"One Retriever to merge them all, One Retriever to expose them, One
Retriever to bring them all and in and process them with Document
formatters."
Hi @dev2049! Here bothering people again!
I'm using this simple idea to deal with merging the output of several
retrievers into one.
I'm aware of DocumentCompressorPipeline and
ContextualCompressionRetriever but I don't think they allow us to do
something like this. Also I was getting in trouble to get the pipeline
working too. Please correct me if i'm wrong.
This allow to do some sort of "retrieval" preprocessing and then using
the retrieval with the curated results anywhere you could use a
retriever.
My use case is to generate diff indexes with diff embeddings and sources
for a more colorful results then filtering them with one or many
document formatters.
I saw some people looking for something like this, here:
https://github.com/hwchase17/langchain/issues/3991
and something similar here:
https://github.com/hwchase17/langchain/issues/5555
This is just a proposal I know I'm missing tests , etc. If you think
this is a worth it idea I can work on tests and anything you want to
change.
Let me know!
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR adds the possibility of specifying the endpoint URL to AWS in
the DynamoDBChatMessageHistory, so that it is possible to target not
only the AWS cloud services, but also a local installation.
Specifying the endpoint URL, which is normally not done when addressing
the cloud services, is very helpful when targeting a local instance
(like [Localstack](https://localstack.cloud/)) when running local tests.
Fixes#5835
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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#### Add start index to metadata in TextSplitter
- Modified method `create_documents` to track start position of each
chunk
- The `start_index` is included in the metadata if the `add_start_index`
parameter in the class constructor is set to `True`
This enables referencing back to the original document, particularly
useful when a specific chunk is retrieved.
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This PR adds a Baseten integration. I've done my best to follow the
contributor's guidelines and add docs, an example notebook, and an
integration test modeled after similar integrations' test.
Please let me know if there is anything I can do to improve the PR. When
it is merged, please tag https://twitter.com/basetenco and
https://twitter.com/philip_kiely as contributors (the note on the PR
template said to include Twitter accounts)
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---------
Co-authored-by: rlm <pexpresss31@gmail.com>
# Your PR Title (What it does)
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Fixes # (issue)
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- Added `SingleStoreDB` vector store, which is a wrapper over the
SingleStore DB database, that can be used as a vector storage and has an
efficient similarity search.
- Added integration tests for the vector store
- Added jupyter notebook with the example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Simply fixing a small typo in the memory page.
Also removed an extra code block at the end of the file.
Along the way, the current outputs seem to have changed in a few places
so left that for posterity, and updated the number of runs which seems
harmless, though I can clean that up if preferred.
### Summary
Adds an `UnstructuredCSVLoader` for loading CSVs. One advantage of using
`UnstructuredCSVLoader` relative to the standard `CSVLoader` is that if
you use `UnstructuredCSVLoader` in `"elements"` mode, an HTML
representation of the table will be available in the metadata.
#### Who can review?
@hwchase17
@eyurtsev
Hi! I just added an example of how to use a custom scraping function
with the sitemap loader. I recently used this feature and had to dig in
the source code to find it. I thought it might be useful to other devs
to have an example in the Jupyter Notebook directly.
I only added the example to the documentation page.
@eyurtsev I was not able to run the lint. Please let me know if I have
to do anything else.
I know this is a very small contribution, but I hope it will be
valuable. My Twitter handle is @web3Dav3.
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---------
Co-authored-by: Yessen Kanapin <yessen@deepinfra.com>
just change "to" to "too" so it matches the above prompt
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Fixes # (issue)
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This introduces the `YoutubeAudioLoader`, which will load blobs from a
YouTube url and write them. Blobs are then parsed by
`OpenAIWhisperParser()`, as show in this
[PR](https://github.com/hwchase17/langchain/pull/5580), but we extend
the parser to split audio such that each chuck meets the 25MB OpenAI
size limit. As shown in the notebook, this enables a very simple UX:
```
# Transcribe the video to text
loader = GenericLoader(YoutubeAudioLoader([url],save_dir),OpenAIWhisperParser())
docs = loader.load()
```
Tested on full set of Karpathy lecture videos:
```
# Karpathy lecture videos
urls = ["https://youtu.be/VMj-3S1tku0"
"https://youtu.be/PaCmpygFfXo",
"https://youtu.be/TCH_1BHY58I",
"https://youtu.be/P6sfmUTpUmc",
"https://youtu.be/q8SA3rM6ckI",
"https://youtu.be/t3YJ5hKiMQ0",
"https://youtu.be/kCc8FmEb1nY"]
# Directory to save audio files
save_dir = "~/Downloads/YouTube"
# Transcribe the videos to text
loader = GenericLoader(YoutubeAudioLoader(urls,save_dir),OpenAIWhisperParser())
docs = loader.load()
```
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In the [Databricks
integration](https://python.langchain.com/en/latest/integrations/databricks.html)
and [Databricks
LLM](https://python.langchain.com/en/latest/modules/models/llms/integrations/databricks.html),
we suggestted users to set the ENV variable `DATABRICKS_API_TOKEN`.
