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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
.github Include testing instructions for getting setup in CONTRIBUTING.md (#3020) 2023-04-17 08:34:07 -07:00
docs Fix notebook example (#3142) 2023-04-19 08:55:06 -07:00
langchain Print exception type for Python tool (#3126) 2023-04-18 22:45:06 -07:00
tests Update Tool Input (#3103) 2023-04-18 18:18:33 -07:00
.dockerignore fix: tests with Dockerfile (#2382) 2023-04-04 06:47:19 -07:00
.flake8 change run to use args and kwargs (#367) 2022-12-18 15:54:56 -05:00
.gitignore Fix notebook example (#3142) 2023-04-19 08:55:06 -07:00
CITATION.cff bump version to 0069 (#710) 2023-01-24 00:24:54 -08:00
Dockerfile feat: add pytest-vcr for recording HTTP interactions in integration tests (#2445) 2023-04-07 07:28:57 -07:00
LICENSE add license (#50) 2022-11-01 21:12:02 -07:00
Makefile Add lint_diff command (#2449) 2023-04-05 09:34:24 -07:00
poetry.lock Harrison/confluent loader (#2994) 2023-04-17 20:23:45 -07:00
poetry.toml fix Poetry 1.4.0+ installation (#1935) 2023-03-27 08:27:54 -07:00
pyproject.toml bump version to 144 (#3136) 2023-04-18 23:29:23 -07:00
README.md Update README.md (#2805) 2023-04-12 22:02:06 -07:00
readthedocs.yml update rtd config (#1664) 2023-03-14 10:40:06 -07:00

🦜🔗 LangChain

Building applications with LLMs through composability

lint test linkcheck Downloads License: MIT Twitter

Production Support: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.

Quick Install

pip install langchain or conda install langchain -c conda-forge

🤔 What is this?

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:

Question Answering over specific documents

💬 Chatbots

🤖 Agents

📖 Documentation

Please see here for full documentation on:

  • Getting started (installation, setting up the environment, simple examples)
  • How-To examples (demos, integrations, helper functions)
  • Reference (full API docs)
  • Resources (high-level explanation of core concepts)

🚀 What can this help with?

There are six main areas that LangChain is designed to help with. These are, in increasing order of complexity:

📃 LLMs and Prompts:

This includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.

🔗 Chains:

Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.

📚 Data Augmented Generation:

Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.

🤖 Agents:

Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.

🧠 Memory:

Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.

🧐 Evaluation:

[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.

For more information on these concepts, please see our full documentation.

💁 Contributing

As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.

For detailed information on how to contribute, see here.