- **Description:** Added a retriever for the Outline API to ask
questions on knowledge base
- **Issue:** resolves#11814
- **Dependencies:** None
- **Tag maintainer:** @baskaryan
- **Description:**
I encountered an issue while running the existing sample code on the
page https://python.langchain.com/docs/modules/agents/how_to/agent_iter
in an environment with Pydantic 2.0 installed. The following error was
triggered:
```python
ValidationError Traceback (most recent call last)
<ipython-input-12-2ffff2c87e76> in <cell line: 43>()
41
42 tools = [
---> 43 Tool(
44 name="GetPrime",
45 func=get_prime,
2 frames
/usr/local/lib/python3.10/dist-packages/pydantic/v1/main.py in __init__(__pydantic_self__, **data)
339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
--> 341 raise validation_error
342 try:
343 object_setattr(__pydantic_self__, '__dict__', values)
ValidationError: 1 validation error for Tool
args_schema
subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel)
```
I have made modifications to the example code to ensure it functions
correctly in environments with Pydantic 2.0.
- **Description:** Simple change, I just added title metadata to
GoogleDriveLoader for optional File Loaders
- **Dependencies:** no dependencies
- **Tag maintainer:** @hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR provides idiomatic implementations for the exact-match and the
semantic LLM caches using Astra DB as backend through the database's
HTTP JSON API. These caches require the `astrapy` library as dependency.
Comes with integration tests and example usage in the `llm_cache.ipynb`
in the docs.
@baskaryan this is the Astra DB counterpart for the Cassandra classes
you merged some time ago, tagging you for your familiarity with the
topic. Thank you!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds a chat message history component that uses Astra DB for
persistence through the JSON API.
The `astrapy` package is required for this class to work.
I have added tests and a small notebook, and updated the relevant
references in the other docs pages.
(@rlancemartin this is the counterpart of the Cassandra equivalent class
you so helpfully reviewed back at the end of June)
Thank you!
- **Description:** This commit fixed the problem that Redis vector store
will change the value of a metadata from 0 to empty when saving the
document, which should be an un-intended behavior.
- **Issue:** N/A
- **Dependencies:** N/A
**Description:** Currently, if we pass in a ToolMessage back to the
chain, it crashes with error
`Got unsupported message type: `
This fixes it.
Tested locally
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** BaseStringMessagePromptTemplate.from_template was
passing the value of partial_variables into cls(...) via **kwargs,
rather than passing it to PromptTemplate.from_template. Which resulted
in those *partial_variables being* lost and becoming required
*input_variables*.
Co-authored-by: Josep Pon Farreny <josep.pon-farreny@siemens.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Fix some circular deps:
- move PromptValue into top level module bc both PromptTemplates and
OutputParsers import
- move tracer context vars to `tracers.context` and import them in
functions in `callbacks.manager`
- add core import tests
Adds a cookbook for semi-structured RAG via Docugami. This follows the
same outline as the semi-structured RAG with Unstructured cookbook:
https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb
The main change is this cookbook uses Docugami instead of Unstructured
to find text and tables, and shows how XML markup in the output helps
with retrieval and generation.
We are \@docugami on twitter, I am \@tjaffri
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
- **Description:** We need to update the Dockerfile for templates to
also copy your README.md. This is because poetry requires that a readme
exists if it is specified in the pyproject.toml
