Given a user question, this will -
* Use LLM to generate a set of queries.
* Query for each.
* The URLs from search results are stored in self.urls.
* A check is performed for any new URLs that haven't been processed yet
(not in self.url_database).
* Only these new URLs are loaded, transformed, and added to the
vectorstore.
* The vectorstore is queried for relevant documents based on the
questions generated by the LLM.
* Only unique documents are returned as the final result.
This code will avoid reprocessing of URLs across multiple runs of
similar queries, which should improve the performance of the retriever.
It also keeps track of all URLs that have been processed, which could be
useful for debugging or understanding the retriever's behavior.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Until now, hybrid search was limited to modules requiring external
services, such as Weaviate/Pinecone Hybrid Search. However, I have
developed a hybrid retriever that can merge a list of retrievers using
the [Reciprocal Rank
Fusion](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf)
algorithm. This new approach, similar to Weaviate hybrid search, does
not require the initialization of any external service.
- Dependencies: No - Twitter handle: dayuanjian21687
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
New HTML loader that asynchronously loader a list of urls.
New transformer using [HTML2Text](https://github.com/Alir3z4/html2text/)
for HTML to clean, easy-to-read plain ASCII text (valid Markdown).
I've extended the support of async API to local Qdrant mode. It is faked
but allows prototyping without spinning a container. The tests are
improved to test the in-memory case as well.
@baskaryan @rlancemartin @eyurtsev @agola11
BedrockEmbeddings does not have endpoint_url so that switching to custom
endpoint is not possible. I have access to Bedrock custom endpoint and
cannot use BedrockEmbeddings
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Work in Progress.
WIP
Not ready...
Adds Document Loader support for
[Geopandas.GeoDataFrames](https://geopandas.org/)
Example:
- [x] stub out `GeoDataFrameLoader` class
- [x] stub out integration tests
- [ ] Experiment with different geometry text representations
- [ ] Verify CRS is successfully added in metadata
- [ ] Test effectiveness of searches on geometries
- [ ] Test with different geometry types (point, line, polygon with
multi-variants).
- [ ] Add documentation
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Lance Martin <122662504+rlancemartin@users.noreply.github.com>
** This should land Monday the 17th **
Chroma is upgrading from `0.3.29` to `0.4.0`. `0.4.0` is easier to
build, more durable, faster, smaller, and more extensible. This comes
with a few changes:
1. A simplified and improved client setup. Instead of having to remember
weird settings, users can just do `EphemeralClient`, `PersistentClient`
or `HttpClient` (the underlying direct `Client` implementation is also
still accessible)
2. We migrated data stores away from `duckdb` and `clickhouse`. This
changes the api for the `PersistentClient` that used to reference
`chroma_db_impl="duckdb+parquet"`. Now we simply set
`is_persistent=true`. `is_persistent` is set for you to `true` if you
use `PersistentClient`.
3. Because we migrated away from `duckdb` and `clickhouse` - this also
means that users need to migrate their data into the new layout and
schema. Chroma is committed to providing extension notification and
tooling around any schema and data migrations (for example - this PR!).
After upgrading to `0.4.0` - if users try to access their data that was
stored in the previous regime, the system will throw an `Exception` and
instruct them how to use the migration assistant to migrate their data.
The migration assitant is a pip installable CLI: `pip install
chroma_migrate`. And is runnable by calling `chroma_migrate`
-- TODO ADD here is a short video demonstrating how it works.
Please reference the readme at
[chroma-core/chroma-migrate](https://github.com/chroma-core/chroma-migrate)
to see a full write-up of our philosophy on migrations as well as more
details about this particular migration.
Please direct any users facing issues upgrading to our Discord channel
called
[#get-help](https://discord.com/channels/1073293645303795742/1129200523111841883).
We have also created a [email
listserv](https://airtable.com/shrHaErIs1j9F97BE) to notify developers
directly in the future about breaking changes.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Moving to the latest non-preview Azure OpenAI API version=2023-05-15.
The previous 2023-03-15-preview doesn't have support, SLA etc. For
instance, OpenAI SDK has moved to this version
https://github.com/openai/openai-python/releases/tag/v0.27.7
@baskaryan
Description:
Currently, Zilliz only support dedicated clusters using a pair of
username and password for connection. Regarding serverless clusters,
they can connect to them by using API keys( [ see official note
detail](https://docs.zilliz.com/docs/manage-cluster-credentials)), so I
add API key(token) description in Zilliz docs to make it more obvious
and convenient for this group of users to better utilize Zilliz. No
changes done to code.
