mirror of
https://github.com/hwchase17/langchain
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ArangoDB/AQL support for Graph QA Chain (#7880)
**Description**: Serves as an introduction to LangChain's support for [ArangoDB](https://github.com/arangodb/arangodb), similar to https://github.com/hwchase17/langchain/pull/7165 and https://github.com/hwchase17/langchain/pull/4881 **Issue**: No issue has been created for this feature **Dependencies**: `python-arango` has been added as an optional dependency via the `CONTRIBUTING.md` guidelines **Twitter handle**: [at]arangodb - Integration test has been added - Notebook has been added: [graph_arangodb_qa.ipynb](https://github.com/amahanna/langchain/blob/master/docs/extras/modules/chains/additional/graph_arangodb_qa.ipynb) [](https://colab.research.google.com/github/amahanna/langchain/blob/master/docs/extras/modules/chains/additional/graph_arangodb_qa.ipynb) ``` docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb ``` ``` pip install git+https://github.com/amahanna/langchain.git ``` ```python from arango import ArangoClient from langchain.chat_models import ChatOpenAI from langchain.graphs import ArangoGraph from langchain.chains import ArangoGraphQAChain db = ArangoClient(hosts="localhost:8529").db(name="_system", username="root", password="", verify=True) graph = ArangoGraph(db) chain = ArangoGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph) chain.run("Is Ned Stark alive?") ``` --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Anthony Mahanna
GitHub
parent
3e7d2a1b64
commit
76102971c0
23
docs/extras/integrations/providers/arangodb.mdx
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docs/extras/integrations/providers/arangodb.mdx
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# ArangoDB
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>[ArangoDB](https://github.com/arangodb/arangodb) is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud – anywhere.
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## Dependencies
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Install the [ArangoDB Python Driver](https://github.com/ArangoDB-Community/python-arango) package with
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```bash
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pip install python-arango
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```
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## Graph QA Chain
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Connect your ArangoDB Database with a Chat Model to get insights on your data.
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See the notebook example [here](/docs/modules/chains/additional/graph_arangodb_qa.html).
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```python
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from arango import ArangoClient
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from langchain.graphs import ArangoGraph
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from langchain.chains import ArangoGraphQAChain
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```
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819
docs/extras/modules/chains/additional/graph_arangodb_qa.ipynb
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819
docs/extras/modules/chains/additional/graph_arangodb_qa.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c94240f5",
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"metadata": {
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"id": "c94240f5"
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},
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"source": [
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"# ArangoDB QA chain\n",
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"\n",
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"[](https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/extras/modules/chains/additional/graph_arangodb_qa.ipynb)\n",
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"\n",
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"This notebook shows how to use LLMs to provide a natural language interface to an [ArangoDB](https://github.com/arangodb/arangodb#readme) database."
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]
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},
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{
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"cell_type": "markdown",
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"id": "dbc0ee68",
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"metadata": {
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"id": "dbc0ee68"
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},
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"source": [
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"You can get a local ArangoDB instance running via the [ArangoDB Docker image](https://hub.docker.com/_/arangodb): \n",
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"\n",
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"```\n",
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"docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb\n",
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"```\n",
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"\n",
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"An alternative is to use the [ArangoDB Cloud Connector package](https://github.com/arangodb/adb-cloud-connector#readme) to get a temporary cloud instance running:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "izi6YoFC8KRH",
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"metadata": {
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"id": "izi6YoFC8KRH"
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},
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"outputs": [],
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"source": [
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"%%capture\n",
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"!pip install python-arango # The ArangoDB Python Driver\n",
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"!pip install adb-cloud-connector # The ArangoDB Cloud Instance provisioner\n",
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"!pip install openai\n",
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"!pip install langchain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "62812aad",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "62812aad",
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"outputId": "f7ed8346-d88b-40d1-eaff-68e97e0e157e"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Log: requesting new credentials...\n",
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"Succcess: new credentials acquired\n",
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"{\n",
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" \"dbName\": \"TUT3sp29s3pjf1io0h4cfdsq\",\n",
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" \"username\": \"TUTo6nkwgzkizej3kysgdyeo8\",\n",
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" \"password\": \"TUT9vx0qjqt42i9bq8uik4v9\",\n",
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" \"hostname\": \"tutorials.arangodb.cloud\",\n",
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" \"port\": 8529,\n",
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" \"url\": \"https://tutorials.arangodb.cloud:8529\"\n",
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"}\n"
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]
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}
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],
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"source": [
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"# Instantiate ArangoDB Database\n",
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"import json\n",
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"from arango import ArangoClient\n",
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"from adb_cloud_connector import get_temp_credentials\n",
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"\n",
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"con = get_temp_credentials()\n",
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"\n",
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"db = ArangoClient(hosts=con[\"url\"]).db(\n",
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" con[\"dbName\"], con[\"username\"], con[\"password\"], verify=True\n",
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")\n",
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"\n",
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"print(json.dumps(con, indent=2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "0928915d",
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"metadata": {
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"id": "0928915d"
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},
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"outputs": [],
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"source": [
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"# Instantiate the ArangoDB-LangChain Graph\n",
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"from langchain.graphs import ArangoGraph\n",
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"\n",
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"graph = ArangoGraph(db)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "995ea9b9",
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"metadata": {
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"id": "995ea9b9"
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},
