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13 Commits

Author SHA1 Message Date
Chester Curme
045ac176d8 test o3-mini 2025-01-31 15:50:30 -05:00
A Venkata Sai Krishna Varun
21d8d41595 docs: update delete method in vectorstores.mdx (#29497)
Thank you for contributing to LangChain!

- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
  - Example: "community: add foobar LLM"


- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!


- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
2025-01-31 18:15:28 +00:00
Mark Perfect
b8e218b09f docs: Fix Milvus vector store initialization (#29511)
- [x] **PR title**:


- [x] **PR message**:

- A change in the Milvus API has caused an issue with the local vector
store initialization. Having used an Ollama embedding model, the vector
store initialization results in the following error:

<img width="978" alt="image"
src="https://github.com/user-attachments/assets/d57e495c-1764-4fbe-ab8c-21ee44f1e686"
/>

- This is fixed by setting the index type explicitly:

`vector_store = Milvus(embedding_function=embeddings,
connection_args={"uri": URI}, index_params={"index_type": "FLAT",
"metric_type": "L2"},)`

Other small documentation edits were also made.


- [x] **Add tests and docs**:
  N/A


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
2025-01-31 12:57:36 -05:00
Amit Ghadge
0c405245c4 [Integrations][Tool] Added Jenkins tools support (#29516)
Thank you for contributing to LangChain!

- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
  - Example: "community: add foobar LLM"


- [x] **PR message**: ***Delete this entire checklist*** and replace
with
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!


- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
2025-01-31 12:50:10 -05:00
Subrat Lima
5b826175c9 docs: Update local_llms.ipynb - fixed a typo (#29520)
Description: fixed a typo in the how to > local llma > llamafile section
description.
2025-01-31 11:18:24 -05:00
Christophe Bornet
aab2e42169 core[patch]: Use Blockbuster to detect blocking calls in asyncio during tests (#29043)
This PR uses the [blockbuster](https://github.com/cbornet/blockbuster)
library in langchain-core to detect blocking calls made in the asyncio
event loop during unit tests.
Avoiding blocking calls is hard as these can be deeply buried in the
code or made in 3rd party libraries.
Blockbuster makes it easier to detect them by raising an exception when
a call is made to a known blocking function (eg: `time.sleep`).

Adding blockbuster allowed to find a blocking call in
`aconfig_with_context` (it ends up calling `get_function_nonlocals`
which loads function code).

**Dependencies:**
- blockbuster (test)

**Twitter handle:** cbornet_
2025-01-31 10:06:34 -05:00
Philippe PRADOS
ceda8bc050 community[minor]: 03 - Refactoring PyPDF parser (#29330)
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once.
This specific part focuses on updating the PyPDF parser.

For more details, see [PR
28970](https://github.com/langchain-ai/langchain/pull/28970).
2025-01-31 10:05:07 -05:00
Julian Castro Pulgarin
b7e3e337b1 community: Fix YahooFinanceNewsTool to handle updated yfinance data structure (#29498)
*Description:**
Updates the YahooFinanceNewsTool to handle the current yfinance news
data structure. The tool was failing with a KeyError due to changes in
the yfinance API's response format. This PR updates the code to
correctly extract news URLs from the new structure.

**Issue:** #29495

**Dependencies:** 
No new dependencies required. Works with existing yfinance package.

The changes maintain backwards compatibility while fixing the KeyError
that users were experiencing.

The modified code properly handles the new data structure where:
- News type is now at `content.contentType`
- News URL is now at `content.canonicalUrl.url`

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
2025-01-31 02:31:44 +00:00
Vadym Barda
22219eefaf docs: update README/intro (#29492) 2025-01-29 22:50:00 +00:00
Erick Friis
332e303858 partners/mistralai: release 0.2.6 (#29491) 2025-01-29 22:23:14 +00:00
Erick Friis
2c795f5628 partners/openai: release 0.3.3 (#29490) 2025-01-29 22:23:03 +00:00
Erick Friis
f307b3cc5f langchain: release 0.3.17 (#29485) 2025-01-29 22:22:49 +00:00
Erick Friis
5cad3683b4 partners/groq: release 0.2.4 (#29488) 2025-01-29 22:22:30 +00:00
43 changed files with 3750 additions and 1570 deletions

View File

@@ -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:

View File

@@ -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

View File

@@ -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",

View File

@@ -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

View 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).

View 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

View File

@@ -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

View File

@@ -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):

View File

@@ -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)

View File

@@ -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)

View File

@@ -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")

View File

@@ -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

View File

@@ -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

View File

@@ -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(

View File

@@ -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
View File

@@ -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"

View File

@@ -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"

View File

@@ -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(

View File

@@ -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=" "),

View File

@@ -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

View File

@@ -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(

View File

@@ -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:

View File

@@ -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

View File

@@ -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:

View File

@@ -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:

View File

@@ -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"),

View File

@@ -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

View File

@@ -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"))

View 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()

View File

@@ -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}"
)

View File

@@ -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

View File

@@ -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"},
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@@ -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 = [
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@@ -1978,7 +1978,7 @@ files = [
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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"
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@@ -2430,13 +2430,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
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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 ...`)."
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@@ -5189,4 +5189,4 @@ cffi = ["cffi (>=1.11)"]
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lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "adcdcf3ce76a7951ebd6110c64ceb6ba051d2887109ed1fed439b1aa78a2a5c5"
content-hash = "e771bd2ab1b08c4f6f9e264f9b417a729424660b153cc96e15bc139e778a432e"

View File

@@ -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"

View File

@@ -371,3 +371,7 @@ packages:
downloads: 0
js: '@langchain/deepseek'
provider_page: deepseek
- name: langchain-jenkins
path: .
repo: Amitgb14/langchain_jenkins
downloads: 0

View File

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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"
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@@ -687,15 +771,26 @@ files = [
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View File

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-groq"
version = "0.2.3"
version = "0.2.4"
description = "An integration package connecting Groq and LangChain"
authors = []
readme = "README.md"
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[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.29"
langchain-core = "^0.3.33"
groq = ">=0.4.1,<1"
[tool.ruff.lint]

View File

@@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "annotated-types"
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View File

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-mistralai"
version = "0.2.5"
version = "0.2.6"
description = "An integration package connecting Mistral and LangChain"
authors = []
readme = "README.md"
@@ -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"
httpx = ">=0.25.2,<1"
httpx-sse = ">=0.3.1,<1"

View File

@@ -1,4 +1,4 @@
# 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.
[[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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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"
@@ -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
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version = "1.60.2"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.8"
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version = "2.10.5"
version = "2.10.6"
description = "Data validation using Python type hints"
optional = false
python-versions = ">=3.8"
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View File

@@ -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"

View File

@@ -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: