begining -> beginning
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Fix Issue #7616 with a simpler approach to extract function names (use
`__name__` attribute)
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixes for #8786 @agola11
- Description: The flow of callback is breaking till the last chain, as
callbacks are missed in between chain along nested path. This will help
get full trace and correlate parent child relationship in all nested
chains.
- Issue: the issue #8786
- Dependencies: NA
- Tag maintainer: @agola11
- Twitter handle: Agarwal_Ankur
Description: When using a ReAct Agent with tools and no tool is found,
the InvalidTool gets called. Previously it just asked for a different
action, but I've found that if you list the available actions it
improves the chances of getting a valid action in the next round. I've
added a UnitTest for it also.
@hinthornw
# What
- Add missing test for retrievers self_query
- Add missing import validation
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- Issue: None
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- Twitter handle: @MlopsJ
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- Description: 2 links were not working on Question Answering Use Cases
documentation page. Hence, changed them to nearest useful links,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @baskaryan,
- Twitter handle: NA
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- Description: we expose Kendra result item id and document id as
document metadata.
- Tag maintainer: @3coins @baskaryan
- Twitter handle: wilsonleao
**Why**
The result item id and document id might be used to keep track of the
retrieved resources.
Refactor for the extraction use case documentation
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Added a couple of "integration tests" for these that I ran.
Main design point of feedback: at this point, would it just be better to
have separate arguments for each type? Little confusing what is or isn't
supported and what is the intended usage at this point since I try to
wrap the function as runnable or pack or unpack chains/llms.
```
run_on_dataset(
...
llm_or_chain_factory = None,
llm = None,
chain = NOne,
runnable=None,
function=None
):
# raise error if none set
```
Downside with runnables and arbitrary function support is that you get
much less helpful validation and error messages, but I don't think we
should block you from this, at least.
* Documentation to favor creation without declaring input_variables
* Cut out obvious examples, but add more description in a few places
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Update API reference documentation. This PR will pick up a number of missing classes, it also applies selective formatting based on the class / object type.
Description: Adding support for [Amazon
Textract](https://aws.amazon.com/textract/) as a PDF document loader
---------
Co-authored-by: schadem <45048633+schadem@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Resolves occasional JSON parsing error when some predictions are passed
through a `MultiPromptChain`.
Makes [this
modification](https://github.com/langchain-ai/langchain/issues/5163#issuecomment-1652220401)
to `multi_prompt_prompt.py`, which is much cleaner than appending an
entire example object, which is another community-reported solution.
@hwchase17, @baskaryan
cc: @SimasJan
- Description: Added a missing word and rearranged a sentence in the
documentation of Self Query Retrievers.,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @baskaryan,
- Twitter handle: NA
Thanks for your time.
llamacpp params (per their own code) are unstable, so instead of
adding/deleting them constantly adding a model_kwargs parameter that
allows for arbitrary additional kwargs
cc @jsjolund and @zacps re #8599 and #8704
There is already a `loads()` function which takes a JSON string and
loads it using the Reviver
But in the callbacks system, there is a `serialized` object that is
passed in and that object is already a deserialized JSON-compatible
object. This allows you to call `load(serialized)` and bypass
intermediate JSON encoding.
I found one other place in the code that benefited from this
short-circuiting (string_run_evaluator.py) so I fixed that too.
Tagging @baskaryan for general/utility stuff.
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---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Description: Add ScaNN vectorstore to langchain.
ScaNN is a Open Source, high performance vector similarity library
optimized for AVX2-enabled CPUs.
https://github.com/google-research/google-research/tree/master/scann
- Dependencies: scann
Python notebook to illustrate the usage:
docs/extras/integrations/vectorstores/scann.ipynb
Integration test:
libs/langchain/tests/integration_tests/vectorstores/test_scann.py
@rlancemartin, @eyurtsev for review.
Thanks!
This PR updates _load_reduce_documents_chain to handle
`reduce_documents_chain` and `combine_documents_chain` config
Please review @hwchase17, @baskaryan
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# What
- This is to add filter option to sklearn vectore store functions
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This is to add save_local and load_local to tfidf_vectorizer and docs in
tfidf_retriever to make the vectorizer reusable.
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Removing score threshold parameter of faiss
_similarity_search_with_relevance_scores as the thresholding part is
implemented in similarity_search_with_relevance_scores method which
calls this method.
As this method is supposed to be a private method of faiss.py this will
never receive the score threshold parameter as it is popped in the super
method similarity_search_with_relevance_scores.
@baskaryan @hwchase17
Just a tiny change to use `list.append(...)` and `list.extend(...)`
instead of `list += [...]` so that no unnecessary temporary lists are
created.
Since its a tiny miscellaneous thing I guess @baskaryan is the
maintainer to tag?
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Simple retriever that applies an LLM between the user input and the
query pass the to retriever.
It can be used to pre-process the user input in any way.
The default prompt:
```
DEFAULT_QUERY_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an assistant tasked with taking a natural languge query from a user
and converting it into a query for a vectorstore. In this process, you strip out
information that is not relevant for the retrieval task. Here is the user query: {question} """
)
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
