I would like to contribute with a jupyter notebook example
implementation of an AI Sales Agent using `langchain`.
The bot understands the conversation stage (you can define your own
stages fitting your needs)
using two chains:
1. StageAnalyzerChain - takes context and LLM decides what part of sales
conversation is one in
2. SalesConversationChain - generate next message
Schema:
https://images-genai.s3.us-east-1.amazonaws.com/architecture2.png
my original repo: https://github.com/filip-michalsky/SalesGPT
This example creates a sales person named Ted Lasso who is trying to
sell you mattresses.
Happy to update based on your feedback.
Thanks, Filip
https://twitter.com/FilipMichalsky
Simplifies the [Two Agent
D&D](https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html)
example with a cleaner, simpler interface that is extensible for
multiple agents.
`DialogueAgent`:
- `send()`: applies the chatmodel to the message history and returns the
message string
- `receive(name, message)`: adds the `message` spoken by `name` to
message history
The `DialogueSimulator` class takes a list of agents. At each step, it
performs the following:
1. Select the next speaker
2. Calls the next speaker to send a message
3. Broadcasts the message to all other agents
4. Update the step counter.
The selection of the next speaker can be implemented as any function,
but in this case we simply loop through the agents.
Update Alchemy Key URL in Blockchain Document Loader. I want to say
thank you for the incredible work the LangChain library creators have
done.
I am amazed at how seamlessly the Loader integrates with Ethereum
Mainnet, Ethereum Testnet, Polygon Mainnet, and Polygon Testnet, and I
am excited to see how this technology can be extended in the future.
@hwchase17 - Please let me know if I can improve or if I have missed any
community guidelines in making the edit? Thank you again for your hard
work and dedication to the open source community.
Ran into this issue In vectorstores/redis.py when trying to use the
AutoGPT agent with redis vector store. The error I received was
`
langchain/experimental/autonomous_agents/autogpt/agent.py", line 134, in
run
self.memory.add_documents([Document(page_content=memory_to_add)])
AttributeError: 'RedisVectorStoreRetriever' object has no attribute
'add_documents'
`
Added the needed function to the class RedisVectorStoreRetriever which
did not have the functionality like the base VectorStoreRetriever in
vectorstores/base.py that, for example, vectorstores/faiss.py has
This commit adds a new unit test for the _merge_splits function in the
text splitter. The new test verifies that the function merges text into
chunks of the correct size and overlap, using a specified separator. The
test passes on the current implementation of the function.
The Pandas agent fails to pass callback_manager forward, making it
impossible to use custom callbacks with it. Fix that.
Co-authored-by: Sami Liedes <sami.liedes@rocket-science.ch>
Test for #3434 @eavanvalkenburg
Initially, I was unaware and had submitted a pull request #3450 for the
same purpose, but I have now repurposed the one I used for that. And it
worked.
Improved `arxiv/tool.py` by adding more specific information to the
`description`. It would help with selecting `arxiv` tool between other
tools.
Improved `arxiv.ipynb` with more useful descriptions.
In this notebook, we show how we can use concepts from
[CAMEL](https://www.camel-ai.org/) to simulate a role-playing game with
a protagonist and a dungeon master. To simulate this game, we create a
`TwoAgentSimulator` class that coordinates the dialogue between the two
agents.
Apart from being unnecessary, postgresql is run on its default port,
which means that the langchain-server will fail to start if there is
already a postgresql server running on the host. This is obviously less
than ideal.
(Yeah, I don't understand why "expose" is the syntax that does not
expose the ports to the host...)
Tested by running langchain-server and trying out debugging on a host
that already has postgresql bound to the port 5432.
Co-authored-by: Sami Liedes <sami.liedes@rocket-science.ch>
So, this is basically fixing the same things as #1517 but for GCS.
### Problem
When loading GCS Objects with `/` in the object key (eg.
folder/some-document.txt) using `GCSFileLoader`, the objects are
downloaded into a temporary directory and saved as a file.
This errors out when the parent directory does not exist within the
temporary directory.
### What this pr does
Creates parent directories based on object key.
This also works with deeply nested keys:
folder/subfolder/some-document.txt
Fix for: [Changed regex to cover new line before action
serious.](https://github.com/hwchase17/langchain/issues/3365)
---
This PR fixes the issue where `ValueError: Could not parse LLM output:`
was thrown on seems to be valid input.
Changed regex to cover new lines before action serious (after the
keywords "Action:" and "Action Input:").
regex101: https://regex101.com/r/CXl1kB/1
---------
Co-authored-by: msarskus <msarskus@cisco.com>
My attempt at improving the `Chain`'s `Getting Started` docs and
`LLMChain` docs. Might need some proof-reading as English is not my
first language.
In LLM examples, I replaced the example use case when a simpler one
(shorter LLM output) to reduce cognitive load.
Rewrite of #3368
Mainly an issue for when people are just getting started, but still nice
to not throw an error if the number of docs is < k.
Add a little decorator utility to block mutually exclusive keyword
arguments
At present, the method of generating `point` in qdrant is to use random
`uuid`. The problem with this approach is that even documents with the
same content will be inserted repeatedly instead of updated. Using `md5`
as the `ID` of `point` to insert text can achieve true `update or
insert`.
Co-authored-by: mayue <mayue05@qiyi.com>
Updated `Getting Started` page of `Prompt Templates` to showcase more
features provided by the class. Might need some proof reading because
apparently English is not my first language.
This fixes the below mentioned issue. Instead of simply passing the text
to `tensorflow_hub`, we convert it to a list and then pass it.
https://github.com/hwchase17/langchain/issues/3282
Co-authored-by: Prakhar Agarwal <i.prakhar-agarwal@devrev.ai>
First PR, let me know if this needs anything like unit tests,
reformatting, etc. Seemed pretty straightforward to implement. Only
hitch was that mmap needs to be disabled when loading LoRAs or else you
segfault.
This PR adds support for providing a Weaviate API Key to the VectorStore
methods `from_documents` and `from_texts`. With this addition, users can
authenticate to Weaviate and make requests to private Weaviate servers
when using these methods.
## Motivation
Currently, LangChain's VectorStore methods do not provide a way to
authenticate to Weaviate. This limits the functionality of the library
and makes it more difficult for users to take advantage of Weaviate's
features.
This PR addresses this issue by adding support for providing a Weaviate
API Key as extra parameter used in the `from_texts` method.
## Contributing Guidelines
I have read the [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md)
and the PR code passes the following tests:
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
