langchain-ai/rag-research-agent-template-js
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@@ -255,22 +255,22 @@ You can customize this retrieval agent template in several ways:
1. **Change the retriever**: You can switch between different vector stores (Elasticsearch, MongoDB, Pinecone) by modifying the `retriever_provider` in the configuration. Each provider has its own setup instructions in the "Getting Started" section above.
1. **Modify the embedding model**: You can change the embedding model used for document indexing and query embedding by updating the `embedding_model` in the configuration. Options include various OpenAI and Cohere models.
2. **Modify the embedding model**: You can change the embedding model used for document indexing and query embedding by updating the `embedding_model` in the configuration. Options include various OpenAI and Cohere models.
1. **Adjust search parameters**: Fine-tune the retrieval process by modifying the `search_kwargs` in the configuration. This allows you to control aspects like the number of documents retrieved or similarity thresholds.
3. **Adjust search parameters**: Fine-tune the retrieval process by modifying the `search_kwargs` in the configuration. This allows you to control aspects like the number of documents retrieved or similarity thresholds.
1. **Customize the response generation**: You can modify the `response_system_prompt` to change how the agent formulates its responses. This allows you to adjust the agent's personality or add specific instructions for answer generation.
4. **Customize the response generation**: You can modify the `response_system_prompt` to change how the agent formulates its responses. This allows you to adjust the agent's personality or add specific instructions for answer generation.
1. **Modify prompts**: Update the prompts used for user query routing, research planning, query generation and more in `src/retrieval_graph/prompts.ts` to better suit your specific use case or to improve the agent's performance. You can also modify these directly in LangGraph Studio. For example, you can:
5. **Modify prompts**: Update the prompts used for user query routing, research planning, query generation and more in `src/retrieval_graph/prompts.ts` to better suit your specific use case or to improve the agent's performance. You can also modify these directly in LangGraph Studio. For example, you can:
* Modify system prompt for creating research plan (`research_plan_system_prompt`)
* Modify system prompt for generating search queries based on the research plan (`generate_queries_system_prompt`)
1. **Change the language model**: Update the `response_model` in the configuration to use different language models for response generation. Options include various Claude models from Anthropic, as well as models from other providers like Fireworks AI.
6. **Change the language model**: Update the `response_model` in the configuration to use different language models for response generation. Options include various Claude models from Anthropic, as well as models from other providers like Fireworks AI.
1. **Extend the graph**: You can add new nodes or modify existing ones in the `src/retrieval_graph/graph.ts` file to introduce additional processing steps or decision points in the agent's workflow.
7. **Extend the graph**: You can add new nodes or modify existing ones in the `src/retrieval_graph/graph.ts` file to introduce additional processing steps or decision points in the agent's workflow.
1. **Add tools**: Implement tools to expand the researcher agent's capabilities beyond simple retrieval generation.
8. **Add tools**: Implement tools to expand the researcher agent's capabilities beyond simple retrieval generation.
Remember to test your changes thoroughly to ensure they improve the agent's performance for your specific use case.