However, this is inconsistent with the other Databricks library. To make
it consistent, this PR changes the variable from `DATABRICKS_API_TOKEN`
to `DATABRICKS_TOKEN`
After changes, there is no more `DATABRICKS_API_TOKEN` in the doc
```
$ git grep DATABRICKS_API_TOKEN|wc -l
0
$ git grep DATABRICKS_TOKEN|wc -l
8
```
cc @hwchase17 @dev2049 @mengxr since you have reviewed the previous PRs.
# Scores in Vectorestores' Docs Are Explained
Following vectorestores can return scores with similar documents by
using `similarity_search_with_score`:
- chroma
- docarray_hnsw
- docarray_in_memory
- faiss
- myscale
- qdrant
- supabase
- vectara
- weaviate
However, in documents, these scores were either not explained at all or
explained in a way that could lead to misunderstandings (e.g., FAISS).
For instance in FAISS document: if we consider the score returned by the
function as a similarity score, we understand that a document returning
a higher score is more similar to the source document. However, since
the scores returned by the function are distance scores, we should
understand that smaller scores correspond to more similar documents.
For the libraries other than Vectara, I wrote the scores they use by
investigating from the source libraries. Since I couldn't be certain
about the score metric used by Vectara, I didn't make any changes in its
documentation. The links mentioned in Vectara's documentation became
broken due to updates, so I replaced them with working ones.
VectorStores / Retrievers / Memory
- @dev2049
my twitter: [berkedilekoglu](https://twitter.com/berkedilekoglu)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Aviary is an open source toolkit for evaluating and deploying open
source LLMs. You can find out more about it on
[http://github.com/ray-project/aviary). You can try it out at
[http://aviary.anyscale.com](aviary.anyscale.com).
This code adds support for Aviary in LangChain. To minimize
dependencies, it connects directly to the HTTP endpoint.
The current implementation is not accelerated and uses the default
implementation of `predict` and `generate`.
It includes a test and a simple example.
@hwchase17 and @agola11 could you have a look at this?
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# OpenAIWhisperParser
This PR creates a new parser, `OpenAIWhisperParser`, that uses the
[OpenAI Whisper
model](https://platform.openai.com/docs/guides/speech-to-text/quickstart)
to perform transcription of audio files to text (`Documents`). Please
see the notebook for usage.
# Token text splitter for sentence transformers
The current TokenTextSplitter only works with OpenAi models via the
`tiktoken` package. This is not clear from the name `TokenTextSplitter`.
In this (first PR) a token based text splitter for sentence transformer
models is added. In the future I think we should work towards injecting
a tokenizer into the TokenTextSplitter to make ti more flexible.
Could perhaps be reviewed by @dev2049
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Fixes#5638. Retitles "Amazon Bedrock" page to "Bedrock" so that the
Integrations section of the left nav is properly sorted in alphabetical
order.
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# like
[StdoutCallbackHandler](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/stdout.py),
but writes to a file
When running experiments I have found myself wanting to log the outputs
of my chains in a more lightweight way than using WandB tracing. This PR
contributes a callback handler that writes to file what
`StdoutCallbackHandler` would print.
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## Example Notebook
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See the included `filecallbackhandler.ipynb` notebook for usage. Would
it be better to include this notebook under `modules/callbacks` or under
`integrations/`?
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Created fix for 5475
Currently in PGvector, we do not have any function that returns the
instance of an existing store. The from_documents always adds embeddings
and then returns the store. This fix is to add a function that will
return the instance of an existing store
Also changed the jupyter example for PGVector to show the example of
using the function
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Fixes # 5475
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@dev2049
@hwchase17
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---------
Co-authored-by: rajib76 <rajib76@yahoo.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR corrects a minor typo in the Momento chat message history
notebook and also expands the title from "Momento" to "Momento Chat
History", inline with other chat history storage providers.
