mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
137 lines
3.6 KiB
Python
137 lines
3.6 KiB
Python
# Get LLM
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import os
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from pathlib import Path
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import requests
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from langchain.memory import ConversationBufferMemory
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from langchain_community.llms import LlamaCpp
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from langchain_community.utilities import SQLDatabase
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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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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# File name and URL
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file_name = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"
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url = (
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"https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/"
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"mistral-7b-instruct-v0.1.Q4_K_M.gguf"
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)
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# Check if file is present in the current directory
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if not os.path.exists(file_name):
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print(f"'{file_name}' not found. Downloading...")
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# Download the file
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response = requests.get(url)
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response.raise_for_status() # Raise an exception for HTTP errors
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with open(file_name, "wb") as f:
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f.write(response.content)
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print(f"'{file_name}' has been downloaded.")
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else:
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print(f"'{file_name}' already exists in the current directory.")
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# Add the LLM downloaded from HF
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model_path = file_name
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n_gpu_layers = 1 # Metal set to 1 is enough.
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# Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.
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n_batch = 512
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llm = LlamaCpp(
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model_path=model_path,
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n_gpu_layers=n_gpu_layers,
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n_batch=n_batch,
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n_ctx=2048,
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# f16_kv MUST set to True
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# otherwise you will run into problem after a couple of calls
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f16_kv=True,
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verbose=True,
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)
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db_path = Path(__file__).parent / "nba_roster.db"
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rel = db_path.relative_to(Path.cwd())
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db_string = f"sqlite:///{rel}"
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db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0)
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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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# Prompt
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template = """Based on the table schema below, write a SQL query that would answer the user's question:
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{schema}
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Question: {question}
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SQL Query:""" # noqa: E501
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "Given an input question, convert it to a SQL query. No pre-amble."),
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MessagesPlaceholder(variable_name="history"),
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("human", template),
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]
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)
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memory = ConversationBufferMemory(return_messages=True)
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# Chain to query with memory
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sql_chain = (
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RunnablePassthrough.assign(
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schema=get_schema,
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history=RunnableLambda(lambda x: memory.load_memory_variables(x)["history"]),
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)
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| prompt
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| llm.bind(stop=["\nSQLResult:"])
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| StrOutputParser()
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)
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def save(input_output):
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output = {"output": input_output.pop("output")}
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memory.save_context(input_output, output)
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return output["output"]
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sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save
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# Chain to answer
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template = """Based on the table schema below, question, sql query, and sql response, write a natural language response:
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{schema}
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Question: {question}
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SQL Query: {query}
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SQL Response: {response}""" # noqa: E501
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prompt_response = 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 SQL response, convert it to a natural "
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"language answer. No pre-amble.",
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),
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("human", template),
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]
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)
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# Supply the input types to the prompt
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class InputType(BaseModel):
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question: str
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chain = (
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RunnablePassthrough.assign(query=sql_response_memory).with_types(
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input_type=InputType
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)
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| RunnablePassthrough.assign(
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schema=get_schema,
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response=lambda x: db.run(x["query"]),
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)
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| prompt_response
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| llm
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)
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