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⚡ Building applications with LLMs through composability ⚡
0a177ec2cc
### Description: This pull request significantly enhances the MongodbLoader class in the LangChain community package by adding robust metadata customization and improved field extraction capabilities. The updated class now allows users to specify additional metadata fields through the metadata_names parameter, enabling the extraction of both top-level and deeply nested document attributes as metadata. This flexibility is crucial for users who need to include detailed contextual information without altering the database schema. Moreover, the include_db_collection_in_metadata flag offers optional inclusion of database and collection names in the metadata, allowing for even greater customization depending on the user's needs. The loader's field extraction logic has been refined to handle missing or nested fields more gracefully. It now employs a safe access mechanism that avoids the KeyError previously encountered when a specified nested field was absent in a document. This update ensures that the loader can handle diverse and complex data structures without failure, making it more resilient and user-friendly. ### Issue: This pull request addresses a critical issue where the MongodbLoader class in the LangChain community package could throw a KeyError when attempting to access nested fields that may not exist in some documents. The previous implementation did not handle the absence of specified nested fields gracefully, leading to runtime errors and interruptions in data processing workflows. This enhancement ensures robust error handling by safely accessing nested document fields, using default values for missing data, thus preventing KeyError and ensuring smoother operation across various data structures in MongoDB. This improvement is crucial for users working with diverse and complex data sets, ensuring the loader can adapt to documents with varying structures without failing. ### Dependencies: Requires motor for asynchronous MongoDB interaction. ### Twitter handle: N/A ### Add tests and docs Tests: Unit tests have been added to verify that the metadata inclusion toggle works as expected and that the field extraction correctly handles nested fields. Docs: An example notebook demonstrating the use of the enhanced MongodbLoader is included in the docs/docs/integrations directory. This notebook includes setup instructions, example usage, and outputs. (Here is the notebook link : [colab link](https://colab.research.google.com/drive/1tp7nyUnzZa3dxEFF4Kc3KS7ACuNF6jzH?usp=sharing)) Lint and test Before submitting, I ran make format, make lint, and make test as per the contribution guidelines. All tests pass, and the code style adheres to the LangChain standards. ```python import unittest from unittest.mock import patch, MagicMock import asyncio from langchain_community.document_loaders.mongodb import MongodbLoader class TestMongodbLoader(unittest.TestCase): def setUp(self): """Setup the MongodbLoader test environment by mocking the motor client and database collection interactions.""" # Mocking the AsyncIOMotorClient self.mock_client = MagicMock() self.mock_db = MagicMock() self.mock_collection = MagicMock() self.mock_client.get_database.return_value = self.mock_db self.mock_db.get_collection.return_value = self.mock_collection # Initialize the MongodbLoader with test data self.loader = MongodbLoader( connection_string="mongodb://localhost:27017", db_name="testdb", collection_name="testcol" ) @patch('langchain_community.document_loaders.mongodb.AsyncIOMotorClient', return_value=MagicMock()) def test_constructor(self, mock_motor_client): """Test if the constructor properly initializes with the correct database and collection names.""" loader = MongodbLoader( connection_string="mongodb://localhost:27017", db_name="testdb", collection_name="testcol" ) self.assertEqual(loader.db_name, "testdb") self.assertEqual(loader.collection_name, "testcol") def test_aload(self): """Test the aload method to ensure it correctly queries and processes documents.""" # Setup mock data and responses for the database operations self.mock_collection.count_documents.return_value = asyncio.Future() self.mock_collection.count_documents.return_value.set_result(1) self.mock_collection.find.return_value = [ {"_id": "1", "content": "Test document content"} ] # Run the aload method and check responses loop = asyncio.get_event_loop() results = loop.run_until_complete(self.loader.aload()) self.assertEqual(len(results), 1) self.assertEqual(results[0].page_content, "Test document content") def test_construct_projection(self): """Verify that the projection dictionary is constructed correctly based on field names.""" self.loader.field_names = ['content', 'author'] self.loader.metadata_names = ['timestamp'] expected_projection = {'content': 1, 'author': 1, 'timestamp': 1} projection = self.loader._construct_projection() self.assertEqual(projection, expected_projection) if __name__ == '__main__': unittest.main() ``` ### Additional Example for Documentation Sample Data: ```json [ { "_id": "1", "title": "Artificial Intelligence in Medicine", "content": "AI is transforming