- Description: Fix mangling issue affecting a couple of VectorStore
classes including Redis.
- Issue: https://github.com/langchain-ai/langchain/issues/8185
- @rlancemartin
This is a simple issue but I lack of some context in the original
implementation.
My changes perhaps are not the definitive fix but to start a quick
discussion.
@hinthornw Tagging you since one of your changes introduced this
[here.](c38965fcba)
I have some Prompt subclasses in my project that I'd like to be able to
deserialize in callbacks. Right now `loads()`/`load()` will bail when it
encounters my object, but I know I can trust the objects because they're
in my own projects.
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### Description
This PR includes the following changes:
- Adds AOSS (Amazon OpenSearch Service Serverless) support to
OpenSearch. Please refer to the documentation on how to use it.
- While creating an index, AOSS only supports Approximate Search with
`nmslib` and `faiss` engines. During Search, only Approximate Search and
Script Scoring (on doc values) are supported.
- This PR also adds support to `efficient_filter` which can be used with
`faiss` and `lucene` engines.
- The `lucene_filter` is deprecated. Instead please use the
`efficient_filter` for the lucene engine.
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
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>
Added a quick check to make integration easier with Databricks; another
option would be to make a new class, but this seemed more
straightfoward.
cc: @liangz1 Can this be done in a more straightfoward way?
This PR removes operator overloading for base message.
Removing the `+` operating from base message will help make sure that:
1) There's no need to re-define `+` for message chunks
2) That there's no unexpected behavior in terms of types changing
(adding two messages yields a ChatPromptTemplate which is not a message)
- Description: Small change to fix broken Azure streaming. More complete
migration probably still necessary once the new API behavior is
finalized.
- Issue: Implements fix by @rock-you in #6462
- Dependencies: N/A
There don't seem to be any tests specifically for this, and I was having
some trouble adding some. This is just a small temporary fix to allow
for the new API changes that OpenAI are releasing without breaking any
other code.
---------
Co-authored-by: Jacob Swe <jswe@polencapital.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
# What
- This is to add test for faiss vector store with score threshold
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- Issue: None
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- Twitter handle: @MlopsJ
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- Models / Prompts: @hwchase17, @baskaryan
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# What
- Use `logger` instead of using logging directly.
<!-- Thank you for contributing to LangChain!
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- Description: Use `logger` instead of using logging directly.
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
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- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
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Refactored `requests.py`. The same as
https://github.com/langchain-ai/langchain/pull/7961#8098#8099
requests.py is in the root code folder. This creates the
`langchain.requests: Requests` group on the API Reference navigation
ToC, on the same level as Chains and Agents which is incorrect.
Refactoring:
- copied requests.py content into utils/requests.py
- I added the backwards compatibility ref in the original requests.py.
- updated imports to requests objects
@hwchase17, @baskaryan
Addresses #7578. `run()` can return dictionaries, Pydantic objects or
strings, so the type hints should reflect that. See the chain from
`create_structured_output_chain` for an example of a non-string return
type from `run()`.
I've updated the BaseLLMChain return type hint from `str` to `Any`.
Although, the differences between `run()` and `__call__()` seem less
clear now.
CC: @baskaryan
Co-authored-by: Bagatur <baskaryan@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>
- Description: Changed "SELECT" and "UPDTAE" intent check from "=" to
"in",
- Issue: Based on my own testing, most of the LLM (StarCoder, NeoGPT3,
etc..) doesn't return a single word response ("SELECT" / "UPDATE")
through this modification, we can accomplish the same output without
curated prompt engineering.
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @aditya_0290
Thank you for maintaining this library, Keep up the good efforts.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Stop sequences are useful if you are doing long-running completions and
need to early-out rather than running for the full max_length... not
only does this save inference cost on Replicate, it is also much faster
if you are going to truncate the output later anyway.
Other LLMs support stop sequences natively (e.g. OpenAI) but I didn't
see this for Replicate so adding this via their prediction cancel
method.
Housekeeping: I ran `make format` and `make lint`, no issues reported in
the files I touched.
I did update the replicate integration test and ran `poetry run pytest
tests/integration_tests/llms/test_replicate.py` successfully.
Finally, I am @tjaffri https://twitter.com/tjaffri for feature
announcement tweets... or if you could please tag @docugami
https://twitter.com/docugami we would really appreciate that :-)
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
@rlancemartin
The modification includes:
* etherscanLoader
* test_etherscan
* document ipynb
I have run the test, lint, format, and spell check. I do encounter a
linting error on ipynb, I am not sure how to address that.
