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‎supporting-blog-content/langraph-retrieval-agent-template-demo/.github/workflows/integration-tests.yml

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# This workflow will run integration tests for the current project once per day
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name: Integration Tests
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on:
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schedule:
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- cron: "37 14 * * *" # Run at 7:37 AM Pacific Time (14:37 UTC) every day
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workflow_dispatch: # Allows triggering the workflow manually in GitHub UI
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# If another scheduled run starts while this workflow is still running,
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# cancel the earlier run in favor of the next run.
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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integration-tests:
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name: Integration Tests
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strategy:
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matrix:
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os: [ubuntu-latest]
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python-version: ["3.11", "3.12"]
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runs-on: ${{ matrix.os }}
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services:
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elasticsearch:
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image: elasticsearch:8.13.0
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env:
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discovery.type: single-node
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xpack.license.self_generated.type: trial
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xpack.security.enabled: false # disable password and TLS; never do this in production!
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ports:
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- 9200:9200
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options: >-
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--health-cmd "curl --fail http://localhost:9200/_cluster/health"
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--health-start-period 10s
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--health-timeout 3s
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--health-interval 3s
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--health-retries 10
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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curl -LsSf https://astral.sh/uv/install.sh | sh
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uv venv
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uv pip install -r pyproject.toml
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uv pip install -U pytest-asyncio
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- name: Run integration tests
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env:
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ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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ELASTICSEARCH_URL: http://localhost:9200
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ELASTICSEARCH_USER: elastic
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ELASTICSEARCH_PASSWORD: ""
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LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
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LANGSMITH_TRACING: true
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run: |
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uv run pytest tests/integration_tests

‎supporting-blog-content/langraph-retrieval-agent-template-demo/.github/workflows/unit-tests.yml

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# This workflow will run unit tests for the current project
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name: CI
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on:
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push:
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branches: ["main"]
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pull_request:
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workflow_dispatch: # Allows triggering the workflow manually in GitHub UI
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# If another push to the same PR or branch happens while this workflow is still running,
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# cancel the earlier run in favor of the next run.
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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unit-tests:
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name: Unit Tests
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strategy:
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matrix:
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os: [ubuntu-latest]
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python-version: ["3.11", "3.12"]
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runs-on: ${{ matrix.os }}
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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curl -LsSf https://astral.sh/uv/install.sh | sh
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uv venv
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uv pip install -r pyproject.toml
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- name: Lint with ruff
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run: |
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uv pip install ruff
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uv run ruff check .
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- name: Lint with mypy
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run: |
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uv pip install mypy
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uv run mypy --strict src/
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- name: Check README spelling
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uses: codespell-project/actions-codespell@v2
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with:
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ignore_words_file: .codespellignore
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path: README.md
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- name: Check code spelling
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uses: codespell-project/actions-codespell@v2
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with:
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ignore_words_file: .codespellignore
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path: src/
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- name: Run tests with pytest
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run: |
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uv pip install pytest
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uv run pytest tests/unit_tests

‎supporting-blog-content/langraph-retrieval-agent-template-demo/LICENSE

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MIT License
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Copyright (c) 2024 LangChain
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎supporting-blog-content/langraph-retrieval-agent-template-demo/Makefile

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.PHONY: all format lint test tests test_watch integration_tests docker_tests help extended_tests
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# Default target executed when no arguments are given to make.
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all: help
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# Define a variable for the test file path.
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TEST_FILE ?= tests/unit_tests/
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test:
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python -m pytest $(TEST_FILE)
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test_watch:
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python -m ptw --snapshot-update --now . -- -vv tests/unit_tests
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test_profile:
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python -m pytest -vv tests/unit_tests/ --profile-svg
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extended_tests:
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python -m pytest --only-extended $(TEST_FILE)
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######################
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# LINTING AND FORMATTING
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######################
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# Define a variable for Python and notebook files.
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PYTHON_FILES=src/
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MYPY_CACHE=.mypy_cache
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lint format: PYTHON_FILES=.
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lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep -E '\.py$$|\.ipynb$$')
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lint_package: PYTHON_FILES=src
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lint_tests: PYTHON_FILES=tests
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lint_tests: MYPY_CACHE=.mypy_cache_test
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lint lint_diff lint_package lint_tests:
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python -m ruff check .
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[ "$(PYTHON_FILES)" = "" ] || python -m ruff format $(PYTHON_FILES) --diff
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[ "$(PYTHON_FILES)" = "" ] || python -m ruff check --select I $(PYTHON_FILES)
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[ "$(PYTHON_FILES)" = "" ] || python -m mypy --strict $(PYTHON_FILES)
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[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && python -m mypy --strict $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
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format format_diff:
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ruff format $(PYTHON_FILES)
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ruff check --select I --fix $(PYTHON_FILES)
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spell_check:
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codespell --toml pyproject.toml
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spell_fix:
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codespell --toml pyproject.toml -w
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######################
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# HELP
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######################
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help:
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@echo '----'
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@echo 'format - run code formatters'
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@echo 'lint - run linters'
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@echo 'test - run unit tests'
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@echo 'tests - run unit tests'
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@echo 'test TEST_FILE=<test_file> - run all tests in file'
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@echo 'test_watch - run unit tests in watch mode'
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‎supporting-blog-content/langraph-retrieval-agent-template-demo/README.md