---------
Co-authored-by: Robin.Wang <3Jg$94sbQ@q1>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: This PR adds the option to retrieve scores and explanations
in the WeaviateHybridSearchRetriever. This feature improves the
usability of the retriever by allowing users to understand the scoring
logic behind the search results and further refine their search queries.
Issue: This PR is a solution to the issue #7855
Dependencies: This PR does not introduce any new dependencies.
Tag maintainer: @rlancemartin, @eyurtsev
I have included a unit test for the added feature, ensuring that it
retrieves scores and explanations correctly. I have also included an
example notebook demonstrating its use.
Motivation, it seems that when dealing with a long context and "big"
number of relevant documents we must avoid using out of the box score
ordering from vector stores.
See: https://arxiv.org/pdf/2306.01150.pdf
So, I added an additional parameter that allows you to reorder the
retrieved documents so we can work around this performance degradation.
The relevance respect the original search score but accommodates the
lest relevant document in the middle of the context.
Extract from the paper (one image speaks 1000 tokens):
This seems to be common to all diff arquitectures. SO I think we need a
good generic way to implement this reordering and run some test in our
already running retrievers.
It could be that my approach is not the best one from the architecture
point of view, happy to have a discussion about that.
For me this was the best place to introduce the change and start
retesting diff implementations.
@rlancemartin, @eyurtsev
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
- Description: Add a BM25 Retriever that do not need Elastic search
- Dependencies: rank_bm25(if it is not installed it will be install by
using pip, just like TFIDFRetriever do)
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: DayuanJian21687
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Support Redis Sentinel database connections
This PR adds the support to connect not only to Redis standalone servers
but High Availability Replication sets too
(https://redis.io/docs/management/sentinel/)
Redis Replica Sets have on Master allowing to write data and 2+ replicas
with read-only access to the data. The additional Redis Sentinel
instances monitor all server and reconfigure the RW-Master on the fly if
it comes unavailable.
Therefore all connections must be made through the Sentinels the query
the current master for a read-write connection. This PR adds basic
support to also allow a redis connection url specifying a Sentinel as
Redis connection.
Redis documentation and Jupyter notebook with Redis examples are updated
to mention how to connect to a redis Replica Set with Sentinels
-
Remark - i did not found test cases for Redis server connections to add
new cases here. Therefor i tests the new utility class locally with
different kind of setups to make sure different connection urls are
working as expected. But no test case here as part of this PR.
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Integrate [Rockset](https://rockset.com/docs/) as a document loader.
Issue: None
Dependencies: Nothing new (rockset's dependency was already added
[here](https://github.com/hwchase17/langchain/pull/6216))
Tag maintainer: @rlancemartin
I have added a test for the integration and an example notebook showing
its use. I ran `make lint` and everything looks good.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Multiple people have asked in #5081 for a way to limit the documents
returned from an AzureCognitiveSearchRetriever. This PR adds the `top_n`
parameter to allow that.
Twitter handle:
[@UmerHAdil](twitter.com/umerHAdil)
# Browserless
Added support for Browserless' `/content` endpoint as a document loader.
### About Browserless
Browserless is a cloud service that provides access to headless Chrome
browsers via a REST API. It allows developers to automate Chromium in a
serverless fashion without having to configure and maintain their own
Chrome infrastructure.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
- Description: Add two new document transformers that translates
documents into different languages and converts documents into q&a
format to improve vector search results. Uses OpenAI function calling
via the [doctran](https://github.com/psychic-api/doctran/tree/main)
library.
- Issue: N/A
- Dependencies: `doctran = "^0.0.5"`
- Tag maintainer: @rlancemartin @eyurtsev @hwchase17
- Twitter handle: @psychicapi or @jfan001
Notes
- Adheres to the `DocumentTransformer` abstraction set by @dev2049 in
#3182
- refactored `EmbeddingsRedundantFilter` to put it in a file under a new
`document_transformers` module
- Added basic docs for `DocumentInterrogator`, `DocumentTransformer` as
well as the existing `EmbeddingsRedundantFilter`
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Probably the most boring PR to review ;)
Individual commits might be easier to digest
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description: a description of the change**
Fixed `make docs_build` and related scripts which caused errors. There
are several changes.
First, I made the build of the documentation and the API Reference into
two separate commands. This is because it takes less time to build. The
commands for documents are `make docs_build`, `make docs_clean`, and
`make docs_linkcheck`. The commands for API Reference are `make
api_docs_build`, `api_docs_clean`, and `api_docs_linkcheck`.
