- Description: support [ERNIE
Embedding-V1](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/alj562vvu),
which is part of ERNIE ecology
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Updating documentation to add AmazonTextractPDFLoader
according to
[comment](https://github.com/langchain-ai/langchain/pull/8661#issuecomment-1666572992)
from [baskaryan](https://github.com/baskaryan)
Adding one notebook and instructions to the
modules/data_connection/document_loaders/pdf.mdx
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Made the notion document of how Langchain executes agents method by
method in the codebase.
Can be helpful for developers that just started working with the
Langchain codebase.
The current Collab URL returns a 404, since there is no `chatbots`
directory under `use_cases`.
<!-- Thank you for contributing to LangChain!
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
### Summary
Fixes a bug from #7850 where post processing functions in Unstructured
loaders were not apply. Adds a assertion to the test to verify the post
processing function was applied and also updates the explanation in the
example notebook.
Fix spelling errors in the text: 'Therefore' and 'Retrying
I want to stress that your feedback is invaluable to us and is genuinely
cherished.
With gratitude,
@baskaryan @hwchase17
Removed extra "the" in the sentence about the chicken crossing the road
in fallbacks.ipynb. The sentence now reads correctly: "Why did the
chicken cross the road?" This resolves the grammatical error and
improves the overall quality of the content.
@baskaryan , @hinthornw , @hwchase17
I want to extend my heartfelt gratitude to the creator for masterfully
crafting this remarkable application. 🙌 I am truly impressed by the
meticulous attention to grammar and spelling in the documentation, which
undoubtedly contributes to a polished and seamless reader experience.
As always, your feedback holds immense value and is greatly appreciated.
@baskaryan , @hwchase17
I want to convey my deep appreciation to the creator for their expert
craftsmanship in developing this exceptional application. 👏 The
remarkable dedication to upholding impeccable grammar and spelling in
the documentation significantly enhances the polished and seamless
experience for readers.
I want to stress that your feedback is invaluable to us and is genuinely
cherished.
With gratitude,
@baskaryan, @hwchase17
In this commit, I have made a modification to the term "Langchain" to
correctly reflect the project's name as "LangChain". This change ensures
consistency and accuracy throughout the codebase and documentation.
@baskaryan , @hwchase17
Refined the example in router.ipynb by addressing a minor typographical
error. The typo "rins" has been corrected to "rains" in the code snippet
that demonstrates the usage of the MultiPromptChain. This change ensures
accuracy and consistency in the provided code example.
This improvement enhances the readability and correctness of the
notebook, making it easier for users to understand and follow the
demonstration. The commit aims to maintain the quality and accuracy of
the content within the repository.
Thank you for your attention to detail, and please review the change at
your convenience.
@baskaryan , @hwchase17
- Description: Fix a minor variable naming inconsistency in a code
snippet in the docs
- Issue: N/A
- Dependencies: none
- Tag maintainer: N/A
- Twitter handle: N/A
- Description: Added improvements in Nebula LLM to perform auto-retry;
more generation parameters supported. Conversation is no longer required
to be passed in the LLM object. Examples are updated.
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @baskaryan
- Twitter handle: symbldotai
---------
Co-authored-by: toshishjawale <toshish@symbl.ai>
Update documentation and URLs for the Langchain Context integration.
We've moved from getcontext.ai to context.ai \o/
Thanks in advance for the review!
Now with ElasticsearchStore VectorStore merged, i've added support for
the self-query retriever.
I've added a notebook also to demonstrate capability. I've also added
unit tests.
**Credit**
@elastic and @phoey1 on twitter.
Todo:
- [x] Connection options (cloud, localhost url, es_connection) support
- [x] Logging support
- [x] Customisable field support
- [x] Distance Similarity support
- [x] Metadata support
- [x] Metadata Filter support
- [x] Retrieval Strategies
- [x] Approx
- [x] Approx with Hybrid
- [x] Exact
- [x] Custom
- [x] ELSER (excluding hybrid as we are working on RRF support)
- [x] integration tests
- [x] Documentation
👋 this is a contribution to improve Elasticsearch integration with
Langchain. Its based loosely on the changes that are in master but with
some notable changes:
## Package name & design improvements
The import name is now `ElasticsearchStore`, to aid discoverability of
the VectorStore.
```py
## Before
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch, ElasticKnnSearch
## Now
from langchain.vectorstores.elasticsearch import ElasticsearchStore
```
## Retrieval Strategy support
Before we had a number of classes, depending on the strategy you wanted.
