viruPVS/langchain-docs huggingface dataset:
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_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import (
    CYPHER_QA_PROMPT,
    GREMLIN_GENERATION_PROMPT,
)
from langchain.chains.llm import LLMChain
from langchain.graphs.hugegraph import HugeGraph
[docs]class HugeGraphQAChain(Chain):
    """Chain for question-answering against a graph by generating gremlin statements.
    *Security note*: Make sure that the database connection uses credentials
        that are narrowly-scoped to only include necessary permissions.
        Failure to do so may result in data corruption or loss, since the calling
        code may attempt commands that would result in deletion, mutation
        of data if appropriately prompted or reading sensitive data if such
        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: HugeGraph = Field(exclude=True)
    gremlin_generation_chain: LLMChain
    qa_chain: LLMChain
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    @property
    def input_keys(self) -> List[str]:
        """Input keys.
def input_keys(self) -> List[str]:
        """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,
        llm: BaseLanguageModel,
        *,
        qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
        gremlin_prompt: BasePromptTemplate = GREMLIN_GENERATION_PROMPT,
        **kwargs: Any,
    ) -> HugeGraphQAChain:
        """Initialize from LLM."""
        qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
        gremlin_generation_chain = LLMChain(llm=llm, prompt=gremlin_prompt)
        return cls(
            qa_chain=qa_chain,
            gremlin_generation_chain=gremlin_generation_chain,
            **kwargs,
        )
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        """Generate gremlin statement, use it to look up in db and answer question."""
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        question = inputs[self.input_key]
        generated_gremlin = self.gremlin_generation_chain.run(
            {"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
        )
)
        _run_manager.on_text("Generated gremlin:", end="\n", verbose=self.verbose)
        _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", verbose=self.verbose
        )
        result = self.qa_chain(
            {"question": question, "context": context},
            callbacks=callbacks,
        )
        return {self.output_key: result[self.qa_chain.output_key]}
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_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
from langchain.chains.llm import LLMChain
from langchain.graphs import FalkorDBGraph
INTERMEDIATE_STEPS_KEY = "intermediate_steps"
[docs]def extract_cypher(text: str) -> str:
    """
    Extract Cypher code from a text.
    Args:
        text: Text to extract Cypher code from.
    Returns:
        Cypher code extracted from the text.
    """
    # 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]class FalkorDBQAChain(Chain):
    """Chain for question-answering against a graph by generating Cypher statements.
    *Security note*: Make sure that the database connection uses credentials
        that are narrowly-scoped to only include necessary permissions.
        Failure to do so may result in data corruption or loss, since the calling
        code may attempt commands that would result in deletion, mutation
        of data if appropriately prompted or reading sensitive data if such
        data is present in the database.
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=True)
    cypher_generation_chain: LLMChain
    qa_chain: LLMChain
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    top_k: int = 10
    """Number of results to return from the query"""
    return_intermediate_steps: bool = False
    """Whether or not to return the intermediate steps along with the final answer."""
    return_direct: bool = False
    """Whether or not to return the result of querying the graph directly."""
    @property
    def input_keys(self) -> List[str]:
        """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
    @property
    def _chain_type(self) -> str:
        return "graph_cypher_chain"
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        *,
        qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
        cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
        **kwargs: Any,
    ) -> FalkorDBQAChain:
        """Initialize from LLM."""
) -> 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,
        )
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """Generate Cypher statement, use it to look up in db and answer question."""
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        question = inputs[self.input_key]
        intermediate_steps: List = []
        generated_cypher = self.cypher_generation_chain.run(
            {"question": question, "schema": self.graph.schema}, callbacks=callbacks
        )
        # Extract Cypher code if it is wrapped in backticks
        generated_cypher = extract_cypher(generated_cypher)
        _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
        _run_manager.on_text(
            generated_cypher, color="green", end="\n", verbose=self.verbose
        )
        intermediate_steps.append({"query": generated_cypher})
        # Retrieve and limit the number of results
        context = self.graph.query(generated_cypher)[: self.top_k]
        if self.return_direct:
            final_result = context
        else:
            _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
            _run_manager.on_text(
_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,
            )
            final_result = result[self.qa_chain.output_key]
        chain_result: Dict[str, Any] = {self.output_key: final_result}
        if self.return_intermediate_steps:
            chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
        return chain_result
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_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import CYPHER_QA_PROMPT, NGQL_GENERATION_PROMPT
from langchain.chains.llm import LLMChain
from langchain.graphs.nebula_graph import NebulaGraph
[docs]class NebulaGraphQAChain(Chain):
    """Chain for question-answering against a graph by generating nGQL statements.
