[Text Generation
Inference](https://github.com/huggingface/text-generation-inference) is
a Rust, Python and gRPC server for generating text using LLMs.
This pull request add support for self hosted Text Generation Inference
servers.
feature: #4280
---------
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Rebased Mahmedk's PR with the callback refactor and added the example
requested by hwchase plus a couple minor fixes
---------
Co-authored-by: Ahmed K <77802633+mahmedk@users.noreply.github.com>
Co-authored-by: Ahmed K <mda3k27@gmail.com>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Corey Zumar <39497902+dbczumar@users.noreply.github.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
We're fans of the LangChain framework thus we wanted to make sure we
provide an easy way for our customers to be able to utilize this
framework for their LLM-powered applications at our platform.
# Add option to `load_huggingface_tool`
Expose a method to load a huggingface Tool from the HF hub
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Thanks to @anna-charlotte and @jupyterjazz for the contribution! Made
few small changes to get it across the finish line
---------
Signed-off-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Co-authored-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: Saba Sturua <45267439+jupyterjazz@users.noreply.github.com>
# ODF File Loader
Adds a data loader for handling Open Office ODT files. Requires
`unstructured>=0.6.3`.
### Testing
The following should work using the `fake.odt` example doc from the
[`unstructured` repo](https://github.com/Unstructured-IO/unstructured).
```python
from langchain.document_loaders import UnstructuredODTLoader
loader = UnstructuredODTLoader(file_path="fake.odt", mode="elements")
loader.load()
loader = UnstructuredODTLoader(file_path="fake.odt", mode="single")
loader.load()
```
- added `Wikipedia` retriever. It is effectively a wrapper for
`WikipediaAPIWrapper`. It wrapps load() into get_relevant_documents()
- sorted `__all__` in the `retrievers/__init__`
- added integration tests for the WikipediaRetriever
- added an example (as Jupyter notebook) for the WikipediaRetriever
# Minor Wording Documentation Change
```python
agent_chain.run("When's my friend Eric's surname?")
# Answer with 'Zhu'
```
is change to
```python
agent_chain.run("What's my friend Eric's surname?")
# Answer with 'Zhu'
```
I think when is a residual of the old query that was "When’s my friends
Eric`s birthday?".
# Fix grammar in Text Splitters docs
Just a small fix of grammar in the documentation:
"That means there two different axes" -> "That means there are two
different axes"
Related: #4028, I opened a new PR because (1) I was unable to unstage
mistakenly committed files (I'm not familiar with git enough to resolve
this issue), (2) I felt closing the original PR and opening a new PR
would be more appropriate if I changed the class name.
This PR creates HumanInputLLM(HumanLLM in #4028), a simple LLM wrapper
class that returns user input as the response. I also added a simple
Jupyter notebook regarding how and why to use this LLM wrapper. In the
notebook, I went over how to use this LLM wrapper and showed example of
testing `WikipediaQueryRun` using HumanInputLLM.
I believe this LLM wrapper will be useful especially for debugging,
educational or testing purpose.
- Added the `Wikipedia` document loader. It is based on the existing
`unilities/WikipediaAPIWrapper`
- Added a respective ut-s and example notebook
- Sorted list of classes in __init__
- made notebooks consistent: titles, service/format descriptions.
- corrected short names to full names, for example, `Word` -> `Microsoft
Word`
- added missed descriptions
- renamed notebook files to make ToC correctly sorted
This implements a loader of text passages in JSON format. The `jq`
syntax is used to define a schema for accessing the relevant contents
from the JSON file. This requires dependency on the `jq` package:
https://pypi.org/project/jq/.
---------
Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>
In the example for creating a Python REPL tool under the Agent module,
the ".run" was omitted in the example. I believe this is required when
defining a Tool.
In the section `Get Message Completions from a Chat Model` of the quick
start guide, the HumanMessage doesn't need to include `Translate this
sentence from English to French.` when there is a system message.
Simplify HumanMessages in these examples can further demonstrate the
power of LLM.
* implemented arun, results, and aresults. Reuses aiosession if
available.
* helper tools GoogleSerperRun and GoogleSerperResults
* support for Google Images, Places and News (examples given) and
filtering based on time (e.g. past hour)
* updated docs
Google Scholar outputs a nice list of scientific and research articles
that use LangChain.
I added a link to the Google Scholar page to the `gallery` doc page
Single edit to: models/text_embedding/examples/openai.ipynb - Line 88:
changed from: "embeddings = OpenAIEmbeddings(model_name=\"ada\")" to
"embeddings = OpenAIEmbeddings()" as model_name is no longer part of the
OpenAIEmbeddings class.
