@agola11
Issue
#6193
I added the new pricing for the new models.
Also, now gpt-3.5-turbo got split into "input" and "output" pricing. It
currently does not support that.
can't pass system_message argument, the prompt always show default
message "System: You are a helpful AI assistant."
```
system_message = SystemMessage(
content="You are an AI that provides information to Human regarding documentation."
)
agent = initialize_agent(
tools,
llm=openai_llm_chat,
agent=AgentType.OPENAI_FUNCTIONS,
system_message=system_message,
agent_kwargs={
"system_message": system_message,
},
verbose=False,
)
```
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To bypass SSL verification errors during fetching, you can include the
`verify=False` parameter. This markdown proves useful, especially for
beginners in the field of web scraping.
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Fixes#6079
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@eyurtsev
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
To bypass SSL verification errors during web scraping, you can include
the ssl_verify=False parameter along with the headers parameter. This
combination of arguments proves useful, especially for beginners in the
field of web scraping.
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Fixes#1829
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Hi, I make a small improvement for BaseOpenAI.
I added a max_context_size attribute to BaseOpenAI so that we can get
the max context size directly instead of only getting the maximum token
size of the prompt through the max_tokens_for_prompt method.
Who can review?
@hwchase17 @agola11
I followed the [Common
Tasks](c7db9febb0/.github/CONTRIBUTING.md),
the test is all passed.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
LLM configurations can be loaded from a Python dict (or JSON file
deserialized as dict) using the
[load_llm_from_config](8e1a7a8646/langchain/llms/loading.py (L12))
function.
However, the type string in the `type_to_cls_dict` lookup dict differs
from the type string defined in some LLM classes. This means that the
LLM object can be saved, but not loaded again, because the type strings
differ.
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The current version of chat history with DynamoDB doesn't handle the
case correctly when a table has no chat history. This change solves this
error handling.
<!-- Remove if not applicable -->
Fixes https://github.com/hwchase17/langchain/issues/6088
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Fixes#6131
Simply passes kwargs forward from similarity_search to helper functions
so that search_kwargs are applied to search as originally intended. See
bug for repro steps.
#### Who can review?
@hwchase17
@dev2049
Twitter: poshporcupine
Very small typo in the Constitutional AI critique default prompt. The
negation "If there is *no* material critique of ..." is used two times,
should be used only on the first one.
Cheers,
Pierre
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Fixes https://github.com/hwchase17/langchain/issues/6208
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Fixes # (issue)
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Hot Fixes for Deep Lake [would highly appreciate expedited review]
* deeplake version was hardcoded and since deeplake upgraded the
integration fails with confusing error
* an additional integration test fixed due to embedding function
* Additionally fixed docs for code understanding links after docs
upgraded
* notebook removal of public parameter to make sure code understanding
notebook works
#### Who can review?
@hwchase17 @dev2049
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
Fixes#5807 (issue)
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Related to this https://github.com/hwchase17/langchain/issues/6225
Just copied the implementation from `generate` function to `agenerate`
and tested it.
Didn't run any official tests thought
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Fixes#6225
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The LLM integration
[HuggingFaceTextGenInference](https://github.com/hwchase17/langchain/blob/master/langchain/llms/huggingface_text_gen_inference.py)
already has streaming support.
However, when streaming is enabled, it always returns an empty string as
the final output text when the LLM is finished. This is because `text`
is instantiated with an empty string and never updated.
This PR fixes the collection of the final output text by concatenating
new tokens.
Similar as https://github.com/hwchase17/langchain/pull/5818
Added the functionality to save/load Graph Cypher QA Chain due to a user
reporting the following error
> raise NotImplementedError("Saving not supported for this chain
type.")\nNotImplementedError: Saving not supported for this chain
type.\n'
In LangChain, all module classes are enumerated in the `__init__.py`
file of the correspondent module. But some classes were missed and were
not included in the module `__init__.py`
This PR:
- added the missed classes to the module `__init__.py` files
- `__init__.py:__all_` variable value (a list of the class names) was
sorted
- `langchain.tools.sql_database.tool.QueryCheckerTool` was renamed into
the `QuerySQLCheckerTool` because it conflicted with
`langchain.tools.spark_sql.tool.QueryCheckerTool`
- changes to `pyproject.toml`:
- added `pgvector` to `pyproject.toml:extended_testing`
- added `pandas` to
`pyproject.toml:[tool.poetry.group.test.dependencies]`
- commented out the `streamlit` from `collbacks/__init__.py`, It is
because now the `streamlit` requires Python >=3.7, !=3.9.7
- fixed duplicate names in `tools`
- fixed correspondent ut-s
#### Who can review?
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@dev2049
Fixed PermissionError that occurred when downloading PDF files via http
in BasePDFLoader on windows.
