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<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> Adding rag-opensearch template. --------- Signed-off-by: kalyanr <kalyan.ben10@live.com> Co-authored-by: Erick Friis <erick@langchain.dev>
61 lines
1.5 KiB
Python
61 lines
1.5 KiB
Python
import os
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from openai import OpenAI
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from opensearchpy import OpenSearch
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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OPENSEARCH_URL = os.getenv("OPENSEARCH_URL", "https://localhost:9200")
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OPENSEARCH_USERNAME = os.getenv("OPENSEARCH_USERNAME", "admin")
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OPENSEARCH_PASSWORD = os.getenv("OPENSEARCH_PASSWORD", "admin")
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OPENSEARCH_INDEX_NAME = os.getenv("OPENSEARCH_INDEX_NAME", "langchain-test")
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with open("dummy_data.txt") as f:
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docs = [line.strip() for line in f.readlines()]
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client_oai = OpenAI(api_key=OPENAI_API_KEY)
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client = OpenSearch(
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hosts=[OPENSEARCH_URL],
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http_auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),
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use_ssl=True,
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verify_certs=False,
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)
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# Define the index settings and mappings
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index_settings = {
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"settings": {
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"index": {"knn": True, "number_of_shards": 1, "number_of_replicas": 0}
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},
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"mappings": {
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"properties": {
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"vector_field": {
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"type": "knn_vector",
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"dimension": 1536,
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"method": {"name": "hnsw", "space_type": "l2", "engine": "faiss"},
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}
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}
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},
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}
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response = client.indices.create(index=OPENSEARCH_INDEX_NAME, body=index_settings)
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print(response)
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# Insert docs
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for each in docs:
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res = client_oai.embeddings.create(input=each, model="text-embedding-ada-002")
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document = {
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"vector_field": res.data[0].embedding,
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"text": each,
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}
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response = client.index(index=OPENSEARCH_INDEX_NAME, body=document, refresh=True)
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print(response)
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