    *Security note*: Make sure that the database connection uses credentials
        that are narrowly-scoped to only include necessary permissions.
        Failure to do so may result in data corruption or loss, since the calling
        code may attempt commands that would result in deletion, mutation
        of data if appropriately prompted or reading sensitive data if such
        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: NebulaGraph = Field(exclude=True)
    ngql_generation_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 the input keys.
        :meta private:
"""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 from_llm(
        cls,
        llm: BaseLanguageModel,
        *,
        qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
        ngql_prompt: BasePromptTemplate = NGQL_GENERATION_PROMPT,
        **kwargs: Any,
    ) -> NebulaGraphQAChain:
        """Initialize from LLM."""
        qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
        ngql_generation_chain = LLMChain(llm=llm, prompt=ngql_prompt)
        return cls(
            qa_chain=qa_chain,
            ngql_generation_chain=ngql_generation_chain,
            **kwargs,
        )
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        """Generate nGQL statement, use it to look up in db and answer question."""
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        question = inputs[self.input_key]
        generated_ngql = self.ngql_generation_chain.run(
            {"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
        )
        _run_manager.on_text("Generated nGQL:", end="\n", verbose=self.verbose)
_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", verbose=self.verbose
        )
        result = self.qa_chain(
            {"question": question, "context": context},
            callbacks=callbacks,
        )
        return {self.output_key: result[self.qa_chain.output_key]}
Source code for langchain.chains.graph_qa.cypher
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
from langchain.chains.llm import LLMChain
from langchain.graphs.graph_store import GraphStore
INTERMEDIATE_STEPS_KEY = "intermediate_steps"
[docs]def extract_cypher(text: str) -> str:
    """Extract Cypher code from a text.
    Args:
        text: Text to extract Cypher code from.
    Returns:
        Cypher code extracted from the text.
    """
    # 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 construct_schema(
    structured_schema: Dict[str, Any],
    include_types: List[str],
    exclude_types: List[str],
) -> str:
    """Filter the schema based on included or excluded types"""
    def filter_func(x: str) -> bool:
        return x in include_types if include_types else x not in exclude_types
    filtered_schema: Dict[str, Any] = {
        "node_props": {
filtered_schema: Dict[str, Any] = {
        "node_props": {
            k: v
            for k, v in structured_schema.get("node_props", {}).items()
            if filter_func(k)
        },
        "rel_props": {
            k: v
            for k, v in structured_schema.get("rel_props", {}).items()
            if filter_func(k)
        },
        "relationships": [
            r
            for r in structured_schema.get("relationships", [])
            if all(filter_func(r[t]) for t in ["start", "end", "type"])
        ],
    }
    # Format node properties
    formatted_node_props = []
    for label, properties in filtered_schema["node_props"].items():
        props_str = ", ".join(
            [f"{prop['property']}: {prop['type']}" for prop in properties]
        )
        formatted_node_props.append(f"{label} {{{props_str}}}")
    # Format relationship properties
    formatted_rel_props = []
    for rel_type, properties in filtered_schema["rel_props"].items():
        props_str = ", ".join(
            [f"{prop['property']}: {prop['type']}" for prop in properties]
        )
        formatted_rel_props.append(f"{rel_type} {{{props_str}}}")
    # Format relationships
    formatted_rels = [
        f"(:{el['start']})-[:{el['type']}]->(:{el['end']})"
        for el in filtered_schema["relationships"]
    ]
    return "\n".join(
        [
            "Node properties are the following:",
            ",".join(formatted_node_props),
            "Relationship properties are the following:",
            ",".join(formatted_rel_props),
"Relationship properties are the following:",
            ",".join(formatted_rel_props),
            "The relationships are the following:",
            ",".join(formatted_rels),
        ]
    )
[docs]class GraphCypherQAChain(Chain):
    """Chain for question-answering against a graph by generating Cypher statements.
    *Security note*: Make sure that the database connection uses credentials
        that are narrowly-scoped to only include necessary permissions.
