One of our users noticed a bug when calling streaming models. This is
because those models return an iterator. So, I've updated the Replicate
`_call` code to join together the output. The other advantage of this
fix is that if you requested multiple outputs you would get them all –
previously I was just returning output[0].
I also adjusted the demo docs to use dolly, because we're featuring that
model right now and it's always hot, so people won't have to wait for
the model to boot up.
The error that this fixes:
```
> llm = Replicate(model=“replicate/flan-t5-xl:eec2f71c986dfa3b7a5d842d22e1130550f015720966bec48beaae059b19ef4c”)
> llm(“hello”)
> Traceback (most recent call last):
File "/Users/charlieholtz/workspace/dev/python/main.py", line 15, in <module>
print(llm(prompt))
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 246, in __call__
return self.generate([prompt], stop=stop).generations[0][0].text
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 140, in generate
raise e
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 137, in generate
output = self._generate(prompts, stop=stop)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 324, in _generate
text = self._call(prompt, stop=stop)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/replicate.py", line 108, in _call
return outputs[0]
TypeError: 'generator' object is not subscriptable
```
The sentence transformers was a dup of the HF one.
This is a breaking change (model_name vs. model) for anyone using
`SentenceTransformerEmbeddings(model="some/nondefault/model")`, but
since it was landed only this week it seems better to do this now rather
than doing a wrapper.
This notebook shows how the DialogueAgent and DialogueSimulator class
make it easy to extend the [Two-Player Dungeons & Dragons
example](https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html)
to multiple players.
The main difference between simulating two players and multiple players
is in revising the schedule for when each agent speaks
To this end, we augment DialogueSimulator to take in a custom function
that determines the schedule of which agent speaks. In the example
below, each character speaks in round-robin fashion, with the
storyteller interleaved between each player.
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.
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.
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.
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.
Now it is hard to search for the integration points between
data_loaders, retrievers, tools, etc.
I've placed links to all groups of providers and integrations on the
`ecosystem` page.
So, it is easy to navigate between all integrations from a single
location.
Improvements
* set default num_workers for ingestion to 0
* upgraded notebooks for avoiding dataset creation ambiguity
* added `force_delete_dataset_by_path`
* bumped deeplake to 3.3.0
* creds arg passing to deeplake object that would allow custom S3
Notes
* please double check if poetry is not messed up (thanks!)
Asks
* Would be great to create a shared slack channel for quick questions
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
The detailed walkthrough of the Weaviate wrapper was pointing to the
getting-started notebook. Fixed it to point to the Weaviable notebook in
the examples folder.
This pull request adds a ChatGPT document loader to the document loaders
module in `langchain/document_loaders/chatgpt.py`. Additionally, it
includes an example Jupyter notebook in
`docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
which uses fake sample data based on the original structure of the
`conversations.json` file.
The following files were added/modified:
- `langchain/document_loaders/__init__.py`
- `langchain/document_loaders/chatgpt.py`
- `docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
-
`docs/modules/indexes/document_loaders/examples/example_data/fake_conversations.json`
This pull request was made in response to the recent release of ChatGPT
data exports by email:
https://help.openai.com/en/articles/7260999-how-do-i-export-my-chatgpt-history
Hi there！
I'm excited to open this PR to add support for using a fully Postgres
syntax compatible database 'AnalyticDB' as a vector.
As AnalyticDB has been proved can be used with AutoGPT,
ChatGPT-Retrieve-Plugin, and LLama-Index, I think it is also good for
you.
AnalyticDB is a distributed Alibaba Cloud-Native vector database. It
works better when data comes to large scale. The PR includes:
- [x] A new memory: AnalyticDBVector
- [x] A suite of integration tests verifies the AnalyticDB integration
I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
