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# LangGraph Retrieval Agent Template
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# LangGraph RAG Research Agent Template
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<!-- TODO: Add CI shields -->
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<!-- [](https://github.com/langchain-ai/retrieval-agent-template-js/actions/workflows/unit-tests.yml)
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[](https://github.com/langchain-ai/retrieval-agent-template-js/actions/workflows/integration-tests.yml) -->
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[](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/langchain-ai/rag-research-agent-template-js)
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[](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/langchain-ai/retrieval-agent-template-js)
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This is a starter project to help you get started with developing a retrieval agent using [LangGraph.js](https://github.com/langchain-ai/langgraphjs) in [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio).
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This is a starter project to help you get started with developing a RAG research agent using [LangGraph.js](https://github.com/langchain-ai/langgraphjs) in [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio).
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It contains example graphs exported from `src/retrieval_agent/graph.ts` that implement a retrieval-based question answering system.
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## What it does
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This project has two graphs: an "index" graph, and a "retrieval" graph.
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This project has three graphs:
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The index graph takes in document objects and strings, and it indexes them for the configured `userId`.
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* an "index" graph (`src/index_graph/graph.py`)
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* a "retrieval" graph (`src/retrieval_graph/graph.py`)
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* a "researcher" subgraph (part of the retrieval graph) (`src/researcher_graph/graph.py`)
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The index graph takes in document objects indexes them.
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```json
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[{ "page_content": "I have 1 cat." }]
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[{ "page_content": "LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows." }]
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```
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The retrieval chat bot manages a chat history and responds based on fetched context. It:
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If an empty list is provided (default), a list of sample documents from `src/sample_docs.json` is indexed instead. Those sample documents are based on the conceptual guides for LangChain and LangGraph.
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The retrieval graph manages a chat history and responds based on the fetched documents. Specifically, it:
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1. Takes a user **query** as input
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2. Searches for documents in filtered by userId based on the conversation history
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3. Responds using the retrieved information and conversation context
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By default, it's set up to answer questions based on the user's indexed documents, which are filtered by the user's ID for personalized responses.
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2. Analyzes the query and determines how to route it:
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- if the query is about "LangChain", it creates a research plan based on the user's query and passes the plan to the researcher subgraph
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- if the query is ambiguous, it asks for more information
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- if the query is general (unrelated to LangChain), it lets the user know
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3. If the query is about "LangChain", the researcher subgraph runs for each step in the research plan, until no more steps are left:
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- it first generates a list of queries based on the step
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- it then retrieves the relevant documents in parallel for all queries and return the documents to the retrieval graph
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4. Finally, the retrieval graph generates a response based on the retrieved documents and the conversation context
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## Getting Started
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@@ -73,7 +77,15 @@ ELASTICSEARCH_API_KEY=<API_KEY>
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**Local Elasticsearch (Docker)**
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```
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docker run -p 127.0.0.1:9200:9200 -d --name elasticsearch --network elastic-net -e ELASTIC_PASSWORD=changeme -e "discovery.type=single-node" -e "xpack.security.http.ssl.enabled=false" -e "xpack.license.self_generated.type=trial" docker.elastic.co/elasticsearch/elasticsearch:8.15.1
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docker run \
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-p 127.0.0.1:9200:9200 \
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-d \
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--name elasticsearch \
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-e ELASTIC_PASSWORD=changeme \
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-e "discovery.type=single-node" \
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-e "xpack.security.http.ssl.enabled=false" \
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-e "xpack.license.self_generated.type=trial" \
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docker.elastic.co/elasticsearch/elasticsearch:8.15.1
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```
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See the [official Elastic documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html) for more information on running it locally.
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@@ -231,41 +243,40 @@ End setup instructions
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## Using
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Once you've set up your retriever saved your model secrets, it's time to try it out! First, let's add some information to the index. Open studio, select the "indexer" graph from the dropdown in the top-left, provide an example user ID in the configuration at the bottom, and then add some content to chat over.
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Once you've set up your retriever and saved your model secrets, it's time to try it out! First, let's add some information to the index. Open studio, select the "indexer" graph from the dropdown in the top-left, and then add some content to chat over. You can just invoke it with an empty list (default) to index sample documents from LangChain and LangGraph documentation.
