Commit Graph

14 Commits (9b3a025f9c806a6f8a00030c7058c689536ae5a0)

Author SHA1 Message Date
Joydeep Banik Roy 3796672c67
community, milvus, pinecone, qdrant, mongo: Broadcast operation failure while using simsimd beyond v3.7.7 (#22271)
- [ ] **Packages affected**: 
  - community: fix `cosine_similarity` to support simsimd beyond 3.7.7
- partners/milvus: fix `cosine_similarity` to support simsimd beyond
3.7.7
- partners/mongodb: fix `cosine_similarity` to support simsimd beyond
3.7.7
- partners/pinecone: fix `cosine_similarity` to support simsimd beyond
3.7.7
- partners/qdrant: fix `cosine_similarity` to support simsimd beyond
3.7.7


- [ ] **Broadcast operation failure while using simsimd beyond v3.7.7**:
- **Description:** I was using simsimd 4.3.1 and the unsupported operand
type issue popped up. When I checked out the repo and ran the tests,
they failed as well (have attached a screenshot for that). Looks like it
is a variant of https://github.com/langchain-ai/langchain/issues/18022 .
Prior to 3.7.7, simd.cdist returned an ndarray but now it returns
simsimd.DistancesTensor which is ineligible for a broadcast operation
with numpy. With this change, it also remove the need to explicitly cast
`Z` to numpy array
    - **Issue:** #19905
    - **Dependencies:** No
    - **Twitter handle:** https://x.com/GetzJoydeep

<img width="1622" alt="Screenshot 2024-05-29 at 2 50 00 PM"
src="https://github.com/langchain-ai/langchain/assets/31132555/fb27b383-a9ae-4a6f-b355-6d503b72db56">

- [ ] **Considerations**: 
1. I started with community but since similar changes were there in
Milvus, MongoDB, Pinecone, and QDrant so I modified their files as well.
If touching multiple packages in one PR is not the norm, then I can
remove them from this PR and raise separate ones
2. I have run and verified that the tests work. Since, only MongoDB had
tests, I ran theirs and verified it works as well. Screenshots attached
:
<img width="1573" alt="Screenshot 2024-05-29 at 2 52 13 PM"
src="https://github.com/langchain-ai/langchain/assets/31132555/ce87d1ea-19b6-4900-9384-61fbc1a30de9">
<img width="1614" alt="Screenshot 2024-05-29 at 3 33 51 PM"
src="https://github.com/langchain-ai/langchain/assets/31132555/6ce1d679-db4c-4291-8453-01028ab2dca5">
  

I have added a test for simsimd. I feel it may not go well with the
CI/CD setup as installing simsimd is not a dependency requirement. I
have just imported simsimd to ensure simsimd cosine similarity is
invoked. However, its not a good approach. Suggestions are welcome and I
can make the required changes on the PR. Please provide guidance on the
same as I am new to the community.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
4 months ago
Jib f369495fa0
mongodb: [performance] Increase DEFAULT_INSERT_BATCH_SIZE to 100,000 and introduce sizing constraints (#19608) 4 months ago
Jib a97473c846
mongodb[patch]: Make ObjectId JSON-serializable on generation (#21394) 4 months ago
Erick Friis 91a2ea5cd6
chroma, mongodb: fix docstrings (#21629) 4 months ago
William FH b7b62e29fb
community[patch], mongodb[patch]: Stop spamming SIMD import warnings (#19531)
If you use an embedding dist function in an eval loop, you get warned
every time. Would prefer to just check once and forget about it.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
6 months ago
Ash Vardanian d01bad5169
core[patch]: Convert SimSIMD back to NumPy (#19473)
This patch fixes the #18022 issue, converting the SimSIMD internal
zero-copy outputs to NumPy.

I've also noticed, that oftentimes `dtype=np.float32` conversion is used
before passing to SimSIMD. Which numeric types do LangChain users
generally care about? We support `float64`, `float32`, `float16`, and
`int8` for cosine distances and `float16` seems reasonable for
practically any kind of embeddings and any modern piece of hardware, so
we can change that part as well 🤗
6 months ago
Jib f8078e41e5
mongodb[patch]: Added scoring threshold to caching (#19286)
## Description
Semantic Cache can retrieve noisy information if the score threshold for
the value is too low. Adding the ability to set a `score_threshold` on
cache construction can allow for less noisy scores to appear.


