||||
from langchain_core.rate_limiters import InMemoryRateLimiter
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def deactivate_blockbuster(blockbuster: BlockBuster) -> None:
|
||||
# Deactivate BlockBuster to not disturb the rate limiter timings
|
||||
blockbuster.deactivate()
|
||||
|
||||
|
||||
def test_rate_limit_invoke() -> None:
|
||||
"""Add rate limiter."""
|
||||
model = GenericFakeChatModel(
|
||||
|
||||
@@ -1,5 +1,3 @@
|
||||
import base64
|
||||
import tempfile
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from typing import Any, Union, cast
|
||||
@@ -727,44 +725,39 @@ async def test_chat_tmpl_from_messages_multipart_image() -> None:
|
||||
async def test_chat_tmpl_from_messages_multipart_formatting_with_path() -> None:
|
||||
"""Verify that we cannot pass `path` for an image as a variable."""
|
||||
in_mem = "base64mem"
|
||||
in_file_data = "base64file01"
|
||||
|
||||
with tempfile.NamedTemporaryFile(delete=True, suffix=".jpg") as temp_file:
|
||||
temp_file.write(base64.b64decode(in_file_data))
|
||||
temp_file.flush()
|
||||
|
||||
template = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
("system", "You are an AI assistant named {name}."),
|
||||
(
|
||||
"human",
|
||||
[
|
||||
{"type": "text", "text": "What's in this image?"},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": "data:image/jpeg;base64,{in_mem}",
|
||||
},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {"path": "{file_path}"},
|
||||
},
|
||||
],
|
||||
),
|
||||
]
|
||||
template = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
("system", "You are an AI assistant named {name}."),
|
||||
(
|
||||
"human",
|
||||
[
|
||||
{"type": "text", "text": "What's in this image?"},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": "data:image/jpeg;base64,{in_mem}",
|
||||
},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {"path": "{file_path}"},
|
||||
},
|
||||
],
|
||||
),
|
||||
]
|
||||
)
|
||||
with pytest.raises(ValueError):
|
||||
template.format_messages(
|
||||
name="R2D2",
|
||||
in_mem=in_mem,
|
||||
file_path="some/path",
|
||||
)
|
||||
with pytest.raises(ValueError):
|
||||
template.format_messages(
|
||||
name="R2D2",
|
||||
in_mem=in_mem,
|
||||
file_path=temp_file.name,
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
await template.aformat_messages(
|
||||
name="R2D2",
|
||||
in_mem=in_mem,
|
||||
file_path=temp_file.name,
|
||||
)
|
||||
with pytest.raises(ValueError):
|
||||
await template.aformat_messages(
|
||||
name="R2D2",
|
||||
in_mem=in_mem,
|
||||
file_path="some/path",
|
||||
)
|
||||
|
||||
|
||||
def test_messages_placeholder() -> None:
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
import asyncio
|
||||
from typing import Any, Callable, NamedTuple, Union
|
||||
|
||||
import pytest
|
||||
@@ -330,19 +331,26 @@ test_cases = [
|
||||
|
||||
|
||||
@pytest.mark.parametrize("runnable, cases", test_cases)
|
||||
async def test_context_runnables(
|
||||
def test_context_runnables(
|
||||
runnable: Union[Runnable, Callable[[], Runnable]], cases: list[_TestCase]
|
||||
) -> None:
|
||||
runnable = runnable if isinstance(runnable, Runnable) else runnable()
|
||||
assert runnable.invoke(cases[0].input) == cases[0].output
|
||||
assert await runnable.ainvoke(cases[1].input) == cases[1].output
|
||||
assert runnable.batch([case.input for case in cases]) == [
|
||||
case.output for case in cases
|
||||
]
|
||||
assert add(runnable.stream(cases[0].input)) == cases[0].output
|
||||
|
||||
|
||||
@pytest.mark.parametrize("runnable, cases", test_cases)
|
||||
async def test_context_runnables_async(
|
||||
runnable: Union[Runnable, Callable[[], Runnable]], cases: list[_TestCase]
|
||||
) -> None:
|
||||
runnable = runnable if isinstance(runnable, Runnable) else runnable()
|
||||
assert await runnable.ainvoke(cases[1].input) == cases[1].output
|
||||
assert await runnable.abatch([case.input for case in cases]) == [
|
||||
case.output for case in cases
|
||||
]
|
||||
assert add(runnable.stream(cases[0].input)) == cases[0].output
|
||||
assert await aadd(runnable.astream(cases[1].input)) == cases[1].output
|
||||
|
||||
|
||||
@@ -390,8 +398,7 @@ async def test_runnable_seq_streaming_chunks() -> None:
|
||||
"prompt": Context.getter("prompt"),
|
||||
}
|
||||
)
|
||||
|
||||
chunks = list(chain.stream({"foo": "foo", "bar": "bar"}))
|
||||
chunks = await asyncio.to_thread(list, chain.stream({"foo": "foo", "bar": "bar"}))
|
||||
achunks = [c async for c in chain.astream({"foo": "foo", "bar": "bar"})]
|
||||
for c in chunks:
|
||||
assert c in achunks
|
||||
|
||||
@@ -82,17 +82,25 @@ def chain_pass_exceptions() -> Runnable:
|
||||
"runnable",
|
||||
["llm", "llm_multi", "chain", "chain_pass_exceptions"],
|
||||
)
|
||||
async def test_fallbacks(
|
||||
def test_fallbacks(
|
||||
runnable: RunnableWithFallbacks, request: Any, snapshot: SnapshotAssertion
|
||||
) -> None:
|
||||
runnable = request.getfixturevalue(runnable)
|
||||
assert runnable.invoke("hello") == "bar"
|
||||
assert runnable.batch(["hi", "hey", "bye"]) == ["bar"] * 3
|
||||
assert list(runnable.stream("hello")) == ["bar"]
|
||||
assert dumps(runnable, pretty=True) == snapshot
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"runnable",
|
||||
["llm", "llm_multi", "chain", "chain_pass_exceptions"],
|
||||
)
|
||||
async def test_fallbacks_async(runnable: RunnableWithFallbacks, request: Any) -> None:
|
||||
runnable = request.getfixturevalue(runnable)
|
||||
assert await runnable.ainvoke("hello") == "bar"
|
||||
assert await runnable.abatch(["hi", "hey", "bye"]) == ["bar"] * 3
|
||||
assert list(await runnable.ainvoke("hello")) == list("bar")
|
||||
assert dumps(runnable, pretty=True) == snapshot
|
||||
|
||||
|
||||
def _runnable(inputs: dict) -> str:
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
import asyncio
|
||||
import sys
|
||||
import uuid
|
||||
import warnings
|
||||
@@ -1008,6 +1009,73 @@ def test_configurable_fields_example(snapshot: SnapshotAssertion) -> None:
|
||||
)
|
||||
|
||||
|
||||
def test_passthrough_tap(mocker: MockerFixture) -> None:
|
||||
fake = FakeRunnable()
|
||||
mock = mocker.Mock()
|
||||
|
||||
seq: Runnable = RunnablePassthrough(mock) | fake | RunnablePassthrough(mock)
|
||||
|
||||
assert seq.invoke("hello", my_kwarg="value") == 5 # type: ignore[call-arg]
|
||||
assert mock.call_args_list == [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
]
|
||||
mock.reset_mock()
|
||||
|
||||
assert seq.batch(["hello", "byebye"], my_kwarg="value") == [5, 6]
|
||||
assert len(mock.call_args_list) == 4
|
||||
for call in [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call("byebye", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
mocker.call(6),
|
||||
]:
|
||||
assert call in mock.call_args_list
|
||||
mock.reset_mock()
|
||||
|
||||
assert seq.batch(["hello", "byebye"], my_kwarg="value", return_exceptions=True) == [
|
||||
5,
|
||||
6,
|
||||
]
|
||||
assert len(mock.call_args_list) == 4
|
||||
for call in [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call("byebye", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
mocker.call(6),
|
||||
]:
|
||||
assert call in mock.call_args_list
|
||||
mock.reset_mock()
|
||||
|
||||
assert sorted(
|
||||
a
|
||||
for a in seq.batch_as_completed(
|
||||
["hello", "byebye"], my_kwarg="value", return_exceptions=True
|
||||
)
|
||||
) == [
|
||||
(0, 5),
|
||||
(1, 6),
|
||||
]
|
||||
assert len(mock.call_args_list) == 4
|
||||
for call in [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call("byebye", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
mocker.call(6),
|
||||
]:
|
||||
assert call in mock.call_args_list
|
||||
mock.reset_mock()
|
||||
|
||||
assert list(
|
||||
seq.stream("hello", {"metadata": {"key": "value"}}, my_kwarg="value")
|
||||
) == [5]
|
||||
assert mock.call_args_list == [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
]
|
||||
mock.reset_mock()
|
||||
|
||||
|
||||
async def test_passthrough_tap_async(mocker: MockerFixture) -> None:
|
||||
fake = FakeRunnable()
|
||||
mock = mocker.Mock()
|
||||
@@ -1079,67 +1147,6 @@ async def test_passthrough_tap_async(mocker: MockerFixture) -> None:
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
]
|
||||
mock.reset_mock()
|
||||
|
||||
assert seq.invoke("hello", my_kwarg="value") == 5 # type: ignore[call-arg]
|
||||
assert mock.call_args_list == [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
]
|
||||
mock.reset_mock()
|
||||
|
||||
assert seq.batch(["hello", "byebye"], my_kwarg="value") == [5, 6]
|
||||
assert len(mock.call_args_list) == 4
|
||||
for call in [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call("byebye", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
mocker.call(6),
|
||||
]:
|
||||
assert call in mock.call_args_list
|
||||
