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https://github.com/liteobject/autogen_with_chromadb
This repository contains a Python script that uses the `autogen` and `chromadb` libraries to create a chatbot that can retrieve information from a database and generate responses based on a language model. The chatbot can also execute code and provide answers based on the context of the user's question.
https://github.com/liteobject/autogen_with_chromadb
ai-agent autogen chromadb vector-database
Last synced: about 1 month ago
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This repository contains a Python script that uses the `autogen` and `chromadb` libraries to create a chatbot that can retrieve information from a database and generate responses based on a language model. The chatbot can also execute code and provide answers based on the context of the user's question.
- Host: GitHub
- URL: https://github.com/liteobject/autogen_with_chromadb
- Owner: LiteObject
- Created: 2024-02-15T03:22:19.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-02-17T04:17:20.000Z (10 months ago)
- Last Synced: 2024-02-17T05:28:51.887Z (10 months ago)
- Topics: ai-agent, autogen, chromadb, vector-database
- Language: Python
- Homepage:
- Size: 3.32 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Autogen with Chroma DB 
>AutoGen is an open-source framework that enables the development of conversational AI applications using multiple agents.
>Chroma DB is an open-source vector database for storing and retrieving vector embeddings.
### Create virtual python environment
- `virtualenv -p python3.11 env_name`
- `python -m venv env_name`
### Activate the virtual env
- `env_name/scripts/activate`
---
## Installs AutoGen & Chroms DB
pip install -U "pyautogen[retrievechat]" chromadb
- `-U` tells pip to upgrade any already installed packages to their latest versions before installing.
- `"pyautogen[retrievechat]"` installs the pyautogen package and also installs the optional "retrievechat" extra feature of that package
## Set environment variable AUTOGEN_USE_DOCKER to False
### Bash Command:
export AUTOGEN_USE_DOCKER=False
### PowerShell Command:
$Env:AUTOGEN_USE_DOCKER="False"
Exporting `AUTOGEN_USE_DOCKER=False` tells pyautogen to run its tasks directly on the host rather than using Docker containers. It bypasses the Docker dependency but also loses some of the isolation benefits Docker provides.
## Set environment variable OPENAI_API_KEY=???
### Bash Command:
export OPENAI_API_KEY=Fxxxxxxxxxxxxxxxxxxxxxxxxx
### PowerShell Command:
$Env:OPENAI_API_KEY="xxxxxxxxxxxxxxxxxxxxxxxxx"
## Run `app.py`
python app.py
## Explanation of the code file
This code file defines a chatbot system using the autogen and chromadb libraries. Here's a step-by-step breakdown of the code:
### Importing Libraries
The first step is to import the necessary libraries. In this case, we're using autogen and chromadb to create a chatbot that can retrieve information from a database and generate responses based on a language model.
```python
import autogen
import chromadb
```
### Defining the Chatbot Assistant
Next, we define the chatbot assistant using the AssistantAgent class from the autogen library. This class takes a name, language model configuration, and system message as input.
```python
assistant = AssistantAgent(
name="my_assistant",
llm_config=llm_config_proxy,
system_message="You are a helpful assistant. Provide accurate answers based on the context. Respond 'Unsure about answer' if uncertain."
)
```
### Defining the User
We also define the user using the RetrieveUserProxyAgent class from the autogen.agentchat.contrib module. This class takes a name, human input mode, system message, maximum number of consecutive auto-replies, and configuration for retrieving information from a database as input.
```python
user = RetrieveUserProxyAgent(
name="me_user",
human_input_mode="NEVER",
system_message="Assistant who has extra content retrieval power for solving difficult problems.",
max_consecutive_auto_reply=10,
retrieve_config={
"task": "code",
"docs_path": ['./docs/autogen.pdf'],
"chunk_token_size": 1000,
"model": config_list[0]["model"],
"client": chromadb.PersistentClient(path='/tmp/chromadb'),
"collection_name": "pdfreader",
"get_or_create": True,
},
code_execution_config={"work_dir": "coding"},
)
```
### Defining the User Question
We define the user's question or prompt as a string variable.
```python
user_question = """
Compose a short blog post showcasing how AutoGen is revolutionizing the future of Generative AI
through the collaboration of various agents. Craft an introduction, main body, and a compelling
conclusion. Encourage readers to share the post. Keep the post under 500 words.
"""
```
### Initiating the Chat
Finally, we initiate the chat session between the user and the chatbot using the initiate_chat method of the RetrieveUserProxyAgent class.
```python
user.initiate_chat(assistant, problem=user_question)
```
### Summary
Overall, this code file defines a chatbot system that can respond to user questions or prompts by retrieving information from a database and generating responses based on a language model. The chatbot can also execute code and provide answers based on the context of the user's question.
---
## Links
- [Getting started with Chroma DB](https://docs.trychroma.com/getting-started)
- [AutoGen: Enable Next-Gen Large Language Model Applications](https://microsoft.github.io/autogen/)