Last week Anthropic released version 0.39.0 of its python sdk, which
enabled support for Python 3.13. This release deleted a legacy
`client.count_tokens` method, which we currently access during init of
the `Anthropic` LLM. Anthropic has replaced this functionality with the
[client.beta.messages.count_tokens()
API](https://github.com/anthropics/anthropic-sdk-python/pull/726).
To enable support for `anthropic >= 0.39.0` and Python 3.13, here we
drop support for the legacy token counting method, and add support for
the new method via `ChatAnthropic.get_num_tokens_from_messages`.
To fully support the token counting API, we update the signature of
`get_num_tokens_from_message` to accept tools everywhere.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Description:
* When working with OpenSearchVectorSearch to make
OpenSearchGraphVectorStore (coming soon), I noticed that there wasn't
type hinting for the underlying OpenSearch clients. This fixes that
issue.
* Confirmed tests are still passing with code changes.
Note that there is some additional code duplication now, but I think
this approach is cleaner overall.
## Description
As proposed in our earlier discussion #26977 we have introduced a Google
Books API Tool that leverages the Google Books API found at
[https://developers.google.com/books/docs/v1/using](https://developers.google.com/books/docs/v1/using)
to generate book recommendations.
### Sample Usage
```python
from langchain_community.tools import GoogleBooksQueryRun
from langchain_community.utilities import GoogleBooksAPIWrapper
api_wrapper = GoogleBooksAPIWrapper()
tool = GoogleBooksQueryRun(api_wrapper=api_wrapper)
tool.run('ai')
```
### Sample Output
```txt
Here are 5 suggestions based off your search for books related to ai:
1. "AI's Take on the Stigma Against AI-Generated Content" by Sandy Y. Greenleaf: In a world where artificial intelligence (AI) is rapidly advancing and transforming various industries, a new form of content creation has emerged: AI-generated content. However, despite its potential to revolutionize the way we produce and consume information, AI-generated content often faces a significant stigma. "AI's Take on the Stigma Against AI-Generated Content" is a groundbreaking book that delves into the heart of this issue, exploring the reasons behind the stigma and offering a fresh, unbiased perspective on the topic. Written from the unique viewpoint of an AI, this book provides readers with a comprehensive understanding of the challenges and opportunities surrounding AI-generated content. Through engaging narratives, thought-provoking insights, and real-world examples, this book challenges readers to reconsider their preconceptions about AI-generated content. It explores the potential benefits of embracing this technology, such as increased efficiency, creativity, and accessibility, while also addressing the concerns and drawbacks that contribute to the stigma. As you journey through the pages of this book, you'll gain a deeper understanding of the complex relationship between humans and AI in the realm of content creation. You'll discover how AI can be used as a tool to enhance human creativity, rather than replace it, and how collaboration between humans and machines can lead to unprecedented levels of innovation. Whether you're a content creator, marketer, business owner, or simply someone curious about the future of AI and its impact on our society, "AI's Take on the Stigma Against AI-Generated Content" is an essential read. With its engaging writing style, well-researched insights, and practical strategies for navigating this new landscape, this book will leave you equipped with the knowledge and tools needed to embrace the AI revolution and harness its potential for success. Prepare to have your assumptions challenged, your mind expanded, and your perspective on AI-generated content forever changed. Get ready to embark on a captivating journey that will redefine the way you think about the future of content creation.
Read more at https://play.google.com/store/books/details?id=4iH-EAAAQBAJ&source=gbs_api
2. "AI Strategies For Web Development" by Anderson Soares Furtado Oliveira: From fundamental to advanced strategies, unlock useful insights for creating innovative, user-centric websites while navigating the evolving landscape of AI ethics and security Key Features Explore AI's role in web development, from shaping projects to architecting solutions Master advanced AI strategies to build cutting-edge applications Anticipate future trends by exploring next-gen development environments, emerging interfaces, and security considerations in AI web development Purchase of the print or Kindle book includes a free PDF eBook Book Description If you're a web developer looking to leverage the power of AI in your projects, then this book is for you. Written by an AI and ML expert with more than 15 years of experience, AI Strategies for Web Development takes you on a transformative journey through the dynamic intersection of AI and web development, offering a hands-on learning experience.The first part of the book focuses on uncovering the profound impact of AI on web projects, exploring fundamental concepts, and navigating popular frameworks and tools. As you progress, you'll learn how to build smart AI applications with design intelligence, personalized user journeys, and coding assistants. Later, you'll explore how to future-proof your web development projects using advanced AI strategies and understand AI's impact on jobs. Toward the end, you'll immerse yourself in AI-augmented development, crafting intelligent web applications and navigating the ethical landscape.Packed with insights into next-gen development environments, AI-augmented practices, emerging realities, interfaces, and security governance, this web development book acts as your roadmap to staying ahead in the AI and web development domain. What you will learn Build AI-powered web projects with optimized models Personalize UX dynamically with AI, NLP, chatbots, and recommendations Explore AI coding assistants and other tools for advanced web development Craft data-driven, personalized experiences using pattern recognition Architect effective AI solutions while exploring the future of web development Build secure and ethical AI applications following TRiSM best practices Explore cutting-edge AI and web development trends Who this book is for This book is for web developers with experience in programming languages and an interest in keeping up with the latest trends in AI-powered web development. Full-stack, front-end, and back-end developers, UI/UX designers, software engineers, and web development enthusiasts will also find valuable information and practical guidelines for developing smarter websites with AI. To get the most out of this book, it is recommended that you have basic knowledge of programming languages such as HTML, CSS, and JavaScript, as well as a familiarity with machine learning concepts.