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# LangGraph RAG Workflow with Elasticsearch
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This project contains the code to create a custom agent using the LangGraph Retrieval Agent Template with Elasticsearch to build an efficient Retrieval-Augmented Generation (RAG) workflow for AI-driven responses.
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## Introduction
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LangGraph, developed by LangChain, simplifies the creation of retrieval-based question-answering systems. By using LangGraph Studio and LangGraph CLI, you can quickly build agents that index and retrieve documents using Elasticsearch.
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## Prerequisites
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Before you start, ensure you have the following installed:
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- Elasticsearch (Cloud or on-prem, version 8.0.0 or higher)
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- Python 3.9+
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- Access to an LLM provider like Cohere, OpenAI, or Anthropic
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## Steps to Set Up the LangGraph App
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### 1. Install LangGraph CLI
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```bash
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pip install --upgrade "langgraph-cli[inmem]"
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```
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### 2. Create LangGraph App
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```
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mkdir lg-agent-demo
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cd lg-agent-demo
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langgraph new lg-agent-demo
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```
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### 3. Install Dependencies
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Create a virtual environment and install the dependencies:
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For macOS:
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```
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python3 -m venv lg-demo
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source lg-demo/bin/activate
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pip install -e .
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```
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For Windows:
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```
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python3 -m venv lg-demo
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lg-demo\Scripts\activate
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pip install -e .
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```
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### 4. Set Up Environment
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Create a .env file by copying the example:
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```
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cp .env.example .env
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```
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Then, configure your .env file with your API keys and URLs for Elasticsearch and LLM.
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### 5. Update configuration.py
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Modify the configuration.py file to set up your LLM models, like Cohere (or OpenAI/Anthropic), as shown below:
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```embedding_model = "cohere/embed-english-v3.0"
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response_model = "cohere/command-r-08-2024"
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query_model = "cohere/command-r-08-2024"
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```
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## Running the Agent
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### 1. Launch LangGraph Server
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```
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cd lg-agent-demo
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langgraph dev
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```
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This starts the LangGraph API server locally.
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### 2. Open LangGraph Studio
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You can now access the LangGraph Studio UI and see the following:
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<img width="1306" alt="Screenshot 2025-04-01 at 6 02 31 PM" src="https://github.com/user-attachments/assets/c7c13645-99a1-48b2-8d3c-c1135fd33f54" />
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Indexer Graph: Indexes documents into Elasticsearch.
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<img width="776" alt="Screenshot 2025-03-11 at 6 08 09 PM" src="https://github.com/user-attachments/assets/5d61b9d0-ae9e-4d66-9e99-fa27bce7a1d0" />
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Retrieval Graph: Retrieves data from Elasticsearch and answers queries using the LLM.
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### 3. Index Sample Documents
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Index the sample documents into Elasticsearch (representing the NoveTech Solutions reports).
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### 4. Run the Retrieval Graph
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Enter a query like:
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```
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What was NovaTech Solutions' total revenue in Q1 2025?
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The system will retrieve relevant documents and provide an answer.
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```
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## Customizing the Retrieval Agent
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## Query Prediction
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To enhance user experience, add a query prediction feature based on the context from previous queries and retrieved documents. Here’s what to do:
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1. Add predict_query function in graph.py.
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2. Modify the respond function to return a response object.
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3. Update the graph structure to include a new node for query prediction.
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4. Modify Prompts and Configuration
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Update prompts.py to define a prompt for predicting the next question. Then, modify configuration.py to add this new prompt.
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```
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predict_next_question_prompt: str = "Your prompt here"
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```
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Re-run the Retrieval Graph
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Run the query again to see the predicted next three questions based on the context.
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<img width="732" alt="Screenshot 2025-03-17 at 3 06 54 PM" src="https://github.com/user-attachments/assets/88832fa6-4dc9-41cc-894d-d3d437bf4d80" />
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## Conclusion
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By using the LangGraph Retrieval Agent template with Elasticsearch, you can:
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- Accelerate development by using pre-configured templates.
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- Easily deploy with built-in API support and scaling.
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- Customize workflows to fit your specific use case.

‎supporting-blog-content/langraph-retrieval-agent-template-demo/langgraph.json

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{
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"dependencies": ["."],
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"graphs": {
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"indexer": "./src/retrieval_graph/index_graph.py:graph",
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"retrieval_graph": "./src/retrieval_graph/graph.py:graph"
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},
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"env": ".env"
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}

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