14e4d30659
I made a couple of improvements to the Comet tracker:
* The Comet project name is configurable in various ways (code,
environment variable or file), having a default value in code meant that
users couldn't set the project name in an environment variable or in a
file.
* I added error catching when the `flush_tracker` is called in order to
avoid crashing the whole process. Instead we are gonna display a warning
or error log message (`extra={"show_traceback": True}` is an internal
convention to force the display of the traceback when using our own
logger).
I decided to add the error catching after seeing the following error in
the third example of the notebook:
```
COMET ERROR: Failed to export agent or LLM to Comet
Traceback (most recent call last):
File "/home/lothiraldan/project/cometml/langchain/langchain/callbacks/comet_ml_callback.py", line 484, in _log_model
langchain_asset.save(langchain_asset_path)
File "/home/lothiraldan/project/cometml/langchain/langchain/agents/agent.py", line 591, in save
raise ValueError(
ValueError: Saving not supported for agent executors. If you are trying to save the agent, please use the `.save_agent(...)`
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/lothiraldan/project/cometml/langchain/langchain/callbacks/comet_ml_callback.py", line 449, in flush_tracker
self._log_model(langchain_asset)
File "/home/lothiraldan/project/cometml/langchain/langchain/callbacks/comet_ml_callback.py", line 488, in _log_model
langchain_asset.save_agent(langchain_asset_path)
File "/home/lothiraldan/project/cometml/langchain/langchain/agents/agent.py", line 599, in save_agent
return self.agent.save(file_path)
File "/home/lothiraldan/project/cometml/langchain/langchain/agents/agent.py", line 145, in save
agent_dict = self.dict()
File "/home/lothiraldan/project/cometml/langchain/langchain/agents/agent.py", line 119, in dict
_dict = super().dict()
File "pydantic/main.py", line 449, in pydantic.main.BaseModel.dict
File "pydantic/main.py", line 868, in _iter
File "pydantic/main.py", line 743, in pydantic.main.BaseModel._get_value
File "/home/lothiraldan/project/cometml/langchain/langchain/schema.py", line 381, in dict
output_parser_dict["_type"] = self._type
File "/home/lothiraldan/project/cometml/langchain/langchain/schema.py", line 376, in _type
raise NotImplementedError
NotImplementedError
```
I still need to investigate and try to fix it, it looks related to
saving an agent to a file.
|
||
|---|---|---|
| .github | ||
| docs | ||
| langchain | ||
| tests | ||
| .dockerignore | ||
| .flake8 | ||
| .gitignore | ||
| CITATION.cff | ||
| Dockerfile | ||
| LICENSE | ||
| Makefile | ||
| poetry.lock | ||
| poetry.toml | ||
| pyproject.toml | ||
| README.md | ||
| readthedocs.yml | ||
🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
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
- Documentation
- End-to-end Example: Question Answering over Notion Database
💬 Chatbots
- Documentation
- End-to-end Example: Chat-LangChain
🤖 Agents
- Documentation
- End-to-end Example: GPT+WolframAlpha
📖 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.