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"source": [
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"## Populating the Database\n",
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"\n",
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"We will rely on the Python Driver to import our [GameOfThrones](https://github.com/arangodb/example-datasets/tree/master/GameOfThrones) data into our database."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "fedd26b9",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "fedd26b9",
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"outputId": "fc7d9067-e4f5-495e-cd0c-79135bc16fc2"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'error': False,\n",
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" 'created': 4,\n",
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" 'errors': 0,\n",
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" 'empty': 0,\n",
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" 'updated': 0,\n",
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" 'ignored': 0,\n",
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" 'details': []}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"if db.has_graph(\"GameOfThrones\"):\n",
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" db.delete_graph(\"GameOfThrones\", drop_collections=True)\n",
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"\n",
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"db.create_graph(\n",
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" \"GameOfThrones\",\n",
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" edge_definitions=[\n",
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" {\n",
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" \"edge_collection\": \"ChildOf\",\n",
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" \"from_vertex_collections\": [\"Characters\"],\n",
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" \"to_vertex_collections\": [\"Characters\"],\n",
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" },\n",
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" ],\n",
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")\n",
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"\n",
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"documents = [\n",
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" {\n",
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" \"_key\": \"NedStark\",\n",
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" \"name\": \"Ned\",\n",
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" \"surname\": \"Stark\",\n",
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" \"alive\": True,\n",
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" \"age\": 41,\n",
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" \"gender\": \"male\",\n",
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" },\n",
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" {\n",
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" \"_key\": \"CatelynStark\",\n",
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" \"name\": \"Catelyn\",\n",
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" \"surname\": \"Stark\",\n",
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" \"alive\": False,\n",
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" \"age\": 40,\n",
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" \"gender\": \"female\",\n",
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" },\n",
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" {\n",
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" \"_key\": \"AryaStark\",\n",
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" \"name\": \"Arya\",\n",
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" \"surname\": \"Stark\",\n",
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" \"alive\": True,\n",
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" \"age\": 11,\n",
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" \"gender\": \"female\",\n",
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" },\n",
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" {\n",
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" \"_key\": \"BranStark\",\n",
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" \"name\": \"Bran\",\n",
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" \"surname\": \"Stark\",\n",
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" \"alive\": True,\n",
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" \"age\": 10,\n",
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" \"gender\": \"male\",\n",
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" },\n",
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"]\n",
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"\n",
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"edges = [\n",
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" {\"_to\": \"Characters/NedStark\", \"_from\": \"Characters/AryaStark\"},\n",
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" {\"_to\": \"Characters/NedStark\", \"_from\": \"Characters/BranStark\"},\n",
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" {\"_to\": \"Characters/CatelynStark\", \"_from\": \"Characters/AryaStark\"},\n",
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" {\"_to\": \"Characters/CatelynStark\", \"_from\": \"Characters/BranStark\"},\n",
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"]\n",
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"\n",
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"db.collection(\"Characters\").import_bulk(documents)\n",
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"db.collection(\"ChildOf\").import_bulk(edges)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "58c1a8ea",
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"metadata": {
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"id": "58c1a8ea"
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},
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"source": [
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"## Getting & Setting the ArangoDB Schema\n",
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"\n",
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"An initial ArangoDB Schema is generated upon instantiating the `ArangoDBGraph` object. Below are the schema's getter & setter methods should you be interested in viewing or modifying the schema:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "4e3de44f",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "4e3de44f",
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"outputId": "6102f0c6-4a94-4e00-b93e-eb8de2f7d9d5"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" \"Graph Schema\": [],\n",
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" \"Collection Schema\": []\n",
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"}\n"
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]
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}
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],
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"source": [
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"# The schema should be empty here,\n",
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"# since `graph` was initialized prior to ArangoDB Data ingestion (see above).\n",
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"\n",
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"import json\n",
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"\n",
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"print(json.dumps(graph.schema, indent=4))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "1fe76ccd",
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"metadata": {
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"id": "1fe76ccd"
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},
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"outputs": [],
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"source": [
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"graph.set_schema()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "mZ679anj_-Er",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "mZ679anj_-Er",
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"outputId": "e05229c7-bc61-4803-d720-47e3e9b2b350"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" \"Graph Schema\": [\n",
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" {\n",
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" \"graph_name\": \"GameOfThrones\",\n",
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" \"edge_definitions\": [\n",
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" {\n",
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" \"edge_collection\": \"ChildOf\",\n",
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" \"from_vertex_collections\": [\n",
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" \"Characters\"\n",
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" ],\n",
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" \"to_vertex_collections\": [\n",
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" \"Characters\"\n",
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" ]\n",
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" }\n",
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" ]\n",
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" }\n",
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" ],\n",
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" \"Collection Schema\": [\n",
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" {\n",
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" \"collection_name\": \"ChildOf\",\n",
