mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +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'
```
118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
import os
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from typing import List, Tuple
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from google.cloud import dlp_v2
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from langchain.chat_models import ChatVertexAI
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_core.runnables import RunnableLambda, RunnableParallel
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# Formatting for chat history
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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def _deidentify_with_replace(
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input_str: str,
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info_types: List[str],
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project: str,
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) -> str:
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"""Uses the Data Loss Prevention API to deidentify sensitive data in a
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string by replacing matched input values with the info type.
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Args:
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project: The Google Cloud project id to use as a parent resource.
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input_str: The string to deidentify (will be treated as text).
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info_types: A list of strings representing info types to look for.
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Returns:
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str: The input string after it has been deidentified.
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"""
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# Instantiate a client
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dlp = dlp_v2.DlpServiceClient()
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# Convert the project id into a full resource id.
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parent = f"projects/{project}/locations/global"
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if info_types is None:
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info_types = ["PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD_NUMBER"]
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# Construct inspect configuration dictionary
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inspect_config = {"info_types": [{"name": info_type} for info_type in info_types]}
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# Construct deidentify configuration dictionary
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deidentify_config = {
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"info_type_transformations": {
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"transformations": [
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{"primitive_transformation": {"replace_with_info_type_config": {}}}
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]
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}
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}
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# Construct item
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item = {"value": input_str}
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# Call the API
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response = dlp.deidentify_content(
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request={
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"parent": parent,
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"deidentify_config": deidentify_config,
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"inspect_config": inspect_config,
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"item": item,
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}
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)
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# Print out the results.
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return response.item.value
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# Prompt we will use
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful assistant who translates to pirate",
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),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{question}"),
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]
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)
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# Create Vertex AI retriever
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project_id = os.environ.get("GOOGLE_CLOUD_PROJECT_ID")
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model_type = os.environ.get("MODEL_TYPE")
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# Set LLM and embeddings
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model = ChatVertexAI(model_name=model_type, temperature=0.0)
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class ChatHistory(BaseModel):
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question: str
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chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}})
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_inputs = RunnableParallel(
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{
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"question": RunnableLambda(
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lambda x: _deidentify_with_replace(
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input_str=x["question"],
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info_types=["PERSON_NAME", "PHONE_NUMBER", "EMAIL_ADDRESS"],
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project=project_id,
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)
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).with_config(run_name="<lambda> _deidentify_with_replace"),
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"chat_history": RunnableLambda(
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lambda x: _format_chat_history(x["chat_history"])
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).with_config(run_name="<lambda> _format_chat_history"),
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}
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)
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# RAG
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chain = _inputs | prompt | model | StrOutputParser()
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chain = chain.with_types(input_type=ChatHistory).with_config(run_name="Inputs")
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