Read more at https://play.google.com/store/books/details?id=FzYZEQAAQBAJ&source=gbs_api
3. "Artificial Intelligence for Students" by Vibha Pandey: A multifaceted approach to develop an understanding of AI and its potential applications KEY FEATURES ● AI-informed focuses on AI foundation, applications, and methodologies. ● AI-inquired focuses on computational thinking and bias awareness. ● AI-innovate focuses on creative and critical thinking and the Capstone project. DESCRIPTION AI is a discipline in Computer Science that focuses on developing intelligent machines, machines that can learn and then teach themselves. If you are interested in AI, this book can definitely help you prepare for future careers in AI and related fields. The book is aligned with the CBSE course, which focuses on developing employability and vocational competencies of students in skill subjects. The book is an introduction to the basics of AI. It is divided into three parts – AI-informed, AI-inquired and AI-innovate. It will help you understand AI's implications on society and the world. You will also develop a deeper understanding of how it works and how it can be used to solve complex real-world problems. Additionally, the book will also focus on important skills such as problem scoping, goal setting, data analysis, and visualization, which are essential for success in AI projects. Lastly, you will learn how decision trees, neural networks, and other AI concepts are commonly used in real-world applications. By the end of the book, you will develop the skills and competencies required to pursue a career in AI. WHAT YOU WILL LEARN ● Get familiar with the basics of AI and Machine Learning. ● Understand how and where AI can be applied. ● Explore different applications of mathematical methods in AI. ● Get tips for improving your skills in Data Storytelling. ● Understand what is AI bias and how it can affect human rights. WHO THIS BOOK IS FOR This book is for CBSE class XI and XII students who want to learn and explore more about AI. Basic knowledge of Statistical concepts, Algebra, and Plotting of equations is a must. TABLE OF CONTENTS 1. Introduction: AI for Everyone 2. AI Applications and Methodologies 3. Mathematics in Artificial Intelligence 4. AI Values (Ethical Decision-Making) 5. Introduction to Storytelling 6. Critical and Creative Thinking 7. Data Analysis 8. Regression 9. Classification and Clustering 10. AI Values (Bias Awareness) 11. Capstone Project 12. Model Lifecycle (Knowledge) 13. Storytelling Through Data 14. AI Applications in Use in Real-World
Read more at https://play.google.com/store/books/details?id=ptq1EAAAQBAJ&source=gbs_api
4. "The AI Book" by Ivana Bartoletti, Anne Leslie and Shân M. Millie: Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important
Read more at http://books.google.ca/books?id=oE3YDwAAQBAJ&dq=ai&hl=&source=gbs_api
5. "Artificial Intelligence in Society" by OECD: The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises.
Read more at https://play.google.com/store/books/details?id=eRmdDwAAQBAJ&source=gbs_api
```
## Issue
This closes#27276
## Dependencies
No additional dependencies were added
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
all template installs will now have to declare `--branch v0.2` to make
clear they aren't compatible with langchain 0.3 (most have a pydantic v1
setup). e.g.
```
langchain-cli app add pirate-speak --branch v0.2
```
Thank you for contributing to LangChain!
- [x] **PR title**: "community: chat models wrapper for Cloudflare
Workers AI"
- [x] **PR message**:
- **Description:** Add chat models wrapper for Cloudflare Workers AI.
Enables Langgraph intergration via ChatModel for tool usage, agentic
usage.
- [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: Erick Friis <erick@langchain.dev>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** This PR adds functionality to pass in in-memory bytes
as a source to `AzureAIDocumentIntelligenceLoader`.