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" \"collection_type\": \"edge\",\n",
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" \"edge_properties\": [\n",
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" {\n",
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" \"name\": \"_key\",\n",
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" \"type\": \"str\"\n",
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" },\n",
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" {\n",
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" \"name\": \"_id\",\n",
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" \"type\": \"str\"\n",
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" },\n",
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" {\n",
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" \"name\": \"_from\",\n",
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" \"type\": \"str\"\n",
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" },\n",
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" {\n",
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" \"name\": \"_to\",\n",
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" \"type\": \"str\"\n",
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" },\n",
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" {\n",
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" \"name\": \"_rev\",\n",
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" \"type\": \"str\"\n",
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" }\n",
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" ],\n",
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" \"example_edge\": {\n",
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" \"_key\": \"266218884025\",\n",
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" \"_id\": \"ChildOf/266218884025\",\n",
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" \"_from\": \"Characters/AryaStark\",\n",
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" \"_to\": \"Characters/NedStark\",\n",
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||||||
|
" \"_rev\": \"_gVPKGSq---\"\n",
|
||||||
|
" }\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"collection_name\": \"Characters\",\n",
|
||||||
|
" \"collection_type\": \"document\",\n",
|
||||||
|
" \"document_properties\": [\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"_key\",\n",
|
||||||
|
" \"type\": \"str\"\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"_id\",\n",
|
||||||
|
" \"type\": \"str\"\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"_rev\",\n",
|
||||||
|
" \"type\": \"str\"\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"name\",\n",
|
||||||
|
" \"type\": \"str\"\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"surname\",\n",
|
||||||
|
" \"type\": \"str\"\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"alive\",\n",
|
||||||
|
" \"type\": \"bool\"\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"age\",\n",
|
||||||
|
" \"type\": \"int\"\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"gender\",\n",
|
||||||
|
" \"type\": \"str\"\n",
|
||||||
|
" }\n",
|
||||||
|
" ],\n",
|
||||||
|
" \"example_document\": {\n",
|
||||||
|
" \"_key\": \"NedStark\",\n",
|
||||||
|
" \"_id\": \"Characters/NedStark\",\n",
|
||||||
|
" \"_rev\": \"_gVPKGPi---\",\n",
|
||||||
|
" \"name\": \"Ned\",\n",
|
||||||
|
" \"surname\": \"Stark\",\n",
|
||||||
|
" \"alive\": true,\n",
|
||||||
|
" \"age\": 41,\n",
|
||||||
|
" \"gender\": \"male\"\n",
|
||||||
|
" }\n",
|
||||||
|
" }\n",
|
||||||
|
" ]\n",
|
||||||
|
"}\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# We can now view the generated schema\n",
|
||||||
|
"\n",
|
||||||
|
"import json\n",
|
||||||
|
"\n",
|
||||||
|
"print(json.dumps(graph.schema, indent=4))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "68a3c677",
|
||||||
|
"metadata": {
|
||||||
|
"id": "68a3c677"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"## Querying the ArangoDB Database\n",
|
||||||
|
"\n",
|
||||||
|
"We can now use the ArangoDB Graph QA Chain to inquire about our data"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "635c4018",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import os\n",
|
||||||
|
"\n",
|
||||||
|
"os.environ[\"OPENAI_API_KEY\"] = \"your-key-here\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 9,
|
||||||
|
"id": "7476ce98",
|
||||||
|
"metadata": {
|
||||||
|
"id": "7476ce98"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from langchain.chat_models import ChatOpenAI\n",
|
||||||
|
"from langchain.chains import ArangoGraphQAChain\n",
|
||||||
|
"\n",
|
||||||
|
"chain = ArangoGraphQAChain.from_llm(\n",
|
||||||
|
" ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 10,
|
||||||
|
"id": "ef8ee27b",
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/",
|
||||||
|
"height": 261
|
||||||
|
},
|
||||||
|
"id": "ef8ee27b",
|
||||||
|
"outputId": "6008ee92-a3ec-4968-d48d-6a5b66403959"
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
||||||
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
||||||
|
"WITH Characters\n",
|
||||||
|
"FOR character IN Characters\n",
|
||||||
|
"FILTER character.name == \"Ned\" AND character.surname == \"Stark\"\n",
|
||||||
|
"RETURN character.alive\n",
|
||||||
|
"\u001b[0m\n",
|
||||||
|
"AQL Result:\n",
|
||||||
|
"\u001b[32;1m\u001b[1;3m[True]\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
||||||
|
"type": "string"
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
"'Yes, Ned Stark is alive.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 10,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(\"Is Ned Stark alive?\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 11,
|
||||||
|
"id": "9CSig1BgA76q",
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/",
|
||||||
|
"height": 261
|
||||||
|
},
|
||||||
|
"id": "9CSig1BgA76q",
|
||||||
|
"outputId": "3060cf15-68e0-4f8a-cdfd-68af3f0e5fbf"
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
||||||
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
||||||
|
"WITH Characters\n",
|
||||||
|
"FOR character IN Characters\n",
|
||||||
|
"FILTER character.name == \"Arya\" && character.surname == \"Stark\"\n",
|
||||||
|
"RETURN character.age\n",
|
||||||
|
"\u001b[0m\n",
|
||||||
|
"AQL Result:\n",
|
||||||
|
"\u001b[32;1m\u001b[1;3m[11]\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
||||||
|
"type": "string"
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
"'Arya Stark is 11 years old.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 11,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(\"How old is Arya Stark?\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 12,
|
||||||
|
"id": "9Fzdic_pA_4y",
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/",
|
||||||
|
"height": 298
|
||||||
|
},
|
||||||
|
"id": "9Fzdic_pA_4y",
|
||||||
|
"outputId": "9bd93580-964e-4c53-e273-6723dad5f375"
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
||||||
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
||||||
|
"WITH Characters, ChildOf\n",
|
||||||
|
"FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf\n",
|
||||||
|
" FILTER p.vertices[-1]._key == 'NedStark'\n",
|
||||||
|
" RETURN p\n",
|
||||||
|
"\u001b[0m\n",
|
||||||
|
"AQL Result:\n",
|
||||||
|
"\u001b[32;1m\u001b[1;3m[{'vertices': [{'_key': 'AryaStark', '_id': 'Characters/AryaStark', '_rev': '_gVPKGPi--B', 'name': 'Arya', 'surname': 'Stark', 'alive': True, 'age': 11, 'gender': 'female'}, {'_key': 'NedStark', '_id': 'Characters/NedStark', '_rev': '_gVPKGPi---', 'name': 'Ned', 'surname': 'Stark', 'alive': True, 'age': 41, 'gender': 'male'}], 'edges': [{'_key': '266218884025', '_id': 'ChildOf/266218884025', '_from': 'Characters/AryaStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq---'}], 'weights': [0, 1]}]\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
||||||
|
"type": "string"
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
"'Yes, Arya Stark and Ned Stark are related. According to the information retrieved from the database, there is a relationship between them. Arya Stark is the child of Ned Stark.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 12,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(\"Are Arya Stark and Ned Stark related?\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 13,
|
||||||
|
"id": "zq_oeDpAOXpF",
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/",
|
||||||
|
"height": 261
|
||||||
|
},
|
||||||
|
"id": "zq_oeDpAOXpF",
|
||||||
|
"outputId": "a47f37b5-4d7b-41c6-fbc7-7b1abc25fa20"
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
||||||
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
||||||
|
"WITH Characters, ChildOf\n",
|
||||||
|
"FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf\n",
|
||||||
|
"FILTER v.alive == false\n",
|
||||||
|
"RETURN e\n",
|
||||||
|
"\u001b[0m\n",
|
||||||
|
"AQL Result:\n",
|
||||||
|
"\u001b[32;1m\u001b[1;3m[{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}]\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
||||||
|
"type": "string"
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
"'Yes, Arya Stark has a dead parent. The parent is Catelyn Stark.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 13,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(\"Does Arya Stark have a dead parent?\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "Ob_3aGauGd7d",
|
||||||
|
"metadata": {
|
||||||
|
"id": "Ob_3aGauGd7d"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"## Chain Modifiers"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "3P490E2dGiBp",
|
||||||
|
"metadata": {
|
||||||
|
