id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
77f2edfc50f7-0 | Source code for langchain.chains.graph_qa.hugegraph
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts imp... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/hugegraph.html |
77f2edfc50f7-1 | """Input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/hugegraph.html |
77f2edfc50f7-2 | _run_manager.on_text(
generated_gremlin, color="green", end="\n", verbose=self.verbose
)
context = self.graph.query(generated_gremlin)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(context), color="green", end="\n"... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/hugegraph.html |
8f92bd80470c-0 | Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import E... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
8f92bd80470c-1 | """
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
qa_promp... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
8f92bd80470c-2 | all_triplets.extend(self.graph.get_entity_knowledge(entity))
context = "\n".join(all_triplets)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(context, color="green", end="\n", verbose=self.verbose)
result = self.qa_chain(
{"question... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
c17c1e7e05b7-0 | Source code for langchain.chains.graph_qa.sparql
"""
Question answering over an RDF or OWL graph using SPARQL.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.cha... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
c17c1e7e05b7-1 | sparql_intent_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
return [self.input_key]
@property
def output_keys(self) -> List[str]:
_output_keys = [self.o... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
c17c1e7e05b7-2 | **kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""
Generate SPARQL query, use it to retrieve a response from the gdb and answer
the question.
"""
_run_mana... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
c17c1e7e05b7-3 | )
if intent == "SELECT":
context = self.graph.query(generated_sparql)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(context), color="green", end="\n", verbose=self.verbose
)
result = sel... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
19a254c2de1d-0 | Source code for langchain.chains.graph_qa.neptune_cypher
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langcha... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
19a254c2de1d-1 | """Extract Cypher code from text using Regex."""
# The pattern to find Cypher code enclosed in triple backticks
pattern = r"```(.*?)```"
# Find all matches in the input text
matches = re.findall(pattern, text, re.DOTALL)
return matches[0] if matches else text
[docs]def use_simple_prompt(llm: BaseLan... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
19a254c2de1d-2 | See https://python.langchain.com/docs/security for more information.
Example:
.. code-block:: python
chain = NeptuneOpenCypherQAChain.from_llm(
llm=llm,
graph=graph
)
response = chain.run(query)
"""
graph: NeptuneGraph = Field(exclude=True)
cypher_... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
19a254c2de1d-3 | **kwargs: Any,
) -> NeptuneOpenCypherQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
_cypher_prompt = cypher_prompt or PROMPT_SELECTOR.get_prompt(llm)
cypher_generation_chain = LLMChain(llm=llm, prompt=_cypher_prompt)
return cls(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
19a254c2de1d-4 | )
intermediate_steps.append({"query": generated_cypher})
context = self.graph.query(generated_cypher)
if self.return_direct:
final_result = context
else:
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
cd3cd25fe848-0 | Source code for langchain.chains.graph_qa.kuzu
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import C... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
cd3cd25fe848-1 | """Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def fro... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
cd3cd25fe848-2 | _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_cypher, color="green", end="\n", verbose=self.verbose
)
context = self.graph.query(generated_cypher)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
b7ebf2a07f04-0 | Source code for langchain.chains.graph_qa.nebulagraph
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts i... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
b7ebf2a07f04-1 | """Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def fro... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
b7ebf2a07f04-2 | _run_manager.on_text(
generated_ngql, color="green", end="\n", verbose=self.verbose
)
context = self.graph.query(generated_ngql)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(context), color="green", end="\n", verb... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
a85a14f9a237-0 | Source code for langchain.chains.graph_qa.falkordb
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chai... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/falkordb.html |
a85a14f9a237-1 | data is present in the database.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this tool.
See https://python.langchain.com/docs/security for more information.
"""
graph: FalkorDBGraph = Field(exclude... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/falkordb.html |
a85a14f9a237-2 | ) -> FalkorDBQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
return cls(
qa_chain=qa_chain,
cypher_generation_chain=cypher_generation_chain,
**kwargs,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/falkordb.html |
a85a14f9a237-3 | _run_manager.on_text(
str(context), color="green", end="\n", verbose=self.verbose
)
intermediate_steps.append({"context": context})
result = self.qa_chain(
{"question": question, "context": context},
callbacks=callbacks,
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/falkordb.html |
e3cbe8b0e7bb-0 | Source code for langchain.chains.constitutional_ai.base
"""Chain for applying constitutional principles to the outputs of another chain."""
