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README.md
286
README.md
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@ -50,6 +50,10 @@ Pulsar provides two types of connectivity:
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processed, the output is delivered to a separate queue so that the caller
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can collect the data.
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All the code is bundled into a single Python package which can be used to
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use all the functionality. There is also a container image with the
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package installed which can be used to run everything.
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## Included modules
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- `chunker-recursive` - Accepts text documents and uses LangChain recurse
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@ -108,9 +112,285 @@ Using the Docker Compose you should be able to...
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- Run a query which uses the vector and graph stores to produce a prompt
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which is answered using an LLM.
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If you get a Graph RAG response to the query, everything is working
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If you get a Graph RAG response to the query, everything is working.
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### Docker compose
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### Clone the Github repo
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TBD
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```
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git clone https://github.com/trustgraph-ai/trustgraph trustgraph
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cd trustgraph
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```
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### Docker compose files
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There are 4 docker compose files to choose from depending on the LLM you
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wish to use:
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- `docker-compose-azure.yaml`. This is for a serverless AI endpoint
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hosted on Azure. Set `AZURE_TOKEN` to the secret token and
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`AZURE_ENDPOINT` to the endpoint address.
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- `docker-compose-claude.yaml`. This is for using Anthropic Claude LLM.
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Set `CLAUDE_KEY` to the API key.
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- `docker-compose-ollama.yaml`. This is for a local LLM - gemma2 hosted
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using Ollama. Set `OLLAMA_HOST` to the host running Ollama (e.g.
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`localhost` to talk to a locally hosted Ollama.
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- `docker-compose-vertexai.yaml`. This is for using Google Cloud VertexAI.
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You need a private.json authentication file for your Google Cloud.
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Should be at path `vertexai/private.json`.
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#### docker-compose-azure.yaml
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```
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export AZURE_ENDPOINT=https://ENDPOINT.HOST.GOES.HERE/
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export AZURE_TOKEN=TOKEN-GOES-HERE
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docker-compose -f docker-compose-azure.yaml up -d
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```
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#### docker-compose-claude.yaml
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```
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export CLAUDE_KEY=TOKEN-GOES-HERE
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docker-compose -f docker-compose-claude.yaml up -d
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```
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#### docker-compose-ollama.yaml
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```
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export OLLAMA_HOST=localhost # Set to hostname of Ollama host
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docker-compose -f docker-compose-ollama.yaml up -d
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```
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#### docker-compose-azure.yaml
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```
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mkdir -p vertexai
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cp {whatever} vertexai/private.json
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docker-compose -f docker-compose-vertexai.yaml up -d
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```
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On Linux if running SELinux you may need to set the permissions on the
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VertexAI directory so that the key file can be mounted on a docker
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container...
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```
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chcon -Rt svirt_sandbox_file_t vertexai/
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```
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### Check things are running
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Check that you have a set of containers running...
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```docker ps
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```
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You might want to look at containers which are down to see if any
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have exited unexpectedly - look at the STATUS field.
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```docker ps -a
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```
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### Wait
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Before proceeding, you should leave enough time for the system to
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settle into a working state. On my Macbook, it takes about 30 seconds
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for Pulsar to start, before which, nothing works.
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The system uses Cassandra for a Graph store, takes around 60-70 seconds
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to achieve a working state. For your first go, I would advise just letting
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everything settle for a couple of minutes before doing anything else, so
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that if there are errors you know it's not just that the system is starting
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up.
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### Install requirements
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```
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python3 -m venv env
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. env/bin/activate
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pip3 install pulsar-client
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pip3 install cassandra-driver
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```
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### Load some data
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Create a sources directory and get a test file...
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```
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mkdir sources
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curl -o sources/Challenger-Report-Vol1.pdf https://sma.nasa.gov/SignificantIncidents/assets/rogers_commission_report.pdf
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```
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Then load the file...
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```
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scripts/loader
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```
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You get some output on the screen, if nothing looks like errors (has the
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ERROR tag) you should be good.
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### Check logs
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Look at the PDF decoder...
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```
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docker logs trustgraph_pdf-decoder_1
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```
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which should contain some text like...
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```
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Decoding 1f7b7055...
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Done.
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```
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Look at the chunker output...
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```
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docker logs trustgraph_chunker_1
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```
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You will see similar output, except many entries instead of 1.
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Look at the vectorizer output...
