Currently `langchain.agents.agent_toolkits.SQLDatabaseToolkit` has a
field `llm` with type `BaseLLM`. This breaks initialization for some
LLMs. For example, trying to use it with GPT4:
```
from langchain.sql_database import SQLDatabase
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
db = SQLDatabase.from_uri("some_db_uri")
llm = ChatOpenAI(model_name="gpt-4")
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
# pydantic.error_wrappers.ValidationError: 1 validation error for SQLDatabaseToolkit
# llm
# Can't instantiate abstract class BaseLLM with abstract methods _agenerate, _generate, _llm_type (type=type_error)
```
Seems like much of the rest of the codebase has switched from BaseLLM to
BaseLanguageModel. This PR makes the change for SQLDatabaseToolkit as
well
In the current solution, AgentType and AGENT_TO_CLASS are placed in two
separate files and both manually maintained. This might cause
inconsistency when we update either of them.
— latest —
based on the discussion with hwchase17, we don’t know how to further use
the newly introduced AgentTypeConfig type, so it doesn’t make sense yet
to add it. Instead, it’s better to move the dictionary to another file
to keep the loading.py file clear. The consistency is a good point.
Instead of asserting the consistency during linting, we added a unittest
for consistency check. I think it works as auto unittest is triggered
every time with clear failure notice. (well, force push is possible, but
we all know what we are doing, so let’s show trust. :>)
~~This PR includes~~
- ~~Introduced AgentTypeConfig as the source of truth of all AgentType
related meta data.~~
- ~~Each AgentTypeConfig is a annotated class type which can be used for
annotation in other places.~~
- ~~Each AgentTypeConfig can be easily extended when we have more meta
data needs.~~
- ~~Strong assertion to ensure AgentType and AGENT_TO_CLASS are always
consistent.~~
- ~~Made AGENT_TO_CLASS automatically generated.~~
~~Test Plan:~~
- ~~since this change is focusing on annotation, lint is the major test
focus.~~
- ~~lint, format and test passed on local.~~
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>
The character code mismatches occurred when character information was
not included in the response header (In my case, a Japanese web page).
I solved this issue by changing the encoding setting to
apparent_encoding.
This PR makes the `"\n\n"` string with which `StuffDocumentsChain` joins
formatted documents a property so it can be configured. The new
`document_separator` property defaults to `"\n\n"` so the change is
backwards compatible.
During the import of langchain, SQLAlchemy was throeing an errror
`ImportError: cannot import name 'Mapped' from 'sqlalchemy.orm'`. This
is becaue the Mapped name was introduced in v1.4
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}
```
At the moment all content in Confluence is retrieved by default,
including archived content.
Often, this is undesired as the content is not relevant anymore.
**Notes**
Fetching pages by label does not support excluding archived content.
This may lead to unexpected results.
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)).
This **partially** addresses
https://github.com/hwchase17/langchain/issues/1524, but it's also useful
for some of our use cases.
This `DocstoreFn` allows to lookup a document given a function that
accepts the `search` string without the need to implement a custom
`Docstore`.
This could be useful when:
* you don't want to implement a `Docstore` just to provide a custom
`search`
* it's expensive to construct an `InMemoryDocstore`/dict
* you retrieve documents from remote sources
* you just want to reuse existing objects
- 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.)
