mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +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'
```
135 lines
3.8 KiB
Python
135 lines
3.8 KiB
Python
from typing import List, Optional
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from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
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from langchain.chains.openai_functions import create_structured_output_chain
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from langchain.chat_models import ChatOpenAI
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from langchain.graphs import Neo4jGraph
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from langchain.prompts import ChatPromptTemplate
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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 RunnablePassthrough
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# Connection to Neo4j
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graph = Neo4jGraph()
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# Cypher validation tool for relationship directions
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corrector_schema = [
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Schema(el["start"], el["type"], el["end"])
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for el in graph.structured_schema.get("relationships")
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]
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cypher_validation = CypherQueryCorrector(corrector_schema)
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# LLMs
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cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0)
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qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0)
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# Extract entities from text
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class Entities(BaseModel):
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"""Identifying information about entities."""
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names: List[str] = Field(
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...,
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description="All the person, organization, or business entities that "
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"appear in the text",
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)
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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 extracting organization and person entities from the text.",
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),
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(
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"human",
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"Use the given format to extract information from the following "
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"input: {question}",
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),
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]
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)
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# Fulltext index query
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def map_to_database(entities: Entities) -> Optional[str]:
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result = ""
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for entity in entities.names:
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response = graph.query(
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"CALL db.index.fulltext.queryNodes('entity', $entity + '*', {limit:1})"
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" YIELD node,score RETURN node.name AS result",
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{"entity": entity},
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)
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try:
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result += f"{entity} maps to {response[0]['result']} in database\n"
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except IndexError:
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pass
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return result
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entity_chain = create_structured_output_chain(Entities, qa_llm, prompt)
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# Generate Cypher statement based on natural language input
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cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:
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{schema}
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Entities in the question map to the following database values:
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{entities_list}
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Question: {question}
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Cypher query:""" # noqa: E501
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cypher_prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"Given an input question, convert it to a Cypher query. No pre-amble.",
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),
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("human", cypher_template),
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]
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)
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cypher_response = (
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RunnablePassthrough.assign(names=entity_chain)
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| RunnablePassthrough.assign(
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entities_list=lambda x: map_to_database(x["names"]["function"]),
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schema=lambda _: graph.get_schema,
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)
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| cypher_prompt
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| cypher_llm.bind(stop=["\nCypherResult:"])
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| StrOutputParser()
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)
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# Generate natural language response based on database results
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response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
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Question: {question}
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Cypher query: {query}
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Cypher Response: {response}""" # noqa: E501
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response_prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"Given an input question and Cypher response, convert it to a natural"
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" language answer. No pre-amble.",
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),
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("human", response_template),
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]
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)
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chain = (
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RunnablePassthrough.assign(query=cypher_response)
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| RunnablePassthrough.assign(
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response=lambda x: graph.query(cypher_validation(x["query"])),
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)
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| response_prompt
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| qa_llm
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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question: str
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chain = chain.with_types(input_type=Question)
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