mock.reset_mock()
|
||||
|
||||
assert seq.batch(["hello", "byebye"], my_kwarg="value", return_exceptions=True) == [
|
||||
5,
|
||||
6,
|
||||
]
|
||||
assert len(mock.call_args_list) == 4
|
||||
for call in [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call("byebye", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
mocker.call(6),
|
||||
]:
|
||||
assert call in mock.call_args_list
|
||||
mock.reset_mock()
|
||||
|
||||
assert sorted(
|
||||
a
|
||||
for a in seq.batch_as_completed(
|
||||
["hello", "byebye"], my_kwarg="value", return_exceptions=True
|
||||
)
|
||||
) == [
|
||||
(0, 5),
|
||||
(1, 6),
|
||||
]
|
||||
assert len(mock.call_args_list) == 4
|
||||
for call in [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call("byebye", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
mocker.call(6),
|
||||
]:
|
||||
assert call in mock.call_args_list
|
||||
mock.reset_mock()
|
||||
|
||||
assert list(
|
||||
seq.stream("hello", {"metadata": {"key": "value"}}, my_kwarg="value")
|
||||
) == [5]
|
||||
assert mock.call_args_list == [
|
||||
mocker.call("hello", my_kwarg="value"),
|
||||
mocker.call(5),
|
||||
]
|
||||
mock.reset_mock()
|
||||
|
||||
|
||||
async def test_with_config_metadata_passthrough(mocker: MockerFixture) -> None:
|
||||
@@ -1170,7 +1177,7 @@ async def test_with_config_metadata_passthrough(mocker: MockerFixture) -> None:
|
||||
spy.reset_mock()
|
||||
|
||||
|
||||
async def test_with_config(mocker: MockerFixture) -> None:
|
||||
def test_with_config(mocker: MockerFixture) -> None:
|
||||
fake = FakeRunnable()
|
||||
spy = mocker.spy(fake, "invoke")
|
||||
|
||||
@@ -1276,7 +1283,11 @@ async def test_with_config(mocker: MockerFixture) -> None:
|
||||
for i, call in enumerate(spy.call_args_list):
|
||||
assert call.args[0] == ("hello" if i == 0 else "wooorld")
|
||||
assert call.args[1].get("tags") == ["a-tag"]
|
||||
spy.reset_mock()
|
||||
|
||||
|
||||
async def test_with_config_async(mocker: MockerFixture) -> None:
|
||||
fake = FakeRunnable()
|
||||
spy = mocker.spy(fake, "invoke")
|
||||
|
||||
handler = ConsoleCallbackHandler()
|
||||
assert (
|
||||
@@ -1372,7 +1383,7 @@ async def test_with_config(mocker: MockerFixture) -> None:
|
||||
)
|
||||
|
||||
|
||||
async def test_default_method_implementations(mocker: MockerFixture) -> None:
|
||||
def test_default_method_implementations(mocker: MockerFixture) -> None:
|
||||
fake = FakeRunnable()
|
||||
spy = mocker.spy(fake, "invoke")
|
||||
|
||||
@@ -1413,7 +1424,11 @@ async def test_default_method_implementations(mocker: MockerFixture) -> None:
|
||||
for call in spy.call_args_list:
|
||||
assert call.args[1].get("tags") == ["a-tag"]
|
||||
assert call.args[1].get("metadata") == {}
|
||||
spy.reset_mock()
|
||||
|
||||
|
||||
async def test_default_method_implementations_async(mocker: MockerFixture) -> None:
|
||||
fake = FakeRunnable()
|
||||
spy = mocker.spy(fake, "invoke")
|
||||
|
||||
assert await fake.ainvoke("hello", config={"callbacks": []}) == 5
|
||||
assert spy.call_args_list == [
|
||||
@@ -1442,7 +1457,7 @@ async def test_default_method_implementations(mocker: MockerFixture) -> None:
|
||||
}
|
||||
|
||||
|
||||
async def test_prompt() -> None:
|
||||
def test_prompt() -> None:
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
messages=[
|
||||
SystemMessage(content="You are a nice assistant."),
|
||||
@@ -1475,6 +1490,21 @@ async def test_prompt() -> None:
|
||||
|
||||
assert [*prompt.stream({"question": "What is your name?"})] == [expected]
|
||||
|
||||
|
||||
async def test_prompt_async() -> None:
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
messages=[
|
||||
SystemMessage(content="You are a nice assistant."),
|
||||
HumanMessagePromptTemplate.from_template("{question}"),
|
||||
]
|
||||
)
|
||||
expected = ChatPromptValue(
|
||||
messages=[
|
||||
SystemMessage(content="You are a nice assistant."),
|
||||
HumanMessage(content="What is your name?"),
|
||||
]
|
||||
)
|
||||
|
||||
assert await prompt.ainvoke({"question": "What is your name?"}) == expected
|
||||
|
||||
assert await prompt.abatch(
|
||||
@@ -2770,9 +2800,7 @@ def test_seq_prompt_dict(mocker: MockerFixture, snapshot: SnapshotAssertion) ->
|
||||
|
||||
|
||||
@freeze_time("2023-01-01")
|
||||
async def test_router_runnable(
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion
|
||||
) -> None:
|
||||
def test_router_runnable(mocker: MockerFixture, snapshot: SnapshotAssertion) -> None:
|
||||
chain1: Runnable = ChatPromptTemplate.from_template(
|
||||
"You are a math genius. Answer the question: {question}"
|
||||
) | FakeListLLM(responses=["4"])
|
||||
@@ -2797,17 +2825,6 @@ async def test_router_runnable(
|
||||
)
|
||||
assert result2 == ["4", "2"]
|
||||
|
||||
result = await chain.ainvoke({"key": "math", "question": "2 + 2"})
|
||||
assert result == "4"
|
||||
|
||||
result2 = await chain.abatch(
|
||||
[
|
||||
{"key": "math", "question": "2 + 2"},
|
||||
{"key": "english", "question": "2 + 2"},
|
||||
]
|
||||
)
|
||||
assert result2 == ["4", "2"]
|
||||
|
||||
# Test invoke
|
||||
router_spy = mocker.spy(router.__class__, "invoke")
|
||||
tracer = FakeTracer()
|
||||
@@ -2827,8 +2844,33 @@ async def test_router_runnable(
|
||||
assert len(router_run.child_runs) == 2
|
||||
|
||||
|
||||
async def test_router_runnable_async() -> None:
|
||||
chain1: Runnable = ChatPromptTemplate.from_template(
|
||||
"You are a math genius. Answer the question: {question}"
|
||||
) | FakeListLLM(responses=["4"])
|
||||
chain2: Runnable = ChatPromptTemplate.from_template(
|
||||
"You are an english major. Answer the question: {question}"
|
||||
) | FakeListLLM(responses=["2"])
|
||||
router: Runnable = RouterRunnable({"math": chain1, "english": chain2})
|
||||
chain: Runnable = {
|
||||
"key": lambda x: x["key"],
|
||||
"input": {"question": lambda x: x["question"]},
|
||||
} | router
|
||||
|
||||
result = await chain.ainvoke({"key": "math", "question": "2 + 2"})
|
||||
assert result == "4"
|
||||
|
||||
result2 = await chain.abatch(
|
||||
[
|
||||
{"key": "math", "question": "2 + 2"},
|
||||
{"key": "english", "question": "2 + 2"},
|
||||
]
|
||||
)
|
||||
assert result2 == ["4", "2"]
|
||||
|
||||
|
||||
@freeze_time("2023-01-01")
|
||||
async def test_higher_order_lambda_runnable(
|
||||
def test_higher_order_lambda_runnable(
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion
|
||||
) -> None:
|
||||
math_chain: Runnable = ChatPromptTemplate.from_template(
|
||||
@@ -2865,17 +2907,6 @@ async def test_higher_order_lambda_runnable(
|
||||
)
|
||||
assert result2 == ["4", "2"]
|
||||
|
||||
result = await chain.ainvoke({"key": "math", "question": "2 + 2"})
|
||||
assert result == "4"
|
||||
|
||||
result2 = await chain.abatch(
|
||||
[
|
||||
{"key": "math", "question": "2 + 2"},
|
||||
{"key": "english", "question": "2 + 2"},
|
||||
]
|
||||
)
|
||||
assert result2 == ["4", "2"]
|
||||
|
||||
# Test invoke
|
||||
math_spy = mocker.spy(math_chain.__class__, "invoke")
|
||||
tracer = FakeTracer()
|
||||
@@ -2897,6 +2928,41 @@ async def test_higher_order_lambda_runnable(
|
||||
assert math_run.name == "RunnableSequence"
|
||||
assert len(math_run.child_runs) == 3
|
||||
|
||||
|
||||
async def test_higher_order_lambda_runnable_async(mocker: MockerFixture) -> None:
|
||||
math_chain: Runnable = ChatPromptTemplate.from_template(
|
||||
"You are a math genius. Answer the question: {question}"
|
||||
) | FakeListLLM(responses=["4"])
|
||||
english_chain: Runnable = ChatPromptTemplate.from_template(
|
||||
"You are an english major. Answer the question: {question}"
|
||||
) | FakeListLLM(responses=["2"])
|
||||
input_map: Runnable = RunnableParallel(
|
||||
key=lambda x: x["key"],
|
||||
input={"question": lambda x: x["question"]},
|
||||
)
|
||||
|
||||
def router(input: dict[str, Any]) -> Runnable:
|
||||
if input["key"] == "math":
|
||||
return itemgetter("input") | math_chain
|
||||
elif input["key"] == "english":
|
||||
return itemgetter("input") | english_chain
|
||||
else:
|
||||
msg = f"Unknown key: {input['key']}"
|
||||
raise ValueError(msg)
|
||||
|
||||
chain: Runnable = input_map | router
|
||||
|
||||
result = await chain.ainvoke({"key": "math", "question": "2 + 2"})
|
||||
assert result == "4"
|
||||
|
||||
result2 = await chain.abatch(
|
||||
[
|
||||
{"key": "math", "question": "2 + 2"},
|
||||
{"key": "english", "question": "2 + 2"},
|
||||
]
|
||||
)
|
||||
assert result2 == ["4", "2"]
|
||||
|
||||
# Test ainvoke
|
||||
async def arouter(input: dict[str, Any]) -> Runnable:
|
||||
if input["key"] == "math":
|
||||
@@ -4651,7 +4717,7 @@ async def test_tool_from_runnable() -> None:
|
||||
}
|
||||
|
||||
|
||||
async def test_runnable_gen() -> None:
|
||||
def test_runnable_gen() -> None:
|
||||
"""Test that a generator can be used as a runnable."""