`ElasticKnnSearch` for approx, `ElasticVectorSearch` for exact / brute
force.
With `ElasticsearchStore` we have retrieval strategies:
### Approx Example
Default strategy for the vast majority of developers who use
Elasticsearch will be inferring the embeddings from outside of
Elasticsearch. Uses KNN functionality of _search.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index"
)
output = docsearch.similarity_search("foo", k=1)
```
### Approx, with hybrid
Developers who want to search, using both the embedding and the text
bm25 match. Its simple to enable.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True)
)
output = docsearch.similarity_search("foo", k=1)
```
### Approx, with `query_model_id`
Developers who want to infer within Elasticsearch, using the model
loaded in the ml node.
This relies on the developer to setup the pipeline and index if they
wish to embed the text in Elasticsearch. Example of this in the test.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ApproxRetrievalStrategy(
query_model_id="sentence-transformers__all-minilm-l6-v2"
),
)
output = docsearch.similarity_search("foo", k=1)
```
### I want to provide my own custom Elasticsearch Query
You might want to have more control over the query, to perform
multi-phase retrieval such as LTR, linearly boosting on document
parameters like recently updated or geo-distance. You can do this with
`custom_query_fn`
```py
def my_custom_query(query_body: dict, query: str) -> dict:
return {"query": {"match": {"text": {"query": "bar"}}}}
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts, FakeEmbeddings(), **elasticsearch_connection, index_name=index_name
)
docsearch.similarity_search("foo", k=1, custom_query=my_custom_query)
```
### Exact Example
Developers who have a small dataset in Elasticsearch, dont want the cost
of indexing the dims vs tradeoff on cost at query time. Uses
script_score.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ExactRetrievalStrategy(),
)
output = docsearch.similarity_search("foo", k=1)
```
### ELSER Example
Elastic provides its own sparse vector model called ELSER. With these
changes, its really easy to use. The vector store creates a pipeline and
index thats setup for ELSER. All the developer needs to do is configure,
ingest and query via langchain tooling.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.SparseVectorStrategy(),
)
output = docsearch.similarity_search("foo", k=1)
```
## Architecture
In future, we can introduce new strategies and allow us to not break bwc
as we evolve the index / query strategy.
## Credit
On release, could you credit @elastic and @phoey1 please? Thank you!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adds [DeepSparse](https://github.com/neuralmagic/deepsparse) as an LLM
backend. DeepSparse supports running various open-source sparsified
models hosted on [SparseZoo](https://sparsezoo.neuralmagic.com/) for
performance gains on CPUs.
Twitter handles: @mgoin_ @neuralmagic
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Added SmartGPT workflow by providing SmartLLM wrapper around LLMs
Edit:
As @hwchase17 suggested, this should be a chain, not an LLM. I have
adapted the PR.
It is used like this:
```
from langchain.prompts import PromptTemplate
from langchain.chains import SmartLLMChain
from langchain.chat_models import ChatOpenAI
hard_question = "I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?"
hard_question_prompt = PromptTemplate.from_template(hard_question)
llm = ChatOpenAI(model_name="gpt-4")
prompt = PromptTemplate.from_template(hard_question)
chain = SmartLLMChain(llm=llm, prompt=prompt, verbose=True)
chain.run({})
```
Original text:
Added SmartLLM wrapper around LLMs to allow for SmartGPT workflow (as in
https://youtu.be/wVzuvf9D9BU). SmartLLM can be used wherever LLM can be
used. E.g:
```
smart_llm = SmartLLM(llm=OpenAI())
smart_llm("What would be a good company name for a company that makes colorful socks?")
```
or
```
smart_llm = SmartLLM(llm=OpenAI())
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=smart_llm, prompt=prompt)
chain.run("colorful socks")
```
SmartGPT consists of 3 steps:
1. Ideate - generate n possible solutions ("ideas") to user prompt
2. Critique - find flaws in every idea & select best one
3. Resolve - improve upon best idea & return it
Fixes#4463
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @hwchase17
- @agola11
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit adds the LangChain utility which allows for the real-time
retrieval of cryptocurrency exchange prices. With LangChain, users can
easily access up-to-date pricing information by running the command
".run(from_currency, to_currency)". This new feature provides a
convenient way to stay informed on the latest exchange rates and make
informed decisions when trading crypto.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Adds the ArcGISLoader class to
`langchain.document_loaders`
- Allows users to load data from ArcGIS Online, Portal, and similar
- Users can authenticate with `arcgis.gis.GIS` or retrieve public data
anonymously
- Uses the `arcgis.features.FeatureLayer` class to retrieve the data
- Defines the most relevant keywords arguments and accepts `**kwargs`
- Dependencies: Using this class requires `arcgis` and, optionally,
`bs4.BeautifulSoup`.