- **Issue:** I needed the functionality, so I added it.
- **Dependencies:** NA
- **Twitter handle:** @akseljoonas if this is a big enough change :)
---------
Co-authored-by: Aksel Joonas Reedi <aksel@klippa.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
# OCR-based PDF loader
This implements [Zerox](https://github.com/getomni-ai/zerox) PDF
document loader.
Zerox utilizes simple but very powerful (even though slower and more
costly) approach to parsing PDF documents: it converts PDF to series of
images and passes it to a vision model requesting the contents in
markdown.
It is especially suitable for complex PDFs that are not parsed well by
other alternatives.
## Example use:
```python
from langchain_community.document_loaders.pdf import ZeroxPDFLoader
os.environ["OPENAI_API_KEY"] = "" ## your-api-key
model = "gpt-4o-mini" ## openai model
pdf_url = "https://assets.ctfassets.net/f1df9zr7wr1a/soP1fjvG1Wu66HJhu3FBS/034d6ca48edb119ae77dec5ce01a8612/OpenAI_Sacra_Teardown.pdf"
loader = ZeroxPDFLoader(file_path=pdf_url, model=model)
docs = loader.load()
```
The Zerox library supports wide range of provides/models. See Zerox
documentation for details.
- **Dependencies:** `zerox`
- **Twitter handle:** @martintriska1
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
## Description
This PR adds support for Memcached as a usable LLM model cache by adding
the ```MemcachedCache``` implementation relying on the
[pymemcache](https://github.com/pinterest/pymemcache) client.
Unit test-wise, the new integration is generally covered under existing
import testing. All new functionality depends on pymemcache if
instantiated and used, so to comply with the other cache implementations
the PR also adds optional integration tests for ```MemcachedCache```.
Since this is a new integration, documentation is added for Memcached as
an integration and as an LLM Cache.
## Issue
This PR closes#27275 which was originally raised as a discussion in
#27035
## Dependencies
There are no new required dependencies for langchain, but
[pymemcache](https://github.com/pinterest/pymemcache) is required to
instantiate the new ```MemcachedCache```.
## Example Usage
```python3
from langchain.globals import set_llm_cache
from langchain_openai import OpenAI
from langchain_community.cache import MemcachedCache
from pymemcache.client.base import Client
llm = OpenAI(model="gpt-3.5-turbo-instruct", n=2, best_of=2)
set_llm_cache(MemcachedCache(Client('localhost')))
# The first time, it is not yet in cache, so it should take longer
llm.invoke("Which city is the most crowded city in the USA?")
# The second time it is, so it goes faster
llm.invoke("Which city is the most crowded city in the USA?")
```
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
## What this PR does?
### Currently `O365BaseLoader` (and consequently both derived loaders)
are limited to `pdf`, `doc`, `docx` files.
- **Solution: here we introduce _handlers_ attribute that allows for
custom handlers to be passed in. This is done in _dict_ form:**
**Example:**
```python
from langchain_community.document_loaders.parsers.documentloader_adapter import DocumentLoaderAsParser
# PR for DocumentLoaderAsParser here: https://github.com/langchain-ai/langchain/pull/27749
from langchain_community.document_loaders.excel import UnstructuredExcelLoader
xlsx_parser = DocumentLoaderAsParser(UnstructuredExcelLoader, mode="paged")
# create dictionary mapping file types to handlers (parsers)
handlers = {
"doc": MsWordParser()
"pdf": PDFMinerParser()
"txt": TextParser()
"xlsx": xlsx_parser
}
loader = SharePointLoader(document_library_id="...",
handlers=handlers # pass handlers to SharePointLoader
)
documents = loader.load()
# works the same in OneDriveLoader
loader = OneDriveLoader(document_library_id="...",
handlers=handlers
)
```
This dictionary is then passed to `MimeTypeBasedParser` same as in the
[current
implementation](5a2cfb49e0/libs/community/langchain_community/document_loaders/parsers/registry.py (L13)).
### Currently `SharePointLoader` and `OneDriveLoader` are separate
loaders that both inherit from `O365BaseLoader`
However both of these implement the same functionality. The only
differences are:
- `SharePointLoader` requires argument `document_library_id` whereas
`OneDriveLoader` requires `drive_id`. These are just different names for
the same thing.
- `SharePointLoader` implements significantly more features.
- **Solution: `OneDriveLoader` is replaced with an empty shell just
renaming `drive_id` to `document_library_id` and inheriting from
`SharePointLoader`**
**Dependencies:** None
**Twitter handle:** @martintriska1
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
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, "templates:
..." for template changes, "infra: ..." for CI changes.