|
||||
|
||||
def gen(input: Iterator[Any]) -> Iterator[int]:
|
||||
@@ -4671,6 +4737,10 @@ async def test_runnable_gen() -> None:
|
||||
assert list(runnable.stream(None)) == [1, 2, 3]
|
||||
assert runnable.batch([None, None]) == [6, 6]
|
||||
|
||||
|
||||
async def test_runnable_gen_async() -> None:
|
||||
"""Test that a generator can be used as a runnable."""
|
||||
|
||||
async def agen(input: AsyncIterator[Any]) -> AsyncIterator[int]:
|
||||
yield 1
|
||||
yield 2
|
||||
@@ -4693,14 +4763,14 @@ async def test_runnable_gen() -> None:
|
||||
assert [p async for p in arunnablecallable.astream(None)] == [1, 2, 3]
|
||||
assert await arunnablecallable.abatch([None, None]) == [6, 6]
|
||||
with pytest.raises(NotImplementedError):
|
||||
arunnablecallable.invoke(None)
|
||||
await asyncio.to_thread(arunnablecallable.invoke, None)
|
||||
with pytest.raises(NotImplementedError):
|
||||
arunnablecallable.stream(None)
|
||||
await asyncio.to_thread(arunnablecallable.stream, None)
|
||||
with pytest.raises(NotImplementedError):
|
||||
arunnablecallable.batch([None, None])
|
||||
await asyncio.to_thread(arunnablecallable.batch, [None, None])
|
||||
|
||||
|
||||
async def test_runnable_gen_context_config() -> None:
|
||||
def test_runnable_gen_context_config() -> None:
|
||||
"""Test that a generator can call other runnables with config
|
||||
propagated from the context.
|
||||
"""
|
||||
@@ -4769,9 +4839,16 @@ async def test_runnable_gen_context_config() -> None:
|
||||
assert [r.inputs["input"] for r in tracer.runs[1].child_runs] == ["a", "aa", "aaa"]
|
||||
assert [(r.outputs or {})["output"] for r in tracer.runs[1].child_runs] == [1, 2, 3]
|
||||
|
||||
if sys.version_info < (3, 11):
|
||||
# Python 3.10 and below don't support running async tasks in a specific context
|
||||
return
|
||||
|
||||
@pytest.mark.skipif(
|
||||
sys.version_info < (3, 11),
|
||||
reason="Python 3.10 and below don't support running async tasks in a specific context",
|
||||
)
|
||||
async def test_runnable_gen_context_config_async() -> None:
|
||||
"""Test that a generator can call other runnables with config
|
||||
propagated from the context."""
|
||||
|
||||
fake = RunnableLambda(len)
|
||||
|
||||
async def agen(input: AsyncIterator[Any]) -> AsyncIterator[int]:
|
||||
yield await fake.ainvoke("a")
|
||||
@@ -4835,7 +4912,7 @@ async def test_runnable_gen_context_config() -> None:
|
||||
assert [(r.outputs or {})["output"] for r in tracer.runs[1].child_runs] == [1, 2, 3]
|
||||
|
||||
|
||||
async def test_runnable_iter_context_config() -> None:
|
||||
def test_runnable_iter_context_config() -> None:
|
||||
"""Test that a generator can call other runnables with config
|
||||
propagated from the context.
|
||||
"""
|
||||
@@ -4888,9 +4965,16 @@ async def test_runnable_iter_context_config() -> None:
|
||||
assert [r.inputs["input"] for r in tracer.runs[1].child_runs] == ["a", "aa", "aaa"]
|
||||
assert [(r.outputs or {})["output"] for r in tracer.runs[1].child_runs] == [1, 2, 3]
|
||||
|
||||
if sys.version_info < (3, 11):
|
||||
# Python 3.10 and below don't support running async tasks in a specific context
|
||||
return
|
||||
|
||||
@pytest.mark.skipif(
|
||||
sys.version_info < (3, 11),
|
||||
reason="Python 3.10 and below don't support running async tasks in a specific context",
|
||||
)
|
||||
async def test_runnable_iter_context_config_async() -> None:
|
||||
"""Test that a generator can call other runnables with config
|
||||
propagated from the context."""
|
||||
|
||||
fake = RunnableLambda(len)
|
||||
|
||||
@chain
|
||||
async def agen(input: str) -> AsyncIterator[int]:
|
||||
@@ -4952,7 +5036,7 @@ async def test_runnable_iter_context_config() -> None:
|
||||
assert [(r.outputs or {})["output"] for r in tracer.runs[1].child_runs] == [1, 2, 3]
|
||||
|
||||
|
||||
async def test_runnable_lambda_context_config() -> None:
|
||||
def test_runnable_lambda_context_config() -> None:
|
||||
"""Test that a function can call other runnables with config
|
||||
propagated from the context.
|
||||
"""
|
||||
@@ -5003,9 +5087,16 @@ async def test_runnable_lambda_context_config() -> None:
|
||||
assert [r.inputs["input"] for r in tracer.runs[1].child_runs] == ["a", "aa", "aaa"]
|
||||
assert [(r.outputs or {})["output"] for r in tracer.runs[1].child_runs] == [1, 2, 3]
|
||||
|
||||
if sys.version_info < (3, 11):
|
||||
# Python 3.10 and below don't support running async tasks in a specific context
|
||||
return
|
||||
|
||||
@pytest.mark.skipif(
|
||||
sys.version_info < (3, 11),
|
||||
reason="Python 3.10 and below don't support running async tasks in a specific context",
|
||||
)
|
||||
async def test_runnable_lambda_context_config_async() -> None:
|
||||
"""Test that a function can call other runnables with config
|
||||
propagated from the context."""
|
||||
|
||||
fake = RunnableLambda(len)
|
||||
|
||||
@chain
|
||||
async def afun(input: str) -> int:
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
"""Module that contains tests for runnable.astream_events API."""
