commit 299332dd4ef5f28197d30f087bc9d70a296d2f0e Author: Cyber MacGeddon Date: Wed Jul 10 17:04:24 2024 +0100 Trustgraph, first drop of code diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..fdd19e0a --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +*~ +__pycache__/ +env/ +*.egg_info/ diff --git a/Containerfile b/Containerfile new file mode 100644 index 00000000..e01bd7a2 --- /dev/null +++ b/Containerfile @@ -0,0 +1,54 @@ + +# ---------------------------------------------------------------------------- +# Build an AI container. This does the torch install which is huge, and I +# like to avoid re-doing this. +# ---------------------------------------------------------------------------- + +FROM docker.io/fedora:40 AS ai + +ENV PIP_BREAK_SYSTEM_PACKAGES=1 + +RUN dnf install -y python3 python3-pip python3-wheel python3-aiohttp \ + python3-rdflib + +RUN pip3 install torch --index-url https://download.pytorch.org/whl/cpu + +RUN pip3 install anthropic google-cloud-aiplatform langchain langchain-core \ + langchain-huggingface langchain-text-splitters langchain-community \ + pymilvus sentence-transformers transformers huggingface-hub \ + pulsar-client && \ + pip3 cache purge + +# ---------------------------------------------------------------------------- +# Build a container which contains the built Python package. The build +# creates a bunch of left-over cruft, a separate phase means this is only +# needed to support package build +# ---------------------------------------------------------------------------- + +FROM ai AS build + +env PACKAGE_VERSION=0.0.0 + +COPY setup.py /root/build/ +COPY README.md /root/build/ +COPY scripts/ /root/build/scripts/ +COPY trustgraph/ root/build/trustgraph/ + +RUN (cd /root/build && pip3 wheel -w /root/wheels --no-deps .) + +# ---------------------------------------------------------------------------- +# Finally, the target container. Start with base and add the package. +# ---------------------------------------------------------------------------- + +FROM ai + +COPY --from=build /root/wheels /root/wheels + +RUN pip3 install /root/wheels/trustgraph-* && \ + pip3 cache purge && \ + rm -rf /root/wheels + +WORKDIR / + +CMD sleep 1000000 + diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..d6456956 --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/Makefile b/Makefile new file mode 100644 index 00000000..f7fbdc72 --- /dev/null +++ b/Makefile @@ -0,0 +1,30 @@ + +# VERSION=$(shell git describe | sed 's/^v//') +VERSION=0.1.16 + +all: container + +CONTAINER=docker.io/trustgraph/trustgraph-flow + +container: + podman build -f Containerfile -t ${CONTAINER}:${VERSION} \ + --format docker + +push: + podman push ${CONTAINER}:${VERSION} + +start: + podman run -i -t --name ${NAME} \ + -i -t \ + -p 8081:8081 \ + -v $$(pwd)/keys:/keys \ + -v $$(pwd)/configs:/configs \ + ${CONTAINER}:${VERSION} + +stop: + podman rm -f ${NAME} + +clean: + rm -rf wheels/ + +# sed -i 's/0.1.15/0.1.16/' docker-compose*.yaml diff --git a/README.md b/README.md new file mode 100644 index 00000000..a6c28209 --- /dev/null +++ b/README.md @@ -0,0 +1,151 @@ + +# For AMD... +# pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.1/ + +---------------------------------------------------------------------------- + +docker network create trustgraph --driver bridge + +docker volume create cassandra +docker volume create pulsar-conf +docker volume create pulsar-data +docker volume create etcd +docker volume create minio-data +docker volume create milvus + +---------------------------------------------------------------------------- + +Cassandra: +docker run -i -t \ + --name cassandra \ + --network trustgraph \ + -p 9042:9042 \ + -v cassandra:/var/lib/cassandra \ + docker.io/cassandra:4.1.5 + +---------------------------------------------------------------------------- + +Pulsar: + +docker run -it \ + --name pulsar \ + --network trustgraph \ + -p 6650:6650 \ + -p 8080:8080 \ + -v pulsar-conf:/pulsar/conf \ + -v pulsar-data:/pulsar/data \ + docker.io/apachepulsar/pulsar:3.3.0 \ + bin/pulsar standalone + +---------------------------------------------------------------------------- + +Milvus: + +docker run -i -t \ + --name etcd \ + --network trustgraph \ + -p 2379:2379 \ + -e ETCD_AUTO_COMPACTION_MODE=revision \ + -e ETCD_AUTO_COMPACTION_RETENTION=1000 \ + -e ETCD_QUOTA_BACKEND_BYTES=4294967296 \ + -e ETCD_SNAPSHOT_COUNT=50000 \ + -v etcd:/etcd \ + quay.io/coreos/etcd:v3.5.5 \ + etcd -advertise-client-urls=http://127.0.0.1:2379 \ + -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd + +docker run -i -t \ + --name minio \ + --network trustgraph \ + -p 9000:9000 -p 9001:9001 \ + -e MINIO_ACCESS_KEY=minioadmin \ + -e MINIO_SECRET_KEY=minioadmin \ + -v minio-data:/minio_data \ + docker.io/minio/minio:RELEASE.2024-07-04T14-25-45Z \ + minio server /minio_data --console-address :9001 + +docker run -i -t \ + --name milvus \ + --network trustgraph \ + -p 19530:19530 -p 9091:9091 \ + -e ETCD_ENDPOINTS=etcd:2379 \ + -e MINIO_ADDRESS=minio:9000 \ + -v milvus:/var/lib/milvus \ + docker.io/milvusdb/milvus:v2.4.5 \ + milvus run standalone + +---------------------------------------------------------------------------- + +. env/bin/activate + +scripts/pdf-decoder -p pulsar://localhost:6650/ +scripts/chunker -p pulsar://localhost:6650/ +scripts/vectorize-minilm -p pulsar://localhost:6650/ +scripts/kg-extract-definitions -p pulsar://localhost:6650/ +scripts/kg-extract-relationships -p pulsar://localhost:6650/ +scripts/vector-write-milvus -p pulsar://localhost:6650/ +scripts/graph-write-cassandra -p pulsar://localhost:6650/ +scripts/llm-ollama-text -p pulsar://localhost:6650/ -r http://monster:11434/ + +---------------------------------------------------------------------------- + +The pub/sub infra means individual processors can be switched out for other +processors which do the same thing e.g. + +- graph-write writes to Cassandra but could be switched out for something else + which uses a different store +- vector-store writes to Milvus but could be switched out for something which + writes to a different store +- llm-ollama invokes Ollama, could be switched out for a different LLM with a + different API + +The vectorizer is a bit hard to switch out ATM, different embedding algorithms +have different vector dimensions, but can probably make that work. + +kg-extractor turns chunks into edges, so maybe that could be switched out for +different algorithms & different schemas. Potentially a good place for 'value +add' processing to be added. + +It would also be possible to have more than one processor running where +kg-extractor sits to have different kinds of semantic extraction: other +proceessors could take as input the output of vectorizer, and drop edges and +edge associations into graph-write and vector-store + +Actually, as it stands, kg-extractor does 2 different kinds of processing +itself: + +- Creates edges for relationships, and +- Creates edges for definitions + +Each of those uses its own LLM invocation and mashes the output into RDF. It +would be useful to split kg-extractor into 2 pieces to demonstrate this bit of +the architecture. + +---------------------------------------------------------------------------- + +- pdf-extractor turns a PDF doc into plain text. For each PDF page, a + separate blob of text is emitted currently. +- chunker applies chunking to turn 1 PDF page into several chunks +- vectorizer invokes a sentence embedding model to create vectors. Doesn't + store anything. Output of vectorizer is chunk plus embedding. +- kg-extract turns chunks into edges using LLM prompting. There are 2 outputs: + - RDF triples + - An association between the vector embedding and RDF entities. For every + triple, there's an association between the S, P and O entiites and the + embedding. +- graph-write loads the triples into a store +- vector-store store the association between the vector and the RDF URI in a + vector store + +---------------------------------------------------------------------------- + +The demo query algorithm is... + +- Take the question, extract embeddings using a sentence model +- Use the vector store to lookup RDF URIs +- Use the RDF URIs to find relevant RDF triples in Cassandra +- Also use Cassandra to turn RDF URIs into plain-text labels if they are known +- Construct the prompt, turn RDF triples into a knowledge graph format (used + Cypher, seems to work but not sure that's the best choice) +- LLM prompt and show the answer + diff --git a/architecture.png b/architecture.png new file mode 100644 index 00000000..deb30c7c Binary files /dev/null and b/architecture.png differ diff --git a/architecture.svg b/architecture.svg new file mode 100644 index 00000000..86a0cad5 --- /dev/null +++ b/architecture.svg @@ -0,0 +1,598 @@ + + + + + + + + + + + + + + + + + loader + + + + + pdfdecoder + + + + chunker + + + + vectorizer + + + + kgextractor + + + + llmollama + + + + graphwrite + + + + vectorstore + + + + Pulsar + + + + Ollama + + + + GPUhardware + + + + Milvus + + + + Cassandra + + + + + + + + + + + + + + + + + + Embeds + + + + + RDFedges + + + + + diff --git a/collate-graph b/collate-graph new file mode 100755 index 00000000..c43bca90 --- /dev/null +++ b/collate-graph @@ -0,0 +1,24 @@ +#!/usr/bin/env python3 + +import os +import rdflib + +dir="graph" +g = rdflib.Graph() + +files = os.listdir(dir) + +for file in files: + + try: + cg = rdflib.Graph() + file = dir + "/" + file + cg.parse(file, format="turtle") + + for t in cg: + g.add(t) + except Exception as e: + print(f"{file}: {str(e)}") + +g.serialize(destination="graph.ttl") + diff --git a/do-query b/do-query new file mode 100644 index 00000000..af2841ce --- /dev/null +++ b/do-query @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 + +from trustgraph.graph_rag import GraphRag + +gr = GraphRag() + +query="""This knowledge graph describes the Space Shuttle disaster. +Present 20 facts which are present in the knowledge graph.""" + +# embed = gr.get_vector(query) +# print(embed) + +#entities = gr.get_entities(query) +#for ent in entities: +# print(ent) + +# nodes = gr.get_nodes(query) +# for node in nodes: +# print(node) + +#sg = gr.get_subgraph(query) +#for edge in sg: +# print(edge) + +#lg = gr.get_labelgraph(query) +#for edge in lg: +# print(edge) + +# oc = gr.get_cypher(query) +# print(oc) + +# pr = gr.get_graph_prompt(query) +# print(pr) + +resp = gr.query(query) + +print(resp) + diff --git a/docker-compose-azure.yaml b/docker-compose-azure.yaml new file mode 100644 index 00000000..1a86ed9e --- /dev/null +++ b/docker-compose-azure.yaml @@ -0,0 +1,172 @@ + +volumes: + cassandra: + pulsar-conf: + pulsar-data: + etcd: + minio-data: + milvus: + +services: + + cassandra: + image: docker.io/cassandra:4.1.5 + ports: + - "9042:9042" + volumes: + - "cassandra:/var/lib/cassandra" + restart: on-failure:100 + + pulsar: + image: docker.io/apachepulsar/pulsar:3.3.0 + command: bin/pulsar standalone + ports: + - "6650:6650" + - "8080:8080" + volumes: + - "pulsar-conf:/pulsar/conf" + - "pulsar-data:/pulsar/data" + restart: on-failure:100 + + pulsar-manager: + image: docker.io/apachepulsar/pulsar-manager:v0.3.0 + ports: + - "9527:9527" + - "7750:7750" + environment: + SPRING_CONFIGURATION_FILE: /pulsar-manager/pulsar-manager/application.properties + restart: on-failure:100 + + etcd: + image: quay.io/coreos/etcd:v3.5.5 + command: + - "etcd" + - "-advertise-client-urls=http://127.0.0.1:2379" + - "-listen-client-urls" + - "http://0.0.0.0:2379" + - "--data-dir" + - "/etcd" + environment: + ETCD_AUTO_COMPACTION_MODE: revision + ETCD_AUTO_COMPACTION_RETENTION: "1000" + ETCD_QUOTA_BACKEND_BYTES: "4294967296" + ETCD_SNAPSHOT_COUNT: "50000" + ports: + - "2379:2379" + volumes: + - "etcd:/etcd" + restart: on-failure:100 + + minio: + image: docker.io/minio/minio:RELEASE.2024-07-04T14-25-45Z + command: + - "minio" + - "server" + - "/minio_data" + - "--console-address" + - ":9001" + environment: + MINIO_ROOT_USER: minioadmin + MINIO_ROOT_PASSWORD: minioadmin + ports: + - "9001:9001" + volumes: + - "minio-data:/minio_data" + restart: on-failure:100 + + milvus: + image: docker.io/milvusdb/milvus:v2.4.5 + command: + - "milvus" + - "run" + - "standalone" + environment: + ETCD_ENDPOINTS: etcd:2379 + MINIO_ADDRESS: minio:9000 + ports: + - "9091:9091" + - "19530:19530" + volumes: + - "milvus:/var/lib/milvus" + restart: on-failure:100 + + pdf-decoder: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "pdf-decoder" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + chunker: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "chunker-recursive" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + vectorize: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "embeddings-vectorize" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + embeddings: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "embeddings-hf" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + kg-extract-definitions: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "kg-extract-definitions" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + kg-extract-relationships: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "kg-extract-relationships" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + vector-write: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "vector-write-milvus" + - "-p" + - "pulsar://pulsar:6650" + - "-t" + - "http://milvus:19530" + restart: on-failure:100 + + graph-write: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "graph-write-cassandra" + - "-p" + - "pulsar://pulsar:6650" + - "-g" + - "cassandra" + restart: on-failure:100 + + llm: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "llm-azure-text" + - "-p" + - "pulsar://pulsar:6650" + - "-k" + - ${AZURE_TOKEN} + - "-e" + - ${AZURE_ENDPOINT} + restart: on-failure:100 + diff --git a/docker-compose-claude.yaml b/docker-compose-claude.yaml new file mode 100644 index 00000000..5999725e --- /dev/null +++ b/docker-compose-claude.yaml @@ -0,0 +1,170 @@ + +volumes: + cassandra: + pulsar-conf: + pulsar-data: + etcd: + minio-data: + milvus: + +services: + + cassandra: + image: docker.io/cassandra:4.1.5 + ports: + - "9042:9042" + volumes: + - "cassandra:/var/lib/cassandra" + restart: on-failure:100 + + pulsar: + image: docker.io/apachepulsar/pulsar:3.3.0 + command: bin/pulsar standalone + ports: + - "6650:6650" + - "8080:8080" + volumes: + - "pulsar-conf:/pulsar/conf" + - "pulsar-data:/pulsar/data" + restart: on-failure:100 + + pulsar-manager: + image: docker.io/apachepulsar/pulsar-manager:v0.3.0 + ports: + - "9527:9527" + - "7750:7750" + environment: + SPRING_CONFIGURATION_FILE: /pulsar-manager/pulsar-manager/application.properties + restart: on-failure:100 + + etcd: + image: quay.io/coreos/etcd:v3.5.5 + command: + - "etcd" + - "-advertise-client-urls=http://127.0.0.1:2379" + - "-listen-client-urls" + - "http://0.0.0.0:2379" + - "--data-dir" + - "/etcd" + environment: + ETCD_AUTO_COMPACTION_MODE: revision + ETCD_AUTO_COMPACTION_RETENTION: "1000" + ETCD_QUOTA_BACKEND_BYTES: "4294967296" + ETCD_SNAPSHOT_COUNT: "50000" + ports: + - "2379:2379" + volumes: + - "etcd:/etcd" + restart: on-failure:100 + + minio: + image: docker.io/minio/minio:RELEASE.2024-07-04T14-25-45Z + command: + - "minio" + - "server" + - "/minio_data" + - "--console-address" + - ":9001" + environment: + MINIO_ROOT_USER: minioadmin + MINIO_ROOT_PASSWORD: minioadmin + ports: + - "9001:9001" + volumes: + - "minio-data:/minio_data" + restart: on-failure:100 + + milvus: + image: docker.io/milvusdb/milvus:v2.4.5 + command: + - "milvus" + - "run" + - "standalone" + environment: + ETCD_ENDPOINTS: etcd:2379 + MINIO_ADDRESS: minio:9000 + ports: + - "9091:9091" + - "19530:19530" + volumes: + - "milvus:/var/lib/milvus" + restart: on-failure:100 + + pdf-decoder: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "pdf-decoder" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + chunker: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "chunker-recursive" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + vectorize: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "embeddings-vectorize" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + embeddings: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "embeddings-hf" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + kg-extract-definitions: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "kg-extract-definitions" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + kg-extract-relationships: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "kg-extract-relationships" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + vector-write: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "vector-write-milvus" + - "-p" + - "pulsar://pulsar:6650" + - "-t" + - "http://milvus:19530" + restart: on-failure:100 + + graph-write: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "graph-write-cassandra" + - "-p" + - "pulsar://pulsar:6650" + - "-g" + - "cassandra" + restart: on-failure:100 + + llm: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "llm-claude-text" + - "-p" + - "pulsar://pulsar:6650" + - "-k" + - ${CLAUDE_KEY} + restart: on-failure:100 + diff --git a/docker-compose-ollama.yaml b/docker-compose-ollama.yaml new file mode 100644 index 00000000..d7d0f36f --- /dev/null +++ b/docker-compose-ollama.yaml @@ -0,0 +1,178 @@ + +volumes: + cassandra: + pulsar-conf: + pulsar-data: + etcd: + minio-data: + milvus: + +services: + + cassandra: + image: docker.io/cassandra:4.1.5 + ports: + - "9042:9042" + volumes: + - "cassandra:/var/lib/cassandra" + restart: on-failure:100 + + pulsar: + image: docker.io/apachepulsar/pulsar:3.3.0 + command: bin/pulsar standalone + ports: + - "6650:6650" + - "8080:8080" + volumes: + - "pulsar-conf:/pulsar/conf" + - "pulsar-data:/pulsar/data" + restart: on-failure:100 + + pulsar-manager: + image: docker.io/apachepulsar/pulsar-manager:v0.3.0 + ports: + - "9527:9527" + - "7750:7750" + environment: + SPRING_CONFIGURATION_FILE: /pulsar-manager/pulsar-manager/application.properties + restart: on-failure:100 + + etcd: + image: quay.io/coreos/etcd:v3.5.5 + command: + - "etcd" + - "-advertise-client-urls=http://127.0.0.1:2379" + - "-listen-client-urls" + - "http://0.0.0.0:2379" + - "--data-dir" + - "/etcd" + environment: + ETCD_AUTO_COMPACTION_MODE: revision + ETCD_AUTO_COMPACTION_RETENTION: "1000" + ETCD_QUOTA_BACKEND_BYTES: "4294967296" + ETCD_SNAPSHOT_COUNT: "50000" + ports: + - "2379:2379" + volumes: + - "etcd:/etcd" + restart: on-failure:100 + + minio: + image: docker.io/minio/minio:RELEASE.2024-07-04T14-25-45Z + command: + - "minio" + - "server" + - "/minio_data" + - "--console-address" + - ":9001" + environment: + MINIO_ROOT_USER: minioadmin + MINIO_ROOT_PASSWORD: minioadmin + ports: + - "9001:9001" + volumes: + - "minio-data:/minio_data" + restart: on-failure:100 + + milvus: + image: docker.io/milvusdb/milvus:v2.4.5 + command: + - "milvus" + - "run" + - "standalone" + environment: + ETCD_ENDPOINTS: etcd:2379 + MINIO_ADDRESS: minio:9000 + ports: + - "9091:9091" + - "19530:19530" + volumes: + - "milvus:/var/lib/milvus" + restart: on-failure:100 + + pdf-decoder: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "pdf-decoder" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + chunker: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "chunker-recursive" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + vectorize: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "embeddings-vectorize" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + embeddings: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "embeddings-hf" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + kg-extract-definitions: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "kg-extract-definitions" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + kg-extract-relationships: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "kg-extract-relationships" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + vector-write: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "vector-write-milvus" + - "-p" + - "pulsar://pulsar:6650" + - "-t" + - "http://milvus:19530" + restart: on-failure:100 + + graph-write: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "graph-write-cassandra" + - "-p" + - "pulsar://pulsar:6650" + - "-g" + - "cassandra" + restart: on-failure:100 + + llm: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "llm-ollama-text" + - "-p" + - "pulsar://pulsar:6650" + - "-r" + - "http://${OLLAMA_HOST}:11434/" + restart: on-failure:100 + + graph-rag: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "graph-rag" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + diff --git a/docker-compose-vertexai.yaml b/docker-compose-vertexai.yaml new file mode 100644 index 00000000..e7e14be2 --- /dev/null +++ b/docker-compose-vertexai.yaml @@ -0,0 +1,174 @@ + +volumes: + cassandra: + pulsar-conf: + pulsar-data: + etcd: + minio-data: + milvus: + +services: + + cassandra: + image: docker.io/cassandra:4.1.5 + ports: + - "9042:9042" + volumes: + - "cassandra:/var/lib/cassandra" + restart: on-failure:100 + + pulsar: + image: docker.io/apachepulsar/pulsar:3.3.0 + command: bin/pulsar standalone + ports: + - "6650:6650" + - "8080:8080" + volumes: + - "pulsar-conf:/pulsar/conf" + - "pulsar-data:/pulsar/data" + restart: on-failure:100 + + pulsar-manager: + image: docker.io/apachepulsar/pulsar-manager:v0.3.0 + ports: + - "9527:9527" + - "7750:7750" + environment: + SPRING_CONFIGURATION_FILE: /pulsar-manager/pulsar-manager/application.properties + restart: on-failure:100 + + etcd: + image: quay.io/coreos/etcd:v3.5.5 + command: + - "etcd" + - "-advertise-client-urls=http://127.0.0.1:2379" + - "-listen-client-urls" + - "http://0.0.0.0:2379" + - "--data-dir" + - "/etcd" + environment: + ETCD_AUTO_COMPACTION_MODE: revision + ETCD_AUTO_COMPACTION_RETENTION: "1000" + ETCD_QUOTA_BACKEND_BYTES: "4294967296" + ETCD_SNAPSHOT_COUNT: "50000" + ports: + - "2379:2379" + volumes: + - "etcd:/etcd" + restart: on-failure:100 + + minio: + image: docker.io/minio/minio:RELEASE.2024-07-04T14-25-45Z + command: + - "minio" + - "server" + - "/minio_data" + - "--console-address" + - ":9001" + environment: + MINIO_ROOT_USER: minioadmin + MINIO_ROOT_PASSWORD: minioadmin + ports: + - "9001:9001" + volumes: + - "minio-data:/minio_data" + restart: on-failure:100 + + milvus: + image: docker.io/milvusdb/milvus:v2.4.5 + command: + - "milvus" + - "run" + - "standalone" + environment: + ETCD_ENDPOINTS: etcd:2379 + MINIO_ADDRESS: minio:9000 + ports: + - "9091:9091" + - "19530:19530" + volumes: + - "milvus:/var/lib/milvus" + restart: on-failure:100 + + pdf-decoder: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "pdf-decoder" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + chunker: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "chunker-recursive" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + vectorize: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "embeddings-vectorize" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + embeddings: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "embeddings-hf" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + kg-extract-definitions: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "kg-extract-definitions" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + kg-extract-relationships: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "kg-extract-relationships" + - "-p" + - "pulsar://pulsar:6650" + restart: on-failure:100 + + vector-write: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "vector-write-milvus" + - "-p" + - "pulsar://pulsar:6650" + - "-t" + - "http://milvus:19530" + restart: on-failure:100 + + graph-write: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "graph-write-cassandra" + - "-p" + - "pulsar://pulsar:6650" + - "-g" + - "cassandra" + restart: on-failure:100 + + llm: + image: docker.io/trustgraph/trustgraph-flow:0.1.16 + command: + - "llm-vertexai-text" + - "-p" + - "pulsar://pulsar:6650" + - "-k" + - "/vertexai/private.json" + - "-r" + - "us-west1" + volumes: + - "./vertexai:/vertexai" + restart: on-failure:100 + diff --git a/etl b/etl new file mode 100755 index 00000000..0191d766 --- /dev/null +++ b/etl @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 + +import pulsar +from pulsar.schema import JsonSchema, Bytes +from schema import Chunk, Triple, Value +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_community.llms import Ollama +from trustgraphETL import scholar, callmixtral, build_graph_robust +import sys +import rdflib + +g = rdflib.Graph() +g.parse("out2.ttl") + +client = pulsar.Client("pulsar://localhost:6650") + +consumer = client.subscribe( + 'chunk-load', 'etl', + schema=JsonSchema(Chunk), +) + +producer = client.create_producer( + topic='graph-load', + schema=JsonSchema(Triple), +) + +while True: + + msg = consumer.receive() + + try: + + v = msg.value() + print("Indexing {} {}...".format(v.path, v.num)) + + chunk = v.chunk.decode("utf-8") + + s = scholar(chunk) + resp = callmixtral(s) + + try: + g = build_graph_robust([resp]) + + for s, p, o in g: + + sv = Value(value=str(s), is_uri=True) + pv = Value(value=str(p), is_uri=True) + + if isinstance(o, rdflib.term.URIRef): + ov = Value(value=str(o), is_uri=True) + else: + ov = Value(value=str(o), is_uri=False) + + t = Triple(s=sv, p=pv, o=ov) + + producer.send(t) + + except Exception as e: + print("Exception: ", e) + + print("Done.") + + # Acknowledge successful processing of the message + consumer.acknowledge(msg) + + except Exception as e: + + print(e) + + # Message failed to be processed + consumer.negative_acknowledge(msg) + +client.close() + diff --git a/etl2 b/etl2 new file mode 100755 index 00000000..43adefb6 --- /dev/null +++ b/etl2 @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 + +import pulsar +from pulsar.schema import JsonSchema, Bytes +from schema import Chunk, Triple, Value +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_community.llms import Ollama +from trustgraphETL2 import scholar, callmixtral, build_graph_robust +import sys +import rdflib + +g = rdflib.Graph() +g.parse("out2.ttl") + +client = pulsar.Client("pulsar://localhost:6650") + +consumer = client.subscribe( + 'chunk-load', 'etl', + schema=JsonSchema(Chunk), +) + +producer = client.create_producer( + topic='graph-load', + schema=JsonSchema(Triple), +) + +while True: + + msg = consumer.receive() + + try: + + v = msg.value() + print("Indexing {} {}...".format(v.path, v.num)) + + chunk = v.chunk.decode("utf-8") + + s = scholar(chunk) + resp = callmixtral(s) + + try: + g = build_graph_robust([resp]) + + for s, p, o in g: + + sv = Value(value=str(s), is_uri=True) + pv = Value(value=str(p), is_uri=True) + + if isinstance(o, rdflib.term.URIRef): + ov = Value(value=str(o), is_uri=True) + else: + ov = Value(value=str(o), is_uri=False) + + t = Triple(s=sv, p=pv, o=ov) + + producer.send(t) + + except Exception as e: + print("Exception: ", e) + + print("Done.") + + # Acknowledge successful processing of the message + consumer.acknowledge(msg) + + except Exception as e: + + print(e) + + # Message failed to be processed + consumer.negative_acknowledge(msg) + +client.close() + diff --git a/graph-clear b/graph-clear new file mode 100755 index 00000000..9633a08f --- /dev/null +++ b/graph-clear @@ -0,0 +1,8 @@ +#!/usr/bin/env python3 + +from trustgraph import TrustGraph + +t = TrustGraph() + +t.clear() + diff --git a/graph-dump b/graph-dump new file mode 100755 index 00000000..e31a1c3b --- /dev/null +++ b/graph-dump @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 + +import pulsar +from pulsar.schema import JsonSchema, Bytes +from schema import Chunk, Triple +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_community.llms import Ollama +from trustgraphETL import scholar, callmixtral, build_graph_robust +import sys +import rdflib +import uuid + +g = rdflib.Graph() + +client = pulsar.Client("pulsar://localhost:6650") + +consumer = client.subscribe( + 'graph-load', 'graph-dump', + schema=JsonSchema(Triple), +) + +g = rdflib.Graph() +count = 0 +limit = 100 + +while True: + + msg = consumer.receive() + + try: + + v = msg.value() + + if v.o.is_uri: + g.add(( + rdflib.term.URIRef(v.s.value), + rdflib.term.URIRef(v.p.value), + rdflib.term.URIRef(v.o.value), + )) + else: + g.add(( + rdflib.term.URIRef(v.s.value), + rdflib.term.URIRef(v.p.value), + rdflib.term.Literal(v.o.value), + )) + + count += 1 + + if count > limit: + + id = str(uuid.uuid4()) + path = f"graph/{id}.ttl" + g.serialize(destination=path) + g = rdflib.Graph() + print(f"Written {path}") + + count = 0 + + # Acknowledge successful processing of the message + consumer.acknowledge(msg) + + except Exception as e: + + print(e) + + # Message failed to be processed + consumer.negative_acknowledge(msg) + +client.close() + diff --git a/graph-out b/graph-out new file mode 100755 index 00000000..4c305d92 --- /dev/null +++ b/graph-out @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 + +import pulsar +from pulsar.schema import JsonSchema, Bytes +from schema import Chunk, Triple +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_community.llms import Ollama +from trustgraphETL import scholar, callmixtral, build_graph_robust +import sys +import rdflib + +g = rdflib.Graph() +g.parse("out2.ttl") + +client = pulsar.Client("pulsar://localhost:6650") + +consumer = client.subscribe( + 'graph-load', 'graph-dump', + schema=JsonSchema(Triple), +) + +while True: + + msg = consumer.receive() + + try: + + v = msg.value() + + print( + v.s.value, + v.p.value, + v.o.value, + ) + + # Acknowledge successful processing of the message + consumer.acknowledge(msg) + + except Exception as e: + + print(e) + + # Message failed to be processed + consumer.negative_acknowledge(msg) + +client.close() + diff --git a/query b/query new file mode 100755 index 00000000..7a6a3599 --- /dev/null +++ b/query @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 + +from langchain_huggingface import HuggingFaceEmbeddings +from edge_map import VectorStore +from trustgraph import TrustGraph +from llm_ollama import Llm + +LABEL="http://www.w3.org/2000/01/rdf-schema#label" +DEFINITION="http://www.w3.org/2004/02/skos/core#definition" + +embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") + +client = VectorStore() + +tg = TrustGraph() + +entity_limit=50 +query_limit=30 +max_sg_size=3000 + +query = """Explain how CISA contributes to incident management in the +US government economy""" + +# query = """Explain in a step-by-step form how CISA helps new legislation to emerge for national security""" + +query = """An insider threat has been discovered inside CISA. +The employee has been discovered to be stealing data using admin credentials. +Provide 3 theories as to the cause of the failure in hiring this individual, +referencing oversight information in the knowledge graph provided. +""" + +query = """An insider threat has been discovered inside CISA. +Their job title is Protective Security Advisor. What threats are posed +to to CISA by the compromise of somoene in this role? +Provide 3 hypothetical compromise scenarios which could be cause by +someone in such a role being compromised as an inside threat. +""" + +query = """Provide three hypothetical causes for the space shuttle +disaster. For each hypothesis, provided a set of step-by-step events +which lead to the disaster. +""" + +qembeds = embeddings.embed_documents([query])[0] + +res = client.search( + qembeds, + limit=entity_limit +) + +entities = set([ + item["entity"]["entity"] + for item in res +]) + +subgraph = set() + +for e in entities: + + res = tg.get_s(e, limit=query_limit) + for p, o in res: + subgraph.add((e, p, o)) + + res = tg.get_p(e, limit=query_limit) + for s, o in res: + subgraph.add((s, e, o)) + + res = tg.get_o(e, limit=query_limit) + for s, p in res: + subgraph.add((s, p, e)) + +subgraph = list(subgraph) + +subgraph = subgraph[0:max_sg_size] + +cache = {} + +def maybe_label(e): + + if e in cache: + return cache[e] + + res = tg.get_sp(e, LABEL) + res = list(res) + + if len(res) == 0: + cache[e] = e + return e + + cache[e] = res[0][0] + return cache[e] + +sg2 = [] + +for edge in subgraph: + + if edge[1] == LABEL: + continue + + s = maybe_label(edge[0]) + p = maybe_label(edge[1]) + o = maybe_label(edge[2]) + + sg2.append(f"({s})-[{p}]->({o})") + +prompt=f"""Study the knowledge graph provided, and use +the information to answer the question. The question should be answered +in plain English only. + + +{"\n".join(sg2)} + + +{query} + +""" + +print(prompt) + +print(len(prompt)) + +# llm = Llm("deepseek-v2") +# llm = Llm("gemma2") +llm = Llm("phi3:14b") +resp = llm(prompt) + +print(resp) + diff --git a/receive b/receive new file mode 100755 index 00000000..c32a3492 --- /dev/null +++ b/receive @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 + +import pulsar + +client = pulsar.Client("pulsar://localhost:6650") + +consumer = client.subscribe('my-topic', 'my-subscription') + +while True: + msg = consumer.receive() + try: + print("Received message '{}' id='{}'".format( + msg.data(), msg.message_id() + )) + # Acknowledge successful processing of the message + consumer.acknowledge(msg) + except Exception: + # Message failed to be processed + consumer.negative_acknowledge(msg) + +client.close() + diff --git a/receive2 b/receive2 new file mode 100755 index 00000000..82c013fe --- /dev/null +++ b/receive2 @@ -0,0 +1,39 @@ +#!/usr/bin/env python3 + +import pulsar +from pulsar.schema import JsonSchema +from schema import Triple, Value + +client = pulsar.Client("pulsar://localhost:6650") + +consumer = client.subscribe( + 'my-topic2', 'my-subscription', + schema=JsonSchema(Triple), +) + +while True: + msg = consumer.receive() + try: + +# print("Received message '{}' id='{}'".format( +# msg.data(), msg.message_id() +# )) + + v = msg.value() + + print( + "{} -- {} --> {}".format( + v.s.value, + v.p.value, + v.o.value, + ) + ) + + # Acknowledge successful processing of the message + consumer.acknowledge(msg) + except Exception: + # Message failed to be processed + consumer.negative_acknowledge(msg) + +client.close() + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..7bfcf3ba --- /dev/null +++ b/requirements.txt @@ -0,0 +1,16 @@ +torch +urllib3 +transformers +sentence-transformers +rdflib +pymilvus +langchain +langchain-core +langchain-huggingface +langchain-text-splitters +langchain-community +huggingface-hub +cassandra-driver +pulsar-client +anthropic +google-cloud-aiplatform diff --git a/scripts/chunker-recursive b/scripts/chunker-recursive new file mode 100755 index 00000000..2356903d --- /dev/null +++ b/scripts/chunker-recursive @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.chunker.recursive import run + +run() + diff --git a/scripts/embeddings-hf b/scripts/embeddings-hf new file mode 100755 index 00000000..a7d84d04 --- /dev/null +++ b/scripts/embeddings-hf @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.embeddings.hf import run + +run() + diff --git a/scripts/embeddings-vectorize b/scripts/embeddings-vectorize new file mode 100755 index 00000000..3de1e3a9 --- /dev/null +++ b/scripts/embeddings-vectorize @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.embeddings.vectorize import run + +run() + diff --git a/scripts/graph-rag b/scripts/graph-rag new file mode 100755 index 00000000..a6dab1f3 --- /dev/null +++ b/scripts/graph-rag @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.rag.graph import run + +run() + diff --git a/scripts/graph-show b/scripts/graph-show new file mode 100755 index 00000000..26e6cbff --- /dev/null +++ b/scripts/graph-show @@ -0,0 +1,10 @@ +#!/usr/bin/env python3 + +from trustgraph.trustgraph import TrustGraph + +t = TrustGraph() + +rows = t.get_all(limit=100_000_000) +for s, p, o in rows: + print(s, p, o) + diff --git a/scripts/graph-to-turtle b/scripts/graph-to-turtle new file mode 100755 index 00000000..1bd48802 --- /dev/null +++ b/scripts/graph-to-turtle @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 + +from trustgraph.trustgraph import TrustGraph +import rdflib +import sys +import io + +t = TrustGraph() + +g = rdflib.Graph() + +rows = t.get_all(limit=100_000_000) +for s, p, o in rows: + +# print(s, p, o) + sv = rdflib.term.URIRef(s) + pv = rdflib.term.URIRef(p) + + if o.startswith("https://") or o.startswith("http://"): + + # Skip malformed URLs with spaces in + if " " in o: + continue + + ov = rdflib.term.URIRef(o) + else: + ov = rdflib.term.Literal(o) + + g.add((sv, pv, ov)) + +g.serialize(destination="output.ttl", format="turtle") + +buf = io.BytesIO() + +g.serialize(destination=buf, format="turtle") + +sys.stdout.write(buf.getvalue().decode("utf-8")) diff --git a/scripts/graph-write-cassandra b/scripts/graph-write-cassandra new file mode 100755 index 00000000..7fc3d0c8 --- /dev/null +++ b/scripts/graph-write-cassandra @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.graph.cassandra_write import run + +run() + diff --git a/scripts/init-pulsar-manager b/scripts/init-pulsar-manager new file mode 100755 index 00000000..6e855ffb --- /dev/null +++ b/scripts/init-pulsar-manager @@ -0,0 +1,11 @@ +#!