"id": "3P490E2dGiBp"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"You can alter the values of the following `ArangoDBGraphQAChain` class variables to modify the behaviour of your chain results\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 14,
|
||||||
|
"id": "1B9h3PvzJ41T",
|
||||||
|
"metadata": {
|
||||||
|
"id": "1B9h3PvzJ41T"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Specify the maximum number of AQL Query Results to return\n",
|
||||||
|
"chain.top_k = 10\n",
|
||||||
|
"\n",
|
||||||
|
"# Specify whether or not to return the AQL Query in the output dictionary\n",
|
||||||
|
"chain.return_aql_query = True\n",
|
||||||
|
"\n",
|
||||||
|
"# Specify whether or not to return the AQL JSON Result in the output dictionary\n",
|
||||||
|
"chain.return_aql_result = True\n",
|
||||||
|
"\n",
|
||||||
|
"# Specify the maximum amount of AQL Generation attempts that should be made\n",
|
||||||
|
"chain.max_aql_generation_attempts = 5\n",
|
||||||
|
"\n",
|
||||||
|
"# Specify a set of AQL Query Examples, which are passed to\n",
|
||||||
|
"# the AQL Generation Prompt Template to promote few-shot-learning.\n",
|
||||||
|
"# Defaults to an empty string.\n",
|
||||||
|
"chain.aql_examples = \"\"\"\n",
|
||||||
|
"# Is Ned Stark alive?\n",
|
||||||
|
"RETURN DOCUMENT('Characters/NedStark').alive\n",
|
||||||
|
"\n",
|
||||||
|
"# Is Arya Stark the child of Ned Stark?\n",
|
||||||
|
"FOR e IN ChildOf\n",
|
||||||
|
" FILTER e._from == \"Characters/AryaStark\" AND e._to == \"Characters/NedStark\"\n",
|
||||||
|
" RETURN e\n",
|
||||||
|
"\"\"\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 15,
|
||||||
|
"id": "49cnjYV-PUv3",
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/",
|
||||||
|
"height": 209
|
||||||
|
},
|
||||||
|
"id": "49cnjYV-PUv3",
|
||||||
|
"outputId": "f05f0a86-f922-47d1-91b9-5380ef1f996d"
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
||||||
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
||||||
|
"RETURN DOCUMENT('Characters/NedStark').alive\n",
|
||||||
|
"\u001b[0m\n",
|
||||||
|
"AQL Result:\n",
|
||||||
|
"\u001b[32;1m\u001b[1;3m[True]\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
||||||
|
"type": "string"
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
"'Yes, according to the information in the database, Ned Stark is alive.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 15,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(\"Is Ned Stark alive?\")\n",
|
||||||
|
"\n",
|
||||||
|
"# chain(\"Is Ned Stark alive?\") # Returns a dictionary with the AQL Query & AQL Result"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 16,
|
||||||
|
"id": "nWfALJ8dPczE",
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/",
|
||||||
|
"height": 244
|
||||||
|
},
|
||||||
|
"id": "nWfALJ8dPczE",
|
||||||
|
"outputId": "1235baae-f3f7-438e-ef24-658cd17f727d"
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
||||||
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
||||||
|
"FOR e IN ChildOf\n",
|
||||||
|
" FILTER e._from == \"Characters/BranStark\" AND e._to == \"Characters/NedStark\"\n",
|
||||||
|
" RETURN e\n",
|
||||||
|
"\u001b[0m\n",
|
||||||
|
"AQL Result:\n",
|
||||||
|
"\u001b[32;1m\u001b[1;3m[{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}]\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
||||||
|
"type": "string"
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
"'Yes, according to the information in the ArangoDB database, Bran Stark is indeed the child of Ned Stark.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 16,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(\"Is Bran Stark the child of Ned Stark?\")"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"collapsed_sections": [
|
||||||
|
"995ea9b9",
|
||||||
|
"58c1a8ea",
|
||||||
|
"68a3c677",
|
||||||
|
"Ob_3aGauGd7d"
|
||||||
|
],
|
||||||
|
"provenance": []
|
||||||
|
},
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.8.8"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@ -28,6 +28,7 @@ from langchain.chains.conversational_retrieval.base import (
|
|||||||
)
|
)
|
||||||
from langchain.chains.example_generator import generate_example
|
from langchain.chains.example_generator import generate_example
|
||||||
from langchain.chains.flare.base import FlareChain
|
from langchain.chains.flare.base import FlareChain
|
||||||
|
from langchain.chains.graph_qa.arangodb import ArangoGraphQAChain
|
||||||
from langchain.chains.graph_qa.base import GraphQAChain
|
from langchain.chains.graph_qa.base import GraphQAChain
|
||||||
from langchain.chains.graph_qa.cypher import GraphCypherQAChain
|
from langchain.chains.graph_qa.cypher import GraphCypherQAChain
|
||||||
from langchain.chains.graph_qa.hugegraph import HugeGraphQAChain
|
from langchain.chains.graph_qa.hugegraph import HugeGraphQAChain
|
||||||
@ -77,6 +78,7 @@ from langchain.chains.transform import TransformChain
|
|||||||
__all__ = [
|
__all__ = [
|
||||||
"APIChain",
|
"APIChain",
|
||||||
"AnalyzeDocumentChain",
|
"AnalyzeDocumentChain",
|
||||||
|
"ArangoGraphQAChain",
|
||||||
"ChatVectorDBChain",
|
"ChatVectorDBChain",
|
||||||
"ConstitutionalChain",
|
"ConstitutionalChain",
|
||||||
"ConversationChain",
|
"ConversationChain",
|
||||||
|
|||||||
229
libs/langchain/langchain/chains/graph_qa/arangodb.py
Normal file
229
libs/langchain/langchain/chains/graph_qa/arangodb.py
Normal file
@ -0,0 +1,229 @@
|
|||||||
|
"""Question answering over a graph."""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import re
|
||||||
|
from typing import Any, Dict, List, Optional
|
||||||
|
|
||||||
|
from pydantic import Field
|
||||||
|
|
||||||
|
from langchain.base_language import BaseLanguageModel
|
||||||
|
from langchain.callbacks.manager import CallbackManagerForChainRun
|
||||||
|
from langchain.chains.base import Chain
|
||||||
|
from langchain.chains.graph_qa.prompts import (
|
||||||
|
AQL_FIX_PROMPT,
|
||||||
|
AQL_GENERATION_PROMPT,
|
||||||
|
AQL_QA_PROMPT,
|
||||||
|
)
|
||||||
|
from langchain.chains.llm import LLMChain
|
||||||
|
from langchain.graphs.arangodb_graph import ArangoGraph
|
||||||
|
from langchain.schema import BasePromptTemplate
|
||||||
|
|
||||||
|
|
||||||
|
class ArangoGraphQAChain(Chain):
|
||||||
|
"""Chain for question-answering against a graph by generating AQL statements."""
|
||||||
|
|
||||||
|
graph: ArangoGraph = Field(exclude=True)
|
||||||
|
aql_generation_chain: LLMChain
|
||||||
|
aql_fix_chain: LLMChain
|
||||||
|
qa_chain: LLMChain
|
||||||
|
input_key: str = "query" #: :meta private:
|
||||||
|
output_key: str = "result" #: :meta private:
|
||||||
|
|
||||||
|
# Specifies the maximum number of AQL Query Results to return
|
||||||
|
top_k = 10
|
||||||
|
|
||||||
|
# Specifies the set of AQL Query Examples that promote few-shot-learning
|
||||||
|
aql_examples = ""
|
||||||
|
|
||||||
|
# Specify whether to return the AQL Query in the output dictionary
|
||||||
|
return_aql_query: bool = False
|
||||||
|
|
||||||
|
# Specify whether to return the AQL JSON Result in the output dictionary
|
||||||
|
return_aql_result: bool = False
|
||||||
|
|
||||||
|
# Specify the maximum amount of AQL Generation attempts that should be made
|
||||||
|
max_aql_generation_attempts = 3
|
||||||
|
|
||||||
|
@property
|
||||||
|
def input_keys(self) -> List[str]:
|
||||||
|
return [self.input_key]
|
||||||
|
|
||||||
|
@property
|
||||||
|
def output_keys(self) -> List[str]:
|
||||||
|
return [self.output_key]
|
||||||
|
|
||||||
|
@property
|
||||||
|
def _chain_type(self) -> str:
|
||||||
|
return "graph_aql_chain"
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_llm(
|
||||||
|
cls,
|
||||||
|
llm: BaseLanguageModel,
|
||||||
|
*,
|
||||||
|
qa_prompt: BasePromptTemplate = AQL_QA_PROMPT,
|
||||||
|
aql_generation_prompt: BasePromptTemplate = AQL_GENERATION_PROMPT,
|
||||||
|
aql_fix_prompt: BasePromptTemplate = AQL_FIX_PROMPT,
|
||||||
|
**kwargs: Any,
|
||||||
|
) -> ArangoGraphQAChain:
|
||||||
|
"""Initialize from LLM."""
|
||||||
|
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
|
||||||
|
aql_generation_chain = LLMChain(llm=llm, prompt=aql_generation_prompt)
|
||||||
|
aql_fix_chain = LLMChain(llm=llm, prompt=aql_fix_prompt)
|
||||||
|
|
||||||
|
return cls(
|
||||||
|
qa_chain=qa_chain,
|
||||||
|
aql_generation_chain=aql_generation_chain,
|
||||||
|
aql_fix_chain=aql_fix_chain,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
def _call(
|
||||||
|
self,
|
||||||
|
inputs: Dict[str, Any],
|
||||||
|
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||||||
|
) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
Generate an AQL statement from user input, use it retrieve a response
|
||||||
|
from an ArangoDB Database instance, and respond to the user input
|
||||||
|
in natural language.
|
||||||
|
|
||||||
|
Users can modify the following ArangoGraphQAChain Class Variables:
|
||||||
|
|
||||||
|
:var top_k: The maximum number of AQL Query Results to return
|
||||||
|
:type top_k: int
|
||||||
|
|
||||||
|
:var aql_examples: A set of AQL Query Examples that are passed to
|
||||||
|
the AQL Generation Prompt Template to promote few-shot-learning.
|
||||||
|
Defaults to an empty string.
|
||||||
|
:type aql_examples: str
|
||||||
|
|
||||||
|
:var return_aql_query: Whether to return the AQL Query in the
|
||||||
|
output dictionary. Defaults to False.
|
||||||
|
:type return_aql_query: bool
|
||||||
|
|
||||||
|
:var return_aql_result: Whether to return the AQL Query in the
|
||||||
|
output dictionary. Defaults to False
|
||||||
|
:type return_aql_result: bool
|
||||||
|
|
||||||
|
:var max_aql_generation_attempts: The maximum amount of AQL
|
||||||
|
Generation attempts to be made prior to raising the last
|
||||||
|
AQL Query Execution Error. Defaults to 3.