        Failure to do so may result in data corruption or loss, since the calling
        code may attempt commands that would result in deletion, mutation
        of data if appropriately prompted or reading sensitive data if such
        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: GraphStore = Field(exclude=True)
    cypher_generation_chain: LLMChain
    qa_chain: LLMChain
    graph_schema: str
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    top_k: int = 10
    """Number of results to return from the query"""
    return_intermediate_steps: bool = False
    """Whether or not to return the intermediate steps along with the final answer."""
    return_direct: bool = False
    """Whether or not to return the result of querying the graph directly."""
    cypher_query_corrector: Optional[CypherQueryCorrector] = None
    """Optional cypher validation tool"""
    @property
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
"""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
    @property
    def _chain_type(self) -> str:
        return "graph_cypher_chain"
[docs]    @classmethod
    def from_llm(
        cls,
        llm: Optional[BaseLanguageModel] = None,
        *,
        qa_prompt: Optional[BasePromptTemplate] = None,
        cypher_prompt: Optional[BasePromptTemplate] = None,
        cypher_llm: Optional[BaseLanguageModel] = None,
        qa_llm: Optional[BaseLanguageModel] = None,
        exclude_types: List[str] = [],
        include_types: List[str] = [],
        validate_cypher: bool = False,
        qa_llm_kwargs: Optional[Dict[str, Any]] = None,
        cypher_llm_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> GraphCypherQAChain:
        """Initialize from LLM."""
        if not cypher_llm and not llm:
            raise ValueError("Either `llm` or `cypher_llm` parameters must be provided")
        if not qa_llm and not llm:
            raise ValueError("Either `llm` or `qa_llm` parameters must be provided")
        if cypher_llm and qa_llm and llm:
            raise ValueError(
                "You can specify up to two of 'cypher_llm', 'qa_llm'"
", and 'llm', but not all three simultaneously."
            )
        if cypher_prompt and cypher_llm_kwargs:
            raise ValueError(
                "Specifying cypher_prompt and cypher_llm_kwargs together is"
                " not allowed. Please pass prompt via cypher_llm_kwargs."
            )
        if qa_prompt and qa_llm_kwargs:
            raise ValueError(
                "Specifying qa_prompt and qa_llm_kwargs together is"
                " not allowed. Please pass prompt via qa_llm_kwargs."
            )
        use_qa_llm_kwargs = qa_llm_kwargs if qa_llm_kwargs is not None else {}
        use_cypher_llm_kwargs = (
            cypher_llm_kwargs if cypher_llm_kwargs is not None else {}
        )
        if "prompt" not in use_qa_llm_kwargs:
            use_qa_llm_kwargs["prompt"] = (
                qa_prompt if qa_prompt is not None else CYPHER_QA_PROMPT
            )
        if "prompt" not in use_cypher_llm_kwargs:
            use_cypher_llm_kwargs["prompt"] = (
                cypher_prompt if cypher_prompt is not None else CYPHER_GENERATION_PROMPT
            )
        qa_chain = LLMChain(llm=qa_llm or llm, **use_qa_llm_kwargs)
        cypher_generation_chain = LLMChain(
            llm=cypher_llm or llm, **use_cypher_llm_kwargs
        )
        if exclude_types and include_types:
            raise ValueError(
                "Either `exclude_types` or `include_types` "
                "can be provided, but not both"
            )
        graph_schema = construct_schema(
)
        graph_schema = construct_schema(
            kwargs["graph"].get_structured_schema, include_types, exclude_types
        )
        cypher_query_corrector = None
        if validate_cypher:
            corrector_schema = [
                Schema(el["start"], el["type"], el["end"])
                for el in kwargs["graph"].structured_schema.get("relationships")
            ]
            cypher_query_corrector = CypherQueryCorrector(corrector_schema)
        return cls(
            graph_schema=graph_schema,
            qa_chain=qa_chain,
            cypher_generation_chain=cypher_generation_chain,
            cypher_query_corrector=cypher_query_corrector,
            **kwargs,
        )
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """Generate Cypher statement, use it to look up in db and answer question."""
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        question = inputs[self.input_key]
        intermediate_steps: List = []
        generated_cypher = self.cypher_generation_chain.run(
            {"question": question, "schema": self.graph_schema}, callbacks=callbacks
        )
        # Extract Cypher code if it is wrapped in backticks
        generated_cypher = extract_cypher(generated_cypher)
        # Correct Cypher query if enabled
        if self.cypher_query_corrector:
            generated_cypher = self.cypher_query_corrector(generated_cypher)
        _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
        _run_manager.on_text(
_run_manager.on_text(
            generated_cypher, color="green", end="\n", verbose=self.verbose
        )
        intermediate_steps.append({"query": generated_cypher})
        # Retrieve and limit the number of results
        # Generated Cypher be null if query corrector identifies invalid schema
        if generated_cypher:
            context = self.graph.query(generated_cypher)[: self.top_k]
        else:
            context = []
        if self.return_direct:
            final_result = context
        else:
            _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
            _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,
            )
            final_result = result[self.qa_chain.output_key]
        chain_result: Dict[str, Any] = {self.output_key: final_result}
        if self.return_intermediate_steps:
            chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
        return chain_result