langchain/templates/elastic-query-generator/elastic_query_generator/chain.py

from elasticsearch import Elasticsearch
from langchain.chat_models import ChatOpenAI
from langchain.output_parsers.json import SimpleJsonOutputParser
from langchain.pydantic_v1 import BaseModel

from .elastic_index_info import get_indices_infos
from .prompts import DSL_PROMPT

# Setup Elasticsearch
# This shows how to set it up for a cloud hosted version

# Password for the 'elastic' user generated by Elasticsearch
ELASTIC_PASSWORD = "..."

# Found in the 'Manage Deployment' page
CLOUD_ID = "..."

# Create the client instance
db = Elasticsearch(
    cloud_id=CLOUD_ID,
    basic_auth=("elastic", ELASTIC_PASSWORD)
)

# Specify indices to include
# If you want to use on your own indices, you will need to change this.
INCLUDE_INDICES = ["customers"]

# With the Elasticsearch connection created, we can now move on to the chain

_model = ChatOpenAI(temperature=0, model="gpt-4")

chain = {
    "input": lambda x: x["input"],
    # This line only get index info for "customers" index.
    # If you are running this on your own data, you will want to change.
    "indices_info": lambda _: get_indices_infos(db, include_indices=INCLUDE_INDICES),
    "top_k": lambda x: x.get("top_k", 5),
} | DSL_PROMPT | _model | SimpleJsonOutputParser()


# Nicely typed inputs for playground
class ChainInputs(BaseModel):
    input: str
    top_k: int = 5


chain = chain.with_types(input_type=ChainInputs)