When downloading PDF files via http in BasePDFLoader, NamedTemporaryFile
is used.
This function cannot open the file again on **Windows**.[Python
Doc](https://docs.python.org/3.9/library/tempfile.html#tempfile.NamedTemporaryFile)
So, we created a **temporary directory** with TemporaryDirectory and
placed the downloaded file there.
temporary directory is deleted in the deconstruct.
Fixes#2698
#### Who can review?
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- @eyurtsev
- @hwchase17
This will add the ability to add an AsyncCallbackManager (handler) for
the reducer chain, which would be able to stream the tokens via the
`async def on_llm_new_token` callback method
Fixes # (issue)
[5532](https://github.com/hwchase17/langchain/issues/5532)
@hwchase17 @agola11
The following code snippet explains how this change would be used to
enable `reduce_llm` with streaming support in a `map_reduce` chain
I have tested this change and it works for the streaming use-case of
reducer responses. I am happy to share more information if this makes
solution sense.
```
AsyncHandler
..........................
class StreamingLLMCallbackHandler(AsyncCallbackHandler):
"""Callback handler for streaming LLM responses."""
def __init__(self, websocket):
self.websocket = websocket
# This callback method is to be executed in async
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
resp = ChatResponse(sender="bot", message=token, type="stream")
await self.websocket.send_json(resp.dict())
Chain
..........
stream_handler = StreamingLLMCallbackHandler(websocket)
stream_manager = AsyncCallbackManager([stream_handler])
streaming_llm = ChatOpenAI(
streaming=True,
callback_manager=stream_manager,
verbose=False,
temperature=0,
)
main_llm = OpenAI(
temperature=0,
verbose=False,
)
doc_chain = load_qa_chain(
llm=main_llm,
reduce_llm=streaming_llm,
chain_type="map_reduce",
callback_manager=manager
)
qa_chain = ConversationalRetrievalChain(
retriever=vectorstore.as_retriever(),
combine_docs_chain=doc_chain,
question_generator=question_generator,
callback_manager=manager,
)
# Here `acall` will trigger `acombine_docs` on `map_reduce` which should then call `_aprocess_result` which in turn will call `self.combine_document_chain.arun` hence async callback will be awaited
result = await qa_chain.acall(
{"question": question, "chat_history": chat_history}
)
```
Hi again @agola11! 🤗
## What's in this PR?
After playing around with different chains we noticed that some chains
were using different `output_key`s and we were just handling some, so
we've extended the support to any output, either if it's a Python list
or a string.
Kudos to @dvsrepo for spotting this!
---------
Co-authored-by: Daniel Vila Suero <daniel@argilla.io>
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Fixes https://github.com/ShreyaR/guardrails/issues/155
Enables guardrails reasking by specifying an LLM api in the output
parser.
skip building preview of docs for anything branch that doesn't start
with `__docs__`. will eventually update to look at code diff directories
but patching for now
We propose an enhancement to the web-based loader initialize method by
introducing a "verify" option. This enhancement addresses the issue of
SSL verification errors encountered on certain web pages. By providing
users with the option to set the verify parameter to False, we offer
greater flexibility and control.
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### Fixes#6079
#### Who can review?
@eyurtsev @hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Fixes # (issue)
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[Feature] User can custom the Anthropic API URL
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Models
- @hwchase17
- @agola11
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Added support to `search_by_vector` to Qdrant Vector store.
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@eyurtsev
The existing GoogleDrive implementation always needs a service account
to be available at the credentials location. When running on GCP
services such as Cloud Run, a service account already exists in the
metadata of the service, so no physical key is necessary. This change
adds a check to see if it is running in such an environment, and uses
that authentication instead.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Add oobabooga/text-generation-webui support as an LLM. Currently,
supports using text-generation-webui's non-streaming API interface.
Allows users who already have text-gen running to use the same models
with langchain.
#### Before submitting
Simple usage, similar to existing LLM supported:
```
from langchain.llms import TextGen
llm = TextGen(model_url = "http://localhost:5000")
```
#### Who can review?
@hwchase17 - project lead
---------
Co-authored-by: Hien Ngo <Hien.Ngo@adia.ae>