/usr/bin/env bash + +CSRF_TOKEN=$(curl http://localhost:7750/pulsar-manager/csrf-token) + +curl \ + -H "X-XSRF-TOKEN: $CSRF_TOKEN" \ + -H "Cookie: XSRF-TOKEN=$CSRF_TOKEN;" \ + -H 'Content-Type: application/json' \ + -X PUT \ + http://localhost:7750/pulsar-manager/users/superuser \ + -d '{"name": "admin", "password": "apachepulsar", "description": "test", "email": "username@test.org"}' diff --git a/scripts/kg-extract-definitions b/scripts/kg-extract-definitions new file mode 100755 index 00000000..327ec06f --- /dev/null +++ b/scripts/kg-extract-definitions @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.kg.extract_definitions import run + +run() + diff --git a/scripts/kg-extract-relationships b/scripts/kg-extract-relationships new file mode 100755 index 00000000..91040589 --- /dev/null +++ b/scripts/kg-extract-relationships @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.kg.extract_relationships import run + +run() + diff --git a/scripts/llm-azure-text b/scripts/llm-azure-text new file mode 100755 index 00000000..cdaea4b8 --- /dev/null +++ b/scripts/llm-azure-text @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.llm.azure_text import run + +run() + diff --git a/scripts/llm-claude-text b/scripts/llm-claude-text new file mode 100755 index 00000000..496d1440 --- /dev/null +++ b/scripts/llm-claude-text @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.llm.claude_text import run + +run() + diff --git a/scripts/llm-ollama-text b/scripts/llm-ollama-text new file mode 100755 index 00000000..cb7a4ebc --- /dev/null +++ b/scripts/llm-ollama-text @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.llm.ollama_text import run + +run() + diff --git a/scripts/llm-vertexai-text b/scripts/llm-vertexai-text new file mode 100755 index 00000000..4634015f --- /dev/null +++ b/scripts/llm-vertexai-text @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.llm.vertexai_text import run + +run() + diff --git a/scripts/loader b/scripts/loader new file mode 100755 index 00000000..a6dc4450 --- /dev/null +++ b/scripts/loader @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 + +import pulsar +from pulsar.schema import JsonSchema, Bytes, String +from trustgraph.schema import Document, Source +import base64 +import hashlib + +# client = pulsar.Client("pulsar://localhost:6650") +host="10.89.1.246" +host="localhost" +client = pulsar.Client(f"pulsar://{host}:6650") + +producer = client.create_producer( + topic='document-load', + schema=JsonSchema(Document), + chunking_enabled=True, +) + +files=[ + "Challenger-Report-Vol1.pdf", +# "columbia-accident-investigation-board-report-volume-1.pdf", +# "Proposed_CIRCIA_Rules.pdf", +] + +for file in files: + + path = "sources/" + file + data = open(path, "rb").read() + + id = hashlib.sha256(path.encode("utf-8")).hexdigest()[0:8] + + r = Document( + source=Source( + source=path, + title=path, + id=id, + ), + data=base64.b64encode(data), + ) + + resp = producer.send(r) + + print(resp) + +client.close() + diff --git a/scripts/pdf-decoder b/scripts/pdf-decoder new file mode 100755 index 00000000..82b89298 --- /dev/null +++ b/scripts/pdf-decoder @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.decoder.pdf import run + +run() + diff --git a/scripts/query b/scripts/query new file mode 100755 index 00000000..5cb5c0c6 --- /dev/null +++ b/scripts/query @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 + +from trustgraph.graph_rag import GraphRag +import sys + +query = " ".join(sys.argv[1:]) + +gr = GraphRag(verbose=True) + +if query == "": + query="""This knowledge graph describes the Space Shuttle disaster. +Present 20 facts which are present in the knowledge graph.""" + +resp = gr.query(query) +print(resp) + diff --git a/scripts/vector-write-milvus b/scripts/vector-write-milvus new file mode 100755 index 00000000..952e22cf --- /dev/null +++ b/scripts/vector-write-milvus @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from trustgraph.vector.milvus_write import run + +run() + diff --git a/send b/send new file mode 100755 index 00000000..7f5c454a --- /dev/null +++ b/send @@ -0,0 +1,12 @@ +#!/usr/bin/env python3 + +import pulsar + +client = pulsar.Client("pulsar://localhost:6650") + +producer = client.create_producer('my-topic') + +for i in range(10): + producer.send(('Hello-%d' % i).encode('utf-8')) + +client.close() diff --git a/send2 b/send2 new file mode 100755 index 00000000..762a50cc --- /dev/null +++ b/send2 @@ -0,0 +1,25 @@ +#!/usr/bin/env python3 + +import pulsar +from pulsar.schema import JsonSchema +from schema import Triple, Value + +client = pulsar.Client("pulsar://localhost:6650") + +producer = client.create_producer( + topic='my-topic2', + schema=JsonSchema(Triple), +) + +for i in range(10): + + r = Triple( + s = Value(is_uri=True, value="https://example.org"), + p = Value(is_uri=True, value="https://example.org"), + o = Value(is_uri=True, value="https://example.org"), + ) + + producer.send(r) + +client.close() + diff --git a/setup.py b/setup.py new file mode 100644 index 00000000..2ad4db47 --- /dev/null +++ b/setup.py @@ -0,0 +1,65 @@ +import setuptools +import os + +with open("README.md", "r") as fh: + long_description = fh.read() + +version = "0.0.0" + +setuptools.setup( + name="trustgraph", + version=version, + author="trustgraph.ai", + author_email="security@trustgraph.ai", + description="trustgraph.ai", + long_description=long_description, + long_description_content_type="text/markdown", + url="https://github.com/trustgraph.ai/FIXME.git", + packages=setuptools.find_packages(), + classifiers=[ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", + "Operating System :: OS Independent", + ], + python_requires='>=3.8', + download_url = "https://github.com/trustgraph.ai/FIXME.git/archive/refs/tags/v" + version + ".tar.gz", + install_requires=[ + "torch", + "urllib3", + "transformers", + "sentence-transformers", + "rdflib", + "pymilvus", + "langchain", + "langchain-core", + "langchain-huggingface", + "langchain-text-splitters", + "langchain-community", + "huggingface-hub", + "requests", + "cassandra-driver", + "pulsar-client", + "pypdf", + "anthropic", + "google-cloud-aiplatform", + ], + scripts=[ + "scripts/chunker-recursive", + "scripts/graph-show", + "scripts/graph-to-turtle", + "scripts/graph-write-cassandra", + "scripts/kg-extract-definitions", + "scripts/kg-extract-relationships", + "scripts/llm-ollama-text", + "scripts/llm-vertexai-text", + "scripts/llm-claude-text", + "scripts/llm-azure-text", + "scripts/loader", + "scripts/pdf-decoder", + "scripts/query", + "scripts/embeddings-vectorize", + "scripts/embeddings-hf", + "scripts/vector-write-milvus", + "scripts/graph-rag", + ] +) diff --git a/sources/Challenger-Report-Vol1.pdf b/sources/Challenger-Report-Vol1.pdf new file mode 100644 index 00000000..58dc559e Binary files /dev/null and b/sources/Challenger-Report-Vol1.pdf differ diff --git a/sources/Columbia-Aeromedical.pdf b/sources/Columbia-Aeromedical.pdf new file mode 100644 index 00000000..eb408700 Binary files /dev/null and b/sources/Columbia-Aeromedical.pdf differ diff --git a/sources/Columbia-Ars-Technica.pdf b/sources/Columbia-Ars-Technica.pdf new file mode 100644 index 00000000..ae543f4e Binary files /dev/null and b/sources/Columbia-Ars-Technica.pdf differ diff --git a/sources/Columbia-Crew-Survivability.pdf b/sources/Columbia-Crew-Survivability.pdf new file mode 100644 index 00000000..c2e73bd5 Binary files /dev/null and b/sources/Columbia-Crew-Survivability.pdf differ diff --git a/sources/Columbia-Disaster-Wiki.pdf b/sources/Columbia-Disaster-Wiki.pdf new file mode 100644 index 00000000..40964223 Binary files /dev/null and b/sources/Columbia-Disaster-Wiki.pdf differ diff --git a/sources/Columbia-NASA-Decisions.pdf b/sources/Columbia-NASA-Decisions.pdf new file mode 100644 index 00000000..5535781d Binary files /dev/null and b/sources/Columbia-NASA-Decisions.pdf differ diff --git a/sources/Columbia-Options-Assessment.pdf b/sources/Columbia-Options-Assessment.pdf new file mode 100644 index 00000000..1bf4341c Binary files /dev/null and b/sources/Columbia-Options-Assessment.pdf differ diff 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n +0000609183 00000 n +0000609229 00000 n +trailer +<]/Root 442 0 R/Size 444/Info 443 0 R>> +startxref +609385 +%%EOF diff --git a/test-cassandra b/test-cassandra new file mode 100644 index 00000000..7c8f7ee2 --- /dev/null +++ b/test-cassandra @@ -0,0 +1,93 @@ + +from cassandra.cluster import Cluster +from cassandra.auth import PlainTextAuthProvider + +import sys + +def init(): + + cluster = Cluster(["localhost"]) + + session = cluster.connect() + + session.execute(""" + drop keyspace if exists trustgraph; + """); + + print(1/0) + + session.execute(""" + create keyspace if not exists trustgraph + with replication = { + 'class' : 'SimpleStrategy', + 'replication_factor' : 1 + }; + """); + + session.set_keyspace('trustgraph') + + session.execute(""" + create table if not exists triples ( + s text, + p text, + o text, + PRIMARY KEY (s, p) + ); + """); + + session.execute(""" + create index if not exists triples_p + ON triples (p); + """); + + session.execute(""" + create index if not exists triples_o + ON triples (o); + """); + + return session + +try: + session = init() + + session.execute( + "insert into triples (s, p, o) values (%s, %s, %s)", + ( + "http://example.com", + "http://example.org", + "http://example.net", + ) + ) + + session.execute( + "insert into triples (s, p, o) values (%s, %s, %s)", + ( + "http://example.com/2", + "http://example.org/2", + "http://example.net/1", + ) + ) + + session.execute( + "insert into triples (s, p, o) values (%s, %s, %s)", + ( + "http://example.com", + "http://example.org/2", + "http://example.net/3", + ) + ) + + rows = session.execute( + "select s, p, o from triples" + ) + + for s, p, o in rows: + print(s, p, o) + +except Exception as e: + + print("Exception: ", e) + + sys.exit(1) + + diff --git a/test-trustgraph b/test-trustgraph new file mode 100644 index 00000000..5d6950e3 --- /dev/null +++ b/test-trustgraph @@ -0,0 +1,82 @@ + +from trustgraph import TrustGraph + +t = TrustGraph() + +t.clear() + +t.insert( + "http://example.com", + "http://example.org", + "http://example.net" +) + +t.insert( + "http://example.com/2", + "http://example.org/2", + "http://example.net/1" +) + +t.insert( + "http://example.com", + "http://example.org/2", + "http://example.net" +) + +print("====") + +print("====") + +rows = t.get_all() +for s, p, o in rows: + print(s, p, o) + +print("====") + +rows = t.get_s("http://example.com/2") +for p, o in rows: + print(p, o) + +print("====") + +rows = t.get_p("http://example.org/2") +for s, o in rows: + print(s, o) + +print("====") + +rows = t.get_o("http://example.net") +for s, p in rows: + print(s, p) + +print("====") + +rows = t.get_sp("http://example.com", "http://example.org/2") +for o, in rows: + print(o) + +print("====") + +rows = t.get_po("http://example.com", "http://example.org/2") +for s, in rows: + print(s) + +print("====") + +rows = t.get_os("http://example.com", "http://example.org/2") +for p, in rows: + print(p) + +print("====") + +rows = t.get_spo( + "http://example.com", + "http://example.org", + "http://example.net" +) + +for p, in rows: + print(p) + +print("====") + diff --git a/tests/test-embeddings b/tests/test-embeddings new file mode 100755 index 00000000..e2bcdbde --- /dev/null +++ b/tests/test-embeddings @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 + +import pulsar +from trustgraph.embeddings_client import EmbeddingsClient + +embed = EmbeddingsClient(pulsar_host="pulsar://localhost:6650") + +prompt="Write a funny limerick about a llama" + +resp = embed.request(prompt) + +print(resp) + + + diff --git a/tests/test-graph-rag b/tests/test-graph-rag new file mode 100755 index 00000000..c6abfe05 --- /dev/null +++ b/tests/test-graph-rag @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 + +import pulsar +from trustgraph.graph_rag_client import GraphRagClient + +rag = GraphRagClient(pulsar_host="pulsar://localhost:6650") + +query="""This knowledge graph describes the Space Shuttle disaster. +Present 20 facts which are present in the knowledge graph.""" + +resp = rag.request(query) + +print(resp) + diff --git a/tests/test-llm b/tests/test-llm new file mode 100755 index 00000000..35177e81 --- /dev/null +++ b/tests/test-llm @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 + +import pulsar +from trustgraph.llm_client import LlmClient + +llm = LlmClient(pulsar_host="pulsar://localhost:6650") + +prompt="Write a funny limerick about a llama" + +resp = llm.request(prompt) + +print(resp) + +llm.close() + diff --git a/tests/test-milvus b/tests/test-milvus new file mode 100755 index 00000000..e95955dc --- /dev/null +++ b/tests/test-milvus @@ -0,0 +1,35 @@ +#!/usr/bin/env python3 + +from langchain_huggingface import HuggingFaceEmbeddings + +from edge_map import VectorStore + +client = VectorStore() + +embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") + +text="""A cat is a small animal. A dog is a large animal. +Cats say miaow. Dogs go woof. +""" + +embeds = embeddings.embed_documents([text])[0] + +text2="""If you couldn't download the model due to network issues, as a walkaround, you can use random vectors to represent the text and still finish the example. Just note that the search result won't reflect semantic similarity as the vectors are fake ones. +""" + +embeds2 = embeddings.embed_documents([text2])[0] + +client.insert(embeds, "animals") +client.insert(embeds, "vectors") + +query="""What noise does a cat make?""" + +qembeds = embeddings.embed_documents([query])[0] + +res = client.search( + qembeds, + limit=2 +) + +print(res) + diff --git a/trustgraph/__init__.py b/trustgraph/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/chunker/__init__.py b/trustgraph/chunker/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/chunker/recursive/__init__.py b/trustgraph/chunker/recursive/__init__.py new file mode 100644 index 00000000..3b816664 --- /dev/null +++ b/trustgraph/chunker/recursive/__init__.py @@ -0,0 +1,3 @@ + +from . chunker import * + diff --git a/trustgraph/chunker/recursive/__main__.py b/trustgraph/chunker/recursive/__main__.py new file mode 100644 index 00000000..18e14ad5 --- /dev/null +++ b/trustgraph/chunker/recursive/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . chunker import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/chunker/recursive/chunker.py b/trustgraph/chunker/recursive/chunker.py new file mode 100755 index 00000000..ba5eb939 --- /dev/null +++ b/trustgraph/chunker/recursive/chunker.py @@ -0,0 +1,164 @@ + +""" +Simple decoder, accepts text documents on input, outputs chunks from the +as text as separate output objects. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +from langchain_text_splitters import RecursiveCharacterTextSplitter +import time + +from ... schema import TextDocument, Chunk, Source +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(TextDocument), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(Chunk), + ) + + self.text_splitter = RecursiveCharacterTextSplitter( + chunk_size=1000, + chunk_overlap=20, + length_function=len, + is_separator_regex=False, + ) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + print(f"Chunking {v.source.id}...", flush=True) + + texts = self.text_splitter.create_documents( + [v.text.decode("utf-8")] + ) + + for ix, chunk in enumerate(texts): + + id = v.source.id + "-c" + str(ix) + + r = Chunk( + source=Source( + source=v.source.source, + id=id, + title=v.source.title + ), + chunk=chunk.page_content.encode("utf-8"), + ) + + self.producer.send(r) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + print("Done.", flush=True) + + except Exception as e: + print(e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='pdf-decoder', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'text-doc-load' + default_output_queue = 'chunk-load' + default_subscriber = 'chunker-recursive' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + args = parser.parse_args() + + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + + diff --git a/trustgraph/decoder/__init__.py b/trustgraph/decoder/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/decoder/pdf/__init__.py b/trustgraph/decoder/pdf/__init__.py new file mode 100644 index 00000000..0d8d9c78 --- /dev/null +++ b/trustgraph/decoder/pdf/__init__.py @@ -0,0 +1,3 @@ + +from . pdf_decoder import * + diff --git a/trustgraph/decoder/pdf/__main__.py b/trustgraph/decoder/pdf/__main__.py new file mode 100755 index 00000000..44dd026d --- /dev/null +++ b/trustgraph/decoder/pdf/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . pdf_decoder import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/decoder/pdf/pdf_decoder.py b/trustgraph/decoder/pdf/pdf_decoder.py new file mode 100755 index 00000000..f892ebac --- /dev/null +++ b/trustgraph/decoder/pdf/pdf_decoder.py @@ -0,0 +1,159 @@ + +""" +Simple decoder, accepts PDF documents on input, outputs pages from the +PDF document as text as separate output objects. +""" + +import pulsar +from pulsar.schema import JsonSchema +from langchain_community.document_loaders import PyPDFLoader +import tempfile +import base64 +import os +import argparse +import time + +from ... schema import Document, TextDocument, Source +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(Document), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(TextDocument), + ) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + print(f"Decoding {v.source.id}...", flush=True) + + with tempfile.NamedTemporaryFile(delete_on_close=False) as fp: + + fp.write(base64.b64decode(v.data)) + fp.close() + + with open(fp.name, mode='rb') as f: + + loader = PyPDFLoader(fp.name) + pages = loader.load() + + for ix, page in enumerate(pages): + + id = v.source.id + "-p" + str(ix) + r = TextDocument( + source=Source( + source=v.source.source, + title=v.source.title, + id=id, + ), + text=page.page_content.encode("utf-8"), + ) + + self.producer.send(r) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + print("Done.", flush=True) + + except Exception as e: + print(e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='pdf-decoder', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'document-load' + default_output_queue = 'text-doc-load' + default_subscriber = 'pdf-decoder' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + args = parser.parse_args() + + while True: + + try: + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + diff --git a/trustgraph/edge_map.py b/trustgraph/edge_map.py new file mode 100644 index 00000000..55d9077f --- /dev/null +++ b/trustgraph/edge_map.py @@ -0,0 +1,102 @@ + +from pymilvus import MilvusClient, CollectionSchema, FieldSchema, DataType + +class VectorStore: + + def __init__(self, uri="http://localhost:19530"): + + self.client = MilvusClient(uri=uri) + + self.collection = "edges" + self.dimension = 384 + + if not self.client.has_collection(collection_name=self.collection): + self.init_collection() + + def init_collection(self): + + pkey_field = FieldSchema( + name="id", + dtype=DataType.INT64, + is_primary=True, + auto_id=True, + ) + + vec_field = FieldSchema( + name="vector", + dtype=DataType.FLOAT_VECTOR, + dim=self.dimension, + ) + + entity_field = FieldSchema( + name="entity", + dtype=DataType.VARCHAR, + max_length=65535, + ) + + schema = CollectionSchema( + fields = [pkey_field, vec_field, entity_field], + description = "Edge map schema", + ) + + self.client.create_collection( + collection_name=self.collection, + schema=schema, + metric_type="IP", + ) + + index_params = MilvusClient.prepare_index_params() + + index_params.add_index( + field_name="vector", + metric_type="COSINE", + index_type="FLAT", # IVF_FLAT?! + index_name="vector_index", + params={ "nlist": 128 } + ) + + self.client.create_index( + collection_name=self.collection, + index_params=index_params + ) + + def insert(self, embeds, entity): + + data = [ + { + "vector": embeds, + "entity": entity, + } + ] + + self.client.insert(collection_name=self.collection, data=data) + + def search(self, embeds, fields=["entity"], limit=10): + + search_params = { + "metric_type": "COSINE", + "params": { + "radius": 0.1, + "range_filter": 0.8 + } + } + + self.client.load_collection( + collection_name=self.collection, +# replica_number=1 + ) + + res = self.client.search( + collection_name=self.collection, + data=[embeds], + limit=limit, + output_fields=fields, + search_params=search_params, + )[0] + + self.client.release_collection( + collection_name=self.collection, + ) + + return res + diff --git a/trustgraph/embeddings/__init__.py b/trustgraph/embeddings/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/embeddings/hf/__init__.py b/trustgraph/embeddings/hf/__init__.py new file mode 100644 index 00000000..7ccb0b37 --- /dev/null +++ b/trustgraph/embeddings/hf/__init__.py @@ -0,0 +1,3 @@ + +from . hf import * + diff --git a/trustgraph/embeddings/hf/__main__.py b/trustgraph/embeddings/hf/__main__.py new file mode 100755 index 00000000..89684e3e --- /dev/null +++ b/trustgraph/embeddings/hf/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . hf import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/embeddings/hf/hf.py b/trustgraph/embeddings/hf/hf.py new file mode 100755 index 00000000..33890483 --- /dev/null +++ b/trustgraph/embeddings/hf/hf.py @@ -0,0 +1,161 @@ + +""" +Simple LLM service, performs text prompt completion using an Ollama service. +Input is prompt, output is response. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +from langchain_huggingface import HuggingFaceEmbeddings +import time + +from ... schema import EmbeddingsRequest, EmbeddingsResponse +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + model, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(EmbeddingsRequest), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(EmbeddingsResponse), + ) + + self.embeddings = HuggingFaceEmbeddings(model_name=model) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + + # Sender-produced ID + + id = msg.properties()["id"] + + print(f"Handling input {id}...", flush=True) + + text = v.text + embeds = self.embeddings.embed_documents([text]) + + print("Send response...", flush=True) + r = EmbeddingsResponse(vectors=embeds) + self.producer.send(r, properties={"id": id}) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + print("Closing", flush=True) + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='llm-ollama-text', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'embeddings' + default_output_queue = 'embeddings-response' + default_subscriber = 'embeddings-hf' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-m', '--model', + default="all-MiniLM-L6-v2", + help=f'LLM model (default: all-MiniLM-L6-v2)' + ) + + args = parser.parse_args() + + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + model=args.model, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + diff --git a/trustgraph/embeddings/vectorize/__init__.py b/trustgraph/embeddings/vectorize/__init__.py new file mode 100644 index 00000000..31596b8c --- /dev/null +++ b/trustgraph/embeddings/vectorize/__init__.py @@ -0,0 +1,3 @@ + +from . vectorize import * + diff --git a/trustgraph/embeddings/vectorize/__main__.py b/trustgraph/embeddings/vectorize/__main__.py new file mode 100755 index 00000000..a578de8a --- /dev/null +++ b/trustgraph/embeddings/vectorize/__main__.py @@ -0,0 +1,6 @@ + +from . vectorize import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/embeddings/vectorize/vectorize.py b/trustgraph/embeddings/vectorize/vectorize.py new file mode 100755 index 00000000..b362470d --- /dev/null +++ b/trustgraph/embeddings/vectorize/vectorize.py @@ -0,0 +1,167 @@ + +""" +Vectorizer, applies an embedding algorithm to a chunk. Input is a chunk, +output is chunk and vectors. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +import time + +from ... schema import Chunk, VectorsChunk +from ... embeddings_client import EmbeddingsClient +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + model, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(Chunk), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(VectorsChunk), + ) + + self.embeddings = EmbeddingsClient(pulsar_host=pulsar_host) + + def emit(self, source, chunk, vectors): + + r = VectorsChunk(source=source, chunk=chunk, vectors=vectors) + self.producer.send(r) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + print(f"Indexing {v.source.id}...", flush=True) + + chunk = v.chunk.decode("utf-8") + + try: + + vectors = self.embeddings.request(chunk) + + self.emit( + source=v.source, + chunk=chunk.encode("utf-8"), + vectors=vectors + ) + + except Exception as e: + print("Exception:", e, flush=True) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='embeddings-vectorizer', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'chunk-load' + default_output_queue = 'vectors-chunk-load' + default_subscriber = 'embeddings-vectorizer' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-m', '--model', + default="all-MiniLM-L6-v2", + help=f'LLM model (default: all-MiniLM-L6-v2)' + ) + + args = parser.parse_args() + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + model=args.model, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + diff --git a/trustgraph/embeddings_client.py b/trustgraph/embeddings_client.py new file mode 100644 index 00000000..e464e02f --- /dev/null +++ b/trustgraph/embeddings_client.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 + +import pulsar +import _pulsar +from pulsar.schema import JsonSchema +from trustgraph.schema import EmbeddingsRequest, EmbeddingsResponse +import hashlib +import uuid + +# Ugly +ERROR=_pulsar.LoggerLevel.Error +WARN=_pulsar.LoggerLevel.Warn +INFO=_pulsar.LoggerLevel.Info +DEBUG=_pulsar.LoggerLevel.Debug + +class EmbeddingsClient: + + def __init__( + self, log_level=ERROR, client_id=None, + pulsar_host="pulsar://pulsar:6650", + ): + + if client_id == None: + client_id = str(uuid.uuid4()) + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level), + ) + + self.producer = self.client.create_producer( + topic='embeddings', + schema=JsonSchema(EmbeddingsRequest), + chunking_enabled=True, + ) + + self.consumer = self.client.subscribe( + 'embeddings-response', client_id, + schema=JsonSchema(EmbeddingsResponse), + ) + + def request(self, text, timeout=500): + + id = str(uuid.uuid4()) + + r = EmbeddingsRequest( + text=text + ) + self.producer.send(r, properties={ "id": id }) + + while True: + + msg = self.consumer.receive(timeout_millis=timeout * 1000) + + mid = msg.properties()["id"] + + if mid == id: + resp = msg.value().vectors + self.consumer.acknowledge(msg) + return resp + + # Ignore messages with wrong ID + self.consumer.acknowledge(msg) + + def __del__(self): + + self.producer.close() + self.consumer.close() + self.client.close() + diff --git a/trustgraph/graph/__init__.py b/trustgraph/graph/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/graph/cassandra_write/__init__.py b/trustgraph/graph/cassandra_write/__init__.py new file mode 100644 index 00000000..d891d55f --- /dev/null +++ b/trustgraph/graph/cassandra_write/__init__.py @@ -0,0 +1,3 @@ + +from . write import * + diff --git a/trustgraph/graph/cassandra_write/__main__.py b/trustgraph/graph/cassandra_write/__main__.py new file mode 100755 index 00000000..c05d8c6d --- /dev/null +++ b/trustgraph/graph/cassandra_write/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . write import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/graph/cassandra_write/write.py b/trustgraph/graph/cassandra_write/write.py new file mode 100755 index 00000000..1b56b5ab --- /dev/null +++ b/trustgraph/graph/cassandra_write/write.py @@ -0,0 +1,144 @@ + +""" +Simple decoder, accepts PDF documents on input, outputs pages from the +PDF document as text as separate output objects. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +import time + +from ... trustgraph import TrustGraph +from ... schema import Triple +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + subscriber, + log_level, + graph_host, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(Triple), + ) + + self.tg = TrustGraph([graph_host]) + + self.count = 0 + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + + self.tg.insert( + v.s.value, + v.p.value, + v.o.value + ) + + self.count += 1 + + if (self.count % 1000) == 0: + print(self.count, "...", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='graph-write-cassandra', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'graph-load' + default_subscriber = 'graph-write-cassandra' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-g', '--graph-host', + default="localhost", + help=f'Output queue (default: localhost)' + ) + + args = parser.parse_args() + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + subscriber=args.subscriber, + log_level=args.log_level, + graph_host=args.graph_host, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + + diff --git a/trustgraph/graph_rag.py b/trustgraph/graph_rag.py new file mode 100644 index 00000000..4175698e --- /dev/null +++ b/trustgraph/graph_rag.py @@ -0,0 +1,227 @@ + +from trustgraph.trustgraph import TrustGraph +from trustgraph.edge_map import VectorStore +from trustgraph.trustgraph import TrustGraph +from trustgraph.llm_client import LlmClient +from trustgraph.embeddings_client import EmbeddingsClient + +LABEL="http://www.w3.org/2000/01/rdf-schema#label" +DEFINITION="http://www.w3.org/2004/02/skos/core#definition" + +class GraphRag: + + def __init__( + self, + graph_hosts=None, + pulsar_host="pulsar://pulsar:6650", + vector_store="http://milvus:19530", + verbose=False + ): + + self.verbose=verbose + + if graph_hosts == None: + graph_hosts = ["cassandra"] + + if self.verbose: + print("Initialising...", flush=True) + + self.graph = TrustGraph(graph_hosts) + + self.embeddings = EmbeddingsClient(pulsar_host=pulsar_host) + + self.vecstore = VectorStore(vector_store) + + self.entity_limit=50 + self.query_limit=30 + self.max_sg_size=3000 + + self.label_cache = {} + + self.llm = LlmClient(pulsar_host=pulsar_host) + + if self.verbose: + print("Initialised", flush=True) + + def get_vector(self, query): + + if self.verbose: + print("Compute embeddings...", flush=True) + + qembeds = self.embeddings.request(query) + + if self.verbose: + print("Done.", flush=True) + + return qembeds + + def get_entities(self, query): + + everything = [] + + vectors = self.get_vector(query) + + if self.verbose: + print("Get entities...", flush=True) + + for vector in vectors: + + res = self.vecstore.search( + vector, + limit=self.entity_limit + ) + + entities = set([ + item["entity"]["entity"] + for item in res + ]) + + everything.extend(entities) + + if self.verbose: + print("Entities:", flush=True) + for ent in everything: + print(" ", ent, flush=True) + + return everything + + def maybe_label(self, e): + + if e in self.label_cache: + return self.label_cache[e] + + res = self.graph.get_sp(e, LABEL) + res = list(res) + + if len(res) == 0: + self.label_cache[e] = e + return e + + self.label_cache[e] = res[0][0] + return self.label_cache[e] + + def get_nodes(self, query): + + ents = self.get_entities(query) + + if self.verbose: + print("Get labels...", flush=True) + + nodes = [ + self.maybe_label(e) + for e in ents + ] + + if self.verbose: + print("Nodes:", flush=True) + for node in nodes: + print(" ", node, flush=True) + + return nodes + + def get_subgraph(self, query): + + entities = self.get_entities(query) + + subgraph = set() + + if self.verbose: + print("Get subgraph...", flush=True) + + for e in entities: + + res = self.graph.get_s(e, limit=self.query_limit) + for p, o in res: + subgraph.add((e, p, o)) + + res = self.graph.get_p(e, limit=self.query_limit) + for s, o in res: + subgraph.add((s, e, o)) + + res = self.graph.get_o(e, limit=self.query_limit) + for s, p in res: + subgraph.add((s, p, e)) + + subgraph = list(subgraph) + + subgraph = subgraph[0:self.max_sg_size] + + if self.verbose: + print("Subgraph:", flush=True) + for edge in subgraph: + print(" ", str(edge), flush=True) + + if self.verbose: + print("Done.", flush=True) + + return subgraph + + def get_labelgraph(self, query): + + subgraph = self.get_subgraph(query) + + sg2 = [] + + for edge in subgraph: + + if edge[1] == LABEL: + continue + + s = self.maybe_label(edge[0]) + p = self.maybe_label(edge[1]) + o = self.maybe_label(edge[2]) + + sg2.append((s, p, o)) + + return sg2 + + def get_cypher(self, query): + + sg = self.get_labelgraph(query) + + sg2 = [] + + for s, p, o in sg: + + sg2.append(f"({s})-[{p}]->({o})") + + kg = "\n".join(sg2) + kg = kg.replace("\\", "-") + + return kg + + def get_graph_prompt(self, query): + + kg = self.get_cypher(query) + + prompt=f"""Study the knowledge graph provided, and use +the information to answer the question. The question should be answered +in plain English only. + + +{kg} + + +{query} + +""" + + return prompt + + def query(self, query): + + if self.verbose: + print("Construct prompt...", flush=True) + + prompt = self.get_graph_prompt(query) + + if self.verbose: + print("Invoke LLM...", flush=True) + + resp = self.llm.request(prompt) + + if self.verbose: + print("Done", flush=True) + + return resp + diff --git a/trustgraph/graph_rag_client.py b/trustgraph/graph_rag_client.py new file mode 100644 index 00000000..6f48e772 --- /dev/null +++ b/trustgraph/graph_rag_client.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 + +import pulsar +import _pulsar +from pulsar.schema import JsonSchema +from trustgraph.schema import GraphRagQuery, GraphRagResponse +import hashlib +import uuid + +# Ugly +ERROR=_pulsar.LoggerLevel.Error +WARN=_pulsar.LoggerLevel.Warn +INFO=_pulsar.LoggerLevel.Info +DEBUG=_pulsar.LoggerLevel.Debug + +class GraphRagClient: + + def __init__( + self, log_level=ERROR, client_id=None, + pulsar_host="pulsar://pulsar:6650", + ): + + if client_id == None: + client_id = str(uuid.uuid4()) + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level), + ) + + self.producer = self.client.create_producer( + topic='graph-rag-query', + schema=JsonSchema(GraphRagQuery), + chunking_enabled=True, + ) + + self.consumer = self.client.subscribe( + 'graph-rag-response', client_id, + schema=JsonSchema(GraphRagResponse), + ) + + def request(self, query, timeout=500): + + id = str(uuid.uuid4()) + + r = GraphRagQuery( + query=query + ) + self.producer.send(r, properties={ "id": id }) + + while True: + + msg = self.consumer.receive(timeout_millis=timeout * 1000) + + mid = msg.properties()["id"] + + if mid == id: + resp = msg.value().response + self.consumer.acknowledge(msg) + return resp + + # Ignore messages with wrong ID + self.consumer.acknowledge(msg) + + def __del__(self): + + self.client.close() + diff --git a/trustgraph/kg/__init__.py b/trustgraph/kg/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/kg/extract_definitions/__init__.py b/trustgraph/kg/extract_definitions/__init__.py new file mode 100644 index 00000000..81287a3c --- /dev/null +++ b/trustgraph/kg/extract_definitions/__init__.py @@ -0,0 +1,3 @@ + +from . extract import * + diff --git a/trustgraph/kg/extract_definitions/__main__.py b/trustgraph/kg/extract_definitions/__main__.py new file mode 100755 index 00000000..403fe672 --- /dev/null +++ b/trustgraph/kg/extract_definitions/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . extract import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/kg/extract_definitions/extract.py b/trustgraph/kg/extract_definitions/extract.py new file mode 100755 index 00000000..369aef59 --- /dev/null +++ b/trustgraph/kg/extract_definitions/extract.py @@ -0,0 +1,193 @@ + +""" +Simple decoder, accepts PDF documents on input, outputs pages from the +PDF document as text as separate output objects. +""" + +import pulsar +from pulsar.schema import JsonSchema +from langchain_community.document_loaders import PyPDFLoader +import tempfile +import base64 +import os +import argparse +import rdflib +import json +import urllib.parse +import time + +from ... schema import VectorsChunk, Triple, Source, Value +from ... log_level import LogLevel +from ... llm_client import LlmClient +from ... prompts import to_definitions +from ... rdf import TRUSTGRAPH_ENTITIES, DEFINITION + +DEFINITION_VALUE = Value(value=DEFINITION, is_uri=True) + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(VectorsChunk), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(Triple), + ) + + self.llm = LlmClient(pulsar_host=pulsar_host) + + def to_uri(self, text): + + part = text.replace(" ", "-").lower().encode("utf-8") + quoted = urllib.parse.quote(part) + uri = TRUSTGRAPH_ENTITIES + quoted + + return uri + + def get_definitions(self, chunk): + + prompt = to_definitions(chunk) + resp = self.llm.request(prompt) + + defs = json.loads(resp) + + return defs + + def emit_edge(self, s, p, o): + + t = Triple(s=s, p=p, o=o) + self.producer.send(t) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + print(f"Indexing {v.source.id}...", flush=True) + + chunk = v.chunk.decode("utf-8") + + g = rdflib.Graph() + + try: + + defs = self.get_definitions(chunk) + print(json.dumps(defs, indent=4), flush=True) + + for defn in defs: + + s = defn["entity"] + s_uri = self.to_uri(s) + + o = defn["definition"] + + s_value = Value(value=str(s_uri), is_uri=True) + o_value = Value(value=str(o), is_uri=False) + + self.emit_edge(s_value, DEFINITION_VALUE, o_value) + + except Exception as e: + print("Exception: ", e, flush=True) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception: ", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='pdf-decoder', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'vectors-chunk-load' + default_output_queue = 'graph-load' + default_subscriber = 'kg-extract-definitions' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + args = parser.parse_args() + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + diff --git a/trustgraph/kg/extract_relationships/__init__.py b/trustgraph/kg/extract_relationships/__init__.py new file mode 100644 index 00000000..81287a3c --- /dev/null +++ b/trustgraph/kg/extract_relationships/__init__.py @@ -0,0 +1,3 @@ + +from . extract import * + diff --git a/trustgraph/kg/extract_relationships/__main__.py b/trustgraph/kg/extract_relationships/__main__.py new file mode 100755 index 00000000..403fe672 --- /dev/null +++ b/trustgraph/kg/extract_relationships/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . extract import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/kg/extract_relationships/extract.py b/trustgraph/kg/extract_relationships/extract.py new file mode 100755 index 00000000..a47c3b6e --- /dev/null +++ b/trustgraph/kg/extract_relationships/extract.py @@ -0,0 +1,252 @@ + +""" +Simple decoder, accepts PDF documents on input, outputs pages from the +PDF document as text as separate output objects. +""" + +import pulsar +from pulsar.schema import JsonSchema +from langchain_community.document_loaders import PyPDFLoader +import tempfile +import base64 +import os +import argparse +import rdflib +import json +import urllib.parse +import time + +from ... schema import VectorsChunk, Triple, VectorsAssociation, Source, Value +from ... log_level import LogLevel +from ... llm_client import LlmClient +from ... prompts import to_relationships +from ... rdf import RDF_LABEL, TRUSTGRAPH_ENTITIES + +RDF_LABEL_VALUE = Value(value=RDF_LABEL, is_uri=True) + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + vec_queue, + subscriber, + log_level, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(VectorsChunk), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(Triple), + ) + + self.vec_prod = self.client.create_producer( + topic=vec_queue, + schema=JsonSchema(VectorsAssociation), + ) + + self.llm = LlmClient(pulsar_host=pulsar_host) + + def to_uri(self, text): + + part = text.replace(" ", "-").lower().encode("utf-8") + quoted = urllib.parse.quote(part) + uri = TRUSTGRAPH_ENTITIES + quoted + + return uri + + def get_relationships(self, chunk): + + prompt = to_relationships(chunk) + resp = self.llm.request(prompt) + + rels = json.loads(resp) + + return rels + + def emit_edge(self, s, p, o): + + t = Triple(s=s, p=p, o=o) + self.producer.send(t) + + def emit_vec(self, ent, vec): + + r = VectorsAssociation(entity=ent, vectors=vec) + self.vec_prod.send(r) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + print(f"Indexing {v.source.id}...", flush=True) + + chunk = v.chunk.decode("utf-8") + + g = rdflib.Graph() + + try: + + rels = self.get_relationships(chunk) + print(json.dumps(rels, indent=4), flush=True) + + for rel in rels: + + s = rel["subject"] + p = rel["predicate"] + o = rel["object"] + + s_uri = self.to_uri(s) + s_value = Value(value=str(s_uri), is_uri=True) + + p_uri = self.to_uri(p) + p_value = Value(value=str(p_uri), is_uri=True) + + if rel["object-entity"]: + o_uri = self.to_uri(o) + o_value = Value(value=str(o_uri), is_uri=True) + else: + o_value = Value(value=str(o), is_uri=False) + + self.emit_edge( + s_value, + p_value, + o_value + ) + + # Label for s + self.emit_edge( + s_value, + RDF_LABEL_VALUE, + Value(value=str(s), is_uri=False) + ) + + # Label for p + self.emit_edge( + p_value, + RDF_LABEL_VALUE, + Value(value=str(p), is_uri=False) + ) + + if rel["object-entity"]: + # Label for o + self.emit_edge( + o_value, + RDF_LABEL_VALUE, + Value(value=str(o), is_uri=False) + ) + + self.emit_vec(s_value, v.vectors) + self.emit_vec(p_value, v.vectors) + if rel["object-entity"]: + self.emit_vec(o_value, v.vectors) + + except Exception as e: + print("Exception: ", e, flush=True) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception: ", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='kg-extract-relationships', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'vectors-chunk-load' + default_output_queue = 'graph-load' + default_subscriber = 'kg-extract-relationships' + default_vector_queue='vectors-load' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-c', '--vector-queue', + default=default_vector_queue, + help=f'Vector output queue (default: {default_vector_queue})' + ) + + args = parser.parse_args() + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + vec_queue=args.vector_queue, + subscriber=args.subscriber, + log_level=args.log_level, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + + diff --git a/trustgraph/llm/__init__.py b/trustgraph/llm/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/llm/azure_text/__init__.py b/trustgraph/llm/azure_text/__init__.py new file mode 100644 index 00000000..f2017af8 --- /dev/null +++ b/trustgraph/llm/azure_text/__init__.py @@ -0,0 +1,3 @@ + +from . llm import * + diff --git a/trustgraph/llm/azure_text/__main__.py b/trustgraph/llm/azure_text/__main__.py new file mode 100755 index 00000000..91342d2d --- /dev/null +++ b/trustgraph/llm/azure_text/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . llm import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/llm/azure_text/llm.py b/trustgraph/llm/azure_text/llm.py new file mode 100755 index 00000000..c4f47b6a --- /dev/null +++ b/trustgraph/llm/azure_text/llm.py @@ -0,0 +1,213 @@ + +""" +Simple LLM service, performs text prompt completion using an Ollama service. +Input is prompt, output is response. