# 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>
# Ensure deployment_id is set to provided deployment, required for Azure
OpenAI.
---------
Co-authored-by: Lucas Pickup <lupickup@microsoft.com>
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>
Formatted docstrings from different formats to consistent format, lile:
>Loads processed docs from Docugami.
"Load from `Docugami`."
>Loader that uses Unstructured to load HTML files.
"Load `HTML` files using `Unstructured`."
>Load documents from a directory.
"Load from a directory."
- `Load` - no `Loads`
- DocumentLoader always loads Documents, so no more
"documents/docs/texts/ etc"
- integrated systems and APIs enclosed in backticks,
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>
As stated in the title the SVM retriever discarded the metadata of
passed in docs. This code fixes that. I also added one unit test that
should test that.
---------
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 change updates the central utility class to recognize a Redis
cluster server after connection and returns an new cluster aware Redis
client. The "normal" Redis client would not be able to talk to a cluster
node because keys might be stored on other shards of the Redis cluster
and therefor not readable or writable.
With this patch clients do not need to know what Redis server it is,
they just connect though the same API calls for standalone and cluster
server.
There are no dependencies added due to this MR.
Remark - with current redis-py client library (4.6.0) a cluster cannot
be used as VectorStore. It can be used for other use-cases. There is a
bug / missing feature(?) in the Redis client breaking the VectorStore
implementation. I opened an issue at the client library too
(redis/redis-py#2888) to fix this. As soon as this is fixed in
`redis-py` library it should be usable there too.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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>
This MR corrects the IndexError arising in prep_prompts method when no
documents are returned from a similarity search.
Fixes#1733
Co-authored-by: Sam Groenjes <sam.groenjes@darkwolfsolutions.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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>
### Description:
`ConversationBufferTokenMemory` should have a simple way of returning
the conversation messages as a string.
Previously to complete this, you would only have the option to return
memory as an array through the buffer method and call
`get_buffer_string` by importing it from `langchain.schema`, or use the
`load_memory_variables` method and key into `self.memory_key`.
### Maintainer
@hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Now that we accept any runnable or arbitrary function to evaluate, we
don't always look up the input keys. If an evaluator requires
references, we should try to infer if there's one key present. We only
have delayed validation here but it's better than nothing
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: updated BabyAGI examples and experimental to append the
iteration to the result id to fix error storing data to vectorstore.
Issue: 7445
Dependencies: no
Tag maintainer: @eyurtsev
This fix worked for me locally. Happy to take some feedback and iterate
on a better solution. I was considering appending a uuid instead but
didn't want to over complicate the example.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Add convenience methods to `ConversationBufferMemory` and
`ConversationBufferWindowMemory` to get buffer either as messages or as
string.
Helps when `return_messages` is set to `True` but you want access to the
messages as a string, and vice versa.
@hwchase17
One use case: Using a `MultiPromptRouter` where `default_chain` is
`ConversationChain`, but destination chains are `LLMChains`. Injecting
chat memory into prompts for destination chains prints a stringified
`List[Messages]` in the prompt, which creates a lot of noise. These
convenience methods allow caller to choose either as needed.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Due to some issue on the test, this is a separate PR with
the test for #8502
Tag maintainer: @rlancemartin
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Current regex only extracts agent's action between '` ``` ``` `', this
commit will extract action between both '` ```json ``` `' and '` ``` ```
`'
This is very similar to #7511
Co-authored-by: zjl <junlinzhou@yzbigdata.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?
