mirror of
https://github.com/hwchase17/langchain
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480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
137 lines
2.9 KiB
Plaintext
137 lines
2.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "052dfe58",
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"metadata": {},
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"source": [
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"# Fake LLM\n",
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"LangChain provides a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.\n",
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"\n",
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"In this notebook we go over how to use this.\n",
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"\n",
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"We start this with using the FakeLLM in an agent."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ef97ac4d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.llms.fake import FakeListLLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9a0a160f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentType, initialize_agent, load_tools"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b272258c",
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"metadata": {},
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"outputs": [],
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"source": [
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"tools = load_tools([\"python_repl\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "94096c4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"responses = [\"Action: Python REPL\\nAction Input: print(2 + 2)\", \"Final Answer: 4\"]\n",
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"llm = FakeListLLM(responses=responses)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "da226d02",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = initialize_agent(\n",
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" tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "44c13426",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mAction: Python REPL\n",
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"Action Input: print(2 + 2)\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m4\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3mFinal Answer: 4\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'4'"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"whats 2 + 2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "814c2858",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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