|
||||||
|
:type max_aql_generation_attempts: int
|
||||||
|
"""
|
||||||
|
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
||||||
|
callbacks = _run_manager.get_child()
|
||||||
|
user_input = inputs[self.input_key]
|
||||||
|
|
||||||
|
#########################
|
||||||
|
# Generate AQL Query #
|
||||||
|
aql_generation_output = self.aql_generation_chain.run(
|
||||||
|
{
|
||||||
|
"adb_schema": self.graph.schema,
|
||||||
|
"aql_examples": self.aql_examples,
|
||||||
|
"user_input": user_input,
|
||||||
|
},
|
||||||
|
callbacks=callbacks,
|
||||||
|
)
|
||||||
|
#########################
|
||||||
|
|
||||||
|
aql_query = ""
|
||||||
|
aql_error = ""
|
||||||
|
aql_result = None
|
||||||
|
aql_generation_attempt = 1
|
||||||
|
|
||||||
|
while (
|
||||||
|
aql_result is None
|
||||||
|
and aql_generation_attempt < self.max_aql_generation_attempts + 1
|
||||||
|
):
|
||||||
|
#####################
|
||||||
|
# Extract AQL Query #
|
||||||
|
pattern = r"```(?i:aql)?(.*?)```"
|
||||||
|
matches = re.findall(pattern, aql_generation_output, re.DOTALL)
|
||||||
|
if not matches:
|
||||||
|
_run_manager.on_text(
|
||||||
|
"Invalid Response: ", end="\n", verbose=self.verbose
|
||||||
|
)
|
||||||
|
_run_manager.on_text(
|
||||||
|
aql_generation_output, color="red", end="\n", verbose=self.verbose
|
||||||
|
)
|
||||||
|
raise ValueError(f"Response is Invalid: {aql_generation_output}")
|
||||||
|
|
||||||
|
aql_query = matches[0]
|
||||||
|
#####################
|
||||||
|
|
||||||
|
_run_manager.on_text(
|
||||||
|
f"AQL Query ({aql_generation_attempt}):", verbose=self.verbose
|
||||||
|
)
|
||||||
|
_run_manager.on_text(
|
||||||
|
aql_query, color="green", end="\n", verbose=self.verbose
|
||||||
|
)
|
||||||
|
|
||||||
|
#####################
|
||||||
|
# Execute AQL Query #
|
||||||
|
from arango import AQLQueryExecuteError
|
||||||
|
|
||||||
|
try:
|
||||||
|
aql_result = self.graph.query(aql_query, self.top_k)
|
||||||
|
except AQLQueryExecuteError as e:
|
||||||
|
aql_error = e.error_message
|
||||||
|
|
||||||
|
_run_manager.on_text(
|
||||||
|
"AQL Query Execution Error: ", end="\n", verbose=self.verbose
|
||||||
|
)
|
||||||
|
_run_manager.on_text(
|
||||||
|
aql_error, color="yellow", end="\n\n", verbose=self.verbose
|
||||||
|
)
|
||||||
|
|
||||||
|
########################
|
||||||
|
# Retry AQL Generation #
|
||||||
|
aql_generation_output = self.aql_fix_chain.run(
|
||||||
|
{
|
||||||
|
"adb_schema": self.graph.schema,
|
||||||
|
"aql_query": aql_query,
|
||||||
|
"aql_error": aql_error,
|
||||||
|
},
|
||||||
|
callbacks=callbacks,
|
||||||
|
)
|
||||||
|
########################
|
||||||
|
|
||||||
|
#####################
|
||||||
|
|
||||||
|
aql_generation_attempt += 1
|
||||||
|
|
||||||
|
if aql_result is None:
|
||||||
|
m = f"""
|
||||||
|
Maximum amount of AQL Query Generation attempts reached.
|
||||||
|
Unable to execute the AQL Query due to the following error:
|
||||||
|
{aql_error}
|
||||||
|
"""
|
||||||
|
raise ValueError(m)
|
||||||
|
|
||||||
|
_run_manager.on_text("AQL Result:", end="\n", verbose=self.verbose)
|
||||||
|
_run_manager.on_text(
|
||||||
|
str(aql_result), color="green", end="\n", verbose=self.verbose
|
||||||
|
)
|
||||||
|
|
||||||
|
########################
|
||||||
|
# Interpret AQL Result #
|
||||||
|
result = self.qa_chain(
|
||||||
|
{
|
||||||
|
"adb_schema": self.graph.schema,
|
||||||
|
"user_input": user_input,
|
||||||
|
"aql_query": aql_query,
|
||||||
|
"aql_result": aql_result,
|
||||||
|
},
|
||||||
|
callbacks=callbacks,
|
||||||
|
)
|
||||||
|
########################
|
||||||
|
|
||||||
|
# Return results #
|
||||||
|
result = {self.output_key: result[self.qa_chain.output_key]}
|
||||||
|
|
||||||
|
if self.return_aql_query:
|
||||||
|
result["aql_query"] = aql_query
|
||||||
|
|
||||||
|
if self.return_aql_result:
|
||||||
|
result["aql_result"] = aql_result
|
||||||
|
|
||||||
|
return result
|
||||||
@ -198,6 +198,123 @@ SPARQL_QA_PROMPT = PromptTemplate(
|
|||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
|
AQL_GENERATION_TEMPLATE = """Task: Generate an ArangoDB Query Language (AQL) query from a User Input.
|
||||||
|
|
||||||
|
You are an ArangoDB Query Language (AQL) expert responsible for translating a `User Input` into an ArangoDB Query Language (AQL) query.
|
||||||
|
|
||||||
|
You are given an `ArangoDB Schema`. It is a JSON Object containing:
|
||||||
|
1. `Graph Schema`: Lists all Graphs within the ArangoDB Database Instance, along with their Edge Relationships.
|
||||||
|
2. `Collection Schema`: Lists all Collections within the ArangoDB Database Instance, along with their document/edge properties and a document/edge example.
|
||||||
|
|
||||||
|
You may also be given a set of `AQL Query Examples` to help you create the `AQL Query`. If provided, the `AQL Query Examples` should be used as a reference, similar to how `ArangoDB Schema` should be used.
|
||||||
|
|
||||||
|
Things you should do:
|
||||||
|
- Think step by step.
|
||||||
|
- Rely on `ArangoDB Schema` and `AQL Query Examples` (if provided) to generate the query.
|
||||||
|
- Begin the `AQL Query` by the `WITH` AQL keyword to specify all of the ArangoDB Collections required.
|
||||||
|
- Return the `AQL Query` wrapped in 3 backticks (```).
|
||||||
|
- Use only the provided relationship types and properties in the `ArangoDB Schema` and any `AQL Query Examples` queries.
|
||||||
|
- Only answer to requests related to generating an AQL Query.
|
||||||
|
- If a request is unrelated to generating AQL Query, say that you cannot help the user.
|
||||||
|
|
||||||
|
Things you should not do:
|
||||||
|
- Do not use any properties/relationships that can't be inferred from the `ArangoDB Schema` or the `AQL Query Examples`.
|
||||||
|
- Do not include any text except the generated AQL Query.
|
||||||
|
- Do not provide explanations or apologies in your responses.
|
||||||
|
- Do not generate an AQL Query that removes or deletes any data.
|
||||||
|
|
||||||
|
Under no circumstance should you generate an AQL Query that deletes any data whatsoever.