the medical industry by providing personalized medicine solutions.", "author": { "name": "John Doe", "email": "john.doe@example.com" }, "tags": ["AI", "Healthcare", "Innovation"] }, { "_id": "2", "title": "Data Science in Sports", "content": "Data science provides insights into player performance and strategic planning in sports.", "author": { "name": "Jane Smith", "email": "jane.smith@example.com" }, "tags": ["Data Science", "Sports", "Analytics"] } ] ``` Example Code: ```python loader = MongodbLoader( connection_string="mongodb://localhost:27017", db_name="example_db", collection_name="articles", filter_criteria={"tags": "AI"}, field_names=["title", "content"], metadata_names=["author.name", "author.email"], include_db_collection_in_metadata=True ) documents = loader.load() for doc in documents: print("Page Content:", doc.page_content) print("Metadata:", doc.metadata) ``` Expected Output: ``` Page Content: Artificial Intelligence in Medicine AI is transforming the medical industry by providing personalized medicine solutions. Metadata: {'author_name': 'John Doe', 'author_email': 'john.doe@example.com', 'database': 'example_db', 'collection': 'articles'} ``` Thank you. --- Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. --------- Co-authored-by: ccurme <chester.curme@gmail.com> |
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🦜️🔗 LangChain
⚡ Build context-aware reasoning applications ⚡
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 large language models (LLMs).
For these applications, LangChain simplifies the entire application lifecycle:
- Open-source libraries: Build your applications using LangChain's open-source building blocks, components, and third-party integrations. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support.
- Productionization: Inspect, monitor, and evaluate your apps with LangSmith so that you can constantly optimize and deploy with confidence.
- Deployment: Turn your LangGraph applications into production-ready APIs and Assistants with LangGraph Cloud.
Open-source libraries
langchain-core: Base abstractions and LangChain Expression Language.langchain-community: Third party integrations.- Some integrations have been further split into partner packages that only rely on
langchain-core. Examples includelangchain_openaiandlangchain_anthropic.
- Some integrations have been further split into partner packages that only rely on
langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.LangGraph: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, Introduction to LangGraph, available here.
Productionization:
- LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
Deployment:
- LangGraph Cloud: Turn your LangGraph applications into production-ready APIs and Assistants.
🧱 What can you build with LangChain?
❓ Question answering with RAG
- Documentation
- End-to-end Example: Chat LangChain and repo
🧱 Extracting structured output
- Documentation
- End-to-end Example: SQL Llama2 Template
🤖 Chatbots
- Documentation
- End-to-end Example: Web LangChain (web researcher chatbot) and repo
And much more! Head to the Tutorials section of the docs for more.
🚀 How does LangChain help?
The main value props of the LangChain libraries are:
- Components: composable building blocks, 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
- 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.
LangChain Expression Language (LCEL)
LCEL is a key part of LangChain, allowing you to build and organize chains of processes in a straightforward, declarative manner. It was designed to support taking prototypes directly into production without needing to alter any code. This means you can use LCEL to set up everything from basic "prompt + LLM" setups to intricate, multi-step workflows.
- Overview: LCEL and its benefits
- Interface: The standard Runnable interface for LCEL objects
- Primitives: More on the primitives LCEL includes
- Cheatsheet: Quick overview of the most common usage patterns
Components
Components fall into the following modules:
📃 Model I/O
This includes prompt management, prompt optimization, a generic interface for chat models and LLMs, and common utilities for working with model outputs.
📚 Retrieval
Retrieval Augmented Generation involves loading data from a variety of sources, preparing it, then searching over (a.k.a. retrieving from) it for use in the generation step.
🤖 Agents
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a standard interface for agents, along with LangGraph for building custom agents.
📖 Documentation
Please see here for full documentation, which includes:
- Introduction: Overview of the framework and the structure of the docs.
- Tutorials: If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
- How-to guides: Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
- Conceptual guide: Conceptual explanations of the key parts of the framework.
- API Reference: Thorough documentation of every class and method.
🌐 Ecosystem
- 🦜🛠️ LangSmith: Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
- 🦜🕸️ LangGraph: Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
- 🦜🏓 LangServe: Deploy LangChain runnables and chains as REST APIs.
💁 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.