Tagging maintainers:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Updated interactive walkthrough link in index.md to resolve 404 error.
Also, expressing deep gratitude to LangChain library developers for
their exceptional efforts 🥇 .
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Added a new use case category called "Web Scraping", and
a tutorial to scrape websites using OpenAI Functions Extraction chain to
the docs.
- Tag maintainer:@baskaryan @hwchase17 ,
- Twitter handle: https://www.linkedin.com/in/haiphunghiem/ (I'm on
LinkedIn mostly)
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
This PR introduces [Label Studio](https://labelstud.io/) integration
with LangChain via `LabelStudioCallbackHandler`:
- sending data to the Label Studio instance
- labeling dataset for supervised LLM finetuning
- rating model responses
- tracking and displaying chat history
- support for custom data labeling workflow
### Example
```
chat_llm = ChatOpenAI(callbacks=[LabelStudioCallbackHandler(mode="chat")])
chat_llm([
SystemMessage(content="Always use emojis in your responses."),
HumanMessage(content="Hey AI, how's your day going?"),
AIMessage(content="🤖 I don't have feelings, but I'm running smoothly! How can I help you today?"),
HumanMessage(content="I'm feeling a bit down. Any advice?"),
AIMessage(content="🤗 I'm sorry to hear that. Remember, it's okay to seek help or talk to someone if you need to. 💬"),
HumanMessage(content="Can you tell me a joke to lighten the mood?"),
AIMessage(content="Of course! 🎭 Why did the scarecrow win an award? Because he was outstanding in his field! 🌾"),
HumanMessage(content="Haha, that was a good one! Thanks for cheering me up."),
AIMessage(content="Always here to help! 😊 If you need anything else, just let me know."),
HumanMessage(content="Will do! By the way, can you recommend a good movie?"),
])
```
<img width="906" alt="image"
src="https://github.com/langchain-ai/langchain/assets/6087484/0a1cf559-0bd3-4250-ad96-6e71dbb1d2f3">
### Dependencies
- [label-studio](https://pypi.org/project/label-studio/)
- [label-studio-sdk](https://pypi.org/project/label-studio-sdk/)
https://twitter.com/labelstudiohq
---------
Co-authored-by: nik <nik@heartex.net>
As of the recent PR at #9043, after some testing we've realised that the
default values were not being used for `api_key` and `api_url`. Besides
that, the default for `api_key` was set to `argilla.apikey`, but since
the default values are intended for people using the Argilla Quickstart
(easy to run and setup), the defaults should be instead `owner.apikey`
if using Argilla 1.11.0 or higher, or `admin.apikey` if using a lower
version of Argilla.
Additionally, we've removed the f-string replacements from the
docstrings.
---------
Co-authored-by: Gabriel Martin <gabriel@argilla.io>
In second section it looks like a copy/paste from the first section and
doesn't include the specific embedding model mentioned in the example so
I added it for clarity.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The table creation process in these examples commands do not match what
the recently updated functions in these example commands is looking for.
This change updates the type in the table creation command.
Issue Number for my report of the doc problem #7446
@rlancemartin and @eyurtsev I believe this is your area
Twitter: @j1philli
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description**: [BagelDB](bageldb.ai) a collaborative vector
database. Integrated the bageldb PyPi package with langchain with
related tests and code.
- **Issue**: Not applicable.
- **Dependencies**: `betabageldb` PyPi package.
- **Tag maintainer**: @rlancemartin, @eyurtsev, @baskaryan
- **Twitter handle**: bageldb_ai (https://twitter.com/BagelDB_ai)
We ran `make format`, `make lint` and `make test` locally.
Followed the contribution guideline thoroughly
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
---------
Co-authored-by: Towhid1 <nurulaktertowhid@gmail.com>
## Description
This PR adds the `aembed_query` and `aembed_documents` async methods for
improving the embeddings generation for large documents. The
implementation uses asyncio tasks and gather to achieve concurrency as
there is no bedrock async API in boto3.
### Maintainers
@agola11
@aarora79
### Open questions
To avoid throttling from the Bedrock API, should there be an option to
limit the concurrency of the calls?
