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…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
from elasticsearch import Elasticsearch
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from langchain.output_parsers.json import SimpleJsonOutputParser
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from langchain_community.chat_models import ChatOpenAI
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from langchain_core.pydantic_v1 import BaseModel
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from .elastic_index_info import get_indices_infos
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from .prompts import DSL_PROMPT
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# Setup Elasticsearch
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# This shows how to set it up for a cloud hosted version
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# Password for the 'elastic' user generated by Elasticsearch
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ELASTIC_PASSWORD = "..."
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# Found in the 'Manage Deployment' page
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CLOUD_ID = "..."
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# Create the client instance
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db = Elasticsearch(cloud_id=CLOUD_ID, basic_auth=("elastic", ELASTIC_PASSWORD))
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# Specify indices to include
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# If you want to use on your own indices, you will need to change this.
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INCLUDE_INDICES = ["customers"]
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# With the Elasticsearch connection created, we can now move on to the chain
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_model = ChatOpenAI(temperature=0, model="gpt-4")
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chain = (
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{
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"input": lambda x: x["input"],
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# This line only get index info for "customers" index.
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# If you are running this on your own data, you will want to change.
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"indices_info": lambda _: get_indices_infos(
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db, include_indices=INCLUDE_INDICES
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),
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"top_k": lambda x: x.get("top_k", 5),
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}
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| DSL_PROMPT
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| _model
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| SimpleJsonOutputParser()
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)
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# Nicely typed inputs for playground
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class ChainInputs(BaseModel):
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input: str
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top_k: int = 5
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chain = chain.with_types(input_type=ChainInputs)
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