|
||||
|
||||
import asyncio
|
||||
import sys
|
||||
from collections.abc import AsyncIterator, Sequence
|
||||
from itertools import cycle
|
||||
@@ -1957,9 +1958,12 @@ async def test_runnable_with_message_history() -> None:
|
||||
]
|
||||
}
|
||||
|
||||
with_message_history.with_config(
|
||||
{"configurable": {"session_id": "session-123"}}
|
||||
).invoke({"question": "meow"})
|
||||
await asyncio.to_thread(
|
||||
with_message_history.with_config(
|
||||
{"configurable": {"session_id": "session-123"}}
|
||||
).invoke,
|
||||
{"question": "meow"},
|
||||
)
|
||||
assert store == {
|
||||
"session-123": [
|
||||
HumanMessage(content="hello"),
|
||||
|
||||
@@ -13,6 +13,7 @@ from typing import (
|
||||
)
|
||||
|
||||
import pytest
|
||||
from blockbuster import BlockBuster
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForRetrieverRun, Callbacks
|
||||
@@ -38,7 +39,9 @@ from langchain_core.runnables import (
|
||||
chain,
|
||||
ensure_config,
|
||||
)
|
||||
from langchain_core.runnables.config import get_callback_manager_for_config
|
||||
from langchain_core.runnables.config import (
|
||||
get_async_callback_manager_for_config,
|
||||
)
|
||||
from langchain_core.runnables.history import RunnableWithMessageHistory
|
||||
from langchain_core.runnables.schema import StreamEvent
|
||||
from langchain_core.runnables.utils import Input, Output
|
||||
@@ -1923,9 +1926,12 @@ async def test_runnable_with_message_history() -> None:
|
||||
]
|
||||
}
|
||||
|
||||
with_message_history.with_config(
|
||||
{"configurable": {"session_id": "session-123"}}
|
||||
).invoke({"question": "meow"})
|
||||
await asyncio.to_thread(
|
||||
with_message_history.with_config(
|
||||
{"configurable": {"session_id": "session-123"}}
|
||||
).invoke,
|
||||
{"question": "meow"},
|
||||
)
|
||||
assert store == {
|
||||
"session-123": [
|
||||
HumanMessage(content="hello"),
|
||||
@@ -1995,8 +2001,9 @@ EXPECTED_EVENTS = [
|
||||
]
|
||||
|
||||
|
||||
async def test_sync_in_async_stream_lambdas() -> None:
|
||||
async def test_sync_in_async_stream_lambdas(blockbuster: BlockBuster) -> None:
|
||||
"""Test invoking nested runnable lambda."""
|
||||
blockbuster.deactivate()
|
||||
|
||||
def add_one(x: int) -> int:
|
||||
return x + 1
|
||||
@@ -2085,8 +2092,8 @@ class StreamingRunnable(Runnable[Input, Output]):
|
||||
**kwargs: Optional[Any],
|
||||
) -> AsyncIterator[Output]:
|
||||
config = ensure_config(config)
|
||||
callback_manager = get_callback_manager_for_config(config)
|
||||
run_manager = callback_manager.on_chain_start(
|
||||
callback_manager = get_async_callback_manager_for_config(config)
|
||||
run_manager = await callback_manager.on_chain_start(
|
||||
None,
|
||||
input,
|
||||
name=config.get("run_name", self.get_name()),
|
||||
@@ -2109,9 +2116,9 @@ class StreamingRunnable(Runnable[Input, Output]):
|
||||
final_output = element
|
||||
|
||||
# set final channel values as run output
|
||||
run_manager.on_chain_end(final_output)
|
||||
await run_manager.on_chain_end(final_output)
|
||||
except BaseException as e:
|
||||
run_manager.on_chain_error(e)
|
||||
await run_manager.on_chain_error(e)
|
||||
raise
|
||||
|
||||
|
||||
|
||||
@@ -1,8 +1,11 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
import uuid
|
||||
from collections.abc import AsyncGenerator, Generator
|
||||
from typing import Any
|
||||
from collections.abc import AsyncGenerator, Coroutine, Generator
|
||||
from inspect import isasyncgenfunction
|
||||
from typing import Any, Callable, Optional
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
@@ -12,6 +15,7 @@ from langsmith.run_trees import RunTree
|
||||
from langsmith.utils import get_env_var
|
||||
from typing_extensions import Literal
|
||||
|
||||
from langchain_core.callbacks import BaseCallbackHandler
|
||||
from langchain_core.runnables.base import RunnableLambda, RunnableParallel
|
||||
from langchain_core.tracers.langchain import LangChainTracer
|
||||
|
||||
@@ -35,6 +39,17 @@ def _get_posts(client: Client) -> list:
|
||||
return posts
|
||||
|
||||
|
||||
def _create_tracer_with_mocked_client(
|
||||
project_name: Optional[str] = None,
|
||||
tags: Optional[list[str]] = None,
|
||||
) -> LangChainTracer:
|
||||
mock_session = MagicMock()
|
||||
mock_client_ = Client(
|
||||
session=mock_session, api_key="test", auto_batch_tracing=False
|
||||
)
|
||||
return LangChainTracer(client=mock_client_, project_name=project_name, tags=tags)
|
||||
|
||||
|
||||
def test_tracing_context() -> None:
|
||||
mock_session = MagicMock()
|
||||
mock_client_ = Client(
|
||||
@@ -56,12 +71,8 @@ def test_tracing_context() -> None:
|
||||
|
||||
def test_config_traceable_handoff() -> None:
|
||||
get_env_var.cache_clear()
|
||||
mock_session = MagicMock()
|
||||
mock_client_ = Client(
|
||||
session=mock_session, api_key="test", auto_batch_tracing=False
|
||||
)
|
||||
tracer = LangChainTracer(
|
||||
client=mock_client_, project_name="another-flippin-project", tags=["such-a-tag"]
|
||||
tracer = _create_tracer_with_mocked_client(
|
||||
project_name="another-flippin-project", tags=["such-a-tag"]
|
||||
)
|
||||
|
||||
@traceable
|
||||
@@ -100,7 +111,7 @@ def test_config_traceable_handoff() -> None:
|
||||
my_parent_runnable = RunnableLambda(my_parent_function)
|
||||
|
||||
assert my_parent_runnable.invoke(1, {"callbacks": [tracer]}) == 6
|
||||
posts = _get_posts(mock_client_)
|
||||
posts = _get_posts(tracer.client)
|
||||
assert all(post["session_name"] == "another-flippin-project" for post in posts)
|
||||
# There should have been 6 runs created,
|
||||
# one for each function invocation
|
||||
@@ -143,11 +154,7 @@ def test_config_traceable_handoff() -> None:
|
||||
sys.version_info < (3, 11), reason="Asyncio context vars require Python 3.11+"
|
||||
)
|
||||
async def test_config_traceable_async_handoff() -> None:
|
||||
mock_session = MagicMock()
|
||||
mock_client_ = Client(
|
||||
session=mock_session, api_key="test", auto_batch_tracing=False
|
||||
)
|
||||
tracer = LangChainTracer(client=mock_client_)
|
||||
tracer = _create_tracer_with_mocked_client()
|
||||
|
||||
@traceable
|
||||
def my_great_great_grandchild_function(a: int) -> int:
|
||||
@@ -175,7 +182,7 @@ async def test_config_traceable_async_handoff() -> None:
|
||||
my_parent_runnable = RunnableLambda(my_parent_function) # type: ignore
|
||||
result = await my_parent_runnable.ainvoke(1, {"callbacks": [tracer]})
|
||||
assert result == 6
|
||||
posts = _get_posts(mock_client_)
|
||||
posts = _get_posts(tracer.client)
|
||||
# There should have been 6 runs created,
|
||||
# one for each function invocation
|
||||
assert len(posts) == 6
|
||||
@@ -245,144 +252,172 @@ def test_tracing_enable_disable(
|
||||
assert not mock_posts
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"method", ["invoke", "stream", "batch", "ainvoke", "astream", "abatch"]
|
||||
)
|
||||
async def test_runnable_sequence_parallel_trace_nesting(method: str) -> None:
|
||||
if method.startswith("a") and sys.version_info < (3, 11):
|
||||
pytest.skip("Asyncio context vars require Python 3.11+")
|
||||
mock_session = MagicMock()
|
||||
mock_client_ = Client(
|
||||
session=mock_session, api_key="test", auto_batch_tracing=False
|
||||
class TestRunnableSequenceParallelTraceNesting:
|
||||
@pytest.fixture(autouse=True)
|
||||
def _setup(self) -> None:
|
||||
self.tracer = _create_tracer_with_mocked_client()
|
||||
|
||||
@staticmethod
|
||||
def _create_parent(
|
||||
other_thing: Callable[
|
||||
[int], Generator[int, None, None] | AsyncGenerator[int, None]
|
||||
],
|
||||
) -> RunnableLambda:
|
||||
@RunnableLambda
|
||||
def my_child_function(a: int) -> int:
|
||||
return a + 2
|
||||
|
||||
parallel = RunnableParallel(
|
||||
chain_result=my_child_function.with_config(tags=["atag"]),
|
||||
other_thing=other_thing,
|
||||
)
|
||||
|
||||
def before(x: int) -> int:
|
||||
return x
|
||||
|
||||
def after(x: dict) -> int:
|
||||
return x["chain_result"]
|
||||
|
||||
sequence = before | parallel | after
|
||||
if isasyncgenfunction(other_thing):
|
||||
|
||||
@RunnableLambda # type: ignore
|
||||
async def parent(a: int) -> int:
|
||||
return await sequence.ainvoke(a)
|
||||
|
||||
else:
|
||||
|
||||
@RunnableLambda
|
||||
def parent(a: int) -> int:
|
||||
return sequence.invoke(a)
|
||||
|
||||
return parent
|
||||
|
||||
def _check_posts(self) -> None:
|
||||
posts = _get_posts(self.tracer.client)
|
||||
name_order = [
|
||||
"parent",
|
||||
"RunnableSequence",
|
||||
"before",
|
||||
"RunnableParallel<chain_result,other_thing>",
|
||||
["my_child_function", "other_thing"],
|
||||
"after",
|
||||
]
|
||||
expected_parents = {
|
||||
"parent": None,
|
||||
"RunnableSequence": "parent",
|
||||
"before": "RunnableSequence",
|
||||
"RunnableParallel<chain_result,other_thing>": "RunnableSequence",
