mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
74 lines
1.6 KiB
Plaintext
74 lines
1.6 KiB
Plaintext
### Setup
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To start we'll need to install the OpenAI Python package:
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```bash
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pip install openai
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```
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Accessing the API requires an API key, which you can get by creating an account and heading [here](https://platform.openai.com/account/api-keys). Once we have a key we'll want to set it as an environment variable by running:
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```bash
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export OPENAI_API_KEY="..."
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```
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If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:
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```python
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from langchain.embeddings import OpenAIEmbeddings
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embeddings_model = OpenAIEmbeddings(openai_api_key="...")
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```
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otherwise you can initialize without any params:
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```python
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from langchain.embeddings import OpenAIEmbeddings
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embeddings_model = OpenAIEmbeddings()
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```
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### `embed_documents`
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#### Embed list of texts
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```python
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embeddings = embeddings_model.embed_documents(
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[
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"Hi there!",
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"Oh, hello!",
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"What's your name?",
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"My friends call me World",
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"Hello World!"
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]
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)
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len(embeddings), len(embeddings[0])
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```
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<CodeOutputBlock language="python">
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```
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(5, 1536)
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```
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</CodeOutputBlock>
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### `embed_query`
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#### Embed single query
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Embed a single piece of text for the purpose of comparing to other embedded pieces of texts.
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```python
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embedded_query = embeddings_model.embed_query("What was the name mentioned in the conversation?")
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embedded_query[:5]
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```
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<CodeOutputBlock language="python">
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```
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[0.0053587136790156364,
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-0.0004999046213924885,
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0.038883671164512634,
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-0.003001077566295862,
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-0.00900818221271038]
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```
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</CodeOutputBlock>
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