# Improve pinecone hybrid search retriever adding metadata support
I simply remove the hardwiring of metadata to the existing
implementation allowing one to pass `metadatas` attribute to the
constructors and in `get_relevant_documents`. I also add one missing pip
install to the accompanying notebook (I am not adding dependencies, they
were pre-existing).
First contribution, just hoping to help, feel free to critique :)
my twitter username is `@andreliebschner`
While looking at hybrid search I noticed #3043 and #1743. I think the
former can be closed as following the example right now (even prior to
my improvements) works just fine, the latter I think can be also closed
safely, maybe pointing out the relevant classes and example. Should I
reply those issues mentioning someone?
@dev2049, @hwchase17
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Co-authored-by: Andreas Liebschner <a.liebschner@shopfully.com>
# Add self query translator for weaviate vectorstore
Adds support for the EQ comparator and the AND/OR operators.
Co-authored-by: Dominic Chan <dchan@cppib.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Zep Retriever - Vector Search Over Chat History with the Zep Long-term
Memory Service
More on Zep: https://github.com/getzep/zep
Note: This PR is related to and relies on
https://github.com/hwchase17/langchain/pull/4834. I did not want to
modify the `pyproject.toml` file to add the `zep-python` dependency a
second time.
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
# Docs: improvements in the `retrievers/examples/` notebooks
Its primary purpose is to make the Jupyter notebook examples
**consistent** and more suitable for first-time viewers.
- add links to the integration source (if applicable) with a short
description of this source;
- removed `_retriever` suffix from the file names (where it existed) for
consistency;
- removed ` retriever` from the notebook title (where it existed) for
consistency;
- added code to install necessary Python package(s);
- added code to set up the necessary API Key.
- very small fixes in notebooks from other folders (for consistency):
- docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb
- docs/modules/indexes/vectorstores/examples/pinecone.ipynb
- docs/modules/models/llms/integrations/cohere.ipynb
- fixed misspelling in langchain/retrievers/time_weighted_retriever.py
comment (sorry, about this change in a .py file )
## Who can review
@dev2049
# Fixed typos (issues #4818 & #4668 & more typos)
- At some places, it said `model = ChatOpenAI(model='gpt-3.5-turbo')`
but should be `model = ChatOpenAI(model_name='gpt-3.5-turbo')`
- Fixes some other typos
Fixes#4818, #4668
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
- added `Wikipedia` retriever. It is effectively a wrapper for
`WikipediaAPIWrapper`. It wrapps load() into get_relevant_documents()
- sorted `__all__` in the `retrievers/__init__`
- added integration tests for the WikipediaRetriever
- added an example (as Jupyter notebook) for the WikipediaRetriever
Alternate implementation of #3452 that relies on a generic query
constructor chain and language and then has vector store-specific
translation layer. Still refactoring and updating examples but general
structure is there and seems to work s well as #3452 on exampels
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
## Use `index_id` over `app_id`
We made a major update to index + retrieve based on Metal Indexes
(instead of apps). With this change, we accept an index instead of an
app in each of our respective core apis. [More details
here](https://docs.getmetal.io/api-reference/core/indexing).
Add a time-weighted memory retriever and a notebook that approximates a
Generative Agent from https://arxiv.org/pdf/2304.03442.pdf
The "daily plan" components are removed for now since they are less
useful without a virtual world, but the memory is an interesting
component to build off.
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Add SVM retriever class, based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb.
Testing still WIP, but the logic is correct (I have a local
implementation outside of Langchain working).
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Co-authored-by: Lance Martin <122662504+PineappleExpress808@users.noreply.github.com>
Co-authored-by: rlm <31treehaus@31s-MacBook-Pro.local>
Improvements to Deep Lake Vector Store
- much faster view loading of embeddings after filters with
`fetch_chunks=True`
- 2x faster ingestion
- use np.float32 for embeddings to save 2x storage, LZ4 compression for
text and metadata storage (saves up to 4x storage for text data)
- user defined functions as filters
Docs
- Added retriever full example for analyzing twitter the-algorithm
source code with GPT4
- Added a use case for code analysis (please let us know your thoughts
how we can improve it)
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Co-authored-by: Davit Buniatyan <d@activeloop.ai>
