mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
import os
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from git import Repo
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from langchain_chroma import Chroma
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from langchain_community.document_loaders.generic import GenericLoader
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from langchain_community.document_loaders.parsers import LanguageParser
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from langchain_community.embeddings import GPT4AllEmbeddings
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from langchain_community.llms.fireworks import Fireworks
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from langchain_text_splitters import Language, RecursiveCharacterTextSplitter
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# Check API key
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if os.environ.get("FIREWORKS_API_KEY", None) is None:
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raise Exception("Missing `FIREWORKS_API_KEY` environment variable.")
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# Load codebase
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# Set local path
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repo_path = "/Users/rlm/Desktop/tmp_repo"
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# Use LangChain as an example
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repo = Repo.clone_from("https://github.com/langchain-ai/langchain", to_path=repo_path)
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loader = GenericLoader.from_filesystem(
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repo_path + "/libs/langchain/langchain",
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glob="**/*",
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suffixes=[".py"],
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parser=LanguageParser(language=Language.PYTHON, parser_threshold=500),
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)
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documents = loader.load()
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# Split
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python_splitter = RecursiveCharacterTextSplitter.from_language(
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language=Language.PYTHON, chunk_size=2000, chunk_overlap=200
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)
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texts = python_splitter.split_documents(documents)
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# Add to vectorDB
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vectorstore = Chroma.from_documents(
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documents=texts,
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collection_name="codebase-rag",
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embedding=GPT4AllEmbeddings(),
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)
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retriever = vectorstore.as_retriever()
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# Initialize a Fireworks model
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model = Fireworks(model="accounts/fireworks/models/llama-v2-34b-code-instruct")
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# RAG chain
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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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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__root__: str
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chain = chain.with_types(input_type=Question)
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