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CrewAI Rag tool set a custom vectordb
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| from crewai_tools import RagTool | |
| from dotenv import load_dotenv | |
| from crewai import Agent, Crew, Process, Task | |
| load_dotenv() | |
| rag_tool = RagTool( | |
| config={ | |
| "vectordb": { | |
| "provider": "weaviate", # add your provider: we support embedchain support vectordb providers: https://docs.embedchain.ai/components/vector-databases/ | |
| "config": { | |
| "collection_name": "contracts_business_latest", # ENSURE YOU HAVE THE APPROPRIATE KEYS IN LOADED IN ENV like API_KEY, ENDPOINT, etc | |
| }, | |
| } | |
| } | |
| ) | |
| agent = Agent( | |
| role="Contract Analyst Agent", | |
| goal="You know everything about the text. Ensure the {question} gets passed to the rag_tool as query and get the answer.", | |
| backstory="""You are a master at understanding text and its details.""", | |
| verbose=True, | |
| allow_delegation=False, | |
| tools=[rag_tool], | |
| llm="gpt-4o", | |
| ) | |
| task = Task( | |
| description="Answer the following questions about the text: {question}.", | |
| expected_output="An answer to the question", | |
| agent=agent, | |
| output_file="output.txt", | |
| ) | |
| crew = Crew( | |
| agents=[agent], | |
| tasks=[task], | |
| verbose=True, | |
| process=Process.sequential, | |
| memory=True, | |
| ) | |
| result = crew.kickoff( | |
| inputs={ | |
| "question": "What are the differences in how contracts define warranties within creditcardscominc and digitalcinemadestination, provide the sources for each of them as well", | |
| }, | |
| ) | |
| print("result", result) |
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