mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
9ffca3b92a
Update imports to use core for the low-hanging fruit changes. Ran
following
```bash
git grep -l 'langchain.schema.runnable' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g'
git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g'
git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g'
git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g'
git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g'
git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g'
git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g'
git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g'
git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g'
git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g'
git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g'
git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g'
git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g'
git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g'
git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g'
git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g'
git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g'
git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g'
```
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
from elasticsearch import Elasticsearch
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from langchain.chat_models import ChatOpenAI
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from langchain.output_parsers.json import SimpleJsonOutputParser
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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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