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +from langchain_community.llms import Ollama +import requests +import time +import json + +from ... schema import TextCompletionRequest, TextCompletionResponse +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + endpoint, + token, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(TextCompletionRequest), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(TextCompletionResponse), + ) + + self.endpoint = endpoint + self.token = token + + def build_prompt(self, system, content): + + data = { + "messages": [ + { + "role": "system", "content": system + }, + { + "role": "user", "content": content + } + ], + "max_tokens": 4192, + "temperature": 0.2, + "top_p": 1 + } + + body = json.dumps(data) + + return body + + def call_llm(self, body): + + url = self.endpoint + + # Replace this with the primary/secondary key, AMLToken, or + # Microsoft Entra ID token for the endpoint + api_key = self.token + + headers = { + 'Content-Type': 'application/json', + 'Authorization': f'Bearer {api_key}' + } + + resp = requests.post(url, data=body, headers=headers) + result = resp.json() + + message_content = result['choices'][0]['message']['content'] + + return message_content + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + + # Sender-produced ID + + id = msg.properties()["id"] + + print(f"Handling prompt {id}...", flush=True) + + prompt = self.build_prompt( + "You are a helpful chatbot", + v.prompt + ) + + response = self.call_llm(prompt) + + print("Send response...", flush=True) + r = TextCompletionResponse(response=response) + self.producer.send(r, properties={"id": id}) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='llm-ollama-text', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'llm-complete-text' + default_output_queue = 'llm-complete-text-response' + default_subscriber = 'llm-ollama-text' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-e', '--endpoint', + help=f'LLM model endpoint' + ) + + parser.add_argument( + '-k', '--token', + help=f'LLM model token' + ) + + args = parser.parse_args() + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + endpoint=args.endpoint, + token=args.token, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + + diff --git a/trustgraph/llm/claude_text/__init__.py b/trustgraph/llm/claude_text/__init__.py new file mode 100644 index 00000000..f2017af8 --- /dev/null +++ b/trustgraph/llm/claude_text/__init__.py @@ -0,0 +1,3 @@ + +from . llm import * + diff --git a/trustgraph/llm/claude_text/__main__.py b/trustgraph/llm/claude_text/__main__.py new file mode 100755 index 00000000..91342d2d --- /dev/null +++ b/trustgraph/llm/claude_text/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . llm import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/llm/claude_text/llm.py b/trustgraph/llm/claude_text/llm.py new file mode 100755 index 00000000..85ffc9c7 --- /dev/null +++ b/trustgraph/llm/claude_text/llm.py @@ -0,0 +1,190 @@ + +""" +Simple LLM service, performs text prompt completion using Claude. +Input is prompt, output is response. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +import anthropic +import time + +from ... schema import TextCompletionRequest, TextCompletionResponse +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + model, + api_key, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(TextCompletionRequest), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(TextCompletionResponse), + ) + + self.model = model + + self.claude = anthropic.Anthropic(api_key=api_key) + + print("Initialised", flush=True) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + + # Sender-produced ID + + id = msg.properties()["id"] + + print(f"Handling prompt {id}...", flush=True) + + prompt = v.prompt + response = message = self.claude.messages.create( + model=self.model, + max_tokens=1000, + temperature=0.1, + system = "You are a helpful chatbot.", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": prompt + } + ] + } + ] + ) + + resp = response.content[0].text + print(resp, flush=True) + + print("Send response...", flush=True) + r = TextCompletionResponse(response=resp) + self.producer.send(r, properties={"id": id}) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='llm-ollama-text', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'llm-complete-text' + default_output_queue = 'llm-complete-text-response' + default_subscriber = 'llm-claude-text' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-m', '--model', + default="claude-3-5-sonnet-20240620", + help=f'LLM model (default: claude-3-5-sonnet-20240620)' + ) + + parser.add_argument( + '-k', '--api-key', + help=f'Claude API key' + ) + + args = parser.parse_args() + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + model=args.model, + api_key=args.api_key, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + + diff --git a/trustgraph/llm/ollama_text/__init__.py b/trustgraph/llm/ollama_text/__init__.py new file mode 100644 index 00000000..f2017af8 --- /dev/null +++ b/trustgraph/llm/ollama_text/__init__.py @@ -0,0 +1,3 @@ + +from . llm import * + diff --git a/trustgraph/llm/ollama_text/__main__.py b/trustgraph/llm/ollama_text/__main__.py new file mode 100755 index 00000000..91342d2d --- /dev/null +++ b/trustgraph/llm/ollama_text/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . llm import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/llm/ollama_text/llm.py b/trustgraph/llm/ollama_text/llm.py new file mode 100755 index 00000000..9d9c7dad --- /dev/null +++ b/trustgraph/llm/ollama_text/llm.py @@ -0,0 +1,169 @@ + +""" +Simple LLM service, performs text prompt completion using an Ollama service. +Input is prompt, output is response. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +from langchain_community.llms import Ollama +import time + +from ... schema import TextCompletionRequest, TextCompletionResponse +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + model, + ollama, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(TextCompletionRequest), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(TextCompletionResponse), + ) + + self.llm = Ollama(base_url=ollama, model=model) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + + # Sender-produced ID + + id = msg.properties()["id"] + + print(f"Handling prompt {id}...", flush=True) + + prompt = v.prompt + response = self.llm.invoke(prompt) + + print("Send response...", flush=True) + r = TextCompletionResponse(response=response) + self.producer.send(r, properties={"id": id}) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + print("Closing") + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='llm-ollama-text', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'llm-complete-text' + default_output_queue = 'llm-complete-text-response' + default_subscriber = 'llm-ollama-text' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-m', '--model', + default="gemma2", + help=f'LLM model (default: gemma2)' + ) + + parser.add_argument( + '-r', '--ollama', + default="http://localhost:11434", + help=f'ollama (default: http://localhost:11434)' + ) + + args = parser.parse_args() + + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + model=args.model, + ollama=args.ollama, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + diff --git a/trustgraph/llm/vertexai_text/__init__.py b/trustgraph/llm/vertexai_text/__init__.py new file mode 100644 index 00000000..f2017af8 --- /dev/null +++ b/trustgraph/llm/vertexai_text/__init__.py @@ -0,0 +1,3 @@ + +from . llm import * + diff --git a/trustgraph/llm/vertexai_text/__main__.py b/trustgraph/llm/vertexai_text/__main__.py new file mode 100755 index 00000000..91342d2d --- /dev/null +++ b/trustgraph/llm/vertexai_text/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . llm import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/llm/vertexai_text/llm.py b/trustgraph/llm/vertexai_text/llm.py new file mode 100755 index 00000000..44b55e62 --- /dev/null +++ b/trustgraph/llm/vertexai_text/llm.py @@ -0,0 +1,254 @@ + +""" +Simple LLM service, performs text prompt completion using an Ollama service. +Input is prompt, output is response. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +import vertexai +import time + +from google.oauth2 import service_account +import google + +from vertexai.preview.generative_models import ( + Content, + FunctionDeclaration, + GenerativeModel, + GenerationConfig, + HarmCategory, + HarmBlockThreshold, + Part, + Tool, +) + +from ... schema import TextCompletionRequest, TextCompletionResponse +from ... log_level import LogLevel + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + credentials, + region, + model, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(TextCompletionRequest), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(TextCompletionResponse), + ) + + self.parameters = { + "temperature": 0.2, + "top_p": 1.0, + "top_k": 32, + "candidate_count": 1, + "max_output_tokens": 8192, + } + + self.generation_config = GenerationConfig( + temperature=0.2, + top_p=1.0, + top_k=10, + candidate_count=1, + max_output_tokens=8191, + ) + + # Block none doesn't seem to work + block_level = HarmBlockThreshold.BLOCK_ONLY_HIGH + # block_level = HarmBlockThreshold.BLOCK_NONE + + self.safety_settings = { + HarmCategory.HARM_CATEGORY_HARASSMENT: block_level, + HarmCategory.HARM_CATEGORY_HATE_SPEECH: block_level, + HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: block_level, + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: block_level, + } + + print("Initialise VertexAI...", flush=True) + + if credentials: + vertexai.init( + location=region, + credentials=credentials, + project=credentials.project_id, + ) + else: + vertexai.init( + location=region + ) + + print(f"Initialise model {model}", flush=True) + self.llm = GenerativeModel(model) + + print("Initialisation complete", flush=True) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + + # Sender-produced ID + + id = msg.properties()["id"] + + print(f"Handling prompt {id}...", flush=True) + + prompt = v.prompt + + resp = self.llm.generate_content( + prompt, generation_config=self.generation_config, + safety_settings=self.safety_settings + ) + + resp = resp.text + + resp = resp.replace("```json", "") + resp = resp.replace("```", "") + + print("Send response...", flush=True) + r = TextCompletionResponse(response=resp) + self.producer.send(r, properties={"id": id}) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except google.api_core.exceptions.ResourceExhausted: + + print("429, resource busy, sleeping", flush=True) + time.sleep(15) + self.consumer.negative_acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='llm-ollama-text', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'llm-complete-text' + default_output_queue = 'llm-complete-text-response' + default_subscriber = 'llm-vertexai-text' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-m', '--model', + default="gemini-1.0-pro-001", + help=f'LLM model (default: gemini-1.0-pro-001)' + ) + # Also: text-bison-32k + + parser.add_argument( + '-k', '--private-key', + help=f'Google Cloud private JSON file' + ) + + parser.add_argument( + '-r', '--region', + default='us-west1', + help=f'Google Cloud region (default: us-west1)', + ) + + args = parser.parse_args() + + if args.private_key: + credentials = service_account.Credentials.from_service_account_file( + args.private_key + ) + else: + credentials = None + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + credentials=credentials, + region=args.region, + model=args.model, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + diff --git a/trustgraph/llm_client.py b/trustgraph/llm_client.py new file mode 100644 index 00000000..5e0df96d --- /dev/null +++ b/trustgraph/llm_client.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 + +import pulsar +import _pulsar +from pulsar.schema import JsonSchema +from trustgraph.schema import TextCompletionRequest, TextCompletionResponse +import hashlib +import uuid + +# Ugly +ERROR=_pulsar.LoggerLevel.Error +WARN=_pulsar.LoggerLevel.Warn +INFO=_pulsar.LoggerLevel.Info +DEBUG=_pulsar.LoggerLevel.Debug + +class LlmClient: + + def __init__( + self, log_level=ERROR, client_id=None, + pulsar_host="pulsar://pulsar:6650", + ): + + if client_id == None: + client_id = str(uuid.uuid4()) + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level), + ) + + self.producer = self.client.create_producer( + topic='llm-complete-text', + schema=JsonSchema(TextCompletionRequest), + chunking_enabled=True, + ) + + self.consumer = self.client.subscribe( + 'llm-complete-text-response', client_id, + schema=JsonSchema(TextCompletionResponse), + ) + + def request(self, prompt, timeout=500): + + id = str(uuid.uuid4()) + + + r = TextCompletionRequest( + prompt=prompt + ) + self.producer.send(r, properties={ "id": id }) + + while True: + + msg = self.consumer.receive(timeout_millis=timeout * 1000) + + mid = msg.properties()["id"] + + if mid == id: + resp = msg.value().response + self.consumer.acknowledge(msg) + return resp + + # Ignore messages with wrong ID + self.consumer.acknowledge(msg) + + def __del__(self): + + self.producer.close() + self.consumer.close() + self.client.close() + diff --git a/trustgraph/log_level.py b/trustgraph/log_level.py new file mode 100644 index 00000000..65486b29 --- /dev/null +++ b/trustgraph/log_level.py @@ -0,0 +1,20 @@ + +from enum import Enum +import _pulsar + +class LogLevel(Enum): + DEBUG = 'debug' + INFO = 'info' + WARN = 'warn' + ERROR = 'error' + + def __str__(self): + return self.value + + def to_pulsar(self): + if self == LogLevel.DEBUG: return _pulsar.LoggerLevel.Debug + if self == LogLevel.INFO: return _pulsar.LoggerLevel.Info + if self == LogLevel.WARN: return _pulsar.LoggerLevel.Warn + if self == LogLevel.ERROR: return _pulsar.LoggerLevel.Error + raise RuntimeError("Log level mismatch") + diff --git a/trustgraph/prompts.py b/trustgraph/prompts.py new file mode 100644 index 00000000..c6b91ff2 --- /dev/null +++ b/trustgraph/prompts.py @@ -0,0 +1,138 @@ + +def turtle_extract(text): + + prompt = f""" +Study the following text and extract knowledge as +information in Turtle RDF format. +When declaring any new URIs, use prefix, +and declare appropriate namespace tags. + + + +{text} + + + +Do not use placeholders for information you do not know. +You will respond only with raw Turtle RDF data. Do not provide +explanations. Do not use special characters in the abstract text. The +abstract must be written as plain text. Do not add markdown formatting. +""" + + return prompt + +def scholar(text): + + # Build the prompt for Article style extraction + jsonexample = """{ + "title": "Article title here", + "abstract": "Abstract text here", + "keywords": ["keyword1", "keyword2", "keyword3"], + "people": ["person1", "person2", "person3"] +}""" + + promptscholar = f"""Your task is to read the provided text and write a scholarly abstract to fully explain all of the concepts described in the provided text. The abstract must include all conceptual details. + +{text} + + + +- Structure: For the provided text, write a title, abstract, keywords, + and people for the concepts found in the provided text. Ignore + document formatting in the provided text such as table of contents, + headers, footers, section metadata, and URLs. +- Focus on Concepts The abstract must focus on concepts found in the + provided text. The abstract must be factually accurate. Do not + write any concepts not found in the provided text. Do not + speculate. Do not omit any conceptual details. +- Completeness: The abstract must capture all topics the reader will + need to understand the concepts found in the provided text. Describe + all terms, definitions, entities, people, events, concepts, + conceptual relationships, and any other topics necessary for the + reader to understand the concepts of the provided text. + +- Format: Respond in the form of a valid JSON object. + + +{jsonexample} + + +You will respond only with the JSON object. Do not provide +explanations. Do not use special characters in the abstract text. The +abstract must be written as plain text. +""" + + return promptscholar + +def to_json_ld(text): + + prompt = f""" +Study the following text and output any facts you discover in +well-structured JSON-LD format. +Use any schema you understand from schema.org to describe the facts. + + + +{text} + + + +You will respond only with raw JSON-LD data in JSON format. Do not provide +explanations. Do not use special characters in the abstract