I have added a reddit document loader which fetches the text from the
Posts of Subreddits or Reddit users, using the `praw` Python package. I
have also added an example notebook reddit.ipynb in order to guide users
to use this dataloader.
This code was made in format similar to twiiter document loader. I have
run code formating, linting and also checked the code myself for
different scenarios.
This is my first contribution to an open source project and I am really
excited about this. If you want to suggest some improvements in my code,
I will be happy to do it. :)
Co-authored-by: Taaha Bajwa <taaha.s.bajwa@gmail.com>
This PR includes some minor alignment updates, including:
- metadata object extended to support contractAddress, blockchainType,
and tokenId
- notebook doc better aligned to standard langchain format
- startToken changed from int to str to support multiple hex value types
on the Alchemy API
The updated metadata will look like the below. It's possible for a
single contractAddress to exist across multiple blockchains (e.g.
Ethereum, Polygon, etc.) so it's important to include the
blockchainType.
```
metadata = {"source": self.contract_address,
"blockchain": self.blockchainType,
"tokenId": tokenId}
```
- Added links to the vectorstore providers
- Added installation code (it is not clear that we have to go to the
`LangChan Ecosystem` page to get installation instructions.)
Add other File Utilities, include
- List Directory
- Search for file
- Move
- Copy
- Remove file
Bundle as toolkit
Add a notebook that connects to the Chat Agent, which somewhat supports
multi-arg input tools
Update original read/write files to return the original dir paths and
better handle unsupported file paths.
Add unit tests
Adds a PlayWright web browser toolkit with the following tools:
- NavigateTool (navigate_browser) - navigate to a URL
- NavigateBackTool (previous_page) - wait for an element to appear
- ClickTool (click_element) - click on an element (specified by
selector)
- ExtractTextTool (extract_text) - use beautiful soup to extract text
from the current web page
- ExtractHyperlinksTool (extract_hyperlinks) - use beautiful soup to
extract hyperlinks from the current web page
- GetElementsTool (get_elements) - select elements by CSS selector
- CurrentPageTool (current_page) - get the current page URL
Alternate implementation of #3452 that relies on a generic query
constructor chain and language and then has vector store-specific
translation layer. Still refactoring and updating examples but general
structure is there and seems to work s well as #3452 on exampels
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR
* Adds `clear` method for `BaseCache` and implements it for various
caches
* Adds the default `init_func=None` and fixes gptcache integtest
* Since right now integtest is not running in CI, I've verified the
changes by running `docs/modules/models/llms/examples/llm_caching.ipynb`
(until proper e2e integtest is done in CI)
It makes sense to use `arxiv` as another source of the documents for
downloading.
- Added the `arxiv` document_loader, based on the
`utilities/arxiv.py:ArxivAPIWrapper`
- added tests
- added an example notebook
- sorted `__all__` in `__init__.py` (otherwise it is hard to find a
class in the very long list)
Tools for Bing, DDG and Google weren't consistent even though the
underlying implementations were.
All three services now have the same tools and implementations to easily
switch and experiment when building chains.
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.
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.
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.
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>
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
First cut of a supabase vectorstore loosely patterned on the langchainjs
equivalent. Doesn't support async operations which is a limitation of
the supabase python client.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
I have noticed a typo error in the `custom_mrkl_agents.ipynb` document
while trying the example from the documentation page. As a result, I
have opened a pull request (PR) to address this minor issue, even though
it may seem insignificant 😂.
The following calls were throwing an exception:
575b717d10/docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb (L192)575b717d10/docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb (L239)
Exception:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[14], line 1
----> 1 chain_sota = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0), chain_type="stuff", retriever=vectorstore_sota, input_key="question")
File ~/github/langchain/venv/lib/python3.9/site-packages/langchain/chains/retrieval_qa/base.py:89, in BaseRetrievalQA.from_chain_type(cls, llm, chain_type, chain_type_kwargs, **kwargs)
85 _chain_type_kwargs = chain_type_kwargs or {}
86 combine_documents_chain = load_qa_chain(
87 llm, chain_type=chain_type, **_chain_type_kwargs
88 )
---> 89 return cls(combine_documents_chain=combine_documents_chain, **kwargs)
File ~/github/langchain/venv/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for RetrievalQA
retriever
instance of BaseRetriever expected (type=type_error.arbitrary_type; expected_arbitrary_type=BaseRetriever)
```
The vectorstores had to be converted to retrievers:
`vectorstore_sota.as_retriever()` and `vectorstore_pg.as_retriever()`.
The PR also:
- adds the file `paul_graham_essay.txt` referenced by this notebook
- adds to gitignore *.pkl and *.bin files that are generated by this
notebook
Interestingly enough, the performance of the prediction greatly
increased (new version of langchain or ne version of OpenAI models since
the last run of the notebook): from 19/33 correct to 28/33 correct!
- Remove dynamic model creation in the `args()` property. _Only infer
for the decorator (and add an argument to NOT infer if someone wishes to
only pass as a string)_
- Update the validation example to make it less likely to be
misinterpreted as a "safe" way to run a repl
There is one example of "Multi-argument tools" in the custom_tools.ipynb
from yesterday, but we could add more. The output parsing for the base
MRKL agent hasn't been adapted to handle structured args at this point
in time
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
## Use `index_id` over `app_id`
We made a major update to index + retrieve based on Metal Indexes
(instead of apps). With this change, we accept an index instead of an
app in each of our respective core apis. [More details
here](https://docs.getmetal.io/api-reference/core/indexing).
## What is this PR for:
* This PR adds a commented line of code in the documentation that shows
how someone can use the Pinecone client with an already existing
Pinecone index
* The documentation currently only shows how to create a pinecone index
from langchain documents but not how to load one that already exists
- Updated `langchain/docs/modules/models/llms/integrations/` notebooks:
added links to the original sites, the install information, etc.
- Added the `nlpcloud` notebook.
- Removed "Example" from Titles of some notebooks, so all notebook
titles are consistent.
### https://github.com/hwchase17/langchain/issues/2997
Replaced `conversation.memory.store` to
`conversation.memory.entity_store.store`
As conversation.memory.store doesn't exist and re-ran the whole file.
Add a time-weighted memory retriever and a notebook that approximates a
Generative Agent from https://arxiv.org/pdf/2304.03442.pdf
The "daily plan" components are removed for now since they are less
useful without a virtual world, but the memory is an interesting
component to build off.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
