These currently read off AIMessage.tool_calls, and only fall back to
OpenAI parsing if tool calls aren't populated.
Importing these from `openai_tools` (e.g., in our [tool calling
docs](https://python.langchain.com/v0.2/docs/how_to/tool_calling/#tool-calls))
can lead to confusion.
After landing, would need to release core and update docs.
Pydantic allows empty strings:
```
from langchain.pydantic_v1 import Field, BaseModel
class Property(BaseModel):
"""A single property consisting of key and value"""
key: str = Field(..., description="key")
value: str = Field(..., description="value")
x = Property(key="", value="")
```
Which can produce errors downstream. We simply ignore those records
bing_search_url is an endpoint to requests bing search resource and is
normally invariant to users, we can give it the default value to simply
the uesages of this utility/tool
Description: Add classifier_location feature flag. This flag enables
Pebblo to decide the classifier location, local or pebblo-cloud.
Unit Tests: N/A
Documentation: N/A
---------
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
The code snippet under ‘pdfs_qa’ contains an small incorrect code
example , resulting in users getting errors. This pr replaces ‘llm’
variable with ‘model’ to help user avoid a NameError message.
Resolves#22689
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Adds options for configuring MongoDBChatMessageHistory
(no breaking changes):
- session_id_key: name of the field that stores the session id
- history_key: name of the field that stores the chat history
- create_index: whether to create an index on the session id field
- index_kwargs: additional keyword arguments to pass to the index
creation
**Discussion:**
https://github.com/langchain-ai/langchain/discussions/22918
**Twitter handle:** @userlerueda
---------
Co-authored-by: Jib <Jibzade@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Add standard tests to base store abstraction. These only work on [str,
str] right now. We'll need to check if it's possible to add
encoder/decoders to generalize
**Description:**
This PR addresses an issue in the `MongodbLoader` where nested fields
were not being correctly extracted. The loader now correctly handles
nested fields specified in the `field_names` parameter.
**Issue:**
Fixes an issue where attempting to extract nested fields from MongoDB
documents resulted in `KeyError`.
**Dependencies:**
No new dependencies are required for this change.
**Twitter handle:**
(Optional, your Twitter handle if you'd like a mention when the PR is
announced)
### Changes
1. **Field Name Parsing**:
- Added logic to parse nested field names and safely extract their
values from the MongoDB documents.
2. **Projection Construction**:
- Updated the projection dictionary to include nested fields correctly.
3. **Field Extraction**:
- Updated the `aload` method to handle nested field extraction using a
recursive approach to traverse the nested dictionaries.
### Example Usage
Updated usage example to demonstrate how to specify nested fields in the
`field_names` parameter:
```python
loader = MongodbLoader(
connection_string=MONGO_URI,
db_name=MONGO_DB,
collection_name=MONGO_COLLECTION,
filter_criteria={"data.job.company.industry_name": "IT", "data.job.detail": { "$exists": True }},
field_names=[
"data.job.detail.id",
"data.job.detail.position",
"data.job.detail.intro",
"data.job.detail.main_tasks",
"data.job.detail.requirements",
"data.job.detail.preferred_points",
"data.job.detail.benefits",
],
)
docs = loader.load()
print(len(docs))
for doc in docs:
print(doc.page_content)
```
### Testing
Tested with a MongoDB collection containing nested documents to ensure
that the nested fields are correctly extracted and concatenated into a
single page_content string.
### Note
This change ensures backward compatibility for non-nested fields and
improves functionality for nested field extraction.
### Output Sample
```python
print(docs[:3])
```
```shell
# output sample:
[
Document(
# Here in this example, page_content is the combined text from the fields below
# "position", "intro", "main_tasks", "requirements", "preferred_points", "benefits"
page_content='all combined contents from the requested fields in the document',
metadata={'database': 'Your Database name', 'collection': 'Your Collection name'}
),
...
]
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] PR title:
community: Add OCI Generative AI new model support
- [x] PR message:
- Description: adding support for new models offered by OCI Generative
AI services. This is a moderate update of our initial integration PR
16548 and includes a new integration for our chat models under
/langchain_community/chat_models/oci_generative_ai.py
- Issue: NA
- Dependencies: No new Dependencies, just latest version of our OCI sdk
- Twitter handle: NA
- [x] Add tests and docs:
1. we have updated our unit tests
2. we have updated our documentation including a new ipynb for our new
chat integration
- [x] Lint and test:
`make format`, `make lint`, and `make test` run successfully
---------
Co-authored-by: RHARPAZ <RHARPAZ@RHARPAZ-5750.us.oracle.com>
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
** Description**
This is the community integration of ZenGuard AI - the fastest
guardrails for GenAI applications. ZenGuard AI protects against:
- Prompts Attacks
- Veering of the pre-defined topics
- PII, sensitive info, and keywords leakage.
- Toxicity
- Etc.
**Twitter Handle** : @zenguardai
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. Added an integration test
2. Added colab
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.
---------
Co-authored-by: Nuradil <nuradil.maksut@icloud.com>
Co-authored-by: Nuradil <133880216+yaksh0nti@users.noreply.github.com>
They are now rejecting with code 401 calls from users with expired or
invalid tokens (while before they were being considered anonymous).
Thus, the authorization header has to be removed when there is no token.
Related to: #23178
---------
Signed-off-by: Joffref <mariusjoffre@gmail.com>
Description: 2 feature flags added to SharePointLoader in this PR:
1. load_auth: if set to True, adds authorised identities to metadata
2. load_extended_metadata, adds source, owner and full_path to metadata
Unit tests:N/A
Documentation: To be done.
---------
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
This fixes processing issue for nodes with numbers in their labels (e.g.
`"node_1"`, which would previously be relabeled as `"node__"`, and now
are correctly processed as `"node_1"`)
**Description:**
Fix "`TypeError: 'NoneType' object is not iterable`" when the
auth_context is absent in PebbloRetrievalQA. The auth_context is
optional; hence, PebbloRetrievalQA should work without it, but it throws
an error at the moment. This PR fixes that issue.
**Issue:** NA
**Dependencies:** None
**Unit tests:** NA
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description: file_metadata_ was not getting propagated to returned
documents. Changed the lookup key to the name of the blob's path.
Changed blob.path key to blob.path.name for metadata_dict key lookup.
Documentation: N/A
Unit tests: N/A
Co-authored-by: ccurme <chester.curme@gmail.com>