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.consti... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
e3cbe8b0e7bb-1 | critique_chain: LLMChain
revision_chain: LLMChain
return_intermediate_steps: bool = False
[docs] @classmethod
def get_principles(
cls, names: Optional[List[str]] = None
) -> List[ConstitutionalPrinciple]:
if names is None:
return list(PRINCIPLES.values())
else:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
e3cbe8b0e7bb-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_manager.get_child("original"),
)
initial_response = response
input_prompt = self.chain.prompt.format(**inpu... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
e3cbe8b0e7bb-3 | _run_manager.on_text(
text=f"Applying {constitutional_principle.name}..." + "\n\n",
verbose=self.verbose,
color="green",
)
_run_manager.on_text(
text="Critique: " + critique + "\n\n",
verbose=self.verbose,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
661e38815525-0 | Source code for langchain.chains.constitutional_ai.models
"""Models for the Constitutional AI chain."""
from langchain.pydantic_v1 import BaseModel
[docs]class ConstitutionalPrinciple(BaseModel):
"""Class for a constitutional principle."""
critique_request: str
revision_request: str
name: str = "Constit... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/models.html |
d2bd37184976-0 | Source code for langchain.chains.openai_functions.citation_fuzzy_match
from typing import Iterator, List
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain.output_parsers.openai_functions import (
PydanticOutputFunctionsParser,
)
from langchai... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
d2bd37184976-1 | if s is not None:
yield from s.spans()
[docs] def get_spans(self, context: str) -> Iterator[str]:
for quote in self.substring_quote:
yield from self._get_span(quote, context)
[docs]class QuestionAnswer(BaseModel):
"""A question and its answer as a list of facts each one should hav... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
d2bd37184976-2 | HumanMessagePromptTemplate.from_template("Question: {question}"),
HumanMessage(
content=(
"Tips: Make sure to cite your sources, "
"and use the exact words from the context."
)
),
]
prompt = ChatPromptTemplate(messages=messages)
chain =... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
40e078c7ee99-0 | Source code for langchain.chains.openai_functions.base
"""Methods for creating chains that use OpenAI function-calling APIs."""
import inspect
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
from langchain.base_language import BaseL... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-1 | for block in docstring_blocks:
if block.startswith("Args:"):
args_block = block
break
elif block.startswith("Returns:") or block.startswith("Example:"):
# Don't break in case Args come after
past_descriptors = True
elif ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-2 | if arg in arg_descriptions:
if arg not in properties:
properties[arg] = {}
properties[arg]["description"] = arg_descriptions[arg]
return properties
def _get_python_function_required_args(function: Callable) -> List[str]:
"""Get the required arguments for a Python function... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-3 | """Convert a raw function/class to an OpenAI function.
Args:
function: Either a dictionary, a pydantic.BaseModel class, or a Python function.
If a dictionary is passed in, it is assumed to already be a valid OpenAI
function.
Returns:
A dict version of the passed in functi... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-4 | function_names = [convert_to_openai_function(f)["name"] for f in functions]
if isinstance(functions[0], type) and issubclass(functions[0], BaseModel):
if len(functions) > 1:
pydantic_schema: Union[Dict, Type[BaseModel]] = {
name: fn for name, fn in zip(function_names, functions)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-5 | Google Python style args descriptions in the docstring. Additionally,
Python functions should only use primitive types (str, int, float, bool) or
pydantic.BaseModels for arguments.
llm: Language model to use, assumed to support the OpenAI function-calling API.
prompt: BasePromptT... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-6 | name: str = Field(..., description="The dog's name")
color: str = Field(..., description="The dog's color")
fav_food: Optional[str] = Field(None, description="The dog's favorite food")
llm = ChatOpenAI(model="gpt-4", temperature=0)
prompt = ChatPro... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-7 | llm: Runnable,
prompt: BasePromptTemplate,
*,
output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
**kwargs: Any,
) -> Runnable:
"""Create a runnable that uses an OpenAI function to get a structured output.
Args:
output_schema: Either a dictionary or pydan... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-8 | fav_food: Optional[str] = Field(None, description="The dog's favorite food")
llm = ChatOpenAI(model="gpt-3.5-turbo-0613", temperature=0)
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a world class algorithm for extracting inf... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-9 | )
""" --- Legacy --- """
[docs]def create_openai_fn_chain(
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
llm: BaseLanguageModel,
prompt: BasePromptTemplate,
*,
output_key: str = "function",
output_parser: Optional[BaseLLMOutputParser] = None,
**kwargs: Any,
) -> LLMC... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-10 | model outputs will simply be parsed as JSON. If multiple functions are
passed in and they are not pydantic.BaseModels, the chain output will
include both the name of the function that was returned and the arguments
to pass to the function.