|
||||||
|
|
||||||
|
ArangoDB Schema:
|
||||||
|
{adb_schema}
|
||||||
|
|
||||||
|
AQL Query Examples (Optional):
|
||||||
|
{aql_examples}
|
||||||
|
|
||||||
|
User Input:
|
||||||
|
{user_input}
|
||||||
|
|
||||||
|
AQL Query:
|
||||||
|
"""
|
||||||
|
|
||||||
|
AQL_GENERATION_PROMPT = PromptTemplate(
|
||||||
|
input_variables=["adb_schema", "aql_examples", "user_input"],
|
||||||
|
template=AQL_GENERATION_TEMPLATE,
|
||||||
|
)
|
||||||
|
|
||||||
|
AQL_FIX_TEMPLATE = """Task: Address the ArangoDB Query Language (AQL) error message of an ArangoDB Query Language query.
|
||||||
|
|
||||||
|
You are an ArangoDB Query Language (AQL) expert responsible for correcting the provided `AQL Query` based on the provided `AQL Error`.
|
||||||
|
|
||||||
|
The `AQL Error` explains why the `AQL Query` could not be executed in the database.
|
||||||
|
The `AQL Error` may also contain the position of the error relative to the total number of lines of the `AQL Query`.
|
||||||
|
For example, 'error X at position 2:5' denotes that the error X occurs on line 2, column 5 of the `AQL Query`.
|
||||||
|
|
||||||
|
You are also given the `ArangoDB Schema`. It is a JSON Object containing:
|
||||||
|
1. `Graph Schema`: Lists all Graphs within the ArangoDB Database Instance, along with their Edge Relationships.
|
||||||
|
2. `Collection Schema`: Lists all Collections within the ArangoDB Database Instance, along with their document/edge properties and a document/edge example.
|
||||||
|
|
||||||
|
You will output the `Corrected AQL Query` wrapped in 3 backticks (```). Do not include any text except the Corrected AQL Query.
|
||||||
|
|
||||||
|
Remember to think step by step.
|
||||||
|
|
||||||
|
ArangoDB Schema:
|
||||||
|
{adb_schema}
|
||||||
|
|
||||||
|
AQL Query:
|
||||||
|
{aql_query}
|
||||||
|
|
||||||
|
AQL Error:
|
||||||
|
{aql_error}
|
||||||
|
|
||||||
|
Corrected AQL Query:
|
||||||
|
"""
|
||||||
|
|
||||||
|
AQL_FIX_PROMPT = PromptTemplate(
|
||||||
|
input_variables=[
|
||||||
|
"adb_schema",
|
||||||
|
"aql_query",
|
||||||
|
"aql_error",
|
||||||
|
],
|
||||||
|
template=AQL_FIX_TEMPLATE,
|
||||||
|
)
|
||||||
|
|
||||||
|
AQL_QA_TEMPLATE = """Task: Generate a natural language `Summary` from the results of an ArangoDB Query Language query.
|
||||||
|
|
||||||
|
You are an ArangoDB Query Language (AQL) expert responsible for creating a well-written `Summary` from the `User Input` and associated `AQL Result`.
|
||||||
|
|
||||||
|
A user has executed an ArangoDB Query Language query, which has returned the AQL Result in JSON format.
|
||||||
|
You are responsible for creating an `Summary` based on the AQL Result.
|
||||||
|
|
||||||
|
You are given the following information:
|
||||||
|
- `ArangoDB Schema`: contains a schema representation of the user's ArangoDB Database.
|
||||||
|
- `User Input`: the original question/request of the user, which has been translated into an AQL Query.
|
||||||
|
- `AQL Query`: the AQL equivalent of the `User Input`, translated by another AI Model. Should you deem it to be incorrect, suggest a different AQL Query.
|
||||||
|
- `AQL Result`: the JSON output returned by executing the `AQL Query` within the ArangoDB Database.
|
||||||
|
|
||||||
|
Remember to think step by step.
|
||||||
|
|
||||||
|
Your `Summary` should sound like it is a response to the `User Input`.
|
||||||
|
Your `Summary` should not include any mention of the `AQL Query` or the `AQL Result`.
|
||||||
|
|
||||||
|
ArangoDB Schema:
|
||||||
|
{adb_schema}
|
||||||
|
|
||||||
|
User Input:
|
||||||
|
{user_input}
|
||||||
|
|
||||||
|
AQL Query:
|
||||||
|
{aql_query}
|
||||||
|
|
||||||
|
AQL Result:
|
||||||
|
{aql_result}
|
||||||
|
"""
|
||||||
|
AQL_QA_PROMPT = PromptTemplate(
|
||||||
|
input_variables=["adb_schema", "user_input", "aql_query", "aql_result"],
|
||||||
|
template=AQL_QA_TEMPLATE,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
NEPTUNE_OPENCYPHER_EXTRA_INSTRUCTIONS = """
|
NEPTUNE_OPENCYPHER_EXTRA_INSTRUCTIONS = """
|
||||||
Instructions:
|
Instructions:
|
||||||
Generate the query in openCypher format and follow these rules:
|
Generate the query in openCypher format and follow these rules:
|
||||||
|
|||||||
@ -1,4 +1,5 @@
|
|||||||
"""Graph implementations."""
|
"""Graph implementations."""
|
||||||
|
from langchain.graphs.arangodb_graph import ArangoGraph
|
||||||
from langchain.graphs.hugegraph import HugeGraph
|
from langchain.graphs.hugegraph import HugeGraph
|
||||||
from langchain.graphs.kuzu_graph import KuzuGraph
|
from langchain.graphs.kuzu_graph import KuzuGraph
|
||||||
from langchain.graphs.nebula_graph import NebulaGraph
|
from langchain.graphs.nebula_graph import NebulaGraph
|
||||||
@ -15,4 +16,5 @@ __all__ = [
|
|||||||
"KuzuGraph",
|
"KuzuGraph",
|
||||||
"HugeGraph",
|
"HugeGraph",
|
||||||
"RdfGraph",
|
"RdfGraph",
|
||||||
|
"ArangoGraph",
|
||||||
]
|
]
|
||||||
|
|||||||
166
libs/langchain/langchain/graphs/arangodb_graph.py
Normal file
166
libs/langchain/langchain/graphs/arangodb_graph.py
Normal file
@ -0,0 +1,166 @@
|
|||||||
|
import os
|
||||||
|
from math import ceil
|
||||||
|
from typing import Any, Dict, List, Optional
|
||||||
|
|
||||||
|
|
||||||
|
class ArangoGraph:
|
||||||
|
"""ArangoDB wrapper for graph operations."""
|
||||||
|
|
||||||
|
def __init__(self, db: Any) -> None:
|
||||||
|
"""Create a new ArangoDB graph wrapper instance."""
|
||||||
|
self.set_db(db)
|
||||||
|
self.set_schema()
|
||||||
|
|
||||||
|
@property
|
||||||
|
def db(self) -> Any:
|
||||||
|
return self.__db
|
||||||
|
|
||||||
|
@property
|
||||||
|
def schema(self) -> Dict[str, Any]:
|
||||||
|
return self.__schema
|
||||||
|
|
||||||
|
def set_db(self, db: Any) -> None:
|
||||||
|
from arango.database import Database
|
||||||
|
|
||||||
|
if not isinstance(db, Database):
|
||||||
|
msg = "**db** parameter must inherit from arango.database.Database"
|
||||||
|
raise TypeError(msg)
|
||||||
|
|
||||||
|
self.__db: Database = db
|
||||||
|
self.set_schema()
|
||||||
|
|
||||||
|
def set_schema(self, schema: Optional[Dict[str, Any]] = None) -> None:
|
||||||
|
"""
|
||||||
|
Set the schema of the ArangoDB Database.