Seems the pyllamacpp package is no longer the supported bindings from
gpt4all. Tested that this works locally.
Given that the older models weren't very performant, I think it's better
to migrate now without trying to include a lot of try / except blocks
---------
Co-authored-by: Nissan Pow <npow@users.noreply.github.com>
Co-authored-by: Nissan Pow <pownissa@amazon.com>
### Summary
Adds `UnstructuredAPIFileLoaders` and `UnstructuredAPIFIleIOLoaders`
that partition documents through the Unstructured API. Defaults to the
URL for hosted Unstructured API, but can switch to a self hosted or
locally running API using the `url` kwarg. Currently, the Unstructured
API is open and does not require an API, but it will soon. A note was
added about that to the Unstructured ecosystem page.
### Testing
```python
from langchain.document_loaders import UnstructuredAPIFileIOLoader
filename = "fake-email.eml"
with open(filename, "rb") as f:
loader = UnstructuredAPIFileIOLoader(file=f, file_filename=filename)
docs = loader.load()
docs[0]
```
```python
from langchain.document_loaders import UnstructuredAPIFileLoader
filename = "fake-email.eml"
loader = UnstructuredAPIFileLoader(file_path=filename, mode="elements")
docs = loader.load()
docs[0]
```
Modified Modern Treasury and Strip slightly so credentials don't have to
be passed in explicitly. Thanks @mattgmarcus for adding Modern Treasury!
---------
Co-authored-by: Matt Marcus <matt.g.marcus@gmail.com>
Haven't gotten to all of them, but this:
- Updates some of the tools notebooks to actually instantiate a tool
(many just show a 'utility' rather than a tool. More changes to come in
separate PR)
- Move the `Tool` and decorator definitions to `langchain/tools/base.py`
(but still export from `langchain.agents`)
- Add scene explain to the load_tools() function
- Add unit tests for public apis for the langchain.tools and langchain.agents modules
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}
```
For many applications of LLM agents, the environment is real (internet,
database, REPL, etc). However, we can also define agents to interact in
simulated environments like text-based games. This is an example of how
to create a simple agent-environment interaction loop with
[Gymnasium](https://github.com/Farama-Foundation/Gymnasium) (formerly
[OpenAI Gym](https://github.com/openai/gym)).
- 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
This notebook showcases how to implement a multi-agent simulation where
a privileged agent decides who to speak.
This follows the polar opposite selection scheme as [multi-agent
decentralized speaker
selection](https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html).
We show an example of this approach in the context of a fictitious
simulation of a news network. This example will showcase how we can
implement agents that
- think before speaking
- terminate the conversation
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)
This notebook showcases how to implement a multi-agent simulation
without a fixed schedule for who speaks when. Instead the agents decide
for themselves who speaks. We can implement this by having each agent
bid to speak. Whichever agent's bid is the highest gets to speak.
We will show how to do this in the example below that showcases a
fictitious presidential debate.
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.
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
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.
- Most important - fixes the relevance_fn name in the notebook to align
with the docs
- Updates comments for the summary:
<img width="787" alt="image"
src="https://user-images.githubusercontent.com/130414180/232520616-2a99e8c3-a821-40c2-a0d5-3f3ea196c9bb.png">
- The new conversation is a bit better, still unfortunate they try to
schedule a followup.
- Rm the max dialogue turns argument to the conversation function
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>
Use numexpr evaluate instead of the python REPL to avoid malicious code
injection.
Tested against the (limited) math dataset and got the same score as
before.
For more permissive tools (like the REPL tool itself), other approaches
ought to be provided (some combination of Sanitizer + Restricted python
+ unprivileged-docker + ...), but for a calculator tool, only
mathematical expressions should be permitted.
See https://github.com/hwchase17/langchain/issues/814
Last week I added the `PDFMinerPDFasHTMLLoader`. I am adding some
example code in the notebook to serve as a tutorial for how that loader
can be used to create snippets of a pdf that are structured within
sections. All the other loaders only provide the `Document` objects
segmented by pages but that's pretty loose given the amount of other
metadata that can be extracted.
With the new loader, one can leverage font-size of the text to decide
when a new sections starts and can segment the text more semantically as
shown in the tutorial notebook. The cell shows that we are able to find
the content of entire section under **Related Work** for the example pdf
which is spread across 2 pages and hence is stored as two separate
documents by other loaders
Add SVM retriever class, based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb.
Testing still WIP, but the logic is correct (I have a local
implementation outside of Langchain working).
---------
Co-authored-by: Lance Martin <122662504+PineappleExpress808@users.noreply.github.com>
Co-authored-by: rlm <31treehaus@31s-MacBook-Pro.local>