text. The +abstract must be written as plain text. Do not add markdown formatting +or headers or prefixes. Do not use information which is not present in +the input text. +""" + + return prompt + + +def to_relationships(text): + + prompt = f""" +Study the following text and derive entity relationships. For each +relationship, derive the subject, predicate and object of the relationship. +Output relationships in JSON format as an arary of objects with fields: +- subject: the subject of the relationship +- predicate: the predicate +- object: the object of the relationship +- object-entity: false if the object is a simple data type: name, value or date. true if it is an entity. + + + +{text} + + + +You will respond only with raw JSON format data. Do not provide +explanations. Do not use special characters in the abstract text. The +abstract must be written as plain text. Do not add markdown formatting +or headers or prefixes. +""" + + return prompt + +def to_definitions(text): + + prompt = f""" +Study the following text and derive definitions for any discovered entities. +Do not provide definitions for entities whose definitions are incomplete +or unknown. +Output relationships in JSON format as an arary of objects with fields: +- entity: the name of the entity +- definition: English text which defines the entity + + + +{text} + + + +You will respond only with raw JSON format data. Do not provide +explanations. Do not use special characters in the abstract text. The +abstract will be written as plain text. Do not add markdown formatting +or headers or prefixes. Do not include null or unknown definitions. +""" + + return prompt + diff --git a/trustgraph/rag/__init__.py b/trustgraph/rag/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/rag/graph/__init__.py b/trustgraph/rag/graph/__init__.py new file mode 100644 index 00000000..432ae594 --- /dev/null +++ b/trustgraph/rag/graph/__init__.py @@ -0,0 +1,3 @@ + +from . rag import * + diff --git a/trustgraph/rag/graph/__main__.py b/trustgraph/rag/graph/__main__.py new file mode 100755 index 00000000..89ebb780 --- /dev/null +++ b/trustgraph/rag/graph/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . rag import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/rag/graph/rag.py b/trustgraph/rag/graph/rag.py new file mode 100755 index 00000000..28ad298b --- /dev/null +++ b/trustgraph/rag/graph/rag.py @@ -0,0 +1,172 @@ + +""" +Simple RAG service, performs query using graph RAG an LLM. +Input is query, output is response. +""" + +import pulsar +from pulsar.schema import JsonSchema +import tempfile +import base64 +import os +import argparse +import time + +from ... schema import GraphRagQuery, GraphRagResponse +from ... log_level import LogLevel +from ... graph_rag import GraphRag + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + output_queue, + subscriber, + log_level, + graph_hosts, + vector_store, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(GraphRagQuery), + ) + + self.producer = self.client.create_producer( + topic=output_queue, + schema=JsonSchema(GraphRagResponse), + ) + + self.rag = GraphRag( + pulsar_host=pulsar_host, + graph_hosts=graph_hosts, + vector_store=vector_store, + verbose=True, + ) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + + # Sender-produced ID + + id = msg.properties()["id"] + + print(f"Handling input {id}...", flush=True) + + response = self.rag.query(v.query) + + print("Send response...", flush=True) + r = GraphRagResponse(response = response) + self.producer.send(r, properties={"id": id}) + + print("Done.", flush=True) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + print("Closing", flush=True) + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='llm-ollama-text', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'graph-rag-query' + default_output_queue = 'graph-rag-response' + default_subscriber = 'graph-rag' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-o', '--output-queue', + default=default_output_queue, + help=f'Output queue (default: {default_output_queue})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-g', '--graph-hosts', + default='cassandra', + help=f'Graph hosts, comma separated (default: cassandra)' + ) + + parser.add_argument( + '-v', '--vector-store', + default='http://milvus:19530', + help=f'Vector host (default: http://milvus:19530)' + ) + + args = parser.parse_args() + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + output_queue=args.output_queue, + subscriber=args.subscriber, + log_level=args.log_level, + graph_hosts=args.graph_hosts.split(","), + vector_store=args.vector_store, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + diff --git a/trustgraph/schema.py b/trustgraph/schema.py new file mode 100644 index 00000000..d8fd4af7 --- /dev/null +++ b/trustgraph/schema.py @@ -0,0 +1,67 @@ + +from pulsar.schema import Record, Bytes, String, Boolean, Integer, Array, Double + +from enum import Enum + +#class Command(Enum): +# reindex = 1 + +#class IndexCommand(Record): +# command = Command + +class Value(Record): + value = String() + is_uri = Boolean() + type = String() + +class Source(Record): + source = String() + id = String() + title = String() + +class Document(Record): + source = Source() + data = Bytes() + +class TextDocument(Record): + source = Source() + text = Bytes() + +class Chunk(Record): + source = Source() + chunk = Bytes() + +class VectorsChunk(Record): + source = Source() + vectors = Array(Array(Double())) + chunk = Bytes() + +class VectorsAssociation(Record): + source = Source() + vectors = Array(Array(Double())) + entity = Value() + +class Triple(Record): + source = Source() + s = Value() + p = Value() + o = Value() + +class TextCompletionRequest(Record): + prompt = String() + +class TextCompletionResponse(Record): + response = String() + +class EmbeddingsRequest(Record): + text = String() + +class EmbeddingsResponse(Record): + vectors = Array(Array(Double())) + +class GraphRagQuery(Record): + query = String() + +class GraphRagResponse(Record): + response = String() + diff --git a/trustgraph/trustgraph.py b/trustgraph/trustgraph.py new file mode 100644 index 00000000..a7b53a06 --- /dev/null +++ b/trustgraph/trustgraph.py @@ -0,0 +1,108 @@ + +from cassandra.cluster import Cluster +from cassandra.auth import PlainTextAuthProvider + +class TrustGraph: + + def __init__(self, hosts=None): + + if hosts is None: + hosts = ["localhost"] + + self.cluster = Cluster(hosts) + self.session = self.cluster.connect() + + self.init() + + def clear(self): + + self.session.execute(""" + drop keyspace if exists trustgraph; + """); + + self.init() + + def init(self): + + self.session.execute(""" + create keyspace if not exists trustgraph + with replication = { + 'class' : 'SimpleStrategy', + 'replication_factor' : 1 + }; + """); + + self.session.set_keyspace('trustgraph') + + self.session.execute(""" + create table if not exists triples ( + s text, + p text, + o text, + PRIMARY KEY (s, p) + ); + """); + + self.session.execute(""" + create index if not exists triples_p + ON triples (p); + """); + + self.session.execute(""" + create index if not exists triples_o + ON triples (o); + """); + + def insert(self, s, p, o): + + self.session.execute( + "insert into triples (s, p, o) values (%s, %s, %s)", + (s, p, o) + ) + + def get_all(self, limit=50): + return self.session.execute( + f"select s, p, o from triples limit {limit}" + ) + + def get_s(self, s, limit=10): + return self.session.execute( + f"select p, o from triples where s = %s", + (s,) + ) + + def get_p(self, p, limit=10): + return self.session.execute( + f"select s, o from triples where p = %s limit {limit}", + (p,) + ) + + def get_o(self, o, limit=10): + return self.session.execute( + f"select s, p from triples where o = %s limit {limit}", + (o,) + ) + + def get_sp(self, s, p, limit=10): + return self.session.execute( + f"select o from triples where s = %s and p = %s limit {limit}", + (s, p) + ) + + def get_po(self, p, o, limit=10): + return self.session.execute( + f"select s from triples where p = %s and o = %s allow filtering limit {limit}", + (p, o) + ) + + def get_os(self, o, s, limit=10): + return self.session.execute( + f"select s from triples where o = %s and s = %s limit {limit}", + (o, s) + ) + + def get_spo(self, s, p, o, limit=10): + return self.session.execute( + f"""select s as x from triples where s = %s and p = %s and o = %s limit {limit}""", + (s, p, o) + ) diff --git a/trustgraph/vector/__init__.py b/trustgraph/vector/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trustgraph/vector/milvus_write/__init__.py b/trustgraph/vector/milvus_write/__init__.py new file mode 100644 index 00000000..d891d55f --- /dev/null +++ b/trustgraph/vector/milvus_write/__init__.py @@ -0,0 +1,3 @@ + +from . write import * + diff --git a/trustgraph/vector/milvus_write/__main__.py b/trustgraph/vector/milvus_write/__main__.py new file mode 100755 index 00000000..c05d8c6d --- /dev/null +++ b/trustgraph/vector/milvus_write/__main__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from . write import run + +if __name__ == '__main__': + run() + diff --git a/trustgraph/vector/milvus_write/write.py b/trustgraph/vector/milvus_write/write.py new file mode 100755 index 00000000..6a5700d9 --- /dev/null +++ b/trustgraph/vector/milvus_write/write.py @@ -0,0 +1,136 @@ + +""" +Simple decoder, accepts PDF documents on input, outputs pages from the +PDF document as text as separate output objects. +""" + +import pulsar +from pulsar.schema import JsonSchema +from langchain_community.document_loaders import PyPDFLoader +import tempfile +import base64 +import os +import argparse +import time + +from ... schema import VectorsAssociation +from ... log_level import LogLevel +from ... edge_map import VectorStore + +class Processor: + + def __init__( + self, + pulsar_host, + input_queue, + subscriber, + store_uri, + log_level, + ): + + self.client = pulsar.Client( + pulsar_host, + logger=pulsar.ConsoleLogger(log_level.to_pulsar()) + ) + + self.consumer = self.client.subscribe( + input_queue, subscriber, + schema=JsonSchema(VectorsAssociation), + ) + + self.vecstore = VectorStore(store_uri) + + def run(self): + + while True: + + msg = self.consumer.receive() + + try: + + v = msg.value() + + if v.entity.value != "": + for vec in v.vectors: + self.vecstore.insert(vec, v.entity.value) + + # Acknowledge successful processing of the message + self.consumer.acknowledge(msg) + + except Exception as e: + + print("Exception:", e, flush=True) + + # Message failed to be processed + self.consumer.negative_acknowledge(msg) + + def __del__(self): + self.client.close() + +def run(): + + parser = argparse.ArgumentParser( + prog='pdf-decoder', + description=__doc__, + ) + + default_pulsar_host = os.getenv("PULSAR_HOST", 'pulsar://pulsar:6650') + default_input_queue = 'vectors-load' + default_subscriber = 'vector-write-milvus' + + parser.add_argument( + '-p', '--pulsar-host', + default=default_pulsar_host, + help=f'Pulsar host (default: {default_pulsar_host})', + ) + + parser.add_argument( + '-i', '--input-queue', + default=default_input_queue, + help=f'Input queue (default: {default_input_queue})' + ) + + parser.add_argument( + '-s', '--subscriber', + default=default_subscriber, + help=f'Queue subscriber name (default: {default_subscriber})' + ) + + parser.add_argument( + '-l', '--log-level', + type=LogLevel, + default=LogLevel.INFO, + choices=list(LogLevel), + help=f'Output queue (default: info)' + ) + + parser.add_argument( + '-t', '--store-uri', + default="http://localhost:19530", + help=f'Milvus store URI (default: http://localhost:19530)' + ) + + args = parser.parse_args() + + while True: + + try: + + p = Processor( + pulsar_host=args.pulsar_host, + input_queue=args.input_queue, + subscriber=args.subscriber, + store_uri=args.store_uri, + log_level=args.log_level, + ) + + p.run() + + except Exception as e: + + print("Exception:", e, flush=True) + print("Will retry...", flush=True) + + time.sleep(10) + + diff --git a/trustgraphETL.py b/trustgraphETL.py new file mode 100644 index 00000000..91892625 --- /dev/null +++ b/trustgraphETL.py @@ -0,0 +1,718 @@ + +# import boto3 +import json +import re +import rdflib +import time +from langchain_community.document_loaders import PyPDFLoader +from langchain_community.llms import Ollama + +from mistralai.client import MistralClient +from mistralai.models.chat_completion import ChatMessage + +llm = Ollama( + base_url="http://monster:11434", +# model="deepseek-v2" +# model="phi3:14b" + model="gemma2" +) + +api_key = open("mistral-ai.key").read().strip() + +client = MistralClient(api_key=api_key) +model = "open-mixtral-8x7b" + +# from llama_index.core import Document +# from llama_index.core.node_parser import SentenceSplitter +# from unisim import TextSim + +# bedrock = boto3.client(service_name='bedrock-runtime', region_name="us-west-2") + +exampleXML = f"""## RDF/XML Examples: + +```xml + + + + + + + + + + + +``` + +```xml + + + + + RDF 1.1 XML Syntax + RDF 1.1 XML Syntax + RDF 1.1 XML Syntax + + + + Der Baum + Das Buch ist außergewöhnlich + The Tree + + +``` + +```xml + + + + + + + + + + + + +``` + +## RDF/XML Instructions: +1. All valid RDF/XML shall written with the following first line: +2. The subject of a RDF triple must be a URI, Uniform Resource Identifier. +3. The predicate of a RDF triple must be a URI, Uniform Resource Identier. +4. All URIs must use the following base URL: http://example.org/ +5. Predicates should be written using knowledge schema using the SKOS, Simple Knowledge Organization System, approach. +6. The object of a RDF triple can be a URI or a value. +7. If the object of a RDF triple is a value, the object should have a XSD type. +""" + +exampleTurtle = """Knowledge is written between <<<>>>>. That knowledge will be rewritten as RDF Turtle. + +<<>> + +Here's the RDF Turtle for the knowledge +```turtle +@prefix rdf: . +@prefix animal: . +@prefix prop: . +@prefix type: . +@prefix rdfs: . + +animal:fred + prop:has-legs 4 ; + prop:lives-with animal:hope ; + a type:cat ; + rdfs:label "Fred" . + +animal:hope + prop:has-legs 4 ; + prop:lives-with animal:fred ; + a type:cat ; + rdfs:label "Hope" . + +prop:has-legs + a rdfs:Property ; + rdfs:label "has legs" . + +prop:lives-with + a rdfs:Property ; + rdfs:label "lives with" . + +type:cat + a rdfs:Class ; + rdfs:label "cat" . +``` + +# RDF Turtle Rules +1. The object of a RDF triple can be a URI or a value. +2. If the object of a RDF triple is a value, the object should have a XSD type. +3. All triples must have a subject, predicate, and object. +4. A subject shall always be followed by a predicate and object. +5. When a subject has multiple sets of predicates and objects, use a semicolon character to separate each set of predicates and objects. +6. When a subject and predicate have multiple objects, use a comma character to separate the set of objects. +7. Use the 'a' character in place of the predicate 'is'. +8. Use @prefix rdf: . +9. Use @prefix rdfs: . +10. Use @prefix xsd: . +""" + +# Build the prompt for Article style extraction +def scholar(chunk): + promptscholar = f"""[INST]You are a scholar. You have profound knowledge of the provided text. Your task is to read the provided text and write an article to fully explain the provided text. + + # Provided Text: + {chunk} + + # Instructions: + ## Focus on Concepts + The article should focus on concepts in the provided text. The article should be factually accurate. Do not write any concepts not found in the provided text. Do not speculate. + + ## Completeness + The article should capture all topics the reader will need to understand the concepts found in the provided text. Describe all terms, definitions, entities, people, events, concepts, conceptual relationships, and any other topics necessary for the reader to understand the concepts of the provided text. + [/INST] + """ + return promptscholar + +# Build the prompt for the custom RDF/XML schema +def schema(article): + promptschema = f"""[INST]You are a scholar. You have profound understanding of knowledge schemas that are parsed into RDF knowledge graphs. Your task is to read the provided article and write a complete RDF/XML document for the provided article. You will respond only with the RDF/XML document. Do not provide explanations. Obey all RDF/XML instructions. + + # Provided Article: + {article} + + # Example RDF/XML and Instructions: + {exampleXML} + [/INST] + """ + return promptschema + +# Build the prompt for the SKOS style RDF/XML schema +def skos(article): + promptconcepts = f"""[INST]You are a scholar. You have profound understanding of knowledge schemas that are parsed into RDF knowledge graphs. Your task is to read the provided article and write a complete RDF/XML document for the provided article that uses SKOS, Simple Knowledge Organization System, to structure the conceptual relationships in the article. You will respond only with the RDF/XML document. Do not provide explanations. Obey all RDF/XML instructions. + + # Provided Article: + {article} + + # RDF/XML Example and Instructions: + {exampleXML} + [/INST] + """ + return promptconcepts + +# Add the outputs from the first pass to stabilize the build process +def example_follow(x1, x2): + exampleXML = f"""## RDF/XML Examples: + +```xml + + + + + + + + + + + +``` + +```xml + + + + + RDF 1.1 XML Syntax + RDF 1.1 XML Syntax + RDF 1.1 XML Syntax + + + + Der Baum + Das Buch ist außergewöhnlich + The Tree + + +``` + +```xml + + + + + + + + + + + + +``` + +```xml +{x1} +``` + +```xml +{x2} +``` + +## RDF/XML Instructions: +1. All valid RDF/XML shall written with the following first line: +2. The subject of a RDF triple must be a URI, Uniform Resource Identifier. +3. The predicate of a RDF triple must be a URI, Uniform Resource Identier. +4. All URIs must use the following base URL: http://example.org/ +5. Predicates should be written using knowledge schema using the SKOS, Simple Knowledge Organization System, approach. +6. The object of a RDF triple can be a URI or a value. +7. If the object of a RDF triple is a value, the object should have a XSD type. +""" + return exampleXML + +# Build the follow prompt for the custom RDF/XML schema +def schema_follow(article, example_follow): + promptschema = f"""[INST]You are a scholar. You have profound understanding of knowledge schemas that are parsed into RDF knowledge graphs. Your task is to read the provided article and write a complete RDF/XML document for the provided article. You will respond only with the RDF/XML document. Do not provide explanations. Obey all RDF/XML instructions. + + # Provided Article: + {article} + + # Example RDF/XML and Instructions: + {example_follow} + [/INST] + """ + return promptschema + +# Build the follow prompt for the SKOS style RDF/XML schema +def skos_follow(article, example_follow): + promptconcepts = f"""[INST]You are a scholar. You have profound understanding of knowledge schemas that are parsed into RDF knowledge graphs. Your task is to read the provided article and write a complete RDF/XML document for the provided article that uses SKOS, Simple Knowledge Organization System, to structure the conceptual relationships in the article. You will respond only with the RDF/XML document. Do not provide explanations. Obey all RDF/XML instructions. + + # Provided Article: + {article} + + # RDF/XML Example and Instructions: + {example_follow} + [/INST] + """ + return promptconcepts + + +def p1(chunk): + promptcustom = f"""[INST]You write knowledge schemas that are parsed into RDF knowledge graphs. You read the provided text and develop a knwoledge schema to represent the concepts. Your task is to read the provided text and write a RDF Turtle schema for the concepts found in the provided text. You will respond only with the RDF Turtle schema. Do not provide explanations. Obey all of the provided RDF Turtle rules. + + # RDF Turtle Example and Rules + {exampleTurtle} + + # Provided Text + {chunk} + [/INST] + """ + return promptcustom + +def p2(chunk): + promptconcepts = f"""[INST]You write knowledge schemas that are parsed into RDF knowledge graphs. You read the provided text and develop a knwoledge schema to represent the conceptual relationships in the text. Your task is to read the provided text and write a RDF Turtle schema for the concepts found in the provided text using SKOS, Simple Knowledge Organization System. You will respond only with the RDF Turtle schema. Do not provide explanations. Obey all of the provided RDF Turtle rules. + + # Valid RDF Turtle Example and Rules + {exampleTurtle} + + # Provided Text + {chunk} + [/INST] + """ + return promptconcepts + +def summaryprompt(chunk): + promptsummary = f"""[INST]You are an analyst. You read complex text and write a detailed summary. Your task is to read the provided text and write a thorough summary that captures all of the key points. + + # Provided text: + {chunk} + + # Instructions: + ## Summarize: + In clear and concise language, summarize the key points in the provided text. + + ## Key Points: + It is important to capture unique terms and their definitions, entities, people, events, concepts, conceptual relationships, and relationships between entities and people. + + ## Requirements: + Include all key points in the summary. Do not omit any facts that might be considered a key point. + [/INST] + """ + return promptsummary + +def extractpdf(path): + loader = PyPDFLoader(path) + pages = loader.load_and_split() + pdf = f"" + for page in pages: + content = page.page_content + pdf += "".join(content) + return pdf + +def chunktext(splitpdf, chunk_size, chunk_overlap): + node_parser = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) + nodes = node_parser.get_nodes_from_documents([Document(text=splitpdf)], show_progress=False) + textlist = [] + for node in nodes: + textnode = node.text + textlist.append(textnode) + return textlist + +def OLDcallmixtral(prompt): + + chat_response = client.chat( + model=model, + messages=[ + ChatMessage( + role="user", content=prompt + ) + ] + ) + + print(chat_response) + resp = chat_response.choices[0].message.content + print(resp) + + return resp + +def callmixtral(prompt): + + print(prompt) + resp = llm.invoke(prompt) + print(resp) + + return resp + +def extract_xml(text): + cleantext = replace_ampersand(text) + pattern = r'<\?xml version="1\.0"\?>.*?' + match = re.search(pattern, cleantext, re.DOTALL) + + if match: + return match.group() + else: + return None + +def replace_ampersand(text): + return text.replace('&', 'and') + +def convert_summary(dataset): + num_passes = len(dataset) + print("Beginning Converting to Summaries") + + start_time = time.time() # Start the timer + + summaries = [] + + for i in range(5): + print(f"Begin Pass {i+1}") + promptchunk = dataset[i] + prompt = summaryprompt(promptchunk) + output = callmixtral(prompt) + summaries.append(output) + print(f"Finished Pass {i+1}") + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + print(f"Total time taken: {total_time:.2f} seconds") + return summaries + +def extract_articles(dataset): + num_passes = len(dataset) + print("Beginning Converting to Articles") + + start_time = time.time() # Start the timer + + articles = [] + + # Only Doing 5 chunks for now... + for i in range(5): + print(f"Begin Pass {i+1}") + promptchunk = dataset[i] + prompt = scholar(promptchunk) #Build the prompt + output = callmixtral(prompt) + articles.append(output) + print(f"Finished Pass {i+1}") + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + print(f"Total time taken: {total_time:.2f} seconds") + return articles + +def full_extract(dataset): + g = rdflib.Graph() + num_passes = len(dataset) + print(f"Initializing Graph and beginning extraction:") + + start_time = time.time() # Start the timer + + for i in range(num_passes): + print(f"Begin Pass {i+1}") + output1 = "" + output2 = "" + promptchunk = dataset[i] + prompt1 = p1(promptchunk) + prompt2 = p2(promptchunk) + + output1 = callmixtral(prompt1) + + # Extract text between ### characters from output1 + match1 = re.search(r'###(.*?)###', output1, re.DOTALL) + if match1: + data1 = match1.group() + g.parse(data=data1, format="turtle") + print(f"Complete first half of Pass {i+1} from ###") + else: + g.parse(data=output1, format="turtle") + print(f"Complete first half of Pass {i+1} raw") + + output2 = callmixtral(prompt2) + + # Extract text between ### characters from output2 + match2 = re.search(r'###(.*?)###', output2, re.DOTALL) + if match2: + data2 = match2.group() + g.parse(data=data2, format="turtle") + print(f"Completed Pass {i+1} from ###") + else: + g.parse(data=output2, format="turtle") + print(f"Complete Pass {i+1} raw") + + running_length = len(g) + print(f"Pass {i+1} Graph Length: {running_length}") + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + + print("Holy shit! This actually worked?!?!?!") + print(f"Total time taken: {total_time:.2f} seconds") + return g + +def firstpass_build(dataset): + firstpass = [] + out1 = "" + out2 = "" + chunk = dataset[0] + p1 = schema(chunk) + p2 = skos(chunk) + out1 = callmixtral(p1) + out2 = callmixtral(p2) + xml1 = extract_xml(out1) + xml2 = extract_xml(out2) + firstpass.append(xml1) + firstpass.append(xml2) + return firstpass + +def build_graph(dataset): + start_time = time.time() # Start the timer + + #Build the first set of outputs + firstset = [] + firstset = firstpass_build(dataset) + example1 = firstset[0] + example2 = firstset[1] + + #Build the new set of examples using first pass outputs + newexamples = example_follow(example1, example2) + + print(f"Initializing Graph:") + g = rdflib.Graph() + # Parse the outputs from the first pass function + g.parse(data=firstset[0], format="xml") + g.parse(data=firstset[1], format="xml") + print(f"Parsed First Pass, Graph Length: {len(g)}") + + num_passes = len(dataset) + + for i in range(1, num_passes, 1): + output1 = "" + output2 = "" + promptchunk = dataset[i] + prompt1 = schema_follow(promptchunk, newexamples) #Build the schema follow prompt + prompt2 = skos_follow(promptchunk, newexamples) #Build the SKOS follow prompt + + # Get schema style output + output1 = callmixtral(prompt1) + + # Extract text between ```xml``` characters from output1 + match1 = extract_xml(output1) + if match1: + data1 = match1 + g.parse(data=data1, format="xml") + + # Get SKOS style output + output2 = callmixtral(prompt2) + + # Extract text between ```xml``` characters from output2 + match2 = extract_xml(output2) + if match2: + data2 = match2 + g.parse(data=data2, format="xml") + + running_length = len(g) + print(f"Parsed Pass {i+1}, Graph Length: {running_length}") + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + + outputpath = f"../Columbia_graph_extract.ttl" + g.serialize(destination=outputpath, format="turtle") + print("Holy shit! This actually worked?!?!?!") + print(f"Total time taken: {total_time:.2f} seconds") + return + +# Graph Building with some error handling and return the graph +def build_graph_robust(dataset): + start_time = time.time() # Start the timer + + #Build the first set of outputs + firstset = [] + firstset = firstpass_build(dataset) + example1 = firstset[0] + example2 = firstset[1] + + #Build the new set of examples using first pass outputs + newexamples = example_follow(example1, example2) + + print(f"Initializing Graph:") + g = rdflib.Graph() + # Parse the outputs from the first pass function + try: + g.parse(data=firstset[0], format="xml") + g.parse(data=firstset[1], format="xml") + print(f"Parsed First Pass, Graph Length: {len(g)}") + except Exception as e: + print(f"Error parsing first pass outputs: {str(e)}") + return None + + num_passes = len(dataset) + + i = 1 + while i < num_passes: + output1 = "" + output2 = "" + promptchunk = dataset[i] + prompt1 = schema_follow(promptchunk, newexamples) #Build the schema follow prompt + prompt2 = skos_follow(promptchunk, newexamples) #Build the SKOS follow prompt + + # Get schema style output + output1 = callmixtral(prompt1) + + # Extract text between ```xml``` characters from output1 + match1 = extract_xml(output1) + if match1: + data1 = match1 + try: + g.parse(data=data1, format="xml") + except Exception as e: + print(f"Error parsing schema output in pass {i+1}: {str(e)}\n\nTrying Again...") + continue + + # Get SKOS style output + output2 = callmixtral(prompt2) + + # Extract text between ```xml``` characters from output2 + match2 = extract_xml(output2) + if match2: + data2 = match2 + try: + g.parse(data=data2, format="xml") + except Exception as e: + print(f"Error parsing SKOS output in pass {i+1}: {str(e)}\n\nTrying Again...") + continue + + running_length = len(g) + print(f"Parsed Pass {i+1}, Graph Length: {running_length}") + i += 1 + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + +# outputpath = f"./Columbia_graph_extract.ttl" +# g.serialize(destination=outputpath, format="turtle") + print("Holy shit! This actually worked?!?!?!") + print(f"Total time taken: {total_time:.2f} seconds") + return g + +# Get a list of subjects from graph as URIs +def get_subject_list(graph): + query = """ + SELECT DISTINCT ?subject + WHERE { + ?subject ?predicate ?object . + } + """ + + subjects = graph.query(query) + + subjectlist = [] + for row in subjects: + text = f"{row.subject}" + subjectlist.append(text) + + return subjectlist + +# Function for removing URI artifacts to return clean text +def clean_resource(resource): + # Remove the URL prefixes + cleaned_resource = resource.replace("http://example.org/", "") + cleaned_resource = cleaned_resource.replace("http://www.w3.org/2004/02/skos/core#", "") + cleaned_resource = cleaned_resource.replace("http://www.w3.org/1999/02/22-rdf-syntax-ns#", "") + cleaned_resource = cleaned_resource.replace("http://www.w3.org/2001/XMLSchema#", "") + cleaned_resource = cleaned_resource.replace("http://purl.org/dc/elements/1.1/", "") + + # Replace special symbols with a space + cleaned_resource = re.sub(r'[_#/]', ' ', cleaned_resource) + + return cleaned_resource + +# Clean the entire subject list +def clean_subject_list(subjectlist): + cleaned_subjects = [] + for subject in subjectlist: + clean = f"{clean_resource(subject)}" + cleaned_subjects.append(clean) + + return cleaned_subjects + +# Dump all the triples from the Graph +def get_triples_list(graph): + triples_list = list(graph.triples((None, None, None))) + + semantic_list = [] + for subject, predicate, object in triples_list: + triple = f"{subject} {predicate} {object}" + semantic_list.append(triple) + + return semantic_list + +#Clean the entire triples list +def clean_triples_list(tripleslist): + cleaned_triples = [] + for triple in tripleslist: + cleaned_triple = clean_resource(triple) + cleaned_triples.append(cleaned_triple) + + return cleaned_triples + +# Build UniSim instance for graph subjects +def build_subject_Sim(graph): + subject_sim = TextSim() + subject_sim.reset_index() # Make sure it's empty + subject_sim = TextSim( + store_data=True, # set to False for large datasets to save memory + index_type="exact", # set to "approx" for large datasets to use ANN search + batch_size=128, # increasing batch_size on GPU may be faster + use_accelerator=True, # uses GPU if available, otherwise uses CPU + ) + + subjects = get_subject_list(graph) # Get the subjects + clean_subjects = clean_subject_list(subjects) # Clean the subjects + idx = subject_sim.add(clean_subjects) # Build sim + return subject_sim + +# Build UniSim instance for graph triples +def build_triples_Sim(graph): + triples_sim = TextSim() + triples_sim.reset_index() # Make sure it's empty + triples_sim = TextSim( + store_data=True, # set to False for large datasets to save memory + index_type="exact", # set to "approx" for large datasets to use ANN search + batch_size=128, # increasing batch_size on GPU may be faster + use_accelerator=True, # uses GPU if available, otherwise uses CPU + ) + + triples = get_triples_list(graph) # Get the triples + clean_triples = clean_triples_list(triples) # Clean the triples + idx = triples_sim.add(clean_triples) # Build sim + return triples_sim diff --git a/trustgraphETL2.py b/trustgraphETL2.py new file mode 100644 index 00000000..6e1dea79 --- /dev/null +++ b/trustgraphETL2.py @@ -0,0 +1,875 @@ + +import json +import re +import rdflib +import time +from langchain_community.document_loaders import PyPDFLoader +from llama_index.core import Document +from llama_index.core.node_parser import SentenceSplitter +from langchain_community.llms import Ollama + +llm = Ollama( + base_url="http://monster:11434", +# model="deepseek-v2" + model="phi3:14b" +# model="gemma2" +) + +exampleXML = f"""## RDF/XML Examples: + +```xml + + + + + + + + + + + +``` + +```xml + + + + + RDF 1.1 XML Syntax + RDF 1.1 XML Syntax + RDF 1.1 XML Syntax + + + + Der Baum + Das Buch ist außergewöhnlich + The Tree + + +``` + +```xml + + + + + + + + + + + + +``` + +## RDF/XML Instructions: +1. All valid RDF/XML shall written with the following first line: +2. The subject of a RDF triple must be a URI, Uniform Resource Identifier. +3. The predicate of a RDF triple must be a URI, Uniform Resource Identier. +4. All URIs must use the following base URL: http://example.org/ +5. Predicates should be written using knowledge schema using the SKOS, Simple Knowledge Organization System, approach. +6. The object of a RDF triple can be a URI or a value. +7. If the object of a RDF triple is a value, the object should have a XSD type. +""" + +exampleTurtle = """Knowledge is written between <<<>>>>. That knowledge will be rewritten as RDF Turtle. + +<<>> + +Here's the RDF Turtle for the knowledge +```turtle +@prefix rdf: . +@prefix animal: . +@prefix prop: . +@prefix type: . +@prefix rdfs: . + +animal:fred + prop:has-legs 4 ; + prop:lives-with animal:hope ; + a type:cat ; + rdfs:label "Fred" . + +animal:hope + prop:has-legs 4 ; + prop:lives-with animal:fred ; + a type:cat ; + rdfs:label "Hope" . + +prop:has-legs + a rdfs:Property ; + rdfs:label "has legs" . + +prop:lives-with + a rdfs:Property ; + rdfs:label "lives with" . + +type:cat + a rdfs:Class ; + rdfs:label "cat" . +``` + +# RDF Turtle Rules +1. The object of a RDF triple can be a URI or a value. +2. If the object of a RDF triple is a value, the object should have a XSD type. +3. All triples must have a subject, predicate, and object. +4. A subject shall always be followed by a predicate and object. +5. When a subject has multiple sets of predicates and objects, use a semicolon character to separate each set of predicates and objects. +6. When a subject and predicate have multiple objects, use a comma character to separate the set of objects. +7. Use the 'a' character in place of the predicate 'is'. +8. Use @prefix rdf: . +9. Use @prefix rdfs: . +10. Use @prefix xsd: . +""" + +# Build the prompt for Article style extraction +jsonexample = """{ + "title": "Article title here", + "abstract": "Abstract text here", + "keywords": ["keyword1", "keyword2", "keyword3"], + "people": ["person1", "person2", "person3"] +}""" + +def scholar(chunk): + promptscholar = f"""Your task is to read the provided text and write a scholarly abstract to fully explain all of the concepts described in the provided text. The abstract should include all conceptual details. + +{chunk} + + + +- Structure: For the provided text, write a title, abstract, keywords, + and people for the concepts found in the provided text. Ignore + document formatting in the provided text such as table of contents, + headers, footers, section metadata, and URLs. +- Focus on Concepts The abstract should focus on concepts found in the + provided text. The abstract should be factually accurate. Do not + write any concepts not found in the provided text. Do not + speculate. Do not omit any conceptual details. +- Completeness: The abstract should capture all topics the reader will + need to understand the concepts found in the provided text. Describe + all terms, definitions, entities, people, events, concepts, + conceptual relationships, and any other topics necessary for the + reader to understand the concepts of the provided text. + +- Format: Respond in the form of a valid JSON object. + + +{jsonexample} + + +You will respond only with the JSON object. Do not provide +explanations. Do not use special characters in the abstract text. The +abstract should be written as plain text. +""" + + return promptscholar + +# Build the prompt for the custom RDF/XML schema +def schema(article): + promptschema = f"""[INST]You are a scholar. Your subject of expertise is {article['title']}. You have expert understanding of knowledge schemas that are parsed into RDF knowledge graphs. Your task is to read the provided article and write a complete RDF/XML document for the provided article. You will respond only with the RDF/XML document. Do not provide explanations. Obey all RDF/XML instructions. + + # Provided Article: + {article['abstract']} + + # Example RDF/XML and Instructions: + {exampleXML} + [/INST] + + # Keyword Annotations: + Add RDF/XML labels to the applicable concepts using the following list of keywords: + {article['keywords']} + + # People Annotations: + + Add RDF/XML labels to the applicable concepts using the following list of people: + {article['people']} + """ + return promptschema + +# Build the prompt for the SKOS style RDF/XML schema +def skos(article): + promptconcepts = f"""[INST]You are a scholar. Your subject of expertise is {article['title']}. You have expert understanding of knowledge schemas that are parsed into RDF knowledge graphs. Your task is to read the provided article and write a complete RDF/XML document for the provided article that uses SKOS, Simple Knowledge Organization System, to structure the conceptual relationships in the article. You will respond only with the RDF/XML document. Do not provide explanations. Obey all RDF/XML instructions. + + # Provided Article: + {article['abstract']} + + # RDF/XML Example and Instructions: + {exampleXML} + + # Keyword Annotations: + Add RDF/XML labels to the applicable concepts using the following list of keywords: + {article['keywords']} + + # People Annotations: + Add RDF/XML labels to the applicable concepts using the following list of people: + {article['people']} + [/INST] + """ + return promptconcepts + +# Add the outputs from the first pass to stabilize the build process +def example_follow(x1, x2): + exampleXML = f"""## RDF/XML Examples: + +```xml + + + + + + + + + + + +``` + +```xml + + + + + RDF 1.1 XML Syntax + RDF 1.1 XML Syntax + RDF 1.1 XML Syntax + + + + Der Baum + Das Buch ist außergewöhnlich + The Tree + + +``` + +```xml + + + + + + + + + + + + +``` + +```xml +{x1} +``` + +```xml +{x2} +``` + +## RDF/XML Instructions: +1. All valid RDF/XML shall written with the following first line: +2. The