It looked like `docs/.local_build.sh` could be used to build the
documentation, so I used that. Since `.local_build.sh` was also building
API Rerefence internally, I removed that process. `.local_build.sh` also
added some Bash options to stop in error or so. Futher more added `cd
"${SCRIPT_DIR}"` at the beginning so that the script will work no matter
which directory it is executed in.
`docs/api_reference/api_reference.rst` is removed, because which is
generated by `docs/api_reference/create_api_rst.py`, and added it to
.gitignore.
Finally, the description of CONTRIBUTING.md was modified.
**Issue: the issue # it fixes (if applicable)**
https://github.com/hwchase17/langchain/issues/6413
**Dependencies: any dependencies required for this change**
`nbdoc` was missing in group docs so it was added. I installed it with
the `poetry add --group docs nbdoc` command. I am concerned if any
modifications are needed to poetry.lock. I would greatly appreciate it
if you could pay close attention to this file during the review.
**Tag maintainer**
- General / Misc / if you don't know who to tag: @baskaryan
If this PR needs any additional changes, I'll be happy to make them!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR changes the behavior of `Qdrant.from_texts` so the collection is
reused if not requested to recreate it. Previously, calling
`Qdrant.from_texts` or `Qdrant.from_documents` resulted in removing the
old data which was confusing for many.
Improve documentation for a central use-case, qa / chat over documents.
This will be merged as an update to `index.mdx`
[here](https://python.langchain.com/docs/use_cases/question_answering/).
Testing w/ local Docusaurus server:
```
From `docs` directory:
mkdir _dist
cp -r {docs_skeleton,snippets} _dist
cp -r extras/* _dist/docs_skeleton/docs
cd _dist/docs_skeleton
yarn install
yarn start
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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Fixes # (issue)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
### Description
Created a Loader to get a list of specific logs from Datadog Logs.
### Dependencies
`datadog_api_client` is required.
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [Xorbits](https://doc.xorbits.io/en/latest/) is an open-source
computing framework that makes it easy to scale data science and machine
learning workloads in parallel. Xorbits can leverage multi cores or GPUs
to accelerate computation on a single machine, or scale out up to
thousands of machines to support processing terabytes of data.
- This PR added support for the Xorbits document loader, which allows
langchain to leverage Xorbits to parallelize and distribute the loading
of data.
- Dependencies: This change requires the Xorbits library to be installed
in order to be used.
`pip install xorbits`
- Request for review: @rlancemartin, @eyurtsev
- Twitter handle: https://twitter.com/Xorbitsio
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adding a maximal_marginal_relevance method to the
MongoDBAtlasVectorSearch vectorstore enhances the user experience by
providing more diverse search results
Issue: #7304
### Summary
Adds an `UnstructuredTSVLoader` for TSV files. Also updates the doc
strings for `UnstructuredCSV` and `UnstructuredExcel` loaders.
### Testing
```python
from langchain.document_loaders.tsv import UnstructuredTSVLoader
loader = UnstructuredTSVLoader(
file_path="example_data/mlb_teams_2012.csv", mode="elements"
)
docs = loader.load()
```
Continuing with Tolkien inspired series of langchain tools. I bring to
you:
**The Fellowship of the Vectors**, AKA EmbeddingsClusteringFilter.
This document filter uses embeddings to group vectors together into
clusters, then allows you to pick an arbitrary number of documents
vector based on proximity to the cluster centers. That's a
representative sample of the cluster.
The original idea is from [Greg Kamradt](https://github.com/gkamradt)
from this video (Level4):
https://www.youtube.com/watch?v=qaPMdcCqtWk&t=365s
I added few tricks to make it a bit more versatile, so you can
parametrize what to do with duplicate documents in case of cluster
overlap: replace the duplicates with the next closest document or remove
it. This allow you to use it as an special kind of redundant filter too.
Additionally you can choose 2 diff orders: grouped by cluster or
respecting the original retriever scores.
In my use case I was using the docs grouped by cluster to run refine
chains per cluster to generate summarization over a large corpus of
documents.
Let me know if you want to change anything!
@rlancemartin, @eyurtsev, @hwchase17,
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
This PR improves the example notebook for the Marqo vectorstore
implementation by adding a new RetrievalQAWithSourcesChain example. The
`embedding` parameter in `from_documents` has its type updated to
`Union[Embeddings, None]` and a default parameter of None because this
is ignored in Marqo.
This PR also upgrades the Marqo version to 0.11.0 to remove the device
parameter after a breaking change to the API.
Related to #7068 @tomhamer @hwchase17
---------
Co-authored-by: Tom Hamer <tom@marqo.ai>
