While we work on solidifying the memory interfaces, handle common chat
history formats.
This may break linting on anyone who has been passing in
`get_chat_history` .
Somewhat handles #3077
Alternative to #3078 that updates the typing
First cut of a supabase vectorstore loosely patterned on the langchainjs
equivalent. Doesn't support async operations which is a limitation of
the supabase python client.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
Separated the deployment from model to support Azure OpenAI Embeddings
properly.
Also removed the deprecated document_model_name and query_model_name
attributes.
- Permit the specification of a `root_dir` to the read/write file tools
to specify a working directory
- Add validation for attempts to read/write outside the directory (e.g.,
through `../../` or symlinks or `/abs/path`'s that don't lie in the
correct path)
- Add some tests for all
One question is whether we should make a default root directory for
these? tradeoffs either way
This occurred when redis_url was not passed as a parameter even though a
REDIS_URL env variable was present.
This occurred for all methods that eventually called any of:
(from_texts, drop_index, from_existing_index) - i.e. virtually all
methods in the class.
This fixes it
`langchain.prompts.PromptTemplate` and
`langchain.prompts.FewShotPromptTemplate` do not validate
`input_variables` when initialized as `jinja2` template.
```python
# Using langchain v0.0.144
template = """"\
Your variable: {{ foo }}
{% if bar %}
You just set bar boolean variable to true
{% endif %}
"""
# Missing variable, should raise ValueError
prompt_template = PromptTemplate(template=template,
input_variables=["bar"],
template_format="jinja2",
validate_template=True)
# Extra variable, should raise ValueError
prompt_template = PromptTemplate(template=template,
input_variables=["bar", "foo", "extra", "thing"],
template_format="jinja2",
validate_template=True)
```
### Summary
Updates the `UnstructuredURLLoader` to support passing in headers for
non HTML content types. While this update maintains backward
compatibility with older versions of `unstructured`, we strongly
recommended upgrading to `unstructured>=0.5.13` if you are using the
`UnstructuredURLLoader`.
### Testing
#### With headers
```python
from langchain.document_loaders import UnstructuredURLLoader
urls = ["https://www.understandingwar.org/sites/default/files/Russian%20Offensive%20Campaign%20Assessment%2C%20April%2011%2C%202023.pdf"]
loader = UnstructuredURLLoader(urls=urls, headers={"Accept": "application/json"}, strategy="fast")
docs = loader.load()
print(docs[0].page_content[:1000])
```
#### Without headers
```python
from langchain.document_loaders import UnstructuredURLLoader
urls = ["https://www.understandingwar.org/sites/default/files/Russian%20Offensive%20Campaign%20Assessment%2C%20April%2011%2C%202023.pdf"]
loader = UnstructuredURLLoader(urls=urls, strategy="fast")
docs = loader.load()
print(docs[0].page_content[:1000])
```
---------
Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
Add DocumentTransformer abstraction so that in #2915 we don't have to
wrap TextSplitter and RedundantEmbeddingFilter (neither of which uses
the query) in the contextual doc compression abstractions. with this
change, doc filter (doc extractor, whatever we call it) would look
something like
```python
class BaseDocumentFilter(BaseDocumentTransformer[_RetrievedDocument], ABC):
@abstractmethod
def filter(self, documents: List[_RetrievedDocument], query: str) -> List[_RetrievedDocument]:
...
def transform_documents(self, documents: List[_RetrievedDocument], query: Optional[str] = None, **kwargs: Any) -> List[_RetrievedDocument]:
if query is None:
raise ValueError("Must pass in non-null query to DocumentFilter")
return self.filter(documents, query)
```
I have noticed a typo error in the `custom_mrkl_agents.ipynb` document
while trying the example from the documentation page. As a result, I
have opened a pull request (PR) to address this minor issue, even though
it may seem insignificant 😂.
The following calls were throwing an exception:
575b717d10/docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb (L192)575b717d10/docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb (L239)
Exception:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[14], line 1
----> 1 chain_sota = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0), chain_type="stuff", retriever=vectorstore_sota, input_key="question")
File ~/github/langchain/venv/lib/python3.9/site-packages/langchain/chains/retrieval_qa/base.py:89, in BaseRetrievalQA.from_chain_type(cls, llm, chain_type, chain_type_kwargs, **kwargs)
85 _chain_type_kwargs = chain_type_kwargs or {}
86 combine_documents_chain = load_qa_chain(
87 llm, chain_type=chain_type, **_chain_type_kwargs
88 )
---> 89 return cls(combine_documents_chain=combine_documents_chain, **kwargs)
File ~/github/langchain/venv/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for RetrievalQA
retriever
instance of BaseRetriever expected (type=type_error.arbitrary_type; expected_arbitrary_type=BaseRetriever)
```
The vectorstores had to be converted to retrievers:
`vectorstore_sota.as_retriever()` and `vectorstore_pg.as_retriever()`.
The PR also:
- adds the file `paul_graham_essay.txt` referenced by this notebook
- adds to gitignore *.pkl and *.bin files that are generated by this
notebook
Interestingly enough, the performance of the prediction greatly
increased (new version of langchain or ne version of OpenAI models since
the last run of the notebook): from 19/33 correct to 28/33 correct!
- Remove dynamic model creation in the `args()` property. _Only infer
for the decorator (and add an argument to NOT infer if someone wishes to
only pass as a string)_
- Update the validation example to make it less likely to be
misinterpreted as a "safe" way to run a repl
There is one example of "Multi-argument tools" in the custom_tools.ipynb
from yesterday, but we could add more. The output parsing for the base
MRKL agent hasn't been adapted to handle structured args at this point
in time
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
