! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain youtube-transcript-api pytube

import os
os.environ['LANGCHAIN_TRACING_V2'] = 'true'
os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'
os.environ['LANGCHAIN_API_KEY'] = <your-api-key>

os.environ['OPENAI_API_KEY'] = <your-api-key>

question = """Why doesn't the following code work:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(["human", "speak in {language}"])
prompt.invoke("french")
"""

result = router.invoke({"question": question})

def choose_route(result):
    if "python_docs" in result.datasource.lower():
        ### Logic here 
        return "chain for python_docs"
    elif "js_docs" in result.datasource.lower():
        ### Logic here 
        return "chain for js_docs"
    else:
        ### Logic here 
        return "golang_docs"

from langchain_core.runnables import RunnableLambda

full_chain = router | RunnableLambda(choose_route)

import datetime
from typing import Literal, Optional, Tuple
from langchain_core.pydantic_v1 import BaseModel, Field

class TutorialSearch(BaseModel):
    """Search over a database of tutorial videos about a software library."""

    content_search: str = Field(
        ...,
        description="Similarity search query applied to video transcripts.",
    )
    title_search: str = Field(
        ...,
        description=(
            "Alternate version of the content search query to apply to video titles. "
            "Should be succinct and only include key words that could be in a video "
            "title."
        ),
    )
    min_view_count: Optional[int] = Field(
        None,
        description="Minimum view count filter, inclusive. Only use if explicitly specified.",
    )
    max_view_count: Optional[int] = Field(
        None,
        description="Maximum view count filter, exclusive. Only use if explicitly specified.",
    )
    earliest_publish_date: Optional[datetime.date] = Field(
        None,
        description="Earliest publish date filter, inclusive. Only use if explicitly specified.",
    )
    latest_publish_date: Optional[datetime.date] = Field(
        None,
        description="Latest publish date filter, exclusive. Only use if explicitly specified.",
    )
    min_length_sec: Optional[int] = Field(
        None,
        description="Minimum video length in seconds, inclusive. Only use if explicitly specified.",
    )
    max_length_sec: Optional[int] = Field(
        None,
        description="Maximum video length in seconds, exclusive. Only use if explicitly specified.",
    )

    def pretty_print(self) -> None:
        for field in self.__fields__:
            if getattr(self, field) is not None and getattr(self, field) != getattr(
                self.__fields__[field], "default", None
            ):
                print(f"{field}: {getattr(self, field)}")

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

system = """You are an expert at converting user questions into database queries. \
You have access to a database of tutorial videos about a software library for building LLM-powered applications. \
Given a question, return a database query optimized to retrieve the most relevant results.

If there are acronyms or words you are not familiar with, do not try to rephrase them."""
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "{question}"),
    ]
)
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(TutorialSearch)
query_analyzer = prompt | structured_llm