subject of a RDF triple must be a URI, Uniform Resource Identifier. +3. The predicate of a RDF triple must be a URI, Uniform Resource Identier. +4. All URIs must use the following base URL: http://example.org/ +5. Predicates should be written using knowledge schema using the SKOS, Simple Knowledge Organization System, approach. +6. The object of a RDF triple can be a URI or a value. +7. If the object of a RDF triple is a value, the object should have a XSD type. +""" + return exampleXML + +# Build the follow prompt for the custom RDF/XML schema +def schema_follow(article, example_follow): + promptschema = f"""[INST]You are a scholar. Your subject of expertise is {article['title']}. You have expert understanding of knowledge schemas that are parsed into RDF knowledge graphs. Your task is to read the provided article and write a complete RDF/XML document for the provided article. You will respond only with the RDF/XML document. Do not provide explanations. Obey all RDF/XML instructions. + + # Provided Article: + {article['abstract']} + + # Example RDF/XML and Instructions: + {example_follow} + + # Keyword Annotations: + Add RDF/XML labels to the applicable concepts using the following list of keywords: + {article['keywords']} + + # People Annotations: + Add RDF/XML labels to the applicable concepts using the following list of people: + {article['people']} + [/INST] + """ + return promptschema + +# Build the follow prompt for the SKOS style RDF/XML schema +def skos_follow(article, example_follow): + promptconcepts = f"""[INST]You are a scholar. Your subject of expertise is {article['title']}. You have expert understanding of knowledge schemas that are parsed into RDF knowledge graphs. Your task is to read the provided article and write a complete RDF/XML document for the provided article that uses SKOS, Simple Knowledge Organization System, to structure the conceptual relationships in the article. You will respond only with the RDF/XML document. Do not provide explanations. Obey all RDF/XML instructions. + + # Provided Article: + {article['abstract']} + + # RDF/XML Example and Instructions: + {example_follow} + + # Keyword Annotations: + Add RDF/XML labels to the applicable concepts using the following list of keywords: + {article['keywords']} + + # People Annotations: + Add RDF/XML labels to the applicable concepts using the following list of people: + {article['people']} + [/INST] + """ + return promptconcepts + + +def p1(chunk): + promptcustom = f"""[INST]You write knowledge schemas that are parsed into RDF knowledge graphs. You read the provided text and develop a knwoledge schema to represent the concepts. Your task is to read the provided text and write a RDF Turtle schema for the concepts found in the provided text. You will respond only with the RDF Turtle schema. Do not provide explanations. Obey all of the provided RDF Turtle rules. + + # RDF Turtle Example and Rules + {exampleTurtle} + + # Provided Text + {chunk} + [/INST] + """ + return promptcustom + +def p2(chunk): + promptconcepts = f"""[INST]You write knowledge schemas that are parsed into RDF knowledge graphs. You read the provided text and develop a knwoledge schema to represent the conceptual relationships in the text. Your task is to read the provided text and write a RDF Turtle schema for the concepts found in the provided text using SKOS, Simple Knowledge Organization System. You will respond only with the RDF Turtle schema. Do not provide explanations. Obey all of the provided RDF Turtle rules. + + # Valid RDF Turtle Example and Rules + {exampleTurtle} + + # Provided Text + {chunk} + [/INST] + """ + return promptconcepts + +def summaryprompt(chunk): + promptsummary = f"""[INST]You are an analyst. You read complex text and write a detailed summary. Your task is to read the provided text and write a thorough summary that captures all of the key points. + + # Provided text: + {chunk} + + # Instructions: + ## Summarize: + In clear and concise language, summarize the key points in the provided text. + + ## Key Points: + It is important to capture unique terms and their definitions, entities, people, events, concepts, conceptual relationships, and relationships between entities and people. + + ## Requirements: + Include all key points in the summary. Do not omit any facts that might be considered a key point. + [/INST] + """ + return promptsummary + +def extractpdf(path): + loader = PyPDFLoader(path) + pages = loader.load_and_split() + pdf = f"" + for page in pages: + content = page.page_content + pdf += "".join(content) + return pdf + +def chunktext(splitpdf, chunk_size, chunk_overlap): + node_parser = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) + nodes = node_parser.get_nodes_from_documents([Document(text=splitpdf)], show_progress=False) + textlist = [] + for node in nodes: + textnode = node.text + textlist.append(textnode) + return textlist + +def OLDcallmixtral(prompt): + body = json.dumps({ + "prompt": prompt, + "max_tokens": 4096, + "temperature": 0.0, + "top_p": 0.95, + "top_k": 30 + }) + + modelId = 'mistral.mixtral-8x7b-instruct-v0:1' + accept = 'application/json' + contentType = 'application/json' + response = bedrock.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType) + metadata = response['ResponseMetadata']['HTTPHeaders'] + inputtokens = metadata['x-amzn-bedrock-input-token-count'] + outputtokens = metadata['x-amzn-bedrock-output-token-count'] + response_body = json.loads(response.get("body").read()) + outputtext = response_body['outputs'][0]['text'] + output = { + "text": outputtext, + "input_tokens": inputtokens, + "output_tokens": outputtokens + } + return output + +def callmixtral(prompt): + + resp = llm.invoke(prompt) + return resp + +def count_tokens(metadata): + inputtokens + + +def extract_xml(text): + cleantext = replace_ampersand(text) + pattern = r'<\?xml version="1\.0"\?>.*?' + match = re.search(pattern, cleantext, re.DOTALL) + + if match: + return match.group() + else: + return None + +def replace_ampersand(text): + return text.replace('&', 'and') + +# Not currently using +def load_json_from_string(json_string): + # Remove newline characters + json_string = json_string.replace('\n', '') + + # Check if the string contains ```json delimiters + if "```json" in json_string: + # Find the start and end indices of the JSON object + start_index = json_string.find("```json") + 7 + end_index = json_string.rfind("```") + + # Extract the JSON object from the string + json_object = json_string[start_index:end_index].strip() + else: + # Assume the entire string is a valid JSON object + json_object = json_string + + try: + # Load the JSON object as a dictionary + data_dict = json.loads(json_object) + return data_dict + except json.JSONDecodeError: + # Return None if the string is not a valid JSON object + return None + +# Returns JSON for the Scholar Abstract process +def extract_json_from_output(output): + # Get the text content from the output dictionary + text_content = output['text'] + + # Define the keys we're interested in + target_keys = ['title', 'abstract', 'keywords', 'people'] + + # Initialize an empty dictionary to store our extracted data + extracted_data = {} + + # Use regex to find JSON-like structures for each key + for key in target_keys: + pattern = r'"{0}"\s*:\s*(\[.*?\]|\{{.*?\}}|".*?")'.format(re.escape(key)) + match = re.search(pattern, text_content, re.DOTALL) + if match: + # Extract the value and remove any leading/trailing whitespace + value = match.group(1).strip() + + # If the value is a list or dict, parse it + if value.startswith('[') or value.startswith('{'): + try: + extracted_data[key] = json.loads(value) + except json.JSONDecodeError: + # If parsing fails, store it as a string + extracted_data[key] = value + else: + # Remove quotes for string values + extracted_data[key] = value.strip('"') + + return extracted_data + +# Not currently using +def convert_summary(dataset): + num_passes = len(dataset) + print("Beginning Converting to Summaries") + + start_time = time.time() # Start the timer + + summaries = [] + + for i in range(5): + print(f"Begin Pass {i+1}") + promptchunk = dataset[i] + prompt = summaryprompt(promptchunk) + output = callmixtral(prompt) + outputtext = output['text'] + summaries.append(outputtext) + print(f"Finished Pass {i+1}") + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + print(f"Total time taken: {total_time:.2f} seconds") + return summaries + +def count_tokens(in_tokens, out_tokens, running_total): + running_total['input_tokens'] = running_total['input_tokens'] + in_tokens + running_total['output_tokens'] = running_total['output_tokens'] + out_tokens + running_count = {'input_tokens': running_total['input_tokens'], 'output_tokens': running_total['output_tokens']} + return running_count + +def extract_articles(dataset): + num_passes = len(dataset) + + # Initialize the token counts since this is the first step + in_tokens = 0 + out_tokens = 0 + running_total = {'input_tokens': 0, 'output_tokens': 0} + running = count_tokens(in_tokens, out_tokens, running_total) + print("Beginning Converting to Articles") + print(f"Current Input Tokens: {running['input_tokens']}\nCurrent Output Tokens: {running['output_tokens']}") + + start_time = time.time() # Start the timer + + articles = [] + + i = 0 + while i < num_passes: + print(f"Begin Pass {i+1}") + promptchunk = dataset[i] + prompt = scholar(promptchunk) #Build the prompt + output = callmixtral(prompt) + outputjson = extract_json_from_output(output) + + # Check for valid JSON + if outputjson is None: + print(f"Failed to parse JSON in Pass {i+1}. Retrying...") + continue # Restart the loop for this index + + articles.append(outputjson) + running = count_tokens(int(output['input_tokens']), int(output['output_tokens']), running) + print(f"Finished Pass {i+1}") + print(f"Pass {i+1} Input Tokens: {output['input_tokens']}\nPass{i+1} Output Tokens: {output['output_tokens']}") + + i += 1 # Only increment if successful + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + + inputcost = running['input_tokens'] * priceperinput + outputcost = running['output_tokens'] * priceperoutput + totalcost = round(inputcost + outputcost,2) + + print(f"Total time taken: {total_time:.2f} seconds") + print(f"Total Input Tokens: {running['input_tokens']}\nTotal Output Tokens: {running['output_tokens']}") + print(f"Total Cost: ${totalcost}") + return articles + +# NOT CURRENTLY USING +def full_extract(dataset): + g = rdflib.Graph() + num_passes = len(dataset) + print(f"Initializing Graph and beginning extraction:") + + start_time = time.time() # Start the timer + + for i in range(num_passes): + print(f"Begin Pass {i+1}") + output1 = "" + output2 = "" + promptchunk = dataset[i] + prompt1 = p1(promptchunk) + prompt2 = p2(promptchunk) + + output1 = callmixtral(prompt1) + output1text = output1['text'] + + # Extract text between ### characters from output1 + match1 = re.search(r'###(.*?)###', output1text, re.DOTALL) + if match1: + data1 = match1.group() + g.parse(data=data1, format="turtle") + print(f"Complete first half of Pass {i+1} from ###") + else: + g.parse(data=output1text, format="turtle") + print(f"Complete first half of Pass {i+1} raw") + + output2 = callmixtral(prompt2) + output2text = output2['text'] + + # Extract text between ### characters from output2 + match2 = re.search(r'###(.*?)###', output2text, re.DOTALL) + if match2: + data2 = match2.group() + g.parse(data=data2, format="turtle") + print(f"Completed Pass {i+1} from ###") + else: + g.parse(data=output2text, format="turtle") + print(f"Complete Pass {i+1} raw") + + running_length = len(g) + print(f"Pass {i+1} Graph Length: {running_length}") + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + + outputpath = f"../Columbia_graph_extract.ttl" + g.serialize(destination=outputpath) + print("Holy shit! This actually worked?!?!?!") + print(f"Total time taken: {total_time:.2f} seconds") + return + +def firstpass_build(dataset): + firstpass = [] + out1 = "" + out2 = "" + chunk = dataset[0] + p1 = schema(chunk) + p2 = skos(chunk) + out1 = callmixtral(p1) + out1text = out1['text'] + out2 = callmixtral(p2) + out2text = out2['text'] + xml1 = extract_xml(out1text) + xml2 = extract_xml(out2text) + firstpass.append(xml1) + firstpass.append(xml2) + return firstpass + +# NOT CURRENTLY USING +def build_graph(dataset): + start_time = time.time() # Start the timer + + #Build the first set of outputs + firstset = [] + firstset = firstpass_build(dataset) + example1 = firstset[0] + example2 = firstset[1] + + #Build the new set of examples using first pass outputs + newexamples = example_follow(example1, example2) + + print(f"Initializing Graph:") + g = rdflib.Graph() + # Parse the outputs from the first pass function + g.parse(data=firstset[0], format="xml") + g.parse(data=firstset[1], format="xml") + print(f"Parsed First Pass, Graph Length: {len(g)}") + + num_passes = len(dataset) + + for i in range(1, num_passes, 1): + output1 = "" + output2 = "" + promptchunk = dataset[i] + prompt1 = schema_follow(promptchunk, newexamples) #Build the schema follow prompt + prompt2 = skos_follow(promptchunk, newexamples) #Build the SKOS follow prompt + + # Get schema style output + output1 = callmixtral(prompt1) + output1text = output1['text'] + + # Extract text between ```xml``` characters from output1 + match1 = extract_xml(output1text) + if match1: + data1 = match1 + g.parse(data=data1, format="xml") + + # Get SKOS style output + output2 = callmixtral(prompt2) + output2text = output2['text'] + + # Extract text between ```xml``` characters from output2 + match2 = extract_xml(output2text) + if match2: + data2 = match2 + g.parse(data=data2, format="xml") + + running_length = len(g) + print(f"Parsed Pass {i+1}, Graph Length: {running_length}") + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + + outputpath = f"../Columbia_graph_extract.ttl" + g.serialize(destination=outputpath, format="turtle") + print("Holy shit! This actually worked?!?!?!") + print(f"Total time taken: {total_time:.2f} seconds") + return + +# Graph Building with some error handling and return the graph +def build_graph_robust(dataset): + start_time = time.time() # Start the timer + + #Build the first set of outputs + firstset = [] + firstset = firstpass_build(dataset) + example1 = firstset[0] + example2 = firstset[1] + + #Build the new set of examples using first pass outputs + newexamples = example_follow(example1, example2) + + print(f"Initializing Graph:") + g = rdflib.Graph() + # Parse the outputs from the first pass function + try: + g.parse(data=firstset[0], format="xml") + g.parse(data=firstset[1], format="xml") + print(f"Parsed First Pass, Graph Length: {len(g)}") + except Exception as e: + print(f"Error parsing first pass outputs: {str(e)}") + return + + num_passes = len(dataset) + + i = 1 + while i < num_passes: + output1 = "" + output2 = "" + promptchunk = dataset[i] + prompt1 = schema_follow(promptchunk, newexamples) #Build the schema follow prompt + prompt2 = skos_follow(promptchunk, newexamples) #Build the SKOS follow prompt + + # Get schema style output + output1 = callmixtral(prompt1) + output1text = output1['text'] + + # Extract text between ```xml``` characters from output1 + match1 = extract_xml(output1text) + if match1: + data1 = match1 + try: + g.parse(data=data1, format="xml") + except Exception as e: + print(f"Error parsing schema output in pass {i+1}: {str(e)}\n\nTrying Again...") + continue + + # Get SKOS style output + output2 = callmixtral(prompt2) + output2text = output2['text'] + + # Extract text between ```xml``` characters from output2 + match2 = extract_xml(output2text) + if match2: + data2 = match2 + try: + g.parse(data=data2, format="xml") + except Exception as e: + print(f"Error parsing SKOS output in pass {i+1}: {str(e)}\n\nTrying Again...") + continue + + running_length = len(g) + print(f"Parsed Pass {i+1}, Graph Length: {running_length}") + i += 1 + + end_time = time.time() # End the timer + total_time = end_time - start_time # Calculate the total time + + # Don't write graph file for testing + #outputpath = f"./Columbia_graph_extract.ttl" + #g.serialize(destination=outputpath, format="turtle") + print("Holy shit! This actually worked?!?!?!") + print(f"Total time taken: {total_time:.2f} seconds") + return g + +# Get a list of subjects from graph as URIs +def get_subject_list(graph): + query = """ + SELECT DISTINCT ?subject + WHERE { + ?subject ?predicate ?object . + } + """ + + subjects = graph.query(query) + + subjectlist = [] + for row in subjects: + text = f"{row.subject}" + subjectlist.append(text) + + return subjectlist + +# Function for removing URI artifacts to return clean text +def clean_resource(resource): + # Remove the URL prefixes + cleaned_resource = resource.replace("http://example.org/", "") + cleaned_resource = cleaned_resource.replace("http://www.w3.org/2004/02/skos/core#", "") + cleaned_resource = cleaned_resource.replace("http://www.w3.org/1999/02/22-rdf-syntax-ns#", "") + cleaned_resource = cleaned_resource.replace("http://www.w3.org/2001/XMLSchema#", "") + cleaned_resource = cleaned_resource.replace("http://purl.org/dc/elements/1.1/", "") + + # Replace special symbols with a space + cleaned_resource = re.sub(r'[_#/]', ' ', cleaned_resource) + + return cleaned_resource + +# Clean the entire subject list +def clean_subject_list(subjectlist): + cleaned_subjects = [] + for subject in subjectlist: + clean = f"{clean_resource(subject)}" + cleaned_subjects.append(clean) + + return cleaned_subjects + +# Dump all the triples from the Graph +def get_triples_list(graph): + triples_list = list(graph.triples((None, None, None))) + + semantic_list = [] + for subject, predicate, object in triples_list: + triple = f"{subject} {predicate} {object}" + semantic_list.append(triple) + + return semantic_list + +#Clean the entire triples list +def clean_triples_list(tripleslist): + cleaned_triples = [] + for triple in tripleslist: + cleaned_triple = clean_resource(triple) + cleaned_triples.append(cleaned_triple) + + return cleaned_triples + +# Build UniSim instance for graph subjects +def build_subject_Sim(graph): + subject_sim = TextSim() + subject_sim.reset_index() # Make sure it's empty + subject_sim = TextSim( + store_data=True, # set to False for large datasets to save memory + index_type="exact", # set to "approx" for large datasets to use ANN search + batch_size=128, # increasing batch_size on GPU may be faster + use_accelerator=True, # uses GPU if available, otherwise uses CPU + ) + + subjects = get_subject_list(graph) # Get the subjects + clean_subjects = clean_subject_list(subjects) # Clean the subjects + idx = subject_sim.add(clean_subjects) # Build sim + return subject_sim + +# Build UniSim instance for graph triples +def build_triples_Sim(graph): + triples_sim = TextSim() + triples_sim.reset_index() # Make sure it's empty + triples_sim = TextSim( + store_data=True, # set to False for large datasets to save memory + index_type="exact", # set to "approx" for large datasets to use ANN search + batch_size=128, # increasing batch_size on GPU may be faster + use_accelerator=True, # uses GPU if available, otherwise uses CPU + ) + + triples = get_triples_list(graph) # Get the triples + clean_triples = clean_triples_list(triples) # Clean the triples + idx = triples_sim.add(clean_triples) # Build sim + return triples_sim