|
||||
"my_child_function": "RunnableParallel<chain_result,other_thing>",
|
||||
"other_thing": "RunnableParallel<chain_result,other_thing>",
|
||||
"after": "RunnableSequence",
|
||||
}
|
||||
assert len(posts) == sum(
|
||||
1 if isinstance(n, str) else len(n) for n in name_order
|
||||
)
|
||||
prev_dotted_order = None
|
||||
dotted_order_map = {}
|
||||
id_map = {}
|
||||
parent_id_map = {}
|
||||
i = 0
|
||||
for name in name_order:
|
||||
if isinstance(name, list):
|
||||
for n in name:
|
||||
matching_post = next(
|
||||
p for p in posts[i : i + len(name)] if p["name"] == n
|
||||
)
|
||||
assert matching_post
|
||||
dotted_order = matching_post["dotted_order"]
|
||||
if prev_dotted_order is not None:
|
||||
assert dotted_order > prev_dotted_order
|
||||
dotted_order_map[n] = dotted_order
|
||||
id_map[n] = matching_post["id"]
|
||||
parent_id_map[n] = matching_post.get("parent_run_id")
|
||||
i += len(name)
|
||||
continue
|
||||
else:
|
||||
assert posts[i]["name"] == name
|
||||
dotted_order = posts[i]["dotted_order"]
|
||||
if prev_dotted_order is not None and not str(
|
||||
expected_parents[name]
|
||||
).startswith("RunnableParallel"):
|
||||
assert dotted_order > prev_dotted_order, (
|
||||
f"{name} not after {name_order[i - 1]}"
|
||||
)
|
||||
prev_dotted_order = dotted_order
|
||||
if name in dotted_order_map:
|
||||
msg = f"Duplicate name {name}"
|
||||
raise ValueError(msg)
|
||||
dotted_order_map[name] = dotted_order
|
||||
id_map[name] = posts[i]["id"]
|
||||
parent_id_map[name] = posts[i].get("parent_run_id")
|
||||
i += 1
|
||||
|
||||
# Now check the dotted orders
|
||||
for name, parent_ in expected_parents.items():
|
||||
dotted_order = dotted_order_map[name]
|
||||
if parent_ is not None:
|
||||
parent_dotted_order = dotted_order_map[parent_]
|
||||
assert dotted_order.startswith(parent_dotted_order), (
|
||||
f"{name}, {parent_dotted_order} not in {dotted_order}"
|
||||
)
|
||||
assert str(parent_id_map[name]) == str(id_map[parent_])
|
||||
else:
|
||||
assert dotted_order.split(".")[0] == dotted_order
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"method",
|
||||
[
|
||||
lambda parent, cb: parent.invoke(1, {"callbacks": cb}),
|
||||
lambda parent, cb: list(parent.stream(1, {"callbacks": cb}))[-1],
|
||||
lambda parent, cb: parent.batch([1], {"callbacks": cb})[0],
|
||||
],
|
||||
ids=["invoke", "stream", "batch"],
|
||||
)
|
||||
tracer = LangChainTracer(client=mock_client_)
|
||||
|
||||
@RunnableLambda
|
||||
def my_child_function(a: int) -> int:
|
||||
return a + 2
|
||||
|
||||
if method.startswith("a"):
|
||||
|
||||
async def other_thing(a: int) -> AsyncGenerator[int, None]:
|
||||
yield 1
|
||||
|
||||
else:
|
||||
|
||||
def test_sync(
|
||||
self, method: Callable[[RunnableLambda, list[BaseCallbackHandler]], int]
|
||||
) -> None:
|
||||
def other_thing(a: int) -> Generator[int, None, None]: # type: ignore
|
||||
yield 1
|
||||
|
||||
parallel = RunnableParallel(
|
||||
chain_result=my_child_function.with_config(tags=["atag"]),
|
||||
other_thing=other_thing,
|
||||
parent = self._create_parent(other_thing)
|
||||
|
||||
# Now run the chain and check the resulting posts
|
||||
assert method(parent, [self.tracer]) == 3
|
||||
|
||||
self._check_posts()
|
||||
|
||||
@staticmethod
|
||||
async def ainvoke(parent: RunnableLambda, cb: list[BaseCallbackHandler]) -> int:
|
||||
return await parent.ainvoke(1, {"callbacks": cb})
|
||||
|
||||
@staticmethod
|
||||
async def astream(parent: RunnableLambda, cb: list[BaseCallbackHandler]) -> int:
|
||||
return [res async for res in parent.astream(1, {"callbacks": cb})][-1]
|
||||
|
||||
@staticmethod
|
||||
async def abatch(parent: RunnableLambda, cb: list[BaseCallbackHandler]) -> int:
|
||||
return (await parent.abatch([1], {"callbacks": cb}))[0]
|
||||
|
||||
@pytest.mark.skipif(
|
||||
sys.version_info < (3, 11), reason="Asyncio context vars require Python 3.11+"
|
||||
)
|
||||
@pytest.mark.parametrize("method", [ainvoke, astream, abatch])
|
||||
async def test_async(
|
||||
self,
|
||||
method: Callable[
|
||||
[RunnableLambda, list[BaseCallbackHandler]], Coroutine[Any, Any, int]
|
||||
],
|
||||
) -> None:
|
||||
async def other_thing(a: int) -> AsyncGenerator[int, None]:
|
||||
yield 1
|
||||
|
||||
def before(x: int) -> int:
|
||||
return x
|
||||
parent = self._create_parent(other_thing)
|
||||
|
||||
def after(x: dict) -> int:
|
||||
return x["chain_result"]
|
||||
# Now run the chain and check the resulting posts
|
||||
assert await method(parent, [self.tracer]) == 3
|
||||
|
||||
sequence = before | parallel | after
|
||||
if method.startswith("a"):
|
||||
|
||||
@RunnableLambda # type: ignore
|
||||
async def parent(a: int) -> int:
|
||||
return await sequence.ainvoke(a)
|
||||
|
||||
else:
|
||||
|
||||
@RunnableLambda
|
||||
def parent(a: int) -> int:
|
||||
return sequence.invoke(a)
|
||||
|
||||
# Now run the chain and check the resulting posts
|
||||
cb = [tracer]
|
||||
if method == "invoke":
|
||||
res: Any = parent.invoke(1, {"callbacks": cb}) # type: ignore
|
||||
elif method == "ainvoke":
|
||||
res = await parent.ainvoke(1, {"callbacks": cb}) # type: ignore
|
||||
elif method == "stream":
|
||||
results = list(parent.stream(1, {"callbacks": cb})) # type: ignore
|
||||
res = results[-1]
|
||||
elif method == "astream":
|
||||
results = [res async for res in parent.astream(1, {"callbacks": cb})] # type: ignore
|
||||
res = results[-1]
|
||||
elif method == "batch":
|
||||
res = parent.batch([1], {"callbacks": cb})[0] # type: ignore
|
||||
elif method == "abatch":
|
||||
res = (await parent.abatch([1], {"callbacks": cb}))[0] # type: ignore
|
||||
else:
|
||||
msg = f"Unknown method {method}"
|
||||
raise ValueError(msg)
|
||||
assert res == 3
|
||||
posts = _get_posts(mock_client_)
|
||||
name_order = [
|
||||
"parent",
|
||||
"RunnableSequence",
|
||||
"before",
|
||||
"RunnableParallel<chain_result,other_thing>",
|
||||
["my_child_function", "other_thing"],
|
||||
"after",
|
||||
]
|
||||
expected_parents = {
|
||||
"parent": None,
|
||||
"RunnableSequence": "parent",
|
||||
"before": "RunnableSequence",
|
||||
"RunnableParallel<chain_result,other_thing>": "RunnableSequence",
|
||||
"my_child_function": "RunnableParallel<chain_result,other_thing>",
|
||||
"other_thing": "RunnableParallel<chain_result,other_thing>",
|
||||
"after": "RunnableSequence",
|
||||
}
|
||||
assert len(posts) == sum(1 if isinstance(n, str) else len(n) for n in name_order)
|
||||
prev_dotted_order = None
|
||||
dotted_order_map = {}
|
||||
id_map = {}
|
||||
parent_id_map = {}
|
||||
i = 0
|
||||
for name in name_order:
|
||||
if isinstance(name, list):
|
||||
for n in name:
|
||||
matching_post = next(
|
||||
p for p in posts[i : i + len(name)] if p["name"] == n
|
||||
)
|
||||
assert matching_post
|
||||
dotted_order = matching_post["dotted_order"]
|
||||
if prev_dotted_order is not None:
|
||||
assert dotted_order > prev_dotted_order
|
||||
dotted_order_map[n] = dotted_order
|
||||
id_map[n] = matching_post["id"]
|
||||
parent_id_map[n] = matching_post.get("parent_run_id")
|
||||
i += len(name)
|
||||
continue
|
||||
else:
|
||||
assert posts[i]["name"] == name
|
||||
dotted_order = posts[i]["dotted_order"]
|
||||
if prev_dotted_order is not None and not str(
|
||||
expected_parents[name]
|
||||
).startswith("RunnableParallel"):
|
||||
assert dotted_order > prev_dotted_order, (
|
||||
f"{name} not after {name_order[i - 1]}"
|
||||
)
|
||||
prev_dotted_order = dotted_order
|
||||
if name in dotted_order_map:
|
||||
msg = f"Duplicate name {name}"
|
||||
raise ValueError(msg)
|
||||
dotted_order_map[name] = dotted_order
|
||||
id_map[name] = posts[i]["id"]
|
||||
parent_id_map[name] = posts[i].get("parent_run_id")
|
||||
i += 1
|
||||
|
||||
# Now check the dotted orders
|
||||
for name, parent_ in expected_parents.items():
|
||||
dotted_order = dotted_order_map[name]
|
||||
if parent_ is not None:
|
||||
parent_dotted_order = dotted_order_map[parent_]
|
||||
assert dotted_order.startswith(parent_dotted_order), (
|
||||
f"{name}, {parent_dotted_order} not in {dotted_order}"
|
||||
)
|
||||
assert str(parent_id_map[name]) == str(id_map[parent_])
|
||||
else:
|
||||
assert dotted_order.split(".")[0] == dotted_order
|
||||
self._check_posts()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("parent_type", ("ls", "lc"))
|
||||
|
||||
15
libs/core/tests/unit_tests/test_setup.py
Normal file
15
libs/core/tests/unit_tests/test_setup.py
Normal file
@@ -0,0 +1,15 @@
|
||||
import time
|
||||
|
||||
import pytest
|
||||
from blockbuster import BlockingError
|
||||
|
||||
from langchain_core import sys_info
|
||||
|
||||
|
||||
async def test_blockbuster_setup() -> None:
|
||||
"""Check if blockbuster is correctly setup."""
