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'
```
119 lines
3.2 KiB
Python
119 lines
3.2 KiB
Python
import os
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import re
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.prompts import ChatPromptTemplate
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from langchain.sql_database import SQLDatabase
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableLambda, RunnablePassthrough
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from sql_pgvector.prompt_templates import final_template, postgresql_template
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"""
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IMPORTANT: For using this template, you will need to
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follow the setup steps in the readme file
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"""
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if os.environ.get("OPENAI_API_KEY", None) is None:
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raise Exception("Missing `OPENAI_API_KEY` environment variable")
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postgres_user = os.environ.get("POSTGRES_USER", "postgres")
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postgres_password = os.environ.get("POSTGRES_PASSWORD", "test")
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postgres_db = os.environ.get("POSTGRES_DB", "vectordb")
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postgres_host = os.environ.get("POSTGRES_HOST", "localhost")
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postgres_port = os.environ.get("POSTGRES_PORT", "5432")
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# Connect to DB
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# Replace with your own
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CONNECTION_STRING = (
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f"postgresql+psycopg2://{postgres_user}:{postgres_password}"
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f"@{postgres_host}:{postgres_port}/{postgres_db}"
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)
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db = SQLDatabase.from_uri(CONNECTION_STRING)
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# Choose LLM and embeddings model
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llm = ChatOpenAI(temperature=0)
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embeddings_model = OpenAIEmbeddings()
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# # Ingest code - you will need to run this the first time
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# # Insert your query e.g. "SELECT Name FROM Track"
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# column_to_embed = db.run('replace-with-your-own-select-query')
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# column_values = [s[0] for s in eval(column_to_embed)]
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# embeddings = embeddings_model.embed_documents(column_values)
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# for i in range(len(embeddings)):
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# value = column_values[i].replace("'", "''")
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# embedding = embeddings[i]
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# # Replace with your own SQL command for your column and table.
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# sql_command = (
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# f'UPDATE "Track" SET "embeddings" = ARRAY{embedding} WHERE "Name" ='
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# + f"'{value}'"
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# )
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# db.run(sql_command)
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# -----------------
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# Define functions
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# -----------------
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def get_schema(_):
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return db.get_table_info()
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def run_query(query):
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return db.run(query)
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def replace_brackets(match):
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words_inside_brackets = match.group(1).split(", ")
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embedded_words = [
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str(embeddings_model.embed_query(word)) for word in words_inside_brackets
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]
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return "', '".join(embedded_words)
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def get_query(query):
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sql_query = re.sub(r"\[([\w\s,]+)\]", replace_brackets, query)
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return sql_query
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# -----------------------
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# Now we create the chain
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# -----------------------
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query_generation_prompt = ChatPromptTemplate.from_messages(
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[("system", postgresql_template), ("human", "{question}")]
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)
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sql_query_chain = (
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RunnablePassthrough.assign(schema=get_schema)
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| query_generation_prompt
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| llm.bind(stop=["\nSQLResult:"])
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| StrOutputParser()
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)
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final_prompt = ChatPromptTemplate.from_messages(
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[("system", final_template), ("human", "{question}")]
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)
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full_chain = (
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RunnablePassthrough.assign(query=sql_query_chain)
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| RunnablePassthrough.assign(
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schema=get_schema,
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response=RunnableLambda(lambda x: db.run(get_query(x["query"]))),
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)
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| final_prompt
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| llm
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)
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class InputType(BaseModel):
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question: str
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chain = full_chain.with_types(input_type=InputType)
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