Returns:
An LLMChain that will p... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-11 | ]
)
chain = create_openai_fn_chain([RecordPerson, RecordDog], llm, prompt)
chain.run("Harry was a chubby brown beagle who loved chicken")
# -> RecordDog(name="Harry", color="brown", fav_food="chicken")
""" # noqa: E501
if not functions:
ra... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-12 | output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary
is passed in, it's assumed to already be a valid JsonSchema.
For best results, pydantic.BaseModels should have docstrings describing what
the schema represents and descriptions for the parameters.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
40e078c7ee99-13 | prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a world class algorithm for extracting information in structured formats."),
("human", "Use the given format to extract information from the following input: {input}"),
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
c12915812514-0 | Source code for langchain.chains.openai_functions.qa_with_structure
from typing import Any, List, Optional, Type, Union
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain.output_parsers.openai_functions import (
OutputFunctionsParser,
Pyda... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
c12915812514-1 | prompt: Optional prompt to use for the chain.
Returns:
"""
if output_parser == "pydantic":
if not (isinstance(schema, type) and issubclass(schema, BaseModel)):
raise ValueError(
"Must provide a pydantic class for schema when output_parser is "
"'pydantic'.... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
c12915812514-2 | llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=_output_parser,
verbose=verbose,
)
return chain
[docs]def create_qa_with_sources_chain(
llm: BaseLanguageModel, verbose: bool = False, **kwargs: Any
) -> LLMChain:
"""Create a question answering chain that retur... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
42e0fb958e32-0 | Source code for langchain.chains.openai_functions.utils
from typing import Any, Dict
def _resolve_schema_references(schema: Any, definitions: Dict[str, Any]) -> Any:
"""
Resolves the $ref keys in a JSON schema object using the provided definitions.
"""
if isinstance(schema, list):
for i, item in... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/utils.html |
d255c12e79cc-0 | Source code for langchain.chains.openai_functions.tagging
from typing import Any, Optional
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import _convert_schema, get_llm_kwargs
from langchain.output_parsers.openai_functions import (
Jso... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
d255c12e79cc-1 | llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
**kwargs,
)
return chain
[docs]def create_tagging_chain_pydantic(
pydantic_schema: Any,
llm: BaseLanguageModel,
prompt: Optional[ChatPromptTemplate] = None,
**kwargs: Any,
) -> Chain:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
4eea511ec31a-0 | Source code for langchain.chains.openai_functions.extraction
from typing import Any, List, Optional
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import (
_convert_schema,
_resolve_schema_references,
get_llm_kwargs,
)
from lang... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
4eea511ec31a-1 | ) -> Chain:
"""Creates a chain that extracts information from a passage.
Args:
schema: The schema of the entities to extract.
llm: The language model to use.
prompt: The prompt to use for extraction.
verbose: Whether to run in verbose mode. In verbose mode, some intermediate
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
4eea511ec31a-2 | logs will be printed to the console. Defaults to the global `verbose` value,
accessible via `langchain.globals.get_verbose()`
Returns:
Chain that can be used to extract information from a passage.
"""
class PydanticSchema(BaseModel):
info: List[pydantic_schema] # type: ignore
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
fe2132fb8960-0 | Source code for langchain.chains.openai_functions.openapi
from __future__ import annotations
import json
import re
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
import requests
from requests import Response
from langchain.callbacks.manager import... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
fe2132fb8960-1 | elif param[0] == ";":
sep = f"{clean_param}=" if param[-1] == "*" else ","
new_val = f"{clean_param}=" + sep.join(val)
else:
new_val = ",".join(val)
elif isinstance(val, dict):
kv_sep = "=" if param[-1] == "*" else ","
kv_strs =... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
fe2132fb8960-2 | if p.required:
required.append(p.name)
return {"type": "object", "properties": properties, "required": required}
[docs]def openapi_spec_to_openai_fn(
spec: OpenAPISpec,
) -> Tuple[List[Dict[str, Any]], Callable]:
"""Convert a valid OpenAPI spec to the JSON Schema format expected for OpenAI
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
fe2132fb8960-3 | params_by_type[param_loc], spec
)
request_body = spec.get_request_body_for_operation(op)
# TODO: Support more MIME types.
if request_body and request_body.content:
media_types = {}
for media_type, media_type_object in request_body.c... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
fe2132fb8960-4 | url = _name_to_call_map[name]["url"]
path_params = fn_args.pop("path_params", {})
url = _format_url(url, path_params)
if "data" in fn_args and isinstance(fn_args["data"], dict):
fn_args["data"] = json.dumps(fn_args["data"])
_kwargs = {**fn_args, **kwargs}
if headers i... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
fe2132fb8960-5 | _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
name = inputs[self.input_key].pop("name")
args = inputs[self.input_key].pop("arguments")
_pretty_name = get_colored_text(name, "green")
_pretty_args = get_colored_text(json.dumps(args, indent=2), "green")
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
fe2132fb8960-6 | spec: OpenAPISpec or url/file/text string corresponding to one.
llm: language model, should be an OpenAI function-calling model, e.g.