|
||||||
|
Auto-generates Schema if **schema** is None.
|
||||||
|
"""
|
||||||
|
self.__schema = self.generate_schema() if schema is None else schema
|
||||||
|
|
||||||
|
def generate_schema(
|
||||||
|
self, sample_ratio: float = 0
|
||||||
|
) -> Dict[str, List[Dict[str, Any]]]:
|
||||||
|
"""
|
||||||
|
Generates the schema of the ArangoDB Database and returns it
|
||||||
|
User can specify a **sample_ratio** (0 to 1) to determine the
|
||||||
|
ratio of documents/edges used (in relation to the Collection size)
|
||||||
|
to render each Collection Schema.
|
||||||
|
"""
|
||||||
|
if not 0 <= sample_ratio <= 1:
|
||||||
|
raise ValueError("**sample_ratio** value must be in between 0 to 1")
|
||||||
|
|
||||||
|
# Stores the Edge Relationships between each ArangoDB Document Collection
|
||||||
|
graph_schema: List[Dict[str, Any]] = [
|
||||||
|
{"graph_name": g["name"], "edge_definitions": g["edge_definitions"]}
|
||||||
|
for g in self.db.graphs()
|
||||||
|
]
|
||||||
|
|
||||||
|
# Stores the schema of every ArangoDB Document/Edge collection
|
||||||
|
collection_schema: List[Dict[str, Any]] = []
|
||||||
|
|
||||||
|
for collection in self.db.collections():
|
||||||
|
if collection["system"]:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Extract collection name, type, and size
|
||||||
|
col_name: str = collection["name"]
|
||||||
|
col_type: str = collection["type"]
|
||||||
|
col_size: int = self.db.collection(col_name).count()
|
||||||
|
|
||||||
|
# Set number of ArangoDB documents/edges to retrieve
|
||||||
|
limit_amount = ceil(sample_ratio * col_size) or 1
|
||||||
|
|
||||||
|
aql = f"""
|
||||||
|
FOR doc in {col_name}
|
||||||
|
LIMIT {limit_amount}
|
||||||
|
RETURN doc
|
||||||
|
"""
|
||||||
|
|
||||||
|
doc: Dict[str, Any]
|
||||||
|
properties: List[Dict[str, str]] = []
|
||||||
|
for doc in self.__db.aql.execute(aql):
|
||||||
|
for key, value in doc.items():
|
||||||
|
properties.append({"name": key, "type": type(value).__name__})
|
||||||
|
|
||||||
|
collection_schema.append(
|
||||||
|
{
|
||||||
|
"collection_name": col_name,
|
||||||
|
"collection_type": col_type,
|
||||||
|
f"{col_type}_properties": properties,
|
||||||
|
f"example_{col_type}": doc,
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
return {"Graph Schema": graph_schema, "Collection Schema": collection_schema}
|
||||||
|
|
||||||
|
def query(
|
||||||
|
self, query: str, top_k: Optional[int] = None, **kwargs: Any
|
||||||
|
) -> List[Dict[str, Any]]:
|
||||||
|
"""Query the ArangoDB database."""
|
||||||
|
import itertools
|
||||||
|
|
||||||
|
cursor = self.__db.aql.execute(query, **kwargs)
|
||||||
|
return [doc for doc in itertools.islice(cursor, top_k)]
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_db_credentials(
|
||||||
|
cls,
|
||||||
|
url: Optional[str] = None,
|
||||||
|
dbname: Optional[str] = None,
|
||||||
|
username: Optional[str] = None,
|
||||||
|
password: Optional[str] = None,
|
||||||
|
) -> Any:
|
||||||
|
"""Convenience constructor that builds Arango DB from credentials.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
url: Arango DB url. Can be passed in as named arg or set as environment
|
||||||
|
var ``ARANGODB_URL``. Defaults to "http://localhost:8529".
|
||||||
|
dbname: Arango DB name. Can be passed in as named arg or set as
|
||||||
|
environment var ``ARANGODB_DBNAME``. Defaults to "_system".
|
||||||
|
username: Can be passed in as named arg or set as environment var
|
||||||
|
``ARANGODB_USERNAME``. Defaults to "root".
|
||||||
|
password: Can be passed ni as named arg or set as environment var
|
||||||
|
``ARANGODB_PASSWORD``. Defaults to "".
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
An arango.database.StandardDatabase.
|
||||||
|
"""
|
||||||
|
db = get_arangodb_client(
|
||||||
|
url=url, dbname=dbname, username=username, password=password
|
||||||
|
)
|
||||||
|
return cls(db)
|
||||||
|
|
||||||
|
|
||||||
|
def get_arangodb_client(
|
||||||
|
url: Optional[str] = None,
|
||||||
|
dbname: Optional[str] = None,
|
||||||
|
username: Optional[str] = None,
|
||||||
|
password: Optional[str] = None,
|
||||||
|
) -> Any:
|
||||||
|
"""Convenience method that gets Arango DB from credentials.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
url: Arango DB url. Can be passed in as named arg or set as environment
|
||||||
|
var ``ARANGODB_URL``. Defaults to "http://localhost:8529".
|
||||||
|
dbname: Arango DB name. Can be passed in as named arg or set as
|
||||||
|
environment var ``ARANGODB_DBNAME``. Defaults to "_system".
|
||||||
|
username: Can be passed in as named arg or set as environment var
|
||||||
|
``ARANGODB_USERNAME``. Defaults to "root".
|
||||||
|
password: Can be passed ni as named arg or set as environment var
|
||||||
|
``ARANGODB_PASSWORD``. Defaults to "".
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
An arango.database.StandardDatabase.
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
from arango import ArangoClient
|
||||||
|
except ImportError as e:
|
||||||
|
raise ImportError(
|
||||||
|
"Unable to import arango, please install with `pip install python-arango`."