- Example: "community: add foobar LLM"
- [ ] **description**
langchain_core.runnables.graph_mermaid.draw_mermaid_png calls this
function, but the Mermaid API returns JPEG by default. To be consistent,
add the option `file_type` with the default `png` type.
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
With this small change, I didn't add tests and docs.
- [ ] **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:
One long sentence was divided into two.
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.
Now `encode_kwargs` used for both for documents and queries and this
leads to wrong embeddings. E. g.:
```python
model_kwargs = {"device": "cuda", "trust_remote_code": True}
encode_kwargs = {"normalize_embeddings": False, "prompt_name": "s2p_query"}
model = HuggingFaceEmbeddings(
model_name="dunzhang/stella_en_400M_v5",
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
)
query_embedding = np.array(
model.embed_query("What are some ways to reduce stress?",)
)
document_embedding = np.array(
model.embed_documents(
[
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
)
)
print(model._client.similarity(query_embedding, document_embedding)) # output: tensor([[0.8421, 0.3317]], dtype=torch.float64)
```
But from the [model
card](https://huggingface.co/dunzhang/stella_en_400M_v5#sentence-transformers)
expexted like this:
```python
model_kwargs = {"device": "cuda", "trust_remote_code": True}
encode_kwargs = {"normalize_embeddings": False}
query_encode_kwargs = {"normalize_embeddings": False, "prompt_name": "s2p_query"}
model = HuggingFaceEmbeddings(
model_name="dunzhang/stella_en_400M_v5",
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_encode_kwargs=query_encode_kwargs,
)
query_embedding = np.array(
model.embed_query("What are some ways to reduce stress?", )
)
document_embedding = np.array(
model.embed_documents(
[
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
)
)
print(model._client.similarity(query_embedding, document_embedding)) # tensor([[0.8398, 0.2990]], dtype=torch.float64)
```
There was a change of attribute name which was "max_batch_size". It's
now "get_max_batch_size" method.
I want to use "create_batches" which is right down below.
Please check this PR link.
reference: https://github.com/chroma-core/chroma/pull/2305
---------
Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
Co-authored-by: Prithvi Kannan <46332835+prithvikannan@users.noreply.github.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Jun Yamog <jkyamog@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ono-hiroki <86904208+ono-hiroki@users.noreply.github.com>
Co-authored-by: Dobiichi-Origami <56953648+Dobiichi-Origami@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Co-authored-by: Duy Huynh <vndee.huynh@gmail.com>
Co-authored-by: Rashmi Pawar <168514198+raspawar@users.noreply.github.com>
Co-authored-by: sifatj <26035630+sifatj@users.noreply.github.com>
Co-authored-by: Eric Pinzur <2641606+epinzur@users.noreply.github.com>
Co-authored-by: Daniel Vu Dao <danielvdao@users.noreply.github.com>
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
Co-authored-by: Stéphane Philippart <wildagsx@gmail.com>
- **Description:** change to do the batch embedding server side and not
client side
- **Twitter handle:** @wildagsx
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Description:
This fixes an issue that mistakenly created in
https://github.com/langchain-ai/langchain/pull/27253. The issue
currently exists only in `langchain-community==0.3.4`.
Test cases were added to prevent this issue in the future.
Co-authored-by: Erick Friis <erick@langchain.dev>
### Description:
This PR sets a default value of `output_token_limit = 4000` for the
`PowerBIToolkit` to fix the unintentionally validation error.
### Problem:
When attempting to run a code snippet from [Langchain's PowerBI toolkit
documentation](https://python.langchain.com/v0.1/docs/integrations/toolkits/powerbi/)
to interact with a `PowerBIDataset`, the following error occurs:
```
pydantic.v1.error_wrappers.ValidationError: 1 validation error for QueryPowerBITool
output_token_limit
none is not an allowed value (type=type_error.none.not_allowed)
```
### Root Cause:
The issue arises because when creating a `QueryPowerBITool`, the
`output_token_limit` parameter is unintentionally set to `None`, which
is the current default for `PowerBIToolkit`. However, `QueryPowerBITool`
expects a default value of `4000` for `output_token_limit`. This
unintended override causes the error.
17659ca2cd/libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py (L63)17659ca2cd/libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py (L72-L79)17659ca2cd/libs/community/langchain_community/tools/powerbi/tool.py (L39)
### Solution:
To resolve this, the default value of `output_token_limit` is now
explicitly set to `4000` in `PowerBIToolkit` to prevent the accidental
assignment of `None`.
Co-authored-by: ccurme <chester.curme@gmail.com>