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#### Who can review?
cc @dev2049 who reviewed the original integration
# Your PR Title (What it does)
Fixes the pgvector python example notebook : one of the variables was
not referencing anything
## Before submitting
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VectorStores / Retrievers / Memory
- @dev2049
# Make FinalStreamingStdOutCallbackHandler more robust by ignoring new
lines & white spaces
`FinalStreamingStdOutCallbackHandler` doesn't work out of the box with
`ChatOpenAI`, as it tokenized slightly differently than `OpenAI`. The
response of `OpenAI` contains the tokens `["\nFinal", " Answer", ":"]`
while `ChatOpenAI` contains `["Final", " Answer", ":"]`.
This PR make `FinalStreamingStdOutCallbackHandler` more robust by
ignoring new lines & white spaces when determining if the answer prefix
has been reached.
Fixes#5433
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Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
# Implements support for Personal Access Token Authentication in the
ConfluenceLoader
Fixes#5191
Implements a new optional parameter for the ConfluenceLoader: `token`.
This allows the use of personal access authentication when using the
on-prem server version of Confluence.
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@eyurtsev @Jflick58
Twitter Handle: felipe_yyc
---------
Co-authored-by: Felipe <feferreira@ea.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# docs: modules pages simplified
Fixied #5627 issue
Merged several repetitive sections in the `modules` pages. Some texts,
that were hard to understand, were also simplified.
## Who can review?
@hwchase17
@dev2049
# Fixed multi input prompt for MapReduceChain
Added `kwargs` support for inner chains of `MapReduceChain` via
`from_params` method
Currently the `from_method` method of intialising `MapReduceChain` chain
doesn't work if prompt has multiple inputs. It happens because it uses
`StuffDocumentsChain` and `MapReduceDocumentsChain` underneath, both of
them require specifying `document_variable_name` if `prompt` of their
`llm_chain` has more than one `input`.
With this PR, I have added support for passing their respective `kwargs`
via the `from_params` method.
## Fixes https://github.com/hwchase17/langchain/issues/4752
## Who can review?
@dev2049 @hwchase17 @agola11
---------
Co-authored-by: imeckr <chandanroutray2012@gmail.com>
# Unstructured Excel Loader
Adds an `UnstructuredExcelLoader` class for `.xlsx` and `.xls` files.
Works with `unstructured>=0.6.7`. A plain text representation of the
Excel file will be available under the `page_content` attribute in the
doc. If you use the loader in `"elements"` mode, an HTML representation
of the Excel file will be available under the `text_as_html` metadata
key. Each sheet in the Excel document is its own document.
### Testing
```python
from langchain.document_loaders import UnstructuredExcelLoader
loader = UnstructuredExcelLoader(
"example_data/stanley-cups.xlsx",
mode="elements"
)
docs = loader.load()
```
## Who can review?
@hwchase17
@eyurtsev
Co-authored-by: Alvaro Bartolome <alvarobartt@gmail.com>
Co-authored-by: Daniel Vila Suero <daniel@argilla.io>
Co-authored-by: Tom Aarsen <37621491+tomaarsen@users.noreply.github.com>
Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>
# Create elastic_vector_search.ElasticKnnSearch class
This extends `langchain/vectorstores/elastic_vector_search.py` by adding
a new class `ElasticKnnSearch`
Features:
- Allow creating an index with the `dense_vector` mapping compataible
with kNN search
- Store embeddings in index for use with kNN search (correct mapping
creates HNSW data structure)
- Perform approximate kNN search
- Perform hybrid BM25 (`query{}`) + kNN (`knn{}`) search
- perform knn search by either providing a `query_vector` or passing a
hosted `model_id` to use query_vector_builder to automatically generate
a query_vector at search time
Connection options
- Using `cloud_id` from Elastic Cloud
- Passing elasticsearch client object
search options
- query
- k
- query_vector
- model_id
- size
- source
- knn_boost (hybrid search)
- query_boost (hybrid search)
- fields
This also adds examples to
`docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb`
Fixes # [5346](https://github.com/hwchase17/langchain/issues/5346)
cc: @dev2049
-->
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