- [x] **Add tests and docs**
  1. Added tests that confirm the `score_threshold` query is valid.


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
6 months ago
Jib 516cc44b3f
langchain-mongodb: [test-fix] add explicit index_name setting on test vector creation (#19245)
- **Description:** Tests fail to do value lookup because it does not
specify the index name
  - **Issue:** the issue # Failing integration test
 

- [x] **Add tests and docs**: Tests now pass


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
6 months ago
Jib ec026004cb
mongodb[patch]: Remove in-memory cache from cache abstractions (#18987)
## Description
* In memory cache easily gets out of sync with the server cache, so we
will remove it entirely to reduce the issues around invalidated caches.

## Dependencies
None

- [x]  If you're adding a new integration, please include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Co-authored-by: Erick Friis <erick@langchain.dev>
6 months ago
Jib 866d6408af
mongodb[patch]: Remove embedding retrieval from mongodb payload (#19035)
## Description
Returning the embedding is not necessary in the vector search
functionality unless specified as a debugging step. This change defaults
the behavior such that the server _only_ returns the embedding key if
explicitly requested, such as in the case of
`max_marginal_relevance_search`.


- [x] **Add tests and docs**: If you're adding a new integration, please
include
* Added `test_from_documents_no_embedding_return`


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
6 months ago
Jib 8bc347c5fc
mongodb[patch]: include LLM caches in toplevel library import (#18601) 7 months ago
Jib 9da1e0cf34
mongodb[patch]: Migrate MongoDBChatMessageHistory (#18590)
## **Description** 
Migrate the `MongoDBChatMessageHistory` to the managed
`langchain-mongodb` partner-package
## **Dependencies**
None
## **Twitter handle**
@mongodb

## **tests and docs**
- [x] Migrate existing integration test
- [x ]~ Convert existing integration test to a unit test~ Creation is
out of scope for this ticket
- [x ] ~Considering delaying work until #17470 merges to leverage the
`MockCollection` object. ~
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
7 months ago
Jib f92f7d2e03
mongodb[minor]: Add MongoDB LLM Cache (#17470)
# Description

- **Description:** Adding MongoDB LLM Caching Layer abstraction
- **Issue:** N/A
- **Dependencies:** None
- **Twitter handle:** @mongodb

Checklist:

- [x] PR title: Please title your PR "package: description", where
"package" is whichever of langchain, community, core, experimental, etc.
is being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
  - Example: "community: add foobar LLM"
- [x] PR Message (above)
- [x] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [ ] Add tests and docs: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @efriis, @eyurtsev, @hwchase17.

---------

Co-authored-by: Jib <jib@byblack.us>
7 months ago
Jib 72bfc1d3db
mongodb[minor]: MongoDB Partner Package -- Porting MongoDBAtlasVectorSearch (#17652)
This PR migrates the existing MongoDBAtlasVectorSearch abstraction from
the `langchain_community` section to the partners package section of the
codebase.
- [x] Run the partner package script as advised in the partner-packages
documentation.
- [x] Add Unit Tests
- [x] Migrate Integration Tests
- [x] Refactor `MongoDBAtlasVectorStore` (autogenerated) to
`MongoDBAtlasVectorSearch`
- [x] ~Remove~ deprecate the old `langchain_community` VectorStore
references.

## Additional Callouts
- Implemented the `delete` method
- Included any missing async function implementations
  - `amax_marginal_relevance_search_by_vector`
  - `adelete` 
- Added new Unit Tests that test for functionality of
`MongoDBVectorSearch` methods
- Removed [`del
res[self._embedding_key]`](e0c81e1cb0/libs/community/langchain_community/vectorstores/mongodb_atlas.py (L218))
in `_similarity_search_with_score` function as it would make the
`maximal_marginal_relevance` function fail otherwise. The `Document`
needs to store the embedding key in metadata to work.

Checklist:

- [x] PR title: Please title your PR "package: description", where
"package" is whichever of langchain, community, core, experimental, etc.
is being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
  - Example: "community: add foobar LLM"
- [x] PR message
- [x] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [x] Add tests and docs: If you're adding a new integration, please
include
1. Existing tests supplied in docs/docs do not change. Updated
docstrings for new functions like `delete`
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory. (This already exists)

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.

---------

Co-authored-by: Steven Silvester <steven.silvester@ieee.org>
Co-authored-by: Erick Friis <erick@langchain.dev>
7 months ago