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Juan Jose Miguel Ovalle Villamil 51baa1b5cf
langchain[patch]: fix-cohere-reranker-rerank-method with cohere v5 (#19486)
#### Description
Fixed the following error with `rerank` method from `CohereRerank`:
```
---> [79](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:79) results = self.client.rerank(
     [80](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:80)     query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
     [81](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:81) )
     [82](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:82) result_dicts = []
     [83](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:83) for res in results.results:

TypeError: BaseCohere.rerank() takes 1 positional argument but 4 positional arguments (and 2 keyword-only arguments) were given
```
This was easily fixed going from this:
```
   def rerank(
        self,
        documents: Sequence[Union[str, Document, dict]],
        query: str,
        *,
        model: Optional[str] = None,
        top_n: Optional[int] = -1,
        max_chunks_per_doc: Optional[int] = None,
    ) -> List[Dict[str, Any]]:
         ...
        if len(documents) == 0:  # to avoid empty api call
            return []
        docs = [
            doc.page_content if isinstance(doc, Document) else doc for doc in documents
        ]
        model = model or self.model
        top_n = top_n if (top_n is None or top_n > 0) else self.top_n
        results = self.client.rerank(
            query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
        )
        result_dicts = []
        for res in results:
            result_dicts.append(
                {"index": res.index, "relevance_score": res.relevance_score}
            )
        return result_dicts
```
to this:
```
    def rerank(
        self,
        documents: Sequence[Union[str, Document, dict]],
        query: str,
        *,
        model: Optional[str] = None,
        top_n: Optional[int] = -1,
        max_chunks_per_doc: Optional[int] = None,
    ) -> List[Dict[str, Any]]:
         ...
        if len(documents) == 0:  # to avoid empty api call
            return []
        docs = [
            doc.page_content if isinstance(doc, Document) else doc for doc in documents
        ]
        model = model or self.model
        top_n = top_n if (top_n is None or top_n > 0) else self.top_n
        results = self.client.rerank(
            query=query, documents=docs, model=model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc <-------------
        )
        result_dicts = []
        for res in results.results:  <-------------
            result_dicts.append(
                {"index": res.index, "relevance_score": res.relevance_score}
            )
        return result_dicts
```
#### Unit & Integration tests
I added a unit test to check the behaviour of `rerank`. Also fixed the
original integration test which was failing.

#### Format & Linting
Everything worked properly with `make lint_diff`, `make format_diff` and
`make format`. However I noticed an error coming from other part of the
library when doing `make lint`:

```
(langchain-py3.9) ➜  langchain git:(master) make format
[ "." = "" ] || poetry run ruff format .
1636 files left unchanged
[ "." = "" ] || poetry run ruff --select I --fix .
(langchain-py3.9) ➜  langchain git:(master) make lint
./scripts/check_pydantic.sh .
./scripts/lint_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run ruff format . --diff
1636 files already formatted
[ "." = "" ] || poetry run ruff --select I .
[ "." = "" ] || mkdir -p .mypy_cache && poetry run mypy . --cache-dir .mypy_cache
langchain/agents/openai_assistant/base.py:252: error: Argument "file_ids" to "create" of "Assistants" has incompatible type "Optional[Any]"; expected "Union[list[str], NotGiven]"  [arg-type]
langchain/agents/openai_assistant/base.py:374: error: Argument "file_ids" to "create" of "AsyncAssistants" has incompatible type "Optional[Any]"; expected "Union[list[str], NotGiven]"  [arg-type]
Found 2 errors in 1 file (checked 1634 source files)
make: *** [Makefile:65: lint] Error 1
```

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-03-28 06:32:03 +00:00
.devcontainer Update README.md (#8570) 2023-11-12 22:07:49 -08:00
.github github action: Add dead link check for .mdx files (#19492) 2024-03-26 08:42:34 -07:00
cookbook community[minor]: Add VDMS vectorstore (#19551) 2024-03-28 03:12:11 +00:00
docker community[minor]: Add VDMS vectorstore (#19551) 2024-03-28 03:12:11 +00:00
docs community[minor]: Update Azure Cognitive Services to Azure AI Services (#19488) 2024-03-28 03:19:02 +00:00
libs langchain[patch]: fix-cohere-reranker-rerank-method with cohere v5 (#19486) 2024-03-28 06:32:03 +00:00
templates infra: Update package version to apply CVE-related patch (#19490) 2024-03-25 07:11:23 +00:00
.gitattributes Update dev container (#6189) 2023-06-16 15:42:14 -07:00
.gitignore Add docstrings for Clickhouse class methods (#19195) 2024-03-19 04:03:12 +00:00
.readthedocs.yaml infra: update rtd yaml (#17502) 2024-02-13 18:16:44 -08:00
CITATION.cff rename repo namespace to langchain-ai (#11259) 2023-10-01 15:30:58 -04:00
LICENSE Library Licenses (#13300) 2023-11-28 17:34:27 -08:00
Makefile infra: makefile api_docs_clean fix (#19405) 2024-03-22 15:45:55 -07:00
MIGRATE.md Update main readme (#13298) 2023-11-13 17:37:54 -08:00
poetry.lock infra: Consistent lxml requirements (#19520) 2024-03-27 20:27:59 +00:00
poetry.toml Unbreak devcontainer (#8154) 2023-07-23 19:33:47 -07:00
pyproject.toml infra: Update package version to apply CVE-related patch (#19490) 2024-03-25 07:11:23 +00:00
README.md docs: update readme diagram (#18929) 2024-03-11 11:17:45 -07:00
SECURITY.md Updated security policy (#19089) 2024-03-14 20:58:47 +00:00

🦜🔗 LangChain

Build context-aware reasoning applications

Release Notes CI Downloads License: MIT Twitter Open in Dev Containers Open in GitHub Codespaces GitHub star chart Dependency Status Open Issues

Looking for the JS/TS library? Check out LangChain.js.

To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to speak with our sales team.

Quick Install

With pip:

pip install langchain

With conda:

conda install langchain -c conda-forge

🤔 What is LangChain?

LangChain is a framework for developing applications powered by language models. It enables applications that:

  • Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
  • Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)

This framework consists of several parts.

  • LangChain Libraries: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
  • LangChain Templates: A collection of easily deployable reference architectures for a wide variety of tasks.
  • LangServe: A library for deploying LangChain chains as a REST API.
  • LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
  • LangGraph: LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.

The LangChain libraries themselves are made up of several different packages.

  • langchain-core: Base abstractions and LangChain Expression Language.
  • langchain-community: Third party integrations.
  • langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.

Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.

🧱 What can you build with LangChain?

Retrieval augmented generation

💬 Analyzing structured data

🤖 Chatbots

And much more! Head to the Use cases section of the docs for more.

🚀 How does LangChain help?

The main value props of the LangChain libraries are:

  1. Components: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
  2. Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks

Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.

Components fall into the following modules:

📃 Model I/O:

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

📚 Retrieval:

Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples 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.

📖 Documentation

Please see here for full documentation, which includes:

💁 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 infrastructure, or better documentation.

For detailed information on how to contribute, see here.

🌟 Contributors

langchain contributors