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"""Ingest examples into Weaviate."""
import os
from pathlib import Path
import weaviate
WEAVIATE_URL = os.environ["WEAVIATE_URL"]
client = weaviate.Client(
url=WEAVIATE_URL,
additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
)
client.schema.delete_class("Rephrase")
client.schema.delete_class("QA")
client.schema.get()
schema = {
"classes": [
{
"class": "Rephrase",
"description": "Rephrase Examples",
"vectorizer": "text2vec-openai",
"moduleConfig": {
"text2vec-openai": {
"model": "ada",
"modelVersion": "002",
"type": "text",
}
},
"properties": [
{
"dataType": ["text"],
"moduleConfig": {
"text2vec-openai": {
"skip": False,
"vectorizePropertyName": False,
}
},
"name": "content",
},
{
"dataType": ["text"],
"description": "The link",
"moduleConfig": {
"text2vec-openai": {
"skip": True,
"vectorizePropertyName": False,
}
},
"name": "question",
},
{
"dataType": ["text"],
"description": "The link",
"moduleConfig": {
"text2vec-openai": {
"skip": True,
"vectorizePropertyName": False,
}
},
"name": "answer",
},
{
"dataType": ["text"],
"description": "The link",
"moduleConfig": {
"text2vec-openai": {
"skip": True,
"vectorizePropertyName": False,
}
},
"name": "chat_history",
},
],
},
]
}
client.schema.create(schema)
documents = [
{
"question": "how do i load those?",
"chat_history": "Human: What types of memory exist?\nAssistant: \n\nThere are a few different types of memory: Buffer, Summary, and Conversational Memory.",
"answer": "How do I load Buffer, Summary, and Conversational Memory",
},
{
"question": "how do i install this package?",
"chat_history": "",
"answer": "How do I install langchain?",
},
{
"question": "how do I set serpapi_api_key?",
"chat_history": "Human: can you write me a code snippet for that?\nAssistant: \n\nYes, you can create an Agent with a custom LLMChain in LangChain. Here is a [link](https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html) to the documentation that provides a code snippet for creating a custom Agent.",
"answer": "How do I set the serpapi_api_key?",
},
{
"question": "What are some methods for data augmented generation?",
"chat_history": "Human: List all methods of an Agent class please\nAssistant: \n\nTo answer your question, you can find a list of all the methods of the Agent class in the [API reference documentation](https://langchain.readthedocs.io/en/latest/modules/agents/reference.html).",
"answer": "What are some methods for data augmented generation?",
},
{
"question": "can you write me a code snippet for that?",
"chat_history": "Human: how do I create an agent with custom LLMChain?\nAssistant: \n\nTo create an Agent with a custom LLMChain in LangChain, you can use the [Custom Agent example](https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html). This example shows how to create a custom LLMChain and use an existing Agent class to parse the output. For more information on Agents and Tools, check out the [Key Concepts](https://langchain.readthedocs.io/en/latest/modules/agents/key_concepts.html) documentation.",
"answer": "Can you provide a code snippet for creating an Agent with a custom LLMChain?",
},
]
from langchain.prompts.example_selector.semantic_similarity import \
sorted_values
for d in documents:
d["content"] = " ".join(sorted_values(d))
with client.batch as batch:
for text in documents:
batch.add_data_object(
text,
"Rephrase",
)
client.schema.get()
schema = {
"classes": [
{
"class": "QA",
"description": "Rephrase Examples",
"vectorizer": "text2vec-openai",
"moduleConfig": {
"text2vec-openai": {
"model": "ada",
"modelVersion": "002",
"type": "text",
}
},
"properties": [
{
"dataType": ["text"],
"moduleConfig": {
"text2vec-openai": {
"skip": False,
"vectorizePropertyName": False,
}
},
"name": "content",
},
{
"dataType": ["text"],
"description": "The link",
"moduleConfig": {
"text2vec-openai": {
"skip": True,
"vectorizePropertyName": False,
}
},
"name": "question",
},
{
"dataType": ["text"],
"description": "The link",
"moduleConfig": {
"text2vec-openai": {
"skip": True,
"vectorizePropertyName": False,
}
},
"name": "answer",
},
{
"dataType": ["text"],
"description": "The link",
"moduleConfig": {
"text2vec-openai": {
"skip": True,
"vectorizePropertyName": False,
}
},
"name": "summaries",
},
{
"dataType": ["text"],
"description": "The link",
"moduleConfig": {
"text2vec-openai": {
"skip": True,
"vectorizePropertyName": False,
}
},
"name": "sources",
},
],
},
]
}
client.schema.create(schema)
documents = [
{
"question": "how do i install langchain?",
"answer": "```pip install langchain```",
"summaries": ">Example:\nContent:\n---------\nYou can pip install langchain package by running 'pip install langchain'\n----------\nSource: foo.html",
"sources": "foo.html",
},
{
"question": "how do i import an openai LLM?",
"answer": "```from langchain.llm import OpenAI```",
"summaries": ">Example:\nContent:\n---------\nyou can import the open ai wrapper (OpenAI) from the langchain.llm module\n----------\nSource: bar.html",
"sources": "bar.html",
},
]
from langchain.prompts.example_selector.semantic_similarity import \
sorted_values
for d in documents:
d["content"] = " ".join(sorted_values(d))
with client.batch as batch:
for text in documents:
batch.add_data_object(
text,
"QA",
)
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