mirror of
https://github.com/Mintplex-Labs/langchain-python.git
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blob42
3ecdea8be4
This is a work in progress PR to track my progres.
## TODO:
- [x] Get results using the specifed searx host
- [x] Prioritize returning an `answer` or results otherwise
- [ ] expose the field `infobox` when available
- [ ] expose `score` of result to help agent's decision
- [ ] expose the `suggestions` field to agents so they could try new
queries if no results are found with the orignial query ?
- [ ] Dynamic tool description for agents ?
- Searx offers many engines and a search syntax that agents can take
advantage of. It would be nice to generate a dynamic Tool description so
that it can be used many times as a tool but for different purposes.
- [x] Limit number of results
- [ ] Implement paging
- [x] Miror the usage of the Google Search tool
- [x] easy selection of search engines
- [x] Documentation
- [ ] update HowTo guide notebook on Search Tools
- [ ] Handle async
- [ ] Tests
### Add examples / documentation on possible uses with
- [ ] getting factual answers with `!wiki` option and `infoboxes`
- [ ] getting `suggestions`
- [ ] getting `corrections`
---------
Co-authored-by: blob42 <spike@w530>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
95 lines
2.3 KiB
Python
95 lines
2.3 KiB
Python
"""Main entrypoint into package."""
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from typing import Optional
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from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
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from langchain.cache import BaseCache
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from langchain.callbacks import (
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set_default_callback_manager,
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set_handler,
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set_tracing_callback_manager,
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)
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from langchain.chains import (
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ConversationChain,
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LLMBashChain,
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LLMChain,
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LLMCheckerChain,
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LLMMathChain,
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PALChain,
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QAWithSourcesChain,
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SQLDatabaseChain,
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VectorDBQA,
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VectorDBQAWithSourcesChain,
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)
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from langchain.docstore import InMemoryDocstore, Wikipedia
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from langchain.llms import (
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Anthropic,
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CerebriumAI,
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Cohere,
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ForefrontAI,
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GooseAI,
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HuggingFaceHub,
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OpenAI,
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Petals,
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)
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from langchain.llms.huggingface_pipeline import HuggingFacePipeline
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from langchain.prompts import (
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BasePromptTemplate,
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FewShotPromptTemplate,
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Prompt,
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PromptTemplate,
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)
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from langchain.serpapi import SerpAPIChain, SerpAPIWrapper
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from langchain.sql_database import SQLDatabase
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from langchain.utilities.google_search import GoogleSearchAPIWrapper
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from langchain.utilities.google_serper import GoogleSerperAPIWrapper
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from langchain.utilities.searx_search import SearxSearchWrapper
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from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
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from langchain.vectorstores import FAISS, ElasticVectorSearch
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verbose: bool = False
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llm_cache: Optional[BaseCache] = None
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set_default_callback_manager()
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__all__ = [
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"LLMChain",
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"LLMBashChain",
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"LLMCheckerChain",
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"LLMMathChain",
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"SelfAskWithSearchChain",
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"SerpAPIWrapper",
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"SerpAPIChain",
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"SearxSearchWrapper",
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"GoogleSearchAPIWrapper",
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"GoogleSerperAPIWrapper",
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"WolframAlphaAPIWrapper",
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"Anthropic",
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"CerebriumAI",
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"Cohere",
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"ForefrontAI",
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"GooseAI",
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"OpenAI",
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"Petals",
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"BasePromptTemplate",
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"Prompt",
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"FewShotPromptTemplate",
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"PromptTemplate",
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"ReActChain",
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"Wikipedia",
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"HuggingFaceHub",
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"HuggingFacePipeline",
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"SQLDatabase",
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"SQLDatabaseChain",
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"FAISS",
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"MRKLChain",
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"VectorDBQA",
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"ElasticVectorSearch",
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"InMemoryDocstore",
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"ConversationChain",
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"VectorDBQAWithSourcesChain",
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"QAWithSourcesChain",
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"PALChain",
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"set_handler",
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"set_tracing_callback_manager",
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]
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