|
||||
# Blocking call outside of langchain_core is allowed.
|
||||
time.sleep(0.01) # noqa: ASYNC251
|
||||
with pytest.raises(BlockingError):
|
||||
# Blocking call from langchain_core raises BlockingError.
|
||||
sys_info.print_sys_info()
|
||||
@@ -2,7 +2,6 @@ import asyncio
|
||||
import math
|
||||
import time
|
||||
from collections.abc import AsyncIterator
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
from langchain_core.tracers.memory_stream import _MemoryStream
|
||||
|
||||
@@ -70,7 +69,7 @@ async def test_queue_for_streaming_via_sync_call() -> None:
|
||||
"""Produce items with slight delay."""
|
||||
tic = time.time()
|
||||
for i in range(3):
|
||||
await asyncio.sleep(0.10)
|
||||
await asyncio.sleep(0.2)
|
||||
toc = time.time()
|
||||
await writer.send(
|
||||
{
|
||||
@@ -93,9 +92,11 @@ async def test_queue_for_streaming_via_sync_call() -> None:
|
||||
**item,
|
||||
}
|
||||
|
||||
with ThreadPoolExecutor() as executor:
|
||||
executor.submit(sync_call)
|
||||
items = [item async for item in consumer()]
|
||||
task = asyncio.create_task(asyncio.to_thread(sync_call))
|
||||
items = [item async for item in consumer()]
|
||||
await task
|
||||
|
||||
assert len(items) == 3
|
||||
|
||||
for item in items:
|
||||
delta_time = item["receive_time"] - item["produce_time"]
|
||||
@@ -107,7 +108,7 @@ async def test_queue_for_streaming_via_sync_call() -> None:
|
||||
# To verify that the producer and consumer are running in parallel, we
|
||||
# expect the delta_time to be smaller than the sleep delay in the producer
|
||||
# * # of items = 30 ms
|
||||
assert math.isclose(delta_time, 0, abs_tol=0.010) is True, (
|
||||
assert math.isclose(delta_time, 0, abs_tol=0.020) is True, (
|
||||
f"delta_time: {delta_time}"
|
||||
)
|
||||
|
||||
|
||||
@@ -83,17 +83,17 @@ async def test_inmemory_mmr() -> None:
|
||||
assert output[1] == _any_id_document(page_content="fou")
|
||||
|
||||
|
||||
async def test_inmemory_dump_load(tmp_path: Path) -> None:
|
||||
def test_inmemory_dump_load(tmp_path: Path) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
embedding = DeterministicFakeEmbedding(size=6)
|
||||
store = await InMemoryVectorStore.afrom_texts(["foo", "bar", "baz"], embedding)
|
||||
output = await store.asimilarity_search("foo", k=1)
|
||||
store = InMemoryVectorStore.from_texts(["foo", "bar", "baz"], embedding)
|
||||
output = store.similarity_search("foo", k=1)
|
||||
|
||||
test_file = str(tmp_path / "test.json")
|
||||
store.dump(test_file)
|
||||
|
||||
loaded_store = InMemoryVectorStore.load(test_file, embedding)
|
||||
loaded_output = await loaded_store.asimilarity_search("foo", k=1)
|
||||
loaded_output = loaded_store.similarity_search("foo", k=1)
|
||||
|
||||
assert output == loaded_output
|
||||
|
||||
|
||||
28
libs/langchain/poetry.lock
generated
28
libs/langchain/poetry.lock
generated
@@ -704,13 +704,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[[package]]
|
||||
name = "codespell"
|
||||
version = "2.4.0"
|
||||
version = "2.4.1"
|
||||
description = "Fix common misspellings in text files"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "codespell-2.4.0-py3-none-any.whl", hash = "sha256:b4c5b779f747dd481587aeecb5773301183f52b94b96ed51a28126d0482eec1d"},
|
||||
{file = "codespell-2.4.0.tar.gz", hash = "sha256:587d45b14707fb8ce51339ba4cce50ae0e98ce228ef61f3c5e160e34f681be58"},
|
||||
{file = "codespell-2.4.1-py3-none-any.whl", hash = "sha256:3dadafa67df7e4a3dbf51e0d7315061b80d265f9552ebd699b3dd6834b47e425"},
|
||||
{file = "codespell-2.4.1.tar.gz", hash = "sha256:299fcdcb09d23e81e35a671bbe746d5ad7e8385972e65dbb833a2eaac33c01e5"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@@ -1895,13 +1895,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
|
||||
|
||||
[[package]]
|
||||
name = "jupyterlab"
|
||||
version = "4.3.4"
|
||||
version = "4.3.5"
|
||||
description = "JupyterLab computational environment"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "jupyterlab-4.3.4-py3-none-any.whl", hash = "sha256:b754c2601c5be6adf87cb5a1d8495d653ffb945f021939f77776acaa94dae952"},
|
||||
{file = "jupyterlab-4.3.4.tar.gz", hash = "sha256:f0bb9b09a04766e3423cccc2fc23169aa2ffedcdf8713e9e0fb33cac0b6859d0"},
|
||||
{file = "jupyterlab-4.3.5-py3-none-any.whl", hash = "sha256:571bbdee20e4c5321ab5195bc41cf92a75a5cff886be5e57ce78dfa37a5e9fdb"},
|
||||
{file = "jupyterlab-4.3.5.tar.gz", hash = "sha256:c779bf72ced007d7d29d5bcef128e7fdda96ea69299e19b04a43635a7d641f9d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1978,7 +1978,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.32"
|
||||
version = "0.3.33"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.9,<4.0"
|
||||
@@ -2205,13 +2205,13 @@ traitlets = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mistune"
|
||||
version = "3.1.0"
|
||||
version = "3.1.1"
|
||||
description = "A sane and fast Markdown parser with useful plugins and renderers"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mistune-3.1.0-py3-none-any.whl", hash = "sha256:b05198cf6d671b3deba6c87ec6cf0d4eb7b72c524636eddb6dbf13823b52cee1"},
|
||||
{file = "mistune-3.1.0.tar.gz", hash = "sha256:dbcac2f78292b9dc066cd03b7a3a26b62d85f8159f2ea5fd28e55df79908d667"},
|
||||
{file = "mistune-3.1.1-py3-none-any.whl", hash = "sha256:02106ac2aa4f66e769debbfa028509a275069dcffce0dfa578edd7b991ee700a"},
|
||||
{file = "mistune-3.1.1.tar.gz", hash = "sha256:e0740d635f515119f7d1feb6f9b192ee60f0cc649f80a8f944f905706a21654c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -2430,13 +2430,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
|
||||
|
||||
[[package]]
|
||||
name = "nbconvert"
|
||||
version = "7.16.5"
|
||||
version = "7.16.6"
|
||||
description = "Converting Jupyter Notebooks (.ipynb files) to other formats. Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script. nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "nbconvert-7.16.5-py3-none-any.whl", hash = "sha256:e12eac052d6fd03040af4166c563d76e7aeead2e9aadf5356db552a1784bd547"},
|
||||
{file = "nbconvert-7.16.5.tar.gz", hash = "sha256:c83467bb5777fdfaac5ebbb8e864f300b277f68692ecc04d6dab72f2d8442344"},
|
||||
{file = "nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b"},
|
||||
{file = "nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -5189,4 +5189,4 @@ cffi = ["cffi (>=1.11)"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<4.0"
|
||||
content-hash = "adcdcf3ce76a7951ebd6110c64ceb6ba051d2887109ed1fed439b1aa78a2a5c5"
|
||||
content-hash = "e771bd2ab1b08c4f6f9e264f9b417a729424660b153cc96e15bc139e778a432e"
|
||||
|
||||
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "langchain"
|
||||
version = "0.3.16"
|
||||
version = "0.3.17"
|
||||
description = "Building applications with LLMs through composability"
|
||||
authors = []
|
||||
license = "MIT"
|
||||
@@ -34,7 +34,7 @@ langchain-server = "langchain.server:main"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.9,<4.0"
|
||||
langchain-core = "^0.3.32"
|
||||
langchain-core = "^0.3.33"
|
||||
langchain-text-splitters = "^0.3.3"
|
||||
langsmith = ">=0.1.17,<0.4"
|
||||
pydantic = "^2.7.4"
|
||||
|
||||
@@ -371,3 +371,7 @@ packages:
|
||||
downloads: 0
|
||||
js: '@langchain/deepseek'
|
||||
provider_page: deepseek
|
||||
- name: langchain-jenkins
|
||||
path: .
