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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
77 lines
2.5 KiB
Markdown
77 lines
2.5 KiB
Markdown
# Solo performance prompting agent
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This template creates an agent that transforms a single LLM
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into a cognitive synergist by engaging in multi-turn self-collaboration
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with multiple personas.
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A `cognitive synergist` refers to an intelligent agent that collaborates
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with multiple minds, combining their individual strengths and knowledge,
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to enhance problem-solving and overall performance in complex tasks.
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By dynamically identifying and simulating different personas based
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on task inputs, SPP unleashes the potential of cognitive synergy in LLMs.
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This template will use the `DuckDuckGo` search API.
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## Environment Setup
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This template will use `OpenAI` by default.
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Be sure that `OPENAI_API_KEY` is set in your environment.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package solo-performance-prompting-agent
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add solo-performance-prompting-agent
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```
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And add the following code to your `server.py` file:
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```python
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from solo_performance_prompting_agent.agent import agent_executor as solo_performance_prompting_agent_chain
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add_routes(app, solo_performance_prompting_agent_chain, path="/solo-performance-prompting-agent")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/solo-performance-prompting-agent/playground](http://127.0.0.1:8000/solo-performance-prompting-agent/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/solo-performance-prompting-agent")
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``` |