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```
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docker logs trustgraph_vectorize_1
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```
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You will see similar output, except many entries instead of 1.
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Look at the LLM output...
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```
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docker logs trustgraph_llm_1
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```
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You will see output like this...
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```
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Handling prompt fa1b98ae-70ef-452b-bcbe-21a867c5e8e2...
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Send response...
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Done.
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```
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Two more log outputs to look at...
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```
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docker logs trustgraph_kg-extract-definitions_1
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docker logs trustgraph_kg-extract-relationships_1
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```
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Definitions output similar to this should be visible
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```
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Indexing 1f7b7055-p11-c1...
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[
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{
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"entity": "Orbiter",
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"definition": "A spacecraft designed for spaceflight."
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},
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{
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"entity": "flight deck",
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"definition": "The top level of the crew compartment, typically where flight controls are located."
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},
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{
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"entity": "middeck",
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"definition": "The lower level of the crew compartment, used for sleeping, working, and storing equipment."
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}
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]
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Done.
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```
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and Relationships output...
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```
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Indexing 1f7b7055-p11-c3...
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[
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{
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"subject": "Space Shuttle",
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"predicate": "carry",
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"object": "16 tons of cargo",
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"object-entity": false
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},
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{
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"subject": "friction",
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"predicate": "generated by",
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"object": "atmosphere",
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"object-entity": true
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}
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]
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Done.
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```
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### Check graph is loading
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```
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scripts/graph-show
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```
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You should see some output along the lines of a load of lines like this...
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```
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http://trustgraph.ai/e/enterprise http://trustgraph.ai/e/was-carried to altitude and released for a gliding approach and landing at the Mojave Desert test center
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http://trustgraph.ai/e/enterprise http://www.w3.org/2000/01/rdf-schema#label Enterprise
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http://trustgraph.ai/e/enterprise http://www.w3.org/2004/02/skos/core#definition A prototype space shuttle orbiter used for atmospheric flight testing.
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```
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Any output at all is a good sign - indicates the graph is loading.
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### Query time
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With the graph loading, you should be able to see the number of graph edges
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loaded...
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```
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scripts/graph-show | wc -l
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```
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You need a good few hundred edges to be loaded for the query to work on that
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particular document, because it's the point where the indexer has passed
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the mundane intro parts of the document and got into the interesting
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parts.
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```
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tests/graph/rag
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```
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You should give the command at least a minute to run before being
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concerned. The output should look like this...
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```
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Here are 20 facts from the provided knowledge graph about the Space Shuttle disaster:
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1. **Space Shuttle Challenger was a Space Shuttle spacecraft.**
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2. **The third Spacelab mission was carried by Orbiter Challenger.**
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3. **Francis R. Scobee was the Commander of the Challenger crew.**
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4. **Earth-to-orbit systems are designed to transport payloads and humans from Earth's surface into orbit.**
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5. **The Space Shuttle program involved the Space Shuttle.**
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6. **Orbiter Challenger flew on mission 41-B.**
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7. **Orbiter Challenger was used on STS-7 and STS-8 missions.**
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8. **Columbia completed the orbital test.**
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9. **The Space Shuttle flew 24 successful missions.**
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10. **One possibility for the Space Shuttle was a winged but unmanned recoverable liquid-fuel vehicle based on the Saturn 5 rocket.**
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11. **A Commission was established to investigate the space shuttle Challenger accident.**
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12. **Judit h Arlene Resnik was Mission Specialist Two.**
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13. **Mission 51-L was originally scheduled for December 1985 but was delayed until January 1986.**
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14. **The Corporation's Space Transportation Systems Division was responsible for the design and development of the Space Shuttle Orbiter.**
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15. **Michael John Smith was the Pilot of the Challenger crew.**
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16. **The Space Shuttle is composed of two recoverable Solid Rocket Boosters.**
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17. **The Space Shuttle provides for the broadest possible spectrum of civil/military missions.**
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18. **Mission 51-L consisted of placing one satellite in orbit, deploying and retrieving Spartan, and conducting six experiments.**
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19. **The Space Shuttle became the focus of NASA's near-term future.**
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20. **The Commission focused its attention on safety aspects of future flights.**
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```
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If it looks like something isn't working, try following the graph-rag
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logs:
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```
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docker logs -f trustgraph_graph-rag_1
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```
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If you get an answer to your query, Graph RAG is working!
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If you want to try different queries try modifying the
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script you ran at `tests/test-graph-rag`.
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