|
||||
repo: Amitgb14/langchain_jenkins
|
||||
downloads: 0
|
||||
|
||||
515
libs/partners/groq/poetry.lock
generated
515
libs/partners/groq/poetry.lock
generated
@@ -44,6 +44,85 @@ files = [
|
||||
{file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cffi"
|
||||
version = "1.17.1"
|
||||
description = "Foreign Function Interface for Python calling C code."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
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{file = "cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824"},
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]
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||||
|
||||
[package.dependencies]
|
||||
pycparser = "*"
|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.4.1"
|
||||
@@ -147,13 +226,13 @@ files = [
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||||
|
||||
[[package]]
|
||||
name = "codespell"
|
||||
version = "2.3.0"
|
||||
description = "Codespell"
|
||||
version = "2.4.1"
|
||||
description = "Fix common misspellings in text files"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
{file = "codespell-2.3.0-py3-none-any.whl", hash = "sha256:a9c7cef2501c9cfede2110fd6d4e5e62296920efe9abfb84648df866e47f58d1"},
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{file = "codespell-2.3.0.tar.gz", hash = "sha256:360c7d10f75e65f67bad720af7007e1060a5d395670ec11a7ed1fed9dd17471f"},
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{file = "codespell-2.4.1.tar.gz", hash = "sha256:299fcdcb09d23e81e35a671bbe746d5ad7e8385972e65dbb833a2eaac33c01e5"},
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||||
]
|
||||
|
||||
[package.extras]
|
||||
@@ -200,13 +279,13 @@ test = ["pytest (>=6)"]
|
||||
|
||||
[[package]]
|
||||
name = "groq"
|
||||
version = "0.13.1"
|
||||
version = "0.16.0"
|
||||
description = "The official Python library for the groq API"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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{file = "groq-0.13.1.tar.gz", hash = "sha256:588fd5bee984f4eb46ec89552778d5698b9e9614435defef868645c19463cbcc"},
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]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -325,7 +404,7 @@ files = [
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||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.29"
|
||||
version = "0.3.33"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.9,<4.0"
|
||||
@@ -334,7 +413,7 @@ develop = true
|
||||
|
||||
[package.dependencies]
|
||||
jsonpatch = "^1.33"
|
||||
langsmith = ">=0.1.125,<0.3"
|
||||
langsmith = ">=0.1.125,<0.4"
|
||||
packaging = ">=23.2,<25"
|
||||
pydantic = [
|
||||
{version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
|
||||
@@ -350,7 +429,7 @@ url = "../../core"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-tests"
|
||||
version = "0.3.7"
|
||||
version = "0.3.9"
|
||||
description = "Standard tests for LangChain implementations"
|
||||
optional = false
|
||||
python-versions = ">=3.9,<4.0"
|
||||
@@ -359,7 +438,7 @@ develop = true
|
||||
|
||||
[package.dependencies]
|
||||
httpx = ">=0.25.0,<1"
|
||||
langchain-core = "^0.3.22"
|
||||
langchain-core = "^0.3.31"
|
||||
numpy = [
|
||||
{version = ">=1.24.0,<2.0.0", markers = "python_version < \"3.12\""},
|
||||
{version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
|
||||
@@ -375,13 +454,13 @@ url = "../../standard-tests"
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.2.10"
|
||||
version = "0.3.2"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
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||||
{file = "langsmith-0.2.10-py3-none-any.whl", hash = "sha256:b02f2f174189ff72e54c88b1aa63343defd6f0f676c396a690c63a4b6495dcc2"},
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|
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|
||||
[package.dependencies]
|
||||
@@ -393,10 +472,11 @@ pydantic = [
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||||
]
|
||||
requests = ">=2,<3"
|
||||
requests-toolbelt = ">=1.0.0,<2.0.0"
|
||||
zstandard = ">=0.23.0,<0.24.0"
|
||||
|
||||
[package.extras]
|
||||
compression = ["zstandard (>=0.23.0,<0.24.0)"]
|
||||
langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
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||||
pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "mypy"
|
||||
@@ -515,150 +595,154 @@ files = [
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|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "2.2.1"
|
||||
version = "2.2.2"
|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = false
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||||
python-versions = ">=3.10"
|
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files = [
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[package.dependencies]
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||||
cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
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||||
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||||
[package.extras]
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cffi = ["cffi (>=1.11)"]
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[metadata]
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||||
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||||
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||||
[tool.poetry]
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||||
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authors = []
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readme = "README.md"
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@@ -20,7 +20,7 @@ disallow_untyped_defs = "True"
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||||
|
||||
[tool.poetry.dependencies]
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||||
python = ">=3.9,<4.0"
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langchain-core = "^0.3.29"
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langchain-core = "^0.3.33"
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groq = ">=0.4.1,<1"
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[tool.ruff.lint]
|
||||
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||||
827
libs/partners/mistralai/poetry.lock
generated
827
libs/partners/mistralai/poetry.lock
generated
@@ -1,4 +1,4 @@
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||||
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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||||
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||||
[[package]]
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||||
name = "annotated-types"
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||||
@@ -13,13 +13,13 @@ files = [
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||||
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||||
[[package]]
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||||
name = "anyio"
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||||
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||||
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]
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||||
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||||
[package.dependencies]
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||||
@@ -30,7 +30,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
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||||
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||||
[package.extras]
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||||
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||||
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||||
trio = ["trio (>=0.26.1)"]
|
||||
|
||||
[[package]]
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||||
@@ -44,129 +44,195 @@ files = [
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{file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
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|
||||
[[package]]
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||||
name = "cffi"
|
||||
version = "1.17.1"
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description = "Foreign Function Interface for Python calling C code."