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```json
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[{ "page_content": "My cat knows python." }]
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```
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You'll know that the indexing is complete when the indexer "delete"'s the content from its graph memory (since it's been persisted in your configured storage provider).
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When you upload content, it will be indexed under the configured user ID. You know it's complete when the indexer "delete"'s the content from its graph memory (since it's been persisted in your configured storage provider).
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Next, open the "retrieval_graph" using the dropdown in the top-left. Ask it about your cat to confirm it can fetch the required information! If you change the `userId` at any time, notice how it no longer has access to your information. The graph is doing simple filtering of content so you only access the information under the provided ID.
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Next, open the "retrieval_graph" using the dropdown in the top-left. Ask it questions about LangChain to confirm it can fetch the required information!
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## How to customize
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You can customize this retrieval agent template in several ways:
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1. **Change the retriever**: You can switch between different vector stores (Elasticsearch, MongoDB, Pinecone) by modifying the `retrieverProvider` in the configuration. Each provider has its own setup instructions in the "Getting Started" section above.
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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.
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2. **Modify the embedding model**: You can change the embedding model used for document indexing and query embedding by updating the `embeddingModel` in the configuration. Options include various OpenAI and Cohere models.
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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.
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3. **Adjust search parameters**: Fine-tune the retrieval process by modifying the `searchKwargs` in the configuration. This allows you to control aspects like the number of documents retrieved or similarity thresholds.
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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.
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4. **Customize the response generation**: You can modify the `responseSystemPrompt` to change how the agent formulates its responses. This allows you to adjust the agent's personality or add specific instructions for answer generation.
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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.
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5. **Change the language model**: Update the `responseModel` 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.
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1. **Modify prompts**: Update the prompts used for user query routing, research planning, query generation and more in `src/retrieval_graph/prompts.py` 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:
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6. **Extend the graph**: You can add new nodes or modify existing ones in the `src/retrieval_agent/graph.ts` file to introduce additional processing steps or decision points in the agent's workflow.
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* Modify system prompt for creating research plan (`research_plan_system_prompt`)
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* Modify system prompt for generating search queries based on the research plan (`generate_queries_system_prompt`)
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7. **Add new tools**: Implement new tools or API integrations in `src/retrieval_agent/tools.ts` to expand the agent's capabilities beyond simple retrieval and response generation.
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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.
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8. **Modify prompts**: Update the prompts used for query generation and response formulation in `src/retrieval_agent/prompts.ts` to better suit your specific use case or to improve the agent's performance.
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1. **Extend the graph**: You can add new nodes or modify existing ones in the `src/retrieval_graph/graph.py` file to introduce additional processing steps or decision points in the agent's workflow.
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1. **Add tools**: Implement tools to expand the researcher agent's capabilities beyond simple retrieval generation.
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Remember to test your changes thoroughly to ensure they improve the agent's performance for your specific use case.
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## Development
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While iterating on your graph, you can edit past state and rerun your app from past states to debug specific nodes. Local changes will be automatically applied via hot reload. Try adding an interrupt before the agent calls tools, updating the default system message in `src/retrieval_agent/utils.ts` to take on a persona, or adding additional nodes and edges!
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While iterating on your graph, you can edit past state and rerun your app from past states to debug specific nodes. Local changes will be automatically applied via hot reload. Try adding an interrupt before the agent calls the researcher subgraph, updating the default system message in `src/retrieval_graph/prompts.ts` to take on a persona, or adding additional nodes and edges!
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Follow up requests will be appended to the same thread. You can create an entirely new thread, clearing previous history, using the `+` button in the top right.
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@@ -329,12 +340,12 @@ Configuration auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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},
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"retrieverProvider": {
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"enum": [
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"elastic",
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"elastic-local",
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"elastic",
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"mongodb",
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"pinecone"
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],
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"default": "elastic",
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"default": "elastic-local",
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"description": "The vector store provider to use for retrieval. Options are 'elastic', 'pinecone', or 'mongodb'.",
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"environment": [
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{
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},
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"retrieverProvider": {
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"enum": [
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"elastic",
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"elastic-local",
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"elastic",
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"mongodb",
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"pinecone"
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],
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"default": "elastic",
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"default": "elastic-local",
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"description": "The vector store provider to use for retrieval. Options are 'elastic', 'pinecone', or 'mongodb'.",
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"environment": [
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{
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