### Description
Changed the value specified for `content_key` in JSONLoader from a
single key to a value based on jq schema.
I created [similar
PR](https://github.com/langchain-ai/langchain/pull/11255) before, but it
has several conflicts because of the architectural change associated
stable version release, so I re-create this PR to fit new architecture.
### Why
For json data like the following, specify `.data[].attributes.message`
for page_content and `.data[].attributes.id` or
`.data[].attributes.attributes. tags`, etc., the `content_key` must also
parse the json structure.
<details>
<summary>sample json data</summary>
```json
{
"data": [
{
"attributes": {
"message": "message1",
"tags": [
"tag1"
]
},
"id": "1"
},
{
"attributes": {
"message": "message2",
"tags": [
"tag2"
]
},
"id": "2"
}
]
}
```
</details>
<details>
<summary>sample code</summary>
```python
def metadata_func(record: dict, metadata: dict) -> dict:
metadata["source"] = None
metadata["id"] = record.get("id")
metadata["tags"] = record["attributes"].get("tags")
return metadata
sample_file = "sample1.json"
loader = JSONLoader(
file_path=sample_file,
jq_schema=".data[]",
content_key=".attributes.message", ## content_key is parsable into jq schema
is_content_key_jq_parsable=True, ## this is added parameter
metadata_func=metadata_func
)
data = loader.load()
data
```
</details>
### Dependencies
none
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
Neo4j tools use particular node labels and relationship types to store
metadata, but are irrelevant for text2cypher or graph generation, so we
want to ignore them in the schema representation.
Deprecates the old langchain-hub repository. Does *not* deprecate the
new https://smith.langchain.com/hub
@PinkDraconian has correctly raised that in the event someone is loading
unsanitized user input into the `try_load_from_hub` function, they have
the ability to load files from other locations in github than the
hwchase17/langchain-hub repository.
This PR adds some more path checking to that function and deprecates the
functionality in favor of the hub built into LangSmith.
## Description
Adding in Unit Test variation for `MongoDBChatMessageHistory` package
Follow-up to #18590
- [x] **Add tests and docs**: Unit test is what's being added
- [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/
## **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>
# 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>
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: deprecate vectorstores.MatchingEngine"
- [ ] **PR message**:
- **Description:** announced a deprecation since this integration has
been moved to langchain_google_vertexai
**Description:** Update docstrings of ChatAnthropic class
**Issue:** Change to ChatAnthropic from ChatAnthropicMessages
**Dependencies:** None
**Lint and test**: `make format`, `make lint` and `make test` passed
- **Description:** finishes adding the you.com functionality including:
- add async functions to utility and retriever
- add the You.com Tool
- add async testing for utility, retriever, and tool
- add a tool integration notebook page
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** @scottnath
- **Description:** add tools_renderer for various non-openai agents,
make tools can be render in different ways for your LLM.
- **Issue:** N/A
- **Dependencies:** N/A
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description:
This pull request introduces several enhancements for Azure Cosmos
Vector DB, primarily focused on improving caching and search
capabilities using Azure Cosmos MongoDB vCore Vector DB. Here's a
summary of the changes:
- **AzureCosmosDBSemanticCache**: Added a new cache implementation
called AzureCosmosDBSemanticCache, which utilizes Azure Cosmos MongoDB
vCore Vector DB for efficient caching of semantic data. Added
comprehensive test cases for AzureCosmosDBSemanticCache to ensure its
correctness and robustness. These tests cover various scenarios and edge
cases to validate the cache's behavior.
- **HNSW Vector Search**: Added HNSW vector search functionality in the
CosmosDB Vector Search module. This enhancement enables more efficient
and accurate vector searches by utilizing the HNSW (Hierarchical
Navigable Small World) algorithm. Added corresponding test cases to
validate the HNSW vector search functionality in both
AzureCosmosDBSemanticCache and AzureCosmosDBVectorSearch. These tests
ensure the correctness and performance of the HNSW search algorithm.
- **LLM Caching Notebook** - The notebook now includes a comprehensive
example showcasing the usage of the AzureCosmosDBSemanticCache. This
example highlights how the cache can be employed to efficiently store
and retrieve semantic data. Additionally, the example provides default
values for all parameters used within the AzureCosmosDBSemanticCache,
ensuring clarity and ease of understanding for users who are new to the
cache implementation.
@hwchase17,@baskaryan, @eyurtsev,
### Description
Fixed a small bug in chroma.py add_images(), previously whenever we are
not passing metadata the documents is containing the base64 of the uris
passed, but when we are passing the metadata the documents is containing
normal string uris which should not be the case.
### Issue
In add_images() method when we are calling upsert() we have to use
"b64_texts" instead of normal string "uris".
### Twitter handle
https://twitter.com/whitepegasus01
* **Description:** adds `LlamafileEmbeddings` class implementation for
generating embeddings using
[llamafile](https://github.com/Mozilla-Ocho/llamafile)-based models.
Includes related unit tests and notebook showing example usage.
* **Issue:** N/A
* **Dependencies:** N/A
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Remove the assert statement on the `count_documents`
in setup_class. It should just delete if there are documents present
- **Issue:** the issue # Crashes on class setup
- **Dependencies:** None
- **Twitter handle:** @mongodb
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. N/A
- [ ] **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/
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>
Current implementation doesn't have an indexed property that would
optimize the import. I have added a `baseEntityLabel` parameter that
allows you to add a secondary node label, which has an indexed id
`property`. By default, the behaviour is identical to previous version.
Since multi-labeled nodes are terrible for text2cypher, I removed the
secondary label from schema representation object and string, which is
used in text2cypher.
This PR makes `cohere_api_key` in `llms/cohere` a SecretStr, so that the
API Key is not leaked when `Cohere.cohere_api_key` is represented as a
string.
---------
Signed-off-by: Arun <arun@arun.blog>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