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optional = false
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||||
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[[package]]
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||||
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||||
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||||
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||||
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optional = false
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{file = "charset_normalizer-3.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:49402233c892a461407c512a19435d1ce275543138294f7ef013f0b63d5d3765"},
|
||||
{file = "charset_normalizer-3.4.1-py3-none-any.whl", hash = "sha256:d98b1668f06378c6dbefec3b92299716b931cd4e6061f3c875a71ced1780ab85"},
|
||||
{file = "charset_normalizer-3.4.1.tar.gz", hash = "sha256:44251f18cd68a75b56585dd00dae26183e102cd5e0f9f1466e6df5da2ed64ea3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "codespell"
|
||||
version = "2.3.0"
|
||||
description = "Codespell"
|
||||
version = "2.4.1"
|
||||
description = "Fix common misspellings in text files"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "codespell-2.3.0-py3-none-any.whl", hash = "sha256:a9c7cef2501c9cfede2110fd6d4e5e62296920efe9abfb84648df866e47f58d1"},
|
||||
{file = "codespell-2.3.0.tar.gz", hash = "sha256:360c7d10f75e65f67bad720af7007e1060a5d395670ec11a7ed1fed9dd17471f"},
|
||||
{file = "codespell-2.4.1-py3-none-any.whl", hash = "sha256:3dadafa67df7e4a3dbf51e0d7315061b80d265f9552ebd699b3dd6834b47e425"},
|
||||
{file = "codespell-2.4.1.tar.gz", hash = "sha256:299fcdcb09d23e81e35a671bbe746d5ad7e8385972e65dbb833a2eaac33c01e5"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@@ -202,29 +268,29 @@ test = ["pytest (>=6)"]
|
||||
|
||||
[[package]]
|
||||
name = "filelock"
|
||||
version = "3.16.1"
|
||||
version = "3.17.0"
|
||||
description = "A platform independent file lock."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
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||||
{file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
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||||
{file = "filelock-3.17.0-py3-none-any.whl", hash = "sha256:533dc2f7ba78dc2f0f531fc6c4940addf7b70a481e269a5a3b93be94ffbe8338"},
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||||
{file = "filelock-3.17.0.tar.gz", hash = "sha256:ee4e77401ef576ebb38cd7f13b9b28893194acc20a8e68e18730ba9c0e54660e"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4.1)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
|
||||
docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"]
|
||||
typing = ["typing-extensions (>=4.12.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "fsspec"
|
||||
version = "2024.10.0"
|
||||
version = "2024.12.0"
|
||||
description = "File-system specification"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
|
||||
{file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
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||||
{file = "fsspec-2024.12.0-py3-none-any.whl", hash = "sha256:b520aed47ad9804237ff878b504267a3b0b441e97508bd6d2d8774e3db85cee2"},
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||||
{file = "fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@@ -324,13 +390,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "0.27.0"
|
||||
version = "0.28.0"
|
||||
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "huggingface_hub-0.27.0-py3-none-any.whl", hash = "sha256:8f2e834517f1f1ddf1ecc716f91b120d7333011b7485f665a9a412eacb1a2a81"},
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||||
{file = "huggingface_hub-0.27.0.tar.gz", hash = "sha256:902cce1a1be5739f5589e560198a65a8edcfd3b830b1666f36e4b961f0454fac"},
|
||||
{file = "huggingface_hub-0.28.0-py3-none-any.whl", hash = "sha256:71cff4e500efe68061d94b7f6d3114e183715088be7a90bf4dd84af83b5f5cdb"},
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||||
{file = "huggingface_hub-0.28.0.tar.gz", hash = "sha256:c2b18c02a47d4384763caddb4d0ab2a8fc6c16e0800d6de4d55d0a896244aba3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -343,13 +409,13 @@ tqdm = ">=4.42.1"
|
||||
typing-extensions = ">=3.7.4.3"
|
||||
|
||||
[package.extras]
|
||||
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|
||||
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
cli = ["InquirerPy (==0.3.4)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
|
||||
hf-transfer = ["hf-transfer (>=0.1.4)"]
|
||||
inference = ["aiohttp"]
|
||||
quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
|
||||
quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
|
||||
tensorflow = ["graphviz", "pydot", "tensorflow"]
|
||||
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
|
||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
|
||||
@@ -408,7 +474,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.32"
|
||||
version = "0.3.33"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.9,<4.0"
|
||||
@@ -458,13 +524,13 @@ url = "../../standard-tests"
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.2.3"
|
||||
version = "0.3.2"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
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||||
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|
||||
|
||||
[package.dependencies]
|
||||
@@ -476,55 +542,63 @@ pydantic = [
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||||
]
|
||||
requests = ">=2,<3"
|
||||
requests-toolbelt = ">=1.0.0,<2.0.0"
|
||||
zstandard = ">=0.23.0,<0.24.0"
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||||
|
||||
[package.extras]
|
||||
langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
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||||
pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
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||||
|
||||
[[package]]
|
||||
name = "mypy"
|
||||
version = "1.13.0"
|
||||
version = "1.14.1"
|
||||
description = "Optional static typing for Python"
|
||||
optional = false
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||||
python-versions = ">=3.8"
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files = [
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[[package]]
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||||
]
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||||
|
||||
[package.dependencies]
|
||||
cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
|
||||
|
||||
[package.extras]
|
||||
cffi = ["cffi (>=1.11)"]
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||||
|
||||
[metadata]
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||||
lock-version = "2.0"
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||||
python-versions = ">=3.9,<4.0"
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||||
content-hash = "ee9ca9455bf313bec56b64ac54a2d61ad43fb71340385f1fe17fb8eed00463b6"
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content-hash = "644eff1696ce042cccc14af0b2c1f7b9ddaaaed3fb9f3ab45f73f058a1c99f0d"
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||||
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||||
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "langchain-mistralai"
|
||||
version = "0.2.5"
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||||
version = "0.2.6"
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||||
description = "An integration package connecting Mistral and LangChain"
|
||||
authors = []
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||||
readme = "README.md"
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||||
@@ -20,7 +20,7 @@ disallow_untyped_defs = "True"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.9,<4.0"
|
||||
langchain-core = "^0.3.32"
|
||||
langchain-core = "^0.3.33"
|
||||
tokenizers = ">=0.15.1,<1"
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||||
httpx = ">=0.25.2,<1"
|
||||
httpx-sse = ">=0.3.1,<1"
|
||||
|
||||
30
libs/partners/openai/poetry.lock
generated
30
libs/partners/openai/poetry.lock
generated
@@ -1,4 +1,4 @@
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||||
# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
@@ -226,13 +226,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "codespell"
|
||||
version = "2.4.0"
|
||||
version = "2.4.1"
|
||||
description = "Fix common misspellings in text files"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
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||||
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||||
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||||
]
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||||
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||||
[package.extras]
|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.31"
|
||||
version = "0.3.33"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.9,<4.0"
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||||
@@ -626,13 +626,13 @@ url = "../../standard-tests"
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.3.1"
|
||||
version = "0.3.2"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
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||||
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|
||||
{file = "langsmith-0.3.1.tar.gz", hash = "sha256:9242a49d37e2176a344ddec97bf57b958dc0e1f0437e150cefd0fb70195f0e26"},
|
||||
{file = "langsmith-0.3.2-py3-none-any.whl", hash = "sha256:48ff6bc5eda62f4729596bb68d4f96166d2654728ac32970b69b1be874c61925"},
|
||||
{file = "langsmith-0.3.2.tar.gz", hash = "sha256:7724668e9705734ab25a7977fc34a9ee15a40ba4108987926c69293a05d40229"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -767,13 +767,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.60.0"
|
||||
version = "1.60.2"
|
||||
description = "The official Python library for the openai API"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "openai-1.60.0-py3-none-any.whl", hash = "sha256:df06c43be8018274980ac363da07d4b417bd835ead1c66e14396f6f15a0d5dda"},
|
||||
{file = "openai-1.60.0.tar.gz", hash = "sha256:7fa536cd4b644718645b874d2706e36dbbef38b327e42ca0623275da347ee1a9"},
|
||||
{file = "openai-1.60.2-py3-none-any.whl", hash = "sha256:993bd11b96900b9098179c728026f016b4982ded7ee30dfcf4555eab1171fff9"},
|
||||
{file = "openai-1.60.2.tar.gz", hash = "sha256:a8f843e10f2855713007f491d96afb2694b11b5e02cb97c7d01a0be60bc5bb51"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1014,13 +1014,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.10.5"
|
||||
version = "2.10.6"
|
||||
description = "Data validation using Python type hints"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pydantic-2.10.5-py3-none-any.whl", hash = "sha256:4dd4e322dbe55472cb7ca7e73f4b63574eecccf2835ffa2af9021ce113c83c53"},
|
||||
{file = "pydantic-2.10.5.tar.gz", hash = "sha256:278b38dbbaec562011d659ee05f63346951b3a248a6f3642e1bc68894ea2b4ff"},
|
||||
{file = "pydantic-2.10.6-py3-none-any.whl", hash = "sha256:427d664bf0b8a2b34ff5dd0f5a18df00591adcee7198fbd71981054cef37b584"},
|
||||
{file = "pydantic-2.10.6.tar.gz", hash = "sha256:ca5daa827cce33de7a42be142548b0096bf05a7e7b365aebfa5f8eeec7128236"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1882,4 +1882,4 @@ cffi = ["cffi (>=1.11)"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<4.0"
|
||||
content-hash = "164cc02f120947dbd289f2ad8aa10f714cf2a983121b4b9c088c07fb1e99e8a4"
|
||||
content-hash = "95ca420eeeb0e48b1bff15bce5ed2bb63ad7b8c94f2975dbc427c397ae62e6b7"
|
||||
|
||||
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "langchain-openai"
|
||||
version = "0.3.2"
|
||||
version = "0.3.3"
|
||||
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 = "^0.3.31"
|
||||
langchain-core = "^0.3.33"
|
||||
openai = "^1.58.1"
|
||||
tiktoken = ">=0.7,<1"
|
||||
|
||||
|
||||
@@ -19,7 +19,7 @@ class TestOpenAIStandard(ChatModelIntegrationTests):
|
||||
|
||||
@property
|
||||
def chat_model_params(self) -> dict:
|
||||
return {"model": "gpt-4o-mini", "stream_usage": True}
|
||||
return {"model": "o3-mini", "stream_usage": True}
|
||||
|
||||
@property
|
||||
def supports_image_inputs(self) -> bool:
|
||||
|
||||
Reference in New Issue
Block a user