`ChatOpenAI(model="gpt-3.5-turbo-0613")`.
prompt: Main prompt template to use.
request_chain: Chain for taking the functions output and executing the ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
fe2132fb8960-7 | request_method=lambda name, args: call_api_fn(
name, args, headers=headers, params=params
),
verbose=verbose,
)
return SequentialChain(
chains=[llm_chain, request_chain],
input_variables=llm_chain.input_keys,
output_variables=["response"],
verbose=verb... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
8dc3fe4576c7-0 | Source code for langchain.chains.elasticsearch_database.base
"""Chain for interacting with Elasticsearch Database."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
8dc3fe4576c7-1 | output_key: str = "result" #: :meta private:
sample_documents_in_index_info: int = 3
return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbi... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
8dc3fe4576c7-2 | if self.sample_documents_in_index_info > 0:
for k, v in mappings.items():
hits = self.database.search(
index=k,
query={"match_all": {}},
size=self.sample_documents_in_index_info,
)["hits"]["hits"]
hit... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
8dc3fe4576c7-3 | es_cmd = self.query_chain.run(
callbacks=_run_manager.get_child(),
**query_inputs,
)
_run_manager.on_text(es_cmd, color="green", verbose=self.verbose)
intermediate_steps.append(
es_cmd
) # output: elasticsearch dsl generati... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
8dc3fe4576c7-4 | [docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
database: Elasticsearch,
*,
query_prompt: Optional[BasePromptTemplate] = None,
answer_prompt: Optional[BasePromptTemplate] = None,
query_output_parser: Optional[BaseLLMOutputParser] = None,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
19cd24c17291-0 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from lan... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
19cd24c17291-1 | output_key="revised_statement",
)
chains = [
create_draft_answer_chain,
list_assertions_chain,
check_assertions_chain,
revised_answer_chain,
]
question_to_checked_assertions_chain = SequentialChain(
chains=chains,
input_variables=["question"],
outp... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
19cd24c17291-2 | arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMCheckerChain with an llm is deprecated. "
"Please instantiate with question_to_checked_a... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
19cd24c17291-3 | ) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs[self.input_key]
output = self.question_to_checked_assertions_chain(
{"question": question}, callbacks=_run_manager.get_child()
)
return {self.output_key:... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
3cc2db946746-0 | Source code for langchain.chains.natbot.crawler
# flake8: noqa
import time
from sys import platform
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Optional,
Set,
Tuple,
TypedDict,
Union,
)
if TYPE_CHECKING:
from playwright.sync_api import Browser, CDPSession, ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-1 | """
[docs] def __init__(self) -> None:
try:
from playwright.sync_api import sync_playwright
except ImportError:
raise ImportError(
"Could not import playwright python package. "
"Please install it with `pip install playwright`."
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-2 | links[i].removeAttribute("target");
}
"""
self.page.evaluate(js)
element = self.page_element_buffer.get(int(id))
if element:
x: float = element["center_x"]
y: float = element["center_y"]
self.page.mouse.click(x, y)
else:
print("Could no... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-3 | percentage_progress_end = 2
page_state_as_text.append(
{
"x": 0,
"y": 0,
"text": "[scrollbar {:0.2f}-{:0.2f}%]".format(
round(percentage_progress_start, 2), round(percentage_progress_end)
),
}
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-4 | elements_in_view_port: List[ElementInViewPort] = []
anchor_ancestry: Dict[str, Tuple[bool, Optional[int]]] = {"-1": (False, None)}
button_ancestry: Dict[str, Tuple[bool, Optional[int]]] = {"-1": (False, None)}
def convert_name(
node_name: Optional[str], has_click_handler: Optional[bo... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-5 | if not parent_id_str in hash_tree:
parent_name = strings[node_names[parent_id]].lower()
grand_parent_id = parent[parent_id]
add_to_hash_tree(
hash_tree, tag, parent_id, parent_name, grand_parent_id
)
is_parent_desc_anchor, a... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-6 | continue
if node_name in black_listed_elements:
continue
[x, y, width, height] = bounds[cursor]
x /= device_pixel_ratio
y /= device_pixel_ratio
width /= device_pixel_ratio
height /= device_pixel_ratio
elem_left_bound = x... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-7 | node_name == "input" and element_attributes.get("type") == "submit"
) or node_name == "button":
node_name = "button"
element_attributes.pop(
"type", None
) # prevent [button ... (button)..]
