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[
"On Unstructured Data, Vector Databases, New AI Age, and Our Seed Round. - Qdrant",
"Fundraising"
] | /articles/seed-round/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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] | p | Sometimes you just need inspiration instead of AI ;-) | [
"articles",
"articles/seed-round"
] | 3,272 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(1) | [
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(2) | [
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"Question Answering with Qdrant in the loop"
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Question Answering with Qdrant in the loop"
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[
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"Question Answering with Qdrant in the loop"
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Question Answering with Qdrant in the loop"
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[
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"Question Answering with Qdrant in the loop"
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[
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"articles",
"articles/langchain-integration"
] | 3,299 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3) | [
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"articles",
"articles/langchain-integration"
] | 3,304 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3) | [
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"articles",
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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"articles",
"articles/langchain-integration"
] | 3,306 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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] | p | First of all, we ask Qdrant to provide the most relevant documents and simply combine all | [
"articles",
"articles/langchain-integration"
] | 3,307 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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"articles",
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] | 3,309 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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"articles",
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] | 3,310 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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"What do we need?"
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] | p | There might be several context documents combined, and it is solely up to LLM to choose the right piece of content. | [
"articles",
"articles/langchain-integration"
] | 3,312 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5) | [
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
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"What do we need?"
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[
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"What do we need?"
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"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
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"What do we need?"
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"What do we need?"
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8) | [
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"articles",
"articles/langchain-integration"
] | 3,324 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8) | [
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[
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"What do we need?"
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[
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"What do we need?"
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"What do we need?"
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"articles",
"articles/langchain-integration"
] | 3,334 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Configuration"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9) | [
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] | p | The same is for OpenAI - the API key has to be obtained from their website. | [
"articles",
"articles/langchain-integration"
] | 3,335 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(3) | [
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] | p | We also need some facts from which the answers will be generated. | [
"articles",
"articles/langchain-integration"
] | 3,337 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Building the knowledge base"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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"articles",
"articles/langchain-integration"
] | 3,338 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Building the knowledge base"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Building the knowledge base"
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] | p | That means we need some preprocessing to extract plain text content. | [
"articles",
"articles/langchain-integration"
] | 3,342 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Building the knowledge base"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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] | p | As a result, we’re going to have two lists of strings - one for questions and | [
"articles",
"articles/langchain-integration"
] | 3,343 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Building the knowledge base"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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[
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"Setting up QA with Qdrant in a loop"
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Setting up QA with Qdrant in a loop"
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant"
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[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
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] | 3,355 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Testing out the chain"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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"articles",
"articles/langchain-integration"
] | 3,356 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Testing out the chain"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18) | [
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] | p | The great thing about such a setup is that the knowledge base might be easily extended with some new facts and those will be included in the prompts | [
"articles",
"articles/langchain-integration"
] | 3,357 |
[
"Question Answering with LangChain and Qdrant without boilerplate - Qdrant",
"Testing out the chain"
] | /articles/langchain-integration/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18) | [
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[
"Extending ChatGPT with a Qdrant-based knowledge base - Qdrant"
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] | p | articles and countless other examples of using Large Language Models to generate content | [
"articles",
"articles/chatgpt-plugin"
] | 3,371 |
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