Commit Graph

5 Commits

Author SHA1 Message Date
Quentin Pleplé
126d7f11dd
Fix notebook example (#3142)
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!
2023-04-19 08:55:06 -07:00
Harrison Chase
c7b083ab56
bump version to 131 (#2391) 2023-04-04 07:21:50 -07:00
Shrined
10dab053b4
Add Enum for agent types (#2321)
This pull request adds an enum class for the various types of agents
used in the project, located in the `agent_types.py` file. Currently,
the project is using hardcoded strings for the initialization of these
agents, which can lead to errors and make the code harder to maintain.
With the introduction of the new enums, the code will be more readable
and less error-prone.

The new enum members include:

- ZERO_SHOT_REACT_DESCRIPTION
- REACT_DOCSTORE
- SELF_ASK_WITH_SEARCH
- CONVERSATIONAL_REACT_DESCRIPTION
- CHAT_ZERO_SHOT_REACT_DESCRIPTION
- CHAT_CONVERSATIONAL_REACT_DESCRIPTION

In this PR, I have also replaced the hardcoded strings with the
appropriate enum members throughout the codebase, ensuring a smooth
transition to the new approach.
2023-04-03 21:56:20 -07:00
Harrison Chase
47d37db2d2
WIP: Harrison/base retriever (#1765) 2023-03-24 07:46:49 -07:00
Harrison Chase
2d098e8869
Harrison/agent eval (#1620)
Co-authored-by: jerwelborn <jeremy.welborn@gmail.com>
2023-03-14 12:37:48 -07:00