for key in el... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-8 | "origin_x": int(x),
"origin_y": int(y),
"center_x": int(x + (width / 2)),
"center_y": int(y + (height / 2)),
}
)
# lets filter further to remove anything that does not hold any text nor has click handlers + merge text from l... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
3cc2db946746-9 | and converted_node_name != "link"
and converted_node_name != "input"
and converted_node_name != "img"
and converted_node_name != "textarea"
) and inner_text.strip() == "":
continue
page_element_buffer[id_counter] = element
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
355519ebce64-0 | Source code for langchain.chains.natbot.base
"""Implement an LLM driven browser."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
355519ebce64-1 | previous_command: str = "" #: :meta private:
output_key: str = "command" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
355519ebce64-2 | """Expect url and browser content.
:meta private:
"""
return [self.input_url_key, self.input_browser_content_key]
@property
def output_keys(self) -> List[str]:
"""Return command.
:meta private:
"""
return [self.output_key]
def _call(
self,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
355519ebce64-3 | }
return self(_inputs)[self.output_key]
@property
def _chain_type(self) -> str:
return "nat_bot_chain" | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
e5e8ce940b6e-0 | Source code for langchain.chains.openai_tools.extraction
from typing import List, Type, Union
from langchain.output_parsers import PydanticToolsParser
from langchain.prompts import ChatPromptTemplate
from langchain.pydantic_v1 import BaseModel
from langchain.schema.language_model import BaseLanguageModel
from langchain... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/openai_tools/extraction.html |
312ed69e5445-0 | Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Cha... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
312ed69e5445-1 | return list(np.array(embeddings).mean(axis=0))
[docs] def embed_query(self, text: str) -> List[float]:
"""Generate a hypothetical document and embedded it."""
var_name = self.llm_chain.input_keys[0]
result = self.llm_chain.generate([{var_name: text}])
documents = [generation.text for ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
c8a1fffc9b01-0 | Source code for langchain.chains.conversation.base
"""Chain that carries on a conversation and calls an LLM."""
from typing import Dict, List
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer import ConversationBufferMemory
from langchain.pyda... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
c8a1fffc9b01-1 | f"The input key {input_key} was also found in the memory keys "
f"({memory_keys}) - please provide keys that don't overlap."
)
prompt_variables = values["prompt"].input_variables
expected_keys = memory_keys + [input_key]
if set(expected_keys) != set(prompt_variables):... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
cd870b8b73a8-0 | Source code for langchain.chains.combine_documents.map_rerank
"""Combining documents by mapping a chain over them first, then reranking results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, Union, cast
from langchain.callbacks.manager import Callbacks
from l... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
cd870b8b73a8-1 | "for water. Output both your answer and a score of how confident "
"you are. Context: {content}"
)
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
prompt = PromptTemplate(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
cd870b8b73a8-2 | ) -> Type[BaseModel]:
schema: Dict[str, Any] = {
self.output_key: (str, None),
}
if self.return_intermediate_steps:
schema["intermediate_steps"] = (List[str], None)
if self.metadata_keys:
schema.update({key: (Any, None) for key in self.metadata_keys})
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
cd870b8b73a8-3 | f"it in the llm_chain output keys ({output_keys})"
)
return values
@root_validator(pre=True)
def get_default_document_variable_name(cls, values: Dict) -> Dict:
"""Get default document variable name, if not provided."""
if "document_variable_name" not in values:
ll... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
cd870b8b73a8-4 | """
results = self.llm_chain.apply_and_parse(
# FYI - this is parallelized and so it is fast.
[{**{self.document_variable_name: d.page_content}, **kwargs} for d in docs],
callbacks=callbacks,
)
return self._process_results(docs, results)
[docs] async def ac... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
cd870b8b73a8-5 | )
output, document = sorted_res[0]
extra_info = {}
if self.metadata_keys is not None:
for key in self.metadata_keys:
extra_info[key] = document.metadata[key]
if self.return_intermediate_steps:
extra_info["intermediate_steps"] = results
retu... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
38c6a19de250-0 | Source code for langchain.chains.combine_documents.stuff
"""Chain that combines documents by stuffing into context."""
from typing import Any, Dict, List, Optional, Tuple
from langchain.callbacks.manager import Callbacks
from langchain.chains.combine_documents.base import (
BaseCombineDocumentsChain,
)
from langcha... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
38c6a19de250-1 | # The prompt here should take as an input variable the
# `document_variable_name`
prompt = PromptTemplate.from_template(
"Summarize this content: {context}"
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
chain = StuffDocumentsChain(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
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