|
||||||
|
) from e
|
||||||
|
|
||||||
|
_url: str = url or os.environ.get("ARANGODB_URL", "http://localhost:8529") # type: ignore[assignment] # noqa: E501
|
||||||
|
_dbname: str = dbname or os.environ.get("ARANGODB_DBNAME", "_system") # type: ignore[assignment] # noqa: E501
|
||||||
|
_username: str = username or os.environ.get("ARANGODB_USERNAME", "root") # type: ignore[assignment] # noqa: E501
|
||||||
|
_password: str = password or os.environ.get("ARANGODB_PASSWORD", "") # type: ignore[assignment] # noqa: E501
|
||||||
|
|
||||||
|
return ArangoClient(_url).db(_dbname, _username, _password, verify=True)
|
||||||
29
libs/langchain/poetry.lock
generated
29
libs/langchain/poetry.lock
generated
@ -4398,7 +4398,6 @@ optional = false
|
|||||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
|
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
|
||||||
files = [
|
files = [
|
||||||
{file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
|
{file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
|
||||||
{file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
|
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
@ -8671,6 +8670,30 @@ files = [
|
|||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
watchdog = ">=2.0.0"
|
watchdog = ">=2.0.0"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "python-arango"
|
||||||
|
version = "7.5.9"
|
||||||
|
description = "Python Driver for ArangoDB"
|
||||||
|
category = "main"
|
||||||
|
optional = true
|
||||||
|
python-versions = ">=3.8"
|
||||||
|
files = [
|
||||||
|
{file = "python-arango-7.5.9.tar.gz", hash = "sha256:c16e5b0bfa7a662015e1708f723c5c324e05a32cf98169229f01fd20200cd0db"},
|
||||||
|
{file = "python_arango-7.5.9-py3-none-any.whl", hash = "sha256:aa8587420e7b704c646bf7791b8149674ee562ec0202ba2363100a9cb5dec00f"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
importlib-metadata = ">=4.7.1"
|
||||||
|
packaging = ">=23.1"
|
||||||
|
PyJWT = "*"
|
||||||
|
requests = "*"
|
||||||
|
requests-toolbelt = "*"
|
||||||
|
setuptools = ">=42"
|
||||||
|
urllib3 = ">=1.26.0"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
dev = ["black (>=22.3.0)", "flake8 (>=4.0.1)", "isort (>=5.10.1)", "mock", "mypy (>=0.942)", "pre-commit (>=2.17.0)", "pytest (>=7.1.1)", "pytest-cov (>=3.0.0)", "sphinx", "sphinx-rtd-theme", "types-pkg-resources", "types-requests", "types-setuptools"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "python-dateutil"
|
name = "python-dateutil"
|
||||||
version = "2.8.2"
|
version = "2.8.2"
|
||||||
@ -12461,7 +12484,7 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
|
|||||||
cffi = ["cffi (>=1.11)"]
|
cffi = ["cffi (>=1.11)"]
|
||||||
|
|
||||||
[extras]
|
[extras]
|
||||||
all = ["O365", "aleph-alpha-client", "amadeus", "anthropic", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "langkit", "lark", "libdeeplake", "lxml", "manifest-ml", "marqo", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "octoai-sdk", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "rdflib", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "spacy", "steamship", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
|
all = ["O365", "aleph-alpha-client", "amadeus", "anthropic", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "langkit", "lark", "libdeeplake", "lxml", "manifest-ml", "marqo", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "octoai-sdk", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "python-arango", "pyvespa", "qdrant-client", "rdflib", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "spacy", "steamship", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
|
||||||
azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"]
|
azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"]
|
||||||
clarifai = ["clarifai"]
|
clarifai = ["clarifai"]
|
||||||
cohere = ["cohere"]
|
cohere = ["cohere"]
|
||||||
@ -12477,4 +12500,4 @@ text-helpers = ["chardet"]
|
|||||||
[metadata]
|
[metadata]
|
||||||
lock-version = "2.0"
|
lock-version = "2.0"
|
||||||
python-versions = ">=3.8.1,<4.0"
|
python-versions = ">=3.8.1,<4.0"
|
||||||
content-hash = "c9c8d7972feb1de7b227f222f53b9a3f9b497c9aa5d4ef7666a9eafe0db423d6"
|
content-hash = "7a8847de4dd88e71b423ff148823523220a5649340178e8ab1f7bafb03a290d2"
|
||||||
|
|||||||
@ -124,6 +124,7 @@ langsmith = "~0.0.11"
|
|||||||
rank-bm25 = {version = "^0.2.2", optional = true}
|
rank-bm25 = {version = "^0.2.2", optional = true}
|
||||||
amadeus = {version = ">=8.1.0", optional = true}
|
amadeus = {version = ">=8.1.0", optional = true}
|
||||||
geopandas = {version = "^0.13.1", optional = true}
|
geopandas = {version = "^0.13.1", optional = true}
|
||||||
|
python-arango = {version = "^7.5.9", optional = true}
|
||||||
|
|
||||||
[tool.poetry.group.test.dependencies]
|
[tool.poetry.group.test.dependencies]
|
||||||
# The only dependencies that should be added are
|
# The only dependencies that should be added are
|
||||||
@ -314,6 +315,7 @@ all = [
|
|||||||
"octoai-sdk",
|
"octoai-sdk",
|
||||||
"rdflib",
|
"rdflib",
|
||||||
"amadeus",
|
"amadeus",
|
||||||
|
"python-arango",
|
||||||
]
|
]
|
||||||
|
|
||||||
# An extra used to be able to add extended testing.
|
# An extra used to be able to add extended testing.
|
||||||
|
|||||||
@ -0,0 +1,78 @@
|
|||||||
|
"""Test Graph Database Chain."""
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
|
from langchain.chains.graph_qa.arangodb import ArangoGraphQAChain
|
||||||
|
from langchain.graphs import ArangoGraph
|
||||||
|
from langchain.graphs.arangodb_graph import get_arangodb_client
|
||||||
|
from langchain.llms.openai import OpenAI
|
||||||
|
|
||||||
|
|
||||||
|
def populate_arangodb_database(db: Any) -> None:
|
||||||
|
if db.has_graph("GameOfThrones"):
|
||||||
|
return
|
||||||
|
|
||||||
|
db.create_graph(
|
||||||
|
"GameOfThrones",
|
||||||
|
edge_definitions=[
|
||||||
|
{
|
||||||
|
"edge_collection": "ChildOf",
|
||||||
|
"from_vertex_collections": ["Characters"],
|
||||||
|
"to_vertex_collections": ["Characters"],
|
||||||
|
},
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
documents = [
|
||||||
|
{
|
||||||
|
"_key": "NedStark",
|
||||||
|
"name": "Ned",
|
||||||
|
"surname": "Stark",
|
||||||
|
"alive": True,
|
||||||
|
"age": 41,
|
||||||
|
"gender": "male",
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_key": "AryaStark",
|
||||||
|
"name": "Arya",
|
||||||
|
"surname": "Stark",
|
||||||
|
"alive": True,
|
||||||
|
"age": 11,
|
||||||
|
"gender": "female",
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
edges = [{"_to": "Characters/NedStark", "_from": "Characters/AryaStark"}]
|
||||||
|
|
||||||
|
db.collection("Characters").import_bulk(documents)
|
||||||
|
db.collection("ChildOf").import_bulk(edges)
|
||||||
|
|
||||||
|
|
||||||
|
def test_connect_arangodb() -> None:
|
||||||
|
"""Test that the ArangoDB database is correctly instantiated and connected."""
|
||||||
|
graph = ArangoGraph(get_arangodb_client())
|
||||||
|
|
||||||
|
sample_aql_result = graph.query("RETURN 'hello world'")
|
||||||
|
assert ["hello_world"] == sample_aql_result
|
||||||
|
|
||||||
|
|
||||||
|
def test_aql_generation() -> None:
|
||||||
|
"""Test that AQL statement is correctly generated and executed."""
|
||||||
|
db = get_arangodb_client()
|
||||||
|
|
||||||
|
populate_arangodb_database(db)
|
||||||
|
|
||||||
|
graph = ArangoGraph(db)
|
||||||
|
chain = ArangoGraphQAChain.from_llm(OpenAI(temperature=0), graph=graph)
|
||||||
|
chain.return_aql_result = True
|
||||||
|
|
||||||
|
output = chain("Is Ned Stark alive?")
|
||||||
|
assert output["aql_result"] == [True]
|
||||||
|
assert "Yes" in output["result"]
|
||||||
|
|
||||||
|
output = chain("How old is Arya Stark?")
|
||||||
|
assert output["aql_result"] == [11]
|
||||||
|
assert "11" in output["result"]
|
||||||
|
|
||||||
|
output = chain("What is the relationship between Arya Stark and Ned Stark?")
|
||||||
|
assert len(output["aql_result"]) == 1
|
||||||
|
assert "child of" in output["result"]
|
||||||
Loading…
Reference in New Issue
Block a user