```
docs/extras/modules/data_connection/document_loaders/integrations/Etherscan.ipynb:55: error: Name "null" is not defined [name-defined]
docs/extras/modules/data_connection/document_loaders/integrations/Etherscan.ipynb:76: error: Name "null" is not defined [name-defined]
Found 2 errors in 1 file (checked 1 source file)
```
- Description: The Etherscan loader uses etherscan api to load
transaction histories under specific accounts on Ethereum Mainnet.
- No dependency is introduced by this PR.
- Twitter handle: glazecl
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
ChatGLM LLM integration will by default accumulate conversation
history(with_history=True) to ChatGLM backend api, which is not expected
in most cases. This PR set with_history=False by default, user should
explicitly set llm.with_history=True to turn this feature on. Related
PR: #8048#7774
---------
Co-authored-by: mlot <limpo2000@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
My team recently faced an issue while using MSSQL and passing a schema
name.
We noticed that "SET search_path TO {self.schema}" is being called for
us, which is not a valid ms-sql query, and is specific to postgresql
dialect.
We were able to run it locally after this fix.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Refactored `example_generator.py`. The same as #7961
`example_generator.py` is in the root code folder. This creates the
`langchain.example_generator: Example Generator ` group on the API
Reference navigation ToC, on the same level as `Chains` and `Agents`
which is not correct.
Refactoring:
- moved `example_generator.py` content into
`chains/example_generator.py` (not in `utils` because the
`example_generator` has dependencies on other LangChain classes. It also
doesn't work for moving into `utilities/`)
- added the backwards compatibility ref in the original
`example_generator.py`
@hwchase17
- **Description:** Simple change of the Class that ContentHandler
inherits from. To create an object of type SagemakerEndpointEmbeddings,
the property content_handler must be of type EmbeddingsContentHandler
not ContentHandlerBase anymore,
- **Twitter handle:** @Juanjo_Torres11
Co-authored-by: Bagatur <baskaryan@gmail.com>
Refactored `input.py`. The same as
https://github.com/langchain-ai/langchain/pull/7961#8098#8099
input.py is in the root code folder. This creates the `langchain.input:
Input` group on the API Reference navigation ToC, on the same level as
Chains and Agents which is incorrect.
Refactoring:
- copied input.py file into utils/input.py
- I added the backwards compatibility ref in the original input.py.
- changed several imports to a new ref
@hwchase17, @baskaryan
Description:
This PR adds embeddings for LocalAI (
https://github.com/go-skynet/LocalAI ), a self-hosted OpenAI drop-in
replacement. As LocalAI can re-use OpenAI clients it is mostly following
the lines of the OpenAI embeddings, however when embedding documents, it
just uses string instead of sending tokens as sending tokens is
best-effort depending on the model being used in LocalAI. Sending tokens
is also tricky as token id's can mismatch with the model - so it's safer
to just send strings in this case.
Partly related to: https://github.com/hwchase17/langchain/issues/5256
Dependencies: No new dependencies
Twitter: @mudler_it
---------
Signed-off-by: mudler <mudler@localai.io>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**PR Description:**
This pull request introduces several enhancements and new features to
the `CubeSemanticLoader`. The changes include the following:
1. Added imports for the `json` and `time` modules.
2. Added new constructor parameters: `load_dimension_values`,
`dimension_values_limit`, `dimension_values_max_retries`, and
`dimension_values_retry_delay`.
3. Updated the class documentation with descriptions for the new
constructor parameters.
4. Added a new private method `_get_dimension_values()` to retrieve
dimension values from Cube's REST API.
5. Modified the `load()` method to load dimension values for string
dimensions if `load_dimension_values` is set to `True`.
6. Updated the API endpoint in the `load()` method from the base URL to
the metadata endpoint.
7. Refactored the code to retrieve metadata from the response JSON.
8. Added the `column_member_type` field to the metadata dictionary to
indicate if a column is a measure or a dimension.
9. Added the `column_values` field to the metadata dictionary to store
the dimension values retrieved from Cube's API.
10. Modified the `page_content` construction to include the column title
and description instead of the table name, column name, data type,
title, and description.
These changes improve the functionality and flexibility of the
`CubeSemanticLoader` class by allowing the loading of dimension values
and providing more detailed metadata for each document.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Refactored `formatting.py`. The same as
https://github.com/langchain-ai/langchain/pull/7961#8098#8099
formatting.py is in the root code folder. This creates the
`langchain.formatting: Formatting` group on the API Reference navigation
ToC, on the same level as Chains and Agents which is incorrect.
Refactoring:
- moved formatting.py content into utils/formatting.py
- I did not add the backwards compatibility ref in the original
formatting.py. It seems unnecessary.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
